Geospatial modeling of urban buildings and land use for ...cj...v RESEARCH PRODUCTS Journal...
Transcript of Geospatial modeling of urban buildings and land use for ...cj...v RESEARCH PRODUCTS Journal...
GEOSPATIAL MODELING OF URBAN BUILDINGS AND LAND USE FOR
CLIMATE CHANGE IMPACTS AND RESOURCE PRODUCTIVITY
A Dissertation Presented
By
Mithun Saha
To
The Department of Civil and Environmental Engineering
In partial fulfillment requirement
for the degree of
Doctor of Philosophy
In the field of
Environmental Engineering
Northeastern University
Boston, Massachusetts
August 2016
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ACKNOWLEDGEMENT
First of all, I cannot express enough thanks to my committee for their continued support
and encouragement: Dr. Auroop Ganguly, Dr. Matthias Ruth, and Dr. James Connolly.
My completion of this dissertation could not have been accomplished without the support
of my academic advisor and mentor. I would like, to thank Dr. Matt Eckelman, for being a
great mentor and colleague throughout every step of the course of my doctoral journey. I
am indebted for his help and support, and for sharing his extensive knowledge and very
contagious passion for our research.
I am also grateful to Professor David Fannon, for his invaluable help and advice.
Appreciations to my former colleagues in NU sustainability research engineering team, Dr.
Pei Zhai, Dr. Leila Pourzahedi, and soon to be Dr. Mahdokht Montazeri for all their helpful
discussions, suggestions, and friendship. Also, thanks to all my friends for encouragement.
In addition, I would like to thank departmental staff member Mr. Michael Macneil for
providing continuous logistical and technical support for conducting doctoral research.
Finally, thanks to the most important people in my life, my family for all their love and
support. My parents who are living 12,500 km far from me and have been waiting
relentlessly to see their elder son be a holder of doctorate someday. Last but not the least,
there is one person whom I can’t thank enough and therefore just want to dedicate this
work, the love of my life, my beautiful wife, Susmita Biswas.
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ABSTRACT
Urbanization is expected to continue, with more than two-thirds of the world’s population
likely to live in urban areas by 2050, leading to a net urban influx of approximately 2.5
billion people. Existing infrastructure must be equipped to address this dramatic urban
growth while also adapting to potential adverse impacts of climate change and other natural
hazards. To be sustainable, cities must themselves, become efficient users of materials and
energy as well as respond to future climatic conditions. Two main urban engineering
strategies are to map how current stocks may respond to climate change and to identify
resources that could be used to improve local productive capacity and reduce dependencies
on distant resources. The dissertation herein addresses these two overarching strategies
through a series of specific case studies for the Boston area using GIS based urban stock
assessment as a framework.
GIS is used extensively in urban system modeling and resource assessment. The geospatial
modeling presented in this dissertation involves the combination of spatial and remote
sensing techniques in a way that multi-dimensional, location-based data can be analyzed
and visualized to assess urban resources at building and sub-parcel-level resolution.
Corrosion models are coupled to climate change projections to estimate the durability of
all concrete buildings throughout the city subject to enhanced carbonation and chlorination
processes. Climate change will decrease the time to corrosion initiation in new concrete
buildings by 10-26 years. Scalable spatial frameworks and models for assessing urban
biomass potential are presented and applied to across urban land-use and building types, to
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address questions of self-sufficiency for local food and bioenergy systems. Through a
series of studies, up to 24% of Boston’s land area and up to 20% of the entire metropolitan
area could be plausibly utilized for bioenergy crops, while 17% of Boston’s area could be
used for urban agriculture, potentially supplying up to 30% of all fruits and vegetables
consumed in the city. Results at the sub-parcel-level have direct utility for recent
government initiatives. This work advances the field of urban engineering through
application of novel coupled geospatial-biophysical analyses to entire urban regions at high
resolution.
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RESEARCH PRODUCTS
Journal publications:
Saha, M., Eckelman, M. (2015) Geospatial assessment of potential bioenergy crop
production on urban marginal land. Applied Energy, 159, 540-547
Saha, M., Eckelman, M. (2014) Urban scale mapping of concrete degradation
from projected climate change. Urban Climate, 9, 101-114
Publications in preparation:
Saha, M., Eckelman, M. Geospatial assessment of urban agriculture potential in
Boston
Saha, M., Eckelman, M. A GIS-based Assessment of Regional Scale Bioenergy
Production Potential on Marginal and Degraded Land
Conference proceedings:
Saha, M., Martin, L., Amidon, J., Ruth, M., Eckelman, M. (2015) 4d-Mapping of
urban biomass production for food and fuel, ISIE Conference, July 07-10,
Guildford, Surrey, UK
Saha, M., Martin, L., Amidon, J., Ruth, M., Eckelman, M. (2015) Mapping urban
biomass production of food and fuel, ISSST Symposium, May 18-20, Dearborn,
Michigan
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TABLE OF CONTENTS
LIST OF FIGURES ..................................................................................................... ix
LIST OF TABLES ....................................................................................................... xi
NOMENCLATURE ................................................................................................... xii
Chapter 1 Introduction to Urban Metabolism and GIS Application .......................1
1.1 Background ...............................................................................................................1
1.1.1 Urban metabolism ...............................................................................................1
1.1.2 Urban stock analysis ...........................................................................................4
1.2. Introduction to geospatial assessment ........................................................................5
1.2.1 Geographic information system (GIS) .................................................................6
1.2.2 GIS applications ..................................................................................................9
1.3. GIS-based urban stock assessment .......................................................................... 11
1.4. Motivation and research objectives ......................................................................... 14
1.5. Dissertation Structure.............................................................................................. 16
Chapter 2 Urban Scale Mapping of Concrete Degradation from Projected Climate
Change ......................................................................................................................... 20
2.1 Introduction ............................................................................................................. 21
2.2 Climate Change Scenarios ....................................................................................... 25
2.2.1 Atmospheric CO2 Concentrations ...................................................................... 25
2.2.2 Temperature Predictions.................................................................................... 27
2.3 Corrosion ................................................................................................................. 28
2.3.1 Concrete Carbonation Modeling ........................................................................ 28
2.3.2 Concrete Chlorination Modeling ....................................................................... 31
2.4 Spatial Analysis ....................................................................................................... 33
2.5 Results ..................................................................................................................... 35
2.5.1 Carbonation and Chlorination Depth. ................................................................ 35
2.5.2 Geospatial Results ............................................................................................. 38
2.6 Discussion ............................................................................................................... 40
2.6.1 Implications for Current Code Requirements for Concrete Cover ...................... 40
2.6.2 Concrete Technologies for Climate Change Adaptation..................................... 42
2.6.3 Future Research Needs ...................................................................................... 43
2.7 Conclusion ............................................................................................................... 44
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Chapter 3 Geospatial Assessment of Potential Bioenergy Crop Production on
Urban Marginal Land ................................................................................................. 46
3.1 Introduction ............................................................................................................. 47
3.2 Methods ................................................................................................................... 52
3.2.1 Land use type screening .................................................................................... 53
3.2.2 Bio-geophysical screening................................................................................. 54
3.2.2.1 Exclusion of parcels by soil quality and slope ................................................. 54
3.2.2.2 Exclusion of areas by shadow analysis ........................................................... 55
3.2.3 Biomass and bioenergy yield ............................................................................. 57
3.3 Results ..................................................................................................................... 58
3.3.1 Marginal land resources in Boston .................................................................... 58
3.3.2 Biomass and bioenergy potential ....................................................................... 61
3.3.3 Spatial Validation.............................................................................................. 63
3.4 Discussion and Implications..................................................................................... 63
3.5 Conclusions ............................................................................................................. 66
Chapter 4 A GIS-based Assessment of Regional Scale Bioenergy Production
Potential on Marginal and Degraded Land ................................................................ 68
4.1 Introduction ............................................................................................................. 69
4.1.1 Regional assessment of marginal land ............................................................... 69
4.2 Methods ................................................................................................................... 71
4.2.1 Marginal Land Assessments .............................................................................. 71
4.2.2 Biomass and bioenergy yield ............................................................................. 74
4.3 Results and Discussion ............................................................................................ 75
4.3.1 MAPC Marginal land ........................................................................................ 75
4.3.2 Biomass and Bioenergy Yield ........................................................................... 78
4.4 Conclusion ............................................................................................................... 80
Chapter 5 Geospatial Assessment of Urban Agriculture Potential in Boston ........ 82
5.1 Introduction ............................................................................................................. 83
5.1.1 Urban agriculture .............................................................................................. 83
5.1.2 Spatial analysis ................................................................................................. 85
5.1.3 Connections to self-sufficiency and resilience ................................................... 88
5.2 Methods ................................................................................................................... 91
5.2.1 Study area and datasets ..................................................................................... 91
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5.2.2 Mapping flat rooftops ........................................................................................ 92
5.2.3 Mapping ground level parcels ........................................................................... 96
5.2.4 Estimating food yields ..................................................................................... 100
5.2.5 Validation ....................................................................................................... 101
5.3 Results ................................................................................................................... 102
5.3.1 Rooftop area mapping ..................................................................................... 102
5.3.2 Ground level farmland mapping ...................................................................... 104
5.3.4 Food yield potential ........................................................................................ 106
5.3.5 Validation ....................................................................................................... 107
5.4 Discussion and Implications................................................................................... 108
5.5 Conclusions ........................................................................................................... 111
Chapter 6 Conclusion and Future Works .............................................................. 113
REFERENCES .......................................................................................................... 118
APPENDIX ................................................................................................................ 138
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LIST OF FIGURES
Fig. 1-1 The urban metabolism of Toronto Port Land………………………… 3
Fig. 1-2 Feature type and their representations in two spatial data models…… 7
Fig. 1-3 A spatial database for urban mining studies…………………………... 8
Fig. 2-1 Predicted estimates of CO2 concentrations…………………………… 27
Fig. 2-2 Predicted mean temperature increase for Boston’s Logan Airport
station for A1FI and B1 scenarios ……………………………………
28
Fig. 2-3 Concrete structures (buildings, in red) within Boston (outlined in gray) 34
Fig. 2-4 Estimated carbonation depth (mm) in BMA for a building constructed
in 2000………………………………………………………………...
37
Fig. 2-5 Estimated chlorination depth (mm) in BMA for building constructed
in 2000………………………………………………………………...
37
Fig. 2-6 a) Location of gridded climate data, b) Climatically vulnerable zones
within Boston………………………………………………………….
39
Fig. 2-7 a) Concrete Structures classified according to different age, b) % of
buildings with compromised cover thickness over the service life……
40
Fig. 3-1 Flowchart of modeling processes used for biomass mapping and
bioenergy assessment…………………………………………………
53
Fig. 3-2 Shadow analysis examples: a) input extruded-2D building footprint
and b) shadow map……………………………………………………
56
Fig. 3-3 Marginal land estimation in Boston…………………………………... 60
Fig. 3-4 Available marginal land in Boston…………………………………… 61
Fig. 3-5 Randomly selected urban marginal land parcels (Scale 1: 600) …….... 63
Fig. 4-1 Study area (MAPC region) …………………………………………… 71
Fig. 4-2 Urban marginal land estimation………………………………………. 74
Fig. 4-3 Available marginal land in MAPC cities……………………………... 77
Fig. 4-4 Available marginal land in four municipalities………………………. 78
Fig. 5-1 Boston rooftops and ground level parcels extraction steps…………… 92
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Fig. 5-2 a) Delineated buildings, b) The division of the Boston area for LiDAR
data……………………………………………………………………
94
Fig. 5-3 a) Boston downtown geographic area of interest; b) High resolution
LiDAR data; c) Extracted building footprint layer……………………
96
Fig. 5-4 Shadow distribution on Boston Downtown at a) 10 AM and b) 4 PM
on July 21st…………………………………………………………….
100
Fig. 5-5 Flat rooftop mapping steps…………………………………………… 102
Fig. 5-6 Flat Roofs in Boston………………………………………………….. 103
Fig. 5-7 a) Flat roof distribution in Boston’s neighborhoods, by; b) overlaid
aerial image of flat roofs in the Dorchester neighborhood ……………
103
Fig. 5-8 Ground level farmland estimation in Boston…………………………. 105
Fig. 5-9 Available ground level areas in Boston………………………………. 106
Fig. 5-10 Randomly selected rooftop (top row) and ground level (bottom row)
parcels…………………………………………………………………
108
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LIST OF TABLES
Table 2-1 Structural durability conditions for concrete used in this study ……... 30
Table 2-2 Recommended concrete properties for corrosion protection ………… 32
Table 2-3 Minimum cover (mm) required to counteract the impact of climate
change on carbonation-induced corrosion damage risks by 2100…….
38
Table 2-4 Minimum cover (mm) required to counteract the impact of climate
change on chlorination-induced corrosion damage risks by 2100…….
38
Table 3-1 Review of studies on marginal land assessment in USA ……………. 49
Table 3-2 Description of parameters used to estimate urban marginal land……. 54
Table 3-3 USDA-NRCS Marginal soils classification………………………...... 55
Table 3-4 Energy-crop yield and heat content information …………………...... 58
Table 3-5 Biomass and bioenergy yield ………………………………………... 62
Table 4-1 Description of parameters used to estimate marginal land area……… 73
Table 4-2 Energy-crop yield and heat content information ……………………. 75
Table 4-3 MAPC marginal land ………………………………………………... 78
Table 4-4 Biomass and bioenergy yield ………………………………………... 79
Table 5-1 Review of studies on urban agriculture potential in North America…. 85
Table 5-2 Description of parameters used to estimate rooftop area……………. 93
Table 5-3 Description of parameters used to estimate ground level area ………. 98
Table 5-4 USDA-NRCS soils classification ……………………………………. 98
Table 5-5 Energy-crop yield and heat content information …………………...... 101
Table 5-6 Potential food production in Boston …………………………………. 107
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NOMENCLATURE
ARRM Asynchronous Regional Regression Model
AOGCM Atmospheric-Oceanographic Global Climate Models
ACI American Concrete Institute
BAU Business as Usual
BETYdb Biofuel Ecophysiological Traits and Yields database
CCSM Community Climate System Model
CHP Combined Heat and Power
DSM Digital Surface Model
EBI Energy Bioscience Institute
GFDI Geophysical Fluid Dynamics Institute Model
GHG Greenhouse Gas
GIS Geographic Information System
HADCM2 Hadley Climate model
HHV High Heating Value
IPCC Intergovernmental Panel on Climate Change
LHV Low Heating Value
LiDAR Light Detection and Ranging Data
Mi Marginal land parcel area (ha)
MAGICC Model for Assessment of Greenhouse-gas induced Climate Change
MAPC Metropolitan Area Planning Council
MATEP Medical Area Total Energy Plant
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NAD North American Datum
NCAR National Center for Atmospheric Research
PCM Parallel Climate Model
SFA Stock and Flow Analysis
SRDBMS Spatial Relational Database Management System
SRES Special Report on Emissions Scenarios
SSURGO Soil Survey Geographic Database
TMDL Total Maximum Daily Load
USDA US Department of Agriculture
UTM Universal Transverse Mercator
WGS World Geodetic System
xc Carbonation depth (mm)
xcl Chlorination depth (mm)
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Chapter 1 Introduction to Urban Metabolism and GIS Application
1.1 Background
1.1.1 Urban metabolism
The global footprint of cities is increasing, with more than 50% of world’s population
currently living in cities and peri-urban areas [1]. Urban populations consume the majority
of the world’s resources and contribute towards environmental deterioration both locally
and globally [2]. Modern cities are the epicenter of resource consumption [3]. While they
aim to improve resource efficiency and reduce (particularly local) environmental burdens,
cities must also adapt to global climate change and be prepared for natural calamities.
Several examples portend the challenges ahead. In 2012, in the aftershock of Hurricane
Sandy, nearly three-quarters of gas stations in New York City remained inoperable, leading
to gasoline rationing [4]. During winter 2015, areas of New England received record
snowfall of nearly 110 inches that left thousands of people without power for multiple days
[5].
Extremes events can severely curtail the functioning or urban systems, with significant
implications for shelter, energy, and food security, three essential pillars of human survival.
In a globalized world, cities are increasingly reliant on global supply chains for energy and
food, while local capacity to provide material resources to meet the needs of residents has
dwindled (with the exception of water) [6]. Many cities are reassessing their self-
sufficiency as one strategy to prepare for the challenges that climate change will bring.
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While developed as a method of quantifying urban systems processes of different
dimension and spatiotemporal variability, the field of urban metabolism has gained
importance both among scientific and urban planning and management communities.
Urban metabolism considers all physical and techno-economical processes that originate
from or flow within an urban ecosystem [3, 4]. Practically, urban metabolism investigates
both material and energy flows across the urban boundary and stocks accumulation within
cities [4]. In general, more research has focused on flow analysis with the ideal of
integrating flows among different actors, so-called industrial symbiosis. For example,
Kennedy et al. examined the Port Lands area of Toronto, mapping out both resource inputs
and emissions, and waste streams that were or could be reused (Fig. 1-1).
Previous studies have also discussed possible synergy of urban metabolism in assessing
urban resource self-sufficiency. According to Sharifi and Yamagata. 2016 [6], “a
sustainable and self-reliant city should be capable of sustained disruptions by leveraging
its resources for material, energy and food production”. This will help cities thriving while
minimizing environmental impact and avoiding great harm to life and devastation to
property. In this way, cities are often comparable to an ecosystem [7]. Many studies
conducted on urban metabolism have been sector specific and focused on issues such as
material [8] and energy [9]. At the same time, a number of studies have focused on
developing criteria and indicators for assessing urban stocks and flows in different domains
[8, 10-13].
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In summary, the studies described above indicate that it may not be possible for cities to
be self-sufficient regarding developing its resources and infrastructure to produce all
energy and materials locally, but knowing the urban productive capacity provides an
opportunity to plan for various future scenarios [7]. Every region has a unique set of
resource demands, stocks, and climate vulnerabilities based on geographic location [14].
Past research has called for site and context specific assessment as imperative to understand
which stocks are most vulnerable to change and which resources can potentially be used to
reduce cities’ reliance on global resources [8, 14, 15].
Fig. 1-1: The urban metabolism of Toronto Port Lands [16]
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1.1.2 Urban stock analysis
As a sub-field of urban metabolism, urban stock analysis is the assessment of stored and
internal urban resources [4, 7, 17]. Stocks can materials and energy that are readily
accessible for use, such as diesel fuel, or ‘hibernating’ stocks that are not in use but still
exist within the city boundary, such as steel in abandoned railroad lines. A bottom-up
approach is common for stock estimation [18], where individual materials are located and
accounted for essentially as an inventory [19]. Stocks can also be assessed using a top-
down approach by conducting system material and energy balances [19]. This approach
includes an indirect measurement of stock’s size by observing a time-dependent flows
through the stock system. With the top-down approach, the estimated material stocks are
measured at the national or regional level and then scaled down to the local level [17],
which may introduce a large uncertainty bound for the spatial distribution [18].
A city’s stocks of buildings and land make up its local resource base. From a self-
sufficiency perspective, it is crucial to develop a framework to quantify these resources,
utilize them efficiency, and minimize any adverse local impacts on the urban environment
[20]. For this purpose, the amounts of resources available for urban activities is necessary
to quantify. In this context, SFA (Stock and Flow Analysis) was developed to quantify the
amount of material or energy that is harvested from nature and its life cycle [16].
So far, numerous studies investigated material stock and flow analysis (MSFA) of different
materials. Gordon et al. [21], Hashimoto et al.[22], and Schandl and West [23] are
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representative studies that leveraged MFSA at national, regional, and local scale. MFSA is
useful for conducting a high-resolution analysis in the space-time scale. For example, a
historical visualization of the cityscape is performed by building a 4d-GIS model where
spatial data contain information about height (3d-GIS) and are arranged in a chronological
order over a defined time period [7].
On the basis of discussion above, it is evident that bottom-up stock and flow analyses can
be conducted with high-resolution spatiotemporal data. Quantifying existing urban stocks
and combining with historical data or future scenarios can provide estimates of the current
physical quality of existing stocks, their capacity for productive activities (to produce
resource flows), or how they may degrade over time, all of which can help to support local
policy making [19].
1.2. Introduction to geospatial assessment
The geospatial assessment involves the combination of geographical information system
(GIS) and remote sensing technique in a way that multi-dimensional location-based data
can be entered, checked, analyzed and visualized as data referenced to the earth [24].
Location-based data are also called georeferenced data or, more commonly, as spatial data.
GIS are scale-independent and can be used for examining, exploring and analyzing spatial data
at global, continental, regional, and, local scales [25]. Therefore, GIS can be a useful tool for
characterizing and visualizing spatial distributions of infrastructures and resources (energy and
food) stocks and flows across the urban environments. Therefore, GIS-based location
intelligence can be extremely effective for informing the policy makers and the broader
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community with accurate and comprehensive information. The following section reviews the
theoretical concept and functionality of GIS and studies the possible role of GIS in urban stocks
assessment. Additionally, it describes how GIS can be used to measure, analyze, and visualize
the geographical characteristics of stored and internal stocks. Finally, the section discusses the
issues and challenges in the use of GIS for urban designers and engineers.
1.2.1 Geographic information system (GIS)
In general terms, GIS is a computer system that can be considered as “a special case of
information systems where the database consists of observations on spatially distributed
features, activities or events, which are defined in space as points, lines, or areas” [25]. A GIS
manipulates data about these points, lines, and areas to retrieve data for ad hoc queries and
analyses” [26]. GIS data are characterized using either a vector or raster data model. The vector
model consists of points, lines, and polygon features plotted as coordinates in space. Whereas,
a raster data model is conceptualized as a continuous gridded surface of equal cell size (Fig. 1-
2). Virtually all current GIS-based software packages are capable of handling both vector and
raster layers. The data layer preference is based on the model appropriateness for specific
phenomena. Using a spatial tool involves capturing the spatial distribution and pattern of
features through the measurement of cartographic representation. Urban stocks and other
related infrastructures are all spatially distributed and, so, can be studied using GIS.
The GIS system performs three specific types of functionality with increasing convolution [27].
The first category is spatial visualization or cartographic representation of location data. It is
an essential function of GIS. A map is a simple form of spatial visualization and representation
of spatial data. GIS maps are in digital form and named map layer which is a set of spatial data
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containing the attributes of the features (e.g., material and energy stocks) and their geographical
locations. The geographic locations are recorded as x, y coordinates with a specific coordinate
system or as latitude and longitude. A GIS system provides a comprehensive set of symbols
and colors for users to choose to symbolize layer attributes. Cartography preparation and
geoprocessing analysis are not new, but GIS system performs this more precisely than other
methods. Current GIS technology provides more user interface with 3D spatial analysis and
automatic model builder capability.
Fig. 1-2: Feature type and their representations in two spatial data models [18]
The second type of GIS feature is spatial data management. In GIS, spatially referenced data
are typically arranged in the form of a layer. For example, a census data layer for the U.S. is a
collection of demography characteristics of the country with related tables of attributes
associated with each census. A collection of data layers forms a spatial database. For example,
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Fig. 1-3 shows a geodatabase for urban stock studies. GIS manages a spatial relational database
management system (SRDBMS) with a structured query that extracts features based on their
locations and spatial relationships. For example, spatial query functions can be used to find the
point locations of cyanobacteria bloom in the Charles River with more than 0.05 mg/L of
nitrate concentration. GIS integrates general database maneuvers, such as a spatial query, and
several geostatistical analyses with the unique spatial visualization and geographical analysis
benefits offered by maps.
Fig. 1-3: A spatial database for urban mining studies [18]
Finally, GIS-based tools are used for spatial analysis and modeling. The analysis and modeling
steps are based on features absolute or relative locations, and the outcome depends on the
locations of the features being analyzed. GIS spatial analysis and modeling functions allow
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users to define and execute spatial and attribute procedures to perform analysis in space. This
is the most important part of GIS functionality. GIS provides a large range of computational
tools that operate on the spatial aspects of the data, on the non-spatial attributes of these data,
or both. These tools range from simple geometric measurements such as area, perimeter and
length, to more complex functions such as spatial interpolation, network analysis, and spatial
statistics. These capabilities separate GIS packages from other information system based
modeling methods and make them valuable for a broad range of engineering and environmental
applications, including explaining natural events, simulating complex processes, predicting
outcomes, and supporting decision making. GIS is supported by substantial research
literature where the growing trends in the design of algorithms and novel computing
techniques for visualization and analysis of georeferenced data are evident [18].
1.2.2 GIS applications
GIS software can address a broad range of spatially related questions or procedures. Below
are some examples of the diverse capabilities of a GIS-based software package [25].
Spatial measurement: “The information of distance or the spatial extent or volume
of a feature or incident will be necessary but essential and, using proximity analysis,
GIS can establish the distance of objects about a theme or other objects. Any units
of measures can be deployed, including statistical measurements such as sum,
mean, mode and standard deviation” [25].
Spatial distributions: “Spatial distributions of features may be either random and
regular or clustered, and GIS have the functionality, usually via the use of the
nearest neighbor analysis, to describe distributions in these terms. Using contiguity
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analysis, they can also calculate the relationship between differing distributions
across a surface, i.e., spatial autocorrelation” [25].
Network analysis: “This analysis applies to linear features such as transport routes,
rivers, pipelines and cable networks. Analyses can establish least costs routes,
shortest path routes, the degree of connectivity, etc. Measurement in network
analyses can be regarding monetary units, distance, time, etc.” [25].
Temporal analysis: “Spatial changes can be in absolute terms or defined over time.
Thus, it is valuable to know, for instance, the varying rates of growth in an urban
area over equal consecutive time periods, or to identify the proportional changes in
land use for a given area over time. The collection of long-term remotely sensed
data has significantly expedited time series analyses” [25, 28].
Modeling: “This is a broad heading that frequently includes “what if” scenarios or
models are constructed to show what a probable spatial distribution of an object
might be, given its known distribution in a sample area, and this can be done for
different temporal scenarios. Optimum location analysis is a modeling step that
optimizes the location of any activity based on known inputs of the principal
production functions. Digital terrain modeling allows for the inclusion of the height
dimension for GIS analyses of slopes, aspect, contours and volumes” [25].
Interpolation: “This is simply the generation of missing values based on a set of
known values within a study area. For instance, if a series of spot heights (altitudes)
are known, then it is possible to interpolate contour lines for the same area.
Interpolation can be applied to a wide range of measured values” [25].
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1.3. GIS-based urban stock assessment
Modeling of urban stocks has been a useful tool in order to assess a city’s response to future
conditions and its resource self-sufficiency in the context of urban metabolism [7]. GIS can
be used to determine where the stocks are located, how much they are and how they are
distributed. Two of the most fundamental categories of urban stocks are buildings and land
parcels. GIS can inform evaluation of a city’s degree of self-reliance, or its ability to fulfill
human needs (shelter, energy, and food) [29]. Also, GIS-based approaches can assess the
environmental and socio-economic impacts or trade-offs among different patterns of urban
stocks and scenarios of use [30].
GIS-based tools are particularly useful for analyzing different urban stocks using a bottom-
up approach [18]. Typically, the process starts with extracting local stock information as a
geodatabase or database with appropriate geolocation or standard Cartesian projection
systems like Universal Transverse Mercator (UTM), or a national, regional or locally-
defined Euclidean grid system. The information stored in a spatial database may include
detailed map data layers describing the spatial distribution, configuration and properties of
urban land, structures, and infrastructures (such as buildings, roads, and bridges), the major
uses of interest (such as driveways, lawns, and food gardens) and other environmental,
demographic and economic data (such as population density, soil quality, or parcel
ownership). Once a comprehensive spatial database is built, geoprocessing and statistical
tools can be used to delineate or summarize spatially and allocate stocks. Spatial
visualization functions are then used to map the spatial distribution of the stocks [7].
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Several studies have been used GIS as a tool for stock assessment, primarily focused on
mapping the distribution of specific materials such as concrete, steel, and other metals.
Tanikawa and Hashimoto [7] applied the GIS-based technology to estimate construction
material stocks over time with spatiotemporal data. Their study involved the use of a
geodatabase containing urban area buildings, roadways or railways, and sewer networks.
Two spatial databases of two urban study areas were constructed. The construction material
stocks were estimated by spatial database of buildings, roadways and railways stocks.
Based on this analyses, they estimated material accumulation both above and below
ground.
Sugimoto et al. [31] developed a 4d-GIS model which is a database of spatial 3D GIS data
with a time scale, to estimate building material stock and flow and visualize the transition
of buildings in urban districts for contribution to spatial designs. By utilizing 4d-GIS, they
observed that steel buildings initially accounted for 60%, but the number of reinforced
concrete buildings increased over the 50-year study period. Building material stock grew
from 0.35 million tons in 1961 to 97.5 million tons in 2010, largely because of the increase
of RC buildings. They also predicted the building renovation for Business as Usual (BAU)
and District Renewal Plan (DRP) scenarios up to 2050. The results show that building
material stock does not differ much between BAU and DRP scenarios in 2050, but it was
observed in the DRP scenario, building material stock increased by 0.2 million tons
because buildings will be rebuilt collectively in units.
13
Wallsten et al. [17] also used GIS to spatial location and distribution of hibernating metal
stocks of copper, aluminum and, iron in the infrastructure systems as a raw material for
local power, telecommunication, gas and district heating in the city of Norrköping,
Sweden. Here, they used a spatial database containing data layers of cables and pipes, and
buildings. The city area was divided into multiple zoning based on building types and local
zoning code. Per housing unit metal concentrations were estimated for each building-type.
This was followed by the differentiation of the active and inactive infrastructure systems.
Then they calculated active and inactive copper and aluminum stocks (concentrations) with
different cable and pipe types and size. Finally, the metal stocks were cataloged for each
urban districts and mapped considering urban district as area features.
Krook et al. [20] used a similar approach. They used spatial data to characterize the power
grids of Gothenburg and Linköping cities in Sweden. The power grid location was
quantified with regards to city’s total cable length, voltage levels, locations and operational
status. The stocks of copper, in-use and hibernating were estimated for local power
networks by multiplying the cable length with an average copper concentration. The
parameter quantified indicates the economic conditions for the recovery of cables in
hibernation located in the urban environment.
Van Beers and Graedel [19] also quantified and mapped end-of-life stocks and flows of
copper and zinc in Australia at local scale area. The study was based on existing stocks in
use, residence times, and historical and projected future evolution. The research showed
that the integration of GIS with stock analysis facilitated the end-of-life copper and zinc
14
comparison for different demography and industrial characteristics. The outcome also
provided useful insights for the optimization of copper and zinc recycling.
Based on the review above it can be summarized that GIS-based tools have been
successfully applied to study urban stocks of materials at high resolution. These studies
are primarily focused on assessing the sustainability of resources from the context of
recovering and reusing materials once they reach end of life. However, there has been
relatively little work on analyzing the degradation of urban material stocks over time.
There is also potential to utilize apply GIS and remote sensing in bottom-up assessment of
existing urban stocks in order to determine the resulting resource flows (e.g., energy, food)
that these stocks could support. These research questions of urban self-sufficiency are of
particular interest when considering possible supply chain disruptions due to extreme
events and global climate change.
1.4. Motivation and research objectives
To, Assessment of potential future climate impacts and self-sufficiency need to be
incorporated into urban planning in order to sustain future resourceful and low impact cities
with increasing populations. To adequately address these modeling challenges requires
state-of-the-art tools and city-specific case studies. Many recent studies have discussed the
importance of GIS and remote sensing based tools as a tool for assessing infrastructure
response to future conditions and urban self-reliance. Stewart et al. [32] conducted one
motivational study that looked at the temporal extent of concrete degradation under climate
change in the main Australian cities. They have pointed out the importance of mapping the
15
vulnerable concrete structures in climatically sensitive and disaster-prone urban regions.
This type of research will be useful in devising appropriate mitigation techniques and
possible concrete design code modification. Another useful study conducted by Niblick et
al. [33] assessed Pittsburgh’s energy potential in producing biofuel from local scale
sunflower agriculture. They showed how GIS-based tools could be used in estimating the
city’s alternative energy resources and contribute to the state’s renewable portfolio
standard. These studies sparked the following questions that motivated the dissertation
research.
1. What is the present and future extent of the urban built environment’s susceptibility
to climate change?
2. How self-sufficient can a city be in producing energy or food from agricultural
activities by leveraging local land and building resources? Which crop species are
most promising? Are there advantages to taking a regional rather than a strictly
urban approach to assessing resource self-sufficiency?
These overarching research questions were explored through literature review to analyze
conducted as part of the dissertation were motivated by the aforementioned articles, to
expand them and fit them to other less research areas in urban stock analysis. The present
work considers the following specific research objectives:
1. Urban scale mapping of both new and existing concrete buildings under projected
climate change.
16
2. GIS-based modeling to assess biomass-based bioenergy production potential on
urban marginal land using Boston as a case study.
3. Application of the geospatial model developed in objective 2 in assessing regional
scale bioenergy production potential using marginal and degraded lands.
4. GIS and remote sensing application in assessing urban food production potential
on underutilized land parcels and building rooftops.
1.5. Dissertation Structure
The work shown in this dissertation was conducted under the scope of a Northeastern
University Tier 1 grant on “Mapping coastal urban production of food and fuel: What do
know and where can we grow”, as well as Adviser’s startup funds. This research connects
urban metabolism and self-sufficiency and presents both novel methods as well as
engineering analyses that are relevant to current policies and the incorporation of urban
resource sustainability metrics in urban design and engineering, as described in each
chapter.
Chapter 2 describes a study on 4D-GIS based assessment of mapping vulnerable building
stocks in Boston under different projected climate change scenarios. As, Anthropogenic
increases in atmospheric greenhouse gas (GHG) concentrations and resultant changes in
climate will have significant detrimental effects to urban infrastructure, from both extreme
events and longer-term processes. Here we investigate impacts to concrete structures due
to enhanced corrosion through increases in carbonation and chlorination rates. High and
low emission scenarios (IPCC A1FI and B1) are used in combination with downscaled
temperature projections and code-recommended material specifications are used to model
17
carbonation and chloride-induced corrosion of concrete structures in the Northeast United
States. Geospatial modeling in the Boston metropolitan area is used to project building and
block-level vulnerability of urban concrete structures to future corrosion, and related
maintenance needs, and to project cover thickness degradation for the existing building
stock. The methods described here can be used for city-specific modeling of long-term
climate impacts on concrete infrastructure and provide a scientific basis for future-oriented
construction codes. This study was published in 2014 [34] and featured in Boston Globe.
Chapter 3 describes another GIS-based study for assessing a city’s energy self-reliance
from leveraging local resources. Urban marginal land can be used for growing high yield
bioenergy crops such as miscanthus and poplar. In this study, a GIS-based modeling
framework was created to assess potential urban marginal lands in Boston that include
vacant lands and under-utilized public and private areas with adequate soil quality and
sunlight. Using ArcGIS model builder as a spatial analysis tool, land parcels were screened
for typical urban features such as buildings, driveways, parking lots, water and protected
areas. The resultant layer was subjected to bio-geophysical modeling that includes soil
quality, slope analysis and, finally shadow analysis. The next study will explore other urban
regions of Massachusetts that might be able to fulfill part of their energy demand locally
while providing benefits in environmental quality, economic development, and urban
resilience. This study was published in 2015 [35].
Chapter 4 describes a study in continuation of the work shown in chapter 3. This is an
application of GIS-based model developed in chapter 3, for regional scale bioenergy
18
production potential on marginal and degraded lands. Eastern Massachusetts was
considered for this study. The ‘marginal’ land use category includes vacant and abandoned
lands, inner city underutilized areas, and degraded lands. Three different energy crops;
miscanthus, poplar and willow were considered as biomass feedstocks for bioenergy
production estimation. Fast-growing poplar was found as most suitable high yield
bioenergy crops for this region. Bioenergy potential calculation revealed that there exists
significant bioenergy potential for this region that can be used for heat, conversion to fuels,
or electricity production, particularly for microgrid or district heating applications. The
outcomes of this study are in line with previous work that evaluated marginal land
availability in Boston and other major Eastern U.S. cities, and confirm the accuracy of the
spatial model constructed in chapter 3. The study outcome is currently in preparation for
journal submission.
Chapter 5 describes another GIS-based assessment of urban scale food system and
production potential. Urban farmlands can be leveraged for developing a sustainable food
system by growing high yield food plants. Here, the GIS and remotely sensed data were
used to develop an automated model to assess Boston’s available area for urban farming,
including both rooftop and ground level areas. Geoprocessing and spatial analysis tools
were used to process geographic data layers for zoning, ownership, slope, soil quality, and
adequate light availability. Surface slope (roof pitch) was determined for all buildings in
the city through creation of a digital surface map from remotely sensed LiDAR data. Also,
ground level public and private vacant lands and underutilized residential and commercial
areas were mapped. Finally, food yield data for typical urban crops were used to estimate
19
the city’s food production potential. The study outcome was compared with other regions
in Eastern United States that might be able to fulfill part of their food demand locally while
providing benefits in local environmental quality and economic development. The study
outcome is in preparation for journal submission.
As the final chapter, Chapter 6 presents critical summary of all the research completed in
this dissertation and explains potential future work to follow these studies.
20
Chapter 2 Urban Scale Mapping of Concrete Degradation from Projected
Climate Change
This study has been published
Saha, M., & Eckelman, M. J. (2014). Urban scale mapping of concrete degradation from projected climate
change, 9, 101-114.
Anthropogenic increases in atmospheric greenhouse gas (GHG) concentrations and
resultant changes in climate will have significant detrimental effects to urban
infrastructure, from both extreme events and longer-term processes. Here we investigate
impacts to concrete structures due to enhanced corrosion through increases in carbonation
and chlorination rates. High and low emission scenarios (IPCC A1FI and B1) are used in
combination with downscaled temperature projections and code-recommended material
specifications are used to model carbonation and chloride-induced corrosion of concrete
structures in the Northeast United States. The results suggest that current concrete
construction projects will experience carbonation and chlorination depths that exceed the
current code-recommended cover thickness by 2077 and 2055, respectively, well within
the lifetimes of these buildings, potentially requiring extensive repairs. Geospatial
modeling in the Boston metropolitan area is used to project building and block-level
vulnerability of urban concrete structures to future corrosion, and related maintenance
needs, and to project cover thickness degradation for the existing building stock. The
methods described here can be used for city-specific modeling of long-term climate
impacts on concrete infrastructure and provide a scientific basis for future-oriented
construction codes.
21
2.1 Introduction
Understanding the implications of climatic variation has become a critical issue for
infrastructure maintenance planning. The Earth’s average temperature has been increased
by 0.6 oC since the 1900s and is expected to increase by approximately 1.4-5.8 oC by the
end of this century [36]. Many of the effects of climate change, including changes in
temperature, pollutant concentrations, relative humidity, precipitation, and wind patterns,
as well as increased frequency of severe events could have significant impacts on the
operations and lifespan of critical and non-critical infrastructure [37]. Infrastructure
capacity could be acutely overwhelmed (e.g., sea walls failing due to storm surge) or
degraded gradually. Assessing the potential impacts of climate change on the built
environment is difficult, as the relationship between material degradation and climate is
complex [38]. The Northeastern United States is likely to see an increase in extreme
precipitation events as well as overall increases in temperature and relative humidity [39].
Climate-induced damages to urban infrastructures are of particular concern. Urban areas
in the United States currently include approximately 250 million residents, projected to
grow to ~365 million by 2050 [40]. While the urban share of population and economic
output in the US has grown in the past decades, much of the existing urban infrastructure
has become increasingly susceptible to failures [41, 42]. Aging buildings and
transportation, energy, water, and sanitation infrastructure are all expected to become more
stressed in their ability to support existing services for urban residents in the coming
decades, especially when the impacts of climate change are added as stressors [43]. Climate
22
change will also contribute directly to physical degradation of infrastructure and building
materials [44].
While much research on climate change impacts has focused on infrastructure
susceptibility to extreme events and flooding from long-term sea level rise [45], relatively
few studies have been carried out on the direct effects of climate change on the structural
deterioration of infrastructure. One direct mechanism is acidic attack of cementitious
materials. Concrete degradation due to acid rain has been extensively studied [46], and
elevated levels of atmospheric CO2 will increase the formation of carbonic acid in
precipitation. Similarly, uptake of CO2 by the oceans and the resulting decrease in pH will
amplify degradation of structures in urban coastal areas that are exposed to seawater [47].
Another mechanism for climate-induced concrete degradation is through early failure of
the protective concrete cover over reinforcing steel, leading to corrosion and spalling, due
to changes in CO2 and temperature [48, 49], which has only recently been analyzed. Yoon
et al. [50] was among the first to consider the effects of climate change on concrete
performance and lifetime, in particular the effect on carbonation rates; however, this model
does not account for the influence of temperature change, which can significantly affect
the diffusion coefficient of CO2 into concrete, the rate of reaction between CO2 and
Ca(OH)2, and the rate of dissolution of CO2 and Ca(OH)2 in pore water. The model is also
a time-independent predictive model that assumes CO2 concentrations to be constant up to
a given time, thereby underestimating carbonation depths under changing atmospheric
conditions [51]. Stewart et al. [52] built on the work by Yoon et al. [50] by taking into
23
account the effect of temperature on the diffusion coefficient, but they did not consider the
influence of temperature on the other aforementioned parameters. Their work looked not
only at carbonation and chlorination, but also at the time to crack initiation, crack
propagation, and failure due to reinforcement corrosion. Similar carbonation and
chlorination models were used by Stewart et al. [52] in their work, who noted that there is
a need for an improved model that considers the time-dependent effect of CO2
concentration and other parameters such as temperature and relative humidity.
Recently, Talukdar et al. [48] estimated carbonation (but not chlorination) penetration
depths in concrete due to projected climate change. Several deterministic model parameters
were experimentally verified using unloaded/undamaged concrete. They reported 25-35
mm increase in penetration depth due to carbonation alone. Separately, Bastidas-Arteaga
et al. [53] investigated the influence of global warming on chloride ingress into concrete
using a stochastic model of chloride penetration and corrosion initiation. Their particular
approach was to model future weather conditions, recognizing that temperatures will
fluctuate not only over the century, but also during a given year, and that the duration of
the hot season throughout most of the world is expected to lengthen over this century. They
found significant correlation between chloride ingress over time associated with projected
global warming. Talukdar et al. [54] then improved their carbonation model and coupled
it to the climate model proposed by Bastidas-Arteaga et al. [53] to project concrete
infrastructure degradation and to consider the suitability of current code requirements.
24
The current study builds on these previous reports by estimating climate-induced changes
in corrosion depths for both carbonation and chloride induced corrosion for multiple
climate scenarios and at a high level of geospatial resolution. A scenario modeling
approach is used to estimate the carbonation and chlorination depth in cement concrete
under both high and low greenhouse gas (GHG) emission scenarios (IPCC A1FI and B1).
The results are then compared to the minimum American Concrete Institute (ACI) code-
recommended concrete cover thickness, which is meant to prevent corrosion of reinforced
concrete structures but does not currently reflect future anticipated changes in climate.
Geographic information system (GIS)-based spatial analysis identifies the location of
vulnerable concrete structures within metropolitan Boston, a densely populated area home
to nearly 4.5 million people and representative of urban coastal areas on the Atlantic
seaboard [40]. The results from this study are both temporally and geographically specific
and relevant to infrastructure policy and construction codes, maintenance planning, and
technology adoption to mitigate corrosion-induced damage of the built environment.
25
2.2 Climate Change Scenarios
Projections of atmospheric and climatic conditions are required at the urban scale for this
study. Earlier work on climate change effects on Boston by Ruth [55] projected increases
in annual temperatures of 2-5 oC (4-9oF) and in annual precipitation up to 25%, with the
frequency and intensity of extreme precipitation events and droughts increasing as well.
Temperature changes will likely be accompanied by continual rise of sea levels with
seasonal and periodic fluctuations, and may exacerbate already existing natural hazards in
the region, including floods, heavy rainstorms, and hurricanes [56]. Temperature changes
and CO2 concentrations were accounted for in this study, as these are likely to have the
most direct effect on concrete deterioration. Rainfall and relative humidity also affect
erosion of concrete surfaces as well as diffusion of pollutants into the subsurface but were
not included for the following reasons. The effects of rainfall on concrete structures are
highly site specific [48], and both precipitation and relative humidity projections carry high
uncertainty in long-term climate models [57].
2.2.1 Atmospheric CO2 Concentrations
Carbonation of concrete is a function of ambient concentrations of CO2. Future
atmospheric CO2 concentrations were projected based on the IPCC A1 and B1 special
report on emission (SRES) scenarios [58]. The A1 scenario indicates very rapid economic
growth, a global population that peaks in mid-century and declines thereafter, and the rapid
introduction of new and more efficient technologies, as well as substantial reduction in
regional differences in per capita income. A1 scenario has two sub-categories that include
A1FI and A1B. A1FI (fossil intensive) scenario considers the rapid introduction of new
26
and more efficient technologies, substantial reduction in regional differences in per capita
income, and intensive fossil energy consumption [58]. (A1B is a moderate scenario that
balances fossil intensive and non-fossil intensive energy sources.) The B1 scenario
assumes the same population trend as A1, but with rapid changes in economic structure
towards a service and information economy, with reduction in material intensity and
introduction of clean and resource-efficient technologies. A control emissions scenario
based on year 2000 ambient CO2 concentrations is also considered to provide a reference
for other emission scenarios.
Fig. 2-1 depicts projected CO2 concentrations from 2000-2099 based on the Model for
Assessment of Greenhouse-gas induced Climate Change (MAGICC) [59]. Boston is an
urban center with a high density of fossil fuel combustion activities, particularly from the
transportation sector. In such urban settings, CO2 concentrations can be 5-30% higher than
nearby rural environments [60]. These elevated urban CO2 concentrations are reflected in
an urban environment term ke, which is introduced as a correction factor in the methods
section.
27
Fig. 2-1: Predicted estimates of CO2 concentrations. Actual atmospheric CO2
concentration has been plotted side by side along with predicted concentrations for 2000-
2013 and shown in the inset panel
2.2.2 Temperature Predictions
Future temperatures for Boston were predicted using data generated from four different
atmospheric-oceanographic global climate models (AOGCM). These include the
Community Climate System Model (CCSM), Hadley Climate model (HADCM2),
Geophysical Fluid Dynamics Institute Model (GFDI) and Parallel Climate Model (PCM).
Each of these were downscaled to give station-based predictions for Boston’s Logan
Airport. Daily average temperature data were extracted for each of the A1FI and B1
emissions scenarios respectively.
28
Downscaling was performed using the asynchronous regional regression model (ARRM)
developed by [61]. This technique was established to bridge the gap between large-scale
outputs from AOGCMs and the fine-scale output required for urban climate impact
assessments. ARRM uses piecewise regression to quantify the relationship between
observed and modeled quantiles and then downscale future projections [62]. Fig. 2-2
presents the change in average annual temperature in next 100 years for different
AOGCMs.
Fig. 2-2: Predicted mean temperature increase for Boston’s Logan Airport station for
A1FI and B1 scenarios; results downscaled from CCSM, HADCM3, GFDI, PCM models
2.3 Corrosion
2.3.1 Concrete Carbonation Modeling
Carbonation depth depends on several parameters such as concrete quality, protective
cover thickness, temperature, and ambient CO2 concentration. Carbonation of concrete has
been studied extensively and various models have been developed for the purpose of
predicting carbonation depths [48, 52]. It has been observed that corrosion may occur when
0
1
2
3
4
5
6
7
2000 2020 2040 2060 2080 2100
Tem
pera
ture
In
cre
ase (
oC
)
Year
CCSM-A1FI
HADCM3-A1FI
GFDI-A1FI
PCM-A1FI
CCSM-B1
HADCM3-B1
GFDI-B1
PCM-B1
29
the distance between the carbonation front and the rebar surface is less than 1-5 mm [50],
although probabilistic analyses for assessing durability design specifications tend to ignore
this effect. Here we assume that corrosion initiation occurs when carbonation front depth
equals the concrete cover thickness.
In the carbonation depth model recommended by DuraCrete [63] and Yoon et al., [50] and
others, carbonation depth (xc in cm) in year t is predicted as a diffusion process:
𝑥𝑐(𝑡) = √(2𝐷𝐶𝑂2
(𝑡)
𝑎) ∙ 𝑘𝑒,𝐶𝑂2 ∙ 𝐶𝐶𝑂2
(𝑡) ∙ (𝑡 − 𝑡0) ∙ (𝑡𝑖,𝐶𝑂2
𝑡−𝑡0)
𝑛𝑚
; 𝑡 ≥ 2000 (i)
The time and temperature-dependent diffusion coefficient DCO2(t) is given by:
𝐷𝐶𝑂2(𝑡) = 𝑓𝑇(𝑡) ∙ 𝐷0,𝐶𝑂2(𝑡 − 𝑡0)−𝑛𝑑,𝐶𝑂2 (ii)
where D0,CO2 is the initial CO2 diffusion coefficient, with a recommended range of 0.5-50
× 10-4 cm2/s ; nd,CO2 is the ageing factor for the CO2 diffusion coefficient determined
empirically; and t0 is the initial year, 1999. The time-dependent temperature factor is
described as:
𝑓𝑇(𝑡) ≈ 𝑒𝑥𝑝 (𝐸
𝑅 −
1
273+𝑇𝑎𝑣𝑔(𝑡)) (iii)
where Tavg(t) is the running average temperature (oC) over the interval t–t0, E is the
activation energy of the diffusion process (40 kJ/mol) [64]; and R is the universal gas
constant (8.314×10−3 kJ/mol-K).
Cement characteristics are combined in Eq. (i) in a single factor a:
𝑎 = 0.75𝐶𝑒 ∙ 𝐶𝐶𝑎𝑂 ∙ 𝛼𝐻 ∙𝑀𝐶𝑂2
𝑀𝐶𝑎𝑂 (iv)
30
where Ce is the cement content (kg/m3); CCaO is the calcium oxide content in cement; MCaO
is the molar mass of CaO; MCO2 is the molar mass of CO2, and αH is the degree of hydration,
given by:
𝛼𝐻 ≈ 1 − 𝑒−3.38(𝑤/𝑐) (v)
The water-cement ratio w/c is a design parameter for concrete and is codified according to
the intended exposure condition (Table 2-1).
The remaining terms in Eq. (i) are as follows: ke,CO2 is a correction factor to account for
increased CO2 levels in urban environments; CCO2(t) is the time-dependent concentration
of ambient CO2 (10−3 kg/m3) in Section 2.1 (using the conversion factor 1 ppm =
0.0019×10−3 kg/m3); and (𝑡𝑖,𝐶𝑂2
𝑡−𝑡0)
𝑛𝑚
reflects the aging of the concrete matrix, including
ti,CO2 a reference time, 1yr; and an ageing factor for microclimatic conditions (nm)
associated with the annual frequency of wetting and drying cycles. nm is 0 for sheltered
outdoor surfaces and 0.12 for unsheltered outdoor surfaces. All parameter values and
supporting references are given in Table 2-1.
Table 2-1. Structural durability conditions for concrete used in this study
Parameters Unit Value Reference
General
f/c MPa 34.5 ACI Code, [65]
w/c dimensionless 0.4 ACI Code, [65]
Ce kg/m3 450 Stewart et al. [51]
CCaO dimensionless 0.65 Stewart et al. [52]
MCO2 g/mol 44 Stewart et al. [52]
MCaO g/mol 56 Stewart et al. [52]
nm dimensionless 0.12 Yoon et al. [50]
Carbonation
D0,CO2 cm2/s 4×10-4 Yoon et al. [50]
ti year 1.0 Yoon et al. [50]
nd,CO2 dimensionless 0.24 Yoon et al. [50]
31
ke,CO2 dimensionless 1.2 Yoon et al. [50]
Chlorination
D0,Cl m2/s 7.0×10-12 Stewart et al. [52]
ti year 0.07 Stewart et al. [52]
nd,Cl dimensionless 0.35 Stewart et al. [52]
Co kg/m3 8.0 Val and Stewart. [66]
Ccr kg/m3 3.35 Val and Stewart. [66]
ke,T dimensionless 0.92 Stewart et al. [52]
kt dimensionless 1.0 Stewart et al. [52]
kc dimensionless 1.0 Stewart et al. [52]
The years 2000 to 2099 were considered. Corrosion depths were also estimated without
any climate change effects by keeping CO2 and temperature constant over the time period
in the corrosion models. This was termed as control condition and will be used in later
sections.
2.3.2 Concrete Chlorination Modeling
Chloride ions can penetrate concrete by capillary absorption, hydrostatic pressure, and
diffusion [67]. The most recognized method is diffusion, governed by Fick’s second law
[68], although chloride penetration processes and field conditions can deviate from those
assumed for Fickian diffusion [66]. Surface chloride concentration, Co and diffusion
coefficients, Dcl are easily calculated by fitting Fick’s law to measured chloride profiles.
An improved model utilizing a time-dependent chloride diffusion coefficient proposed by
DuraCrete [69] is used here to calculate chloride concentration. Corrosion initiation occurs
when the chloride concentration at the level of reinforcement exceeds the critical chloride
concentration, typically 3.35 kg/m3, which is independent of concrete quality [69].
32
The chloride concentration at depth xcl mm in year t has the standard solution:
𝐶(𝑥, 𝑡) = 𝐶0
[
1 − 𝑒𝑟𝑓
(
𝑥𝑐𝑙
2√𝐷𝐶𝑙(𝑡).(𝑡𝑖,𝐶𝑙𝑡−𝑡0
)𝑛𝑚
.(𝑡−𝑡0)
)
]
; t ≥ 2000 (vi)
DCl(t) is the time and temperature-dependent apparent chloride diffusion coefficient:
𝐷𝐶𝑙(𝑡) = 𝑘𝑒,𝑇 ∙ 𝑘𝑡 ∙ 𝑘𝑐 ∙ 𝑓𝑇(𝑡) ∙ 𝐷0,𝐶𝑙(𝑡 − 𝑡0)−𝑛𝑑,𝐶𝑙 (vii)
where ke,T is a correction factor to account for increased temperatures due to urban heat
island effects; kt is the test method factor (1.0); kc is the curing factor (1.0); ti,Cl is the
reference time in years (28 days or 0.077 years); fT(t) is the temperature effect on diffusion
coefficient given by Eq. (iii); and nd,Cl is the chloride-specific ageing factor determined
empirically.
Table 2-2. Recommended concrete properties for corrosion protection (ACI Code, [65])
No. Exposure Condition Maximum
w/c ratio by
weight
Minimum
f/c (MPa)
1 Concrete intended to have low permeability when
exposed to water 0.50 27.5
2 Concrete exposed to freezing and thawing in a
moist condition or to deicing chemicals 0.45 31.0
3 Corrosion protection of reinforcement in concrete
exposed to chlorides from deicing chemicals, salt,
salt water, brackish water, seawater or spray from
these sources
0.40 32.5
The surface chloride concentration Co is considered as a time-independent variable as
exposure to chlorides for a specific structural member would not change significantly from
year to year [52]. However due to climate change, fluctuation in wetting/drying cycles,
rainfall and wind patterns could vary but there is no data to support how this might affect
33
Co. The surface chloride concentration is reported as 7-8 kg/m3 for splash/tidal zones [66].
In this study Co = 8 kg/m3 was assumed. Structural exposure conditions were chosen from
Table 2-2 as condition 3, which references “concrete exposed to chlorides from deicing
chemicals, salt, salt water, brackish water, seawater or spray from these sources” [65]. The
necessary data for estimating DCl(t) were derived from [52]. All parameter values used in
the chlorination model are shown in Table 2-1.
2.4 Spatial Analysis
The analysis described in Sections 2-3 apply generically to new buildings constructed in
the Boston area. In order to identify and evaluate the spatial distribution of climatically
vulnerability for actual concrete structures within the city, geospatial analysis was
performed at higher resolution using ArcGIS 10.2 (ESRI). Gridded temperature data for
the year 2000-2099 were collected from the National Center for Atmospheric Research
(NCAR) climate database [70]. The NCAR database has gridded downscaled data available
from the community climate system model (CCSM) only. Therefore, for spatial analysis
purposes, CCSM is chosen among four different AOGCMs discussed in Section 2.2.
Raster-based geo-statistical analysis has been performed to identify the climatically
vulnerable region within the city according to high (A1FI) and low emission (B1)
scenarios. The resolution of downscaled climate data was 3 miles × 3 miles (0.33o). The
raster analysis of gridded climate data was performed using the ‘geospatial analysis’ tool
in ArcGIS. The raster analysis provided contour plots of temperature within the city.
Ordinary kriging with no transformation was used for spatial interpolation.
34
The goal of the spatial analysis is a building-by-building assessment of concrete
vulnerability to enhanced carbonation and chlorination. Rather than using a baseline
construction year of 2000, here we identified and cataloged concrete structures according
to their actual year of construction. Building age and composition data were obtained from
GIS clearing houses including the city of Boston GIS data hub [71], MassGIS data layers
[72] and Boston Redevelopment Authority planimetric dataset [73]. Using these data, an
atlas of concrete buildings by decadal cohort was created and analyzed to identify
vulnerable building structures. The scope of climate vulnerability was limited to concrete
buildings but could also be extended to roadways and bridges. Fig. 2-3 presents the
locations of all 1,700 concrete buildings in Boston. Concrete buildings represent 3% of
total available land area within the city and 46% of total building area. Carbonation and
chlorination models were run for each building cohort beginning in 1951 to estimate the
year at which the concrete cover for each building would be compromised.
Fig. 2-3: Concrete structures (buildings, in red) within Boston (outlined in gray)
35
2.5 Results
2.5.1 Carbonation and Chlorination Depth.
Results from the corrosion depth analysis indicate significant and prolonged climate-
induced effects on both carbonation and chlorination of concrete structures (Fig. 2-4 and
2-5). Results suggest that for Boston, for the HADCM3 model under the worst case
scenario (A1FI), carbonation depths would equal the protective cover thickness by the year
2077 and would exceed the cover thickness by approximately 15 mm by the end of the
century. This represents an increase in depth of nearly 40% compared to that expected
assuming current temperatures and CO2 concentrations (Table 2-4) for which the ACI code
was designed. Penetration rates for chlorination exceed those for carbonation. For chloride-
induced corrosion under the same HADCM3–A1FI scenario, the protective layer is
exhausted even earlier, by the year 2055, with penetration depths 12% higher than in the
control scenario (Table 2-4).
By 2030, the maximum chloride penetration depth was calculated to be 27 mm, which is
11-12 mm higher than the predicted carbonation depth (Fig. 2-4). After 2030, accelerated
carbonation due to climate change effects noticeably diverges from the control case (Fig.
2-4). This is generally expected as, over time, the temperature differential becomes greater
and hot seasons become extended. Averaging across climate models and emission
scenarios, corrosion damage will be noticeable after 2083 (Fig. 2-4) for carbonation and
by 2062 due to chloride ingress (Fig. 2-5). As, after that timeframe both the carbonation
and chlorination depth will exceed the code recommended protected cover (38-50 mm)
installed in the majority of concrete structures.
36
Predicted carbonation and chlorination depths were compared with results from previous
studies, and were found to be higher than the previous studies for both carbonation and
chloride induced corrosion. Talukdar et al. [54] reported 23 mm for Vancouver, while [32]
found 28 mm for Sydney. Talukdar and Banthia [74] later compared different cities
(London, Mumbai, New York, Toronto and Vancouver) across the globe and estimated
carbonation depths that ranged between 15-35 mm by the end of the century. For New
York City, the estimated depth was 35 mm for a high emission scenario, which is 25% less
than the carbonation depth estimated for Boston in the present work. The higher result here
is due in part to a different assumed initial diffusion coefficient. Here we assumed a value
of D0,CO2 of 4×10-4 cm2/s, following previous work [50], whereas Talukdar and Banthia
assumed a range of 0.5-1.6×10-4 cm2/s. Secondly, this study considered predicted rise in
GHG emissions for SRES scenarios according to IPCC 4th assessment report, which
described higher values than the 3rd assessment report used by Talukdar and Banthia [74].
Chlorination results have been compared with Stewart et al. [32], who investigated major
coastal urban areas in Australia. The results suggest a 9 mm increase in design concrete
cover for Boston by 2100 for the A1FI SRES emission scenario, which is slightly higher
than previous results reported for Australian cities (5-7 mm increase). Differences in
structural exposure conditions that include surface chloride concentration, Co concrete
strength, f/c and regionally typical water cement ratio (w/c) contributed to the higher results
found for Boston.
37
Fig. 2-4: Estimated carbonation depth (mm) in BMA for a building constructed in 2000
Fig. 2-5: Estimated chlorination depth (mm) in BMA for building constructed in 2000
0
10
20
30
40
50
60
2000 2020 2040 2060 2080 2100
Carb
on
ati
on
Dep
th (
mm
)
Year
A1FI-CCSM A1FI-HADCM3 A1FI-GFDI
A1FI-PCM B1-CCSM B1-HADCM3
B1-GFDI B1-PCM Control
0
10
20
30
40
50
60
2000 2020 2040 2060 2080 2100
Ch
lori
nati
on
Dep
th (
mm
)
Year
A1FI-CCSM A1FI-HADCM3 A1FI-GFDI
A1FI-PCM B1-CCSM B1-HADCM3
B1-GFDI B1-PCM Control
38 mm
∆x = 26 yr
∆x = 10 years
38 mm
38
Table 2-3. Minimum cover (mm) required to counteract the impact of climate change on
carbonation-induced corrosion damage risks by 2100.
Emission
scenarios
CCSM HADCM3 GFDI PCM
A1FI 51.1 52.6 51.0 49.8
B1 43.0 43.7 43.1 42.9
Control 37.6 37.6 37.6 37.6
ACI Code 38 38 38 38
Table 2-4. Minimum cover (mm) required to counteract the impact of climate change on
chlorination-induced corrosion damage risks by 2100.
Emission
scenarios
CCSM HADCM3 GFDI PCM
A1FI 47.9 49.6 47.8 46.4
B1 44.7 45.5 44.8 44.5
Control 44.1 44.1 44.1 44.1
ACI Code 38 38 38 38
2.5.2 Geospatial Results
The results above suggest that new concrete structures constructed in urban coastal regions
using current code requirements may degrade prematurely and require costly maintenance
over their service life. Spatially resolved analysis at the urban scale will help to identify
vulnerable zones of existing buildings within the city to anticipate monitoring, testing, and
potential maintenance for specific concrete structures that are part of the current building
stock. The results are summarized in Fig. 2-6 for the high (A1FI) emission scenario.
39
Fig. 2-6: a) Location of gridded climate data, b) Climatically vulnerable zones within
Boston (A1FI emission scenario, predicted mean temperature at 2099, CCSM model)
The kriging analysis identifies the areas within the city those are vulnerable due to increase
in temperature over time (Fig. 2-6b). The average temperature in Boston may increase up
to 4-5oC by the year 2100. Results show that high-density areas of downtown Boston, East
Boston and Charlestown and part of South Boston will be subjected to higher temperature
increases from climate change. These areas are located within a four-mile radius of the
high-temperature increase (red) zones identified by kriging analysis (Fig 2-6b). An earlier
study revealed that these same areas will also be highly vulnerable to storm surge [55].
Consequently, the concrete structures situated in these areas will be vulnerable to higher
degrees of structural deterioration associated with corrosion.
Among the 1,700 concrete buildings identified within the city, approximately 60% are
located within the identified high risk zones for corrosion (Fig. 2-7a). In addition, nearly
45% of the concrete buildings in Boston are more than 35 years old, so that significant
carbonation and chlorination may have already occurred. There are roughly 10-12%
concrete buildings located especially in downtown region that will exceed the typical
a) b)
40
design service life for concrete structures (~ 60-75 years) by 2030 (Fig 2-7b). Their
extended service age and location on the waterfront increases the overall extent of
carbonation and chlorination and puts them at high risk for corrosion initiation over the
next 20-30 years. By 2050, penetration depths in nearly 60% of existing buildings will
exceed the code-recommended cover thickness (Fig 2-7b). Degradation rates will increase
over time with the projected change in climate and eventually affect 100% of the existing
building stock before 2080. Consequently, these buildings may require significant
inspection and maintenance. These spatial and temporal results can be used to formulate
appropriate testing protocols and maintenance schedules.
Fig 2-7: a) Concrete Structures classified according to different age, b) % of buildings
with compromised cover thickness over the service life
2.6 Discussion
2.6.1 Implications for Current Code Requirements for Concrete Cover
The results presented are based on structural exposure condition of ACI Code, 2011 which
only pertains to minimum concrete cover (38.5 mm) and concrete compressive strength
(32.5 MPa). It is important to note that, for a specific structure, more precise results in
a) b)
50%
41
terms of climate change impacts on corrosion can be obtained only by specifying material
and construction specifications and structural detailing. This increase in corrosion
phenomena is driven more by increases in atmospheric CO2 concentration in which there
is high confidence, and less by the less accurate projections in temperature and relative
humidity. Even under the control emission scenario with stable CO2 concentrations and
temperature, the increase in damage risks over the 21st century will still be significant and
cannot be ignored, particularly for chlorination-induced corrosion of structures located on
the coast.
The results also suggest that if concrete buildings in coastal locations are designed with up
to 10-12 mm extra concrete cover or use higher grade concrete or low carbon steel then
this will reduce the effects of climate change even if the climate change trends occur
according to the projected highest emission scenario (A1FI). A less severe emission
scenario (B1) would require less additional cover, perhaps as low as 3-5 mm. Where
stability is governed by chlorides, a 5-10 mm increase in design concrete cover is suitable
for the A1FI emission scenario. Increasing design cover by up to 8 mm or increasing
concrete compressive strength by one grade would increase construction costs by
approximately 2-4% [32], but has the potential to save billions of dollars per year in repairs.
Relevant ACI technical committees have recently discussed the effect of climate change
on concrete durability [75] and emphasized the need to formulate necessary code to
mitigate carbonation ingress in concrete structures. But to our knowledge, no U.S. state or
municipal authority has yet altered concrete cover requirements in response to future
climate projections.
42
2.6.2 Concrete Technologies for Climate Change Adaptation
Several anti-corrosion technologies are available that reduce the vulnerability of reinforced
concrete structures and thus improve their adaptive capacity to changing climatic
conditions. These include protective surface coatings, which are easy to apply to existing
buildings but can carry high costs. Acrylic-based surface coatings can reduce carbonation
depths by 10-65% [32]. Stainless steel, galvanized steel or other methods of cathodic
protection, or glass fiber reinforced polymer rebar can be used instead of low carbon steel
in new buildings. Chlorination can also be reversed through electro-chemical chloride
extraction, though the high cost of this technology means that it would be reserved for
critical structures [76]. These adaptation steps have the benefit of reducing or reversing
diffusion would improve the performance of concrete infrastructures under a changing
climate.
Many existing concrete structures are likely to suffer from decreased durability due to
climate change. As this risk varies widely with location, environmental exposure and
material design is therefore critical to predict for every individual structure. A protective
approach would suggest that increased monitoring and maintenance of concrete structures
would be preferred. Clearly, the costs and benefits of such an approach will vary widely
by location, and a life cycle assessment approach may be useful for efficient practical
implementation [51]. The interaction of atmospheric CO2 with concrete has implications
not just for concrete performance but also for climate change mitigation and GHG
accounting. Carbonate weathering of concrete and natural minerals acts as a carbon sink.
43
Yearly CO2 uptake by existing concrete structures can be estimated and included in
accounts of urban GHG emissions [77].
2.6.3 Future Research Needs
As chlorination induced corrosion is typically more widespread and severe than
carbonation-induced corrosion, especially in coastal regions, more research and field data
is necessary in this regard. One specific need is that the mechanistic relationship between
chlorination and carbonation processes has yet to be properly modeled. Chloride ingression
occurs through the release of bound chloride in the hardened concrete, leading to reduction
in alkalinity and potentially increasing the risk of carbonation-induced corrosion [78].
Therefore, the synergistic interaction between these two processes may result in corrosion
rates higher than those predicted here. Finally, it should also be noted that the results based
on these models depend on downscaled climate data, but that there is significant
uncertainty associated with downscaling and with long-term climate projections generally.
Every model has its associated uncertainty in parameter and data related uncertainty which
may affect the overall modeling performance as well as outcome. Therefore, more
elaborate, site-specific experimental carbonation and chlorination depths estimation are
necessary to compare experimental results with model performance for validation
purposes. The outcome of city-specific studies can also be extended to assess regional or
national level vulnerability assessments and policy modeling of code requirements. This
assessment can be used to anticipate climate change implications in the planning, design
and maintenance of concrete structures.
44
2.7 Conclusion
Relatively little research has been directed to long-term susceptibility of urban
infrastructure and materials to climate change outside of sea-level rise and flooding.
Critical questions remain about how and where climate change will affect the underlying
materials and structures in different types of urban settlement zones. The present study
details both a temporal and geospatial framework for analyzing climate change impact on
urban concrete structural deterioration, using corrosion depth as the primary metric. The
major findings of this study are:
Climate change will accelerate corrosion and degradation of concrete structures in
Boston. By the year 2055, the chlorination-induced corrosion depth in concrete
structures built in year 2000 may exceed the code recommended protected cover
thickness of 38 mm (1.5 in). For carbonation-induced corrosion the threshold year is
2077. Concrete code modification may be required in light of regional climate
projections.
Chlorination-induced corrosion is more prevalent in Boston compared to carbonation.
Carbonation induced corrosion primarily depends on atmospheric CO2 concentration
while chlorination depends on surface chloride concentration.
From the spatial analysis, it can be inferred that nearly 60% of the existing concrete
structures in Boston will be more prone to structural deterioration associated with
corrosion by 2050 compared to control scenario.
In conclusion, accelerated corrosion in concrete will be a long-term, globally prevalent
effect of climate change, particularly in coastal cities. Corrosion is also preventable with
45
appropriate changes to concrete codes and the use of protective coatings or alternative
building materials. Code adjustment can be made based on regional assessments, and finer-
scale modeling of concrete mechanics can extend this work to account for the effect of
loading and cracking. Additional modeling can also be applied under different future
climate scenarios, length scales, and geographic regions to anticipate the pervasive effects
of regional or global climate change on both unstressed and stressed concrete structures.
46
Chapter 3 Geospatial Assessment of Potential Bioenergy Crop Production
on Urban Marginal Land
This study has been published
Saha, M., & Eckelman, M. J. (2015). Geospatial assessment of potential bioenergy crop production on urban marginal land, 159, 540-547
Urban marginal land can be used for growing high yield bioenergy crops such as
miscanthus and poplar. Here, a GIS-based modeling framework was created to assess
potential urban marginal lands in Boston that include vacant lands and under-utilized
public and private areas with adequate soil quality and sunlight. Using ArcGIS model
builder as a spatial analysis tool, land parcels were screened for typical urban features such
as buildings, driveways, parking lots, water and protected areas. The resultant layer was
subjected to bio-geophysical modeling that includes soil quality, slope analysis and finally
shadow analysis. Approximately, 2660 hectares of potential marginal land was identified
as suitable, representing 24% of total land area in Boston. Using crop yield information, it
was estimated that this marginal land could be used to produce up to a total of nearly 42,130
tons of high yield short rotation poplar biomass in a regular growing season. Also,
bioenergy potential calculation revealed that for short rotation poplar, this amount of
biomass can potentially yield up to 745 TJ (LHV) to 830 (HHV) TJ of yearly primary
energy for the city of Boston that can be used for heat or electricity production, particularly
for microgrid or district heating applications. This is equivalent to ~0.6% of Massachusetts
primary energy demand for 2012. Ongoing work will explore other urban regions of
Massachusetts and the Eastern US that might be able to fulfill part of their energy demand
locally while providing benefits in environmental quality, economic development, and
urban resilience.
47
3.1 Introduction
Urban inhabitants represent the majority of global energy demand (75%), with more than
50% of the population currently residing and working in cities [79]. Although densely
settled cities cannot be self-sufficient in food or energy production, many communities are
considering growing dedicated energy crops on under-utilized land to produce food and
fuel for district heating and small-scale electricity production [80]. Such schemes provide
opportunities for public and private actors in municipalities to fulfill part of their energy
demand locally while providing potential benefits to residents in the form of improved
landscapes, economic development, and modulation of urban heat islands [81]. Local
sources of energy may provide a temporary buffer to communities when a power grid
failure or heating fuel supply disruption occurs due to natural catastrophes such as
hurricanes and floods [73]. However, it is important that urban bioenergy production
address community concerns such as odor, noise, or increased traffic [82], and not impede
other beneficial uses of valuable urban land.
A study conducted by U.S. Department of Energy reported an increase of bioenergy
production by more than 300% in the past decade, with potential 1.9 PWh available
annually in the contiguous United States [83, 84]. While much of the recent increase is due
to corn ethanol, woody biomass has seen growth as a primary or secondary fuel in electric
generating units and in residential high-efficiency pellet stoves. In addition to large-scale
agricultural and forestry operations to supply this bioenergy, urban marginal land can also
play a part in larger bioenergy production systems. Previous studies have characterized
urban marginal lands as land parcels that have limited economic value and not suitable for
48
agricultural or residential purposes [82, 85]. A central challenge of this work is to
determine the extent of marginal land for urban regions [86]. Recent urban energy and
geography research has focused on the development of computational tools to acquire data
and estimate urban marginal land for different cities [87]. One study estimated that
approximately 15% of U.S. urban land on average can be considered marginal [88].
Recently, many cities have been reclaiming their marginal or under-utilized land parcels
for use as recreational parks, playgrounds, and community gardens. However, growing
dedicated energy crops on these urban lands is a fairly new concept and needs further
investigation, even just to understand the scale of potential energy benefits.
Marginal land estimations for the U.S. have been conducted nationally, regionally and at
the city scale (Table 3-1). Scale and location are critical issues, as it is not cost-effective to
transport biomass resources over long distances [87]. Limited agreement exists among
techniques used to estimate potential for bioenergy schemes in previous studies [71, 79,
89, 90], but common approaches make use of Geographic Information System (GIS) and
remote sensing based tools [86]. GIS-based tools assess spatial patterns of biomass based
bioenergy production on marginal land for both urban and non-urban areas as well as
availability of suitable land.
49
Table 3-1. Review of studies on marginal land assessment in USA
Year Author Scale (USA) Crop Marginal land
definition
Percentage
of total
area
2011 Gopalakrishnan
et al. [91]
Regional
(Northeast)
Lignocellulosic Contaminated
brownfield
8.0
2013 Gelfand et al. [92]
Regional (Midwest)
Cellulosic Crop land with low soil quality
10
2013 Grewal and
Grewal [73]
Local
(Cleveland, OH)
Algae Vacant lands 40
2013 Niblick
et al. [33]
Local
(Pittsburgh, PA)
Sunflower Vacant and abundant
lands
35
2014 Milbrandt
et al. [93]
National Lignocellulosic Abandoned lands,
brownfield, right-of-ways
8.5
Milbrandt et al. [93] looked at national level for estimating biomass based bioenergy
resources The study reported 8.6 million km2 of marginal land availability in contiguous
U.S., which is equivalent to 8.5% of total land area. They considered non-urban abandoned
lands, brownfield and transportation right-of-ways for marginal land estimation and
herbaceous crops (switch grass and miscanthus) as biomass feedstock. At the regional
level, Gelfand et al. and Gopalakrishnan et al. [91, 92] conducted assessment for the US
Midwest and Northeast, respectively, considering lignocellulosic biofuel based bio-energy
production system in primarily non-urban regions. At these scales, an important benefit of
using marginal land is that it does not diminish agricultural production and use of prime
farmlands and therefore can avoid ensuing impacts due to indirect land use change [94].
At local or municipal scales, several studies have conducted detailed mapping of marginal
land availability through parcel-level screening of land use and soil quality [33, 73, 95-98].
Typically, these local scale studies assessed bioenergy availability in meeting urban or
50
regional renewable portfolio standards, or as a question of urban self-reliance. Proper
quantification and geolocation of practically usable marginal land is critical to the
successful planning of urban bioenergy systems. Unlike regional or national level studies,
local scale studies are capable of incorporating parcel-level ownership and assessment
records, road and riparian boundaries, and socio-economic considerations that are relevant
for municipal authorities. Niblick et al. [33] incorporated several of these aspects in an
urban land study for Pittsburgh, PA, finding 35% of the city as marginal lands of limited
economic value that could be sustainably cultivated for sunflower based biofuel
production. Metal uptake was also considered in a subsequent study, as urban vacant lots
frequently have contaminated soils [99]. Finally, Grewal and Grewal [73] investigated
Cleveland, OH as a test case and assessed vacant lands equivalent to 40% of that city’s
total land area that could be suitably used to develop high-yield algae based biodiesel
production scheme.
Here we built on previous work for the assessment the marginal land and bioenergy
potential at urban scales, using Boston, MA USA as a case city. Additional attribute-based
and geospatial modeling tools were employed, and, to our knowledge, this is the first urban-
scale study to conduct a detailed parcel-level screening of layers for public and private
ownership, zoning, parcel size, slope, soil quality and shadow analysis. Both herbaceous
(miscanthus) and woody based (poplar and willow) energy crops were considered for
biomass and bioenergy yield estimation. These are believed to be the best yielding fast-
growing species in the Northeast U.S. [95]. Each has been shown to have a positive net
energy balance and can be effective in both fulfilling energy demand and mitigating climate
51
change [89]. The outcome of the study looks to provide policy makers and bio-energy
developers with a better understanding of the scale of urban bioenergy opportunities, while
also contributing to the larger research question of urban energy self-sufficiency.
52
3.2 Methods
Estimation of potential bioenergy yield from urban marginal land for Boston was
performed in several steps, represented in Fig. 3-1. A GIS-based site suitability analysis
was performed using ArcMap 10.3 (ESRI, Redlands, CA). Land parcel layer was used as
an input into GIS model and series of linear combinations of spatially referenced layers
were used as screening layer with some boundary conditions to identify land parcels that
can be suitably used. Here urban marginal land was defined as land that is not suitable for
primary agriculture, has a soil slope < 15% and has a minimum parcel size. Land features
that fit these criteria can be diverse especially within built up areas. For Boston, these
included (but may not be limited to) public and private vacant lands, residential and
commercial under-utilized areas, and degraded lands and fill. Finally, total biomass and
bioenergy potential was calculated using estimated urban marginal land, energy crop yield
information, and heat content. The input data sources, estimation approaches, and
validation techniques are explained in detail below for each step.
53
Fig. 3-1: Flowchart of modeling processes used for biomass mapping and bioenergy
assessment
3.2.1 Land use type screening
First, potential marginal land areas were identified using GIS site suitability model
developed for screening purposes (Fig. 3-1). This model consists of an input layer, erase
layers and the output suitable parcel layer (Table 3-2). The input layer was a 2013 record
of all City of Boston parcels. Several screening layers were joined and overlaid with the
input layer to exclude areas unsuitable for biomass cultivation because of existing
improvements or zoning restrictions. These screening layers included area occupied by
buildings, driving lots, parkways, protected areas (parks and recreational areas). These
layers were obtained from City of Boston Department of Innovation and Technology [12],
54
Department of Neighborhood Development [100], MassGIS [101] and Tufts University
GIS database [9]. The scale of all the layers was 1:1,000,000. Streets and highways were
excluded from the input parcel layer and so did not require subsequent exclusion.
3.2.2 Bio-geophysical screening
In this second step, the previously estimated suitable parcel layer was the input into a
separate GIS site suitability model developed for bio-geophysical analysis, consisting of
an input layer, three screening layers and an output layer. The bio-geophysical model
performed minimum area screening, soil quality, slope and shadow analysis. Based on
literature, a minimum parcel size for marginal land was determined of at least 93 m2 (1000
ft2) with a marginal slope of 15% or less to minimize runoff and erosion and for ease of
cultivation and harvesting [102].
Table 3-2. Description of parameters used to estimate urban marginal land
Spatial parameters Scale Projection
Land parcels 1: 1,00,000
North American Datum (NAD) 1983,
State plane Massachusetts, ft
Buildings 1: 1,00,000
Drive ways and
parking lots
1: 1,00,000
Water and
protected areas
1: 1,00,000
Soil types 1: 1,00,000
World Geodetic System (WGS) 1984
Soil slopes 1: 1,00,000
Extruded 2D
buildings
1: 1,00,000
3.2.2.1 Exclusion of parcels by soil quality and slope
Soil quality information for Boston was obtained from the United States Department of
Agricultural Natural Resources Conversation Services (USDA-NRCS) soil survey
55
geographic data base (SSURGO) [103]. The SSURGO database contains information
about soil type and slope information for the entire country. Based on SSURGO data, there
are four distinct classes of soil types (A, B, C and D) exist for Boston. USDA-NRCS has
three major farmland classifications: prime farmland, farmland of statewide importance
and not prime farmland [104]. Areas with soil listed as ‘not prime farmland’ are classified
as marginal (Table 3-3). The bio-geophysical model used spatial selection criteria to select
and erase parcels that are classified as ‘not prime farmland’ and soil slope <= 15% from
the input ‘suitable parcel’ layer.
Table 3-3. USDA-NRCS Marginal soils classification [104]
USDA NRCS classification Soil type Slope (%) Soil type
Prime farmland Silt loam/Shaly silt/loam/clay loam 0-8% A
Farmland of statewide
importance
Silt loam/Shaly silt/loam/clay loam 2-15% B
Not prime farmland Urban/industrial dump/gravel pits 0-35% C/D
3.2.2.2 Exclusion of areas by shadow analysis
Shadow analysis can be used to estimate the distribution and intensity of sunlight over a
geographic area for specific time duration. This tool accounts for how daily and seasonal
shifts of the sun angle, along with variations in elevation, orientation (slope and aspect),
and shadows cast by topographic features (buildings), affect the amount of incoming solar
radiation [105], which is of obvious importance for assessing site suitability for biomass
growth. Here, sun-shadow analysis was performed to identify the suitable parcels that
receive minimum six hours of sunlight during mid-crop season (Fig. 3-2a). Land parcels
with full sun exposure on the south, southeast and southwest with no buildings on those
sides were considered optimal, while parcels with shade during any part of the day were
considered a shadow area (Fig. 3-2b). ArcMap 3D sun-shadow analysis tools was used for
56
performing shadow analysis which requires extruded 2D buildings as an input features.
The analysis was conducted on extruded 2D buildings for July 21st between 8AM – 4PM.
July 21st was considered as the representative day for analysis that lies at the midpoint of
the seven-month growing season (April – October). The shadow map obtained was input
as an erase layer in bio-geophysical model along with soil quality and slope layers. The
marginal land layer was compared by laying with 15 cm high-resolution aerial imagery
obtained from MassGIS data layers. The purpose of the aerial analysis was to ground truth
the mapped parcels by considering criteria that could not be easily applied through GIS
analysis. The two major criteria examined through the use of aerial imagery were light
exposure and vegetation density. The parcel-by-parcel crisscross was performed to
determine the extent of similarities and additional parcels not identified in spatial analysis.
The resultant urban marginal land layer obtained from these models was further compared
with city of Boston’s new zoning code to ensure compliance with the approved zoning
requirements for urban agriculture [26].
Fig. 3-2: Shadow analysis examples: a) input extruded-2D building footprint and b)
shadow map
a)
b)
57
3.2.3 Biomass and bioenergy yield
As an urban coastal area Boston has a subtropical climate that could be favorable for
energy-crop production especially from late spring to early fall between April – October
[106]. A recent study conducted by the Massachusetts Department of Environmental
Resources and the Massachusetts Clean Energy Center reported a list of suitable bioenergy
crops with yield information for different areas within the states [107]. These included
herbaceous crops such as perennial switch grass, miscanthus, and woody biomass such as
short-rotation poplar and willow. Among these, willow, poplar and miscanthus have been
found most suitable for high yield and survival under changing climate scenarios [108],
including resilience to environmental conditions such as flash flood, low temperatures, salt
and alkali [107]. The crop yield information were obtained from Biofuel Ecophysiological
Traits and Yields database (BETYdb) [21]. BETYdb is a national database developed by
Energy Bioscience Institute (EBI) that maintains cellulosic biofuel crop species and yield
information for research, forecasting, and decision making support. For each crop type,
BETYdb has gridded crop yield data availability with 0.1o × 0.3o resolution that also uses
the USDA SSURGO soil quality database in estimating yields [109, 110].
For this study, crop yield values of three different energy crops for Boston area were
spatially interpolated from the BETYbd gridded dataset. Ordinary kriging method with no
transformation was chosen as method of interpolation. Later crop yields for each mapped
parcel were estimated by overlaying centroids of each parcel with the interpolated raster
pixels. The average yield values for each crop types are listed in Table 3-4. Total biomass
yield Mbio (t yr-1) was estimated by multiplying each individual marginal land parcel area
58
Ai (ha) with associated yield data Yi (t ha-1 yr-1) (eq.i) and summing. Bioenergy yield Ebio
was calculated using biomass yield (t yr-1) and heat content H of energy crops (eq.ii).
Higher and lower heating values were obtained for each crop (dry wt) [111]. Table 3-4 lists
the heat content values used.
𝑀𝑏𝑖𝑜 = ∑ 𝐴𝑖 ∙ 𝑌𝑖𝑖 (i)
𝐸𝑏𝑖𝑜 = 𝑀𝑏𝑖𝑜 ∙ 𝐻𝐿𝐻𝑉/𝐻𝐻𝑉 (ii)
Table 3-4. Energy-crop yield and heat content information
Energy crops Crop yield (dry t ha-1) Heating value (dry wt, GJ ha-1)
Lower (LHV) Higher (HHV)
Miscanthus 7.6 18.1 19.6
Poplar 15.8 17.7 19.7
Willow 11.2 18.4 19.7
3.3 Results
3.3.1 Marginal land resources in Boston
The marginal area estimation steps are displayed in panel format in Fig. 3-3. This shows
the a) input parcels, all the exclusion layers including b) buildings, c) water and protected
areas, d) driveways and parking lots e) parcels that are not suitable due to inferior soil
quality and inadequate sunlight and, f) final marginal land layers. As previously described
in Section 2, marginal land estimation was conducted in several steps using GIS based
screening model, bio-geophysical model and shadow analysis. Fig. 3-4 visualizes the total
marginal land availability in the city. Approximately, 2660 ha (26.6 km2) of potential urban
marginal land was mapped out representing 24% of total land area in Boston and have a
minimum of 93 m2 (1000 ft2) parcel size (Fig. 3-4). Of this urban marginal land, 15%
consists of vacant lands and the remainder is mostly under-utilized residential and
59
commercial area. There are approximately 283 ha (0.28 km2) of public and 116 ha (0.12
km2) of private marginal vacant lands available for immediate ground-level crop
production. Fully 70% of under-utilized land use mapped are residential neighborhood
areas that include house backyards excluding paved parking lots. The remaining 640 ha
(0.64 km2) are commercial under-utilized areas that could be categorized as marginal and
used for agricultural purposes. These findings are in the same range as estimated by Niblick
et al. for Pittsburgh, PA [33] and Grewal and Grewal for Cleveland, OH [73].
Marginal land resources are concentrated in southern part of the city. Due to proximity to
water resources, sufficient sunshine and low population density relative to the rest of the
city, the southern neighborhoods of Dorchester, Hyde Park, Mattapan, Roslindale, and
West Roxbury are promising areas for growing dedicated energy crops. These areas have
also been identified by the City of Boston as potential location for developing food-based
urban agriculture [94]. On the other hand, the highly urbanized and densely populated core
of Boston was not found suitable due to the high proportion of impervious areas, limited
parcel sizes, large shadow areas associated with buildings, and proximity to the coast;
however, this part of the city has some potential for rooftop urban agriculture.
60
Land parcels (Input layer)
Buildings
Water and protected areas
Driveways and parking lots
Unsuitable parcels from slope and shadow analysis
Urban marginal land (Output layer)
Fig. 3-3: Marginal land estimation in Boston
e)
b)
a)
c)
d)
f)
Erased
layers
61
Fig. 3-4: Available marginal land in Boston
3.3.2 Biomass and bioenergy potential
Evaluation of bioenergy potential of energy crops in Boston are shown in Table 3-5. These
values should be seen as an upper bound, as not all marginal land available in the city may
be practically be used to cultivate energy crops. Community acceptance, soil
contamination, safety, and traffic considerations could reduce the amount of marginal land
that is practically usable [86].
Boston
62
Table 3-5. Biomass and bioenergy yield
Energy crops Biomass
yield (t yr-1)
Primary
Bioenergy yield
(TJ yr-1, HHV)
Energy
District
heating
(TJ yr-1)
Energy
CHP
(TJ yr-1)
Miscanthus 20,300 398 338 240
Poplar 42,100 830 705 498
Willow 29,800 586 500 352
It was estimated that Boston’s marginal land could be used to produce nearly 42,130 tons
of high yield short rotation poplar biomass in a regular growing season. Potential biomass
yield for willow and miscanthus were found to be nearly equal. Short rotation poplar was
found to be the most promising energy crops based on heating value calculation. This
amount of biomass can potentially yield up to 830 TJ yr-1 (230 GWh yr-1) of primary energy
on average (using high heating values). For context, this is equivalent to ~0.6% of State of
Massachusetts’ primary energy demand for 2012 [97]. This energy could be used directly
in Boston itself for heat and power production, particularly in high-density commercial or
industrial areas. For example, the existing Medical Area Total Energy Plant (MATEP) is a
natural gas fired district heating and power generation facility that serves the Longwood
Medical Area and supplies 2.1 GWh annually, only 2.5% of the potential total heat
available in the city from biomass [70]. With an average 85% conversion efficiency for a
biomass district heating plant that used poplar, this amount of primary energy could yield
up to 705 TJ yr-1 (195 GWh yr-1) of useful energy for heating (Table 3-5) [112]. Also, for
a biomass driven decentralized combined heat and power (CHP) facility with 60% energy
efficiency that used miscanthus as a feedstock could yield up to 240 TJ yr-1 (66 GWh yr-1)
of energy [112].
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3.3.3 Spatial Validation
Validation of geospatial model was conducted to ensure accuracy of identified parcels and
check robustness of the system. To accomplish this, eight suitable parcels were randomly
selected from the 60,000 identified marginal land parcels using ‘Create random point’ tools
of ArcMap. The marginal land layer (Fig. 3-4) was used as an input and mask layer in the
validation model and the python based geo-processing tool generated 20 random points. 8
out of 20 points coincide with centroids of the parcels identified (Fig. 3-5). The resultant
parcels were further layered with high resolution (15 cm) ortho imagery for comparison
and validation.
Fig. 3-5: Randomly selected urban marginal land parcels (Scale 1: 600)
3.4 Discussion and Implications
The preceding analysis shows that urban production and use of bioenergy crops have the
potential to contribute to a city's clean energy economy and supplement existing energy
infrastructure. Multiple conversion pathways are possible. Bioenergy crops can be
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combusted directly in a dedicated biomass power plant; the city of Vienna has recently
installed Europe’s largest such plant with a capacity of 53 MW of combined heat and power
[3]. MATEP and similar plants could also take advantage of local biomass feedstock by
adding a biomass boiler or co-firing. An urban bioenergy production facility can be
operated as an independent energy generation facility, or as part of an integrated district
heating network [113]. Plants operating continuously are adaptable to change in biomass
resources, thus providing predictable and reliable base power [114]. Bioenergy crops could
also be pyrolized to syngas, converted to liquid fuels, or directly combusted in small-scale
commercial or residential heating systems [115-117].
Total bioenergy production could be increased if the definition of marginal land were
expanded to include additional types of under-utilized urban land, particularly on
commercial properties. Marginal land categories of saline lands, abandoned or degraded
forests were excluded from the present study scope, but may be relevant for other urban
areas. Increasing the area of cultivated marginal land or improving crop yields will increase
the potential bioenergy yield. Marginal land yields are by definition lower than for prime
agricultural lands, but may be improved by optimizing planting and maintenance processes
and climatically appropriate crop choices [92].
In addition to considerations of urban energy supply, there are also non-energy benefits of
urban bioenergy schemes. Growing bioenergy crops can promote awareness of
environmental stewardship, improve soil quality and increase soil carbon, reduce
stormwater pollution, support urban biodiversity and habitat, and reduce urban heat island
65
effect [81, 107]. Urban biomass and bioenergy can improve self-sufficiency and physical
resiliency in urban areas, to help protect from risks caused by natural hazards and
infrastructure failures [73]. Local bioenergy production can be a means of community-
based economic development, by creating new jobs and promote innovative clusters of
related businesses [94].
There are obvious challenges to implementing an urban bioenergy scheme on a large scale.
While outside the scope of the present work, the logistics of cultivating, harvesting,
transporting, and storing biomass combined from potentially hundreds of urban plots will
be complex. Previous works describing modeling biomass supply chains and logistics may
be applied, particularly optimization models constructed for urban systems [79, 104].
Previous research has found that to implement a robust local urban bioenergy production
it is critical to locate district heating networks and biomass cogeneration facilities in
proximity to feedstock collection facilities [95]. Distributed bioenergy production may
also suffer from diseconomies of scale. Boston’s land values and labor costs are high,
which will impact capital and operating costs and project financing. A simple cost
estimation by Chin et al. [94] revealed that a half-acre (2000 m2) urban farming parcel
requires capital cost of approximately $10,000 that includes equipment purchase, sales, and
marketing. Operating costs for an urban farm of the same size are estimated to be $5,000-
$10,000 per year with gross revenue of $60,000. Present-day costs may well be much
higher in Boston. Land access and ownership are important considerations, especially
considering that 85% of the marginal lands identified are in private hands and must be
either cultivated by residents or leased to urban farmers [106]. It may also be difficult to
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obtain permits for cultivation or conversion facilities, and there are currently no zoning
ordinances specific to bioenergy cropping.
3.5 Conclusions
Bioenergy production on urban marginal lands have several benefits, such as fulfilling
partial energy demand, increasing resilience and mitigate climate change impact by
lowering GHG emission. Based on reasonable assumptions, precise data acquisition and
analysis methods, the following outcomes have been summarized:
For Boston, total area of marginal land was estimated up to 2660 ha (26.6 km2) that can be
suitably used for bioenergy production and represents 24% of total land area. This area
consists of 85% of residential and commercial unutilized areas and 15% vacant lands.
South and Southwestern part of the city were found most suitable for energy crop
harvesting.
Three species of bioenergy crops were found suitable namely herbaceous miscanthus, short
rotation poplar and willow that could be planted in different experimental areas throughout
the city and have great potential for bioenergy development. However site-specific
experimental crop harvesting is necessary to validate the suitability.
The maximum potential bioenergy yield is approximately 830 TJ for short rotation poplar
if 100% of the marginal lands identified are used for growing the energy crops. Energy
yield obtained from both biomass district heating plant and CHP plant indicate promising
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outcome. However even partial development of bioenergy crop production on urban
marginal land could fulfill significant amount of the city’s heating demand during winter
season.
The present study confirms that the total potential supply of urban bioenergy is significant
even for a densely settled city such as Boston. While Boston was the target of the present
study, these methods can in principle be applied to any city or metropolitan area. Urban
bioenergy schemes may be more appropriate for cities undergoing depopulation and with
a higher proportion of vacant marginal lands and lower economic rents than Boston. In
practice, there exist numerous logistical and financial obstacles to using urban bioenergy
at large scales, but also many potential co-benefits to communities, including measures of
energy security, economic development, and environmental quality that could potentially
be realized.
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Chapter 4 A GIS-based Assessment of Regional Scale Bioenergy
Production Potential on Marginal and Degraded Land
This study presents an application of the GIS-based model developed in Chapter 3, for site-
specific bioenergy production potential on urban marginal lands available in the Eastern
Massachusetts region. The ‘marginal’ land use category includes vacant and abandoned
lands, inner city underutilized areas, and degraded lands. Three different energy crops;
miscanthus, poplar and willow were considered as biomass feedstocks for bioenergy
production. A total area of marginal land was estimated up to 71,200 ha (712 km2) that can
be suitably used for bioenergy production and represents 20% of the MAPC land area.
Among 102 cities assessed in the study region, Boston, Marshfield, Franklin, and Concord
were found to have the greatest bioenergy potential in terms of total marginal land area
availability. Fast-growing poplar was found as most suitable high yield bioenergy crops
for this region. Bioenergy potential calculation revealed that for short rotation poplar, this
amount of biomass can potentially yield up to 22 PJ (HHV) of yearly primary energy for
this region that can be used for heat, conversion to fuels, and/or electricity production. This
is equivalent to ~15% of Massachusetts primary energy demand for 2012. The outcomes
of this study are in line with previous work that evaluated marginal land availability in
Boston and other major Eastern U.S. cities, and confirm the accuracy of the spatial model
constructed in Chapter 3.
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4.1 Introduction
4.1.1 Regional assessment of marginal land
The United States has approximately 120 million ha of marginal land area available that
includes federally funded brownfields, closed landfills and abandoned lands [118].
Approximately 67% of this land area is within the administrative boundary of urban
metropolitan regions. A typical urban community’s reliance on conventional fossil energy
is not only carbon intensive but also vulnerable to supply disruptions due to environmental,
economic and geopolitical factors. In the Northeastern United States, heating demand has
increased significantly in last decade [107]. To reduce supply related uncertainty and
increase energy self-reliance, many cities have started to focus on the sourcing part of its
energy from leveraging local renewable energy resources [119]. However, inner cities are
often densely populated with limited land resources suitable for large-scale renewable
energy production. Research suggests growing energy crops on marginal or degraded land
close to larger metropolis can be a viable solution [33, 35]. Suitable suburban land can be
repurposed to grow high-yield crops that can be used as biomass feedstock for bioenergy
production. Successful implementation however, warrants more site-specific investigation.
The precise definition of marginal land may vary depending on prior uses and geographical
considerations such as location and scale. While different definitions exist [35, 99],
marginal lands can be broadly categorized as “lands that are not suitable for food-based
agriculture and have limited economic potential for fulfilling other ecosystem services”
[120]. The unsuitability can be attributed to poor physical and chemical soil properties
and/or susceptibility to erosion [121]. Several land use categories could be considered as
70
marginal. Furthermore, there is a lack of consensus regarding choice of suitable energy
crops to grow on urban marginal lands. All energy crops require favorable climatic
conditions and a specific amount of water, nutrients and suitable growing conditions [122].
Some feedstocks, however, are more resilient than others and can grow successfully on
marginal lands and under changing climate scenarios [122].
Past studies have claimed that using marginal lands to produce bioenergy on a global scale
is unfeasible due to lack of economic incentives and threats to biodiversity and
conservation areas [123, 124]. However, marginal production levels become more viable
when considered on the local and regional level as certain regions have high land or
biomass availability, existing infrastructure, and suitable population densities [86].
Regional scale study has the advantage of assessing city by city evaluation of land access,
imperviousness, and soil quality to precisely identify areas suitable for specific crop type.
Every geographic and climatic region is different, and therefore spatial analyses are critical
and chosen technique to assess land use suitability for sustainable bioenergy production
and infrastructure systems development.
This study expands the scope of geospatial model developed by Saha and Eckelman. 2015
[35]; to assess large scale urban region surrounding Boston for marginal land availability
for energy crop production. The model description and outcome are also described in
Chapter 3 of this dissertation. Even though Boston’s identified marginal land is
approximately one quarter of its total [35], due to high land prices and competition with
other current or potential economic activities, access and logistical factors, it is unlikely
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that all of these land parcels can suitably be used for cultivation. But crops can be grown
outside the city and still be used for inner city bioenergy production as the transportation
requirement and costs are low [88]. Here, total theoretical marginal land availability and
bioenergy potential for 102 cities and towns in Eastern Massachusetts were assessed.
4.2 Methods
4.2.1 Marginal Land Assessments
As mentioned, the urban marginal land assessment model developed in Chapter 3 was
applied in this study for a larger geography that includes 102 cities and towns located in
Eastern Massachusetts. Together, these 102 cities fall within the planning jurisdiction of
Metropolitan Area Planning Council (MAPC), a state agency (Fig. 4-1). Because of its
strategic location, population density and economic importance, the MAPC region is
considered one of the major energy demand centers of the Eastern United States [88]. The
total land area of MAPC region is 330 km2 and includes more than 3 million inhabitants
[89].
Fig. 4-1: Study area (MAPC region)
72
Here, the general geospatial modeling framework developed by Saha and Eckelman [35]
was used for MAPC marginal area assessment. Based on the literature, marginal land was
characterized as specific land use types with marginal soil quality and soil slope. The most
recent land cover data obtained from USGS national land cover database were used as an
input in the screening model [125]. Here a national scale data layer was used in order to
make it easy to perform a similar analysis for a larger geography. The USGS land cover
layer is a nationwide, seamless digital dataset covering wide range of land cover and land
use types, created using semi-automated methods, and based on 0.5 m resolution digital
orthoimagery. The specified land use classification is attributed in the data layer by two
fields: land use description (LU05_DESC) and land use code (LUCODE) (Table A1).
Land cover types defined as ‘residential and commercial underutilized areas, abandoned
agricultural lands, landfills, and junkyards available within city areas are considered as
marginal land.
The input land cover layer was subjected to screening by series of spatially referenced
layers, starting with minimum area (9.3 m2) screen followed by the impervious layer, water
and conservation land, and soil quality respectively. The remaining screening data layers
were obtained from the MassGIS website [126]. The aggregated impervious surface layer
consists of roads, railways, driveways, parking lots, and other areas where soils are
inaccessible [90]. For biogeophysical analysis, land parcels with soil slope <15% to avoid
erosion and soil quality type C or D based on the USDA NRCS soil classification were
selected [35]. The resultant marginal land area information was later used for biomass and
73
bioenergy yield calculation. The spatial projections and coordinate systems used are
described in Table 4-1.
Table 4-1. Description of parameters used to estimate marginal land area
Spatial parameters Scale Projection
Output layer 1: 1,00,000 Massachusetts State Plane Projection System,
ft
Land cover 1 meter Albers conical equal area
Impervious layer 1 meter
Water and
protected areas
1: 1,00,000 North American Datum (NAD) 1983, State
plane Massachusetts, ft
Soil types 1: 1,00,000
World Geodetic System (WGS) 1984
Soil slopes 1: 1,00,000
Extruded 2D
buildings
1: 1,00,000
Fig. 4-2 summarized the step-by-step spatial overlay analyses in panel format. These
include the input land cover layer (Fig. 4-2a) and overlaid screening layers (Fig 4-2b, 4-
2c, 4-2d). The land cover layer consists of multiple land use types represented by different
colors. In greater Boston, land use mostly consists of high intensity residential and
commercial areas (red), low intensity residential and commercial areas (yellow) and forest
(green), respectively (Fig. 4-2a). The detailed geoprocessing steps used and underlying
assumptions are also described in the Methods section of Chapter 3.
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a) Land cover
b) Impervious layer
c) Water and protected areas
d) Unsuitable parcels from soil
quality and slope analysis
Fig. 4-2: Urban marginal land estimation
4.2.2 Biomass and bioenergy yield
In this study, the theoretical biomass yield was estimated for marginal land across the entire
MAPC region. Three major bioenergy crops miscanthus, poplar and willow were
considered as biomass feedstocks as identified by Massachusetts Department of Energy
Resources [35, 107]. Next, average biomass yield and energy density factors were used to
estimate the total primary bioenergy yield available in an upper-bound, 100% marginal
75
land utilization scenario. The average yield values for each crop type are listed in Table 4-
2, based on the present climatic condition for the region. Total biomass yield Mbio (t yr-1)
was estimated by multiplying each individual marginal land parcel area Ai (ha) with
associated yield data Yi (t ha-1 yr-1) (eq.i) and summing. Bioenergy yield Ebio was calculated
as the product of total using biomass yield (t yr-1) and heat content of energy crops (eq.ii).
𝑀𝑏𝑖𝑜 = ∑ 𝐴𝑖 ∙ 𝑌𝑖𝑖 (i)
𝐸𝑏𝑖𝑜 = 𝑀𝑏𝑖𝑜 ∙ 𝐻𝐿𝑉/𝐻𝑉 (ii)
Table 4-2. Energy-crop yield and heat content information [40]
Energy crops Crop yield (dry t ha-1) Heating value (dry wt, GJ t-1)
Lower (LHV) Higher (HHV)
Miscanthus 7.6 18.1 19.6
Poplar 15.8 17.7 19.7
Willow 11.2 18.4 19.7
4.3 Results and Discussion
4.3.1 MAPC Marginal land
The marginal land assessed are presented in Fig. 4-3. As previously described in Section
2, suitable area estimation for energy crop harvesting was conducted in several steps using
GIS based screening model, bio-geophysical model and shadow analysis. Approximately,
71,200 ha (712 km2) of potential marginal land areas were identified representing 20% of
the total land area of the entire MAPC region (Fig. 4-3). Of this land area, 75% consists of
under-utilized residential and commercial areas that include vacant lots and yards. The
remaining 25% are abandoned cropland, landfill areas, and junkyards that could be
categorized as marginal and repurposed for agricultural purposes. Among 102 cities and
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towns assessed, Boston, Marshfield, Franklin, and Concord were found to be the four most
suitable cities or towns in terms of total marginal land area availability (Fig. 4-4a, 4-4b, 4-
4c, and 4-4d). Together, these four cities account for 12% of total marginal land assessed
for MAPC region (Table 4-5). For Boston, the available area estimated was 2,910 ha, or
which is 23% of total land area. This value is within one percentage point of the result
estimated by Saha and Eckelman for Boston [35]. This shows successful validation of
spatial model construction in Chapter 3 for a larger geography and using larger scale
slightly different starting input data layer. The city of Reading was found most suitable on
the percentage land intensity basis, with 37% of total land area followed by Needham
(33%), Newton, (32%) and Danvers (31%), respectively (Table A2). Because of close
proximity to Boston, these municipalities could in theory supply bioenergy for the city
center without requiring long hauling distances. Although it contains the largest number of
marginal parcels, Boston was ranked 25th on a percentage basis.
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Fig. 4-3: Available marginal land in MAPC cities
Boston Marshfield
a) b)
Boston
Marshfield
Franklin
Concord
78
Fig. 4-4: Available marginal land in four municipalities
Table 4-3. MAPC marginal land
Rank City or town Marginal land
area (ha)
Total land
area
(ha)
Marginal %
of total land
area
MAPC region 71,150 362, 419 20%
1 Boston 2,910 12,500 23%
2 Marshfield 2,110 7,446 28%
3 Franklin 1,850 6,988 27%
4 Concord 1,568 6,684 24%
4.3.2 Biomass and Bioenergy Yield
Marginal lands available in the MAPC region could be used to produce nearly 1.1 million
tons of high yield short rotation poplar biomass in a regular growing season (Table 4-4).
Potential biomass yields for willow and miscanthus were found to be 0.8 million tons and
0.5 million tons, respectively. Short rotation poplar was found to be the most promising
energy crop based on heating value as well. Poplar biomass could potentially yield up to
22 PJ yr-1 of primary energy (using high heating values) in a scenario of 100% marginal
land utilization. This is equivalent to ~15% of Massachusetts primary energy demand for
Concord Franklin
c) d)
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2012 [97]. Boston’s marginal land could alone produce up to 46,000 tons of poplar, 32,600
tons of miscanthus and 22,200 tons of willow, respectively. This may be attributed to
Boston being the largest city with higher amount of land parcels with marginal soil type
(C/D). The results are also closely in line with previously reported values in Saha and
Eckelman [35].
Table 4-4. Biomass and bioenergy yield
Geography Energy crops Biomass
yield (t yr-1)
Primary
Bioenergy yield
(TJ yr-1, HHV)
MAPC Miscanthus 541,000 10,600
Poplar 1,120,000 22,200
Willow 797,000 15,700
Boston Miscanthus 22,100 434
Poplar 46,000 906
Willow 32,600 641
Marshfield Miscanthus 16,000 314
Poplar 33,300 657
Willow 23,600 465
Franklin Miscanthus 14,100 276
Poplar 29,200 576
Willow 20,700 407
Concord Miscanthus 11,900 234
Poplar 24,800 488
Willow 17,600 345
This result shows not insignificant potential for regional bioenergy resources to supply
energy for the highly urbanized and energy-intensive Boston metropolitan area that
includes densely populated and industrialized cities like Cambridge and Somerville.
Introduction of local bioenergy resources and adaptation of existing energy infrastructure
to accommodate biomass could be beneficial in providing local renewable energy (mostly
likely for heating) for one of the nation’s largest educational, health care, and high-tech
clusters.
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4.4 Conclusion
The geospatial model constructed for assessing urban marginal land in Chapter 3 was
successfully used to validate the method and corroborate results for a different larger scale
geography, using larger-scale data layers. The following outcomes have been summarized:
For MAPC region, total area of marginal land was estimated up to 71,200 ha (712 km2)
that can be suitably used for bioenergy production and represents 20% of the total land
area. This area consists of residential and commercial land parcels, vacant lots, abandoned
cropland, landfill, and junkyards. Among 102 cities assessed, Boston, Marshfield,
Franklin, and Concord were the most suitable urban locations in terms of marginal land
area availability. Three species of bioenergy crops were modeled for total biomass and
bioenergy production, namely herbaceous miscanthus, short rotation poplar, and willow.
The maximum potential bioenergy yield is approximately 22 PJ for short rotation poplar if
100% of the marginal lands identified are used for growing the energy crops. However
even partial development of bioenergy crop production on urban marginal land could fulfill
significant amount of the region’s heating demand during colder days.
The present study assesses the physical potential of bioenergy crops production on regional
scale marginal land and validates the previously developed model outcome for a larger
scale densely settled Eastern Massachusetts. In practice, there exist numerous challenges
and trade-offs when pairings lands and energy resources. Existing popular technologies
like solar production and wind farms facilities will likely compete for the same marginal
81
land. Therefore, land optimized technology pairing can create opportunities for increased
energy production, especially in the case of co-located energy crops and wind farms. Also,
not all available lands can ideally be used for bioenergy production. Logistics such as
transportation, transmission and distribution lines, and access to water and other nutrients
also need to be considered. Optimal use of land resources, biomass feedstock, and energy
production technologies will allow marginal lands to play an increasingly vital role in
fulfilling regional energy demands in a sustainable manner.
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Chapter 5 Geospatial Assessment of Urban Agriculture Potential in Boston
Urban parcels can be leveraged for developing a local urban food system by growing high
yield food plants. Here, a remote sensing and GIS-based modeling framework was created
to assess Boston’s available area for urban farming, including both rooftop and ground
level areas. Geoprocessing and spatial analysis tools were used to process geographic data
layers for zoning, ownership, slope, soil quality, and adequate light availability. Surface
slope (roof pitch) was determined for all buildings in the city through the creation of a
digital surface map from remotely sensed LiDAR data. Potential parcels from ground level
public and private vacant lands and underutilized residential and commercial areas were
mapped using publicly available datasets. Approximately 922 hectares of rooftop and 1,250
hectares of ground level parcels have been identified, representing 7.4% and 10% of total
land area in Boston, respectively. Finally, food yield values for common urban agricultural
crops were used to estimate the city’s food production potential from the identified parcels.
Despite Boston’s density, the mapped areas have potential to produce enough fresh fruits
and vegetables for 30% of Boston’s population. The study outcome was compared with
results from other regions in North America that might be able to fulfill partial food demand
leveraging local resources.
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5.1 Introduction
5.1.1 Urban agriculture
Food and energy security are pressing concerns for municipalities in the face of growing
global urban populations. Urban inhabitants represent the majority of global food and
energy demand, with nearly 50% of the total global populations live in cities. Large-scale
industrial agriculture, while inarguably efficient, can incur significant environmental costs,
including deforestation, depletion of cropland, soil, and water pollution, and biodiversity
loss [127, 128]. It is estimated that food in the United States travels an average of 1,500
miles from the farm to plates [129], while food grown in urban areas is coincident with
demand centers. Although dense cities cannot be self-sufficient in food production, urban
food farming could increase food security and help to address various urban environmental,
economic, and social challenges.
Urban agriculture is commonly referred to as the practice of growing, processing and
distributing food in urban and peri-urban areas. Becoming at least partially self-sufficient
through urban agriculture is one way to increase the resiliency of food and energy systems
through diversification of supply, and can bring multiple co-benefits including enhanced
food security, enriched landscapes, local economic development, and improved
environmental quality [130]. Urban agriculture can benefit the local environment through
improvements to urban air quality, increasing rates of carbon sequestration, modulation of
urban heat islands, and mitigation of water pollution problems associated with stormwater
runoff [131]. Producing food locally can also avoid environmental impacts associated with
long-distance food distribution and losses [132]. At the same time, potential economic and
84
social benefits can include employment and local economic activity; redevelopment and
productive use of blighted, marginal urban areas that are frequently located in low-income,
underserved communities; and, depending on the area of the country, noise abatement,
food access and nutrition, and community education [99, 131, 133].
An important preliminary step of planning for local food system development is to estimate
the total available area and potential production volumes for urban agriculture. Urban food
farming can be implemented using both ground level and rooftop areas. Especially in
densely built-up areas where vacant parcels are scarce and with a large number of buildings
available, rooftop farming can be an attractive supplement to more conventional urban
farms and community gardens, but is still a niche form urban farming that has yet to gain
popularity on a large scale. However, there exists a lack of consensus in defining the extent
of a local food system. According to one US Department of Agriculture (USDA) definition,
a local urban food system may consist of an area as large as 644 km2 [134]. Many
municipalities consider their administrative boundaries for delineation of their local food
systems. ‘Foodshed’ is another common framework for regional food sourcing and
distribution system. Kloppenburg et al. suggest that “the foodshed can provide a place for
us to ground ourselves in the biological and social realities of living on the land and from
the land in a place that we can call home, a place to which we are or can become native.”
[135]
Compared to efforts in the developing world, cities in North America only recently have
begun to pay attention to the integration of urban agriculture and land use planning [136],
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with notable progress in New York, San Francisco, Portland, and Vancouver [137]. Boston
is a comparative latecomer in this regard, though up until the 20th century it was one of the
largest agricultural centers in Massachusetts [138]. Following the creation of the City’s
Office of Food Initiatives in 2010 has made significant efforts in focusing attention on land
use planning that includes agricultural uses. In Boston, the Mayor’s Office of Food
Initiatives supports urban agriculture, because it “improves access to fresh, healthy, and
affordable food, with decreased transportation costs and lower carbon emissions.
Furthermore, new farming endeavors can bring communities together, empower small
entrepreneurs, and improve access to fresh food for all Bostonians.” [139] To support
commercial-scale urban agriculture, the City in 2013 passed Zoning Article 89 for urban
agriculture, providing necessary guidelines about urban farming implementation and
municipal support for local food distribution [140]. This was followed by a city-wide
visioning document based on extensive stakeholder engagement, published in 2015 [141].
5.1.2 Spatial analysis
Spatial research methods are central to understanding and evaluating the different
components of local food systems. On the supply side, spatial analysis has primarily
included urban food system mapping and land suitability studies. Notable efforts have been
taken in foodshed analysis in various locations over last several decades [142-144].
Different analytical, numerical, statistical, and artificial intelligence approaches have been
investigated to assess urban agriculture viability and potential extent. Table 5-1
summarizes the study characteristics for different cities in North America. GIS-based
86
analysis using tax assessor or land use layers has been most commonly employed,
occasionally coupled with remote sensing data products for further screening or validation.
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Table 5-1. Review of studies on urban agriculture potential in North America
Author(s) Location Agriculture
type
Used
remotely
sensed data
% of total
municipal
land area
Comments
Ackerman et al. [145] New York, NY Ground level Yes 2.6% Vacant lots
Ballmer et al. [146] Portland, OR Ground level Yes n/a 47 sites < ¼ acre
242 sites > ¼ acre
Berger [147] New York, NY Rooftop Yes n/a N. Brooklyn Industrial
Business Zone
Colasanti and Hamm [148] Detroit, MI Ground level No 5.5% Vacant lots
Eanes and Ventura [149] Madison, WI Ground level No 2.2% Vacant lots
Horst [150] Seattle, WA Ground level Yes 0.02% Public vacant, unused,
or excess right-of-way
Kaethler [151] Vancouver, BC Both Yes 0.29% Public vacant, unused,
or excess right-of-way
Kremer and DeLiberty
[152] Philadelphia, PA Ground level Yes 7.8%
Residential lots
Grewal and Grewal [153] Cleveland, OH Both No
37%;
6.9%
5.8%
Ground level;
Vacant lots;
Rooftop Industrial
Habermen et al. [154] Montreal, QB Both No n/a Vacant lots, residential
yards, industrial roofs
MacRae et al. [155] Toronto, ON Ground level Yes 1.3% Two areas zoned for
agriculture
McClintock et al. [156] Oakland, CA Ground level Yes 4.3% Public land and private
vacant lots
Ryerson [157] Atlanta, GA Ground level Yes 0.01% Single candidate site
Taylor and Lovell [81] Chicago, IL Ground level Yes 0.04% Existing community
and private gardens
Remote sensing imagery has typically been processed manually rather than through
automated means, either to detect existing urban agriculture activity [81] or to identify
potentially suitable parcels [147, 156]. For example, on ground level suitability, Kremer
and DeLiberty [152] analyzed the city of Philadelphia using a procedure integrating
classification techniques for remotely sensed land cover data with GIS data layers
including physical and administrative information about Philadelphia’s parcels, buildings,
and zoning. The authors used a supervised pixel-based classification method and discussed
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the importance of comparing it with object-based classification method for the purpose of
detecting urban yards, and including shape, size, and shade for refining the detection. On
rooftop suitability, Berger [147] looked at potential site suitability of rooftop agriculture in
New York City, which presents many challenges to ground level urban agriculture, primar-
ily through the lack of affordable open space for commercial urban farms due to the densely
built landscape. The model utilizes publicly available datasets to identify the buildings with
the greatest potential for rooftop farms, greenhouses, or intensive green roofs (including
structural considerations), combined with aerial imagery for validation and estimation of
usable area. While not focused on food, Kodysh et al. [158] used light detection and
ranging (LiDAR) data and a geographic information system to conduct a rooftop suitability
study for PV solar installation, considering screening parameters such as elevation, slope,
and shadow effect that influence solar intensity on the building roof. Such LiDAR-based
tools developed for the solar energy industry have also been applied to consider suitability
for rooftop agriculture, such as the NYC Solar Map used by Berger [147].
5.1.3 Connections to self-sufficiency and resilience
Resilience is now a central concept in city planning; Boston was one of the first cities in
the US to appoint a Chief Resilience Officer in 2015. Also in 2015, the City released the
first Boston Food System Resilience Study, describing in mostly qualitative terms some of
the potential risks facing the city’s residents in the case of disruptions [159]. The
vulnerability of Boston’s food system to natural disasters is of interest to decision makers,
due in part to recent multiple extreme events such as Hurricane Sandy in 2012, and the
record-breaking snowfall in the early months of 2015.
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The effects of climate change on agriculture are being assessed in many countries, but the
ability of the existing food systems to recover from natural extremes is not generally
considered in most metropolitan resilience planning [160]. The food distribution network
constitutes a critical ‘lifeline’ system for cities. Food distribution systems that are disrupted
by disasters may not return to normal operations for long periods, which could cause
significant food availability and food access issues. Recent studies have suggested local
and regional food systems as a partial solution to these issues [101, 134, 161], though of
course these may also be disrupted during extreme events. It is estimated that 90% of the
food consumed in Boston is produced outside of the region. While growing, urban
agriculture is only a small portion of local food supply. However, there is significant
interest in Boston to expand local food production and processing in the city and the
metropolitan region. Policies that enable or incentivize urban agriculture may decrease
food shortage risks associated with local natural disasters, while in addition increasing
economic resilience by supporting local food cultivation, processing, and distribution
companies that create local jobs [160].
Following in this paper, we describe an automated procedure for combining GIS and
remote sensing data to determine the suitable areas for urban agriculture at ground level
and rooftops in Boston. Additional attribute-based and geospatial modeling tools were
employed, and to our knowledge, this is the first urban-scale study to conduct both rooftop
and ground level productive area mapping with the detailed exclusion of ownership,
zoning, parcel size, slope, soil quality and shadow analysis for an entire city. This modeling
framework can be extended to other cities with similar data availability and can be used to
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provide detailed estimates of urban agricultural potential, as well as to identify or prioritize
specific parcels or neighborhoods for more in-depth analysis. In addition, the outcome of
the study looks to provide urban planners and food system stakeholders with additional
information about urban farming scale, while also contributing to the larger research
question of urban food self-sufficiency.
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5.2 Methods
5.2.1 Study area and datasets
The current study is focused on developing a GIS-based model to conduct site suitability
analysis of urban farming on rooftops and ground level areas in Boston. Both currently
utilized agricultural sites and potentially suitable sites were mapped. The Boston area was
chosen because the city has a diverse range of buildings and land use types that could be
suitable for urban food production and because of supporting policy on urban agriculture
and food security. Currently, there are nearly 200 community gardens in operation for
vegetable and fresh fruit production in Boston. However, there are very few private
operations in the City currently for commercial-scale food production [94]. Specifically,
there are currently six commercial urban farms in Boston operating on 14 plots throughout
the city named: “Allandale Farm, City Growers, Corner Stalk, The Food Project,
Katsiroubas Brothers Fruit and Produce, and ReVision Urban Farms” [94].
This study was divided into three distinct steps (Fig. 5-1). First, a geospatial model was
developed to map the potential flat rooftop and ground level areas that can be used for
urban farming, using a variety of public geospatial data layers and remote sensing data,
described in detail below. Second, the total theoretical fruit and vegetable yield from these
potentially productive lands was assessed. Finally, validation analysis was conducted to
check the model accuracy by comparing with records from the latest Tax Assessor’s
database for the city that includes characteristics of all buildings and parcels. The input
data sources, estimation approaches, and validation techniques are explained in detail
below for each step.
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Fig. 5-1: Boston rooftops and ground level parcels extraction steps
The spatial extent of study area was easting 656,167 feet and northing 2,460,625 feet (Fig.
5-2). The projected coordinate and projection system of the spatial data frame was NAD
1983 State Plane Massachusetts Mainland FIPS 2001 Feet and Lambert Conformal Conic
respectively. The required datasets for analysis were primarily available from MassGIS,
the state GIS clearinghouse for the Commonwealth of Massachusetts [162].
5.2.2 Mapping flat rooftops
Identification of flat rooftops was conducted using several geoprocessing, spatial analyst
and raster analysis tools in ArcMap 10.3 software package (ESRI Inc., Redlands, CA). The
overall modeling process was then automated using ArcMap Model Builder and python
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script. The flowchart in Fig. 5-1 summarizes the steps involved in rooftop mapping. Here,
suitable flat rooftop for urban farming is defined as roof areas that are located within
permitted zones for urban agriculture according to a recent zoning ordinance [140], with a
minimum roof surface area (1000 ft2), and a maximum building height (100 ft), and surface
slope (< 5o). Typically, rooftop conditions above 100 ft are assumed as being less
hospitable to plants due to high winds, and there are logistical and safety concerns with
access for people and supplies [147]. The first step was to overlay building footprints and
zoning layers. The most recent Boston building footprint data layer was obtained from the
City of Boston GIS Datahub [163]. Surface area and height constraints were then applied
to delineate the suitable buildings. Selections by attributes and Selection by location, Clip,
and Erase operations in ArcMap were used for delineation functions. It was assumed
throughout that the roof area of buildings is identical to the building footprint. The building
footprint area is automatically calculated as the shape geometry in ArcMap. The model
was allowed to combine adjacent rooftops. In those cases, the continuous roof area is
considered same as their total combined building footprint. This relaxation of the single-
parcel area constraint result allows for interesting opportunities for rooftop agriculture
through multi-building partnerships.
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Fig. 5-2: a) Delineated buildings, b) The division of the Boston area for LiDAR data
The delineated building layer from the previous step was then subjected to slope analysis
(Fig. 5-2a). For this purpose, a digital surface model (DSM) of Boston buildings was
created using remotely sensed LiDAR point cloud dataset and the identified building
layers. The LiDAR point cloud database was obtained from MassGIS in LAZ (compressed
LAS) file format. The extracted LAS files are originated from airborne LiDAR cloud data
sources that includes elevation and intensity of the topographical features taken from the
first and last pulse returns from a LiDAR instrument flown on a rotary wing platform [164].
These LAS files can be displayed and analyzed in ArcMap as point clouds. For this study,
373 separate LiDAR LAS files are extracted for Boston which consist of total 698,553,071
measured known points (Fig. 5-2b). The resolution of each LAS file is 10 ft × 10 ft. These
dataset were later combined into a single LiDAR database (.lasd) file in ArcMap using
Create LAS dataset tool. The resultant layer was than post-processed using LAS
Reclassification tools to identify the building features for slope analysis. Table 5-2
summarizes the spatial extent of LiDAR cloud dataset.
a) b)
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Table 5-2. Description of parameters used to estimate rooftop area
Spatial
parameters
Scale Projection Description
Output layer 1: 1,00,000 World Equal Area
projection
Feature layer
Land parcels 1: 1,00,000
North American Datum
1983, State plane
Massachusetts, ft
Feature layer
Buildings 1: 1,00,000 Feature layer
Extruded 2D
buildings
1: 1,00,000 Feature layer
LiDAR Point
Clouds
1: 1,00,000 LAZ layer
The corrected LAS data-file was then converted to raster database using LAS to raster tool.
Elevation was used as a conversion value field and Nearest neighbor raster interpolation
method. Previous research has found the Elevation parameter to be useful in extracting the
height variation within a building with a flat roof and Nearest neighbor interpolation
method suitable over inverse distance weighted methods [158]. The resultant DSM layer
of Boston was then further processed for slope and aspect analysis (Fig. 5-3).
Slope analysis of was performed using the Surface slope tool of ArcMap spatial analyst.
The DSM layer was used as an input and degree (pitch) was chosen as output slope layer
unit. Previous work has found that, for a typical urban region, DSM slope ranges from 0-
25°, 25-50° and 50-90° correlated well with actual rooftop types [164]. Slopes of less than
5° usually indicated a flat roof, 25-50°, a pitched roof, and slopes that were greater than
50° were usually associated with a breakline. Therefore, we reclassified all the building
polygons with average slopes of less than 5o as flat roofs using the Reclassify tool. The
reclassified slope layer was masked using the Extract by mask tool where the slope layer
96
was used as an input and building footprint layer as masked layer to extract the roofs with
slope ranges of 0-5°. Wherever there was a sudden change in elevation―between the
ground and the roof of a building or between two levels of the same building―the raster
pixel has a slope greater than 50o.
Fig. 5-3: a) Boston downtown geographic area of interest; b) High resolution LiDAR
data; c) Extracted building footprint layer
5.2.3 Mapping ground level parcels
Mapping of suitable ground level areas for urban farming was also performed in several
steps (Fig. 5-1). Another GIS-based site suitability model was developed using the Model
Builder automation tool and adapted from the spatial analysis framework constructed in
our previous study on bioenergy crop potential in the city [35]. Boston’s land parcel layer
was used as an input into GIS model and a series of linear combinations of spatially
referenced layers were used as screening layers, with some boundary conditions to identify
land parcels that can be suitably used. Here, ground level urban farmland was defined as
parcels that are in areas zoned as suitable for primary agriculture, with a minimum parcel
size of 100 ft2, a soil slope <15%, and adequate soil quality and sunlight hours (6 hours in
growing season). Land use types that fit these criteria can be diverse, especially within built
up areas. For Boston, these included public and private vacant lands, as well as private
residential and commercial underutilized areas.
a) b) c)
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First, an intermediate layer for suitable parcels were identified using spatial screening of
unsuitable areas from the input parcel layer (Table 3), which features of all Boston parcels
estimated for 2014. Screening layers were spatially joined using the Join tool and overlaid
with the input layer using the Erase tool to exclude areas unsuitable for farming because
of zoning, ownership and accessibility restrictions. These screening layers included area
occupied by buildings, driveways, water features, and conservation lands. These layers
were obtained from the City of Boston Department of Innovation and Technology [163],
MassGIS [162], and the Tufts University Boston GIS Database [165]. World equal area
projection system was chosen as the default projected coordinate system for ArcMap data
frame properties to conduct the necessary geoprocessing calculations (Table 5-3). The
scale of all the layers was 1:1,000,000. Streets and highways were pre-excluded from the
input parcel layer and so did not require subsequent exclusion.
Next, the intermediate parcel layer was subjected to bio-geophysical analysis. The bio-
geophysical model performed minimum area screening, marginal slope, soil quality, and
shadow analysis. Minimum parcel size was chosen accordingly to comply with city zoning
restriction and a marginal slope to minimize runoff and erosion and for ease of cultivation
and harvesting, following previous work [152].
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Table 5-3. Description of parameters used to estimate ground level area
Spatial parameters Scale Projection
Output layer 1: 1,000,000 World equal area projection system
Land parcels 1: 1,000,000
North American Datum (NAD) 1983,
State plane Massachusetts, ft
Buildings 1: 1,000,000
Drive ways 1: 1,000,000
Water and protected
areas
1: 1,000,000
Soil types 1: 1,000,000
World Geodetic System (WGS) 1984
Soil slopes 1: 1,000,000
Extruded 2D buildings 1: 1,000,000
Soil quality information for Boston was obtained from the United States Department of
Agricultural Natural Resources Conversation Services (USDA-NRCS) soil survey
geographic database (SSURGO) [166]. The SSURGO database contains information about
soil types based on a soil classification for the entire United States. There are four distinct
classes of soil types (A, B, C and D) and three major farmland classifications available that
include prime farmland, farmland of statewide importance and not prime farmland. Areas
with soils listed as ‘prime farmland’ and ‘farmland of statewide importance’ were
classified as suitable for urban agriculture (Table 5-4). The geoprocessing steps included
selection and simultaneous Erase of unsuitable parcels that are classified as ‘not prime
farmland’, soil type C and D and slope >15%.
Table 5-4. USDA-NRCS soils classification
USDA NRCS classification Soil type Slope (%) Soil type
Prime farmland Silt loam/Shaly silt/loam/clay
loam
0-8% A
Farmland of statewide
importance
Silt loam/Shaly silt/loam/clay
loam
2-15% B
Not prime farmland Urban/industrial dump/gravel
pits
0-35% C/D
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Finally, sun-shadow analysis was conducted to estimate the duration and intensity of
sunlight over the previously identified suitable parcels, based on heights and footprints of
existing buildings. Trees were excluded from the sun-shadow analysis as these could be
removed to prioritize urban agriculture. 3D sun-shadow tool was used to identify the
suitable parcels that receive a minimum of six hours of sunlight during the middle of the
growing season (Fig. 5-4a). Generally, land parcels with full sun exposure on the south,
southeast and southwest with no buildings or other obstruction on those sides were
considered optimal, while parcels with shade during any part of the day were considered a
shadow area and removed (Fig. 5-4b). 3D sun-shadow analysis required extruded 2D
buildings as an input features, created from attribute data from the Boston building
footprint data layer. The analysis was conducted on extruded buildings for July 21st
between 10AM – 4PM. July 21st was considered as the representative day for analysis that
lies at the midpoint of the seven-month growing season (April – October) in Boston. The
analysis yielded composite 2D projections created by shadow volumes on each parcel for
the specified time duration. The projection multipatch features were converted to polygon
features using Multipatch to polygon tool to create the shadow vector map layer, which
was used as an Erase layer in the bio-geophysical model along with unsuitable soil quality
and slope layers.
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Fig. 5-4: Shadow distribution on Boston Downtown at a) 10 AM and b) 4 PM on July
21st
5.2.4 Estimating food yields
As an urban coastal area, Boston has a subtropical climate and can support production of a
multitude of fresh fruits and vegetables, especially between April – October [160]. In this
study, Boston’s total urban food potential was estimated using spatially averaged crop yield
results. Suitable plants species were identified based on agricultural zone information
derived from United States Department of Agriculture (USDA) plant hardiness zone map
[167]. The plant hardiness zone map is the standard by which gardeners and growers can
determine which plants are most likely to thrive at a particular location. The map is based
on the average annual minimum winter temperature, divided into 10-degree Fahrenheit
zones. According to that map, Boston falls into zone 5b, with average minimum
temperatures in the -15 to -10 °F range. Suitable food-plant yield values for Boston were
obtained for both conventional urban gardening and hydroponic rooftop gardening from
literature. These average constant yield values for current climate conditions are listed in
Table 5-5. Total food yield Mf (t yr-1) was estimated by multiplying each individual rooftop
a) b)
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Ar (ha) and land parcel area Ag (ha) with associated yield data Yi (kg m-2 yr-1) (eqs. i and ii)
and summing.
Table 5-5. Plant yield information
Production practice Food types Yield (kg m-2 yr-1) Source
Conventional urban
gardening, Yg
Dark green
vegetables; 1.35 Duchemin et al.
[168] Tree fruits
Hydroponic rooftop
gardening, Yr
Dark green
vegetables 19.5
Grewal and
Grewal [153]
𝑀𝑟 = ∑ 𝐴𝑟 ∙ 𝑌𝑟𝑖 (i)
𝑀𝑔 = ∑ 𝐴𝑔 ∙ 𝑌𝑔𝑖 (ii)
5.2.5 Validation
Validation of results was conducted to ensure accuracy of the spatial models and ground-
truthing of identified rooftop and ground level parcels. To accomplish this, four suitable
rooftops and four ground level parcels were randomly selected from the identified rooftop
and land parcel layers using the Create random point tools of ArcMap. The python-based
geo-processing tool generated 20 random points from the original input parcel layer; 8 out
of 20 points coincide with centroids of the parcels identified. The resultant parcels were
further layered with high resolution (15 cm) orthoimagery for visual comparison and
validation and compared against existing Tax Assessor’s data obtained from the Boston
Redevelopment Authority. The Find similar tool was also used to determine the extent of
similarities and false negative parcels not identified in spatial analysis. Validation could
be further bolstered through on-site inspection, but this was not done here so as to
emphasize automated methods.
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5.3 Results
5.3.1 Rooftop area mapping
The roof area estimation steps are displayed in panel format in Fig. 5-5. This shows the a)
input buildings, b) reclassified LiDAR points with ‘buildings’ object classified as green
colored feature c) digital surface model layer with delineated buildings, d) slope analysis
with color coding represent the slope range (green 0-5°; yellow 5-50°; blue 50-90°), e)
building with slope <5o in green, and f) final flat roof layers in red.
Fig. 5-5: Flat rooftop mapping steps
Fig. 5-6 visualizes the distribution of suitable flat roofs for urban agriculture across the
city. Approximately 922 ha (9.22 km2) of potential roof area was identified, representing
42% of total the roof area and 7.4% of the total land area in Boston, respectively.
Examining rooftop suitability by neighborhood, the communities of Dorchester, Roxbury,
a) Building polygons b) LiDAR point
cloud
f) Extraction of flat rooftops
e) Buildings with slope
<5o
d) Slope analysis of DSM
c) Raster digital surface
model
103
South Boston, Jamaica Plain, and Brighton were found most suitable locations for rooftop
urban farming, where approximately 60% of identified rooftops are located (Fig. 5-7). The
North End, Financial District, Charlestown, and East Boston communities have a lower
proportion of suitable roofs due to lack of sunlight availability and height and zoning
restrictions.
Fig. 5-6: Flat Roofs in Boston
Fig. 5-7: a) Flat roof distribution in Boston’s neighborhoods, by number; b) overlaid
aerial image of flat roofs in the Dorchester neighborhood (suitable roofs filled in grey
with red outline)
a) b)
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5.3.2 Ground level farmland mapping
The ground level area estimation steps are displayed in panel format in Fig. 5-8. This shows
the a) input layer of Boston parcels, all the exclusion layers including b) buildings, c) water
and protected areas, d) driveways, and e) parcels that are not suitable due to inferior soil
quality, slope and inadequate sunlight, resulting in f) final available ground level areas.
Fig. 5-9 visualizes the total ground level land availability across the city. Approximately,
1,250 ha (12.5 km2) of potential urban farmland was mapped, representing 10% of
Boston’s total land area. Of this, 22% consists of vacant lands and the remainder is under-
utilized residential and commercial areas. There are approximately 184 ha (1.84 km2) of
public and 100 ha (1 km2) of private vacant lands available for immediate ground level
food production. Fully 78% of the potential land areas identified are under-utilized parcels,
nearly evenly split between residential areas (458 ha) such as yards (excluding paved
driveways), and government and commercial areas (508 ha) that could potentially be
categorized as urban farmland and used for agricultural purposes.
Suitable ground level farmland resources are concentrated in eastern and southern part of
the city. Due to sufficient sunshine and low building and population density relative to the
rest of the city, the southern neighborhoods of Dorchester, Roxbury, Mattapan, and West
Roxbury are promising areas for growing fruits and vegetables. These areas have also been
identified by the City of Boston as potential location for developing urban agriculture [94].
On the other hand, the highly urbanized and densely populated core of Boston was not
found suitable due to the high proportion of impervious areas, limited parcel sizes, and
large shadow areas associated with buildings; however, this part of the city has some
105
potential for rooftop urban agriculture. The results of this study suggest that Boston has
significant amount of productive farmland and roof area available for neighborhood and
commercial scale food production.
a) Land parcels (input layer)
b) Buildings
c) Driveways and parking
lots
d) Water and protected
areas
e) Unsuitable parcels from
slope and shadow analysis
f) Ground level areas
(output layer)
Fig. 5-8: Ground level farmland estimation in Boston
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Fig. 5-9: Available ground level areas in Boston
5.3.4 Food yield potential
Evaluation of maximum food yield potential in Boston are shown in Table 5-6. It was
estimated that Boston’s ground level parcel and rooftop area have a capacity to produce
annually nearly 17,000 metric tons and 180,000 tons of high-yield fresh fruits and
vegetables respectively, if 100% of available areas is used in a regular growing season.
Potential food yields for hydroponic rooftop-based gardening are higher compared to
conventional ground-based urban gardening (Table 5-6). Actual yields may vary based on
soil quality as well as future climate conditions. For context, this amount of food can
potentially provide enough vegetables and fruits for approximately 280,000 people (30%
of Boston’s population) with an annual average intake of 698 kg fresh fruit and vegetables
[106]. The study shows promising results for increasing local production in Boston’s food
system.
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Table 5-6: Potential food production in Boston
Production practice Yield
(t ha-1yr-1)
Area
(ha)
Food production
(t yr-1)
Conventional urban gardening, Yg 13.5 1250 17,000
Hydroponic rooftop gardening, Yr 195 922 180,000
5.3.5 Validation
Each of the 8 randomly-selected potential ground level and rooftop parcels passed both
visual inspection and validation against data in Boston’s Tax Assessor’s database. Aerial
images, with building id, parcel id and addresses of selected buildings and parcels are
presented in panel format in Fig. 5-10. One of the selected flat-roofed buildings (45 Nevins
St) was a parking garage with rooftop spaces that could be used for greenhouse and/or
hydroponic agriculture but may also be appropriate to screen out. The use of even higher
resolution LiDAR data with precise classification could further improve the assessment
and validation, since features such as rooftop access could be characterized. One of the
selected ground level parcels (ID: 1401121000) was adjacent to several other small parcels,
which could be farmed independently or through community agriculture on shared private
lands.
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Fig. 5-10: Randomly selected rooftop (top row) and ground level (bottom row)
parcels
5.4 Discussion and Implications
The study shows a step-by-step geospatial modeling approach of assessing local food
potential in Boston and how it can supplement the City’s existing food system. In
estimating total potential yield, it was assumed that 100% of available area will be used
towards developing urban agriculture; so these results should be seen as an upper bound,
as not all farm land available in the city may be practically be used. Future changes in land
use patterns and local climate conditions may affect the suitability of the different food
crops and their associated yields. Community acceptance, soil contamination, safety, and
traffic considerations could reduce the amount of available land that is practically usable.
Several previous analyses have incorporated estimates of the proportion of potentially
productive urban land that could practically be utilized, either through application of a city-
875 Morton St, Boston 115 Norfolk St, Boston 761 Gallivan Bl, Boston 45 Nevins St, Boston
Parcel ID: 1601229000 Parcel ID: 1401121000 Parcel ID: 1700979000 Parcel ID: 800943000
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wide average (e.g., 75% for parcels in Oakland by McClintock et al. [156]) or through
creation of different production scenarios (e.g., Grewal and Grewal for Cleveland [153]).
Previous urban agricultural land assessments have taken a diversity of approaches (Table
5-1), with different land types included and different screening criteria. Some have
included only public lands, investigated existing sites, or screened with the intention of
identifying a small number of promising sites. Of those that conducted a city-wide
inventories of potential agricultural land, namely Cleveland [153], Oakland [156], Detroit
[148], and Philadelphia [152], researchers found a range of 2.6-7.8% of total municipal
land areas, with a mix of public and private vacant lots depending on the study. For
Cleveland, the inclusion of occupied residential parcels (i.e., yards) brought the ground
level total to 37% of the city’s total land area, while the flat industrial rooftops were
assessed at 5.8%. The results presented here for Boston for vacant lots correspond to 2.3%,
below the range reported in previous work. This could be due in part to the relative
economic health of Boston compared to the other cities, leading to active development or
conversion of vacant parcels. Boston’s result for total ground level availability of 10% is
well below that for Cleveland, likely due to the multiple screening criteria of slope, soil
quality, impervious surfaces, and shadow included here that were not considered for
Cleveland. The result for rooftops of 7.4% moderately exceeds that for Cleveland but
includes residential and commercial properties, whereas the latter only considers industrial
roofs [153].
110
This recent research indicates that combining high resolution remotely sensed data and GIS
methods in assessment of precision agriculture and urban food potential mapping can
support a wide range of policy and planning efforts. In discussing early efforts in Portland
and Vancouver, Mendes et al. suggest that the very process of carrying out an inventory
can promote stakeholder engagement and assist in consensus community planning [137],
particularly when it is linked to official goals for agricultural land use. Implementation of
this local scale production requires detail food system modeling. The total number of food
based community gardens are growing but represents a very small share of the city’s food
supply, the great majority of which is produced outside of the region [160]. While only a
small portion of the city’s food is currently sourced locally, a network of over 200
community gardens and several dozen local food vendors grow and distribute local fresh
fruits and vegetables in the city. Research on the urban gardening movement in the city
suggests that personal food production in community gardens is helping to fill the gap
particularly in low-income, inner city neighborhoods [94]. Integrating on-the-ground
surveys and interviews with spatial analysis may also help refine modeling efforts and lead
to targeted recommendations for the development of local food systems.
However, several potential challenges need to be carefully considered in expanding the
current scale of city’s local food production activity. Surface runoff and water quality
degradation due to possible additional use of pesticides and fertilizers in the city may
impact local water bodies, especially the Charles River watershed which is already
subjected to cyanobacteria blooms and has a total maximum daily load (TMDL) framework
in place [169]. Neighborhood noise pollution of farming is another key issue that many
111
communities have faced in many other urban regions [133]. Access to adequate fresh water
for some urban area is an important factor that may reduce the rate of community garden
expansion. Also, rooftop farming imposes additional live loads on roofs that may increase
the rate of structural deterioration and maintenance cost.
While Boston was used as a test case in this study, the methods were designed to be applied
to any city or metropolitan area. Urban agriculture inventories may be of particular interest
for cities that are expecting climatic shifts towards more temperate conditions, and/or are
undergoing depopulation and so have a high proportion of underutilized lands and lower
economic rents than Boston.
5.5 Conclusions
Urban agriculture has numerous and varied potential benefits, including community and
economic development, improvements in environmental quality, and partial fulfilment of
food demand, potentially enhancing food security. Here, an automated modeling
framework utilizing GIS tools and remote sensing data was described and used to estimate
potential agricultural land in within Boston, encompassing both ground level parcels and
flat rooftops.
The total available rooftop area was estimated at 922 ha (9.2 km2) that can be suitably used
for food production, representing 42% of city’s total roof area. At ground level, the
estimated area is 1,250 ha (12.5 km2) representing 10% of city’s total available land area,
112
of which 78% is private residential and commercial underutilized areas and 22% is public
and private vacant lands.
If all of the available ground level parcels were converted to agriculture and intensive
hydroponic systems were installed on all suitable roofs, Boston has the capacity to produce
nearly 17,000 metric tons/yr and 180,000 tons/yr of high yield food plants, respectively, in
a regular growing season. This amount is sufficient to supply roughly 30% of Boston’s
fruit and vegetable demand annually. Even partial development of urban agriculture on
these lands could fulfill a significant proportion of the city’s food demand, corroborating
similar results for other cities in North America.
113
Chapter 6 Conclusion and Future Works
The dissertation focused on conducting a series of case studies related to the application of
geographic information system in quantifying the extent of urban system’s response to
climate change induced the atmospheric effect on infrastructure and resource self-
sufficiency. Following the research objectives mentioned in the introduction section, four
specific studies were carried out on different aspects of urban stock assessment and
geospatial modeling.
In chapter 2, describes a study on 4D-GIS based assessment of mapping vulnerable
building stocks in Boston under different projected climate change scenarios. Novel
corrosion model was constructed to assess building and block-level vulnerability of urban
concrete buildings, related maintenance needs, and to project cover thickness degradation
for the existing building stock. This is the first ever urban scale study that looked at both
spatial and temporal aspects of atmospheric CO2 and Chloride induced corrosion
phenomena. The results suggest that climate change may reduce the average design service
life of concrete buildings, potentially requiring extensive repairs well within the average
design service life. Chapter 3 describes another geospatial assessment for quantifying city’s
extent of energy self-reliance from leveraging local resources. Here, a comprehensive,
novel and scalable GIS-based modeling framework was created to assess potential urban
marginal lands that considered ownership, zoning, under-utilized public and private areas
with adequate soil quality and sunlight. This is the most sophisticated screening tool to date
to examine parcel suitability for bioenergy applications, specifically through the
implementation of 3D sun-shade analysis. The detailed analysis found nearly a quarter of
114
city’s land parcel suitable for energy crop agriculture. Chapter 4 describes an application
and validation of GIS framework developed in chapter 3 on a regional scale. Here a site-
specific marginal land assessment for bioenergy production was conducted for the MAPC
cities. The outcomes of this study confirms the model accuracy and provides
comprehensive information about marginal land availability for using in the demand
center. Finally, Chapter 5 describes an automated geospatial analysis of urban land
availability for food-based agriculture purpose. The study considers both ground level and
rooftop areas and estimate the total potential for local food production in the city. High-
resolution LiDAR imagery was used for accurate estimation of suitable rooftops. Overall,
the findings could potentially aid in decision making and prioritize policies regarding urban
system engineering.
The works depicted in this dissertation have made novel contributions to the urban
metabolism and GIS body of knowledge. There is also a novelty in creating spatial
framework and models for assessing urban scale vulnerable infrastructure and sustainable
resource that may be beneficial for improving urban ecosystem health. Especially, a
building-by-building corrosion model was developed for an entire city and the most
sophisticated sub-parcel level screening tool to date to examine parcel suitability for
bioenergy and urban food applications, specifically through the implementation of sun-
shade analysis in custom model builder routine. The models are scalable and robust enough
to explore other urban regions with similar scenarios. With the increase in disaster
dynamics, it’s important to have enough tools to sustain as a resourceful and low impact
city. Therefore, the question of self-sufficiency and resiliency are important. This work
115
fills an important gap of how to leverage existing knowledge of GIS and urban stock
assessment in answering these questions.
There is a wide range future studies that could grow out of this dissertation work. To
realistically include the concept of future climate change impacts and self-sufficiency into
urban engineering, more and more city-specific spatial and temporal study of infrastructure
vulnerability and resource productivity assessment are required. Comparative studies that
apply a unified methodology may help to identify adaptation and other planning measures
(such as changes in building codes or land use zoning) that are appropriate in different
cities.
Several modeling extensions could be made. These studies used a deterministic modeling
framework with average values or fixed ranges. A stochastic analysis using assigned or
fitted parameter value probability distributions could assess uncertainty in the results using
Monte Carlo simulation, while model parametrization and sensitivity analysis could be
performed to determine the most sensitive parameters and test model robustness. For the
concrete buildings work, advanced degradation models are being developed with more
detailed chemistry, specifically that couple the carbonation and chlorination processes,
which could be used in the framework presented in this dissertation. In addition, model-
predicted carbonation and chlorination depths can and should be validated with
experimental site data. For the geospatial modeling of historical concrete building stocks,
instead of constant concrete properties assumed in this work, the temporal variation of code
recommended water-cement ratio, concrete strength, and corrosion protected cover
116
thickness could be considered, or, better, yet, historical construction and material testing
documents could be assembled to determine these modeling parameters on a building-by-
building basis. In addition to revising code modification, as suggested in Chapter 2, the
projected results for corrosion initiation in existing buildings outcome can be used for
devising appropriate building inspection programs.
Possible extensions of urban resource models may include scenario-based land use
combinations for assessing biomass and bioenergy yield. As not all of the identified areas
can be practically used, for reasons described in Chapters 3-5, it could be fruitful to engage
local stakeholders to create probable usage scenarios for the different percentages of
available residential and commercial lands use types, and then estimate potential bioenergy
or food production potential. For example, for total available marginal land assessment for
bioenergy crops, possible scenarios might include only vacant lands, or vacant lands and
50% of commercial under-utilized areas, depending on municipal development and zoning
priorities. For estimating bioenergy and/or food yield, average values reflecting current
climatic conditions were used here, but in a future climate, different crop types and
productivities may be appropriate to use should these studies be extended into the future.
Engineering analysis is just the first step; community scale surveys also need to be
conducted before large-scale urban bioenergy or food agriculture could practically develop.
Successful implementation also requires site validation and planning through soil testing,
logistics planning for harvesting and processing, and financial modeling. A final extension
in scope would be to create a comprehensive model capable of assessing national scale
urban site suitability for urban agriculture implementation. However, this type of modeling
117
will require large scale data acquisition, manipulation, and modeling, using national input
data layers if possible. As more and higher-resolution geospatial data become available, it
will become easier for cities, regions, and entire nations to conduct bottom-up analyses of
buildings, land, and other resources in order to plan for climate change impacts and assess
potential resource productivity.
118
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APPENDIX
Table A1: USGS land cover classification [126]
Land use
Code
Land use
description
Detailed definition
1 Cropland Generally tilled land used to grow row crops.
Boundaries follow the shape of the fields and
include associated buildings (e.g., barns). This
category also includes turf farms that grow sod.
2 Pasture Fields and associated facilities (barns and other
outbuildings) used for animal grazing and for the
growing of grasses for hay.
3 Forest Areas where tree canopy covers at least 50% of the
land. Both coniferous and deciduous forests belong
to this class.
4 Non-Forested
Wetland
DEP Wetlands (1:12,000) WETCODEs 4, 7, 8, 12,
23, 18, 20, and 21.
5 Mining Includes sand and gravel pits, mines and quarries.
The boundaries extend to the edges of the site’s
activities, including on-site machinery, parking lots,
roads and buildings.
6 Open Land Vacant land, idle agriculture, rock outcrops, and
barren areas. Vacant land is not maintained for any
evident purpose and it does not support large plant
growth.
7 Participation
Recreation
Facilities used by the public for active recreation.
Includes ball fields, tennis courts, basketball courts,
athletic tracks, ski areas, playgrounds, and bike
paths plus associated parking lots. Primary and
secondary school recreational facilities are in this
category, but university stadiums and arenas are
considered Spectator Recreation. Recreation
facilities not open to the public such as those
belonging to private residences are mostly labeled
with the associated residential land use class not
participation recreation. However, some private
facilities may also be mapped.
139
8 Spectator
Recreation
University and professional stadiums designed for
spectators as well as zoos, amusement parks, drive-
in theaters, fairgrounds, race tracks and associated
facilities and parking lots.
9 Water-Based
Recreation
Swimming pools, water parks, developed freshwater
and saltwater sandy beach areas and associated
parking lots. Also included are scenic areas
overlooking lakes or other water bodies, which may
or may not include access to the water (such as a
boat launch). Water-based recreation facilities
related to universities are in this class. Private pools
owned by individual residences are usually included
in the Residential category. Marinas are separated
into code 29.
10 Multi-Family
Residential
Duplexes (usually with two front doors, two
entrance pathways, and sometimes two driveways),
apartment buildings, condominium complexes,
including buildings and maintained lawns.
Note: This category was difficult to assess via photo
interpretation, particularly in highly urban areas.
11 High Density
Residential
Housing on smaller than 1/4 acre lots. See notes
below for details on Residential interpretation.
12 Medium Density
Residential
Housing on 1/4 - 1/2 acre lots. See notes below for
details on Residential interpretation.
13 Low Density
Residential
Housing on 1/2 - 1 acre lots. See notes below for
details on Residential interpretation.
14 Saltwater Wetland DEP Wetlands (1:12,000) WETCODEs 11 and 27.
15 Commercial Malls, shopping centers and larger strip commercial
areas, plus neighborhood stores and medical offices
(not hospitals). Lawn and garden centers that do not
produce or grow the product are also considered
commercial.
16 Industrial Light and heavy industry, including buildings,
equipment and parking areas.
17 Transitional Open areas in the process of being developed from
one land use to another (if the future land use is at
all uncertain). Formerly identified as "Urban Open".
18 Transportation Airports (including landing strips, hangars, parking
areas and related facilities), railroads and rail
stations, and divided highways (related facilities
would include rest areas, highway maintenance
areas, storage areas, and on/off ramps). Also
140
includes docks, warehouses, and related land-based
storage facilities, and terminal freight and storage
facilities. Roads and bridges less than 200 feet in
width that are the center of two differing land use
classes will have the land use classes meet at the
center line of the road (i.e., these roads/bridges
themselves will not be separated into this class).
19 Waste Disposal Landfills, dumps, and water and sewage treatment
facilities such as pump houses, and associated
parking lots. Capped landfills that have been
converted to other uses are coded with their present
land use.
20 Water DEP Wetlands (1:12,000) WETCODEs 9 and 22.
23 Cranberry bog Both active and recently inactive cranberry bogs and
the sandy areas adjacent to the bogs that are used in
the growing process. Impervious features associated
with cranberry bogs such as parking lots and
machinery are included. Modified from DEP
Wetlands (1:12,000) WETCODE 5.
24 Powerline/Utility Powerline and other maintained public utility
corridors and associated facilities, including power
plants and their parking areas.
25 Saltwater Sandy
Beach
DEP Wetlands (1:12,000) WETCODEs 1, 2, 3, 6,
10, 13, 17 and 19
26 Golf Course Includes the greenways, sand traps, water bodies
within the course, associated buildings and parking
lots. Large forest patches within the course greater
than 1 acre are classified as Forest (class 3). Does
not include driving ranges or miniature golf courses.
29 Marina Include parking lots and associated facilities but not
docks (in class 18)
31 Urban
Public/Institutional
Lands comprising schools, churches, colleges,
hospitals, museums, prisons, town halls or court
houses, police and fire stations, including parking
lots, dormitories, and university housing. Also may
include public open green spaces like town
commons.
34 Cemetery Includes the gravestones, monuments, parking lots,
road networks and associated buildings.
35 Orchard Fruit farms and associated facilities.
141
36 Nursery Greenhouses and associated buildings as well as any
surrounding maintained lawn. Christmas tree (small
conifer) farms are also classified as Nurseries.
37 Forested Wetland DEP Wetlands (1:12,000) WETCODEs 14, 15, 16,
24, 25 and 26.
38 Very Low Density
Residential
Housing on > 1 acre lots and very remote, rural
housing. See notes below for details on Residential
interpretation.
39 Junkyard Includes the storage of car, metal, machinery and
other debris as well as associated buildings as a
business.
40 Brushland/Success
ional
Predominantly (> 25%) shrub cover, and some
immature trees not large or dense enough to be
classified as forest. It also includes areas that are
more permanently shrubby, such as heath areas,
wild blueberries or mountain laurel.
142
Table A2: Estimated marginal land for MAPC cities
Town
Marginal land
(ha)
Total land area
(ha)
Land
Percentage
BOSTON 2914 12,825 23%
MARSHFIELD 2112 7446 28%
FRANKLIN 1850 6988 26%
CONCORD 1569 6684 23%
MARLBOROUGH 1548 5706 27%
FRAMINGHAM 1546 6869 23%
NEWTON 1527 4711 32%
ACTON 1375 5247 26%
HOPKINTON 1350 7219 19%
SUDBURY 1346 6411 21%
WALPOLE 1312 5467 24%
IPSWICH 1265 8680 15%
NATICK 1236 4138 30%
PEMBROKE 1229 6100 20%
WEYMOUTH 1219 4581 27%
DUXBURY 1217 6273 19%
HANOVER 1190 4060 29%
LEXINGTON 1168 4309 27%
NORWELL 1158 5490 21%
SCITUATE 1122 4475 25%
HOLLISTON 1108 4933 22%
NEEDHAM 1102 3303 33%
DANVERS 1099 3578 31%
BRAINTREE 1075 3720 29%
BELLINGHAM 1039 4898 21%
SHARON 1012 6324 16%
CANTON 996 5053 20%
READING 953 2583 37%
WILMINGTON 947 4442 21%
WESTON 903 4483 20%
NORTH READING 901 3496 26%
MEDWAY 892 3021 30%
HINGHAM 881 5892 15%
FOXBOROUGH 881 5399 16%
LITTLETON 859 4541 19%
PEABODY 850 4350 20%
BEDFORD 847 3589 24%
WAYLAND 838 4104 20%
STOUGHTON 809 4262 19%
143
ROCKLAND 803 2624 31%
BEVERLY 799 4006 20%
QUINCY 771 4335 18%
WRENTHAM 759 5859 13%
MEDFIELD 727 3793 19%
WALTHAM 723 3565 20%
GLOUCESTER 712 6939 10%
MILTON 709 3418 21%
NORFOLK 684 3987 17%
ASHLAND 670 3334 20%
HAMILTON 647 3873 17%
WELLESLEY 630 2720 23%
STOW 625 4672 13%
SHERBORN 622 4180 15%
TOPSFIELD 616 3314 19%
WESTWOOD 614 2889 21%
MILLIS 599 3180 19%
DOVER 594 3998 15%
CARLISLE 576 4012 14%
ESSEX 575 3769 15%
WOBURN 573 3357 17%
MIDDLETON 573 3751 15%
DEDHAM 572 2765 21%
HUDSON 562 3074 18%
LINCOLN 549 3881 14%
BROOKLINE 520 1767 29%
WAKEFIELD 518 2066 25%
AYER 484 2461 20%
COHASSET 435 2604 17%
LYNNFIELD 422 2711 16%
BOXBOROUGH 392 2691 15%
SAUGUS 368 2957 12%
ROCKPORT 363 1849 20%
HOLBROOK 345 1918 18%
WENHAM 344 2102 16%
WINCHESTER 336 1649 20%
NORWOOD 335 2710 12%
RANDOLPH 304 2708 11%
MANCHESTER 235 2020 12%
BURLINGTON 229 3071 7%
MEDFORD 225 2196 10%
MAYNARD 194 1401 14%
SALEM 181 2191 8%
REVERE 168 1540 11%
144
MALDEN 157 1313 12%
BELMONT 156 1234 13%
CAMBRIDGE 147 1856 8%
CHELSEA 145 572 25%
MELROSE 117 1229 9%
STONEHAM 85 1726 5%
HULL 79 755 10%
MARBLEHEAD 73 1155 6%
LYNN 68 2965 2%
SOMERVILLE 63 1072 6%
SWAMPSCOTT 60 787 8%
ARLINGTON 36 1408 3%
WATERTOWN 23 1067 2%
NAHANT 6 296 2%
EVERETT 4 893 0%
Total 71,150 361,885 20%