‘A remote sensing framework for assessing the...
Transcript of ‘A remote sensing framework for assessing the...
‘A remote sensing framework for assessing the microclimatic effects of green infrastructure at local scales’
Carlos Bartesaghi Koc PhD Candidate UNSW, CRC-LCL | MBEnv, BArch Supervisors : Dr Paul Osmond, Prof Alan Peters Co-supervisor : Dr Matthias Irger
GREEN INFRASTRUCTURE
(Tree canopy, green open spaces, green roofs, vertical greenery systems)
URBAN MICROCLIMATE (Surface- & Canopy Layer- Urban
heat island – SUHI, CLUHI)
Airborne
Remote Sensing As a method to map and assess the
thermal effects of GI
Research outline
GI characteristics:
- Multi-functionality - Interconnectivity - Spatial heterogeneity
Climate regulation:
- Shading (LAI-NDVI) - Evaporative cooling (ETc) - Wind modification
What is the thermal
performance of different green
infrastructure typologies on
urban microclimate and which
amounts, compositions and
distributions are more
effective in providing cooling
benefits at the local scales?
Questions & Objectives
O1 Propose a standardised classification scheme for GI.
O2 Evaluate different methods, principles and indicators.
O3 Propose a methodological framework for a more accurate and precise evaluation of GI.
O4 Examine the relationship between different GI characteristics and the thermal profile of a case study in the context of Australia.
O5 Develop a statistical model to predict the thermal performance of different GI typologies.
O6 Propose a list of evidence-based
Data sources & indicators
INFRARED Seasonal / day- & night- time Surface Temperature (SurfT)
IN-SITU MEASUREMENTS
Car transects Relative humidity (RH)
Air Temperature (AirT)
Meteorological stations Wind speed (WS)
Solar radiation (SR)
CADASTRAL
Location Distance to coast (DtC)
Street geometry Street width (W)
Aspect ratio (H/W)
LIDAR
Buildings Building heights (H)
Building surf. Fraction (BSF)
Ground Altitude (DTM/DSM)
Vegetation configuration Patch density (PD), aggregation index (AI), landscape shape index (LSI), contagion (CONTAG)
Vegetation height/extent Low (L), medium (M), high (H) vegetation fractions
HYPER-/MULTI- SPECTRAL
Spectral Reflectivity Impervious surface fraction (ISF)
Water fraction (WF)
NDVI
Deciduous/Evergreen (D/E) fractions
Leaf area index/density (LAI-LAD)
Evapotranspiration (ET)
Climatic indicators
Intervening variables
Independent variables
Urban Morphology indicators
GI- Configurational indicators
GI- Structural indicators
GI- Functional indicators
Dependent variables
Data collection techniques: 1. Airborne remote sensing 2. In-situ measurements (mobile and weather
stations) Initial set of indicators. 95 out of 150 articles reviewed (ongoing systematic review)
Missing data: Summer Data collected: Winter 2012
Data collected and pre-processed by Dimap, and kindly provided by Dr. Matthias Irger
- Hyperspectral - Lidar - Cadastral - Thermal infrared - Car transects’ data
- Multispectral - Thermal infrared - Car transects’ data - Weather stations’ data
Data to be collected as part of a project managed by Dr. Matthias Irger. POSSIBLE PROJECT CANCELATION.
Key issues!
?
Methodological framework
LCZ 2 LCZ 3 LCZ 1
A LCZ classification Hy
Li
In
Image in process of publication, Bartesaghi et al. (2016c)
Ca
- Wind speed
- Dist. to coast - Street width - H/W ratio
- Building heights - Building SF - DSM / DEM
- Impervious SF
II. Classification of case study into LCZs to: - Reduce the effect of
urban morphology aspects.
- Select zones of relatively similar urban characteristics.
I. Control of intervening variables by selecting appropriate location and day for measurements
In= In-situ; Ca= Cadastral; Li= Lidar; Hy= Hyper-/multi- spectral; Th= Thermal
Methodological framework
B
GIT 3 GIT 2 GIT 1 GIT 2
GIT classification
Image in process of publication, Bartesaghi et al. (2016c)
- PD, ED, LSI (Fragstats) - L,M,H Veg. fract.
- Impervious SF - Water fraction
Li
Hy
Hy - Dec./everg. fract. - LAI - NDVI - ET
- RH - Air Temp. - Solar radiation - Wind speed
V. Calculation of NDVI and derivation of LAI VI. Estimation of ET by adapting the FAO-56 Penman-Monteith method. VII. Allocation of functional values (LAI, ET) to each GIT.
In
III. Subdivision of LCZ into GIT. IV. Characterisation and classification of GITs according to structural and configurational indicators.
In= In-situ; Ca= Cadastral; Li= Lidar; Hy= Hyper-/multi- spectral; Th= Thermal
Methodological framework
C Statistical analysis
Image in process of publication, Bartesaghi et al. (2016c)
Th
- Winter & summer, diurnal & nocturnal surface temperature
VIII. Statistical analysis and formulation of a predictive model according to: a. Functional aspects
(LAI; ET; NDVI)
b. Structural aspects (L, M, H; Dec/ev.%)
c. Configurational aspects (PD, AI, LSI, CONTAG).
In= In-situ; Ca= Cadastral; Li= Lidar; Hy= Hyper-/multi- spectral; Th= Thermal
Key interventions & contributions:
• A green infrastructure typology that works in line with LCZ to support climatic studies.
• Use of high resolution imagery for a more precise and accurate analysis.
• Estimation of evapotranspiration in urban areas and heterogeneous contexts.
• Formulation of a framework to evaluate existing urban areas and to predict thermal profiles of vegetation (for end-users i.e. councils and governmental agencies)
• Formulation of guidelines as a communication and visualisation tool for designers and policy-makers. Image: EEA (2013). Building a green infrastructure for Europe.
Key publications
(1) Journal paper (review) submitted to the Urban Ecosystems Journal. (Under review)
(1) Conference paper accepted at IC2UHI June 2016, Singapore. (4th International conference on countermeasures to Urban Heat Islands).
(1)Conference abstract submitted. iHBE (International High-performance Built Environments
Conference) (SBE16 Sydney) November 2016. (1)Conference abstract submitted. Climate Adaptation Conference, Adelaide - July 2016. (1)Poster presentation at the Annual CRC-LCL Forum, November 2015. (1) Abstract and oral presentation at 11th ACCARNSI National Early Career Researcher Forum
and Workshop, February 2016, Canberra.
(1) Working paper for journal > Second systematic literature review (150 pap.) on methodologies and indicators.
Thank you for your attention