Development of Process Algorithms and Datasets for...
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Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Lahouari Bounoua Goddard Space Flight Center
Jeffrey Masek, Marc L. Imhoff and Christa Peters-LidardNASA Goddard Space Flight Center
And
Eric G. MoodyGoddard Space Flight Center
Contract
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
CollaboratorsLahouari Bounoua:• Overall supervision of the project• Development of algorithms for urbanization and their implementation and
testing into the Simple Biosphere (SiB2) land surface model• Design and execute model simulations and• Analysis model outputs for all phases of the project
Jeff Masek:• Development of the automation of the decision and regression tree software necessary for
characterizing the fractions of impervious surfaces in urban area• Development of continental land cover map with fractions of impervious surfaces
Marc Imhoff• Preparation of the MODIS data for the development of the land cover map• Collaboration in the development of the physical algorithms
Christa Peters-Lidard • Collaboration in development of algorithms for urbanization• Implementation of the new land cover map/attributes and the algorithms into the Land Information System
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Background
1. Urbanization is a significant and permanent form of land transformation.
What is the overall impact of urbanization on water, energy and carbon cycles in North America and globally ? And how does it affect climate?
Is there a recognizable effect in the NDVI signal at 1km spatial resolution?
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
DMSP/OLS Urban MapUrban, Peri-urban, Non-urban
AVHRR/MODISMonthly NPP (g Cm-2)
NPP and Local ClimateSatellite Observations
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Winter NPP gain negated during
summer by reduced vegetation and heat
stress.
Seasonal Offset diminishes in
tropics
Urban heating extends the length of growing season locally in cold climates.
In semi-arid regions cities enhance NPP relative to
surrounding areas
North East
Mid-Atlantic
South EastSouth West
Background
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Background
1. Expansion of urbanization is accelerating, especially in developing countries.
Annaba
Annaba
Algiers
Algiers
Casablanca
Casablanca
Oran
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Background1. Urbanization is having an impact on Earth’s water, energy and carbon budgets.
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
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Tem
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Temperature anomalies
Observed monthly mean temperature anomalies (ºC) over Santa Cruz, Bolivia between 1975 and 1999, blank (missing data
The simulated area averaged monthly mean temperature response increased by 0.6 oC for the conversion of broadleaf forest to cropland and 1.2 oC for wooded grassland to cropland.
When averaged over the entire domain, the effect of landscape conversion resulted in a warming of 0.5 oC. This warming is in line with an increasing trend observed in the monthly mean temperature in Santa-Cruz, Bolivia during the same period.
Temperature difference 1999-1975
Warming of 0.6 oC
Warming of 1.2 oC
0warming
Cover type difference 1999-1975
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Objectives
We propose to combine calibrated moderate-resolution data from the MODIS instrument and Landsat digital imagery to:
• Develop a continental land cover map explicitly accounting for the fractions of impervious surfaces within urban areas at the scale of MODIS 1km x 1km with the possibility of extension to the globe.
• Develop and validate process algorithms of urbanization suitable for climate studies and land data assimilation;
• Use the new urban attributes and process algorithms in a land surface model to quantify the impact of urbanization on water, energy and carbon budgets over the continental U.S at time scales ranging from diurnal to annual.
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Background
The Large scale impact ?
While the impact of land cover change to urbanization is perceptible at local scale, its large scale impact on water, energy and carbon budgets is not well comprehended. This can be achieved :
• Evaluating the fractions of impervious surface within urban centers at regional and global scales and• Describing the physical processes associated with urban land use on water, energy and carbon cycles.
It is recognized that characterization of urban areas is best achieved using high resolution data such as Landsat-data. However, one of the objectives of this proposed work is to map urban areas for climate modeling at continental scale; and eventually extend it to global scale. This is best achieved through integration of moderate-resolution (MODIS-scale) data.
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Tasks and Timeline
Develop decision and regression tree software
Algorithms and attributesfor urbanization
Implement SiB2 into theLand Information System (LIS)
Produce preliminary landcover map
Use preliminary map as boundary
condition for SiB2
Develop/Implement algorithms and test model response
Continue development of land cover map
Transfer algorithms and datasets to LIS
Transfer algorithms and datasets to LIS
Perform simulations at continental
scale and analyze outputs
Evaluate impacts on water energy and carbon cycles
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Development of Process Algorithms and Datasets for Urbanization and Climate Studies
MODIS 1km NBAR (Aug-Sep)
MODIS 1km LST(Aug-Sep)
MRLC 30m%impervious averaged to MODIS 1km grid
1 km
Predictor Variables Training Data(950 samples)
Cubist Regression Tree (avg ε = 4.4%)
Apply to all 1km MODIS pixels within EPA Ecoregion
ρ Τ (οK)
Impervious Fraction Mapping
Mapping Approach
Regression to the mean … tendency for many agricultural soils to be labeled 1-2% impervious
Algorithm varies with region
Residual “false positives”
• Perform separate regression for “low” (<10% impervious) values• Mask out very low (<5%) impervious values• Leaf on leaf off
• Derive separate regression trees for EPA Level 2 ecoregions
• Exploring probability field based on proximity to known population centers
Mapping Issues
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Progress
Preliminary map of impervious surfaces for the Eastern US, derived fromMODIS NBAR and LST data, trained using MRLC % impervious coverage for the Philadelphia-New York region. Color scale ranges from black (< 5% impervious cover) to white (> 60% impervious cover).
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Progress
MODIS impervious fraction map for Cleveland, Ohio and Pittsburg,PA showing the location of cities with population > 50,000 persons from ESRI ArcGIS coverage. Note that color scale saturates in these images at ~40% impervious cover (white).
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Surface Layer
Root Zone
Recharge Zone
etc rc
2rbrd
rsoil
λEc
ea
em
λEc + λEg
λEg
ra
S L
Rn
P
+Wleaf
Wc
+ Wthru Wdrip
-Wrun Wg
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W3
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-Wdrain
Um Tm
eci
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z2
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CanopyAir Space
SiB2 Transfer pathway for Latent HeatThe Land Surface Model
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Preliminary Results
Selection of Pixel in Chicago with the highest fraction of impervious surface.
Pixel composition:Class 4 : Needleleaf evergreen (4.2 %)Class 8 : Dwarf trees and Shrubs (2.8 %)Class 12 : Agriculture and C3-grass (20 %)Urban : more than 90 % impervious (73 %)
Run the Land Surface Model for 4 years with Urban Class characterized by:• Low NDVI
• Higher surface reflectance
• Lower specific heat capacity and thermal conductivity
• Low interception capacity
• Low soil porosity
• No interlayer flow between layer 1 and layer 2
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Preliminary ResultsC
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Development of Process Algorithms and Datasets for Urbanization and Climate Studies
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class 4 class 8 class 12 urban weighted avg
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class 4 class 8 class 12 urban weighted avg
Preliminary Results
Physiological Response
Carbon assimilation and Conductance for a mix of land cover co-existing in the same grid.
Class 4 : Needleleaf evergreen (4.2 %)Class 8 : Dwarf trees and Shrubs (2.8%)Class 12 : Agriculture and C3-grass (20%)Urban : more than 90 % impervious (73%)
Grid size Class 4 Class 8 Class12 Urban average avg- urb
1 km 4.4 10.6 10.0 0.05 2.6 2.55
25 km 2735.1 6653.5 6257.9 28.9 1664.2 1635.3
Daily total carbon assimilation (grams) and difference between the weighted average and
the dominant type
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Preliminary Results
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Latent Heat components
total Class 4 Class 8 Class 12 Urban W. avg
LH 169.70 168.30 169.68 68.57 95.86
SH 33.15 -4.15 -6.98 74.83 53.17
Urban area has the minimum LH and the maximum SH
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Preliminary Results
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class 4 class 8 class 12 urban weighted avg prec
Class 4 Class 8 Class12 Urban average
Runoff (mm) 1.39 1.68 1.66 12.1 9.3
run/prec (%) 9.65 11.67 11.53 84.02 64.58
Precipitation and runoff (mm). Daily mean Precipitation 14.4 mm
Urban area loses about 85 % of the water it receives to surface runoff
Precipitation and Runoff
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Preliminary Results
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Class 4 Class 8 Class 12 Urban W. avg
LH 169.70 168.30 169.68 68.57 95.86
SH 33.15 -4.15 -6.98 74.83 53.17
BR= SH/LH 0.20 -0.02 -0.04 1.09 0.56
Typ. values 10 2 - 4 0.4- 0.6 0.2 0.1
desert s. arid Tem for Tr. forest Trop. ocean
At 100% urbanization the model response predicts a reduction in latent heat of about 100Wm-2 (10.7 cm for July)and an increase in sensible heat of about 65 Wm-2. A study covering regional scale Eastern U.S (Dow and DeWalle, 2005 ) suggests that at 100% urbanization decreases annual evaporation by 22 cm and increases SH by 13 W.m-2. Another study over St Louis, Missouri (J.S. Ching, 1985) found maximum Bowen ratio greater than 1.5 over the city and less than 0.2 in non urban areas.
Total Water and Energy Fluxes
Sensible Heat Flux
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Preliminary Results
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class 4 class 8 class 12 urban weighted avg Energy Response
Canopy temperature represents the skin temperature whereas ground temperature is the temperature about 2 cm in the ground.
Because of larger leaf area index-lai, canopy temperature of class 4 (lai = 5.56) is much cooler than that of shorter vegetations, class8 lai = 2.27 and class 12 lai =2.71.TC12 – TC4 = 4.56 oC at 12 noon while that difference is only 0.25 ( at night).
Canopy temperature
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class 4 class 8 class 12 urban weighted avg
Ground temperature Class 4 Class 8 Class12 Urban average
Abs. max 21.33 25.24 25.89 28.87 28.01
Abs. diff 6.68 2.77 2.12 -0.86
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class 4 class 8 class 12 urban weighted avg
Canopy temperature
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Preliminary Results
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Class 4 Class 8 Class 12 urban w..avg.Class 4 Class 8 Class12 Urban average
Abs. dtr 8.47 12.69 13.08 14.78 14.24
Mean dtr 4.20 7.39 7.28 7.85 7.66
Canopy diurnal temperature range
Class 4 Class 8 Class12 Urban average
Abs. dtr 9.76 11.66 11.64 15.48 14.40
Mean dtr 5.27 6.55 6.30 8.13 7.63
Ground diurnal temperature range
For both canopy and ground temperature, urban land cover has the highest diurnal temperature range (dtr). The increase in the dtr is mainly due to an increase in the maximum temperature.
23.1521.1821.07
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Class 4 Class 8 Class12 Urban average
Daytime mean
18.36 21.07 21.18 23.15 22.59
Average minus class
4.23 1.52 1.41 -0.56 0
Ground temperature difference
Development of Process Algorithms and Datasets for Urbanization and Climate Studies
Concluding Remarks
• These results are for a single grid cell and for a month with maximum evaporation (July)
• Results do not include atmospheric feedback which may either exacerbate or mitigate the impact depending on the geographic location and the seasonality of climate
• However, several studies indicate that since pre-industrial times to mid-1980s, the total global effect of the anthropogenic 'greenhouse gases’(not including water vapor) on climate is an energy increase of about 2 Watt/m-2. Regionally, some urban centers may produce heating much greater than that produced by GHG.