development of process algorithms and datasets for ... · development of process algorithms and...

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


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


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?


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


Background

1. Expansion of urbanization is accelerating, especially in developing countries.

Annaba

Annaba

Algiers

Algiers

Casablanca

Casablanca

Oran

Oran

Background1. Urbanization is having an impact on Earth’s water, energy and carbon budgets.


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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


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.


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.


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

Y 1

Y 2

Y 3


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



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).


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).


Surface Layer

Root Zone

Recharge Zone

etc rc

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CanopyAir Space

SiB2 Transfer pathway for Latent HeatThe Land Surface Model


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


Preliminary ResultsC

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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


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


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


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


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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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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Canopy temperature


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


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.

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19.6119.6718.22


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


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.

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