Remote Sensing of Environment and Disaster Laboratory

Institute of Industrial Science, The University of Tokyo, Japan

Urban Morphology Identification using Digital Surface
Model (DSM) over Indian Cities

photoPrakhar Misra, Wataru Takeuchi
Institute of Industrial Science, The University of Tokyo, Japan

11.6: “Reducing impactof adverse air quality onenvironment”

1. INTRODUCTION: Previous researches have indicated that growth in built-up urban area and air pollution for Indian cities arecorrelated. Characterization of urban regions into residential, commercial and industrial zones can throw further light onnature of air quality growth and emission factors of these zones. For this, we investigate the possibility of using freely available
coarse resolution satellite derived digital surface model (DSM) to estimate building height. The most recent DSMs at global
scale are those provided by ASTER GDEMv2 and ALOS World 3D (AW3D) datasets and are distributed freely at 30m resolution.
Since nightlight observed from space are reliable indicator of ground human activity, it is hypothesized that using structureheight in conjunction with nightlight can reveal characterization of urban areas. We consider an urban region (Kanpur city) in
India for comparing the performance of DSM to derive built structure heights. Our results indicate better performance ofAW3D datasets to find built structure heights. Our objective is to investigate freely available satellite datasets for buildingheight estimation. Thereafter urban morphology is identified in terms of: residential, commercial and industrial areas

2. BACKGROUND

3. METHODOLOGY• Air pollution in Indian cities (aerosol andNO2) is correlated with built-up area andpopulation• Construction and industrial time seriesGDP statistically cause air pollution• Spatial urban structure determines
emissions through a landuse-transportemission model (Borrego, 2005)

Residential area (Source:Panoramio)

Study Location –Kanpur city

DEM (AW3D& ASTER)LANDSAT8(2013)

MSD
Filtering

Land coverClassification(Perko, 2015)

Select Builtup pixels

DTM
Commercial area (Source:Flickr)

How urban morphology (residential,commercial, industrial) and its spatiallayout determines urban air pollution?

nDSM

Built nDSM

(Normalized surface model)

Morphologicalerosion

Rule-basedclassifier:
1. Residential
2. Commercial
3. IndustrialUrbanMorphology

Industrial area (Source:Topcem)

Commercial

Thresholds:
h > = 4m
NL >= NL’
Fig.4 Height thresholdis based on uncertaintyof AW3D DSM and local
municipal law.

Behera et al (2011)

Residential
Industrial

Commercial

Unsurec

Fig.1 ASTER DTM underestimates(2014Nightlight)

nDSM = DEM - DTM

Objective: to identify urban morphology in.
Indian cities
4. RESULTS(Digital terrain model)

VIIRS DNB

Fig.6 Urban morphology of Kanpur cityAW3DASTER
Fig.2 Comparison of nDSM

Morphological erosion(Kernel = 50)

Fig.3 Highways & fringe city regions
are removed while estimating ‘core’.

Table 1 Classification
rules based on Fig4.
and Fig3 threshold. 1indicate TRUE.
Height NLCore Class000

Residence

100

Industry

110

Industry

001

Residence

101

Industry

111

Commercial

Industrial

AW3
DRegression relationship:
NL’ = f(h) = 3.16ln(h) + 16.62

TRUE

Fig.5 Regression relationshipbetween nightlight (NL) andAW3D nDSM (h).

FALSE

Fig.7 True and False positives of industrial regions

5. DISCUSSION•••••AW3D is suitable for structure height extraction while ASTER needs error correction or uncertainty estimation. However ASTER can also be used for urban morphology.
Nightlight shows correlation with structure heights and estimated urban morphology appears similar to known map.
Needs processing to get rid of tall vegetation more accurately, possibly using NDVI index.
Ground truth data to resolve ‘unsure’ region. Overpasses, bridges, military bldg. identified as industry/commercial. Using road from OSM to increase DTM accuracy.
Future step: Estimate emission factors of different morphology based on spatial distribution for air pollution studies.

6. REFERENCES
1) Perko, R. et al. Advanced DTM generation from very high resolution satellite stereo images. In: ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci.II-3/W4.March, 2015
2) Misra Prakhar and Takeuchi Wataru. Analysis of Air Quality in Indian Cities Using Remote Sensing and Economic Growth Parameters. 36th Asian Conference of Remote Sensing, Manila, 20153) Borrego et al. How urban structure can affect city sustainability from an air quality perspective. Env. Modelling and Software, 2006 ; 4) Behera et al. Development of GIS-aided emission inventory of air pollutants for an urban environment. Adv Air Pollution, 2011

For further details, contact: Mr. Prakhar Misra, Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: mprakhar@iis.u-tokyo.ac.jp)