Historical Assessment of Spatial Growth of Builtups in Metropolitan areas of Delhi and Mumbai in India and Dhaka in Bangladesh

Transcription

1 Historical Assessment of Spatial Growth of Builtups in Metropolitan areas of Delhi and Mumbai in India and Dhaka in Bangladesh Tomas Soukup, GISAT BBL session, World Bank HQ, Washington DC Date : 10 January 2012

2 Presentation contents Project background the eoworld initiative Spatial data advantage and Earth Observation (EO) data role The eoworld project case: Urban Growth monitoring needs, objectives areas of interest service definition service solution outputs examples Data exploration webtool examples (live)

3 Project background - the eoworld The eoworld initiative based on the previous ESA - WB collaboration pilots joint ESA - WB initiative to bring together both EO and World Bank communities More to be found at observation or in publication to demonstrate the potential of Earth observation to support the World Bank s operations Earth observation information support is scaled up and incorporated into 12 ongoing World Bank activities thematic support in the areas of the marine environment, water resources management, urban development, urban and disaster risk management, and agriculture and forestry. part of overall ESA initiative with several multi-lateral development banks (e.g. European Investment bank, African Development Bank etc..)

4 WB needs for spatial data The World Bank operations deal with dynamic socio-economical processes, but routinely implementing also sustainability principles in project financing (including environmental aspects in addition to social and economical ones) therefore requires data with land related spatial-temporal dimension Data with spatial-temporal dimension crucial for current situation assessment and rational support and monitoring via spatial focus difficult to obtain using conventional sources like statistics (due to missing relevant contents or spatial detail) or topomaps (due to missing relevant contents or temporal comparability)

5 Spatial data EO data role Data with spatial-temporal dimension advantage - example more spatial insight more thematic insight formation structure conventional statistics only conventional statistics only What are the spatial patterns and intensity of changes? How much are losses compensated by gains? How the quality is changing over the time? Which processes are behind class consumption and formation? How is the inner dynamic of classes? consumption structure

6 Spatial data EO data role Earth observation (EO) provides data with spatial-temporal dimension with potential to support both aspects: flexible spatial detail and temporarily comparable contents provides data in a synoptic and consistent manner around the globe, even in the most remote places multi-scale and multi-source solutions long temporal archives free data or competitive pricing advance service provision segment using the state-of-the art image analysis techniques (e.g. object oriented approaches) represents cost effective solution comparing to in-site survey or mapping

7 Envisat MERIS ESA EO data - current market portfolio Medium and Low resolution EO data: ~ 100m - 1km

8 Terra ASTER NASA/GSFC/METI/ERSDAC/JAROS EO data - current market portfolio High resolution EO data: ~ 5-80m

9 Ikonos GeoEye EO data - current market portfolio Very high resolution EO data: ~ 0.5-5m

10 RADARSAT-2 TerraSAR-X Astrium MDA EO data - current market portfolio Radar multi-scale EO data: ~ 1-100m

11 Urban growth monitoring case - intro one case of the 12 selected eoworld projects thematic focus on urban development, urban growth related information - the WB South Asia Megacities Improvement Program spatial focus on Metropolitan areas of Delhi and Mumbai in India and Dhaka in Bangladesh temporal focus on the past two decades ( ) People involved Contractor: GISAT - Project Leader: Mr. Tomas Soukup ESA-WB collaboration TTL : Mr. Anthony G. Bigio - WB Senior Urban Specialist, Earth Observation Coordinator WB Task Team Leader (TTL): Mr. Songsu Choi, South Asia Region supported by Mr. Mats Andersson ESA Technical Officer: Mr. Benjamin Koetz supported by Ms. Anna Burzykowska

12 Urban growth monitoring case - needs Needs, objectives Spatial-explicit information about current state and previous development of land use / land cover resources related to urbanization including: structure of the land cover / land use stocks structure and intensity of change spatial and temporal distribution of both over the past two decades comparability in space (between metropolitan areas) and time

13 Urban growth monitoring case - AOIs Areas of Interest (AOIs) ~ 18,000 km2 1,350km2 11,400km2 5,100km2

14 Urban growth monitoring case - service Service definition Key information - a sequence of land use datasets monitoring urban growth Time series of land use / land cover spatial data Land use / land cover change spatial data Report with change statistics Additional information - urban density mapping and road network mapping Service contents Service specification Thematic 11 classes (including 6 urban classes including density ranges) Spatial Temporal 20m resolution (100x100m - 1ha MMU for densities) Service available for reference years or close to reference years Geometric accuracy < 1 pixel Thematic accuracy overall accuracy > 80%

15 Urban growth monitoring case - solution (1) Technical solution - overall concept inspired by the EEA s Land and ecosystem accounting (LEAC) approach LC Change Flows Classification LC Change Matrix LC Status and Change Accounts LC change DB LC status DBs EO images Reports, statistics, data exploration

16 Urban growth monitoring case - solution (2) Technical solution - EO data based on Spot 2-5 archive and RapidEye up-to-date EO data stack supported by OpenStreetMap for road data extraction Spot 2 (1991) 20m Spot 4 (2003) 10m RapidEye (2011) 5m

17 Urban growth monitoring case - solution (3) Technical solution - standard nomenclatures using standard nomenclatures for land cover / land use classes 15 classes (8 urban related)

18 Urban growth monitoring case - solution (4) Technical solution - standard nomenclatures using standard nomenclatures for land cover change (LCF) flows: L1 7, L2 14

19 Urban growth monitoring case - solution (5) Technical solution - standard quality procedures ISO certification for procedures GMES validated products Local reference data not available - backup strategy based on public sources In Slot 3 (2010 / 2011) each AOI reached overall accuracy higher or equal to 84% and the Kappa coefficient higher or equal to 0,76.

20 Urban growth monitoring case - outputs (1) Service outputs - spatial GIS- ready data + standard ISO metadata + QA report main layers additional layers potential slum areas main road network EO image composite vector data (3x status, 2x changes)

21 Urban growth monitoring case - outputs (2) Service outputs - maps status maps change intensity maps

22 Urban growth monitoring case - outputs (3) Service outputs - Report with detailed statistics in Annex

23 Urban growth monitoring case - outputs (4) Service outputs - data exploration Web Tool

24 Urban growth monitoring case - findings (1) First findings - Land Cover / Land Use share

25 First findings - Land Cover / Land Use share: large districts differences Dhaka Urban growth monitoring case - findings (2) Delhi Mumbai

26 Urban growth monitoring case - findings (3) First findings Artificial area structure: urban fabric densification takes place

27 Urban growth monitoring case - findings (4) First findings Mean annual overall change rate is increasing (exc. Mumbai) City Total turnover [ha] 1 st period No. of years Annual change rate [%] 1 st period Total turnover [ha] 2 nd period No. of years Annual change rate [%] 2 nd period Dhaka , ,99 Delhi , ,73 Mumbai , ,33

28 Urban growth monitoring case - findings (5) First findings Overall change structure: urban sprawl share is increasing

29 Urban growth monitoring case - findings (6) First findings Mean annual artificial land increase is growing

30 Urban growth monitoring case - findings (7) First findings Artificial land formation structure - stable share (exc. Construction sites)

31 Urban growth monitoring case - findings (8) First findings Artificial land uptake (non-artificial land consumption) structure follows overall non-artificial area share

32 Urban growth monitoring case - findings (9) First findings Distribution and intensity of urban sprawl pattern is changing

33 Urban growth monitoring case Benefits Service Benefits harmonized contents for large areas over long time information on spatial/temporal patterns, quality and intensity of changes information comparable between megacities more time & cost efficient then on-site survey change detection (topomaps potential limited), detailed spatial component insight into urban related processes and drivers knowing the past / forecasting the future identification of priority areas Limitations / Constraints EO data availability, quality and acquisition time availability of in-situ information for production QC and verification

34 Urban growth monitoring case Web Tool

35 Urban growth monitoring case Web Tool In addition to proposed services we have provided the WB user also with data in the Web Tool GISAT Web based data exploring environment Why? to integrate presentation modes in easy-to-use integrated environment map graphs, cartograms, cartodiagrams tables to make results available in user-friendly way for user exploring and support utility assessment support collaborative work, data analysis, use for reporting toattract more users within World Bank i.e. have better impact with EObased products - eoworld initiative ultimate goal to test framework in real application and improve based on feedback

36 Urban growth monitoring case Web Tool Technical background Web Tool implementation is based Open Source solutions and the system architecture is based on three-tier client-server architecture. Data layer: PostgreSQL/PostGIS The middle application layer consists of web server (Apache Web Server) layered on an application server and map server: UMN Map Server Web Processing: PostGIS, GRASS, GDAL Authentication: OpenID Presentation layer (client layer) provides the user interface via web and operates by generating requests to the application server via HTTP and displays the results in a web browser. Client web mapping is based on OpenLayers + JavaScript extensions, AJAX Browser support: IE v9.0 on, Google Chrome, Firefox or Safari browsers Development supported by ESA UrbanAtlas+ project

37 Urban growth monitoring case Web Tool Let s go live

38 GISAT - Contractor introduction GISAT company facts: - SME geoinformation company since located in Prague, Czech Republic - committed to remote sensing services - ISO quality certified services Strong expertise in: - land monitoring - urban/spatial planning - environment - agriculture - emergency response & security Clients/customers: - National, European, Worldwide More to be found on company web pages or

39 Thank you Questions & Discussion Date : 10 January 2012