Irrigated Area Mapping, South Asia


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Irrigated Area Mapping, South Asia

  1. 1. Developing a Moderate Resolution Irrigated Area Map forSouth Asia using segmentation and time series analysis Photo: David Brazier/IWMI Water for a food-secure world
  2. 2. Why Irrigated Area Mapping?• Perspective of achieving food security by increasing irrigation• Though 70-85 % of water used• Especially with current situation of population, urbanization , climate change etc.• Important to assess the spatial distribution, intensity, water use etc. Water for a food-secure world
  3. 3. Is it new?• Many products available globally - FAO, IWMI• Also national products- CBIP, India• Global Irrigated Area Map(GIAM) – developed by IWMI in 2006• GIAM -Resolution of 10km and datasets from 1990 -1999, AVHRR• Very course product with detailed classification Water for a food-secure world
  4. 4. Global Irrigated Area Mapping• Product from IWMI - developed using multiple global datasets• Different datasets were used at – Segmentation/Localization of landscape – Classification into different units – Time series analysis to identify irrigation intensity• Nominal resolution of 10KM• Datasets used were from 1990 – 2000 Water for a food-secure world
  5. 5. Need/Opportunity to update GIAM• Data available from 250m spatial resolution• Highly capable HW/SW available for data intensive processes• Good temporal coverage• Extensive change in the landscape would have happened in 12 years• New algorithms in image classification – „object based image analysis‟• Updating the irrigated area map for South East Asia Water for a food-secure world
  6. 6. Datasets - comparisonDataset - GIAM Resolution Proposed Resolution Availability Roletype dataset DatasetNDVI AVHRR 10KM MODIS 250m Free Time series/Reflectance analysisNDVI/Reflec SPOT 1KM IRS P6 - 56m Purchase Single datetance AWIFS classification into objectsDEM GTOPO 1KM SRTM 90m Free conditional segmentationTemperature AVHRR 10km MODIS 1KM Free conditional segmentationPrecipitation CRU 0.5 degree WorldClim 1KM Free conditional segmentation Water for a food-secure world
  7. 7. Updated Level 1 MethodologyEntire processing on minimumMapping unit – like adminboundaries, climatic zones etc. Level 2 Water for a food-secure world
  8. 8. Level1 – Segmentation and HR Land cover map Water for a food-secure world
  9. 9. Optimal segmentation• Region growing algorithm – SPRING open source software• Main parameters; “Similarity” and “Area”• Objective function based on spatial auto correlation to determine best parameters• Optimal segmentation > good classification• Another factor > size of the image• Bigger the size > more mix in clustering results• Optimal size found from trial runs 250km by 200km Water for a food-secure world
  10. 10. Image classification stepsOriginal Image Segmented Image ISOCLASS Classified Image Recoded Image Water for a food-secure world
  11. 11. Level 2 – Time series on MODIS 250m NDVI Water for a food-secure world
  12. 12. MODIS Path/row for South Asia Water for a food-secure world
  13. 13. Class – flow diagram Agriculture Irrigated Rain fed Water sourceSurface water Ground water Conjunctive Irrigation type Continuous Single crop Double crop From MODIS crop Irrigation intensity Example class: Irrigated, surface water, double crop Water for a food-secure world
  14. 14. Irrigated area calculated Country Irrigated Area (million ha) Nepal 4 Pakistan 21 Sri Lanka 1.6 India 169 Bhutan 0.2 Bangladesh 10 Total irrigated area calculated for entire South Asia is 206.74 million hectares. Water for a food-secure world
  15. 15. India Water for a food-secure world
  16. 16. Pakistan Water for a food-secure world
  17. 17. Sri Lanka Water for a food-secure world
  18. 18. NepalWater for a food-secure world
  19. 19. BangladeshWater for a food-secure world
  20. 20. BhutanWater for a food-secure world
  21. 21. Speeding up the localized approach• Use of customizable open source tools• Developing a R package to manage the segmentation• Program in R to control • Dicing the imageries • Segmentation – SPRING software • Classification • Extracting the agc • Time series on agc • Localizing based on secondary datasets • Class assignment based on irrigation intensity• Time consuming/Manual • Class assignment at both classification levels • Comments? Water for a food-secure world
  22. 22. Conclusions• High resolution global datasets available now• Introducing a localized approach to avoid mixes• Key is to identify MMU with homogeneous pattern• Scope for semi automating the process using R scripting• Can‟t avoid the manual interventions though… Water for a food-secure world