irrigated area mapping, south asia
TRANSCRIPT
Phot
o: D
avid
Bra
zier
/IW
MI
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Water for a food-secure world
Developing a Moderate Resolution Irrigated Area Map for South Asia using segmentation and time series analysis
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Water for a food-secure world
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.
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Water for a food-secure world
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
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Water for a food-secure world
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
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Water for a food-secure world
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
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Water for a food-secure world
Datasets - comparisonDataset - type
GIAM dataset
Resolution Proposed Dataset
Resolution Availability Role
NDVI /Reflectance
AVHRR 10KM MODIS 250m Free Time series analysis
NDVI/Reflectance
SPOT 1KM IRS P6 - AWIFS
56m Purchase Single date classification into objects
DEM GTOPO 1KM SRTM 90m Free conditional segmentation
Temperature AVHRR 10km MODIS 1KM Free conditional segmentation
Precipitation CRU 0.5 degree WorldClim 1KM Free conditional segmentation
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Updated Methodology Level 1
Level 2
Entire processing on minimumMapping unit – like admin boundaries, climatic zones etc.
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Level1 – Segmentation and HR Land cover map
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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
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Water for a food-secure world
Image classification steps
Original Image
Segmented Image
ISOCLASS Classified Image
Recoded Image
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Water for a food-secure world
Level 2 – Time series on MODIS 250m NDVI
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MODIS Path/row for South Asia
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Class – flow diagramAgriculture
Irrigated Rain fed
Surface water Ground water Conjunctive
Single crop Double crop Continuous crop
Water source
Irrigation type
From MODIS Irrigation intensity
Example class: Irrigated, surface water, double crop
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Water for a food-secure world
Irrigated area calculatedCountry 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.
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Water for a food-secure world
India
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Water for a food-secure world
Pakistan
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Water for a food-secure world
Sri Lanka
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Water for a food-secure world
Nepal
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Water for a food-secure world
Bangladesh
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Water for a food-secure world
Bhutan
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Water for a food-secure world
• 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?
Speeding up the localized approach
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Water for a food-secure world
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…