Geospatial Delineation of Climate-Smart Agricultural Zones

Project Overview

This project employs multi-temporal remote sensing and GIS-based multi-criteria decision analysis to delineate Climate-Smart Agricultural (CSA) zones in the drought-prone Anuradhapura District of Sri Lanka. The resulting CSA suitability maps for Maha and Yala seasons support climate-resilient agricultural planning and drought risk mitigation.

Study Area

Data Sources

• Vegetation & drought indices – Landsat 8 & 9 (Google Earth Engine)
• Land use / land cover – Sentinel-2
• Rainfall data – Department of Meteorology, Sri Lanka & CHIRPS
• Soil type – Irrigation Department, Sri Lanka
• Slope / topography – SRTM DEM (Google Earth Engine)
• Irrigation infrastructure – Irrigation Department, Sri Lanka
• Administrative boundaries – DIVA GIS

Methodology

• Satellite image preprocessing (Landsat 8 & 9, Sentinel-2)
• Vegetation & drought index calculation (NDVI, EVI, SAVI, NDWI, NDDI, LST, SMI)
• Rainfall, soil, slope, and irrigation data integration
• Land use / land cover classification
• Derivation of biophysical indicators
• Reclassification and weighted overlay analysis (AHP-based)
• CSA suitability mapping for Maha & Yala seasons

Environmental & Biophysical Factors

Soil Type Distribution

Slope Analysis

Land Use & Agricultural Landscape

Land Use / Land Cover Maps

Irrigation & Water Infrastructure

Irrigation Network

Drought & Vegetation Analysis

SAVI (Soil Adjusted Vegetation Index) – Maha Season

EVI (Enhanced Vegetation Index) – Maha Season

NDDI (Normalized Difference Drought Index) – Maha Season

SMI (Soil Moisture Index) – Maha Season

NDWI (Normalized Difference Water Index) – Maha Season

LST (Land Surface Temperature) – Maha Season

Rainfall (Maha Season)

SAVI (Soil-Adjusted Vegetation Index) – Yala Season

EVI (Enhanced Vegetation Index) – Yala Season

NDDI (Normalized Drought Difference Index) – Yala Season

SMI (Soil Moisture Index) – Yala Season

NDWI (Normalized Difference Water Index) – Yala Season

LST (Land Surface Temperature) – Yala Season

Rainfall (Yala Season)

Land Suitability & Multi-Criteria Decision Analysis (MCDA)

To identify Climate-Smart Agricultural suitability, all environmental, climatic, and infrastructural factors were standardized and reclassified into suitability classes. These reclassified layers were later integrated using an Analytical Hierarchy Process (AHP)–based weighted overlay approach in a GIS environment.

Reclassified Indices & Suitability Factors

Reclassified Vegetation & Drought Indices

Reclassified NDVI

Reclassified SAVI

Reclassified EVI

Reclassified NDWI

Reclassified NDDI

Reclassified Climate & Moisture Factors

Reclassified Rainfall

Reclassified Land Surface Temperature (LST)

Reclassified Soil Moisture Index (SMI)

Reclassified Land & Hydrological Factors

Reclassified Land Use

Reclassified Soil Suitability

Reclassified Slope

Reclassified River Buffer

Reclassified Tank Proximity

Delineated CSA Zones (DSD) – Maha & Yala Seasons

Key Observations

• Drought stress is high in specific Maha and Yala seasons, while irrigated areas remain resilient.
• Irrigation, land cover, and elevation strongly affect CSA suitability.
• Some divisions consistently show high suitability; others are vulnerable during dry seasons.
• Water availability, soil moisture, slope, and temperature drive agricultural resilience.
• CSA zoning enables targeted irrigation, adaptive cropping, and climate-resilient planning.
• Promote drought-tolerant crops, micro-irrigation, and regularly update CSA maps.

Skills & Tools Demonstrated

• Software & Platforms: Google Earth Engine (GEE), ArcGIS Pro 3.0.3, MS Excel
• Remote Sensing & Satellite Analysis: Landsat 8/9, Sentinel-2, multi-temporal analysis
• Vegetation & Drought Indices: NDVI, EVI, SAVI, NDWI, NDDI, SMI, LST
• GIS-Based Multi-Criteria Decision Analysis (MCDA) & AHP: Weighted overlay, raster reclassification, slope and soil suitability mapping
• Spatial Analysis & Mapping: Buffers, reclassification, land suitability mapping
• Climate & Soil Data Analysis: Rainfall, soil type, irrigation access
• Climate-Smart Agriculture (CSA) Planning & Zoning

Conclusion

CSA suitability zones were mapped using GIS and remote sensing, highlighting seasonal variability in agricultural potential and drought sensitivity. The maps serve as a practical tool for guiding climate-smart interventions and enhancing resilience to drought.