Spatial Correlation Analysis of Vegetation, Chlorophyll & Water Availability

Using Sentinel-2 Remote Sensing Indices

Project Overview

This project evaluates vegetation health, chlorophyll content, and water availability in the Galenbindunuwewa Divisional Secretariat Division using Sentinel-2 satellite imagery. NDVI, GCI, and LSWI indices were derived and spatially analyzed to assess ecological conditions and inter-index relationships.

Study Area

The study area is the Galenbindunuwewa Divisional Secretariat (DS) Division located in the Anuradhapura District of Sri Lanka. The region represents a dry-zone environment with mixed land cover, including agricultural lands, forests, water bodies, and settlements, making it suitable for vegetation and water-related remote sensing analysis.

Study Area Map of Galenbindunuwewa DS Division

Data Sources

Methodology

Remote Sensing Index Maps

Normalized Difference Vegetation Index (NDVI)

NDVI Map

NDVI values range from -0.098 to 0.633, with higher values indicating healthy and dense vegetation, mainly concentrated in agricultural and forested regions.

Green Chlorophyll Index (GCI)

GCI Map

GCI values range from -0.22 to 2.71, reflecting chlorophyll concentration. Areas with high GCI correspond closely with high NDVI zones.

Land Surface Water Index (LSWI)

LSWI Map

LSWI values range from -0.609 to 0.432. Positive values highlight water bodies and wetter zones, while negative values indicate drier areas.

Spatial Correlation Analysis

Vegetation Health vs Chlorophyll Content (NDVI & GCI)

NDVI vs GCI Graph

A strong positive linear relationship is observed (R² = 0.94), indicating that vegetation health is strongly dependent on chlorophyll content.

Vegetation Health vs Water Availability (NDVI & LSWI)

NDVI vs LSWI Graph

A moderate positive relationship (R² = 0.227) suggests that while water availability supports vegetation health, other environmental factors also play significant roles.

Key Findings

Tools & Skills

Conclusion

This study demonstrates the effectiveness of integrating NDVI, GCI, and LSWI for environmental assessment in dry-zone regions. The results provide valuable insights for sustainable land management, agricultural monitoring, and resource planning.