Vegetation Loss and Gain in Kochi Region (2020–2025): A Remote Sensing Study

 

Vegetation changes detection plays a critical role in understanding urban expansion, ecological degradation, and climate resilience. Here I explore the Normalized Difference Vegetation Index (NDVI) for Ernakulam District, Kerala, using Sentinel-2 satellite imagery for the years 2020 and 2025. The objective is to detect areas of vegetation gain, loss, or stability, and to generate clear visual and quantitative indicators to support future environmental planning.

Data Sources

1. Satellite Imagery: Sentinel-2 Level-2A imagery (10 m resolution) from Copernicus Open Access Hub @ 10% cloud cover.
2. Bands Used:
1. Band 8 (NIR - Near Infrared)
2. Band 4 (Red)

Methodology

1. NDVI Computation NDVI was calculated for both 2020 and 2025 using the formula:

NDVI =

Computation was done in QGIS using the Raster Calculator.

1. Clipping and Masking NDVI rasters were clipped using the Ernakulam district mask to focus the analysis within administrative boundaries.
2. NODATA Handling Areas without data (e.g., outside district boundary or cloud cover) were assigned a NODATA value (-9999) using the 'Build Virtual Raster (VRT)' tool with masking.
3. NDVI Rendering Visual interpretation was improved by applying a pseudocolor ramp:

NDVI < 0: built-up or water (Red)

0.0–0.2: sparse vegetation (Yellow)

0.2–0.4: moderate vegetation (Light Green)

0.4: dense vegetation (Dark Green)

4. Change Detection A raster layer showing difference between 2025 and 2020 was created:

NDVI_change = NDVI₂₀₂₅ – NDVI₂₀₂₀

5. Threshold Classification NDVI changes were categorized:

Gain: > +0.05

Loss: < -0.05

Binary masks were generated for each category.

6. Water Body Isolation Pixels with very low NDVI were mapped to highlight major water bodies (e.g., Lakes, streams, dam storage) using a custom mask.

Visual Outputs:

Figure 1: NDVI condition in 2020; The NDVI analysis for May 2020 uses data from a single Sentinel-2 tile (T43PFM), covering the majority of Ernakulam district. Peripheral slivers that fell outside the tile were not included, as their omission was not expected to affect overall spatial trends or comparative patterns with 2025 data

Figure 2: NDVI condition in 2025

Figure 3: Changes: Composite map showing greening (blue-green), loss (magenta), and stable zones (neutral green)

These visuals help observe spatial patterns: urban fringe vegetation loss, greening in north-eastern hills, and stable agricultural areas.

This NDVI-based approach reveals useful trends in greenness. With additional zoning, socio-economic, or temperature overlays, this framework can evolve into a powerful climate-resilience diagnostic for Kerala districts.

LST map of Kerala District-wise (by the Author)