A Result Paper on Deforestation Monitoring and Cause Detection in India Using Satellite Imagery

V. G.  Bharane; Rajratna Dhende; Nikhil Chavan; Amruta Gaikwad; Nikhil Garje

Authors

V. G. Bharane Dept. of Computer Science, S. B. Patil College of Engineering, Indapur, India
Rajratna Dhende Dept. of Computer Science, S. B. Patil College of Engineering, Indapur, India
Nikhil Chavan Dept. of Computer Science, S. B. Patil College of Engineering, Indapur, India
Amruta Gaikwad Dept. of Computer Science, S. B. Patil College of Engineering, Indapur, India
Nikhil Garje Dept. of Computer Science, S. B. Patil College of Engineering, Indapur, India

Keywords:

Deforestation Satellite Monitoring Sentinel-2 Google Earth Engine Normalized Difference Vegetation Index (NDVI) Artificial Intelligence FastAPI Forest Monitoring Remote Sensing Indian Forests

Abstract

Forests in India are disappearing rapidly. This paper presents an automated system that monitors deforestation using satellite imagery and AI. Users enter any location’s coordinates. The system pulls images from Sentinel-2 via Google Earth Engine and analyzes changes over multiple years. Ten vegetation indices (NDVI, NDWI, EVI, MSAVI2, NDMI, NBR, BSI, NDBI, SAVI,

GNDVI) are calculated to track plant health, soil, and water conditions. A large language model interprets the data and identifies likely causes of forest loss. A web interface built with FastAPI makes the tool accessible to non-experts. Testing on Anamalai Hills, Kerala successfully detected a severe drought event in September 2025 and tracked the forest’s rapid recovery. The system shows that combining satellite data with AI enables scalable, automated forest monitoring.

A Result Paper on Deforestation Monitoring and Cause Detection in India Using Satellite Imagery

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