Satellite-based Change Detection and Analysis for Early Deforestation Prediction

Abstract

Deforestation is a horrific environmental challenge which has direct effects on climate change, biodiversity and sustainable development. Deforestation can be identified by satellite imagery and interventions to be taken to prevent it are made in time when policy measures can be implemented. The present paper introduces a full satellite-based change detection and analysis framework of objectively predicting the deforestation at very early stages based on multi-temporal remote sensing measurements and the power of deep learning. The suggested system combines radiometric correction, analysis of vegetation index, and semantic segmentation based on the convolutional neural network in order to detect changes in the forest cover at an early phase. Multi-date satellite images are used to develop a temporal change detection pipeline that captures vegetation loss pattern variations that are subtle. Experimental findings using publicly available satellite datasets indicate that the proposed method experiences a high detection accuracy and strength relative to the conventional and classical machine learning methods based on thresholds. The effectiveness of the system in terms of early deforestation monitoring is confirmed by quantitative evaluation with the use of metrics like Intersection over Union (IoU), F1-score, and overall accuracy with the accuracy of 93.6 and IoU of 0.85.

Introduction

Deforestation caused by urbanization, agriculture, and illegal logging has become a major environmental challenge, leading to biodiversity loss, increased greenhouse gas emissions, and disruption of ecosystems. Traditional forest monitoring methods based on field surveys and manual satellite interpretation are slow, expensive, and unsuitable for continuous monitoring. Although satellite imagery from platforms such as Landsat and Sentinel enables large-scale observation, early-stage deforestation detection remains difficult because subtle vegetation changes are often masked by seasonal variations, atmospheric effects, and sensor noise.

The proposed research introduces an end-to-end deep learning framework for early deforestation prediction using multi-temporal satellite imagery. The system combines vegetation indices such as NDVI, EVI, and SAVI with a CNN-based semantic segmentation model enhanced by attention mechanisms. Unlike conventional methods that treat vegetation analysis and change detection separately, the proposed framework integrates ecological knowledge directly into the neural network, improving its ability to detect subtle forest degradation while reducing false detections caused by seasonal changes.

The study aims to develop a robust preprocessing pipeline for radiometric correction and temporal normalization, design a hybrid vegetation index–CNN architecture, train a CNN capable of learning spatial-temporal representations from multi-date imagery, and compare its performance with traditional thresholding, Support Vector Machine (SVM), Random Forest (RF), U-Net, and Siamese CNN models. Its major contributions include hybrid feature extraction, multi-scale temporal normalization, early-stage change modeling, a hybrid loss function combining Binary Cross-Entropy, Dice loss, and boundary loss, comprehensive comparative evaluation, and computational optimizations for practical deployment.

The proposed system consists of five modules: satellite data acquisition, preprocessing and radiometric correction, vegetation index computation, deep learning-based change detection, and post-processing with visualization. Satellite images from Landsat-8 and Sentinel-2 are automatically collected, corrected for geometric and atmospheric distortions, normalized across acquisition dates, and processed to compute vegetation indices. A modified U-Net with Siamese encoders, attention mechanisms, and multi-scale feature fusion performs pixel-level deforestation detection by generating probability maps that are converted into binary change maps through adaptive thresholding.

The methodology formulates deforestation detection as a supervised change detection problem using multi-temporal satellite images. Radiometric preprocessing includes atmospheric correction, relative radiometric normalization using pseudo-invariant features, and z-score standardization. Vegetation indices are calculated to enhance forest-related spectral information, while classical machine learning methods such as SVM and Random Forest serve as baseline models for comparison. The proposed attention-based CNN architecture learns hierarchical spatial-temporal features from spectral bands and vegetation indices, providing accurate, scalable, and efficient early deforestation prediction suitable for real-world forest monitoring systems.

Conclusion

The goal of this research is to develop an all-encompassing satellite-based change detection framework for the early prediction of deforestation, combining the use of satellite vegetation indexes with semantic segmentation using deep learning. This proposed hybrid architecture outperforms all existing threshold-based and classical machine learning approaches with 93.6% accuracy, 0.91 F1-score, and 0.85 IoU on various forest ecosystems. Especially the system has shown strong ability to detect deforestation in the early stage (F1-score 0.83), which proves it is appropriate for using it for active monitoring instead of active assessment. Ablation studies validate that every architectural element, vegetation indices, channel attention, and hybrid loss function, offer significant and complementary contribution to performance. High generalization across tropical, temperate, and boreal forests was achieved in the Cross-ecosystem evaluation, with a small decrease in performance (2.8 percentage points). The significant results from the statistical tests confirm that the gains seen are not likely to be random, but rather real advances in methodology. Efficiency analysis by computation also substantiates operational feasibility and allows for near real-time processing of large-scale satellite images within the inference times. The research values in the scientific literature are: (i) proving the effectiveness of hybrid techniques that combine domain knowledge with deep learning; (ii) giving rigorous comparisons between different methodological paradigms; (iii) quantifying the detection capabilities in early stages which are essential for proactive intervention; and (iv) proving the computational feasibility of operational deployment. These contributions, taken together, bring the state of the art in forest monitoring from space to a new level, and offer a basis for further research on forecasting environmental change.

References

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Copyright

Copyright © 2026 Nilesh Sable, Anisha Gondhalekar, Sumiran Kadu, Deep Mehta , Lokesh Bhat. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.