Deforestation detection and reforestation potential due to natural disasters—A case study of floods

Abstract

Forests play a crucial role in our planet’s health and well-being, providing numerous ecological benefits and mitigating climate change. This research addresses the pressing issue of deforestation in flood-affected regions, focusing on the impact of climate change. Our study explores the potential of employing remote sensing data and AI-based analysis to develop a novel unsupervised learning approach for addressing the problem of deforestation detection, an area that has seen limited prior exploration. Utilizing Sentinel-2 data, unsupervised learning techniques are employed to identify deforested areas caused by floods in Pakistan. The quantitative results show that the forests in Pakistan reduced from 3.9% before the floods to about 3.41% after the floods. The study also explores and identifies the potential areas for reforestation which is estimated to be nearly 10.7% of the total area of Pakistan apart from the area covered by the forests. The findings underscore the urgent need for effective conservation strategies and reforestation efforts to mitigate the devastating loss of forest cover and safeguard our planet’s future.