Mapping Deforestation Probability And Understanding The Forest Dynamics In Gazipur, Bangladesh
--
https://doi.org/10.1016/j.envc.2026.101568 <-- shared paper
--
H/T @Pollob Chandra Talukder
“Deforestation poses a growing threat to biodiversity, ecosystem services, and sustainable development in Bangladesh, particularly in Gazipur, home to approximately 86% of the country’s Sal (Shorea robusta) forest. To identify future deforestation hotspots, we developed a machine learning-based framework integrating 12 biophysical, landscape, and anthropogenic conditioning factors with five machine learning algorithms (RF, XGBoost, ANN, NB, and MLP). Random Forest achieved the highest predictive performance (Accuracy = 84%; AUC = 0.93), followed by XGBoost (Accuracy = 83%; AUC = 0.92). The analysis identified rainfall and population density as the dominant drivers of deforestation, while probability maps highlighted the south-western and north-eastern parts of Gazipur as the most vulnerable. The findings provide a scientific basis for evidence-based forest governance, land-use planning, conservation prioritization, and achieving SDG 15 (Life on Land)…”
--
“HIGHLIGHTS
• Rainfall and population density dominated deforestation probability.
• Random Forest provided the most reliable deforestation risk estimates.
• High-risk forests clustered in south-western and north-eastern Gazipur.
• Adaptive zoning linked risk prediction to forest management actions.
• Deforestation risk maps prioritized conservation and restoration measures.
ABSTRACT: Deforestation is a spiralling environmental catastrophe with impervious results for biodiversity, climate change, and human livelihoods, specifically in tropical regions. Being a tropical country, Bangladesh has experienced approximately 40% loss of its forest cover, at Gazipur since 1930, which contains about 86% of the country’s Sal (Shorea robusta) forest, ranging approximately 4,300 hectares per year (2001–2010) to over 19,500 hectares per year (2011–2020), exemplifying an intensification of nearly 353%. The objective of this study is to map deforestation probability at the Gazipur district of Dhaka Division, Bangladesh, by utilising machine learning algorithms along with multi-source geospatial data, with the purpose of identifying high-risk zones and facilitating evidence-based forest governance, land-use development, and prioritizing conservation areas. This study integrated twelve conditioning factors, including biophysical, landscape, and anthropogenic. To identify susceptible zones the study trained and assessed five machine learning algorithms; RF, XGBoost, ANN, NB, and MLP and validating the result through different metrics like sensitivity, specificity, precision, accuracy, F1-score, AUC. The performance of the models was evaluated using Wilcoxon signed-rank tests and marginal response curves (MRC) were used to understand factor contributions. In the result, RF achieved highest performance with accuracy of 84% and AUC of 0.93, followed by XGBoost at 83% accuracy and 0.92 AUC. Rainfall and population density were most dominant conditioning factors among models. Pairwise statistical testing resulted that ensemble-based algorithms (RF, XGBoost) generated statistically comparable and significantly higher predictions compared to NB and MLP. Spatial probability maps indicate areas of high and very high risk in the south-western and north-eastern upazilas. The results can be applicable for forest management authorities, urban planners, and policymakers, and correspond with SDG Indicator 15. An inclusive governance framework containing land zoning, ecological area identification, and compliance with industrial EIA is proposed to persuade probability maps into adaptive forest management strategies…”
#deforestation #probability #machinelearning #algorithms #AI #Gazipur #Bangladesh #GIS #spatial #mapping #spatialanalysis #spatiotemporal #rainfall #precipitation #humanimpacts #populationpressure #risk #prediction #RandomForest #conservation #restoration #environment #biodiversity, #climatechange #human #livelihood #tropical #forestcover #sal #forest #vegetation #tree #upazila #spatialprobability #geostatistics #forestmanagement #planning #policy #urbanplanners #governance #zoning #ecology #habitat