Deforestation Modeling using an Ensemble Machine Learning Approach; an Insight from Nepal

Deforestation Modeling using an Ensemble Machine Learning Approach; an Insight from Nepal | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 August 2025 V1 Latest version Share on Deforestation Modeling using an Ensemble Machine Learning Approach; an Insight from Nepal Author : Sushank Pokhrel 0000-0003-1170-4284 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175588034.46859557/v1 222 views 125 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Despite a net increase in forest cover recent decades, deforestation still remains widespread in many regions of Nepal. In addition, forest fragmentation is also a major concern. Deforestation is one of the widely studied topics but deforestation risk mapping through modeling approach is relatively less explored. To address this gap, the current study ensembles two machine learning models, Random Forest and Extreme Gradient Boosting, to demarcate deforestation risk across five physiographic regions and seven provinces of Nepal at a resolution of 100 m. The land cover maps between 2000 and 2022 and Hansen Forest Cover Loss dataset were utilized to identify deforested regions; while, four biophysical and five anthropogenic factors were used to predict deforestation probability. The models revealed that biophysical factors are predominant in higher elevations, while anthropogenic factors are predominant in lower regions. Approximately one million hectares, corresponding to about 17% of total forest cover of Nepal is under deforestation risk. Middle Mountains has the highest proportion of deforestation risk, whereas the High Himalaya has the lowest. Among provinces, Karnali has the greatest risk, whereas, Madhesh has the lowest. Similarly, fragmented forests are at a greater risk of deforestation. The resulting deforestation risk map can support land use planning, policy development, forest conservation and restoration works. Future studies can enhance modeling accuracy by incorporating additional predictors and field-based data to better simulate real-world scenario. Supplementary Material File (deforestation_modeling v4.pdf) Download 1.05 MB Information & Authors Information Version history V1 Version 1 22 August 2025 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License Keywords deforestation mapping deforestation probability deforestation risk extreme gradient boosting hindu kush himalaya random forest Authors Affiliations Sushank Pokhrel 0000-0003-1170-4284 [email protected] Institute of Forestry, Tribhuvan University View all articles by this author Metrics & Citations Metrics Article Usage 222 views 125 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sushank Pokhrel. Deforestation Modeling using an Ensemble Machine Learning Approach; an Insight from Nepal. Authorea . 22 August 2025. DOI: https://doi.org/10.22541/au.175588034.46859557/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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