DEEP LEARNING APPROACHES FOR PREDICTING DEFORESTATION PATTERNS AND BIODIVERSITY HOTSPOT LOSS IN SUMATRA
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Biodiversity Hotspots, Deforestation, Deep Learning
Abstract
Deforestation in Sumatra, Indonesia, represents a critical environmental challenge that has led to the degradation of biodiversity hotspots and poses serious threats to both local ecosystems and global climate stability, driven largely by rapid forest conversion into agricultural land, illegal logging, and extensive land-use changes, making accurate prediction of deforestation patterns essential for effective conservation planning. This study applies deep learning approaches to predict deforestation patterns in Sumatra while simultaneously assessing their impacts on biodiversity hotspots, with the objective of developing a model capable of identifying areas at high risk of deforestation and estimating potential biodiversity losses. The research employs deep learning algorithms, specifically Convolutional Neural Networks and Recurrent Neural Networks, to analyze satellite imagery, historical deforestation data, land-use changes, and biodiversity hotspot maps, enabling the model to capture both spatial and temporal trends in deforestation dynamics. The results demonstrate that the proposed deep learning model achieves a high prediction accuracy of 92 percent in identifying deforestation hotspots and successfully highlights key biodiversity-rich areas that are highly vulnerable to rapid forest loss, with agricultural expansion and infrastructure development emerging as the dominant drivers of deforestation in these regions. Overall, the findings confirm that deep learning provides a powerful and reliable tool for predicting deforestation patterns and assessing biodiversity hotspot degradation, offering valuable evidence-based insights for policymakers and conservation practitioners to prioritize protection efforts and design targeted interventions aimed at mitigating further environmental damage in Sumatra.
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References
Ahmad, H., Jose, F., Nabi, M. M., Jhara, S. I., & Ong’ondo, F. J. (2025). Land use and land cover dynamics in the Irrawaddy Delta: Remote sensing analysis and projections for the future. Remote Sensing Applications: Society and Environment, 38, 101607. https://doi.org/10.1016/j.rsase.2025.101607
Ansari, M. G. I., Safiullah, D., Cao, J., & Wu, S. (2025). Assessing deforestation and degradation risks in Pakistan (2001–2021): A machine learning and remote sensing perspective. Environmental Technology & Innovation, 40, 104539. https://doi.org/10.1016/j.eti.2025.104539
Assunção da Silva, C. F., dos Santos, A. M., Rudke, A. P., Nunes, F. G., & Alvarado, S. T. (2025). Insights from remote sensing for the study of deforestation drivers in savannas. Journal for Nature Conservation, 86, 126918. https://doi.org/10.1016/j.jnc.2025.126918
Beierkuhnlein, C., Pugh, B., Justice, S., Schrodt, F., El Serafy, G., Karnieli, A., Manakos, I., Nietsch, L., Peñas de Giles, J., Peterek, A., Poursanidis, D., Zwoli?ski, Z., White, T., Wozniak, E., Field, R., & Provenzale, A. (2025). Towards a comprehensive geodiversity—Biodiversity nexus in terrestrial ecosystems. Earth-Science Reviews, 264, 105075. https://doi.org/10.1016/j.earscirev.2025.105075
Bhuyan, J. M., & Udmale, P. D. (2025). Forest fire occurrence patterns and hotspot analysis in Maharashtra using MODIS fire points from FIRMS. Sustainable Geosciences: People, Planet and Prosperity, 1, 100009. https://doi.org/10.1016/j.susgeo.2025.100009
Bhuyan, M., Jeganathan, C., & Pujar, G. S. (2025). Satellite-based assessment of Phenological Informatics and associated drivers over a biodiversity hotspot in the North Eastern Region of India. Environmental and Sustainability Indicators, 26, 100663. https://doi.org/10.1016/j.indic.2025.100663
Bottani, M., Ferro-Famil, L., Doblas Prieto, J., Mermoz, S., Bouvet, A., Koleck, T., & Le Toan, T. (2025). Novel unsupervised Bayesian method for Near Real-Time forest loss detection using Sentinel-1 SAR time series: Assessment over sampled deforestation events in Amazonia and the Cerrado. Remote Sensing of Environment, 331, 115037. https://doi.org/10.1016/j.rse.2025.115037
Chanda, H., Mohareb, E., Peters, M., Harty, C., Green, M., Shibata, N., & Kasanda, E. B. (2025). The African clean energy-deforestation paradox: Examining the sustainability trade-offs of rural solar energy expansion in Zambia. Energy Research & Social Science, 129, 104389. https://doi.org/10.1016/j.erss.2025.104389
Chatrabhuj, Meshram, K., Mishra, U., & Rathnayake, U. (2025). Application of artificial intelligence in agri-tech, environmental and biodiversity conservation. Array, 26, 100412. https://doi.org/10.1016/j.array.2025.100412
Chouikhi, F., Abbes, A. B., & Farah, I. R. (2025). An end-to-end pipeline based on a Two-Dimensional Convolutional Neural Network for monitoring desertification in Africa using remote sensing images. Environmental Modelling & Software, 193, 106622. https://doi.org/10.1016/j.envsoft.2025.106622
García, M. B., Díaz-Gil, C., Aparicio, E., Quintana-Buil, M., Pizarro, M., Gómez, D., & Font, X. (2025). Historical imagery reveals cliffs as refugia for biodiversity in steep mountains. Global Ecology and Conservation, 64, e03965. https://doi.org/10.1016/j.gecco.2025.e03965
Gebrie, A., Mohan, M., Karpowicz, D. A., Dutta Roy, A., Varsha, V., Zambrano, J., Ewane, E. B., Watt, M. S., Macreadie, P. I., Jaffar, A., Calders, K., Dutt, S., Broadbent, E. N., Pandi Selvam, P., Jaafar, W. S. W. M., Zabbey, N., Kasak, K., & Hendy, I. (2025). Potential benefits of biodiversity corridors for fragmented mangrove ecosystems. Biological Conservation, 310, 111309. https://doi.org/10.1016/j.biocon.2025.111309
Guimarães, U. S., Rodrigues, T. B., Vieira, A. C., Hung, E. M., Soja, M. J., Eriksson, L. E. B., & Ulander, L. M. H. (2025). Deep learning models to map deforestation based on Sentinel 1 coherent features in the southern border of Amazon. Science of Remote Sensing, 12, 100279. https://doi.org/10.1016/j.srs.2025.100279
Halder, B., Rana, B., Juneng, L., Alshehri, F., Ahmed, K. O., Pramanik, M., Ahmed, M. F., Pande, C. B., Shafik, S. S., & Yaseen, Z. M. (2025). Wildfire-derived deforestation, climate, and biodiversity variables analysis in Cambodia. Physics and Chemistry of the Earth, Parts A/B/C, 139, 103894. https://doi.org/10.1016/j.pce.2025.103894
Kajuli, J., Mayengo, M., & Fanuel, I. (2025). Mathematical modeling of refugee population dynamics and its impact on deforestation in Tanzania: An ODE-based and neural network-enhanced approach. Results in Control and Optimization, 20, 100591. https://doi.org/10.1016/j.rico.2025.100591
Kasahun, M. (2025). Quantifying deforestation drivers through multi-temporal LULC analysis and population-forest correlation modeling: A case study of Dara Woreda, Ethiopia. Environmental Challenges, 19, 101163. https://doi.org/10.1016/j.envc.2025.101163
Khaskheli, M. A., Nizamani, M. M., Laghari, U. A., Laghari, A. H., Khanzada, A., Sundas, F., Khaskheli, R. A., Khaskheli, R. A., Kalhoro, F. A., Nizamani, M. R., & Wang, Y. (2025). Chapter 5—Application of geographic information system and remote sensing technology in ecosystem services and biodiversity conservation. In U. A. Bhatti, M. M. Nizamani, Y. Wang, & H. Tang (Eds.), Deep Learning for Earth Observation and Climate Monitoring (pp. 97–122). Elsevier. https://doi.org/10.1016/B978-0-443-24712-5.00006-3
Kiflie, M. A., Sharma, D. P., & Haile, M. A. (2024). Deep learning for Ethiopian indigenous medicinal plant species identification and classification. Journal of Ayurveda and Integrative Medicine, 15(6), 100987. https://doi.org/10.1016/j.jaim.2024.100987
Li, C., Huang, Z., Fang, Z., Wang, Y., Ali, M., & Bai, Y. (2025). Deciphering biodiversity drivers in Mainland Southeast Asia: Sensitivity and threshold response hotspots. Environmental and Sustainability Indicators, 27, 100758. https://doi.org/10.1016/j.indic.2025.100758
Lim, A. H., Ng, C. C. Y., Hong, J. H., Lee, E. C., Kwek, K., Tan, P., Kifle, H., Ng, I., & Teh, B. T. (2025). Genomic garden: From societal and scientific impacts to biodiversity conservation. Cell Genomics, 5(4), 100779. https://doi.org/10.1016/j.xgen.2025.100779
Lucey, B. M., Urquhart, A., & Vigne, S. (2025). Biodiversity, financial markets, and systemic risk: A synthesizing review. Journal of Climate Finance, 13, 100078. https://doi.org/10.1016/j.jclimf.2025.100078
Mahefarisoa, K. L., Ratsimbazafy, H. A., Decaestecker, E., Delpy, L., Hugé, J., Antoine-Moussiaux, N., & Aerts, R. (2025). Social network analysis of conservation and one health governance in Madagascar. Environmental Science & Policy, 171, 104132. https://doi.org/10.1016/j.envsci.2025.104132
Maqsood, M. H., Mumtaz, R., & Khan, M. A. (2024). Deforestation detection and reforestation potential due to natural disasters—A case study of floods. Remote Sensing Applications: Society and Environment, 34, 101188. https://doi.org/10.1016/j.rsase.2024.101188
McGunnigle, N., Bardsley, D., Nuberg, I., Cedamon, E., & Pandit, B. H. (2025). Intermediate levels of socio-ecological disturbance drive higher biodiversity in naturally regenerating forests: A case study from Nepal. Journal of Rural Studies, 115, 103582. https://doi.org/10.1016/j.jrurstud.2025.103582
Montero-Botey, M., Kivuyo, E., Sitati, N., & Perea, R. (2024). Deforestation and water availability as main drivers of human-elephant conflict. Global Ecology and Conservation, 54, e03068. https://doi.org/10.1016/j.gecco.2024.e03068
Ngwira, L., & Harris, N. (2025). Bridging global evidence with local needs: A systematic review to inform climate and biodiversity strategies in Malawi. Environmental and Sustainability Indicators, 28, 101010. https://doi.org/10.1016/j.indic.2025.101010
Nogueira Lisboa, S., Grinand, C., Betbeder, J., Montfort, F., & Blanc, L. (2024). Disentangling the drivers of deforestation and forest degradation in the Miombo landscape: A case study from Mozambique. International Journal of Applied Earth Observation and Geoinformation, 130, 103904. https://doi.org/10.1016/j.jag.2024.103904
Ongmu Bhutia, K. D., Mishra, M., Guria, R., Baraj, B., Naik, A. K., Silva, R. M. da, do Nascimento, T. V. M., & Santos, C. A. G. (2024). Evaluation of large-scale deforestation susceptibility mapping in the mountainous region of the Himalayas: A case study of the Khangchendzonga Biosphere Reserve, India. Remote Sensing Applications: Society and Environment, 36, 101285. https://doi.org/10.1016/j.rsase.2024.101285
Ranglong, A., Dutta Roy, A., Das, S., & Deb, S. (2025). Estimating aboveground biomass and biodiversity of a protected area in moist tropical forest ecosystem using remote sensing application. Journal for Nature Conservation, 84, 126823. https://doi.org/10.1016/j.jnc.2024.126823
Rather, S. A., Kumar, A., Liu, H., & Schneider, H. (2025). Rosewoods at crossroads: A modern paradigm to secure the survival of the world’s most trafficked wild species. Biological Conservation, 311, 111399. https://doi.org/10.1016/j.biocon.2025.111399
Ribeiro, V., De Souza Mendes, F., Singh, C., & Heilmayr, R. (2024). Leveraging remote sensing for transparency and accountability in Amazonian commodity supply chains. One Earth, 7(11), 1935–1940. https://doi.org/10.1016/j.oneear.2024.10.011
Sheriff, R., Meer, M. S., Aslam, R. W., & Said, Y. (2025). Machine Learning-Based Forest Fire Susceptibility Mapping Using Random Forest and CART Models. Rangeland Ecology & Management, 102, 96–109. https://doi.org/10.1016/j.rama.2025.06.004
Sietz, D., Niamir, A., Müller, D., Hickler, T., Kanemoto, K., Moran, D. D., & Thonicke, K. (2025). Mapping human-nature archetypes to guide global biodiversity, food security, and land-use policy. One Earth, 8(8), 101416. https://doi.org/10.1016/j.oneear.2025.101416
Silva Filho, P., Persello, C., Maretto, R. V., & Machado, R. (2024). Mapping the Brazilian savanna’s natural vegetation: A SAR-optical uncertainty-aware deep learning approach. ISPRS Journal of Photogrammetry and Remote Sensing, 218, 405–421. https://doi.org/10.1016/j.isprsjprs.2024.09.019
Silva, R., Guimarães, U. S., Garcia, D. C., Vieira, H., & Hung, E. M. (2025). Deep-learning deforestation detection in the Legal Amazon area based on Sentinel-1 data. Remote Sensing Applications: Society and Environment, 40, 101747. https://doi.org/10.1016/j.rsase.2025.101747
Singh, P., Kala, R., Bhavsar, D., Roy, A., & Karnatak, H. (2024). Urban explosion and hotspots of forest loss in western Himalaya: Mapping land use/cover change trends since 1975. Advances in Space Research, 74(3), 1238–1252. https://doi.org/10.1016/j.asr.2024.05.015
Thakur, A. (2025). Sustainable urban policy development in hill cities: A case study of Shimla’s LULC changes and urban regeneration efforts. Urban Governance, 5(3), 342–362. https://doi.org/10.1016/j.ugj.2025.05.009
Vaiyapuri, T., & Julie, G. (2025). AI and Geospatial Technologies for Climate Change Mitigation: Opportunities, Challenges, and Pathways to Sustainability. Sixth International Conference on Futuristic Trends in Networks and Computing Technologies (FTNCT06), Held in Uttarakhand, India, 259, 1346–1355. https://doi.org/10.1016/j.procs.2025.04.089
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Copyright (c) 2025 Rithy Vann, Ming Kiri, aaraf Sharma, Rustiyana Rustiyana
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