[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2681":3},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"view_count":31,"doi":32,"paper":33,"created_at":52},2681,"Forest encroachment prediction using multi-temporal satellite data and machine learning: a case study of Bandipur National Park, India","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70597-0","Abstract Forest encroachment poses a significant threat to protected forest ecosystems due to increasing human activities, including infrastructure development, agricultural expansion, and settlement growth. Continuous monitoring is therefore essential for effective conservation planning and sustainable forest management. This study presents an artificial intelligence-based framework for forest encroachment prediction in Bandipur National Park, India, using multi-temporal Sentinel- 2 satellite imagery integrated with topographic, land-cover, and anthropogenic variables. Multi temporal Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) were combined with elevation, slope, aspect, forest and water masks, and distance-to-road and distance-to-settlement variables. Three predictive models, namely Random Forest (RF), MultiLayer Perceptron (MLP), and a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture, were comparatively evaluated. RF achieved the highest classification accuracy of 94.17%, followed by MLP at 91.50% and CNN–LSTM at 78.69%. Future projections for 2026 and 2030 indicated relatively limited encroachment under RF and MLP, with maximum projected areas of 1.27 km² (0.15%) and 0.64 km² (0.07%), respectively. In contrast, CNN–LSTM projected substantially larger encroached areas of 115.76 km² (13.24%) in 2026 and 71.94 km² (8.23%) in 2030. Overall, RF demonstrated the strongest and most consistent classification performance under the adopted experimental framework, indicating its potential for supporting forest monitoring, encroachment hotspot identification, and evidencebased conservation planning.","摘要 森林侵占对受保护森林生态系统构成重大威胁，其驱动因素包括基础设施建设、农业扩张和聚落增长等日益加剧的人类活动。因此，持续监测对于有效的保护规划和可持续森林管理至关重要。本研究提出了一种基于人工智能的森林侵占预测框架，以印度班迪普尔国家公园为研究区，利用多时相Sentinel-2卫星影像，并结合地形、土地覆盖和人为变量。研究将多时相归一化植被指数（NDVI）和归一化燃烧比（NBR）与高程、坡度、坡向、森林和水体掩膜以及距道路距离和距聚落距离等变量相结合。研究对比评估了三种预测模型，即随机森林（RF）、多层感知机（MLP）以及混合卷积神经网络—长短期记忆网络（CNN–LSTM）架构。RF取得了最高的分类精度，为94.17%，其次是MLP的91.50%和CNN–LSTM的78.69%。2026年和2030年的未来预测表明，在RF和MLP下森林侵占相对有限，最大预测面积分别为1.27 km²（0.15%）和0.64 km²（0.07%）。相比之下，CNN–LSTM预测的侵占面积要大得多，2026年为115.76 km²（13.24%），2030年为71.94 km²（8.23%）。总体而言，在所采用的实验框架下，RF表现出最强且最一致的分类性能，表明其具有支持森林监测、侵占热点识别和循证保护规划的潜力。",null,"Scientific Reports","2026-09-16T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":13,"relevant":21,"comment":22},15,22,18,14,1,"基于多时相Sentinel-2与机器学习预测森林侵占，方法对比扎实、结论明确，对农业遥感与生态监测有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","机器学习","NDVI","生态监测","森林保护",0,"10.1038\u002Fs41598-026-70597-0",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":6,"card":44,"direction":50,"ingested_from":51},"W7213307679",[36,38,40,42],{"name":37,"orcid":9},"Pushpa B. R",{"name":39,"orcid":9},"H. R. Chaitanya",{"name":41,"orcid":9},"Chandhana U. Shankar",{"name":43,"orcid":9},"R. Sudarshan",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"用多时相Sentinel-2影像和机器学习预测印度Bandipur国家公园的森林侵占。","Sentinel-2多时相NDVI\u002FNBR结合地形、土地覆盖和人为变量，比较RF","RF分类精度最高达94.17%，预测2026和2030年侵占面积有限，CNN-LSTM预测值显著偏大","农业遥感与作物表型","可探索多源遥感与深度学习融合提升侵占预测精度，并推广到其他保护区或农业扩张监测。","农业人工智能与决策模型","openalex","2026-09-16T23:30:46.701925Z"]