[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3795":3,"related-3795":91},{"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,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":90},3795,"Remotely sensed vegetation resilience in the four major estuarine ecosystems of eastern China","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffenvs.2026.1912598","Introduction Estuarine wetlands are among the most productive ecosystems on Earth, but they have been experiencing severe degradation induced by human activities and climate change worldwide. Traditional assessments of degradation mostly rely on static indicators, which are insufficient to reflect dynamical ecosystem properties such as systemic resilience. Methods Here, we used remote sensing data and critical slowing down indicators to evaluate ecological resilience of vegetation in four major estuaries in eastern China: the Liaohe Estuary, the Yangtze Estuary, the Minjiang Estuary, and the Pearl Estuary. We identified hotspots of resilience loss during the past two decades, and then assessed relative ecological risk using the Pressure–State–Response (PSR) framework. Using random forest modeling and SHAP (SHapley Additive exPlanations) analysis, we further identified key factors associated with the classification of resilience-loss hotspots. Results Our results showed that all four estuaries have undergone resilience loss, with resilience loss areas accounting for ∼40% of the total study area. Moreover, hotspots of resilience loss were found to be mostly\u002Fstrongly\u002Flargely associated with faster sea level rise and macroclimate conditions, but showed a spatial mismatch with relatively high ecological risk areas. Defined as resilience-loss hotspots with relatively low ecological risk, potential risk areas accounted for ∼35% of the total area. Despite still exhibiting relatively favorable environmental conditions, these areas might degrade quickly under ongoing unprecedented environmental change and thus need further monitoring. Discussion Our study combined risk assessment with resilience evaluation to provide an integrated framework detecting unrecognized ecosystem states. The framework contributes to better understanding resilience dynamics of estuarine wetlands, and has useful implications for adaptive management of these important ecosystems in a changing world.","引言 河口湿地是地球上生产力最高的生态系统之一，但全球范围内正因人类活动和气候变化而经历严重退化。传统的退化评估多依赖静态指标，不足以反映系统恢复力等动态生态系统属性。方法 本研究利用遥感数据和临界慢化指标，评估了中国东部四大河口——辽河口、长江口、闽江口和珠江口——植被的生态恢复力。我们识别了过去二十年恢复力丧失的热点区域，并基于压力—状态—响应（PSR）框架评估了相对生态风险。通过随机森林建模和SHAP（SHapley Additive exPlanations）分析，我们进一步识别了与恢复力丧失热点分类相关的关键因素。结果 结果表明，四大河口均出现了恢复力丧失，恢复力丧失区域约占研究总面积的40%。此外，恢复力丧失热点主要与海平面上升加快和宏观气候条件密切相关，但与相对高生态风险区域存在空间错位。被定义为恢复力丧失热点且生态风险相对较低的区域，即潜在风险区，约占总面积的35%。尽管这些区域仍表现出相对有利的环境条件，但在当前前所未有的环境变化下可能迅速退化，因此需要进一步监测。讨论 本研究将风险评估与恢复力评估相结合，提供了一个识别未被认知生态系统状态的综合框架。该框架有助于更好地理解河口湿地的恢复力动态，并对变化世界中这些重要生态系统的适应性管理具有有益启示。",null,"Frontiers in Environmental Science","2026-09-28T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于遥感与临界慢化指标评估中国四大河口湿地植被韧性，方法新颖、数据扎实，对湿地生态监测与适应性管理有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感监测","植被退化","生态韧性","河口湿地","生态风险评价",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":55},[38,47],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":46},"I3",8,true,[],[44,45],"遥感","remote sensing",[27,26],{"code":48,"score":49,"supported":41,"title":50,"abstract":51,"tags":54},"H7",6,[],[52,53],"气候变化","climate change",[],"农业相关性需要复核",[57,58],"辽河口 长江口 闽江口 珠江口 湿地植被","遥感 生态韧性 河口湿地","辽河口长江口闽江口珠江口湿地植被-3795",2,"10.3389\u002Ffenvs.2026.1912598",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":81,"oa_url":82,"card":83,"direction":87,"ingested_from":89},"W7214657804",[65,67,69,71,73,75,78],{"name":66,"orcid":9},"Qian Dong",{"name":68,"orcid":9},"Yiwen Liu",{"name":70,"orcid":9},"Wensi Hu",{"name":72,"orcid":9},"Babak M. S. Arani",{"name":74,"orcid":9},"Ziyu Zheng",{"name":76,"orcid":77},"Chi Xu","https:\u002F\u002Forcid.org\u002F0000-0001-7389-5763",{"name":79,"orcid":80},"Shuqing N. Teng","https:\u002F\u002Forcid.org\u002F0000-0001-7431-2128",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fenvironmental-science\u002Farticles\u002F10.3389\u002Ffenvs.2026.1912598\u002Fpdf",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"用遥感与临界慢化指标评估中国四大河口湿地植被韧性，识别韧性丧失热点并评估生态风险。","遥感数据、临界慢化指标、PSR框架、随机森林与SHAP分析。","四大河口均出现韧性丧失，约40%区域丧失，热点与海平面上升和宏观气候相关，但与高风险区空间错位。","农业遥感与作物表型","可延伸研究：将韧性评估与风险框架结合，识别低风险但韧性丧失的潜在风险区，用于湿地适应性管理。","openalex","2026-09-29T23:30:30.169110Z",{"total":49,"page":21,"page_size":49,"items":92},[93,149,223,297,359,434],{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":100,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":101,"score_detail":102,"sources":105,"tags":107,"primary_topic":9,"secondary_topics":110,"classification":111,"search_phrases":130,"slug":133,"view_count":81,"doi":134,"paper":135,"created_at":148},4315,"أثرتغير الحرارة والامطار على التدهور البيئي في ناحية الكوير","https:\u002F\u002Fdoi.org\u002F10.66359\u002Fitqan.v2i1.034","يتناول البحث أثر التغيرات المناخية في تدهور الغطاء النباتي بناحية الكوير خلال المدة (1990–2023)، من خلال تحليل بيانات الحرارة والأمطار، واستخدام تقنيات الاستشعار عن بعد ونظم المعلومات الجغرافية (GIS) ومؤشر الغطاء النباتي (NDVI). أظهرت النتائج ارتفاعاً في درجات الحرارة مقابل انخفاض وتذبذب كميات الأمطار، مما أدى إلى تراجع كثافة الغطاء النباتي واتساع مساحات الغطاء الضعيف، خاصة في عامي 2000 و2023 بفعل الجفاف وارتفاع التبخر. كما أكدت الدراسة وجود علاقة واضحة بين التغيرات المناخية وتدهور الغطاء النباتي، مما يبرز أهمية تبني إدارة بيئية مستدامة للحفاظ على الموارد الطبيعية في المنطقة","本研究探讨了1990年至2023年期间气候变化对科韦尔地区植被退化的影响，通过分析气温和降水数据，并运用遥感技术、地理信息系统（GIS）和植被指数（NDVI）进行研究。结果表明，气温上升而降水量减少且波动，导致植被密度下降、稀疏植被面积扩大，尤其是在2000年和2023年，干旱和蒸发加剧了这一趋势。研究还证实气候变化与植被退化之间存在明显关系，凸显了采取可持续环境管理以保护该地区自然资源的重要性。","مجلة الاتقان للعلوم الإنسانية","2026-10-04T00:00:00Z",57,{"impact":40,"substance":17,"depth":103,"authority":13,"freshness":49,"relevant":21,"comment":104},15,"基于遥感与GIS的伊拉克区域植被退化实证研究，方法规范但属地方性案例，公共影响有限。",[106],{"name":99,"url":96},[52,108,26,109,27],"NDVI","GIS",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":112},{"candidates":113,"agricultural_evidence":14,"reason":55},[114,120,126],{"code":48,"score":115,"supported":41,"title":116,"abstract":117,"tags":119},7,[],[52,118],"干旱",[52],{"code":121,"score":115,"supported":41,"title":122,"abstract":123,"tags":125},"I4",[],[124,109],"地理信息",[109],{"code":39,"score":49,"supported":41,"title":127,"abstract":128,"tags":129},[],[44],[108,27,26],[131,132],"الكوير NDVI","تدهور الغطاء النباتي GIS","الكويرNDVI-4315","10.66359\u002Fitqan.v2i1.034",{"doi":134,"openalex_id":136,"authors":137,"venue":99,"cited_by_count":81,"oa_url":142,"card":143,"direction":87,"ingested_from":89},"W7219900093",[138,140],{"name":139,"orcid":9},"معن جاسم محمد",{"name":141,"orcid":9},"أ. م. د    خالد صطم عطية","https:\u002F\u002Falitqan.iraqiqsa.org\u002Findex.php\u002FAIJHS\u002Farticle\u002Fdownload\u002Fitqan-v2i1-034\u002F389",{"tldr":144,"method":145,"finding":146,"direction":87,"opportunity":147},"研究1990–2023年伊拉克Kوير地区气温降水变化对植被退化的影响。","用气温降水数据、遥感、GIS和NDVI分析植被覆盖变化。","气温升高、降水减少且波动导致植被密度下降、弱覆盖扩大，2000和2023年最严重。","可结合多源遥感与气候模型，量化干旱区植被退化驱动机制并预测未来趋势。","2026-10-06T23:30:21.747683Z",{"id":150,"title":151,"url":152,"summary":153,"summary_zh":154,"content":9,"source_name":155,"source_url":152,"published_at":156,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":157,"score_detail":158,"sources":162,"tags":164,"primary_topic":168,"secondary_topics":174,"classification":175,"search_phrases":193,"slug":196,"view_count":81,"doi":197,"paper":198,"created_at":222},4233,"IMPACT OF ANTHROPOGENIC ACTIVITIES (AGRICULTURE, MINING, URBANIZATION) ON VEGETATION AND SOILS IN MARITIME GUINEA (2002–2024)","https:\u002F\u002Fdoi.org\u002F10.30574\u002Fwjarr.2026.32.1.2501","The sub-prefecture of Damakhania, in Maritime Guinea, is undergoing rapid landscape transformation driven by the The landscapes of the Damakhania sub-prefecture, in Maritime Guinea, are being rapidly reshaped by the combined effects of urban expansion, agriculture and climate variability. This study examines changes in vegetation and hydroclimatic conditions between 1986 and 2024, quantifies land-use\u002Fland-cover (LULC) change between 2002 and 2024, and explores possible trajectories up to 2100. Fifteen variables were analysed: seven Landsat-derived indices (NDVI, EVI, SAVI, MSAVI, NBR, NDWI and LST) and eight climatic and water-balance variables. Trends and change points were assessed with the Mann-Kendall, Sen's slope and Pettitt tests, and seasonal contrasts with Student's t-test and the Mann-Whitney test. LULC maps for 2002 (Landsat 5 TM) and 2024 (Landsat 8 OLI) were used to measure change and to build three prospective scenarios. EVI declined significantly (τ = −0.385; p \u003C 0.001), whereas land surface temperature rose by about 0.23 °C per year (τ = 0.397; p \u003C 0.001). The Pettitt test indicated significant inhomogeneity in the EVI, NBR and LST series, while precipitation, maximum air temperature and NDVI showed no significant trend. Over 22 years, wooded\u002Fshrub savanna and open forest lost 47.4% and 45.7% of their area, respectively, to the benefit of urban fabric (+203.4%), degraded areas and bare soil (+189.7%) and crops and fallow land (+33.5%). The scenarios suggest that, without conservation measures, natural vegetation could disappear before 2050. The observed degradation appears more closely related to human pressure than to an unfavourable rainfall trend, which calls for land-use planning that protects the remaining wooded areas.","几内亚沿海地区的达马哈尼亚（Damakhania）副省，其景观正因城市扩张、农业活动和气候变率的综合影响而迅速改变。本研究考察了1986年至2024年间植被与水文气候条件的变化，量化了2002年至2024年间的土地利用\u002F土地覆盖（LULC）变化，并探讨了至2100年的可能演变轨迹。共分析了15个变量：7个基于Landsat的指数（NDVI、EVI、SAVI、MSAVI、NBR、NDWI和LST）以及8个气候与水量平衡变量。趋势与突变点分别采用Mann-Kendall检验、Sen斜率估计和Pettitt检验进行评估，季节性差异则采用Student's t检验和Mann-Whitney检验。利用2002年（Landsat 5 TM）和2024年（Landsat 8 OLI）的LULC图件测量变化并构建了三种情景预测。EVI显著下降（τ = −0.385；p \u003C 0.001），而地表温度以约每年0.23 °C的速率上升（τ = 0.397；p \u003C 0.001）。Pettitt检验表明EVI、NBR和LST序列存在显著的非均一性，而降水、最高气温和NDVI未显示出显著趋势。22年间，疏林\u002F灌丛草原和疏林分别丧失了47.4%和45.7%的面积，而城市建成区（+203.4%）、退化区域和裸地（+189.7%）以及农田和休耕地（+33.5%）则相应增加。情景预测表明，若不采取保护措施，自然植被可能在2050年前消失。观测到的退化似乎与人类压力关系更为密切，而非不利的降水趋势所致，这要求通过土地利用规划来保护残余的林地。","World Journal of Advanced Research and Reviews","2026-10-03T00:00:00Z",68,{"impact":40,"substance":18,"depth":159,"authority":160,"freshness":20,"relevant":21,"comment":161},17,12,"基于Landsat多指数与LULC情景模拟的实证研究，数据扎实、结论明确，但属区域案例，公共影响有限。",[163],{"name":155,"url":152},[26,27,165,166,167],"土地利用变化","荒漠化防治","国土空间规划",{"code":169,"slug":170,"name":171,"path":172,"parent_code":173},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":176,"locked":14,"version":34,"method":35,"evidence":177},"classified",{"candidates":178,"agricultural_evidence":41},[179,184,189],{"code":169,"score":180,"supported":41,"title":181,"abstract":182,"tags":183},3,[],[170],[],{"code":185,"score":60,"supported":14,"title":186,"abstract":187,"tags":188},"H6",[],[],[165,166],{"code":39,"score":60,"supported":14,"title":190,"abstract":191,"tags":192},[],[],[27,26],[194,195],"Damakhania 土地利用 遥感","Maritime Guinea 植被退化","Damakhania土地利用遥感-4233","10.30574\u002Fwjarr.2026.32.1.2501",{"doi":197,"openalex_id":199,"authors":200,"venue":155,"cited_by_count":81,"oa_url":216,"card":217,"direction":87,"ingested_from":89},"W7218954171",[201,203,205,207,210,212,214],{"name":202,"orcid":9},"Alpha Laho Diallo",{"name":204,"orcid":9},"Kadiatou Diallo",{"name":206,"orcid":9},"Thierno Boubacar BAH",{"name":208,"orcid":209},"Mamadou Yaya Baldé","https:\u002F\u002Forcid.org\u002F0000-0002-6439-4702",{"name":211,"orcid":9},"Sory Keita",{"name":213,"orcid":9},"Alpha Oumar",{"name":215,"orcid":9},"DIALLO Alpha Issaga Pallé DIALLO","https:\u002F\u002Fwjarr.com\u002Fsites\u002Fdefault\u002Ffiles\u002Ffulltext_pdf\u002FWJARR-2026-2501.pdf",{"tldr":218,"method":219,"finding":220,"direction":87,"opportunity":221},"分析几内亚达马卡尼亚2002–2024年农业、采矿与城市化对植被和土壤的影响并预测至2100年。","Landsat指数与气候变量结合Mann-Kendall、Sen斜率、Petti","EVI显著下降、地表温度年增约0.23°C，林灌与疏林22年减少约46%，退化主因是人为压力而非降水","可结合高分辨率遥感与农户调查，量化采矿、城市扩张对植被退化的相对贡献并评估保护情景成效。","2026-10-05T23:30:25.259465Z",{"id":224,"title":225,"url":226,"summary":227,"summary_zh":228,"content":9,"source_name":229,"source_url":226,"published_at":230,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":231,"score_detail":232,"sources":235,"tags":237,"primary_topic":9,"secondary_topics":241,"classification":242,"search_phrases":271,"slug":274,"view_count":21,"doi":275,"paper":276,"created_at":296},2416,"Artificial Intelligence for Socio-Ecological Resilience andSustainable Resource Governance","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.256","Climate change, biodiversity loss, resource depletion, and increasingly volatile environmental conditions require governance systems that can move beyond retrospective monitoring toward anticipatory and adaptive action. Artificial intelligence (AI) offers relevant capabilities, but current environmental applications remain fragmented across sensing, prediction, optimization, and decision support. This study develops the AI Enabled SocioEcological Resilience Framework (AI SERF) through a systematic literature review and qualitative conceptual synthesis of peer reviewed studies published between January 2022 and June 2026. The reported review process screened 412 records and retained 73 studies for thematic synthesis. The revised framework links four functional pillars, namely Autonomous Eco Monitoring, Predictive Resource Optimization, Adaptive Algorithmic Governance, and Eco Resilient Feedback Loops, to absorptive, adaptive, and transformative resilience capacities. Its novelty lies not in proposing another isolated AI architecture, but in connecting data acquisition, predictive intelligence, human supervised governance, ecological intervention, and learning within a single resilienceoriented cycle. The framework is operationalized through candidate data sources, AI models, governance actors, performance indicators, and responsible AI safeguards. Particular attention is given to explainability, energy and carbon efficiency, algorithmic bias, cyberphysical security, institutional capacity, and data limitations in tropical and archipelagic settings. The study provides a theoretically grounded and implementation oriented basis for future empirical validation of AI enabled environmental governance and clarifies its contribution to SDGs 9, 11, 13, 14, and 15","气候变化、生物多样性丧失、资源枯竭以及日益不稳定的环境条件，要求治理体系能够超越回溯性监测，转向前瞻性和适应性行动。人工智能（AI）具备相关能力，但当前的环境应用在感知、预测、优化和决策支持方面仍然相互割裂。本研究通过系统文献综述和对2022年1月至2026年6月间发表的同行评审研究的定性概念综合，构建了“人工智能赋能社会生态韧性框架”（AI-SERF）。所述综述过程筛选了412条记录，并保留73项研究进行主题综合。修订后的框架将四个功能支柱，即自主生态监测、预测性资源优化、适应性算法治理和生态韧性反馈回路，与吸收性、适应性和转型性韧性能力相连接。其新颖性不在于提出另一种孤立的AI架构，而在于将数据采集、预测智能、人类监督治理、生态干预和学习连接在一个以韧性为导向的单一循环中。该框架通过候选数据源、AI模型、治理主体、绩效指标和负责任AI保障措施加以操作化。研究特别关注热带和群岛环境中的可解释性、能源与碳效率、算法偏见、网络物理安全、制度能力和数据局限性。本研究为未来对AI赋能环境治理的实证验证提供了具有理论根基且面向实施的基础，并阐明了其对可持续发展目标9、11、13、14和15的贡献。","AI Innovation and Resilience for the Environment (AIR)","2026-09-11T00:00:00Z",77,{"impact":17,"substance":233,"depth":17,"authority":160,"freshness":40,"relevant":21,"comment":234},21,"系统综述构建AI赋能社会生态韧性框架，方法规范、数据规模明确，对农业环境治理与智慧农业有参考价值，但偏理论框架、尚待实证验证。",[236],{"name":229,"url":226},[238,239,26,28,240],"智慧农业","农业人工智能","资源治理",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":243},{"candidates":244,"agricultural_evidence":14,"reason":55},[245,252,258,262,267],{"code":246,"score":103,"supported":41,"title":247,"abstract":249,"tags":251},"I2",[248],"artificial intelligence",[248,250],"decision support",[239],{"code":185,"score":49,"supported":41,"title":253,"abstract":254,"tags":257},[],[255,256],"生物多样性","biodiversity",[],{"code":48,"score":49,"supported":41,"title":259,"abstract":260,"tags":261},[],[52,53],[],{"code":263,"score":21,"supported":14,"title":264,"abstract":265,"tags":266},"I1",[],[],[238],{"code":39,"score":21,"supported":14,"title":268,"abstract":269,"tags":270},[],[],[26],[272,273],"农业人工智能 智慧农业 生态韧性 资源治理","农业人工智能 智慧农业","农业人工智能智慧农业生态韧性资源治理-2416","10.68012\u002Fair.v1i2.256",{"doi":275,"openalex_id":277,"authors":278,"venue":229,"cited_by_count":81,"oa_url":288,"card":289,"direction":295,"ingested_from":89},"W7212282045",[279,281,283,286],{"name":280,"orcid":9},"Ayu Rimanda",{"name":282,"orcid":9},"Prima Wira Nanda",{"name":284,"orcid":285},"Zakia Hary Nisa","https:\u002F\u002Forcid.org\u002F0009-0003-2625-0518",{"name":287,"orcid":9},"Lilik Susilowati","https:\u002F\u002Fjournal.sundarapublishing.com\u002Findex.php\u002Fair\u002Farticle\u002Fdownload\u002F256\u002F135",{"tldr":290,"method":291,"finding":292,"direction":293,"opportunity":294},"通过系统综述构建AI赋能社会生态韧性框架，连接监测、预测、治理与反馈循环。","系统文献综述与定性概念综合，筛选412篇文献保留73篇。","提出AI-SERF框架，整合四大支柱与三类韧性能力，强调负责任AI保障。","农业绿色发展与碳","可在热带与群岛地区实证验证该框架，并探索AI碳效率与算法公平性。","智慧农业 \u002F 农业物联网","2026-09-14T23:30:11.299701Z",{"id":298,"title":299,"url":300,"summary":301,"summary_zh":302,"content":9,"source_name":303,"source_url":300,"published_at":304,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":305,"score_detail":306,"sources":309,"tags":311,"primary_topic":9,"secondary_topics":315,"classification":316,"search_phrases":336,"slug":339,"view_count":21,"doi":340,"paper":341,"created_at":358},2062,"Multi-scale drivers of wildfire burn severity in Central Zagros: insights from remote sensing and machine learning","https:\u002F\u002Fdoi.org\u002F10.3832\u002Fifor5027-019","Over the past few decades, wildfire activity has increased globally. In the Central Zagros Mountains of Iran, widespread oak decline has significantly altered fuel structures and raised ecological concerns. However, the specific contribution of long-term vegetation degradation to wildfire burn severity remains under-quantified. This study addresses this gap by investigating whether decadal declines in the Normalized Difference Vegetation Index (NDVI) are associated with higher burn severity, while assessing their relative importance against short-term pre-fire environmental conditions, fuel types, topography, and human access gradients. Focusing on a major wildfire in June 2024 in the Kashkan Watershed, we mapped burn severity using the differenced Normalized Burn Ratio (dNBR) derived from Sentinel-2 imagery. A comprehensive suite of predictors was evaluated, including the long-term NDVI trend (May- September 2014-2023), pre-fire Normalized Difference Moisture Index (NDMI), Land Surface Temperature (LST), fuel categories based on ESA WorldCover, slope, aspect, spatial texture contrast, and distance to roads and settlements. A stratified sampling design supported statistical inference using ordinary least squares regression and predictive modeling via Random Forest and XGBoost algorithms with five-fold cross-validation. XGBoost demonstrated the highest predictive performance (cross-validated R2 = 0.70; RMSE = 0.11), outperforming Random Forest (R2 = 0.64) and linear regression (R2 = 0.34). Fuel moisture (NDMI) and thermal stress (LST) emerged as the primary drivers of severity, followed by land cover, topography, and spatial texture. While the long-term NDVI decline showed a small but consistent positive association with dNBR at the landscape scale, the results highlight a distinct multi-scale mechanism. In this dynamic, pre-fire moisture and temperature establish the broad watershed-scale environmental susceptibility, whereas specific fuel types and their spatial configurations drive the local-level amplification or damping of burn severity. These findings provide a framework for proactive vulnerability mapping in Zagros oak woodlands, suggesting that integrating dynamic fire-weather data and field-based fuel measurements could further refine future predictability.","过去几十年间，全球野火活动日益加剧。在伊朗中扎格罗斯山脉，大范围的橡树衰退显著改变了可燃物结构，并引发了生态担忧。然而，长期植被退化对野火烧毁严重程度的具体贡献仍缺乏充分的量化研究。本研究通过探讨归一化植被指数（NDVI）的十年尺度下降是否与更高的烧毁严重程度相关，填补了这一空白，同时评估了其相对于短期火前环境条件、可燃物类型、地形和人类可达性梯度的相对重要性。以2024年6月卡什坎流域的一场重大野火为研究对象，我们利用Sentinel-2影像衍生的差分归一化燃烧比（dNBR）绘制了烧毁严重程度图。研究评估了一套综合预测因子，包括长期NDVI趋势（2014—2023年5—9月）、火前归一化差异水分指数（NDMI）、地表温度（LST）、基于ESA WorldCover的可燃物类别、坡度、坡向、空间纹理对比度以及与道路和居民点的距离。分层抽样设计支持了普通最小二乘回归的统计推断，并通过随机森林和XGBoost算法结合五折交叉验证进行预测建模。XGBoost表现出最高的预测性能（交叉验证R² = 0.70；RMSE = 0.11），优于随机森林（R² = 0.64）和线性回归（R² = 0.34）。可燃物湿度（NDMI）和热胁迫（LST）是烧毁严重程度的主要驱动因素，其次是土地覆盖、地形和空间纹理。尽管长期NDVI下降在景观尺度上与dNBR呈现出微小但一致的正相关关系，结果仍揭示了一种独特的多尺度机制。在这一机制中，火前湿度和温度奠定了流域尺度的广泛环境脆弱性，而特定可燃物类型及其空间配置则驱动了局部尺度烧毁严重程度的放大或抑制。这些发现为扎格罗斯橡树林地的主动脆弱性制图提供了框架，并表明整合动态火险天气数据和实地可燃物测量可进一步提升未来的可预测性。","iForest - Biogeosciences and Forestry","2026-09-08T00:00:00Z",66,{"impact":40,"substance":307,"depth":159,"authority":19,"freshness":40,"relevant":21,"comment":308},20,"伊朗扎格罗斯山区野火烧伤严重度多尺度驱动研究，方法扎实但属境外区域案例，对国内农业信息化仅有方法借鉴价值。",[310],{"name":303,"url":300},[312,313,26,314,27],"机器学习","生态保护","森林防火",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":317},{"candidates":318,"agricultural_evidence":14,"reason":55},[319,323,328,332],{"code":39,"score":13,"supported":41,"title":320,"abstract":321,"tags":322},[45],[],[27,26],{"code":246,"score":20,"supported":41,"title":324,"abstract":326,"tags":327},[325],"machine learning",[],[312],{"code":185,"score":21,"supported":14,"title":329,"abstract":330,"tags":331},[],[],[313],{"code":48,"score":21,"supported":14,"title":333,"abstract":334,"tags":335},[],[],[314],[337,338],"机器学习 森林防火 植被退化 生态保护","机器学习 森林防火","机器学习森林防火植被退化生态保护-2062","10.3832\u002Fifor5027-019",{"doi":340,"openalex_id":342,"authors":343,"venue":303,"cited_by_count":81,"oa_url":352,"card":353,"direction":87,"ingested_from":89},"W7211926672",[344,346,348,350],{"name":345,"orcid":9},"F Shahidinejad",{"name":347,"orcid":9},"M Jourgholami",{"name":349,"orcid":9},"MM Pourhanifeh",{"name":351,"orcid":9},"A Esfandyar","https:\u002F\u002Fiforest.sisef.org\u002Fpdf\u002F?id=ifor5027-019",{"tldr":354,"method":355,"finding":356,"direction":87,"opportunity":357},"结合遥感与机器学习，量化伊朗扎格罗斯中部野火烧毁严重度的多尺度驱动因素。","Sentinel-2 dNBR、长期NDVI趋势、随机森林与XGBoost建模。","燃料湿度和热胁迫主导烧毁严重度，长期NDVI下降有微弱正相关，呈现多尺度机制。","可引入动态火险天气与地面燃料实测，提升多尺度火险制图与预测精度。","2026-09-10T23:30:28.899368Z",{"id":360,"title":361,"url":362,"summary":363,"summary_zh":364,"content":9,"source_name":365,"source_url":362,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":367,"score_detail":368,"sources":370,"tags":372,"primary_topic":9,"secondary_topics":377,"classification":378,"search_phrases":404,"slug":407,"view_count":81,"doi":408,"paper":409,"created_at":433},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":17,"substance":18,"depth":159,"authority":160,"freshness":20,"relevant":21,"comment":369},"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[371],{"name":365,"url":362},[26,373,374,375,376],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":379},{"candidates":380,"agricultural_evidence":41,"reason":403},[381,385,389,393,397],{"code":185,"score":115,"supported":41,"title":382,"abstract":383,"tags":384},[],[255,256],[373],{"code":39,"score":115,"supported":41,"title":386,"abstract":387,"tags":388},[],[44,45],[26],{"code":48,"score":49,"supported":41,"title":390,"abstract":391,"tags":392},[],[52,53],[],{"code":169,"score":180,"supported":41,"title":394,"abstract":395,"tags":396},[],[170],[],{"code":398,"score":180,"supported":41,"title":399,"abstract":400,"tags":402},"I7",[],[401],"研究方法",[],"多个产业证据相当，需要确认主归属",[405,406],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":408,"openalex_id":410,"authors":411,"venue":365,"cited_by_count":81,"oa_url":362,"card":428,"direction":87,"ingested_from":89},"W7223141286",[412,415,417,420,423,426],{"name":413,"orcid":414},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":416,"orcid":9},"Gabriel Henrique Santos Silva",{"name":418,"orcid":419},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":421,"orcid":422},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":424,"orcid":425},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":427,"orcid":9},"Sylvana Melo dos Santos",{"tldr":429,"method":430,"finding":431,"direction":87,"opportunity":432},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":439,"content":9,"source_name":440,"source_url":437,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":441,"score_detail":442,"sources":446,"tags":448,"primary_topic":453,"secondary_topics":454,"classification":455,"search_phrases":476,"slug":479,"view_count":81,"doi":480,"paper":481,"created_at":513},5576,"Land cover change and soil salinization in Mozambique: A counterfactual analysis over two decades (2001−2022)","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.catena.2026.110691","Soil salinization is a major issue that constrains the sustainable development of agriculture in arid, semi-arid, and coastal lowland areas. It is of great importance for regional land management and climate adaptation to clarify the influence of land cover change on soil salinization. This study aims to quantitatively assess the contribution of land cover change to soil salinization in Mozambique, where both rapid land cover transformation and salinization issues are evident. Firstly, we integrated soil profile electrical conductivity (EC) data, Landsat time-series remote sensing imagery, TerraClimate meteorological data, and the 30 m resolution land cover data, to construct a remote sensing-based EC inversion model for Mozambique. Secondly, we applied a spatially and environmentally constrained counterfactual matching method to quantitatively identify the contributions of different land cover changes on soil salinization trends. Our results reveal that: (1) the spatial distribution of soil salinization in Mozambique exhibits distinct a clear association with proximity to water bodies, with high EC areas primarily concentrated around Lake Malawi in the north and in the coastal regions of Greater Maputo in the south. (2) 26.98% of Mozambique's territory showed a trend of intensifying soil salinization, while 10.88% exhibited a decreasing trend. Areas with intensifying trends are primarily concentrated in the central alluvial plains, localized agricultural development zones, and coastal lowlands. (3) Urbanization was significantly associated with an increasing salinization trend after false discovery rate correction, suggesting that the expansion of impervious surfaces, changes in drainage patterns, and soil compaction may exacerbate localized salt accumulation. Agricultural expansion showed a negative trend difference, consistent with the potential influence of crop cover, tillage, and wet-season leaching, although this association was not significant after false discovery rate correction. The effects of ecological restoration and other factors were not statistically significant. In addition to elucidating the heterogeneous impacts of land cover changes in Mozambique on soil salinization trends, this study also provides methodological guidance and a scientific basis for monitoring\u002Fmanaging soil salinity risks in other coastal lowlands.","土壤盐渍化是制约干旱、半干旱及沿海低地地区农业可持续发展的主要问题。阐明土地覆盖变化对土壤盐渍化的影响，对区域土地管理和气候适应具有重要意义。本研究旨在定量评估土地覆盖变化对莫桑比克土壤盐渍化的贡献，该国土地覆盖转型迅速且盐渍化问题突出。首先，我们整合了土壤剖面电导率（EC）数据、Landsat时间序列遥感影像、TerraClimate气象数据以及30 m分辨率土地覆盖数据，构建了基于遥感的莫桑比克EC反演模型。其次，我们采用空间与环境约束的反事实匹配方法，定量识别不同土地覆盖变化对土壤盐渍化趋势的贡献。研究结果表明：（1）莫桑比克土壤盐渍化的空间分布与水体邻近性呈现明显的关联，高EC区域主要集中于北部的马拉维湖周边以及南部大马普托沿海地区。（2）莫桑比克26.98%的国土面积呈现土壤盐渍化加剧趋势，而10.88%呈现减轻趋势。加剧趋势区域主要集中于中部冲积平原、局部农业开发区及沿海低地。（3）经错误发现率校正后，城市化与盐渍化加剧趋势显著相关，表明不透水面扩张、排水格局改变及土壤压实可能加剧局部盐分累积。农业扩张呈现负向趋势差异，与作物覆盖、耕作及湿季淋洗的潜在影响一致，但该关联经错误发现率校正后不显著。生态恢复及其他因素的影响在统计上不显著。本研究不仅阐明了莫桑比克土地覆盖变化对土壤盐渍化趋势的异质性影响，还为其他沿海低地土壤盐渍化风险的监测与管理提供了方法学指导和科学依据。","CATENA",79,{"impact":443,"substance":18,"depth":17,"authority":444,"freshness":20,"relevant":21,"comment":445},16,14,"该研究基于遥感与反事实匹配方法，定量评估土地覆盖变化对盐渍化的贡献，方法新颖、结论可靠，对沿海低地盐渍化监测具有参考价值，但属境外区域研究，国内政策与产业影响有限。",[447],{"name":440,"url":437},[26,449,450,451,452],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":169,"slug":170,"name":171,"path":172,"parent_code":173},[],{"status":176,"locked":14,"version":34,"method":35,"evidence":456},{"candidates":457,"agricultural_evidence":41},[458,463,467,472],{"code":169,"score":444,"supported":41,"title":459,"abstract":460,"tags":462},[170],[461,170],"土壤",[],{"code":39,"score":115,"supported":41,"title":464,"abstract":465,"tags":466},[],[44,45],[26],{"code":48,"score":49,"supported":41,"title":468,"abstract":469,"tags":471},[],[470,118],"气候适应",[],{"code":185,"score":21,"supported":14,"title":473,"abstract":474,"tags":475},[],[],[451],[477,478],"莫桑比克 土壤盐渍化 遥感","土地覆盖变化 盐渍化 反事实分析","莫桑比克土壤盐渍化遥感-5576","10.1016\u002Fj.catena.2026.110691",{"doi":480,"openalex_id":482,"authors":483,"venue":440,"cited_by_count":81,"oa_url":437,"card":508,"direction":87,"ingested_from":89},"W7223069171",[484,486,488,490,493,495,498,501,503,505],{"name":485,"orcid":9},"Yue Li",{"name":487,"orcid":9},"Mengfei Shi",{"name":489,"orcid":9},"Fei Zhang",{"name":491,"orcid":492},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":494,"orcid":9},"Gowhar Meraj",{"name":496,"orcid":497},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":499,"orcid":500},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":502,"orcid":9},"Yuanfang Chai",{"name":504,"orcid":9},"Hongtao Xu",{"name":506,"orcid":507},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":509,"method":510,"finding":511,"direction":87,"opportunity":512},"量化莫桑比克2001-2022年土地覆盖变化对土壤盐渍化的贡献。","融合EC剖面、Landsat时序与气候数据反演EC，并用空间环境约束的反事实匹配","26.98%区域盐渍化加剧；城市化显著关联盐渍化上升，农业扩张关联不显著。","可将反事实匹配框架迁移至其他滨海低地，并引入作物类型与灌溉制度细化农业扩张的盐渍化效应。","2026-10-22T11:20:42.713789Z"]