[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3622":3,"related-3622":97},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":69,"slug":72,"view_count":50,"doi":73,"paper":74,"created_at":96},3622,"Assessment and projection of mangrove blue carbon sink along the Guangdong coastline: integrating remote sensing and modeling techniques","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2026.115559","Mangrove ecosystems along the Guangdong coastline serve as a vital blue carbon sink, yet their carbon sequestration potential under changing climatic conditions requires rigorous assessment and forward-looking evaluation. This study develops an integrated framework combining remote sensing techniques and spatial modeling approaches to quantify historical changes and project future trajectories of mangrove blue carbon stocks in Guangdong Province, China. Time-series satellite imagery was used to quantify changes in mangrove extent and associated carbon storage over the period 1994 to 2024. Field-validated sediment core data, coupled with the InVEST and CA-Markov models, were utilized to assess historical carbon storage and forecast future challenges along the coastline. Our results reveal substantial spatial variability in mangrove carbon storage across Guangdong. A non-significant increase in mangrove area was observed during the first decade of the study period (1994–2004). In contrast, a significant area expansion of 10.71% occurred between 2014 and 2024, with projections indicating a further increase of 19.45% by 2034. The eastern coast of Guangdong exhibited greater potential for mangrove expansion than the western coast. Based on the projected mangrove extent and the adopted carbon-density framework, the InVEST model estimated a potential 24.62% increase in mangrove carbon storage by 2034 relative to 2024. By explicitly integrating spatiotemporal remote sensing analysis with predictive land-use and carbon storage modeling, this study advances a scalable and transferable framework for assessing mangrove blue carbon dynamics under climate and development pressures. The findings provide critical evidence to inform adaptive coastal management, strengthen Guangdong's carbon neutrality goals, and support nature-based solutions for climate mitigation. Overall, this integrated approach provides a robust scientific basis for coastal spatial planning and offers actionable insights for conserving and enhancing mangrove blue carbon resources along this ecologically and economically important coastline.","广东沿海的红树林生态系统是重要的蓝碳汇，但其在气候变化条件下的碳封存潜力亟需严谨评估与前瞻性预测。本研究构建了一个集成遥感技术与空间建模方法的框架，用以量化广东省红树林蓝碳储量的历史变化并预测其未来演变轨迹。利用时间序列卫星影像量化了1994年至2024年间红树林面积及相关碳储量的变化。结合实地验证的沉积柱数据，并运用InVEST模型和CA-Markov模型，评估了历史碳储量并预测了沿海地区未来面临的挑战。结果表明，广东红树林碳储量存在显著的空间异质性。在研究期的第一个十年（1994—2004年），红树林面积呈非显著性增加。相比之下，2014年至2024年间出现了10.71%的显著面积扩张，预测表明到2034年将进一步增加19.45%。广东东岸的红树林扩张潜力大于西岸。基于预测的红树林面积和所采用的碳密度框架，InVEST模型估算到2034年红树林碳储量较2024年可能增加24.62%。通过明确整合时空遥感分析与土地利用及碳储量预测建模，本研究提出了一个可扩展、可迁移的框架，用于评估气候与发展压力下红树林蓝碳动态。研究结果为适应性海岸管理提供了关键证据，有助于强化广东的碳中和目标，并支持基于自然的气候减缓方案。总体而言，这一集成方法为海岸空间规划提供了坚实的科学依据，并为保护和提升这一具有重要生态和经济价值海岸线的红树林蓝碳资源提供了可操作的见解。",null,"Ecological Indicators","2026-09-25T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,14,8,1,"融合遥感与InVEST\u002FCA-Markov模型评估并预测广东红树林蓝碳储量，方法可迁移、数据翔实，对沿海生态管理与碳中和工作有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","碳汇核算","蓝碳","红树林","海岸带管理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":68},[39,48,56,63],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":47},"I3",15,true,[44],"remote sensing",[46,44],"遥感",[27],{"code":49,"score":50,"supported":42,"title":51,"abstract":52,"tags":55},"H4",7,[],[53,54],"碳汇","carbon sequestration",[28],{"code":57,"score":58,"supported":42,"title":59,"abstract":60,"tags":62},"H7",3,[],[61],"气候变化",[],{"code":64,"score":22,"supported":14,"title":65,"abstract":66,"tags":67},"H6",[],[],[30],"农业相关性需要复核",[70,71],"广东 红树林 蓝碳","InVEST CA-Markov 碳储量","广东红树林蓝碳-3622","10.1016\u002Fj.ecolind.2026.115559",{"doi":73,"openalex_id":75,"authors":76,"venue":10,"cited_by_count":87,"oa_url":88,"card":89,"direction":93,"ingested_from":95},"W7214279727",[77,80,83,85],{"name":78,"orcid":79},"Hafiz Hassan Javed","https:\u002F\u002Forcid.org\u002F0000-0003-0776-8701",{"name":81,"orcid":82},"You‐Shao Wang","https:\u002F\u002Forcid.org\u002F0000-0001-9565-2666",{"name":84,"orcid":9},"Hao Cheng",{"name":86,"orcid":9},"ABD Ullah",0,"https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1470160X26009611\u002Fpdf",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"融合遥感与模型评估并预测广东红树林蓝碳储量的历史变化与未来趋势。","时间序列卫星影像、沉积柱数据、InVEST与CA-Markov模型。","2014-2024年红树林面积增10.71%，2034年碳储量或较2024年增24.62%，东岸潜力","农业遥感与作物表型","可延伸至多气候情景下红树林碳汇预测及与碳交易、海岸带规划耦合研究。","openalex","2026-09-27T23:30:46.163566Z",{"total":98,"page":22,"page_size":98,"items":99},6,[100,189,252,310,372,433],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":108,"is_selected":14,"score":109,"score_detail":110,"sources":115,"tags":119,"primary_topic":122,"secondary_topics":128,"classification":141,"search_phrases":171,"slug":174,"view_count":22,"doi":175,"paper":176,"created_at":188},2161,"National Carbon Stock and Greenhouse Gas Baseline Assessment for Liberia: Integrating Forest, Blue Carbon, Soil Carbon, and Sectoral Emissions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22685841","This preprint presents an integrated national assessment of carbon stocks and greenhouse gas (GHG) emissions across Liberia, combining forest carbon, blue carbon, soil organic carbon, and major sectoral emission sources within a common spatial framework. The study integrates field measurements, soil and sediment observations, remote sensing, geographic information system (GIS) analysis, spatial modelling, and national activity datasets. Sentinel-2 and Landsat imagery, Digital Earth Africa products, vegetation indices, field-based biomass measurements, and carbon-accounting approaches are used to characterize ecosystem distribution and estimate major carbon pools. The assessment estimates approximately 6.595 million ha of forest and a national forest carbon stock of approximately 2,236 Mt C, equivalent to about 8,200 Mt CO₂e. It also evaluates Liberia's coastal blue-carbon ecosystems and soil organic carbon resources and examines greenhouse gas emissions associated with agriculture, energy, transport, industrial activities, land-use change, and waste. The study provides a spatially integrated scientific framework for strengthening national carbon accounting and Monitoring, Reporting and Verification (MRV), prioritizing ecosystem conservation and restoration, supporting REDD+ and blue-carbon initiatives, and screening potential areas for carbon-market interventions. The manuscript emphasizes the need for continued field measurements, soil and sediment sampling, land-cover validation, explicit uncertainty analysis, and improved sectoral activity data. Author: John Nyemah Natt Jr.Affiliation: Center for Meteorology, Oceanography and Climate Research – LiberiaDocument type: PreprintPublication date: 10 September 2026","本预印本在统一的空间框架内，对利比里亚全国的碳储量与温室气体（GHG）排放进行了综合评估，涵盖森林碳、蓝碳、土壤有机碳以及主要部门排放源。该研究整合了野外实测、土壤与沉积物观测、遥感、地理信息系统（GIS）分析、空间建模以及国家活动数据集。研究采用Sentinel-2与Landsat影像、Digital Earth Africa产品、植被指数、基于野外的生物量测量以及碳核算方法，用以刻画生态系统分布并估算主要碳库。评估估算出森林面积约659.5万公顷，全国森林碳储量约22.36亿吨碳（Mt C），相当于约82亿吨二氧化碳当量（Mt CO₂e）。研究还评估了利比里亚沿海蓝碳生态系统与土壤有机碳资源，并考察了与农业、能源、交通、工业活动、土地利用变化及废弃物相关的温室气体排放。该研究提供了一个空间整合的科学框架，用于加强国家碳核算与监测、报告和核查（MRV），优先开展生态系统保护与恢复，支持REDD+与蓝碳倡议，并筛选适合碳市场干预的潜在区域。本文强调需要持续开展野外测量、土壤与沉积物采样、土地覆盖验证、明确的不确定性分析以及改进的部门活动数据。作者：John Nyemah Natt Jr.所属机构：利比里亚气象、海洋与气候研究中心文献类型：预印本出版日期：2026年9月10日","Zenodo (CERN European Organization for Nuclear Research)","2026-09-10T00:00:00Z",25,84,{"impact":18,"substance":111,"depth":17,"authority":112,"freshness":113,"relevant":22,"comment":114},23,12,9,"利比里亚全国碳储量与温室气体基线评估预印本，整合森林、蓝碳、土壤碳与部门排放，方法体系完整、数据规模可观，对农业土地利用与碳汇监测具有参考价值，但属他国案例、非国内三农政策，故未达每日精选门槛。",[116,117],{"name":106,"url":103},{"name":106,"url":118},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22685842",[27,120,28,29,121],"土壤碳","REDD+",{"code":123,"slug":124,"name":125,"path":126,"parent_code":127},"B7","protected-horticulture","设施园艺与植物工厂","\u002Ftopic\u002Fhorticulture\u002Fprotected-horticulture","B",[129,135],{"code":130,"slug":131,"name":132,"path":133,"parent_code":134},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"code":136,"slug":137,"name":138,"path":139,"parent_code":140},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",{"status":142,"locked":14,"version":35,"method":36,"evidence":143},"classified",{"candidates":144,"agricultural_evidence":42},[145,151,156,163,167],{"code":123,"score":20,"supported":42,"title":146,"abstract":148,"tags":150},[147],"greenhouse",[149,147],"温室",[],{"code":130,"score":20,"supported":42,"title":152,"abstract":153,"tags":155},[131],[154,131],"土壤",[],{"code":136,"score":113,"supported":42,"title":157,"abstract":158,"tags":162},[],[159,160,161],"地理信息","GIS","geographic information",[],{"code":40,"score":50,"supported":42,"title":164,"abstract":165,"tags":166},[],[46,44],[27],{"code":49,"score":22,"supported":14,"title":168,"abstract":169,"tags":170},[],[],[28],[172,173],"碳汇核算 遥感监测 土壤碳 蓝碳","碳汇核算 遥感监测","碳汇核算遥感监测土壤碳蓝碳-2161","10.5281\u002Fzenodo.22685841",{"doi":175,"openalex_id":177,"authors":178,"venue":106,"cited_by_count":87,"oa_url":103,"card":182,"direction":93,"ingested_from":95},"W7212145037",[179],{"name":180,"orcid":181},"JOHN NYEMAH NATT JR","https:\u002F\u002Forcid.org\u002F0000-0003-4084-5916",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"整合森林、蓝碳、土壤碳与部门排放，完成利比里亚国家碳储量与温室气体基线评估。","融合野外实测、Sentinel-2\u002FLandsat遥感、GIS与空间建模及国家活","森林约659.5万公顷，碳储量约2236 Mt C（约8200 Mt CO₂e），并核算多部门排放。","农业绿色发展与碳","可延伸至农业土壤碳汇与部门排放耦合的高分辨率MRV体系及不确定性量化研究。","2026-09-11T23:30:29.631707Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":9,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":203,"tags":205,"primary_topic":9,"secondary_topics":209,"classification":210,"search_phrases":227,"slug":230,"view_count":87,"doi":231,"paper":232,"created_at":251},4689,"Landscape historic trough spatial changes of mangroves in Benin Coastal area (West-Africa) using random forest algorithm applied to Landsat images","https:\u002F\u002Fdoi.org\u002F10.52638\u002Frfpt.2026.736","Mangrove forests, recognized as the main ecosystem of coastal zones and providing various goods and services, are facing are facing increasing anthropogenic pressures and require continuous investigation. Ensuring the sustainable management of these ecosystems implies permanent monitoring of their dynamics. In line with this, the present study carried out on the mangrove forests of Benin (West-Africa) aimed to evaluate the effect of anthropic perturbation on their cover dynamics over the past four decades (including both Ramsar sites 1017 and 1018). The adopted method focused on remote sensing techniques using Landsat images acquired between 1986 and 2023. The Random Forest algorithm was used for image processing. Results revealed that whatever the period, the overall accuracy values obtained after post-classification analysis ranged from 84.56% to 96.52% indicating a correct image classification. From these results, a decline in mangrove cover was observed between 1986 and 2013 followed by an increase from 2013 to 2023. In Ramsar site 1017, mangroves lost about 61.90% (3261.06 ha) of their initial area from 1986 to 2000. Between 2000 and 2014, mangrove cover loss was negligible and estimated at 2.29% (59.04 ha). In contrast, from 2014 to 2023, mangroves experienced an increase estimated at about 16.66% (324.63 ha). The same trend was observed in Ramsar site 1018, with a loss of 31.12% (459.63 ha) and 27.44% (279.18 ha) respectively from 1986 to 2001 and from 2001 to 2013 followed by a gain estimated at 39.89% (279.18 ha) between 2013 and 2023. Landscape changes analysis revealed that regardless of the site or period, mangroves were mainly converted into meadow, other plant and water body (e.g. in Ramsar site 1017, from 1986 to 2001, about 568.26 ha of mangroves were converted into meadows, 33.66 ha into other plant, and 35.46 ha into water body). The principal changes observed in this study focused on Benin coastal zones confirm the vulnerability of mangrove forests facing anthropogenic pressure. Keywords: Mangrove, Dynamics, Remote sensing, Benin.","红树林被公认为海岸带的主要生态系统，提供多种商品和服务，正面临日益增加的人为压力，需要持续开展调查研究。确保这些生态系统的可持续管理意味着对其动态进行长期监测。据此，本研究针对贝宁（西非）的红树林开展，旨在评估人为干扰对其过去四十年（包括拉姆萨尔湿地1017和1018）覆盖动态的影响。所采用的方法侧重于利用1986年至2023年间获取的Landsat影像的遥感技术。采用随机森林（Random Forest）算法进行图像处理。结果表明，无论哪个时期，分类后分析获得的总体精度值介于84.56%至96.52%之间，表明图像分类正确。从这些结果来看，1986年至2013年间红树林覆盖面积下降，随后2013年至2023年间有所增加。在拉姆萨尔湿地1017中，1986年至2000年间红树林丧失了其初始面积的约61.90%（3261.06公顷）。2000年至2014年间，红树林覆盖损失可忽略不计，估计为2.29%（59.04公顷）。相比之下，2014年至2023年间，红树林增加了约16.66%（324.63公顷）。拉姆萨尔湿地1018也观察到相同趋势，1986年至2001年和2001年至2013年分别损失31.12%（459.63公顷）和27.44%（279.18公顷），随后2013年至2023年间增加了约39.89%（279.18公顷）。景观变化分析显示，无论哪个地点或时期，红树林主要转化为草地、其他植物和水体（例如在拉姆萨尔湿地1017中，1986年至2001年间，约568.26公顷红树林转化为草地，33.66公顷转化为其他植物，35.46公顷转化为水体）。本研究中观察到的贝宁海岸带主要变化证实了红树林面对人为压力的脆弱性。关键词：红树林，动态，遥感，贝宁。","Revue Française de Photogrammétrie et de Télédétection","2026-10-09T00:00:00Z",70,{"impact":112,"substance":199,"depth":200,"authority":201,"freshness":21,"relevant":22,"comment":202},20,17,13,"基于Landsat与随机森林的四十年红树林动态监测研究，方法成熟、数据翔实，对滨海湿地遥感监测有参考价值，但属区域案例研究，公共影响有限。",[204],{"name":195,"url":192},[206,27,207,30,208],"随机森林","土地利用变化","滨海湿地",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":211},{"candidates":212,"agricultural_evidence":14,"reason":68},[213,217,222],{"code":40,"score":50,"supported":42,"title":214,"abstract":215,"tags":216},[],[46,44],[27],{"code":64,"score":218,"supported":14,"title":219,"abstract":220,"tags":221},2,[],[],[207,30],{"code":223,"score":22,"supported":14,"title":224,"abstract":225,"tags":226},"I2",[],[],[206],[228,229],"贝宁 红树林 遥感","Landsat 红树林 动态监测","贝宁红树林遥感-4689","10.52638\u002Frfpt.2026.736",{"doi":231,"openalex_id":233,"authors":234,"venue":195,"cited_by_count":87,"oa_url":245,"card":246,"direction":93,"ingested_from":95},"W7221179267",[235,237,239,241,243],{"name":236,"orcid":9},"Mahoutin Gildas Serge Zanvo",{"name":238,"orcid":9},"Nicolas DELBART",{"name":240,"orcid":9},"Paul PASSY",{"name":242,"orcid":9},"Kolawolé Valère Salako",{"name":244,"orcid":9},"Romain Glele-Kakai","https:\u002F\u002Frfpt.sfpt.fr\u002Findex.php\u002FRFPT\u002Farticle\u002Fdownload\u002F736\u002F528",{"tldr":247,"method":248,"finding":249,"direction":93,"opportunity":250},"利用随机森林与Landsat影像分析贝宁沿海红树林1986-2023年时空动态。","Landsat影像（1986-2023）结合随机森林分类与景观变化分析。","红树林先减后增，主要转为草甸、其他植被和水体，人为压力显著。","可结合多源遥感与驱动因子模型，探究红树林恢复的生态与社会机制。","2026-10-11T23:30:33.091927Z",{"id":253,"title":254,"url":255,"summary":256,"summary_zh":257,"content":9,"source_name":258,"source_url":255,"published_at":196,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":260,"sources":263,"tags":265,"primary_topic":9,"secondary_topics":268,"classification":269,"search_phrases":284,"slug":287,"view_count":22,"doi":288,"paper":289,"created_at":309},4582,"Assessing mangrove sensitivity to thermal variations through a satellite-based analysis on the southern São Paulo coast, Brazil","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-16005-6","Abstract Mangroves near subtropical range limits may benefit from fewer cold constraints, yet warmer conditions can also increase atmospheric water demand. These competing possibilities were examined in the Cananéia-Iguape Coastal System, southeastern Brazil, by combining ERA5 2 m air temperature with MODIS normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and leaf area index (LAI). Air-temperature trends were assessed for 1979–2019 with the Mann-Kendall test and Sen’s slope. Temperature-vegetation associations were calculated for 2003–2019 from mangrove-wide means and from individual grid cells at annual, monthly, and fixed calendar-quarter scales. Mean annual air temperature increased by about 0.02 °C year⁻ 1 ; the highest quarter-scale rate occurred in July–September (0.03 °C year⁻ 1 ). Annual temperature correlated positively with NDVI ( r = 0.61, R 2 = 0.38, p = 0.009) and EVI ( r = 0.55, R 2 = 0.30, p = 0.022), whereas the LAI relationship was not significant ( r = 0.27, R 2 = 0.07, p = 0.304). The sign of the association changed within the year: NDVI and EVI were negative in July–September, while positive EVI and LAI relationships occurred in October-December. Significant grid-cell relationships occupied limited and discontinuous areas. The temperature record shows regional warming, whereas canopy associations vary among metrics, periods, and locations. Given the bivariate design and coarse ERA5 grid, these associations should not be interpreted as causal or as responses to temperature alone.","摘要 靠近亚热带分布界限的红树林可能受益于较少的低温限制，但更温暖的条件也可能增加大气水分需求。为检验这两种相互竞争的的可能性，本研究在巴西东南部的卡纳内亚-伊瓜佩海岸系统，将ERA5 2米气温数据与MODIS归一化差异植被指数（NDVI）、增强植被指数（EVI）和叶面积指数（LAI）相结合进行了分析。采用Mann-Kendall检验和Sen斜率法评估了1979—2019年的气温趋势。基于2003—2019年红树林全域均值及单个网格单元，在年、月和固定日历季度尺度上计算了气温-植被关联。年平均气温上升约0.02 °C yr⁻¹；季度尺度最高升温速率出现在7—9月（0.03 °C yr⁻¹）。年气温与NDVI呈正相关（r = 0.61，R² = 0.38，p = 0.009），与EVI也呈正相关（r = 0.55，R² = 0.30，p = 0.022），而与LAI的关系不显著（r = 0.27，R² = 0.07，p = 0.304）。关联方向在年内发生变化：NDVI和EVI在7—9月为负，而EVI和LAI的正相关关系出现在10—12月。显著的网格单元关系占据有限且不连续的区域。气温记录显示区域变暖，而冠层关联因指标、时段和地点而异。鉴于双变量设计和ERA5网格较粗，这些关联不应被解释为因果关系或仅对温度的响应。","Environmental Monitoring and Assessment",66,{"impact":21,"substance":199,"depth":261,"authority":201,"freshness":113,"relevant":22,"comment":262},16,"方法规范、结论审慎的区域性遥感研究，对农业信息化中的生态遥感监测有参考价值，但影响范围限于学术层面。",[264],{"name":258,"url":255},[61,27,266,30,267],"植被指数","海岸带生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":270},{"candidates":271,"agricultural_evidence":14,"reason":68},[272,276,280],{"code":40,"score":218,"supported":14,"title":273,"abstract":274,"tags":275},[],[],[266,27],{"code":64,"score":22,"supported":14,"title":277,"abstract":278,"tags":279},[],[],[30],{"code":57,"score":22,"supported":14,"title":281,"abstract":282,"tags":283},[],[],[61],[285,286],"巴西 圣保罗 红树林 遥感","MODIS NDVI 红树林 温度","巴西圣保罗红树林遥感-4582","10.1007\u002Fs10661-026-16005-6",{"doi":288,"openalex_id":290,"authors":291,"venue":258,"cited_by_count":87,"oa_url":303,"card":304,"direction":93,"ingested_from":95},"W7220983448",[292,295,298,300],{"name":293,"orcid":294},"Jakeline Baratto","https:\u002F\u002Forcid.org\u002F0000-0003-0311-2369",{"name":296,"orcid":297},"Paulo Miguel De Bodas Terassi","https:\u002F\u002Forcid.org\u002F0000-0002-8184-0348",{"name":299,"orcid":9},"Nádia Gilma Beserra de Lima",{"name":301,"orcid":302},"Emerson Galvani","https:\u002F\u002Forcid.org\u002F0000-0002-8082-5963","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10661-026-16005-6.pdf",{"tldr":305,"method":306,"finding":307,"direction":93,"opportunity":308},"结合气温与植被指数分析巴西东南部红树林对温度变化的敏感性。","ERA5气温与MODIS NDVI\u002FEVI\u002FLAI，用Mann-Kendall趋","区域增温，但植被与温度关联随指标、时段和地点变化，非因果。","可引入多源遥感与因果推断，探究亚热带红树林对增温的滞后与非线性响应。","2026-10-10T23:30:27.692383Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":9,"source_name":316,"source_url":313,"published_at":317,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":259,"score_detail":318,"sources":320,"tags":322,"primary_topic":9,"secondary_topics":326,"classification":327,"search_phrases":343,"slug":346,"view_count":22,"doi":347,"paper":348,"created_at":371},4240,"Quantifying the Relationship Between Satellite-Derived Mangrove Canopy Cover and In Situ Air Temperature in Wonorejo Mangrove Ecotourism","https:\u002F\u002Fdoi.org\u002F10.48084\u002Fetasr.19268","Mangrove canopy contributes to local thermal regulation, but field-based evidence linking satellite-derived canopy cover to in situ air temperature remains limited in tropical urban coastal environments. This study quantified the relationship between Normalized Difference Vegetation Index (NDVI)-based canopy values and in situ air temperature at Wonorejo Mangrove Ecotourism by integrating Landsat 8\u002F9-based canopy mapping with field temperature observations. In situ air temperature was measured at 25 observation points across mangrove and non-mangrove settings from November 2024 to January 2025 using an environmental thermometer positioned 1.3 m above the ground. The mapped mangrove area covered 13.14 ha and was dominated by rare to moderate canopy cover, with 11.34 ha classified as rare and 1.80 ha as moderate. Point-based NDVI values ranged from -0.0051 to 0.42428, while mean in situ air temperature ranged from 31.4 °C to 39.4 °C. Lower air temperatures were generally recorded at mangrove points than at adjacent non-mangrove points. Simple linear regression showed a significant negative correlation between NDVI-based canopy values and average in situ air temperature ( = -0.648, = 0.420, \u003C 0.001), suggesting that higher NDVI values related to the canopy are linked with lower local air temperatures. These findings provide site-specific, seasonally bounded evidence of an inverse relationship between NDVI-derived canopy condition and near-surface air temperature in Wonorejo. The empirical trend may support local mangrove management and climate-adaptation planning, but broader application beyond the study site requires additional temporal monitoring and independent validation.","红树林冠层有助于局部热调节，但在热带城市海岸环境中，将卫星衍生的冠层覆盖度与原位气温相联系的实地证据仍然有限。本研究通过整合基于Landsat 8\u002F9的冠层制图与实地温度观测，量化了Wonorejo红树林生态旅游区基于归一化植被指数（NDVI）的冠层值与原地气温之间的关系。2024年11月至2025年1月期间，使用置于地面上方1.3米处的环境温度计，在红树林和非红树林环境中的25个观测点测量了原地气温。制图所得红树林面积为13.14公顷，以稀疏至中等冠层覆盖为主，其中11.34公顷被归类为稀疏，1.80公顷为中等。基于点位的NDVI值范围为-0.0051至0.42428，而原地平均气温范围为31.4 °C至39.4 °C。红树林点位的 recorded 气温通常低于相邻的非红树林点位。简单线性回归显示，基于NDVI的冠层值与原地平均气温之间存在显著的负相关（ = -0.648， = 0.420， \u003C 0.001），表明与冠层相关的较高NDVI值与较低的局部气温有关。这些发现提供了Wonorejo地区NDVI衍生的冠层状况与近地表气温之间反比关系的特定地点、季节性有限的证据。这一经验趋势可能支持当地红树林管理和气候适应规划，但超出研究地点的更广泛应用需要额外的时间监测和独立验证。","Engineering Technology & Applied Science Research","2026-10-02T00:00:00Z",{"impact":21,"substance":199,"depth":261,"authority":201,"freshness":113,"relevant":22,"comment":319},"以Landsat NDVI与实地气温观测量化红树林降温效应，方法清晰、数据具体，但属单点单季案例，应用范围有限。",[321],{"name":316,"url":313},[323,27,30,324,325],"气候适应","城市热环境","生态旅游",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":328},{"candidates":329,"agricultural_evidence":14,"reason":68},[330,335,339],{"code":57,"score":331,"supported":42,"title":332,"abstract":333,"tags":334},4,[],[323],[323],{"code":64,"score":22,"supported":14,"title":336,"abstract":337,"tags":338},[],[],[30],{"code":40,"score":22,"supported":14,"title":340,"abstract":341,"tags":342},[],[],[27],[344,345],"Wonorejo 红树林 NDVI 气温","红树林 冠层覆盖 遥感 气温","Wonorejo红树林NDVI气温-4240","10.48084\u002Fetasr.19268",{"doi":347,"openalex_id":349,"authors":350,"venue":316,"cited_by_count":87,"oa_url":365,"card":366,"direction":93,"ingested_from":95},"W7219808713",[351,353,356,359,362],{"name":352,"orcid":9},"Mohammad Agung Wahyudi Prasetyo",{"name":354,"orcid":355},"Muhammad Naufal Eka Putra","https:\u002F\u002Forcid.org\u002F0009-0001-8821-6070",{"name":357,"orcid":358},"Aulia Lanudia Fathah","https:\u002F\u002Forcid.org\u002F0009-0005-0443-5241",{"name":360,"orcid":361},"Berlania Mahardika Putri","https:\u002F\u002Forcid.org\u002F0009-0006-9898-3085",{"name":363,"orcid":364},"Andik Isdianto","https:\u002F\u002Forcid.org\u002F0000-0002-4808-7868","https:\u002F\u002Fwww.etasr.com\u002Findex.php\u002FETASR\u002Farticle\u002Fdownload\u002F19268\u002F7117",{"tldr":367,"method":368,"finding":369,"direction":93,"opportunity":370},"该研究量化了红树林卫星冠层覆盖与实地气温的关系，发现NDVI越高气温越低。","整合Landsat 8\u002F9冠层制图与25个点实地气温观测，用线性回归分析。","NDVI与实地气温显著负相关（r=-0.648），红树林点气温低于非红树林点。","可扩展至多时相、多区域验证，并耦合微气候模型探究红树林降温机制与规划应用。","2026-10-05T23:30:28.120699Z",{"id":373,"title":374,"url":375,"summary":376,"summary_zh":377,"content":9,"source_name":378,"source_url":375,"published_at":379,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":380,"score_detail":381,"sources":383,"tags":385,"primary_topic":9,"secondary_topics":389,"classification":390,"search_phrases":407,"slug":410,"view_count":218,"doi":411,"paper":412,"created_at":432},4173,"The effect of fire regimes on above-ground biomass change in Baikiaea-dominated woodlands of the Chobe region, northern Botswana, 1991 – 2023","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jenvman.2026.131030","Accurate assessment of fire-vegetation dynamics is crucial for accounting for changes in carbon fluxes. We use a fire frequency index (FFI) against data collected from Permanent Sample Plots (PSPs) in two Forest Reserves, in 1991 and 2023, to assess the effects of the recent fire history (1991-2023) on tree basal area (BA—as a reliable proxy for biomass) changes at PSPs in Chobe Botswana. We explore the efficacy of applying remotely sensed Vegetation Indices (VIs) to map predicted BA on the basis of 1991 assessment data. Our results show a significant increase in plot BA between 1991 ( Mean = 3.73 m 2 \u002Fha) and 2023 ( Mean = 5.63 m 2 \u002Fha) between 1991 and 2023. Fire frequency alone (dominated by severe fires), explained 27% of variation in ΔBA ( p \u003C 0.001); infrequently burnt plots accrued BA, frequently burnt plots lost BA. For mapping predicted BA at landscape scale, we found that 2023 plot BA was most strongly correlated with NDVI from a March 2023 image and plot Canopy Cover; no significant relationships were observed between assessed 1991 VIs and 1991 plot BA, probably as a result of the quality of the available Landsat archive imagery. Our results suggest management of fire alone is key to maintaining and enhancing woody biomass and achieving woodland degradation mitigation and climate initiatives in regional Baikiaea miombo woodlands. The influence of fire on change in woodland structure supports the development of incentivised savanna burning approaches, including carbon sequestration methodologies, for sustainable management of woodland savannas under semi-arid rainfall conditions (ca. 600 mm MAR) in northern Botswana and regionally.","准确评估火-植被动态对于核算碳通量变化至关重要。我们利用火频指数（FFI），结合1991年和2023年在两个森林保护区永久样地（PSPs）采集的数据，评估近期火历史（1991-2023年）对博茨瓦纳乔贝地区永久样地树木断面积（BA——作为生物量的可靠代用指标）变化的影响。我们探讨了应用遥感植被指数（VIs）基于1991年评估数据绘制预测BA图的可行性。结果表明，1991年（均值 = 3.73 m²\u002Fha）至2023年（均值 = 5.63 m²\u002Fha）间样地BA显著增加。火频单独（以严重火灾为主）解释了ΔBA变异的27%（p \u003C 0.001）；低频火烧样地BA增加，高频火烧样地BA减少。在景观尺度预测BA制图方面，我们发现2023年样地BA与2023年3月影像的NDVI及样地冠层盖度相关性最强；1991年评估VIs与1991年样地BA之间未观察到显著关系，可能是由于可用Landsat存档影像的质量所致。我们的结果表明，仅火管理就是维持和增强木本生物量、实现林地退化缓解和气候倡议的关键，适用于区域Baikiaea miombo林地。火对林地结构变化的影响支持发展激励性稀树草原燃烧方法，包括碳封存方法，以在博茨瓦纳北部及区域半干旱降雨条件（约600 mm年平均降雨量）下实现林地稀树草原的可持续管理。","Journal of Environmental Management","2026-10-01T00:00:00Z",75,{"impact":112,"substance":18,"depth":17,"authority":20,"freshness":113,"relevant":22,"comment":382},"基于31年永久样地与遥感指数的火烧制度对稀树草原生物量影响研究，方法扎实、结论对碳汇与火烧管理有参考价值，但属区域案例，公共影响有限。",[384],{"name":378,"url":375},[386,27,28,387,388],"NDVI","草原火管理","植被生物量",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":391},{"candidates":392,"agricultural_evidence":14,"reason":68},[393,398,402],{"code":40,"score":394,"supported":42,"title":395,"abstract":396,"tags":397},5,[],[46],[386,27],{"code":49,"score":331,"supported":42,"title":399,"abstract":400,"tags":401},[],[54],[28],{"code":57,"score":58,"supported":42,"title":403,"abstract":404,"tags":406},[],[405],"干旱",[],[408,409],"Chobe 稀树草原 火烧频率 生物量","Baikiaea 林地 NDVI 生物量估算","Chobe稀树草原火烧频率生物量-4173","10.1016\u002Fj.jenvman.2026.131030",{"doi":411,"openalex_id":413,"authors":414,"venue":378,"cited_by_count":87,"oa_url":426,"card":427,"direction":93,"ingested_from":95},"W7215094148",[415,418,421,424],{"name":416,"orcid":417},"Othusitse Lekoko","https:\u002F\u002Forcid.org\u002F0000-0002-9269-7006",{"name":419,"orcid":420},"Jeremy Russell‐Smith","https:\u002F\u002Forcid.org\u002F0000-0003-1489-2866",{"name":422,"orcid":423},"Andrew Edwards","https:\u002F\u002Forcid.org\u002F0000-0003-2823-4565",{"name":425,"orcid":9},"Cameron Yates","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0301479726024904\u002Fpdf",{"tldr":428,"method":429,"finding":430,"direction":186,"opportunity":431},"研究博茨瓦纳乔贝地区1991-2023年火 regime 对地上生物量变化的影响。","火频指数、永久样地调查数据与遥感植被指数（NDVI）结合分析。","火频解释27%生物量变化，频繁火烧样地生物量下降，稀火样地增加。","可探索将火管理纳入碳汇核算，发展稀树草原燃烧碳补偿方法学。","2026-10-04T23:30:27.108019Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":9,"source_name":439,"source_url":436,"published_at":440,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":441,"score_detail":442,"sources":445,"tags":447,"primary_topic":9,"secondary_topics":451,"classification":452,"search_phrases":482,"slug":485,"view_count":58,"doi":486,"paper":487,"created_at":508},3479,"Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data","https:\u002F\u002Fdoi.org\u002F10.68012\u002Fair.v1i2.252","Mangrove ecosystems play a critical role in coastal protection, carbon sequestration, biodiversity conservation, and climate change mitigation; however, increasing anthropogenic pressures and environmental changes have accelerated mangrove degradation, creating an urgent need for efficient and scalable monitoring approaches. This study aims to develop an Artificial Intelligence-Enabled framework for monitoring mangrove ecosystem conditions by integrating remote sensing imagery with environmental datasets to improve the accuracy and timeliness of ecosystem assessment in tropical coastal regions. The proposed method combines multispectral satellite images, including vegetation indices derived from remote sensing data, with environmental variables such as temperature, precipitation, salinity, and tidal information, which are subsequently processed using a deep learning-based classification model to identify and categorize mangrove health conditions. Experimental evaluation demonstrates that the integration of remote sensing and environmental data significantly enhances model performance compared with approaches relying solely on satellite imagery, achieving high classification accuracy and improving the detection of early signs of ecosystem degradation. The findings further reveal that environmental parameters contribute substantially to distinguishing healthy, moderately degraded, and severely degraded mangrove areas across heterogeneous coastal environments. Consequently, the proposed framework provides an intelligent and reliable decision support tool for environmental monitoring agencies and policymakers while contributing to the development of resilient coastal ecosystem management and sustainable environmental governance in tropical archipelagic regions.","红树林生态系统在海岸防护、碳固存、生物多样性保护及气候变化减缓中发挥着关键作用；然而，日益加剧的人为压力和环境变化加速了红树林退化，亟需高效且可扩展的监测方法。本研究旨在开发一个人工智能驱动的框架，通过整合遥感影像与环境数据集来监测红树林生态系统状况，以提高热带沿海地区生态系统评估的准确性和时效性。所提出的方法将多光谱卫星影像（包括由遥感数据衍生的植被指数）与温度、降水、盐度和潮汐信息等环境变量相结合，随后使用基于深度学习的分类模型进行处理，以识别和分类红树林健康状况。实验评估表明，与仅依赖卫星影像的方法相比，遥感与环境数据的整合显著提升了模型性能，实现了较高的分类精度，并改善了对生态系统退化早期迹象的检测。研究结果进一步揭示，环境参数对于区分异质性沿海环境中健康、中度退化和严重退化的红树林区域具有重要贡献。因此，所提出的框架为环境监测机构和政策制定者提供了一种智能且可靠的决策支持工具，同时有助于热带群岛地区韧性沿海生态系统管理和可持续环境治理的发展。","AI Innovation and Resilience for the Environment (AIR)","2026-09-22T00:00:00Z",76,{"impact":17,"substance":443,"depth":200,"authority":112,"freshness":21,"relevant":22,"comment":444},21,"将遥感与环境数据融合的深度学习框架用于红树林健康监测，方法有创新且结论可靠，对沿海生态治理有参考价值，但属细分领域研究，公共影响有限。",[446],{"name":439,"url":436},[448,449,27,450,30],"农业人工智能","深度学习","生态监测",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":453},{"candidates":454,"agricultural_evidence":14,"reason":68},[455,462,467,473,478],{"code":223,"score":19,"supported":42,"title":456,"abstract":458,"tags":461},[457],"artificial intelligence",[449,459,457,460],"deep learning","decision support",[448,449],{"code":40,"score":17,"supported":42,"title":463,"abstract":464,"tags":466},[44],[46,44,465],"multispectral",[27],{"code":64,"score":21,"supported":42,"title":468,"abstract":469,"tags":472},[],[470,471],"生物多样性","biodiversity",[450,30],{"code":57,"score":98,"supported":42,"title":474,"abstract":475,"tags":477},[],[61,476],"climate change",[],{"code":49,"score":58,"supported":42,"title":479,"abstract":480,"tags":481},[],[54],[],[483,484],"红树林 遥感 人工智能","红树林生态系统 监测","红树林遥感人工智能-3479","10.68012\u002Fair.v1i2.252",{"doi":486,"openalex_id":488,"authors":489,"venue":439,"cited_by_count":87,"oa_url":501,"card":502,"direction":507,"ingested_from":95},"W7213895268",[490,493,496,498],{"name":491,"orcid":492},"Dirvi Surya Abbas","https:\u002F\u002Forcid.org\u002F0000-0002-7819-2837",{"name":494,"orcid":495},"Asep Sutarman","https:\u002F\u002Forcid.org\u002F0009-0002-1029-7963",{"name":497,"orcid":9},"Ryan Davis",{"name":499,"orcid":500},"Maulana Abbas","https:\u002F\u002Forcid.org\u002F0009-0009-0137-9650","https:\u002F\u002Fjournal.sundarapublishing.com\u002Findex.php\u002Fair\u002Farticle\u002Fdownload\u002F252\u002F142",{"tldr":503,"method":504,"finding":505,"direction":93,"opportunity":506},"融合遥感影像与环境数据，用深度学习构建红树林生态系统健康监测框架。","多光谱卫星影像与植被指数，结合温度、降水、盐度、潮汐等环境变量，训练深度学习分类","融合环境数据显著提升分类精度，能更早识别红树林退化迹象，有效区分不同退化程度。","可探索多源时序遥感与环境数据融合的早期退化预警，并迁移至其他滨海湿地生态系统监测。","智慧农业 \u002F 农业物联网","2026-09-25T23:30:14.373467Z"]