[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5171":3,"related-5171":88},{"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":50,"slug":53,"view_count":54,"doi":55,"paper":56,"created_at":87},5171,"Fuel load and potential fire behavior vary seasonally and across grassland plant communities","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs42408-026-00593-2","Abstract Grassland fuels can be patchy across space and time, especially on landscapes where multiple grassland plant communities co-occur. However, the standard fuel models inadequately capture the spatiotemporal variability in grassland fuels for local, strategic planning of fuel treatments. We aimed to better understand the seasonal variation in grassland fuels and fire behavior across grassland types. Here, we developed remote sensing-based grassland fuel maps using multispectral satellite data (10-m resolution) from Sentinel-2, compared seasonal fire behavior across grassland types, and performed a sensitivity analysis of fire behavior models to changes in litter, dead 1 h, and live herbaceous loads, and fuelbed depth estimates. We were able to best predict live herbaceous load (R2 = 0.85), followed by fuelbed depth (R2 = 0.81), dead 1 h fuel load (R2 = 0.76), and litter load (R2 = 0.67) based on remotely sensed predictors. Our spatially predicted grassland fuel maps showed seasonal variability in fuel loads in different places, reflecting the varying timing of curing by grassland types. In particular, we observed more accumulation of dead 1 h load between August and October in early senescing mixedgrass prairies than later senescing tallgrass prairies. We found an overall faster modeled rate of spread in smooth brome-dominated, invasive grasslands and native tallgrass prairies compared to mixedgrass and prairie dog-dominated grasslands. From the sensitivity analysis, we found that modeled fire behavior model is most sensitive to dead 1 h fuel load and least sensitive to litter load in this grassland system. At a time when grassland fires are increasing and resources for fuel management are limited, remote sensing approach to grassland fuel modeling may help land managers make decisions about where and when to prioritize fuel treatments to mitigate the risk of catastrophic wildfires.","摘要 草地可燃物在空间和时间上往往呈斑块状分布，在多种草地植物群落共存的景观中尤为如此。然而，标准可燃物模型难以充分刻画草地可燃物的时空变异性，无法满足局部可燃物处理策略性规划的需求。本研究旨在更好地理解不同草地类型间可燃物和火行为的季节性变化。为此，我们利用Sentinel-2多光谱卫星数据（10 m分辨率）构建了基于遥感的草地可燃物图，比较了不同草地类型间的季节性火行为，并对火行为模型进行了敏感性分析，考察凋落物、1小时时滞死可燃物、活草本可燃物载量及可燃物床深度估算值变化的影响。基于遥感预测因子，我们对活草本可燃物载量的预测效果最佳（R² = 0.85），其次为可燃物床深度（R² = 0.81）、1小时时滞死可燃物载量（R² = 0.76）和凋落物载量（R² = 0.67）。空间预测的草地可燃物图显示，不同地点的可燃物载量存在季节性变异，反映了不同草地类型枯萎时序的差异。特别是，我们观察到早枯的混合草草原在8月至10月间1小时时滞死可燃物载量的累积多于晚枯的高草草原。我们发现，与混合草草原和草原犬鼠主导的草地相比，无芒雀麦主导的入侵草地和原生高草草原的模拟蔓延速率总体更快。敏感性分析表明，在该草地系统中，火行为模型对1小时时滞死可燃物载量最为敏感，对凋落物载量最不敏感。在草地火灾日益增多且可燃物管理资源有限的背景下，基于遥感的草地可燃物建模方法可帮助土地管理者决策在何处、何时优先开展可燃物处理，以降低灾难性野火风险。",null,"Fire Ecology","2026-10-15T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,21,17,13,9,1,"基于 Sentinel-2 的草地可燃物遥感制图与火行为敏感性分析，方法新颖、结论可靠，对草原火险管理有实用价值，但属细分领域研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","卫星遥感","草原火险","可燃物模型","草地管理",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":49},[39],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":48},"I3",11,true,[],[45,46,47],"遥感","remote sensing","multispectral",[28,27],"农业相关性需要复核",[51,52],"Sentinel-2 草地可燃物","草原火行为 遥感制图","Sentinel-2草地可燃物-5171",0,"10.1186\u002Fs42408-026-00593-2",{"doi":55,"openalex_id":57,"authors":58,"venue":10,"cited_by_count":54,"oa_url":79,"card":80,"direction":84,"ingested_from":86},"W7221860401",[59,62,65,68,71,73,76],{"name":60,"orcid":61},"Lina Aoyama","https:\u002F\u002Forcid.org\u002F0000-0001-9677-7268",{"name":63,"orcid":64},"Katharine N. Suding","https:\u002F\u002Forcid.org\u002F0000-0002-5357-0176",{"name":66,"orcid":67},"Advyth Ramachandran","https:\u002F\u002Forcid.org\u002F0009-0005-1440-2726",{"name":69,"orcid":70},"Jordan T. Lee","https:\u002F\u002Forcid.org\u002F0000-0002-3188-9792",{"name":72,"orcid":9},"Paul Dennison",{"name":74,"orcid":75},"Thomas K. Merchant","https:\u002F\u002Forcid.org\u002F0000-0002-6905-0255",{"name":77,"orcid":78},"Jonathan J. Henn","https:\u002F\u002Forcid.org\u002F0000-0003-1551-9238","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs42408-026-00593-2.pdf",{"tldr":81,"method":82,"finding":83,"direction":84,"opportunity":85},"用Sentinel-2遥感构建草地可燃物载量季节图谱并模拟火行为差异。","Sentinel-2多光谱数据回归预测可燃物载量，结合火行为模型敏感性分析。","早枯混草草原死1小时载量积累更多，入侵与高草草原蔓延速率更快。","农业遥感与作物表型","可将遥感可燃物制图与火险预警、放牧管理结合，拓展到多草地类型时空动态监测。","openalex","2026-10-17T23:30:28.706068Z",{"total":89,"page":22,"page_size":89,"items":90},6,[91,170,223,294,377,459],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":106,"tags":108,"primary_topic":112,"secondary_topics":117,"classification":118,"search_phrases":132,"slug":135,"view_count":54,"doi":136,"paper":137,"created_at":169},5318,"Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10668-026-08214-3","Lagged Effects and Mechanisms of Central Environmental Inspection Policies on Agricultural Straw Burning: Evidence from Remote Sensing Data in China。Environment Development and Sustainability","中央环保督察政策对农业秸秆焚烧的滞后效应与作用机制：来自中国遥感数据的证据。《环境、发展与可持续性》","Environment Development and Sustainability","2026-10-19T00:00:00Z",84,{"impact":101,"substance":102,"depth":103,"authority":104,"freshness":13,"relevant":22,"comment":105},22,20,18,14,"基于全国遥感数据评估中央环保督察对秸秆焚烧的滞后效应，数据规模大、方法新颖，对农业环境政策有参考价值，值得进入每日精选。",[107],{"name":97,"url":94},[109,27,28,110,111],"农业政策","秸秆焚烧","环保督察",{"code":40,"slug":113,"name":114,"path":115,"parent_code":116},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":119,"locked":14,"version":35,"method":36,"evidence":120},"classified",{"candidates":121,"agricultural_evidence":42},[122,127],{"code":40,"score":123,"supported":42,"title":124,"abstract":125,"tags":126},16,[46],[45,46],[28,27],{"code":128,"score":22,"supported":14,"title":129,"abstract":130,"tags":131},"H5",[],[],[110],[133,134],"中央环保督察 秸秆焚烧","遥感数据 秸秆焚烧","中央环保督察秸秆焚烧-5318","10.1007\u002Fs10668-026-08214-3",{"doi":136,"openalex_id":138,"authors":139,"venue":97,"cited_by_count":54,"oa_url":9,"card":163,"direction":84,"ingested_from":86},"W7222877438",[140,142,144,146,148,150,152,155,157,159,161],{"name":141,"orcid":9},"Lu Dong",{"name":143,"orcid":9},"Linru Zhang",{"name":145,"orcid":9},"Na Liu",{"name":147,"orcid":9},"Xin Long",{"name":149,"orcid":9},"Ziyi Wang",{"name":151,"orcid":9},"Siyue Yang",{"name":153,"orcid":154},"Jie Tian","https:\u002F\u002Forcid.org\u002F0000-0001-6585-3643",{"name":156,"orcid":9},"Qiyuan Wang",{"name":158,"orcid":9},"Zhanfeng Dong",{"name":160,"orcid":9},"Long Wu",{"name":162,"orcid":9},"Yichen Wang",{"tldr":164,"method":165,"finding":166,"direction":167,"opportunity":168},"利用遥感数据评估中央环保督察政策对秸秆焚烧的滞后效应与作用机制。","基于中国遥感火点数据构建面板，采用滞后效应模型识别政策影响机制。","中央环保督察对秸秆焚烧具有显著但滞后的抑制效应，并存在区域异质性。","农业绿色发展与碳","可结合地块级遥感与农户调查，量化督察滞后期内秸秆离田利用与替代处置的路径。","2026-10-19T23:30:22.693590Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":9,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":182,"tags":184,"primary_topic":9,"secondary_topics":188,"classification":189,"search_phrases":201,"slug":204,"view_count":205,"doi":206,"paper":207,"created_at":222},2800,"Spatial Persistence and Interannual Variability of Floating Algae in Kainji Lake (Nigeria): Insights from Multi-Year Sentinel-2 Observations","https:\u002F\u002Fdoi.org\u002F10.62622\u002Fteiee.026.4.3.55-64","Background: Floating algae are increasingly recognized as indicators of ecological change in freshwater ecosystems. Although satellite remote sensing has been widely applied to monitor algal dynamics, little is known about the long-term spatial organization of floating algae in large tropical reservoirs in West Africa. Objectives: This study investigated the spatio-temporal dynamics of floating algae in Kainji Lake, Nigeria, from 2020 to 2025, with the aim of identifying recurrent hotspot zones, assessing interannual variability, and evaluating the long-term stability of floating algae distribution. Methods: Sentinel-2 Level-2A Surface Reflectance imagery was processed within the Google Earth Engine platform. Images with less than 10% cloud cover were selected and subjected to cloud masking and water extraction procedures. Floating algae were mapped using the Floating Algae Index (FAI), calculated from the red, near-infrared, and short-wave infrared spectral bands. Annual median FAI composites were generated for each year from 2020 to 2025. Descriptive statistics of positive FAI values were calculated to assess temporal variability. Multi-year persistence analysis was then performed to identify recurrent floating algae hotspot zones. Results: The results revealed pronounced spatial heterogeneity in floating algae occurrence across Kainji Lake. Floating algae were consistently concentrated within shoreline and embayment environments, whereas most open-water areas exhibited comparatively low occurrence. Mean annual positive FAI values ranged from 0.0656 to 0.0841, indicating interannual variability in floating algae intensity. Annual bloom extent varied from 139.93 to 168.64 km², representing 12 –15% of the lake surface, with the largest coverage recorded in 2022 and the smallest in 2025. Despite these temporal fluctuations, recurrent hotspot zones remained spatially consistent throughout the study period. Conclusion: Floating algae in Kainji Lake exhibit persistent spatial organization rather than random distribution. This study provides the first multi-year evidence of recurrent floating algae hotspot zones in the reservoir, establishing an important baseline for understanding floating algae dynamics, thereby addressing a significant knowledge gap in West African reservoir ecosystems and providing a foundation for future ecological assessments and monitoring programmes.","背景：浮游藻类日益被视为淡水生态系统生态变化的指示物。尽管卫星遥感已被广泛应用于监测藻类动态，但关于西非大型热带水库中浮游藻类长期空间格局的认识仍然有限。目标：本研究调查了2020年至2025年尼日利亚凯恩吉湖浮游藻类的时空动态，旨在识别反复出现的热点区域、评估年际变异性，并评价浮游藻类分布的长期稳定性。方法：在Google Earth Engine平台上处理Sentinel-2 Level-2A地表反射率影像。选取云量低于10%的影像，并进行去云和水体提取处理。利用浮游藻类指数（Floating Algae Index, FAI）对浮游藻类进行制图，该指数由红光、近红外和短波红外光谱波段计算得出。生成2020年至2025年各年的FAI年中值合成影像。计算FAI正值的描述性统计量以评估时间变异性。随后进行多年持续性分析，以识别反复出现的浮游藻类热点区域。结果：结果揭示了凯恩吉湖浮游藻类 occurrence 的显著空间异质性。浮游藻类持续集中于湖岸和湖湾环境，而大部分开阔水域的出现相对较低。年均FAI正值范围为0.0656至0.0841，表明浮游藻类强度存在年际变异性。年度藻华范围在139.93至168.64 km²之间变化，占湖泊表面积的12%–15%，其中2022年记录到最大覆盖面积，2025年最小。尽管存在这些时间波动，反复出现的热点区域在整个研究期间保持空间一致性。结论：凯恩吉湖的浮游藻类表现出持续的空间组织格局，而非随机分布。本研究首次提供了该水库浮游藻类热点区域反复出现的多年证据，为理解浮游藻类动态建立了重要基线，从而填补了西非水库生态系统中的重大知识空白，并为未来的生态评估和监测计划奠定了基础。","Trends in Ecological and Indoor Environmental Engineering","2026-09-15T00:00:00Z",66,{"impact":180,"substance":102,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":181},8,"基于Sentinel-2多年观测揭示尼日利亚Kainji湖浮藻热点空间稳定性，方法规范、数据扎实，但属区域生态研究，与国内三农信息化关联间接，可作遥感应用参考而非每日必读。",[183],{"name":176,"url":173},[185,27,186,28,187],"非洲农业","水质监测","湖泊生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":190},{"candidates":191,"agricultural_evidence":14,"reason":49},[192,196],{"code":40,"score":180,"supported":42,"title":193,"abstract":194,"tags":195},[],[45,46],[28,27],{"code":197,"score":22,"supported":14,"title":198,"abstract":199,"tags":200},"J3",[],[],[185],[202,203],"卫星遥感 水质监测 湖泊生态 遥感监测","卫星遥感 水质监测","卫星遥感水质监测湖泊生态遥感监测-2800",3,"10.62622\u002Fteiee.026.4.3.55-64",{"doi":206,"openalex_id":208,"authors":209,"venue":176,"cited_by_count":54,"oa_url":216,"card":217,"direction":84,"ingested_from":86},"W7213293788",[210,213],{"name":211,"orcid":212},"Osasere Abike Omoruyi","https:\u002F\u002Forcid.org\u002F0000-0003-4505-9551",{"name":214,"orcid":215},"Ruby Asunomeh","https:\u002F\u002Forcid.org\u002F0009-0009-7529-254X","https:\u002F\u002Fwww.teiee.net\u002Fpdf-226150-145194?filename=Spatial-Persistence-and-I.pdf",{"tldr":218,"method":219,"finding":220,"direction":84,"opportunity":221},"利用2020-2025年Sentinel-2影像分析尼日利亚凯恩吉湖浮藻时空动态与热点区。","Google Earth Engine处理Sentinel-2 L2A影像，用F","浮藻集中于湖岸与湾口，年际面积波动但热点区位置持续稳定。","可结合水质、气象与人类活动数据，探究热带水库浮藻热点持续性的驱动机制与预警模型。","2026-09-17T23:30:38.937817Z",{"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":54,"score_detail":231,"sources":233,"tags":235,"primary_topic":9,"secondary_topics":238,"classification":239,"search_phrases":258,"slug":261,"view_count":262,"doi":263,"paper":264,"created_at":293},2788,"Integrated satellite monitoring and field validation of the periodically outbursting glacier-dammed lake Nedre Demmevatnet, Norway","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-5025","Abstract. Glacial Lake Outburst Floods (GLOFs) present a significant hazard in warming alpine environments, but detailed, sub-seasonal studies of ice-dammed glacier lakes remain rare due to data scarcity. To overcome this challenge, we reconstruct nearly a decade (2016–2025) of drainage timings, outburst volumes, lake levels, and automated, machine learning-based sub-seasonal lake refilling cycles at the ice-dammed lake Nedre Demmevatnet (southwestern Norway), dammed by the glacier Rembesdalskåka, an outlet glacier of the Hardangerjøkulen ice cap. By integrating publicly available satellite and meteorological datasets, we evaluate remote sensing capabilities and establish an error budget for tracking a small and highly dynamic water body. We validate our spaceborne findings using field measurements, including water-level loggers, time-lapse cameras, a local automatic weather station, and high-resolution UAV photogrammetry, complemented by PlanetScope imagery. Combining Sentinel-1 and Sentinel-2 imagery, we constrain GLOF drainage windows to ± 2 days. Lake volume sensitivity tests revealed that while satellite outline errors are minor (2.6–4.2 %), using the static regional digital elevation model ArcticDEM (2014) causes a 25.5 % volume underestimation compared to our 2022 UAV bathymetry due to rapid ice-dam retreat and lakebed erosion. Crucially, the time gap between the last cloud-free satellite image and the GLOF introduces a relative daily volume underestimation of 1.7 %, which scales up significantly during cloudy periods. Our validated multi-sensor remote sensing approach enables valuable glaciological insights, revealing that GLOF timings shifted earlier by an average of 10 days, alongside shortened refilling periods over the past 9 years. Between 2016 and 2022, pre-GLOF lake levels reached 1236–1239 m a.s.l., closely matching the theoretical hydrostatic flotation threshold. In contrast, the 2023 event drained at approximately 1224 m a.s.l. – more than 10 m below this threshold – following a shortened melt period indicated by fewer positive degree days, possibly due to opening of subglacial channels through melt. Finally, we synthesize the workflow developed and tested on our case study into an operational framework that facilitates transferability to other rapidly changing ice-dammed lakes.","摘要：冰川湖溃决洪水（GLOFs）在变暖的高山环境中构成重大危害，但由于数据稀缺，针对冰坝冰川湖的详细次季节研究仍然很少。为克服这一挑战，我们重建了冰坝湖Nedre Demmevatnet（挪威西南部）近十年（2016—2025年）的排水时间、溃决水量、湖泊水位以及基于机器学习的自动化次季节湖泊再蓄水周期。该湖由Hardangerjøkulen冰帽的溢出冰川Rembesdalskåka所阻塞。通过整合公开可用的卫星和气象数据集，我们评估了遥感能力，并建立了追踪一个小型且高度动态水体的误差预算。我们利用实地测量验证了星载观测结果，包括水位记录仪、延时相机、当地自动气象站和高分辨率无人机摄影测量，并辅以PlanetScope影像。结合Sentinel-1和Sentinel-2影像，我们将GLOF排水窗口限定在±2天以内。湖泊体积敏感性测试表明，虽然卫星轮廓误差较小（2.6—4.2%），但由于冰坝快速退缩和湖床侵蚀，使用静态区域数字高程模型ArcticDEM（2014年）会导致体积较我们的2022年无人机测深结果低估25.5%。至关重要的是，最后一幅无云卫星影像与GLOF之间的时间间隔会导致相对日体积低估1.7%，在多云时段这一误差会显著放大。我们经过验证的多传感器遥感方法带来了有价值的冰川学认识，揭示了过去9年中GLOF发生时间平均提前了10天，同时再蓄水期缩短。2016年至2022年间，GLOF前湖泊水位达到海拔1236—1239 m，与理论静水浮力阈值密切吻合。相比之下，2023年事件在海拔约1224 m处排水——低于该阈值超过10 m——此前融化期缩短，表现为正积温日数减少，可能是由于融化导致冰下通道打开。最后，我们将本案例研究中开发和测试的工作流程综合为一个操作框架，以促进其向其他快速变化的冰坝湖推广应用。","OpenAlex","2026-09-16T00:00:00Z",{"impact":54,"substance":54,"depth":54,"authority":54,"freshness":54,"relevant":54,"comment":232},"冰川湖溃决遥感监测研究，属冰川水文与灾害领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[234],{"name":229,"url":226},[27,28,236,237],"灾害预警","冰川湖",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":240},{"candidates":241,"agricultural_evidence":14,"reason":49},[242,246,253],{"code":40,"score":180,"supported":42,"title":243,"abstract":244,"tags":245},[],[45,46],[28,27],{"code":247,"score":89,"supported":42,"title":248,"abstract":249,"tags":252},"I2",[],[250,251],"机器学习","machine learning",[],{"code":254,"score":22,"supported":14,"title":255,"abstract":256,"tags":257},"H7",[],[],[236],[259,260],"卫星遥感 灾害预警 遥感监测 冰川湖","卫星遥感 灾害预警","卫星遥感灾害预警遥感监测冰川湖-2788",2,"10.5194\u002Fegusphere-2026-5025",{"doi":263,"openalex_id":265,"authors":266,"venue":9,"cited_by_count":54,"oa_url":287,"card":288,"direction":84,"ingested_from":86},"W7213228080",[267,269,272,275,278,281,284],{"name":268,"orcid":9},"Ronja Lappe",{"name":270,"orcid":271},"Ursula Enzenhofer","https:\u002F\u002Forcid.org\u002F0009-0005-4802-1243",{"name":273,"orcid":274},"Pascal E. Egli","https:\u002F\u002Forcid.org\u002F0000-0003-2549-8362",{"name":276,"orcid":277},"Yongmei Gong","https:\u002F\u002Forcid.org\u002F0000-0002-3839-5824",{"name":279,"orcid":280},"Liss M. Andreassen","https:\u002F\u002Forcid.org\u002F0000-0001-6494-4252",{"name":282,"orcid":283},"Andreas Kääb","https:\u002F\u002Forcid.org\u002F0000-0002-6017-6564",{"name":285,"orcid":286},"Martina Calovi","https:\u002F\u002Forcid.org\u002F0000-0002-2317-1190","https:\u002F\u002Fegusphere.copernicus.org\u002Fpreprints\u002F2026\u002Fegusphere-2026-5025\u002Fegusphere-2026-5025.pdf",{"tldr":289,"method":290,"finding":291,"direction":84,"opportunity":292},"结合卫星与实地观测重建挪威冰坝湖近十年溃决周期与水量，并建立可迁移监测框架。","Sentinel-1\u002F2、PlanetScope、UAV摄影测量、水位记录仪与气","溃决时间提前约10天，2023年溃决水位低于浮力阈值10米以上。","可将该多源遥感与误差预算框架迁移至其他冰坝湖，并探索云覆盖下水量估算的改进方法。","2026-09-17T23:30:34.612397Z",{"id":295,"title":296,"url":297,"summary":298,"summary_zh":299,"content":9,"source_name":300,"source_url":297,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":302,"score_detail":303,"sources":305,"tags":307,"primary_topic":9,"secondary_topics":312,"classification":313,"search_phrases":347,"slug":350,"view_count":54,"doi":351,"paper":352,"created_at":376},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":103,"substance":101,"depth":19,"authority":17,"freshness":21,"relevant":22,"comment":304},"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。",[306],{"name":300,"url":297},[27,308,309,310,311],"土地利用","森林保护","亚马逊","CBERS-4",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":314},{"candidates":315,"agricultural_evidence":42,"reason":346},[316,324,328,334,340],{"code":317,"score":318,"supported":42,"title":319,"abstract":320,"tags":323},"H6",7,[],[321,322],"生物多样性","biodiversity",[308],{"code":40,"score":318,"supported":42,"title":325,"abstract":326,"tags":327},[],[45,46],[27],{"code":254,"score":89,"supported":42,"title":329,"abstract":330,"tags":333},[],[331,332],"气候变化","climate change",[],{"code":335,"score":205,"supported":42,"title":336,"abstract":337,"tags":339},"H2",[],[338],"soil",[],{"code":341,"score":205,"supported":42,"title":342,"abstract":343,"tags":345},"I7",[],[344],"研究方法",[],"多个产业证据相当，需要确认主归属",[348,349],"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":351,"openalex_id":353,"authors":354,"venue":300,"cited_by_count":54,"oa_url":297,"card":371,"direction":84,"ingested_from":86},"W7223141286",[355,358,360,363,366,369],{"name":356,"orcid":357},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":359,"orcid":9},"Gabriel Henrique Santos Silva",{"name":361,"orcid":362},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":364,"orcid":365},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":367,"orcid":368},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":370,"orcid":9},"Sylvana Melo dos Santos",{"tldr":372,"method":373,"finding":374,"direction":84,"opportunity":375},"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":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":383,"source_url":380,"published_at":301,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":385,"sources":387,"tags":389,"primary_topic":394,"secondary_topics":398,"classification":399,"search_phrases":421,"slug":424,"view_count":54,"doi":425,"paper":426,"created_at":458},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":123,"substance":101,"depth":103,"authority":104,"freshness":21,"relevant":22,"comment":386},"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。",[388],{"name":383,"url":380},[27,390,391,392,393],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":335,"slug":338,"name":395,"path":396,"parent_code":397},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":119,"locked":14,"version":35,"method":36,"evidence":400},{"candidates":401,"agricultural_evidence":42},[402,407,411,417],{"code":335,"score":104,"supported":42,"title":403,"abstract":404,"tags":406},[338],[405,338],"土壤",[],{"code":40,"score":318,"supported":42,"title":408,"abstract":409,"tags":410},[],[45,46],[27],{"code":254,"score":89,"supported":42,"title":412,"abstract":413,"tags":416},[],[414,415],"气候适应","干旱",[],{"code":317,"score":22,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[392],[422,423],"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":425,"openalex_id":427,"authors":428,"venue":383,"cited_by_count":54,"oa_url":380,"card":453,"direction":84,"ingested_from":86},"W7223069171",[429,431,433,435,438,440,443,446,448,450],{"name":430,"orcid":9},"Yue Li",{"name":432,"orcid":9},"Mengfei Shi",{"name":434,"orcid":9},"Fei Zhang",{"name":436,"orcid":437},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":439,"orcid":9},"Gowhar Meraj",{"name":441,"orcid":442},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":444,"orcid":445},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":447,"orcid":9},"Yuanfang Chai",{"name":449,"orcid":9},"Hongtao Xu",{"name":451,"orcid":452},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":454,"method":455,"finding":456,"direction":84,"opportunity":457},"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",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":465,"source_url":462,"published_at":466,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":384,"score_detail":467,"sources":469,"tags":471,"primary_topic":476,"secondary_topics":482,"classification":483,"search_phrases":500,"slug":503,"view_count":54,"doi":504,"paper":505,"created_at":516},5565,"A Unified Survival-Based Energy Framework Linking Plant Color, Stress, and Productivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23540961","Plant coloration, expressed through leaf greenness, floral pigmentation, and fruit color intensity, reflects the integrated outcome of pigment biosynthesis, optical absorption, metabolic regulation, and energy–mass exchange with the environment. While the biochemical pathways governing the synthesis and degradation of major pigment classes—including chlorophylls, carotenoids, and anthocyanins—are well established, existing models largely interpret color expression as a localized biochemical phenomenon. Such approaches do not explicitly account for whole-plant energy balance, transpiration-driven losses, or thermodynamic constraints, limiting their ability to explain widely observed patterns of color saturation, stress-induced fading, and species-specific stability across environmental gradients. Here we develop a unified framework for plant color generation and stability that treats pigmentation as a survival-limited energetic process embedded within the broader dynamics of absorption, transport, metabolism, and entropy production. Central to the framework is a dimensionless energy survival factor, Ψ=AE\u002FTE+ε where AE represents absorbed and retained energetic and elemental resources relevant to pigment formation, TE denotes transpiration-linked, metabolic, and environmental dissipation losses, and ε captures irreducible entropy-producing degradation inherent to non-equilibrium biological systems. By construction, 0\u003CΨ\u003C1, ensuring full compliance with the second law of thermodynamics. When combined with pigment-specific optical absorption coefficients and physiological limitation functions, the framework yields a physically interpretable description of both pigment concentration and color stability. Empirical ranges for absorbed photosynthetically active radiation (≈45–70%), excitation survival (≈85–95%), and respiratory and transpiration losses (≈30–60% of assimilated resources) naturally constrain Ψ, predicting observed limits on sustained pigment accumulation without invoking biochemical inefficiency or ad hoc correction factors. The analysis demonstrates that increased light availability or enhanced biosynthetic capacity alone does not intensify coloration unless absorbed resources survive transport and loss processes. Stress-induced chlorosis, transient anthocyanin accumulation, and saturation of greenness under high irradiance emerge as direct consequences of reduced energy survival rather than impaired pigment synthesis. By explicitly separating survival from synthesis, the framework reconciles molecular-scale pigment biochemistry with whole-plant and ecosystem-scale color dynamics. This survival-based formulation provides a unified physical basis for interpreting plant color variation across species and environments and offers a scalable foundation for crop stress diagnostics, remote-sensing interpretation, and comparative plant phenotyping. More broadly, it extends survival-limited energy principles to visible plant traits, advancing plant science beyond isolated biochemical descriptions toward a systems-level understanding of how energy governs phenotypic expression.","植物色泽通过叶片绿色度、花色苷着色和果实颜色强度体现，反映了色素生物合成、光学吸收、代谢调控以及与环境的能量-物质交换的综合结果。尽管主要色素类别——包括叶绿素、类胡萝卜素和花青素——的合成与降解生化途径已较为明确，但现有模型大多将颜色表达解释为局部生化现象。此类方法未明确考虑整株植物能量平衡、蒸腾驱动的损耗或热力学约束，限制了其解释广泛观察到的颜色饱和度、胁迫诱导褪色以及跨环境梯度的物种特异性稳定性等模式的能力。本文构建了一个统一的植物颜色生成与稳定性框架，将色素沉着视为一种受生存限制的能量过程，并将其嵌入吸收、运输、代谢和熵产生的更广泛动力学之中。该框架的核心是一个无量纲能量生存因子Ψ=AE\u002FTE+ε，其中AE代表与色素形成相关的被吸收并保留的能量和元素资源，TE表示与蒸腾相关的、代谢的和环境的耗散损失，ε则捕捉非平衡生物系统固有的不可约熵产生降解。根据构建方式，0\u003CΨ\u003C1，确保完全符合热力学第二定律。当与色素特异性光学吸收系数和生理限制函数相结合时，该框架可对色素浓度和颜色稳定性给出具有物理可解释性的描述。吸收性光合有效辐射（约45–70%）、激发能存活率（约85–95%）以及呼吸和蒸腾损失（约占同化资源的30–60%）的经验范围自然约束了Ψ，从而预测出所观察到的持续色素积累限度，而无需引入生化低效性或特设校正因子。分析表明，仅增加光照可用性或增强生物合成能力并不会加深颜色，除非被吸收的资源能够在运输和损失过程中存活下来。胁迫诱导的黄化、瞬时花青素积累以及高辐照下绿色度饱和，均表现为能量生存率降低而非色素合成受损的直接后果。通过明确分离生存","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":103,"substance":101,"depth":103,"authority":17,"freshness":21,"relevant":22,"comment":468},"提出以能量生存因子统一解释植物颜色与胁迫的物理框架，方法新颖且对遥感解译和作物诊断有潜在价值，但属预印本理论模型，尚缺田间验证。",[470],{"name":465,"url":462},[472,27,473,474,475],"智慧农业","植物表型","作物胁迫诊断","能量平衡",{"code":477,"slug":478,"name":479,"path":480,"parent_code":481},"B1","vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[],{"status":119,"locked":14,"version":35,"method":36,"evidence":484},{"candidates":485,"agricultural_evidence":42},[486,491,496],{"code":477,"score":205,"supported":42,"title":487,"abstract":488,"tags":490},[],[489],"萝卜",[],{"code":492,"score":22,"supported":14,"title":493,"abstract":494,"tags":495},"I1",[],[],[472],{"code":40,"score":22,"supported":14,"title":497,"abstract":498,"tags":499},[],[],[27],[501,502],"植物颜色 能量生存因子","作物胁迫 遥感诊断","植物颜色能量生存因子-5565","10.5281\u002Fzenodo.23540961",{"doi":504,"openalex_id":506,"authors":507,"venue":465,"cited_by_count":54,"oa_url":462,"card":511,"direction":84,"ingested_from":86},"W7223098474",[508],{"name":509,"orcid":510},"Mokhdum Azam Mashrafi","https:\u002F\u002Forcid.org\u002F0009-0002-1801-1130",{"tldr":512,"method":513,"finding":514,"direction":84,"opportunity":515},"提出基于生存的能量框架，用能量存活因子解释植物颜色生成与稳定性。","构建无量纲能量存活因子Ψ=AE\u002F(TE+ε)，结合色素吸收系数与生理限制函数。","颜色深浅取决于吸收资源经运输损耗后的存活率，而非单纯合成能力。","可将Ψ因子与冠层光谱\u002F表型遥感结合，发展胁迫早期诊断与生产力预测模型。","2026-10-22T11:20:31.195819Z"]