[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2675":3,"related-2675":108},{"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":73,"slug":76,"view_count":57,"doi":77,"paper":78,"created_at":107},2675,"Long-term ecological quality dynamics and spatial associations in arid and semi-arid Northwest China using an adaptive PRSEI","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-71853-z","Long-term ecological monitoring in arid and semi-arid regions requires indices that capture region-specific stressors while remaining comparable through time. We developed an adaptive particular remote-sensing ecological index (PRSEI) for Gansu Province, China, using growing-season kNDVI, wetness (WET), land surface temperature (LST), sandification index (SI), and a PM10-based particulate indicator (TI) from 2000 to 2024. A unified principal component analysis (PCA) with common scaling defined the primary time series, while annual PCA and fixed weights were used for sensitivity testing. Unified PC1 explained 79.12% of total variance, and mean PRSEI increased from 0.343 to 0.419 (slope = 0.00432 yr⁻¹; R² = 0.778). Unified PCA and fixed weights agreed on 99.90% of pixel-level trend directions, whereas annual and unified PCA agreed on only 39.73%, indicating strong methodological sensitivity. Adaptive PRSEI was highly correlated with original PRSEI ( r = 0.991) and traditional RSEI ( r = 0.975), showing the strongest consistency with mapped restoration transitions (81.47%) but weaker consistency with degradation transitions (34.22%). Precipitation, soil type, and land use\u002Fland cover were the most stable spatial explanatory factors. Overall, ecological quality improved broadly but heterogeneously, supporting adaptive PRSEI as a complementary multi-stressor index for drylands rather than a universal replacement for RSEI.","干旱与半干旱地区的长期生态监测需要既能反映区域特有胁迫因子、又能在时间上保持可比性的指数。本研究针对中国甘肃省构建了一种自适应特定遥感生态指数（PRSEI），所用数据为2000—2024年生长季的kNDVI、湿度（WET）、地表温度（LST）、沙化指数（SI）以及基于PM10的颗粒物指标（TI）。采用统一主成分分析（PCA）与共同标准化方案确定主时间序列，同时以逐年PCA和固定权重进行敏感性检验。统一PC1解释了总方差的79.12%，PRSEI均值从0.343上升至0.419（斜率=0.00432 yr⁻¹；R²=0.778）。统一PCA与固定权重在99.90%的像元水平趋势方向上一致，而逐年PCA与统一PCA仅在一致性为39.73%，表明方法敏感性较强。自适应PRSEI与原始PRSEI高度相关（r=0.991），与传统RSEI也高度相关（r=0.975），其与制图恢复转变的一致性最高（81.47%），但与退化转变的一致性较弱（34.22%）。降水、土壤类型和土地利用\u002F土地覆盖是最稳定的空间解释因子。总体而言，生态质量广泛改善但存在空间异质性，支持将自适应PRSEI作为干旱区多胁迫因子的补充性指数，而非RSEI的通用替代方案。",null,"Scientific Reports","2026-09-15T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,22,18,14,8,1,"提出自适应遥感生态指数PRSEI并揭示西北旱区生态质量长期改善趋势，方法新颖、数据扎实，对旱区农业生态监测有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业信息化","甘肃","遥感监测","生态质量","干旱半干旱区",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":72},[39,48,55,62,67],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":47},"H2",6,true,[],[45,46],"土壤","soil",[],{"code":49,"score":50,"supported":42,"title":51,"abstract":52,"tags":54},"I3",4,[],[53],"遥感",[29],{"code":56,"score":57,"supported":42,"title":58,"abstract":59,"tags":61},"H7",3,[],[60],"干旱",[],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"H6",[],[],[30],{"code":68,"score":22,"supported":14,"title":69,"abstract":70,"tags":71},"I1",[],[],[27],"农业相关性需要复核",[74,75],"干旱半干旱区 农业信息化 生态质量 遥感监测","干旱半干旱区 农业信息化","干旱半干旱区农业信息化生态质量遥感监测-2675","10.1038\u002Fs41598-026-71853-z",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":98,"oa_url":99,"card":100,"direction":104,"ingested_from":106},"W7213360699",[81,83,85,87,89,91,94,96],{"name":82,"orcid":9},"Jiangmin Wu",{"name":84,"orcid":9},"Bin Lian",{"name":86,"orcid":9},"Qirui Zhang",{"name":88,"orcid":9},"Zhen Yan",{"name":90,"orcid":9},"Wei Zhao",{"name":92,"orcid":93},"Jiachen Yang","https:\u002F\u002Forcid.org\u002F0000-0003-2558-552X",{"name":95,"orcid":9},"Zaixing Chen",{"name":97,"orcid":9},"Yuxiang Lan",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-71853-z_reference.pdf",{"tldr":101,"method":102,"finding":103,"direction":104,"opportunity":105},"构建自适应PRSEI指数，评估2000-2024年甘肃生态质量动态与空间关联。","基于生长季kNDVI、湿度、地表温度、沙化指数和PM10指标，用统一PCA构建P","生态质量整体改善但异质，PRSEI与RSEI高度一致，方法敏感性显著。","农业遥感与作物表型","可探索多胁迫指数在干旱区不同尺度的适用性，并耦合人类活动与政策因素。","openalex","2026-09-16T23:30:30.948770Z",{"total":41,"page":22,"page_size":41,"items":109},[110,173,243,301,359,423],{"id":111,"title":112,"url":113,"summary":114,"summary_zh":115,"content":9,"source_name":116,"source_url":113,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":118,"score_detail":119,"sources":123,"tags":125,"primary_topic":128,"secondary_topics":133,"classification":134,"search_phrases":156,"slug":159,"view_count":57,"doi":160,"paper":161,"created_at":172},2532,"Remote Sensing-Based Spatiotemporal Assessment of Ecological Quality using RSEI from 2016 to 2025: Gala Lake Basin, Türkiye","https:\u002F\u002Fdoi.org\u002F10.35229\u002Fjaes.1965400","Wetland-agricultural basins are among the most sensitive landscapes to ecological change because vegetation dynamics, surface moisture, bare soil exposure, and thermal stress interact within the same hydrological system. This study assessed the spatiotemporal ecological quality of the Gala Lake Basin, Türkiye, from 2016 to 2025 using the Remote Sensing Ecological Index (RSEI). The basin was selected because it contains Gala Lake, associated wetlands, agricultural lands, and seasonally variable water surfaces, making it a representative and environmentally important wetland-agricultural landscape in northwestern Türkiye. Landsat 8 OLI\u002FTIRS and Landsat 9 OLI-2\u002FTIRS-2 images were processed in Google Earth Engine to derive NDVI, WET, NDBSI, and LST, representing greenness, wetness, dryness, and heat, respectively. MNDWI was used to mask open-water areas before RSEI construction. The four indicators were normalized and integrated using PCA, and annual RSEI maps were classified into five ecological quality levels. The results revealed that ecological quality in the basin showed strong interannual variability rather than a uniform trend. The mean RSEI reached its lowest level in 2022 and its highest in 2025. Class-based analysis showed a clear reduction in low-ecological-quality areas and an expansion of moderate-ecological-quality areas by the end of the study period. PCA results confirmed that PC1 captured the dominant ecological gradient in all years, supporting its use for annual RSEI construction. This study demonstrates that RSEI provides a robust and reproducible framework for monitoring ecological quality in wetland-agricultural basins and offers baseline information for long-term environmental management of the Gala Lake Basin.","湿地-农业流域是对生态变化最为敏感的景观之一，因为植被动态、地表湿度、裸土暴露和热胁迫在同一水文系统内相互作用。本研究利用遥感生态指数（RSEI）评估了土耳其加拉湖流域2016年至2025年的时空生态质量。选择该流域是因为其包含加拉湖、相关湿地、农业用地和季节性变化的水面，使其成为土耳其西北部具有代表性和环境重要性的湿地-农业景观。在Google Earth Engine中处理Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2\u002FTIRS-2影像，以提取NDVI、WET、NDBSI和LST，分别代表绿度、湿度、干度和热度。在构建RSEI之前，使用MNDWI掩膜开阔水域。四个指标经归一化后通过PCA进行整合，并将年度RSEI图分为五个生态质量等级。结果表明，流域生态质量表现出强烈的年际变化，而非一致趋势。RSEI均值在2022年降至最低水平，在2025年达到最高水平。基于等级的分析显示，到研究期末，低生态质量区域明显减少，中等生态质量区域扩大。PCA结果证实，PC1在所有年份均捕捉到了主导生态梯度，支持其用于年度RSEI构建。本研究表明，RSEI为监测湿地-农业流域生态质量提供了一个稳健且可重复的框架，并为加拉湖流域的长期环境管理提供了基线信息。","Journal of Anatolian Environmental and Animal Sciences","2026-09-14T00:00:00Z",63,{"impact":21,"substance":19,"depth":17,"authority":120,"freshness":121,"relevant":22,"comment":122},12,9,"基于Landsat与Google Earth Engine的湿地-农业流域生态质量遥感评估，方法规范、数据扎实，但属区域案例研究，对国内三农实践的直接参考价值有限。",[124],{"name":116,"url":113},[27,29,126,30,127],"湿地农业","RSEI",{"code":49,"slug":129,"name":130,"path":131,"parent_code":132},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":135,"locked":14,"version":35,"method":36,"evidence":136},"classified",{"candidates":137,"agricultural_evidence":42},[138,144,148,152],{"code":49,"score":139,"supported":42,"title":140,"abstract":142,"tags":143},15,[141],"remote sensing",[53,141],[29],{"code":40,"score":57,"supported":42,"title":145,"abstract":146,"tags":147},[],[46],[],{"code":63,"score":22,"supported":14,"title":149,"abstract":150,"tags":151},[],[],[30],{"code":68,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},[],[],[27],[157,158],"农业信息化 湿地农业 生态质量 遥感监测","农业信息化 湿地农业","农业信息化湿地农业生态质量遥感监测-2532","10.35229\u002Fjaes.1965400",{"doi":160,"openalex_id":162,"authors":163,"venue":116,"cited_by_count":98,"oa_url":113,"card":167,"direction":104,"ingested_from":106},"W7213086036",[164],{"name":165,"orcid":166},"Enes Özgenç","https:\u002F\u002Forcid.org\u002F0000-0003-0878-6418",{"tldr":168,"method":169,"finding":170,"direction":104,"opportunity":171},"基于RSEI评估土耳其Gala湖流域2016-2025年生态质量时空变化。","Landsat影像在GEE中提取NDVI、WET、NDBSI、LST，PCA构建","生态质量年际波动明显，2022年最低、2025年最高，低质量区减少、中质量区扩张。","可结合作物物候与灌溉数据，解析湿地-农业复合区生态质量波动的驱动机制。","2026-09-15T23:30:20.761810Z",{"id":174,"title":175,"url":176,"summary":177,"summary_zh":178,"content":9,"source_name":179,"source_url":176,"published_at":180,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":181,"score_detail":182,"sources":185,"tags":187,"primary_topic":191,"secondary_topics":192,"classification":193,"search_phrases":219,"slug":222,"view_count":223,"doi":224,"paper":225,"created_at":242},4355,"Agricultural Flood Damage Assessment: A Systematic Review of Methods, Coverage, and Integration Gaps","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fegusphere-2026-3701","Abstract. Agricultural flooding causes tens of billions of dollars in annual losses globally, yet the methods used to assess these losses systematically capture only a fraction of their true economic scope. This systematic review synthesizes 65 peer-reviewed studies published between 1984 and 2025 to evaluate how agricultural flood damage is measured, what damage categories are covered, and where critical methodological gaps persist. We find a structural asymmetry across the reviewed literature – all 65 reviewed studies address direct immediate damages – primarily crop yield loss estimated through depth-damage functions or remote sensing – while direct induced effects appear in only 8 studies, indirect damages in 9, and intangible damages in fewer than 5. This asymmetry has persisted for four decades, through a major remote sensing revolution, because the dominant methods are structurally incapable of measuring damage categories beyond direct immediate losses. We introduce a Methods-by-Damage-Category Matrix that maps eight assessment methods against five damage categories, making the coverage gap visible and providing a diagnostic framework for method selection. Three post-2013 damage frontiers – long-term land productivity degradation, supply chain finance disruption, and farmer mental health – are identified as emerging categories warranting dedicated methodological development. Existing agricultural flood damage estimates used in benefit–cost analyses and disaster compensation are systematically downward-biased; closing this bias requires integrating remote sensing-based physical detection with economic valuation methods capable of capturing the full damage taxonomy.","摘要：农业洪水每年在全球造成数百亿美元损失，然而用于系统评估这些损失的方法仅能捕捉其真实经济影响的一小部分。本系统综述综合了1984年至2025年间发表的65项同行评审研究，评估农业洪水损失如何被测量、涵盖了哪些损失类别，以及关键方法论空白存在于何处。我们发现，所综述文献中存在结构性不对称——全部65项研究均涉及直接即时损失，主要是通过深度-损失函数或遥感估算的作物产量损失——而直接诱发效应仅出现在8项研究中，间接损失出现在9项研究中，无形损失则不足5项。这种不对称已持续四十年，历经遥感技术的重大革命，因为主流方法在结构上无法测量直接即时损失之外的损失类别。我们引入“方法-损失类别矩阵”，将八种评估方法与五类损失类别进行对应，使覆盖缺口清晰可见，并为方法选择提供诊断框架。2013年后的三个损失前沿——长期土地生产力退化、供应链金融中断和农民心理健康——被识别为值得专门方法论开发的新兴类别。现有用于成本效益分析和灾害补偿的农业洪水损失估算存在系统性低估；弥合这一偏差需要将基于遥感的物理检测与能够捕捉完整损失分类的经济估值方法相结合。","OpenAlex","2026-10-06T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":183,"freshness":21,"relevant":22,"comment":184},13,"系统综述揭示农业洪灾损失评估长期偏低估的方法学缺口，提出方法-损失类别矩阵，对灾害补偿与成本收益分析有实质参考价值。",[186],{"name":179,"url":176},[27,188,189,29,190],"粮食安全","农业保险","灾害评估",{"code":49,"slug":129,"name":130,"path":131,"parent_code":132},[],{"status":135,"locked":14,"version":35,"method":36,"evidence":194},{"candidates":195,"agricultural_evidence":42},[196,201,206,211,215],{"code":49,"score":197,"supported":42,"title":198,"abstract":199,"tags":200},7,[],[53,141],[29],{"code":202,"score":22,"supported":14,"title":203,"abstract":204,"tags":205},"A8",[],[],[188],{"code":207,"score":22,"supported":14,"title":208,"abstract":209,"tags":210},"G3",[],[],[189],{"code":56,"score":22,"supported":14,"title":212,"abstract":213,"tags":214},[],[],[190],{"code":68,"score":22,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[27],[220,221],"农业洪涝灾害 损失评估","遥感 农作物 灾害损失","农业洪涝灾害损失评估-4355",2,"10.5194\u002Fegusphere-2026-3701",{"doi":224,"openalex_id":226,"authors":227,"venue":9,"cited_by_count":98,"oa_url":176,"card":237,"direction":104,"ingested_from":106},"W7220668753",[228,231,234],{"name":229,"orcid":230},"Shokhrukh–Mirzo Jalilov","https:\u002F\u002Forcid.org\u002F0000-0002-2428-9094",{"name":232,"orcid":233},"Haluk Gedikoğlu","https:\u002F\u002Forcid.org\u002F0000-0002-0158-2597",{"name":235,"orcid":236},"Robert Maltsbarger","https:\u002F\u002Forcid.org\u002F0009-0003-7801-8390",{"tldr":238,"method":239,"finding":240,"direction":104,"opportunity":241},"系统综述65项农业洪水损失评估研究，揭示方法仅覆盖直接即时损失的结构性偏差。","系统综述1984-2025年65篇文献，构建方法-损失类别矩阵。","所有研究仅测直接即时损失，间接、诱导及无形损失严重缺失，致损失估计系统性偏低。","可开发融合遥感与经济学估值的方法，量化长期土地退化、供应链金融与农户心理健康等新兴损失类别。","2026-10-07T23:30:25.887527Z",{"id":244,"title":245,"url":246,"summary":247,"summary_zh":248,"content":9,"source_name":249,"source_url":246,"published_at":250,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":251,"sources":253,"tags":255,"primary_topic":259,"secondary_topics":260,"classification":261,"search_phrases":278,"slug":281,"view_count":50,"doi":282,"paper":283,"created_at":300},3361,"Spatiotemporal Patterns and Associations of Ecological Quality and Carbon Storage Across Three Major Agricultural Provinces in Central China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101781","Ecological quality, vegetation productivity, and carbon storage represent different ecosystem properties and may respond differently to land-use change. This study assessed their spatiotemporal patterns and spatial relationships across Hubei, Henan, and Anhui, three major agricultural provinces in central China. Ecological quality was assessed using the Remote Sensing Ecological Index (RSEI), vegetation productivity was estimated as net primary productivity (NPP) using the Carnegie–Ames–Stanford Approach (CASA) model, and carbon storage was estimated using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. Spearman correlation, GeoDetector, and a descriptive coordination index were used to compare their spatial relationships. Ecological quality declined from 2000 to 2010 and recovered only modestly thereafter, with persistently lower values in the eastern and northern plains than in the western and southern mountains. From 1990 to 2020, built-up land expanded by 112.03%, while model-estimated carbon storage declined by 34.59 × 106 t. RSEI and NPP were predominantly positively correlated, but correlations varied substantially among provinces, ecological-quality classes, and land-use types. RSEI had the greatest explanatory power for spatial variation in carbon storage among the composite factors (q = 0.420–0.467), and NDVI was the strongest RSEI component. GeoDetector identified bivariate and nonlinear enhancement, indicating that paired factors explained more spatial variation than individual factors. Moderate and primary coordination together accounted for more than 99% of valid pixels, while severe imbalance declined from 0.88% to 0.44%. These findings show that the three indicators are spatially associated but not interchangeable. Together, the results support protecting forested mountain areas, limiting built-up expansion and cropland loss in plains and urban fringes, and using multiple indicators to identify areas requiring local assessment.","生态质量、植被生产力与碳储量表征不同的生态系统属性，可能对土地利用变化作出不同响应。本研究评估了华中三个农业大省——湖北、河南和安徽——上述指标的时空格局及其空间关系。生态质量采用遥感生态指数（RSEI）评估，植被生产力以Carnegie–Ames–Stanford Approach（CASA）模型估算的净初级生产力（NPP）表征，碳储量则采用生态系统服务与权衡综合评估（InVEST）模型估算。运用Spearman相关分析、地理探测器及描述性协调指数比较其空间关系。2000—2010年生态质量下降，此后仅略有恢复，东部和北部平原的值持续低于西部和南部山区。1990—2020年，建设用地扩张112.03%，而模型估算的碳储量下降34.59 × 10⁶ t。RSEI与NPP总体呈正相关，但相关性在各省份、生态质量等级和土地利用类型间差异显著。在复合因子中，RSEI对碳储量空间变异的解释力最强（q = 0.420–0.467），NDVI是RSEI中最强的组分。地理探测器识别出双因子和非线性增强，表明成对因子解释的空间变异多于单因子。中度协调和初级协调合计占有效像元的99%以上，而严重失调从0.88%降至0.44%。研究结果表明，三个指标在空间上相互关联但不可互换。综合而言，研究结果支持保护山地森林区域、限制平原和城市边缘的建设用地扩张与耕地流失，并利用多指标识别需开展局部评估的区域。","Land","2026-09-23T00:00:00Z",{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":252},"基于RSEI、CASA与InVEST模型对中部三大农业省生态质量与碳储量的长时序空间关联分析，方法规范、数据扎实，对农业生态保护与国土空间管控有参考价值。",[254],{"name":249,"url":246},[256,29,257,30,258],"农业生态","土地利用变化","碳储量",{"code":49,"slug":129,"name":130,"path":131,"parent_code":132},[],{"status":135,"locked":14,"version":35,"method":36,"evidence":262},{"candidates":263,"agricultural_evidence":42},[264,268,273],{"code":49,"score":197,"supported":42,"title":265,"abstract":266,"tags":267},[],[53,141],[29],{"code":63,"score":41,"supported":42,"title":269,"abstract":270,"tags":272},[],[271],"生态系统服务",[256,257,30],{"code":274,"score":22,"supported":14,"title":275,"abstract":276,"tags":277},"H4",[],[],[258],[279,280],"湖北 河南 安徽 农业生态","RSEI 碳储量 遥感","湖北河南安徽农业生态-3361","10.3390\u002Fland15101781",{"doi":282,"openalex_id":284,"authors":285,"venue":249,"cited_by_count":98,"oa_url":246,"card":294,"direction":104,"ingested_from":106},"W7214114544",[286,288,291],{"name":287,"orcid":9},"Xiaohan Liu",{"name":289,"orcid":290},"Lei Wang","https:\u002F\u002Forcid.org\u002F0000-0002-7163-3644",{"name":292,"orcid":293},"Hui Min Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-7587-1116",{"tldr":295,"method":296,"finding":297,"direction":298,"opportunity":299},"评估中部三大农业省生态质量、植被生产力与碳储量的时空格局及空间关联。","用RSEI、CASA模型、InVEST模型结合Spearman相关、GeoDet","生态质量先降后微升，建设用地增112%，碳储减3459万吨；三指标空间关联但不可互换。","农业绿色发展与碳","可探究不同农业省土地利用变化下生态质量与碳储量的非线性驱动机制及分区管控策略。","2026-09-24T23:30:20.927597Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":117,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":308,"sources":310,"tags":312,"primary_topic":9,"secondary_topics":316,"classification":317,"search_phrases":332,"slug":335,"view_count":223,"doi":336,"paper":337,"created_at":358},2526,"Scale-dependent biases in systematic land cover maps undermine freshwater ecological assessment","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-026-15882-1","Abstract Land cover is a key determinant of landscape structure and ecological processes across spatial and temporal scales. In freshwater ecosystems, where riparian and catchment land cover regulate hydrology, nutrient inputs, and habitat quality, most ecological assessments rely on systematically produced land cover maps whose reliability varies with scale and context. Despite their widespread use, the extent to which these products accurately represent landscape patterns and long-term dynamics at ecologically relevant scales remains poorly understood. Here, we evaluate the reliability of systematic land cover maps and assess whether Landsat-based supervised classification (SC) provides more accurate and temporally consistent representations of land cover patterns in draining catchments and riparian areas. We analysed five headwater catchments (\u003C 70 km 2 ) within Natura 2000 sites in northern Spain and reconstructed land cover dynamics over four decades (1984–2023) using Random Forest classification implemented in Google Earth Engine. SC results were compared with CORINE Land Cover, SIOSE, and the Spanish National Forest Inventory across three spatial scales, relevant for freshwater ecological processes: catchment, riparian corridor, and reach. SC achieved consistently high accuracy across all periods (mean overall accuracy = 87.8%), outperforming CORINE and SIOSE and matching NFI only for forest classes. Discrepancies between SC and systematic maps increased at finer spatial scales and varied with landscape context, particularly in heterogeneous riparian environments. Systematic maps also showed limited temporal consistency, frequently misrepresenting land cover trends relative to SC. These scale-dependent and context-specific biases can alter estimates of riparian vegetation and landscape composition and their temporal dynamics, potentially leading to misleading assessments of freshwater ecosystem condition. Our findings demonstrate that reliance on coarse-scale land cover products can distort ecological inference, particularly for freshwater ecosystems where ecological condition depends strongly on catchment and riparian landscape structure. Integrating reproducible, remote sensing–based classifications offers a practical pathway to improving the accuracy and interpretability of land cover indicators in landscape ecological research.","摘要 土地覆盖是跨时空尺度上景观结构和生态过程的关键决定因素。在淡水生态系统中，河岸带和流域土地覆盖调节着水文过程、养分输入和栖息地质量，而大多数生态评估依赖于系统性生产的土地覆盖图，其可靠性随尺度和背景而变化。尽管这些产品被广泛使用，但其在生态相关尺度上准确表征景观格局和长期动态的程度仍鲜为人知。本研究评估了系统性土地覆盖图的可靠性，并检验基于Landsat的监督分类（supervised classification, SC）是否能更准确、更具时间一致性地表征汇水流域和河岸带的土地覆盖格局。我们分析了西班牙北部Natura 2000保护区内五个源头流域（\u003C 70 km²），利用在Google Earth Engine中实现的随机森林分类重建了四十年（1984–2023）的土地覆盖动态。SC结果在三个与淡水生态过程相关的空间尺度——流域、河岸廊道和河段——上与CORINE土地覆盖、SIOSE和西班牙国家森林清查进行了比较。SC在所有时期均达到了持续较高的精度（平均总体精度 = 87.8%），优于CORINE和SIOSE，仅在森林类别上与NFI相当。SC与系统性地图之间的差异在更精细的空间尺度上增大，并随景观背景而变化，尤其是在异质性河岸环境中。系统性地图还表现出有限的时间一致性，相对于SC频繁错误表征土地覆盖趋势。这些尺度依赖性和背景特异性的偏差可能改变对河岸植被和景观组成及其时间动态的估计，从而可能导致对淡水生态系统状况的误导性评估。我们的研究结果表明，依赖粗尺度土地覆盖产品可能扭曲生态推断，尤其是对于生态状况强烈依赖于流域和河岸景观结构的淡水生态系统。整合可重复的、基于遥感的分类为提升景观生态研究中土地覆盖指标的准确性和可解释性提供了一条实用途径。","Environmental Monitoring and Assessment",{"impact":17,"substance":18,"depth":19,"authority":183,"freshness":121,"relevant":22,"comment":309},"该研究揭示系统性土地覆盖图存在尺度依赖偏差，提出基于遥感的可复现分类方法，对农业面源污染评估和流域生态管理有实质参考价值，但属细分领域学术进展，公共影响有限。",[311],{"name":307,"url":304},[27,29,313,314,315],"土地覆盖","淡水生态","生态评估",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":318},{"candidates":319,"agricultural_evidence":14,"reason":72},[320,324,328],{"code":49,"score":197,"supported":42,"title":321,"abstract":322,"tags":323},[],[53,141],[29],{"code":63,"score":22,"supported":14,"title":325,"abstract":326,"tags":327},[],[],[313],{"code":68,"score":22,"supported":14,"title":329,"abstract":330,"tags":331},[],[],[27],[333,334],"农业信息化 土地覆盖 淡水生态 生态评估","农业信息化 土地覆盖","农业信息化土地覆盖淡水生态生态评估-2526","10.1007\u002Fs10661-026-15882-1",{"doi":336,"openalex_id":338,"authors":339,"venue":307,"cited_by_count":98,"oa_url":352,"card":353,"direction":104,"ingested_from":106},"W7212458932",[340,343,346,349],{"name":341,"orcid":342},"Iñaki Fernández de Larrea","https:\u002F\u002Forcid.org\u002F0000-0002-5910-6905",{"name":344,"orcid":345},"Jon Gonzalez-Ibarzabal","https:\u002F\u002Forcid.org\u002F0009-0001-2278-1245",{"name":347,"orcid":348},"Aitor Bastarrika Izaguirre","https:\u002F\u002Forcid.org\u002F0000-0001-8454-7289",{"name":350,"orcid":351},"Aitor Larrañaga","https:\u002F\u002Forcid.org\u002F0000-0002-0185-9154","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10661-026-15882-1.pdf",{"tldr":354,"method":355,"finding":356,"direction":104,"opportunity":357},"评估系统性土地覆盖图在淡水生态评估中的尺度偏差，并用Landsat监督分类重建四十年土地覆盖动态。","随机森林分类与Google Earth Engine，对比CORINE、SIOS","监督分类精度更高，系统图在更细尺度偏差增大且时间一致性差，可能误导淡水生态评估。","可研究多尺度遥感分类产品在河岸带等异质景观中的偏差校正，并开发面向淡水生态的尺度自适应土地覆盖指标。","2026-09-15T23:30:20.336621Z",{"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":371,"tags":373,"primary_topic":9,"secondary_topics":377,"classification":378,"search_phrases":393,"slug":396,"view_count":22,"doi":397,"paper":398,"created_at":422},2421,"Characterizing non-permanent aquatic habitats using a Bayesian-curated remote sensing: insights from a rotifer case study","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10750-026-06348-z","Abstract Remote sensing is an indispensable tool for generating consistent, standardized time-series data across broad spatial scales. However, its usefulness for ecological and evolutionary research depends on how well it captures temporally relevant environmental variation. Non-permanent aquatic systems, such as Mediterranean shallow water bodies, are particularly challenging because their characteristic abrupt wet-dry transitions over short timescales increase noise and misclassification of hydrological states. Organisms inhabiting these systems are tightly coupled to the timing, duration, and predictability of inundation phases. Here, we developed a Bayesian state-space approach to curate Sentinel-2 time-series data, improving the reliability of hydrological state detection in a system of shallow ponds in the eastern Iberian Peninsula. The curated hydrological state data enabled the quantification of non-permanent regimes using ecologically meaningful metrics, revealing substantial differences in water availability and predictability among neighboring ponds under similar climatic conditions. We demonstrate how these metrics are relevant to evolutionary ecology. Specifically, we link hydrological unpredictability to bet-hedging strategies in rotifer populations. Rotifer clones originating from more unpredictable ponds exhibited lower hatching fractions, which is consistent with adaptive risk-spreading strategies. This study highlights the importance of characterizing environmental variability at temporal scales directly relevant to organismal life cycles when linking habitat dynamics to evolutionary responses.","摘要 遥感是生成跨大空间尺度一致、标准化时间序列数据不可或缺的工具。然而，其在生态学与进化研究中的实用性取决于其对时间相关环境变异的捕捉能力。非永久性水生系统，如地中海浅水水体，尤其具有挑战性，因为其在短时间尺度上特有的干湿突变转换会增加噪声和水文状态误分类。栖息于这些系统中的生物与淹水阶段的时机、持续时间和可预测性紧密耦合。在此，我们开发了一种贝叶斯状态空间方法，用于整理哨兵2号（Sentinel-2）时间序列数据，从而提高伊比利亚半岛东部浅塘系统水文状态检测的可靠性。经过整理的水文状态数据使得利用具有生态学意义的指标量化非永久性水文情势成为可能，揭示了在相似气候条件下相邻池塘之间在水体可利用性和可预测性方面的显著差异。我们展示了这些指标如何与进化生态学相关。具体而言，我们将水文不可预测性与轮虫种群的赌注对冲策略联系起来。来自更不可预测池塘的轮虫克隆表现出较低的孵化比例，这与适应性风险分散策略一致。本研究强调了在将栖息地动态与进化响应相联系时，在与生物生活史直接相关的时间尺度上刻画环境变异的重要性。","Hydrobiologia","2026-09-11T00:00:00Z",67,{"impact":21,"substance":369,"depth":19,"authority":183,"freshness":21,"relevant":22,"comment":370},20,"该研究提出贝叶斯状态空间方法优化Sentinel-2时序数据，提升非永久性水体水文状态识别精度，并关联轮虫种群风险分散策略，方法新颖、结论可靠，对农业遥感与生态监测有参考价值，但属基础生态学论文，产业影响有限。",[372],{"name":365,"url":362},[27,374,29,375,376],"生物多样性","水生生态","贝叶斯模型",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":379},{"candidates":380,"agricultural_evidence":14,"reason":72},[381,385,389],{"code":49,"score":139,"supported":42,"title":382,"abstract":383,"tags":384},[141],[53,141],[29],{"code":63,"score":22,"supported":14,"title":386,"abstract":387,"tags":388},[],[],[374],{"code":68,"score":22,"supported":14,"title":390,"abstract":391,"tags":392},[],[],[27],[394,395],"农业信息化 生物多样性 贝叶斯模型 水生生态","农业信息化 生物多样性","农业信息化生物多样性贝叶斯模型水生生态-2421","10.1007\u002Fs10750-026-06348-z",{"doi":397,"openalex_id":399,"authors":400,"venue":365,"cited_by_count":98,"oa_url":416,"card":417,"direction":104,"ingested_from":106},"W7212221912",[401,404,407,410,413],{"name":402,"orcid":403},"Carlota Solano Udina","https:\u002F\u002Forcid.org\u002F0000-0002-7272-8258",{"name":405,"orcid":406},"Eduardo M. García‐Roger","https:\u002F\u002Forcid.org\u002F0000-0001-7112-8464",{"name":408,"orcid":409},"Anabel Forte","https:\u002F\u002Forcid.org\u002F0000-0001-9534-1817",{"name":411,"orcid":412},"Javier Montero‐Pau","https:\u002F\u002Forcid.org\u002F0000-0002-0864-8157",{"name":414,"orcid":415},"María José Carmona","https:\u002F\u002Forcid.org\u002F0000-0002-4835-6933","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10750-026-06348-z.pdf",{"tldr":418,"method":419,"finding":420,"direction":104,"opportunity":421},"用贝叶斯状态空间方法校正Sentinel-2时序，刻画临时性水体水文动态并关联轮虫生活史策略。","贝叶斯状态空间模型校正Sentinel-2时序，提取水体淹没状态指标。","校正后水文指标揭示邻近池塘水分可用性与可预测性差异，且不可预测池塘轮虫克隆孵化率更低。","可将该贝叶斯时序校正框架迁移至农业湿地\u002F灌溉塘遥感监测，并耦合生物生活史模型。","2026-09-14T23:30:18.412621Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":366,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":430,"score_detail":431,"sources":433,"tags":435,"primary_topic":439,"secondary_topics":444,"classification":446,"search_phrases":468,"slug":471,"view_count":22,"doi":472,"paper":473,"created_at":493},2323,"Land cover transformations in northern Algeria from 2001 to 2021: A MODIS-based spatiotemporal analysis","https:\u002F\u002Fdoi.org\u002F10.30493\u002Fdas.2026.011109","Land use\u002Fland cover (LULC) changes in northern Algeria from 2001 to 2021 were investigated in this research. The assessment utilized the MODIS MCD12Q1 dataset combined with Geographic Information System (GIS) techniques. Substantial landscape transformations were highlighted by the findings. Forests (+47.52%), shrublands (+17.65%), wetlands (+58.64%), and natural vegetation (+465.72%) experienced notable growth, whereas a drop of −14.13% was documented for barren lands by the end of study period. Moderate overall growth was displayed by agricultural lands, despite various fluctuations throughout the timeline. Concurrently, a continuous expansion (+3.90%) driven by escalating anthropogenic pressure was recorded in urban and built-up areas. Regional landscape evolution is clearly shaped by a combination of factors. Climatic variability, ecological succession, land management practices, and human activities collectively drive these observed dynamics. Partial alignment with previously documented vegetation recovery and urban expansion trends in Algeria was demonstrated by comparative literature reviews. However, the moderate spatial resolution (500 m) of the MODIS MCD12Q1 product likely caused several identified data discrepancies. Ultimately, the immense value of remote sensing and GIS approaches for regional environmental monitoring is proven by this project. Achieving better classification accuracy in heterogeneous Mediterranean environments, however, strictly necessitates the use of higher-resolution datasets.","本研究探讨了2001年至2021年阿尔及利亚北部土地利用\u002F土地覆盖（LULC）变化。评估采用MODIS MCD12Q1数据集结合地理信息系统（GIS）技术。研究结果凸显了显著的土地景观转变。森林（+47.52%）、灌木地（+17.65%）、湿地（+58.64%）和自然植被（+465.72%）显著增长，而裸地在研究期末则记录了−14.13%的下降。尽管农业用地在时间线上经历了多次波动，但总体上呈现出适度增长。与此同时，城市和建成区因人为压力不断加剧而记录了持续扩张（+3.90%）。区域景观演变显然由多种因素共同塑造。气候变率、生态演替、土地管理实践和人类活动共同驱动了这些观测到的动态。比较文献综述表明，这与阿尔及利亚先前记录的植被恢复和城市扩张趋势部分吻合。然而，MODIS MCD12Q1产品的中等空间分辨率（500米）可能导致了若干已识别的数据差异。最终，本项目证明了遥感和GIS方法在区域环境监测中的巨大价值。然而，要在异质性较强的地中海环境中实现更好的分类精度，严格需要使用更高分辨率的数据集。","DYSONA – Applied Science",62,{"impact":21,"substance":19,"depth":139,"authority":120,"freshness":121,"relevant":22,"comment":432},"基于MODIS数据的阿尔及利亚北部20年土地覆被变化分析，方法规范、数据翔实，对遥感监测与区域生态评估有参考价值，但属境外区域研究，国内产业影响有限。",[434],{"name":429,"url":426},[27,436,29,437,438],"荒漠化","土地利用","植被恢复",{"code":440,"slug":441,"name":442,"path":443,"parent_code":132},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[445],{"code":49,"slug":129,"name":130,"path":131,"parent_code":132},{"status":135,"locked":14,"version":35,"method":36,"evidence":447},{"candidates":448,"agricultural_evidence":42},[449,456,460,464],{"code":440,"score":121,"supported":42,"title":450,"abstract":451,"tags":455},[],[452,453,454],"地理信息","GIS","geographic information",[],{"code":49,"score":21,"supported":42,"title":457,"abstract":458,"tags":459},[],[53,141],[438,29],{"code":63,"score":223,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[437,436],{"code":68,"score":22,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[27],[469,470],"农业信息化 土地利用 植被恢复 遥感监测","农业信息化 土地利用","农业信息化土地利用植被恢复遥感监测-2323","10.30493\u002Fdas.2026.011109",{"doi":472,"openalex_id":474,"authors":475,"venue":429,"cited_by_count":98,"oa_url":426,"card":488,"direction":104,"ingested_from":106},"W7212238590",[476,478,481,484,486],{"name":477,"orcid":9},"Bilel Zerouali",{"name":479,"orcid":480},"Richarde Marques da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-6601-5174",{"name":482,"orcid":483},"Nadjem Bailek","https:\u002F\u002Forcid.org\u002F0000-0001-9051-8548",{"name":485,"orcid":9},"Valeriano Carneiro de Lima Silva",{"name":487,"orcid":9},"Celso Augusto Guimarães Santos",{"tldr":489,"method":490,"finding":491,"direction":104,"opportunity":492},"基于MODIS数据分析了2001—2021年阿尔及利亚北部土地覆盖的时空变化。","使用MODIS MCD12Q1数据集结合GIS技术进行LULC分类与变化检测。","森林、灌丛、湿地和自然植被显著增长，裸地减少，建设用地持续扩张。","可引入高分辨率遥感数据提升地中海异质景观分类精度，并耦合气候与人为驱动因子做归因分析。","2026-09-13T23:30:23.070977Z"]