[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4235":3,"related-4235":132},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":56,"slug":59,"view_count":21,"doi":60,"paper":61,"created_at":131},4235,"Right on time: evaluating accuracy and timing of burn-severity assessments to support post-fire management","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs42408-026-00589-y","Abstract Effective wildfire impact assessment and post-fire management rely on burn-severity maps that accurately characterize fire effects on vegetation and soils. These maps inform time-sensitive activities such as post-fire flood and debris-flow modeling, risk mitigation for homes and communities, and ecosystem recovery planning, but the satellite observations used to generate them are often obstructed or delayed by smoke, clouds, and snow. Across 9,129 large fires (≥ 404 ha) in Canada and the United States from 2016–2024, we quantified post-fire data obstruction and evaluated observational latency – defined as the time required to acquire the first usable scene – for four freely available moderate-resolution remote sensing datasets: Landsat, Sentinel-1, Sentinel-2, and the Harmonized Landsat Sentinel-2 (HLS) product. For multispectral datasets (Landsat, Sentinel-2, and HLS), cloud cover was the primary source of data obstruction, particularly in mesic and coastal regions, whereas Sentinel-1 (synthetic aperture radar) can penetrate clouds. Consequently, Sentinel-1 had the shortest observational latency (median = 4 days), followed by HLS (7 days), Sentinel-2 (10 days), and Landsat (17 days). Likewise, one-month composite imagery from Sentinel-1 (89.6%) and HLS (82.8%) achieved near-complete coverage for most fires, substantially better than Sentinel-2 (70.5%) and Landsat (56.1%). Despite its widespread use in operational burn-severity mapping, Landsat provided the slowest and least complete early post-fire coverage. For 77 fires in forested ecosystems, we also evaluated relationships between early post-fire imagery and field-derived burn-severity measurements collected 0–3 years after fire. All multispectral datasets corresponded well with field data and reliably identified areas of near-total canopy loss using imagery acquired within two months after fire (> 80% balanced accuracy), performing nearly as well as extended assessments using imagery from a full post-fire growing season. Sentinel-1 produced less accurate burn-severity estimates but may remain valuable where persistent cloud cover limits observations, including eastern Canada and the Pacific Northwest. Overall, HLS provided the best combination of latency, spatial coverage, and burn-severity mapping performance, making it a strong candidate for operational use and ecological monitoring across diverse North American ecosystems.","有效的野火影响评估和火后管理依赖于能够准确刻画火灾对植被和土壤影响的火烧严重度（burn severity）制图。这些地图为时间敏感的活动提供信息支持，如火后洪水和泥石流建模、住宅和社区的风险缓解以及生态系统恢复规划，但用于生成这些地图的卫星观测往往受到烟雾、云层和积雪的遮挡或延迟。针对2016—2024年加拿大和美国的9，129场大型火灾（≥ 404公顷），我们量化了火后数据受阻情况，并评估了四种免费可获取的中分辨率遥感数据集的观测延迟——定义为获取首个可用影像所需的时间：Landsat、Sentinel-1、Sentinel-2以及Landsat与Sentinel-2协调产品（HLS）。对于多光谱数据集（Landsat、Sentinel-2和HLS），云覆盖是数据受阻的主要来源，尤其在湿润和沿海地区，而Sentinel-1（合成孔径雷达）能够穿透云层。因此，Sentinel-1的观测延迟最短（中位数=4天），其次是HLS（7天）、Sentinel-2（10天）和Landsat（17天）。同样，Sentinel-1（89.6%）和HLS（82.8%）的月度合成影像对大多数火灾实现了近乎完全的覆盖，显著优于Sentinel-2（70.5%）和Landsat（56.1%）。尽管Landsat在业务化火烧严重度制图中被广泛使用，但其提供的火后早期覆盖最慢且最不完整。针对森林生态系统中的77场火灾，我们还评估了火后早期影像与火后0—3年采集的野外火烧严重度测量值之间的关系。所有多光谱数据集均与野外数据吻合良好，并利用火后两个月内获取的影像可靠地识别了近全部冠层损失区域（平衡精度> 80%），其表现几乎与使用整个火后生长季影像的扩展评估相当。Sentinel-1产生的火烧严重度估算精度较低，但在持续云覆盖限制观测的地区（包括加拿大东部和太平洋西北地区）可能仍有价值。总体而言，HLS在延迟、空间覆盖和火烧严重度制图性能方面提供了最佳组合，使其成为跨北美多样化生态系统业务化应用和生态监测的有力候选方案。",null,"Fire Ecology","2026-10-02T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,8,1,"基于北美9129场大火的遥感时效性评估，数据规模大、结论明确，对灾后遥感监测与生态恢复有直接参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感监测","植被恢复","林火生态","Sentinel-1","灾后评估",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":55},[38,48],{"code":39,"score":40,"supported":41,"title":42,"abstract":43,"tags":47},"I3",11,true,[],[44,45,46],"遥感","remote sensing","multispectral",[27,26],{"code":49,"score":50,"supported":41,"title":51,"abstract":52,"tags":54},"H2",3,[],[53],"土壤",[],"农业相关性需要复核",[57,58],"Sentinel-1 火烧严重度 遥感","HLS 林火 灾后管理","Sentinel-1火烧严重度遥感-4235","10.1186\u002Fs42408-026-00589-y",{"doi":60,"openalex_id":62,"authors":63,"venue":10,"cited_by_count":122,"oa_url":123,"card":124,"direction":128,"ingested_from":130},"W7215928159",[64,67,70,73,76,79,82,85,88,91,94,96,98,101,104,107,110,113,116,119],{"name":65,"orcid":66},"Kyle C. Rodman","https:\u002F\u002Forcid.org\u002F0000-0001-9538-8412",{"name":68,"orcid":69},"Ellen Whitman","https:\u002F\u002Forcid.org\u002F0000-0002-4562-3645",{"name":71,"orcid":72},"Alexander A. Howe","https:\u002F\u002Forcid.org\u002F0000-0002-6815-7536",{"name":74,"orcid":75},"A. Spannuth","https:\u002F\u002Forcid.org\u002F0009-0007-9401-5842",{"name":77,"orcid":78},"Kevin Barrett","https:\u002F\u002Forcid.org\u002F0000-0003-1014-6372",{"name":80,"orcid":81},"Corey Beinhart","https:\u002F\u002Forcid.org\u002F0009-0006-9282-4534",{"name":83,"orcid":84},"Yan Boucher","https:\u002F\u002Forcid.org\u002F0000-0003-0411-6927",{"name":86,"orcid":87},"Sebastian U. Busby","https:\u002F\u002Forcid.org\u002F0000-0003-2084-0106",{"name":89,"orcid":90},"Jorge Téllez Carrasco","https:\u002F\u002Forcid.org\u002F0009-0002-3326-1306",{"name":92,"orcid":93},"Kyra Clark‐Wolf","https:\u002F\u002Forcid.org\u002F0000-0003-4584-0348",{"name":95,"orcid":9},"M. J. Corbett",{"name":97,"orcid":9},"J. E. Crouse",{"name":99,"orcid":100},"Victor Danneyrolles","https:\u002F\u002Forcid.org\u002F0000-0002-4839-8164",{"name":102,"orcid":103},"Patricia Dappen","https:\u002F\u002Forcid.org\u002F0009-0004-9910-7023",{"name":105,"orcid":106},"Kimberley T. Davis","https:\u002F\u002Forcid.org\u002F0000-0001-9727-374X",{"name":108,"orcid":109},"Brian J. Harvey","https:\u002F\u002Forcid.org\u002F0000-0002-5902-4862",{"name":111,"orcid":112},"Zachary A. Holden","https:\u002F\u002Forcid.org\u002F0000-0002-4319-6491",{"name":114,"orcid":115},"Sharon M. Hood","https:\u002F\u002Forcid.org\u002F0000-0002-9544-8208",{"name":117,"orcid":118},"José M. Iniguez","https:\u002F\u002Forcid.org\u002F0000-0002-4566-1297",{"name":120,"orcid":121},"Patrick M. A. James","https:\u002F\u002Forcid.org\u002F0000-0001-7639-7217",0,"https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs42408-026-00589-y.pdf",{"tldr":125,"method":126,"finding":127,"direction":128,"opportunity":129},"评估加拿大和美国9129场大火后四种遥感数据的观测延迟与烧毁严重度制图精度。","利用Landsat、Sentinel-1\u002F2及HLS数据，结合77场火灾的野外烧","Sentinel-1延迟最短（中位4天），HLS综合表现最佳，Landsat最慢且覆盖最差。","农业遥感与作物表型","可探索多源遥感融合与近实时烧毁严重度制图，并迁移至农田火灾或作物灾损评估。","openalex","2026-10-05T23:30:26.576406Z",{"total":133,"page":21,"page_size":133,"items":134},6,[135,213,271,350,423,501],{"id":136,"title":137,"url":138,"summary":139,"summary_zh":140,"content":9,"source_name":141,"source_url":138,"published_at":142,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":143,"score_detail":144,"sources":148,"tags":150,"primary_topic":154,"secondary_topics":160,"classification":161,"search_phrases":187,"slug":190,"view_count":122,"doi":191,"paper":192,"created_at":212},4442,"Effects of annual rainfall variability and agroecological practices on vegetation dynamics in Niger’s Sahelian zone","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jaridenv.2026.105752","In recent years, the Sahel has experienced seasonal regreening, but the relative importance of environmental conditions and land management as drivers remains unclear. This study assesses the influence of rainfall variability and different agroecological interventions in a Sahelian zone of Niger from 2001 to 2025 by integrating rainfall data, Normalized Difference Vegetation Index (NDVI) analysis of satellite imagery, and data on agroecological practices from 70 intervention sites. On the regional scale, our results show a statistically significant positive trend in NDVI values, whereas annual rainfall did not show a significant monotonic trend but exhibited spatial heterogeneity. At the local scale, we used a Before-After-Control-Impact (BACI) design with 70 paired intervention and control sites to disentangle interventions effects from rainfall variability. The overall BACI effect was significant (0.072 ± 0.008, p = 0.001), with the strongest effect for Farmer-Managed Natural Regeneration (FMNR; 0.145 ± 0.021, p = 0.006). Agroecological interventions and regreening are strongly positively correlated (r = 0.64, p \u003C 0.05), whereas the correlation between rainfall and regreening is weaker (r = 0.24, p \u003C 0.05). Our findings suggest that agroecological interventions contribute to regreening trends in the selected region and may enhance the resilience of smallholder farmers to climate variability.","近年来，萨赫勒地区出现了季节性再绿化现象，但环境条件与土地管理作为驱动因素的相对重要性仍不明确。本研究通过整合降雨数据、卫星影像的归一化植被指数（NDVI）分析以及70个干预样点的农业生态实践数据，评估了2001年至2025年尼日尔萨赫勒地区降雨变率与不同农业生态干预措施的影响。在区域尺度上，结果表明NDVI值呈统计上显著的正趋势，而年降雨量未表现出显著的单调趋势，但呈现空间异质性。在局地尺度上，我们采用前后对照影响（BACI）设计，以70对干预与对照样点区分干预效应与降雨变率的影响。整体BACI效应显著（0.072 ± 0.008，p = 0.001），其中农民管理自然再生（FMNR）的效应最强（0.145 ± 0.021，p = 0.006）。农业生态干预与再绿化呈强正相关（r = 0.64，p \u003C 0.05），而降雨与再绿化的相关性较弱（r = 0.24，p \u003C 0.05）。研究结果表明，农业生态干预促进了所选区域的再绿化趋势，并可能增强小农户对气候变率的适应能力。","Journal of Arid Environments","2026-10-06T00:00:00Z",78,{"impact":145,"substance":146,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":147},16,22,"基于2001—2025年遥感与70个样点的BACI设计，量化了农业生态干预对萨赫勒植被恢复的贡献，方法严谨、结论可靠，对干旱区生态农业与遥感监测有参考价值。",[149],{"name":141,"url":138},[151,26,152,27,153],"气候韧性","农林复合","萨赫勒",{"code":155,"slug":156,"name":157,"path":158,"parent_code":159},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":162,"locked":14,"version":34,"method":35,"evidence":163},"classified",{"candidates":164,"agricultural_evidence":41},[165,171,177,182],{"code":155,"score":133,"supported":41,"title":166,"abstract":167,"tags":170},[],[168,169],"小农户","smallholder",[],{"code":172,"score":50,"supported":41,"title":173,"abstract":174,"tags":176},"H6",[],[175],"农业生态",[],{"code":39,"score":178,"supported":14,"title":179,"abstract":180,"tags":181},2,[],[],[27,26],{"code":183,"score":21,"supported":14,"title":184,"abstract":185,"tags":186},"H7",[],[],[151],[188,189],"尼日尔 萨赫勒 NDVI","农民管理自然再生 FMNR","尼日尔萨赫勒NDVI-4442","10.1016\u002Fj.jaridenv.2026.105752",{"doi":191,"openalex_id":193,"authors":194,"venue":141,"cited_by_count":122,"oa_url":206,"card":207,"direction":128,"ingested_from":130},"W7220693911",[195,197,200,203],{"name":196,"orcid":9},"Abdoulkader Amadou Djibo",{"name":198,"orcid":199},"Martin Wiehle","https:\u002F\u002Forcid.org\u002F0000-0002-2054-1555",{"name":201,"orcid":202},"Kira Fastner","https:\u002F\u002Forcid.org\u002F0000-0002-7565-8689",{"name":204,"orcid":205},"Abdoulaye Diouf","https:\u002F\u002Forcid.org\u002F0000-0001-8404-4112","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0140196326002041\u002Fpdf",{"tldr":208,"method":209,"finding":210,"direction":128,"opportunity":211},"研究尼日尔萨赫勒地区2001-2025年植被动态，区分降雨与农生态实践的影响。","整合降雨数据、NDVI卫星影像与70个干预点数据，采用BACI设计。","农生态干预显著促进植被恢复，其中FMNR效果最强，降雨影响较弱。","可结合多源遥感与长期地面观测，量化不同农生态措施的气候韧性贡献。","2026-10-08T23:30:31.109314Z",{"id":214,"title":215,"url":216,"summary":217,"summary_zh":218,"content":9,"source_name":219,"source_url":216,"published_at":220,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":221,"score_detail":222,"sources":228,"tags":230,"primary_topic":9,"secondary_topics":234,"classification":235,"search_phrases":242,"slug":245,"view_count":50,"doi":246,"paper":247,"created_at":270},3853,"Timelines of vegetation recovery following fire in a shrub-dominated Mediterranean-type ecosystem","https:\u002F\u002Fdoi.org\u002F10.32942\u002Fx2vx0p","Fire is a natural disturbance in many Mediterranean-type ecosystems, but wildfires are increasing in frequency and severity in many places worldwide, including California. Vegetation recovery following wildfire is critical for landscape stability and foundational to resilience of many ecosystem processes in the years and decades that follow fire. While remote sensing tools are increasingly important for mapping changes in vegetation following fire, to date these tools have limited application in shrubland systems such as those found in parts of California, hindering assessments of vegetation recovery timelines and inferences about ecological impacts. Using a 100-year fire history dataset alongside both field vegetation surveys (2014-2025) and modeled percent vegetation cover (1986-2025), we analyzed temporal cover dynamics of dominant vegetation classes in the years leading up to, and following, major wildfire events in the Santa Monica Mountains of California. Field survey data broadly corresponded with modeled vegetation cover, particularly for shrubs and herbaceous\u002Fgraminoid cover. We identified consistent patterns in vegetation cover classes among dominant vegetation communities, including reductions in shrub and tree cover following fire in chaparral and grasslands, and pulses in herbaceous and graminoid cover in chaparral and upland woodlands, in the initial 2-5 years following fire. Herbaceous and graminoid cover returned to baseline levels faster than shrubs, which in turn recovered faster than trees. These results corroborate previous field-based studies of vegetation recovery following wildfire, and point toward modeled remote sensing products as a feasible tool for tracking post-fire recovery dynamics in remote and rugged terrain.","火灾是许多地中海型生态系统中的一种自然干扰，但在全球许多地方，包括加利福尼亚，野火的频率和严重程度正在增加。野火后的植被恢复对景观稳定性至关重要，也是火灾后数年乃至数十年间许多生态系统过程恢复力的基础。尽管遥感工具在绘制火灾后植被变化方面日益重要，但迄今为止，这些工具在灌木地系统（如加利福尼亚部分地区所见的灌木地）中的应用仍然有限，阻碍了对植被恢复时间线的评估以及对生态影响的推断。利用百年火灾历史数据集，结合野外植被调查（2014-2025年）和模拟植被覆盖度百分比（1986-2025年），我们分析了加利福尼亚圣莫尼卡山脉在重大野火事件发生前后数年里主要植被类别的覆盖度时间动态。野外调查数据与模拟植被覆盖度总体吻合，尤其是灌木和草本\u002F禾草类覆盖度。我们在主要植被群落中识别出植被覆盖类别的 consistent 模式，包括火灾后 chaparral（硬叶灌木群落）和草地中灌木与乔木覆盖度的下降，以及火灾后最初2-5年内 chaparral 和山地林地中草本与禾草类覆盖度的脉冲式增加。草本和禾草类覆盖度恢复到基线水平的速度快于灌木，而灌木又快于乔木。这些结果印证了此前基于野外的野火后植被恢复研究，并表明模拟遥感产品是追踪偏远崎岖地形火灾后恢复动态的可行工具。","OpenAlex","2026-09-28T00:00:00Z",67,{"impact":20,"substance":223,"depth":224,"authority":225,"freshness":226,"relevant":21,"comment":227},20,17,13,9,"基于百年火史与遥感建模量化灌木地火后恢复时序，方法可迁移至农业遥感与生态监测，但属基础生态研究，产业影响有限。",[229],{"name":219,"url":216},[26,27,231,232,233],"生态遥感","野火","灌丛生态系统",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":236},{"candidates":237,"agricultural_evidence":14,"reason":55},[238],{"code":39,"score":226,"supported":41,"title":239,"abstract":240,"tags":241},[],[44,45],[27,231,26],[243,244],"Santa Monica Mountains 野火 植被恢复","遥感 植被覆盖 火后恢复","SantaMonicaMountains野火植被恢复-3853","10.32942\u002Fx2vx0p",{"doi":246,"openalex_id":248,"authors":249,"venue":9,"cited_by_count":122,"oa_url":264,"card":265,"direction":128,"ingested_from":130},"W7214655153",[250,253,255,257,260,262],{"name":251,"orcid":252},"Christian John","https:\u002F\u002Forcid.org\u002F0000-0002-6249-040X",{"name":254,"orcid":9},"Ellen Bolas",{"name":256,"orcid":9},"Ashley Grupenhoff",{"name":258,"orcid":259},"Mark Mendelsohn","https:\u002F\u002Forcid.org\u002F0009-0005-4266-3894",{"name":261,"orcid":9},"Joseph Algiers",{"name":263,"orcid":9},"Justine Smith","https:\u002F\u002Fecoevorxiv.org\u002Frepository\u002Fobject\u002F15196\u002Fdownload\u002F26361\u002F",{"tldr":266,"method":267,"finding":268,"direction":128,"opportunity":269},"利用百年火史、野外调查与遥感植被覆盖数据，分析加州灌木地火灾后植被恢复时间线。","百年火史数据、2014-2025野外调查、1986-2025遥感植被覆盖模型。","火灾后2-5年草本恢复最快，灌木次之，树木最慢；遥感模型可追踪崎岖地形恢复动态。","可开发针对灌木地生态系统的遥感恢复监测模型，并验证不同火情下的恢复阈值。","2026-09-30T23:30:32.523255Z",{"id":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":9,"source_name":277,"source_url":274,"published_at":278,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":279,"score_detail":280,"sources":282,"tags":284,"primary_topic":288,"secondary_topics":294,"classification":299,"search_phrases":325,"slug":328,"view_count":50,"doi":329,"paper":330,"created_at":349},3550,"Asymmetric Vegetation Responses to Flood Exposure in the Chi River Basin: A Multi-Temporal Remote Sensing and Machine Learning Investigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18101606","Flooding and drought alternate in many tropical floodplains, but how vegetation responds to both hazards over multiple years remains poorly quantified. We examined vegetation–flood associations in the Chi River Basin, Northeast Thailand, drawing on Sentinel-2 imagery from 2020, 2023, and 2024, flood records from 2017, 2018, 2021, and 2022, and machine learning methods across 230,911 point-based spatial units. Nine forms of asymmetry emerged from the analysis. The most striking was recovery asymmetry: areas that flooded at least twice between 2017 and 2022 gained vegetation during 2020–2023 (mean NDVI change = +0.0270), whereas areas with little or no flood exposure lost vegetation (mean change = −0.0430). This gap was statistically significant (Cohen’s d = 0.5586, p \u003C 0.001) and suggests that repeated flooding may buffer vegetation against subsequent drought. Agricultural land declined less (−0.0316) than forest (−0.0937; ANOVA F = 1717.7, p \u003C 0.001). Baseline vegetation condition, measured as NDVI in 2020, contributed 32.9% to model importance, more than twice the contribution of elevation (14.5%). Spectral indices together accounted for 76.6% of importance, compared with 23.5% for topographic variables. The 2023 El Niño year produced the largest difference between high-flood and low-flood areas (+0.0364); because only one year per ENSO phase was available, we treat this as a case-based comparison rather than a general ENSO response. Threshold analysis identified two distinct values: an operational cut-off at NDVI = 0.05 (overall accuracy 82.67%) and an ecological transition around 0.25–0.30. Spatial clustering was weak but significant (Moran’s I = 0.2179, p \u003C 0.001). Spatial block cross-validation gave lower accuracy (0.597) than random cross-validation (0.627), pointing to spatial autocorrelation in the data. High-flood areas had 1.67 times the vulnerability index of low-flood areas (0.4306 vs. 0.2573). These patterns support differentiated management: elevation-based zoning, warning systems calibrated to local flood regimes, and focused interventions at hotspots.","在许多热带洪泛平原，洪水与干旱交替发生，但植被如何在多年间同时响应这两种灾害，仍缺乏充分的定量研究。我们以泰国东北部栖河（Chi River）流域为研究区，考察植被与洪水的关联，数据来源包括2020年、2023年和2024年的Sentinel-2影像，2017年、2018年、2021年和2022年的洪水记录，并采用机器学习方法，覆盖230 911个基于点的空间单元。分析中出现了九种不对称形式。其中最显著的是恢复不对称：2017—2022年间至少发生两次洪水的区域，在2020—2023年间植被增加（NDVI平均变化=+0.0270），而洪水暴露很少或没有洪水暴露的区域则植被减少（平均变化=−0.0430）。这一差异具有统计显著性（Cohen’s d=0.5586，p\u003C0.001），表明反复洪水可能缓冲植被对随后干旱的响应。农田的下降幅度（−0.0316）小于森林（−0.0937；ANOVA F=1717.7，p\u003C0.001）。以2020年NDVI衡量的基线植被状况对模型重要性的贡献为32.9%，是海拔贡献（14.5%）的两倍多。光谱指数合计占重要性的76.6%，而地形变量占23.5%。2023年厄尔尼诺年（El Niño year）高洪水区与低洪水区之间的差异最大（+0.0364）；由于每个ENSO相位仅有1年数据，我们将其视为基于个案的比较，而非一般性的ENSO响应。阈值分析识别出两个不同的值：操作性截断值为NDVI=0.05（总体精度82.67%），生态过渡值约为0.25~0.30。空间聚类较弱但显著（Moran’s I=0.2179，p\u003C0.001）。空间分块交叉验证的精度（0.597）低于随机交叉验证（0.627），表明数据中存在空间自相关。高洪水区的脆弱性指数是低洪水区的1.67倍（0.4306对0.2573）。这些格局支持差异化治理：基于海拔的分区、根据当地洪水情势校准的预警系统，以及在热点区域的重点干预。","Symmetry","2026-09-25T00:00:00Z",70,{"impact":20,"substance":146,"depth":17,"authority":225,"freshness":226,"relevant":21,"comment":281},"基于Sentinel-2与机器学习的多时相洪涝-植被响应研究，数据规模大、结论具体，对农业遥感与灾害风险管理有参考价值，但属区域案例研究，公共影响有限。",[283],{"name":277,"url":274},[285,286,26,287,27],"智慧农业","农业人工智能","洪涝灾害",{"code":289,"slug":290,"name":291,"path":292,"parent_code":293},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[295],{"code":39,"slug":296,"name":297,"path":298,"parent_code":293},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":162,"locked":14,"version":34,"method":35,"evidence":300},{"candidates":301,"agricultural_evidence":41},[302,309,313,320],{"code":289,"score":303,"supported":41,"title":304,"abstract":306,"tags":308},15,[305],"machine learning",[307,305],"机器学习",[286],{"code":39,"score":13,"supported":41,"title":310,"abstract":311,"tags":312},[45],[],[27,26],{"code":183,"score":314,"supported":41,"title":315,"abstract":316,"tags":319},7,[],[317,318],"干旱","drought",[287],{"code":321,"score":21,"supported":14,"title":322,"abstract":323,"tags":324},"I1",[],[],[285],[326,327],"湄公河支流 流域 遥感 洪水","Sentinel-2 NDVI 洪涝 植被","湄公河支流流域遥感洪水-3550","10.3390\u002Fsym18101606",{"doi":329,"openalex_id":331,"authors":332,"venue":277,"cited_by_count":122,"oa_url":274,"card":344,"direction":128,"ingested_from":130},"W7214283372",[333,335,338,341],{"name":334,"orcid":9},"Jiradech Majandang",{"name":336,"orcid":337},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":339,"orcid":340},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":342,"orcid":343},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":345,"method":346,"finding":347,"direction":128,"opportunity":348},"利用多时相遥感和机器学习揭示泰国湄公河支流流域植被对洪水的非对称响应。","Sentinel-2影像、洪水记录与机器学习，分析23万个空间单元。","重复洪水区植被增加，低洪水区减少，表明洪水可缓冲干旱影响。","可探索洪水-干旱交替下植被恢复机制，并开发基于阈值的差异化预警系统。","2026-09-26T23:30:29.514361Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":9,"source_name":356,"source_url":353,"published_at":357,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":358,"score_detail":359,"sources":362,"tags":364,"primary_topic":368,"secondary_topics":373,"classification":375,"search_phrases":397,"slug":400,"view_count":21,"doi":401,"paper":402,"created_at":422},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","2026-09-11T00:00:00Z",62,{"impact":20,"substance":17,"depth":303,"authority":360,"freshness":226,"relevant":21,"comment":361},12,"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。",[363],{"name":356,"url":353},[365,366,26,367,27],"农业信息化","荒漠化","土地利用",{"code":369,"slug":370,"name":371,"path":372,"parent_code":293},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[374],{"code":39,"slug":296,"name":297,"path":298,"parent_code":293},{"status":162,"locked":14,"version":34,"method":35,"evidence":376},{"candidates":377,"agricultural_evidence":41},[378,385,389,393],{"code":369,"score":226,"supported":41,"title":379,"abstract":380,"tags":384},[],[381,382,383],"地理信息","GIS","geographic information",[],{"code":39,"score":20,"supported":41,"title":386,"abstract":387,"tags":388},[],[44,45],[27,26],{"code":172,"score":178,"supported":14,"title":390,"abstract":391,"tags":392},[],[],[367,366],{"code":321,"score":21,"supported":14,"title":394,"abstract":395,"tags":396},[],[],[365],[398,399],"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":401,"openalex_id":403,"authors":404,"venue":356,"cited_by_count":122,"oa_url":353,"card":417,"direction":128,"ingested_from":130},"W7212238590",[405,407,410,413,415],{"name":406,"orcid":9},"Bilel Zerouali",{"name":408,"orcid":409},"Richarde Marques da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-6601-5174",{"name":411,"orcid":412},"Nadjem Bailek","https:\u002F\u002Forcid.org\u002F0000-0001-9051-8548",{"name":414,"orcid":9},"Valeriano Carneiro de Lima Silva",{"name":416,"orcid":9},"Celso Augusto Guimarães Santos",{"tldr":418,"method":419,"finding":420,"direction":128,"opportunity":421},"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",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":143,"score_detail":431,"sources":433,"tags":435,"primary_topic":9,"secondary_topics":439,"classification":440,"search_phrases":471,"slug":474,"view_count":122,"doi":475,"paper":476,"created_at":500},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",{"impact":17,"substance":146,"depth":224,"authority":360,"freshness":226,"relevant":21,"comment":432},"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。",[434],{"name":429,"url":426},[26,367,436,437,438],"森林保护","亚马逊","CBERS-4",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":441},{"candidates":442,"agricultural_evidence":41,"reason":470},[443,449,453,459,464],{"code":172,"score":314,"supported":41,"title":444,"abstract":445,"tags":448},[],[446,447],"生物多样性","biodiversity",[367],{"code":39,"score":314,"supported":41,"title":450,"abstract":451,"tags":452},[],[44,45],[26],{"code":183,"score":133,"supported":41,"title":454,"abstract":455,"tags":458},[],[456,457],"气候变化","climate change",[],{"code":49,"score":50,"supported":41,"title":460,"abstract":461,"tags":463},[],[462],"soil",[],{"code":465,"score":50,"supported":41,"title":466,"abstract":467,"tags":469},"I7",[],[468],"研究方法",[],"多个产业证据相当，需要确认主归属",[472,473],"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":475,"openalex_id":477,"authors":478,"venue":429,"cited_by_count":122,"oa_url":426,"card":495,"direction":128,"ingested_from":130},"W7223141286",[479,482,484,487,490,493],{"name":480,"orcid":481},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":483,"orcid":9},"Gabriel Henrique Santos Silva",{"name":485,"orcid":486},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":488,"orcid":489},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":491,"orcid":492},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":494,"orcid":9},"Sylvana Melo dos Santos",{"tldr":496,"method":497,"finding":498,"direction":128,"opportunity":499},"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":502,"title":503,"url":504,"summary":505,"summary_zh":506,"content":9,"source_name":507,"source_url":504,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":508,"score_detail":509,"sources":511,"tags":513,"primary_topic":518,"secondary_topics":522,"classification":523,"search_phrases":543,"slug":546,"view_count":122,"doi":547,"paper":548,"created_at":580},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":145,"substance":146,"depth":17,"authority":19,"freshness":226,"relevant":21,"comment":510},"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。",[512],{"name":507,"url":504},[26,514,515,516,517],"莫桑比克","土壤盐渍化","土地覆盖变化","盐分反演",{"code":49,"slug":462,"name":519,"path":520,"parent_code":521},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":162,"locked":14,"version":34,"method":35,"evidence":524},{"candidates":525,"agricultural_evidence":41},[526,530,534,539],{"code":49,"score":19,"supported":41,"title":527,"abstract":528,"tags":529},[462],[53,462],[],{"code":39,"score":314,"supported":41,"title":531,"abstract":532,"tags":533},[],[44,45],[26],{"code":183,"score":133,"supported":41,"title":535,"abstract":536,"tags":538},[],[537,317],"气候适应",[],{"code":172,"score":21,"supported":14,"title":540,"abstract":541,"tags":542},[],[],[516],[544,545],"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":547,"openalex_id":549,"authors":550,"venue":507,"cited_by_count":122,"oa_url":504,"card":575,"direction":128,"ingested_from":130},"W7223069171",[551,553,555,557,560,562,565,568,570,572],{"name":552,"orcid":9},"Yue Li",{"name":554,"orcid":9},"Mengfei Shi",{"name":556,"orcid":9},"Fei Zhang",{"name":558,"orcid":559},"Brian Alan Johnson","https:\u002F\u002Forcid.org\u002F0000-0003-1911-3585",{"name":561,"orcid":9},"Gowhar Meraj",{"name":563,"orcid":564},"Jaime Carlos Macuácua","https:\u002F\u002Forcid.org\u002F0000-0002-4822-6098",{"name":566,"orcid":567},"Pankaj Kumar","https:\u002F\u002Forcid.org\u002F0000-0002-5264-3951",{"name":569,"orcid":9},"Yuanfang Chai",{"name":571,"orcid":9},"Hongtao Xu",{"name":573,"orcid":574},"Xu Ma","https:\u002F\u002Forcid.org\u002F0000-0003-0644-7498",{"tldr":576,"method":577,"finding":578,"direction":128,"opportunity":579},"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"]