[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3853":3,"related-3853":81},{"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":48,"slug":51,"view_count":52,"doi":53,"paper":54,"created_at":80},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。",null,"OpenAlex","2026-09-28T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,13,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","植被恢复","生态遥感","野火","灌丛生态系统",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":47},[39],{"code":40,"score":21,"supported":41,"title":42,"abstract":43,"tags":46},"I3",true,[],[44,45],"遥感","remote sensing",[28,29,27],"农业相关性需要复核",[49,50],"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",3,"10.32942\u002Fx2vx0p",{"doi":53,"openalex_id":55,"authors":56,"venue":9,"cited_by_count":71,"oa_url":72,"card":73,"direction":77,"ingested_from":79},"W7214655153",[57,60,62,64,67,69],{"name":58,"orcid":59},"Christian John","https:\u002F\u002Forcid.org\u002F0000-0002-6249-040X",{"name":61,"orcid":9},"Ellen Bolas",{"name":63,"orcid":9},"Ashley Grupenhoff",{"name":65,"orcid":66},"Mark Mendelsohn","https:\u002F\u002Forcid.org\u002F0009-0005-4266-3894",{"name":68,"orcid":9},"Joseph Algiers",{"name":70,"orcid":9},"Justine Smith",0,"https:\u002F\u002Fecoevorxiv.org\u002Frepository\u002Fobject\u002F15196\u002Fdownload\u002F26361\u002F",{"tldr":74,"method":75,"finding":76,"direction":77,"opportunity":78},"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。","农业遥感与作物表型","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。","openalex","2026-09-30T23:30:32.523255Z",{"total":82,"page":22,"page_size":82,"items":83},6,[84,168,244,348,425,495],{"id":85,"title":86,"url":87,"summary":88,"summary_zh":89,"content":9,"source_name":90,"source_url":87,"published_at":91,"category":12,"cover_url":9,"hotness":13,"is_selected":41,"score":92,"score_detail":93,"sources":100,"tags":102,"primary_topic":9,"secondary_topics":106,"classification":107,"search_phrases":130,"slug":133,"view_count":126,"doi":134,"paper":135,"created_at":167},4590,"Earlier green-up exacerbates northern vegetation sensitivity to flash drought","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-026-78296-0","Flash drought–induced relative reduction in vegetation activity and canopy functioning, referred to as vegetation sensitivity, is a crucial measure of drought impacts. While climatic factors influencing vegetation sensitivity are well recognized, the role of phenological shifts remains poorly understood. Using multiple satellite-derived vegetation proxies (NDVI, LAI, SIF, and VOD) during 2001–2021, we show that earlier green-up dates (GUD) exacerbate vegetation sensitivity to flash droughts across 61.2–84.1% of the Northern Hemisphere (>23.5°N). Earlier GUDs enhance pre-drought vegetation growth, especially during the early growing season. Such amplified vegetation growth exacerbates sensitivity by accelerating soil moisture depletion and respiration cost, outweighing the benefit of increased subsequent net carbon assimilation. The contribution of GUD to vegetation sensitivity is comparable to that of major climatic factors. Ignoring phenological changes may lead to underestimation of long-term trends in vegetation sensitivity. These findings underscore the necessity of incorporating the interactions between phenology and flash droughts into projections of ecosystem resistance and resilience. Flash droughts can rapidly reduce vegetation growth, but the role of spring phenology is unclear. The authors show that earlier green-up increases vegetation sensitivity to flash drought across much of the Northern Hemisphere ecosystems.","闪旱导致的植被活动与冠层功能相对下降，即植被敏感性，是衡量干旱影响的关键指标。尽管影响植被敏感性的气候因素已被广泛认识，但物候偏移的作用仍知之甚少。利用2001—2021年多颗卫星衍生的植被代用指标（NDVI、LAI、SIF和VOD），我们发现，在北半球（>23.5°N）61.2%—84.1%的区域，提前的返青期（GUD）加剧了植被对闪旱的敏感性。较早的GUD增强了干旱前的植被生长，尤其是在生长季早期。这种放大的植被生长通过加速土壤水分消耗和呼吸成本加剧了敏感性，其效应超过了随后净碳同化增加带来的益处。GUD对植被敏感性的贡献与主要气候因子相当。忽视物候变化可能导致对植被敏感性长期趋势的低估。这些发现强调了将物候与闪旱之间的相互作用纳入生态系统抵抗力和恢复力预估的必要性。闪旱可迅速降低植被生长，但春季物候的作用尚不清楚。作者表明，在北半球大部分生态系统中，较早的返青增加了植被对闪旱的敏感性。","Nature Communications","2026-10-08T00:00:00Z",86,{"impact":94,"substance":95,"depth":96,"authority":97,"freshness":98,"relevant":22,"comment":99},22,23,19,15,7,"基于多源卫星遥感揭示春季提前返青加剧北半球植被对骤发干旱的敏感性，方法扎实、结论新颖，对农业干旱监测预警有参考价值。",[101],{"name":90,"url":87},[103,27,104,105,29],"气候变化","植被物候","干旱预警",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":108},{"candidates":109,"agricultural_evidence":14,"reason":47},[110,118,125],{"code":111,"score":112,"supported":41,"title":113,"abstract":115,"tags":117},"H7",16,[114],"drought",[116,114],"干旱",[105,103],{"code":119,"score":82,"supported":41,"title":120,"abstract":121,"tags":124},"H2",[],[122,123],"土壤","soil",[],{"code":40,"score":126,"supported":14,"title":127,"abstract":128,"tags":129},2,[],[],[29,27],[131,132],"北半球 植被 干旱 敏感性","NDVI LAI SIF VOD 遥感","北半球植被干旱敏感性-4590","10.1038\u002Fs41467-026-78296-0",{"doi":134,"openalex_id":136,"authors":137,"venue":90,"cited_by_count":71,"oa_url":161,"card":162,"direction":77,"ingested_from":79},"W7115172292",[138,141,144,147,150,153,155,158],{"name":139,"orcid":140},"Wenrui Bai","https:\u002F\u002Forcid.org\u002F0009-0009-4885-3348",{"name":142,"orcid":143},"Quansheng Ge","https:\u002F\u002Forcid.org\u002F0000-0001-8712-8565",{"name":145,"orcid":146},"Constantin M. Zohner","https:\u002F\u002Forcid.org\u002F0000-0002-8302-4854",{"name":148,"orcid":149},"Jun Wang","https:\u002F\u002Forcid.org\u002F0009-0005-5869-832X",{"name":151,"orcid":152},"Shaozhi Lin","https:\u002F\u002Forcid.org\u002F0000-0002-8975-281X",{"name":154,"orcid":9},"Yani Liang",{"name":156,"orcid":157},"Yongshuo H. Fu","https:\u002F\u002Forcid.org\u002F0000-0002-9761-5292",{"name":159,"orcid":160},"Huanjiong Wang","https:\u002F\u002Forcid.org\u002F0000-0002-2325-0120","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41467-026-78296-0_reference.pdf",{"tldr":163,"method":164,"finding":165,"direction":77,"opportunity":166},"利用多源卫星数据揭示北半球早绿期加剧植被对骤旱的敏感性。","2001–2021年NDVI、LAI、SIF、VOD等卫星植被指标分析。","早绿期通过加速土壤水分消耗和呼吸成本，放大植被对骤旱的敏感性。","可探究不同生态系统和气候区中物候-骤旱交互的差异，并纳入陆面模型改进干旱预警。","2026-10-10T23:30:28.234091Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":181,"tags":183,"primary_topic":187,"secondary_topics":193,"classification":194,"search_phrases":218,"slug":221,"view_count":71,"doi":222,"paper":223,"created_at":243},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":112,"substance":94,"depth":178,"authority":179,"freshness":17,"relevant":22,"comment":180},18,14,"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。",[182],{"name":174,"url":171},[184,27,185,28,186],"气候韧性","农林复合","萨赫勒",{"code":188,"slug":189,"name":190,"path":191,"parent_code":192},"G2","farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[],{"status":195,"locked":14,"version":35,"method":36,"evidence":196},"classified",{"candidates":197,"agricultural_evidence":41},[198,204,210,214],{"code":188,"score":82,"supported":41,"title":199,"abstract":200,"tags":203},[],[201,202],"小农户","smallholder",[],{"code":205,"score":52,"supported":41,"title":206,"abstract":207,"tags":209},"H6",[],[208],"农业生态",[],{"code":40,"score":126,"supported":14,"title":211,"abstract":212,"tags":213},[],[],[28,27],{"code":111,"score":22,"supported":14,"title":215,"abstract":216,"tags":217},[],[],[184],[219,220],"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":222,"openalex_id":224,"authors":225,"venue":174,"cited_by_count":71,"oa_url":237,"card":238,"direction":77,"ingested_from":79},"W7220693911",[226,228,231,234],{"name":227,"orcid":9},"Abdoulkader Amadou Djibo",{"name":229,"orcid":230},"Martin Wiehle","https:\u002F\u002Forcid.org\u002F0000-0002-2054-1555",{"name":232,"orcid":233},"Kira Fastner","https:\u002F\u002Forcid.org\u002F0000-0002-7565-8689",{"name":235,"orcid":236},"Abdoulaye Diouf","https:\u002F\u002Forcid.org\u002F0000-0001-8404-4112","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0140196326002041\u002Fpdf",{"tldr":239,"method":240,"finding":241,"direction":77,"opportunity":242},"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":245,"title":246,"url":247,"summary":248,"summary_zh":249,"content":9,"source_name":250,"source_url":247,"published_at":251,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":252,"score_detail":253,"sources":255,"tags":257,"primary_topic":9,"secondary_topics":261,"classification":262,"search_phrases":275,"slug":278,"view_count":22,"doi":279,"paper":280,"created_at":347},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。","Fire Ecology","2026-10-02T00:00:00Z",81,{"impact":178,"substance":95,"depth":178,"authority":179,"freshness":17,"relevant":22,"comment":254},"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。",[256],{"name":250,"url":247},[27,28,258,259,260],"林火生态","Sentinel-1","灾后评估",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":263},{"candidates":264,"agricultural_evidence":14,"reason":47},[265,271],{"code":40,"score":266,"supported":41,"title":267,"abstract":268,"tags":270},11,[],[44,45,269],"multispectral",[28,27],{"code":119,"score":52,"supported":41,"title":272,"abstract":273,"tags":274},[],[122],[],[276,277],"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":279,"openalex_id":281,"authors":282,"venue":250,"cited_by_count":71,"oa_url":341,"card":342,"direction":77,"ingested_from":79},"W7215928159",[283,286,289,292,295,298,301,304,307,310,313,315,317,320,323,326,329,332,335,338],{"name":284,"orcid":285},"Kyle C. Rodman","https:\u002F\u002Forcid.org\u002F0000-0001-9538-8412",{"name":287,"orcid":288},"Ellen Whitman","https:\u002F\u002Forcid.org\u002F0000-0002-4562-3645",{"name":290,"orcid":291},"Alexander A. Howe","https:\u002F\u002Forcid.org\u002F0000-0002-6815-7536",{"name":293,"orcid":294},"A. Spannuth","https:\u002F\u002Forcid.org\u002F0009-0007-9401-5842",{"name":296,"orcid":297},"Kevin Barrett","https:\u002F\u002Forcid.org\u002F0000-0003-1014-6372",{"name":299,"orcid":300},"Corey Beinhart","https:\u002F\u002Forcid.org\u002F0009-0006-9282-4534",{"name":302,"orcid":303},"Yan Boucher","https:\u002F\u002Forcid.org\u002F0000-0003-0411-6927",{"name":305,"orcid":306},"Sebastian U. Busby","https:\u002F\u002Forcid.org\u002F0000-0003-2084-0106",{"name":308,"orcid":309},"Jorge Téllez Carrasco","https:\u002F\u002Forcid.org\u002F0009-0002-3326-1306",{"name":311,"orcid":312},"Kyra Clark‐Wolf","https:\u002F\u002Forcid.org\u002F0000-0003-4584-0348",{"name":314,"orcid":9},"M. J. Corbett",{"name":316,"orcid":9},"J. E. Crouse",{"name":318,"orcid":319},"Victor Danneyrolles","https:\u002F\u002Forcid.org\u002F0000-0002-4839-8164",{"name":321,"orcid":322},"Patricia Dappen","https:\u002F\u002Forcid.org\u002F0009-0004-9910-7023",{"name":324,"orcid":325},"Kimberley T. Davis","https:\u002F\u002Forcid.org\u002F0000-0001-9727-374X",{"name":327,"orcid":328},"Brian J. Harvey","https:\u002F\u002Forcid.org\u002F0000-0002-5902-4862",{"name":330,"orcid":331},"Zachary A. Holden","https:\u002F\u002Forcid.org\u002F0000-0002-4319-6491",{"name":333,"orcid":334},"Sharon M. Hood","https:\u002F\u002Forcid.org\u002F0000-0002-9544-8208",{"name":336,"orcid":337},"José M. Iniguez","https:\u002F\u002Forcid.org\u002F0000-0002-4566-1297",{"name":339,"orcid":340},"Patrick M. A. James","https:\u002F\u002Forcid.org\u002F0000-0001-7639-7217","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1186\u002Fs42408-026-00589-y.pdf",{"tldr":343,"method":344,"finding":345,"direction":77,"opportunity":346},"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。","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。","2026-10-05T23:30:26.576406Z",{"id":349,"title":350,"url":351,"summary":352,"summary_zh":353,"content":9,"source_name":354,"source_url":351,"published_at":355,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":356,"score_detail":357,"sources":360,"tags":362,"primary_topic":366,"secondary_topics":371,"classification":372,"search_phrases":408,"slug":411,"view_count":126,"doi":412,"paper":413,"created_at":424},3894,"Assessment of Spatio-temporal Dynamics of the Remote Sensing Ecological Index (RSEI) based on Landsat 8 Data: A Case Study of Kherson Region, Ukraine (2013–2025)","https:\u002F\u002Fdoi.org\u002F10.36023\u002Fujrs.2026.13.3.310","This scientific paper presents a comprehensive assessment of the spatio-temporal dynamics of the ecological state of the Kherson region from 2013 to 2025, with a particular focus on the consequences of the full-scale military aggression by the Russian Federation. Amidst intensive hostilities, the destruction of critical infrastructure, and global climate challenges, objective geospatial monitoring of the natural environment is a crucial task for documenting damages and forecasting the region's future development.The study is based on the use of modern geographic information systems and remote sensing data. Using the Google Earth Engine cloud computing platform and an archive of multispectral satellite imagery from Landsat 8 OLI\u002FTIRS, the Remote Sensing Ecological Index (RSEI) was calculated. This index comprehensively integrates four key biophysical indicators: greenness (based on the Normalized Difference Vegetation Index, NDVI), wetness (Wet), dryness (based on the Normalized Difference Build-up and Soil Index, NDBSI), and land surface temperature (LST). The application of Principal Component Analysis (PCA) allowed these indicators to be mathematically combined into a single standardized index, where the first principal component (PC1) explains the highest proportion of the total spatial data variance. This indicates that the first principal component (PC1) successfully captures the vast majority of the variance among the four input indicators, which is a basic premise of the RSEI methodology.The results of the cartographic and statistical analyses revealed a sharp, unprecedented deterioration in the ecosystem health of the study region in 2025 compared to retrospective data from previous years. A critical drop in the average RSEI value was established – from 0.381 in the relatively stable year of 2021 to 0.205 in 2025. The spatial assessment demonstrated that the area of territories classified as being in a state of ecological crisis (the «Terrible» category, with an index ranking from 0 to 0.2) more than doubled: from 26% of the region's total area in 2021 to a critical 60% in 2025.The sharp drop in RSEI spatially aligns with the large-scale hydrological transformations caused by the destruction of the Kakhovka Dam; however, this indicator also reflects the synergistic impact of intense hostilities, fires, the cessation of irrigation, and climate anomalies. Kakhovka Dam destruction led to the catastrophic destruction of the hydrographic network, the disappearance of water supply sources for vital agricultural lands, and the disruption of life-support systems in urbanized areas. The only exception against the general backdrop of the ecological catastrophe is the recorded local natural restoration of the old Dnipro riverbed ecosystem, which is gradually returning to its historical state that preceded massive anthropogenic intervention and river regulation in the 1950s.In conclusion, the synergistic impact of continuous large-scale hostilities, the destruction of established ecosystem links, and the exacerbation of global climate change shape an extremely bleak forecast. Further degradation of the soil and vegetation cover and the depletion of water resources create a high risk of transforming a significant part of the Kherson region into a territory unfit for safe living, agriculture, and socio-economic functioning. Disclosure of AI use. In the process of conducting research and preparing the manuscript, the author used the large language model Google Gemini 3 Pro. The tool acted as a technical assistant and consultant at the stages:1. Software development: assistance in writing, debugging and optimizing JavaScript scripts for the Google Earth Engine environment, in particular the implementation of the PCA algorithm.2. Text preparation: providing recommendations on structuring the work, describing the methodology and improving the academic style of presentation.The author carried out a full check of the generated code and text, verified the results obtained and bears sole responsibility for the content of this publication. Data availability and integrity. The JavaScript code for Google Earth Engine developed for this study has been deposited in the Zenodo repository with a reserved DOI to ensure long-term accessibility and reproducibility of the results. The code will be publicly available upon acceptance of the manuscript for publication. The data can be provided by the author upon reasonable request. Conflicts of Interest. The author declares that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements. The author is grateful to the reviewers and editors for their valuable comments, recommendations, and attention to the work.","本文对2013年至2025年赫尔松地区生态状况的时空动态进行了综合评估，尤其关注俄罗斯联邦全面军事侵略所造成的后果。在激烈的敌对行动、关键基础设施的破坏以及全球气候挑战的背景下，对自然环境进行客观的地理空间监测是记录损害和预测该地区未来发展的关键任务。本研究基于现代地理信息系统和遥感数据的应用。利用Google Earth Engine云计算平台和Landsat 8 OLI\u002FTIRS多光谱卫星影像档案，计算了遥感生态指数（RSEI）。该指数综合集成了四个关键生物物理指标：绿度（基于归一化差异植被指数，NDVI）、湿度（Wet）、干度（基于归一化差异建筑与土壤指数，NDBSI）以及地表温度（LST）。主成分分析（PCA）的应用使这些指标能够在数学上组合为一个标准化的单一指数，其中第一主成分（PC1）解释了总空间数据方差的最大比例。这表明第一主成分（PC1）成功捕捉了四个输入指标之间的绝大部分方差，这是RSEI方法论的基本前提。制图与统计分析的结果揭示，与往年回顾性数据相比，2025年研究区域的生态系统健康出现了急剧且前所未有的恶化。研究确定了平均RSEI值的临界下降——从相对稳定的2021年的0.381降至2025年的0.205。空间评估表明，被归类为生态危机状态的领土面积（“极差”类别，指数排名从0到0.2）增加了一倍以上：从2021年占该地区总面积的26%增至2025年临界水平的60%。RSEI的急剧下降在空间上与卡霍夫卡大坝被毁所引发的大规模水文变化相吻合；然而，该指标也反映了激烈敌对行动、火灾、灌溉停止以及气候异常的协同影响。卡霍夫卡大坝被毁导致水文网络的灾难性破坏，","Ukrainian journal of remote sensing","2026-09-30T00:00:00Z",73,{"impact":358,"substance":94,"depth":178,"authority":358,"freshness":21,"relevant":22,"comment":359},12,"基于Landsat 8与Google Earth Engine的RSEI时空动态研究，方法规范、数据翔实，对战争与气候双重压力下的农业生态退化具有实证价值，但属区域案例、与国内三农信息化关联偏弱。",[361],{"name":354,"url":351},[363,27,29,364,365],"NDVI","Landsat 8","乌克兰农业",{"code":40,"slug":367,"name":368,"path":369,"parent_code":370},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":195,"locked":14,"version":35,"method":36,"evidence":373},{"candidates":374,"agricultural_evidence":41},[375,379,383,390,397,402],{"code":40,"score":18,"supported":41,"title":376,"abstract":377,"tags":378},[45],[44,45,269],[363,29,27],{"code":119,"score":82,"supported":41,"title":380,"abstract":381,"tags":382},[],[122,123],[],{"code":384,"score":82,"supported":41,"title":385,"abstract":386,"tags":389},"H3",[],[387,388],"灌溉","irrigation",[],{"code":391,"score":82,"supported":41,"title":392,"abstract":393,"tags":396},"I4",[],[394,395],"地理信息","geographic information",[],{"code":111,"score":52,"supported":41,"title":398,"abstract":399,"tags":401},[],[400],"climate change",[],{"code":403,"score":52,"supported":41,"title":404,"abstract":405,"tags":407},"I2",[],[406],"large language model",[],[409,410],"Kakhovka Dam 生态遥感","乌克兰农业 生态遥感 遥感监测 Landsat 8","KakhovkaDam生态遥感-3894","10.36023\u002Fujrs.2026.13.3.310",{"doi":412,"openalex_id":414,"authors":415,"venue":354,"cited_by_count":71,"oa_url":351,"card":419,"direction":77,"ingested_from":79},"W7214889788",[416],{"name":417,"orcid":418},"Maksym Tymchyshyn","https:\u002F\u002Forcid.org\u002F0000-0003-2966-4794",{"tldr":420,"method":421,"finding":422,"direction":77,"opportunity":423},"基于Landsat 8与GEE计算RSEI，评估乌克兰赫尔松地区2013–2025年生态时空变化。","Google Earth Engine + Landsat 8 OLI\u002FTIRS","2025年RSEI均值由2021年0.381降至0.205，生态危机区由26%扩至60%。","可将RSEI框架迁移至战乱或灌溉中断农区，结合作物物候验证生态退化对农业生产的传导。","2026-10-01T23:30:28.551437Z",{"id":426,"title":427,"url":428,"summary":429,"summary_zh":430,"content":9,"source_name":431,"source_url":428,"published_at":432,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":433,"score_detail":434,"sources":436,"tags":438,"primary_topic":442,"secondary_topics":446,"classification":448,"search_phrases":470,"slug":473,"view_count":52,"doi":474,"paper":475,"created_at":494},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":17,"substance":94,"depth":178,"authority":20,"freshness":21,"relevant":22,"comment":435},"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。",[437],{"name":431,"url":428},[439,440,27,441,28],"智慧农业","农业人工智能","洪涝灾害",{"code":403,"slug":443,"name":444,"path":445,"parent_code":370},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[447],{"code":40,"slug":367,"name":368,"path":369,"parent_code":370},{"status":195,"locked":14,"version":35,"method":36,"evidence":449},{"candidates":450,"agricultural_evidence":41},[451,457,461,465],{"code":403,"score":97,"supported":41,"title":452,"abstract":454,"tags":456},[453],"machine learning",[455,453],"机器学习",[440],{"code":40,"score":13,"supported":41,"title":458,"abstract":459,"tags":460},[45],[],[28,27],{"code":111,"score":98,"supported":41,"title":462,"abstract":463,"tags":464},[],[116,114],[441],{"code":466,"score":22,"supported":14,"title":467,"abstract":468,"tags":469},"I1",[],[],[439],[471,472],"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":474,"openalex_id":476,"authors":477,"venue":431,"cited_by_count":71,"oa_url":428,"card":489,"direction":77,"ingested_from":79},"W7214283372",[478,480,483,486],{"name":479,"orcid":9},"Jiradech Majandang",{"name":481,"orcid":482},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":484,"orcid":485},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":487,"orcid":488},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":490,"method":491,"finding":492,"direction":77,"opportunity":493},"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":496,"title":497,"url":498,"summary":499,"summary_zh":500,"content":9,"source_name":501,"source_url":498,"published_at":502,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":503,"score_detail":504,"sources":506,"tags":508,"primary_topic":512,"secondary_topics":517,"classification":519,"search_phrases":532,"slug":535,"view_count":536,"doi":537,"paper":538,"created_at":566},3084,"Spatiotemporal evolution and drivers of eco-environmental quality for sustainable rural tourism in China’s Yangtze River Basin: an explainable AI approach","https:\u002F\u002Fdoi.org\u002F10.1080\u002F17538947.2026.2735566","Balancing tourism-driven growth (SDG 8.9) with terrestrial ecosystem conservation (SDG 15) is vital for sustainable watershed management. Focusing on 409 Key Rural Tourism Villages in China’s Yangtze River Basin, this study integrates Google Earth Engine and MODIS data to assess eco-environmental quality (EEQ) from 2000 to 2024 using the Remote Sensing Ecological Index (RSEI), employing an XGBoost-SHAP framework to decode driving mechanisms. Results indicate that these villages exhibit a clustered spatial distribution pattern, with three core agglomerations. The mean RSEI across all villages exceeds 0.73, indicating overall improvement and resilient recovery after 2020. However, a distinct north-south spatial disparity emerged: improved villages were predominantly concentrated north of the Yangtze River mainstem, whereas degraded villages clustered in the Taihu, Poyang, and Dongting Lake basins to the south. Elevation, land-use change, and temperature are the primary drivers, with a critical altitudinal threshold near 2,500 metres, where synergistic effects turn antagonistic. Although the XGBoost model achieved strong predictive performance (R2 = 0.853, RMSE = 0.071), 14.7% of the variance remains unexplained, suggesting the influence of additional unmeasured factors. These findings advance SDG 15 localisation by establishing spatial baselines and threshold-guided carrying-capacity regulations for watershed tourism governance.","在可持续流域管理中，平衡旅游驱动型增长（SDG 8.9）与陆地生态系统保护（SDG 15）至关重要。本研究以中国长江流域409个全国乡村旅游重点村为对象，集成Google Earth Engine与MODIS数据，基于遥感生态指数（RSEI）评估2000—2024年生态环境质量（EEQ），并采用XGBoost-SHAP框架解析其驱动机制。结果表明，这些村庄呈集聚型空间分布格局，形成三个核心集聚区。所有村庄的RSEI均值超过0.73，表明生态环境质量总体改善，并在2020年后呈现韧性恢复。然而，南北空间分异显著：改善型村庄主要集中于长江干流以北，而退化型村庄则聚集于以南的太湖、鄱阳湖和洞庭湖流域。海拔、土地利用变化和气温是主要驱动因子，在约2500 m处存在关键海拔阈值，超过该阈值后协同效应转为拮抗效应。尽管XGBoost模型具有较强预测性能（R² = 0.853，RMSE = 0.071），仍有14.7%的方差未能解释，提示其他未测因素的影响。本研究通过建立空间基线和阈值引导的承载力调控措施，推动了SDG 15的本土化，为流域旅游治理提供了依据。","International Journal of Digital Earth","2026-09-18T00:00:00Z",80,{"impact":178,"substance":94,"depth":178,"authority":179,"freshness":17,"relevant":22,"comment":505},"基于GEE与MODIS的乡村文旅生态质量长时序评估，方法新颖、数据规模大，对流域旅游承载力治理有参考价值。",[507],{"name":501,"url":498},[509,27,510,29,511],"长江流域","乡村文旅","可持续旅游",{"code":513,"slug":514,"name":515,"path":516,"parent_code":192},"G4","rural-industry","乡村产业与县域经济","\u002Ftopic\u002Frural-services\u002Frural-industry",[518],{"code":40,"slug":367,"name":368,"path":369,"parent_code":370},{"status":195,"locked":14,"version":35,"method":36,"evidence":520},{"candidates":521,"agricultural_evidence":41},[522,528],{"code":513,"score":179,"supported":41,"title":523,"abstract":525,"tags":527},[524],"rural tourism",[526,524],"乡村旅游",[],{"code":40,"score":17,"supported":41,"title":529,"abstract":530,"tags":531},[],[44,45],[29,27],[533,534],"长江流域 乡村旅游 生态质量","RSEI 遥感生态指数 乡村","长江流域乡村旅游生态质量-3084",5,"10.1080\u002F17538947.2026.2735566",{"doi":537,"openalex_id":539,"authors":540,"venue":501,"cited_by_count":71,"oa_url":558,"card":559,"direction":565,"ingested_from":79},"W7213546376",[541,543,546,548,550,552,555],{"name":542,"orcid":9},"Xiaojuan Zhang",{"name":544,"orcid":545},"Zhe Chen","https:\u002F\u002Forcid.org\u002F0000-0001-7579-1968",{"name":547,"orcid":9},"Libin Guo",{"name":549,"orcid":9},"Guoyan Wang",{"name":551,"orcid":9},"Yongxiu Zhou",{"name":553,"orcid":554},"Hui Li","https:\u002F\u002Forcid.org\u002F0000-0002-3565-1773",{"name":556,"orcid":557},"Zhongchang Sun","https:\u002F\u002Forcid.org\u002F0000-0003-3219-0542","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F17538947.2026.2735566?needAccess=true",{"tldr":560,"method":561,"finding":562,"direction":563,"opportunity":564},"评估长江流域409个乡村旅游村2000-2024年生态环境质量演变并解析驱动机制。","GEE与MODIS数据、RSEI指数、XGBoost-SHAP可解释AI框架。","整体EEQ改善但南北分异明显，海拔、土地利用与温度为主导驱动，2500米为协同转拮抗阈值。","农业绿色发展与碳","可探究未解释的14.7%方差来源，并针对太湖、鄱阳湖、洞庭湖退化区开展旅游承载力调控研究。","数字乡村与农业信息化","2026-09-21T23:30:36.842989Z"]