[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4230":3,"related-4230":72},{"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":46,"slug":49,"view_count":50,"doi":51,"paper":52,"created_at":71},4230,"Vegetation dynamics in Mizoram Northeast India from 2005 to 2025 using landsat imagery and image specific NDVI thresholding","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44415-026-00136-2","Vegetation monitoring is important for evaluating landscape change in ecologically sensitive mountain regions. This study assessed vegetation dynamics across Mizoram, India, an eastern Himalayan-extension landscape, between 2005 and 2025 using Landsat 5 Thematic Mapper and Landsat 9 Operational Land Imager\u002FThermal Infrared Sensor surface-reflectance imagery. Satellite images were processed in Google Earth Engine, and normalized difference vegetation index have been used to derive vegetation and non-vegetation areas using image-specific thresholds. Spatial overlay analysis was subsequently applied to identify vegetation persistence, gain, loss, and persistent non-vegetated land at the state and district levels.Vegetation remained the dominant mapped land-cover category in both years but declined from 20,558.8 km² (97.52%) in 2005 to 20,283.8 km² (96.20%) in 2025, representing a net reduction of 275.0 km². Gross vegetation loss is 602.0 km², exceeding gross vegetation gain of 326.9 km². Vegetation loss was spatially heterogeneous, with the largest net declines observed in Lawngtlai, Kolasib, Saiha, Aizawl, and Lunglei districts, whereas Mamit, Champhai, and Saitual recorded stable net gains. The findings provide baseline information on vegetation-extent dynamics at the state and district level in Mizoram and offer useful insights for future vegetation monitoring, forest conservation, sustainable landscape management, and land-use planning in the eastern Himalayan region.","植被监测对于评估生态敏感山区景观变化具有重要意义。本研究利用Landsat 5专题制图仪（Thematic Mapper）和Landsat 9操作陆地成像仪\u002F热红外传感器（Operational Land Imager\u002FThermal Infrared Sensor）地表反射率影像，评估了2005年至2025年间印度米佐拉姆邦——喜马拉雅东延景观区的植被动态。卫星影像在Google Earth Engine中处理，利用归一化植被指数（normalized difference vegetation index）并结合各影像特定阈值提取植被区与非植被区。随后应用空间叠加分析，在邦和县两级识别植被持续、增加、减少及持续非植被土地。两个年份中植被均为主要土地覆盖类型，但面积从2005年的20558.8 km²（97.52%）下降至2025年的20283.8 km²（96.20%），净减少275.0 km²。植被总减少面积为602.0 km²，超过总增加面积326.9 km²。植被减少在空间上呈异质性，净减少最大的县为Lawngtlai、Kolasib、Saiha、Aizawl和Lunglei，而Mamit、Champhai和Saitual则录得稳定的净增加。研究结果为米佐拉姆邦邦和县两级植被范围动态提供了基线信息，并为喜马拉雅东部地区未来的植被监测、森林保护、可持续景观管理和土地利用规划提供了有益参考。",null,"Discover Forests","2026-10-03T00:00:00Z","论文",10,false,65,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,16,12,9,1,"基于Landsat与GEE的20年植被动态监测，方法规范、数据扎实，但属区域生态研究，与国内农业信息化关联较弱，公共价值有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"NDVI","遥感监测","Landsat","森林保护","植被动态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":45},[39],{"code":40,"score":41,"supported":14,"title":42,"abstract":43,"tags":44},"I3",3,[],[],[29,27,31,28],"农业相关性需要复核",[47,48],"Mizoram Landsat NDVI 植被","印度东北部 植被变化 遥感","MizoramLandsatNDVI植被-4230",0,"10.1007\u002Fs44415-026-00136-2",{"doi":51,"openalex_id":53,"authors":54,"venue":10,"cited_by_count":50,"oa_url":63,"card":64,"direction":68,"ingested_from":70},"W7218499716",[55,58,61],{"name":56,"orcid":57},"K. Lalramngaizuala","https:\u002F\u002Forcid.org\u002F0009-0005-5754-3449",{"name":59,"orcid":60},"Vishwambhar Prasad Sati","https:\u002F\u002Forcid.org\u002F0000-0001-6423-3119",{"name":62,"orcid":9},"F. C. Kypacharili","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44415-026-00136-2.pdf",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"基于Landsat影像与NDVI阈值法评估印度米佐拉姆2005—2025年植被动态。","Landsat 5\u002F9地表反射率影像，Google Earth Engine，影","植被占比由97.52%降至96.20%，净减275 km²，损失602 km²大于增益326.9 k","农业遥感与作物表型","可引入时序NDVI与机器学习区分植被退化驱动因素，并耦合地形与政策数据做多尺度监测。","openalex","2026-10-05T23:30:25.009678Z",{"total":73,"page":22,"page_size":73,"items":74},6,[75,142,204,283,344,391],{"id":76,"title":77,"url":78,"summary":79,"summary_zh":80,"content":9,"source_name":81,"source_url":78,"published_at":82,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":83,"score_detail":84,"sources":90,"tags":92,"primary_topic":9,"secondary_topics":95,"classification":96,"search_phrases":118,"slug":121,"view_count":122,"doi":123,"paper":124,"created_at":141},3791,"Topographic heterogeneity regulates nonlinear vegetation responses to climate and human activities in China’s ecological transition zone","https:\u002F\u002Fdoi.org\u002F10.3389\u002Fffgc.2026.1948232","Ecological transition zones, identified as climate-sensitive areas and biodiversity hotspots, are essential for maintaining regional ecological balance through their vegetation dynamics. However, the impact of topographic heterogeneity on the nonlinear responses of vegetation to climate change and human activities is not well understood. This gap limits our comprehension of ecosystem resilience. This study focuses on the ecological transition zone of the Qinling–Daba Mountains in China. We utilized 30-m resolution Landsat NDVI data from 2000 to 2022, along with climate, human activity, and topographic data. To characterize vegetation dynamics, we employed Theil–Sen trend analysis, the Mann–Kendall test, and the Hurst index. A Bayesian-optimized XGBoost model, combined with SHAP values, was employed to evaluate the relative contributions and response patterns of potential drivers across various vegetation trend zones and topographic gradients. The analysis revealed a significant greening trend, with the normalized difference vegetation index (NDVI) increasing at a rate of 0.0051 yr. −1 ( p \u003C 0.05). Approximately 90.13% of the region displayed an improving trend, while 89.83% indicated a propensity for continued enhancement. The most pronounced vegetation improvements were observed primarily at moderate elevations (1,750–2,250 m), on moderate slopes (15–20°), and on north-facing slopes. Land use and land cover (LULC) (42.5%), elevation (13.2%), population density (8.2%), and precipitation (7.0%) emerged as the most significant contributors to NDVI predictions across the study area. LULC consistently served as the most influential predictor in both regions experiencing significant increases and those facing declines. In contrast, the relative importance and nonlinear response patterns of other factors varied across different trend zones. These findings provide a scientific foundation for targeted ecological restoration and adaptive vegetation management in ecological transition zones.","生态过渡带被认为是气候敏感区和生物多样性热点区，其植被动态对维持区域生态平衡至关重要。然而，地形异质性如何影响植被对气候变化和人类活动的非线性响应，目前尚不明确。这一空白限制了我们对生态系统恢复力的理解。本研究以中国秦岭—大巴山生态过渡带为对象，利用2000—2022年30 m分辨率的Landsat NDVI数据，并结合气候、人类活动和地形数据。为刻画植被动态，我们采用了Theil–Sen趋势分析、Mann–Kendall检验和Hurst指数。采用贝叶斯优化的XGBoost模型，并结合SHAP值，评估了不同植被趋势区和地形梯度上潜在驱动因子的相对贡献及响应模式。分析揭示出显著的绿化趋势，归一化植被指数（NDVI）以0.0051 yr⁻¹的速率增加（p \u003C 0.05）。约90.13%的区域呈改善趋势，89.83%的区域表现出持续增强的倾向。植被改善最显著的区域主要出现在中等海拔（1,750–2,250 m）、中等坡度（15–20°）和北向坡。土地利用与土地覆盖（LULC）（42.5%）、海拔（13.2%）、人口密度（8.2%）和降水（7.0%）是整个研究区NDVI预测中最重要的贡献因子。在显著增加区和下降区，LULC始终是最具影响力的预测因子。相比之下，其他因素的相对重要性和非线性响应模式在不同趋势区存在差异。这些发现为生态过渡带的针对性生态恢复和适应性植被管理提供了科学依据。","Frontiers in Forests and Global Change","2026-09-28T00:00:00Z",76,{"impact":85,"substance":86,"depth":87,"authority":88,"freshness":21,"relevant":22,"comment":89},14,22,18,13,"基于30米Landsat NDVI与XGBoost-SHAP量化地形对植被非线性响应的研究，方法新颖、数据扎实，对生态过渡带精准修复有参考价值，但属学术论文，公共传播价值有限。",[91],{"name":81,"url":78},[93,94,27,28,31],"气候变化","生态修复",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":97},{"candidates":98,"agricultural_evidence":14,"reason":45},[99,108,114],{"code":100,"score":101,"supported":102,"title":103,"abstract":104,"tags":107},"H6",7,true,[],[105,106],"生物多样性","biodiversity",[94],{"code":109,"score":101,"supported":102,"title":110,"abstract":111,"tags":113},"H7",[],[93,112],"climate change",[93],{"code":40,"score":41,"supported":14,"title":115,"abstract":116,"tags":117},[],[],[27,31,28],[119,120],"秦岭大巴山 生态过渡带 植被","NDVI 地形异质性 植被响应","秦岭大巴山生态过渡带植被-3791",2,"10.3389\u002Fffgc.2026.1948232",{"doi":123,"openalex_id":125,"authors":126,"venue":81,"cited_by_count":50,"oa_url":135,"card":136,"direction":68,"ingested_from":70},"W7214562314",[127,129,131,133],{"name":128,"orcid":9},"Xinwen Su",{"name":130,"orcid":9},"Di Wen",{"name":132,"orcid":9},"Jun Li",{"name":134,"orcid":9},"Peng Dailiang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fforests-and-global-change\u002Farticles\u002F10.3389\u002Fffgc.2026.1948232\u002Fpdf",{"tldr":137,"method":138,"finding":139,"direction":68,"opportunity":140},"研究秦岭-大巴山生态过渡带植被非线性响应气候与人类活动，并揭示地形异质性的调控作用。","用2000-2022年30米Landsat NDVI、气候、人类活动和地形数据，","区域显著绿化，NDVI增速0.0051\u002F年；土地利用、海拔、人口密度和降水是主要驱动因子，地形梯度调","可探究其他生态过渡带中地形异质性对植被非线性响应的普适性，并耦合多源遥感与可解释机器学习优化生态修复。","2026-09-29T23:30:28.608664Z",{"id":143,"title":144,"url":145,"summary":146,"summary_zh":147,"content":9,"source_name":148,"source_url":145,"published_at":149,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":150,"score_detail":151,"sources":154,"tags":156,"primary_topic":159,"secondary_topics":164,"classification":165,"search_phrases":179,"slug":182,"view_count":41,"doi":183,"paper":184,"created_at":203},3072,"APPLICATION OF SENTINEL-2 DATA TO ASSESS SEASONAL VEGETATION DYNAMICS IN THE IALPUG RIVER BASIN, SOUTHERN PART OF THE REPUBLIC OF MOLDOVA","https:\u002F\u002Fdoi.org\u002F10.21163\u002Fgt_2027.221.06","This study uses Sentinel-2 imagery to characterize seasonal vegetation dynamics in the analysed part of the Ialpug River Basin from March to October 2025.Monthly NDVI, month-to-month NDVI differences (ΔNDVI) and mean NDVI were calculated to describe vegetation greenness, the direction and magnitude of change between observations, and average greenness across the study period.The results show spring greening followed by spatially contrasting changes in early summer.Moderate vegetation cover occupied its largest share in April, while dense vegetation reached its maximum extent in June.The most widespread decline occurred between June and July, when 54.18% of the study area recorded an NDVI decrease exceeding 0.1.Very sparse vegetation reached its greatest extent in September, followed by limited greening in October.The mean NDVI classification placed 69.20% of the study area in the sparse vegetation class and 24.49% in the very sparse vegetation class, indicating predominantly low to intermediate average greenness across the selected observations.These classes do not establish persistent vegetation conditions or directly identify land-cover types.The summer decline coincided with documented regional drought conditions, but the analysis did not separate climatic effects from crop development, harvesting or other seasonal processes.The combined indicators provide complementary information for identifying areas with pronounced seasonal decline or low average greenness.Multi-year observations and independent climatic, land-cover and field evidence are required to determine the causes of these patterns and assess their longer-term environmental significance.","本研究利用Sentinel-2影像刻画了雅尔普格河（Ialpug River）流域分析区2025年3月至10月的季节性植被动态。通过计算逐月NDVI、月际NDVI差值（ΔNDVI）及平均NDVI，以描述植被绿度、观测间变化的方向与幅度以及研究期内的平均绿度。结果表明，春季出现绿化，随后在初夏呈现空间分异的变化。中度植被覆盖在4月占比最大，而茂密植被在6月达到最大范围。最广泛的下降发生在6月至7月之间，届时研究区54.18%的面积NDVI下降超过0.1。极稀疏植被在9月达到最大范围，随后在10月出现有限绿化。平均NDVI分类将研究区69.20%的面积归为稀疏植被类，24.49%归为极稀疏植被类，表明所选观测期内平均绿度以低至中等为主。这些类别并不确立持续的植被状况，也不直接识别土地覆盖类型。夏季下降与已记录的区域干旱条件相吻合，但分析未将气候影响与作物发育、收割或其他季节性过程区分开来。综合指标为识别季节性下降显著或平均绿度较低的区域提供了互补信息。要确定这些格局的成因并评估其长期环境意义，尚需多年观测以及独立的气候、土地覆盖和野外证据。","Geographia Technica","2026-09-19T00:00:00Z",61,{"impact":17,"substance":87,"depth":152,"authority":20,"freshness":17,"relevant":22,"comment":153},15,"基于Sentinel-2的流域植被季节性动态遥感研究，方法规范、数据翔实，但属区域性案例，对国内农业信息化仅有方法借鉴价值。",[155],{"name":148,"url":145},[27,28,31,157,158],"干旱影响","摩尔多瓦",{"code":109,"slug":160,"name":161,"path":162,"parent_code":163},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[],{"status":166,"locked":14,"version":35,"method":36,"evidence":167},"classified",{"candidates":168,"agricultural_evidence":102},[169,175],{"code":109,"score":73,"supported":102,"title":170,"abstract":171,"tags":174},[],[172,173],"干旱","drought",[],{"code":40,"score":41,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[27,31,28],[180,181],"摩尔多瓦 NDVI 植被动态","干旱影响 摩尔多瓦 植被动态 遥感监测","摩尔多瓦NDVI植被动态-3072","10.21163\u002Fgt_2027.221.06",{"doi":183,"openalex_id":185,"authors":186,"venue":148,"cited_by_count":50,"oa_url":197,"card":198,"direction":68,"ingested_from":70},"W7213674461",[187,190,192,195],{"name":188,"orcid":189},"Ecaterina MELNICENCO","https:\u002F\u002Forcid.org\u002F0000-0002-2706-9417",{"name":191,"orcid":9},"Viorica ANGHELUȚA",{"name":193,"orcid":194},"Iradion Jechiu","https:\u002F\u002Forcid.org\u002F0000-0001-9582-0615",{"name":196,"orcid":9},"Iurie BEJAN","https:\u002F\u002Ftechnicalgeography.org\u002Fpdf\u002F1_2027\u002F06_melnicenco.pdf",{"tldr":199,"method":200,"finding":201,"direction":68,"opportunity":202},"用Sentinel-2影像分析摩尔多瓦Ialpug河流域2025年3-10月植被季节动态。","Sentinel-2月度NDVI、ΔNDVI与均值NDVI分类，覆盖2025年3","6-7月54.18%区域NDVI降幅超0.1，均值NDVI以稀疏植被为主（69.20%）。","可结合多年遥感与气候、物候及田间数据，分离干旱与作物管理对NDVI下降的贡献。","2026-09-21T23:30:25.567291Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":211,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":216,"tags":218,"primary_topic":9,"secondary_topics":222,"classification":223,"search_phrases":253,"slug":256,"view_count":50,"doi":257,"paper":258,"created_at":282},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":87,"substance":86,"depth":214,"authority":20,"freshness":21,"relevant":22,"comment":215},17,"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。",[217],{"name":210,"url":207},[28,219,30,220,221],"土地利用","亚马逊","CBERS-4",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":224},{"candidates":225,"agricultural_evidence":102,"reason":252},[226,230,236,240,246],{"code":100,"score":101,"supported":102,"title":227,"abstract":228,"tags":229},[],[105,106],[219],{"code":40,"score":101,"supported":102,"title":231,"abstract":232,"tags":235},[],[233,234],"遥感","remote sensing",[28],{"code":109,"score":73,"supported":102,"title":237,"abstract":238,"tags":239},[],[93,112],[],{"code":241,"score":41,"supported":102,"title":242,"abstract":243,"tags":245},"H2",[],[244],"soil",[],{"code":247,"score":41,"supported":102,"title":248,"abstract":249,"tags":251},"I7",[],[250],"研究方法",[],"多个产业证据相当，需要确认主归属",[254,255],"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":257,"openalex_id":259,"authors":260,"venue":210,"cited_by_count":50,"oa_url":207,"card":277,"direction":68,"ingested_from":70},"W7223141286",[261,264,266,269,272,275],{"name":262,"orcid":263},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":265,"orcid":9},"Gabriel Henrique Santos Silva",{"name":267,"orcid":268},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":270,"orcid":271},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":273,"orcid":274},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":276,"orcid":9},"Sylvana Melo dos Santos",{"tldr":278,"method":279,"finding":280,"direction":68,"opportunity":281},"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":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":289,"source_url":286,"published_at":290,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":150,"score_detail":291,"sources":293,"tags":295,"primary_topic":9,"secondary_topics":299,"classification":300,"search_phrases":322,"slug":325,"view_count":50,"doi":326,"paper":327,"created_at":343},5481,"Comparison of Satellite-Based Drought Monitoring Indicators (NDVI, NDMI, and LST): The Case of the Northern Aegean Drainage Basin","https:\u002F\u002Fdoi.org\u002F10.30910\u002Fturkjans.2010067","Thanks to advancements in technology, satellite remote sensing provides a valuable means of monitoring the spatial and temporal responses of vegetation, surface moisture conditions, and land surface temperature to drought. Its capacity to monitor large areas with high temporal continuity significantly eliminates the spatial limitations of ground observations. This allows for the simultaneous and comparable analysis of key variables such as vegetation dynamics, soil moisture, and land surface temperature. Indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) provide complementary information on vegetation condition, surface moisture, and thermal stress, thereby supporting drought monitoring. This integrated monitoring framework supports the sustainable management of water resources while strengthening the scientific basis of decision-making processes, significantly contributing to disaster risk reduction. This study comparatively evaluates NDVI, NDMI, and LST values derived from Landsat 8 data obtained in March, April, and May between 2019 and 2022, and examines the relationships between these variables across the Northern Aegean Drainage Basin (NADB). The results show increasingly strong negative relationships between LST and the spectral indices from March to May, with the strongest correlations observed in May for NDMI–LST (r = −0.78) and NDVI–LST (r = −0.74). The NDMI-LST relationship can be said to be slightly better than the NDVI-LST relationship. The correlation coefficient between NDMI and NDVI was calculated to be lowest in March at r=0.54 and highest in May at r=0.90. It was determined that areas with high LST values had lower NDVI and NDMI values. Drought monitoring using remote sensing methods is important in terms of time and accuracy, but it is not sufficient on its own. In addition to problems arising from satellite imagery, land surfaces are affected by short-term climatic factors. Therefore, the indices obtained from satellite images must be evaluated together with meteorological and terrestrial data. However, it is thought that satellite-based drought monitoring will produce more reliable results under current technological and methodological conditions.","得益于技术进步，卫星遥感为监测植被、地表水分状况和地表温度对干旱的时空响应提供了重要手段。其大范围、高时间连续性的监测能力显著消除了地面观测的空间局限性，使得植被动态、土壤水分和地表温度等关键变量能够被同步、可比地分析。归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）和地表温度（LST）等指标提供了关于植被状况、地表水分和热胁迫的互补信息，从而支持干旱监测。这一综合监测框架既支持水资源的可持续管理，又增强了决策过程的科学基础，对减少灾害风险具有重要贡献。本研究对2019年至2022年3月、4月和5月获取的Landsat 8数据所衍生的NDVI、NDMI和LST值进行了比较评估，并考察了北爱琴海流域（NADB）内这些变量之间的关系。结果表明，从3月到5月，LST与光谱指数之间的负相关关系日益增强，5月观测到最强相关性，NDMI–LST（r = −0.78）和NDVI–LST（r = −0.74）。可以认为NDMI-LST关系略优于NDVI-LST关系。NDMI与NDVI之间的相关系数在3月最低（r=0.54），5月最高（r=0.90）。研究确定，LST值较高的区域其NDVI和NDMI值较低。利用遥感方法进行干旱监测在时间和精度方面具有重要意义，但仅靠其自身并不充分。除卫星影像带来的问题外，地表还受到短期气候因素的影响。因此，从卫星影像获得的指数必须与气象和地面数据结合评估。然而，可以认为在当前技术和方法条件下，基于卫星的干旱监测将产生更可靠的结果。","Türk Tarım ve Doğa Bilimleri Dergisi","2026-10-19T00:00:00Z",{"impact":17,"substance":87,"depth":152,"authority":20,"freshness":17,"relevant":22,"comment":292},"方法对比扎实、结论明确，但属区域案例研究，公共影响有限，适合作为遥感干旱监测主题的补充素材而非头条精选。",[294],{"name":289,"url":286},[27,28,296,297,298],"干旱监测","NDMI","LST",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":301},{"candidates":302,"agricultural_evidence":14,"reason":45},[303,307,311,316],{"code":109,"score":152,"supported":102,"title":304,"abstract":305,"tags":306},[173],[172,173],[296],{"code":40,"score":17,"supported":102,"title":308,"abstract":309,"tags":310},[],[233,234],[27,28],{"code":241,"score":73,"supported":102,"title":312,"abstract":313,"tags":315},[],[314,244],"土壤",[],{"code":317,"score":41,"supported":102,"title":318,"abstract":319,"tags":321},"H3",[],[320],"水资源",[],[323,324],"北爱琴海流域 干旱监测","干旱监测 遥感监测 NDMI NDVI","北爱琴海流域干旱监测-5481","10.30910\u002Fturkjans.2010067",{"doi":326,"openalex_id":328,"authors":329,"venue":289,"cited_by_count":50,"oa_url":336,"card":337,"direction":342,"ingested_from":70},"W7222954725",[330,333],{"name":331,"orcid":332},"Arman Monshizadeh Karimi","https:\u002F\u002Forcid.org\u002F0000-0002-2945-5346",{"name":334,"orcid":335},"İ̇smail Taş","https:\u002F\u002Forcid.org\u002F0000-0003-0872-2529","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6296754",{"tldr":338,"method":339,"finding":340,"direction":68,"opportunity":341},"比较Landsat 8的NDVI、NDMI与LST三种遥感干旱指标在北爱琴海流域的相关性。","用2019-2022年3-5月Landsat 8数据计算NDVI、NDMI、LS","LST与NDMI、NDVI负相关随春季增强，5月最强（r=-0.78、-0.74），NDMI略优。","可融合气象与地面数据构建多源干旱指数，并验证NDMI-LST关系在其他气候区的普适性。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:10.758669Z",{"id":345,"title":346,"url":347,"summary":348,"summary_zh":349,"content":9,"source_name":350,"source_url":347,"published_at":351,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":352,"score_detail":353,"sources":355,"tags":357,"primary_topic":9,"secondary_topics":360,"classification":361,"search_phrases":374,"slug":377,"view_count":50,"doi":378,"paper":379,"created_at":390},5182,"Land Use Land Cover (LULC) Change Detection in Delhi NCT Using Multi-Sensor Satellite Data and Google Earth Engine","https:\u002F\u002Fdoi.org\u002F10.21275\u002Fsr26903183702","Land Use Land Cover (LULC) change detection is essential for understanding the dynamics of human modified landscapes and for supporting effective land management and urban planning. This study analyses LULC change across the Delhi National Capital Territory over a fifteen-year period, from 2010 to 2025, to assess urban sprawl and its environmental impact in one of India's fastest growing metropolitan regions. Multi temporal Landsat imagery was used as the primary data source, processed on Google Earth Engine, the cloud-based platform that enabled efficient handling and visualisation of large geospatial datasets. Five classes, built up area, agricultural land, vegetation, water, and barren land, were mapped and monitored, and classification accuracy was assessed for both dates. The Normalised Difference Vegetation Index was additionally employed to track vegetation vigour and ecological change. Results reveal a marked expansion of built-up areas alongside a substantial decline in agricultural and vegetated land, driven by peri urban growth along the northwestern and southwestern fringe. NDVI analysis shows rising greenness across the outer belt, reflecting more intensive cultivation on land that remains under crops, a pattern that complements rather than contradicts the measured loss of vegetated area. Combining classification based and an index-based approach provides a robust, scalable framework for LULC monitoring. By revealing where and how urban growth is reshaping the landscape, the study offers a transferable basis for environmental assessment, resource management, and sustainable urban planning in rapidly urbanising regions worldwide.","土地利用\u002F土地覆盖（LULC）变化检测对于理解人类改造景观的动态以及支撑有效的土地管理和城市规划至关重要。本研究分析了德里国家首都辖区在2010年至2025年十五年间的土地利用\u002F土地覆盖变化，以评估印度增长最快的大都市区之一的城市蔓延及其环境影响。研究以多时相Landsat影像作为主要数据源，在Google Earth Engine这一基于云的平台上进行处理，该平台能够高效处理和可视化大型地理空间数据集。研究对建成区、农业用地、植被、水体与裸地五个类别进行了制图和监测，并对两个时相的分类精度进行了评估。此外，研究采用归一化植被指数（NDVI）追踪植被长势与生态变化。结果表明，建成区显著扩张，农业用地和植被用地大幅减少，这一变化主要由西北部和西南部边缘的近郊增长所驱动。NDVI分析显示外围地带的绿度上升，反映出仍处于耕作状态的土地上集约化种植程度提高，这一格局与所测得的植被面积损失相辅相成而非相互矛盾。将基于分类的方法与基于指数的方法相结合，为土地利用\u002F土地覆盖监测提供了一个稳健且可扩展的框架。通过揭示城市增长在何处以及如何重塑景观，本研究为全球快速城市化地区的环境评估、资源管理和可持续城市规划提供了可迁移的基础。","International Journal of Science and Research (IJSR)","2026-10-15T00:00:00Z",62,{"impact":20,"substance":87,"depth":152,"authority":17,"freshness":21,"relevant":22,"comment":354},"方法可迁移的遥感LULC变化检测研究，对快速城市化地区土地管理有参考价值，但属区域案例、非三农核心议题。",[356],{"name":350,"url":347},[27,28,219,358,359],"Google Earth Engine","城市化",[],{"status":362,"locked":14,"version":35,"method":36,"evidence":363},"unclassified",{"candidates":364,"agricultural_evidence":102,"reason":373},[365,369],{"code":40,"score":41,"supported":14,"title":366,"abstract":367,"tags":368},[],[],[358,27,28],{"code":100,"score":22,"supported":14,"title":370,"abstract":371,"tags":372},[],[],[219],"缺少足够的主题证据",[375,376],"德里 土地利用 遥感","Google Earth Engine 城市扩张","德里土地利用遥感-5182","10.21275\u002Fsr26903183702",{"doi":378,"openalex_id":380,"authors":381,"venue":350,"cited_by_count":50,"oa_url":384,"card":385,"direction":68,"ingested_from":70},"W7222341300",[382],{"name":383,"orcid":9},"Neil Rohatgi","https:\u002F\u002Fwww.ijsr.net\u002Farchive\u002Fv15i10\u002FSR26903183702.pdf",{"tldr":386,"method":387,"finding":388,"direction":68,"opportunity":389},"基于多时相Landsat与GEE分析德里国家首都辖区2010-2025年土地利用变化。","多时相Landsat影像、Google Earth Engine平台、五类分类与","建设用地显著扩张，农业与植被用地大幅减少，城郊西北和西南边缘增长驱动。","可结合分类与指数方法构建可迁移框架，拓展至城乡交错带耕地保护与可持续规划研究。","2026-10-17T23:30:31.767409Z",{"id":392,"title":393,"url":394,"summary":395,"summary_zh":396,"content":9,"source_name":397,"source_url":394,"published_at":351,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":398,"sources":400,"tags":402,"primary_topic":9,"secondary_topics":405,"classification":406,"search_phrases":421,"slug":424,"view_count":22,"doi":425,"paper":426,"created_at":452},5177,"Long-term associations between land-surface indicators and dust-storm occurrence along the margins of the Taklimakan Desert","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75251-3","Land-surface change and atmospheric variability jointly shape dust activity in arid regions. We examined land-cover transitions, station dust-storm records, a Sentinel-1 backscatter index, meteorological variables from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and dust diagnostics from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) along the margins of the Taklimakan Desert. Within the mapped domain, grassland increased from 9.45% in 1990 to 10.59% in 2023, while barren land decreased from 86.45% to 82.30%. Annual dust-storm-day trends were negative at 21 of 30 stations over available 1990–2024 records, and Sentinel-1 index trends were positive at 22 stations during 2015–2024. Mixed-effects path analysis of 3,804 station-month observations showed that the Sentinel-1 index was negatively associated with modelled dust emission (standardized coefficient = − 0.191; 95% confidence interval: −0.289 to − 0.043). The normalized difference vegetation index (NDVI) and friction velocity had the highest training-set feature importance in the random forest analysis.","地表变化与大气变率共同塑造了干旱地区的沙尘活动。我们沿塔克拉玛干沙漠边缘，研究了土地覆盖转变、台站沙尘暴记录、Sentinel-1后向散射指数、来自欧洲中期天气预报中心第五代再分析资料（ERA5）的气象变量，以及来自现代研究与应用回顾分析第2版（MERRA-2）的沙尘诊断量。在制图范围内，草地面积从1990年的9.45%增加到2023年的10.59%，而裸地面积从86.45%减少到82.30%。在1990—2024年现有记录中，30个台站中有21个台站的年沙尘暴日数呈下降趋势；2015—2024年间，22个台站的Sentinel-1指数呈上升趋势。对3804个台站—月观测值的混合效应路径分析表明，Sentinel-1指数与模拟沙尘排放呈负相关（标准化系数=−0.191；95%置信区间：−0.289至−0.043）。在随机森林分析中，归一化植被指数（NDVI）和摩擦速度在训练集中的特征重要性最高。","Scientific Reports",{"impact":19,"substance":86,"depth":87,"authority":85,"freshness":17,"relevant":22,"comment":399},"基于多源遥感与30站长期记录，量化地表指标与沙尘暴关联，方法扎实、数据规模可观，对干旱区生态监测有参考价值。",[401],{"name":397,"url":394},[27,403,28,219,404],"荒漠化","沙尘暴",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":407},{"candidates":408,"agricultural_evidence":14,"reason":45},[409,413,417],{"code":109,"score":41,"supported":102,"title":410,"abstract":411,"tags":412},[],[172],[],{"code":100,"score":122,"supported":14,"title":414,"abstract":415,"tags":416},[],[],[219,403],{"code":40,"score":122,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[27,28],[422,423],"塔克拉玛干沙漠 沙尘暴 遥感","Sentinel-1 土地覆盖 沙尘","塔克拉玛干沙漠沙尘暴遥感-5177","10.1038\u002Fs41598-026-75251-3",{"doi":425,"openalex_id":427,"authors":428,"venue":397,"cited_by_count":50,"oa_url":446,"card":447,"direction":68,"ingested_from":70},"W7222086957",[429,431,434,437,439,441,443],{"name":430,"orcid":9},"Yifei Wang",{"name":432,"orcid":433},"Wen Huo","https:\u002F\u002Forcid.org\u002F0000-0002-7861-2424",{"name":435,"orcid":436},"Mayibaier Maihamuti","https:\u002F\u002Forcid.org\u002F0009-0007-4084-5891",{"name":438,"orcid":9},"Fan Yang",{"name":440,"orcid":9},"Chenglong Zhou",{"name":442,"orcid":9},"Xinghua Yang",{"name":444,"orcid":445},"Ali Mamtimin","https:\u002F\u002Forcid.org\u002F0000-0001-7335-801X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75251-3_reference.pdf",{"tldr":448,"method":449,"finding":450,"direction":68,"opportunity":451},"分析塔克拉玛干沙漠边缘地表指标与沙尘暴发生的长期关联。","结合土地覆盖、站点沙尘记录、Sentinel-1后向散射、ERA5与MERRA-","站点沙尘日数多呈下降趋势，Sentinel-1指数与模拟沙尘排放显著负相关。","可引入多源遥感与机器学习量化地表变化对沙尘的因果贡献，并拓展至绿洲农业防护。","2026-10-17T23:30:30.235613Z"]