[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2514":3,"related-2514":101},{"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":31,"secondary_topics":37,"classification":38,"search_phrases":76,"slug":79,"view_count":21,"doi":80,"paper":81,"created_at":100},2514,"Spatiotemporal Assessment of Land Use and Land Cover Change Effects on Eco- Hydroclimatic Conditions in Kiambu County, Kenya","https:\u002F\u002Fdoi.org\u002F10.54536\u002Fajgt.v5i1.8320","Increasing land-use transitions are major drivers of eco-hydroclimatic changes, particularly in rapidly growing urban areas. This study therefore sought to assess the spatiotemporal effects of land use changes on eco-hydroclimatic conditions in Kiambu County, utilizing remote sensing and Geographic Information Systems technologies. Landsat satellite imagery from 2004 and 2024 was used. Rainfall data, Land Surface Temperature (LST), and Normalized Difference Vegetation Index (NDVI) were used to assess variations in vegetation cover, thermal patterns, and precipitation trends. Land use and land cover maps were generated using supervised classification, and accuracy assessments were performed to validate the classified maps. Statistical analyses were conducted to examine the relationships between LULC dynamics and eco-hydroclimatic parameters. The findings showed a significant expansion of built-up areas, accompanied by declines in cultivated land, vegetation cover, and water bodies. These changes were associated with increased LST, reduced vegetation health, and alterations in rainfall patterns. The study further demonstrated that increased urbanization and substantial landscape changes have significantly affected the county’s eco-hydroclimatic conditions. These changes have significant implications for environmental sustainability, water resources, climate change adaptation and resilience, and land use planning at the county level. The findings are expected to provide evidence to support informed policy formulation and sustainable spatial planning aimed at enhancing ecological resilience in Kiambu County.","土地利用转变日益加剧是生态-水文气候发生变化的主要驱动因素，在快速扩张的城市地区尤为显著。因此，本研究旨在利用遥感与地理信息系统技术，评估基安布县土地利用变化对生态-水文气候条件的时空影响。研究使用了2004年和2024年的Landsat卫星影像，并采用降雨数据、地表温度（LST）和归一化植被指数（NDVI）来评估植被覆盖、热力格局和降水趋势的变化。通过监督分类生成土地利用与土地覆盖图，并进行精度评估以验证分类结果。研究还开展了统计分析，以检验土地利用与土地覆盖动态与生态-水文气候参数之间的关系。研究结果表明，建设用地显著扩张，同时耕地、植被覆盖和水体减少。这些变化与地表温度升高、植被健康状况下降以及降雨格局改变相关。研究进一步表明，城市化加剧和景观的显著变化已对该县的生态-水文气候条件产生了重大影响。这些变化对县域层面的环境可持续性、水资源、气候变化适应与韧性以及土地利用规划具有重要意义。研究结果有望为制定知情政策和可持续空间规划提供证据支持，以增强基安布县的生态韧性。",null,"American Journal of Geospatial Technology","2026-09-14T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,13,1,"基于Landsat遥感与GIS的县域土地利用变化对生态水文气候影响研究，方法规范、数据扎实，但属肯尼亚地方案例，对国内三农信息化参考价值有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"气候变化","遥感","NDVI","土地利用","水资源",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,57,65,71],{"code":32,"score":17,"supported":45,"title":46,"abstract":47,"tags":49},true,[],[27,48],"remote sensing",[28,27],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"H7",7,[],[26,55],"climate change",[26],{"code":58,"score":59,"supported":45,"title":60,"abstract":61,"tags":64},"I4",6,[],[62,63],"地理信息","geographic information",[],{"code":66,"score":67,"supported":45,"title":68,"abstract":69,"tags":70},"H3",4,[],[30],[30],{"code":72,"score":21,"supported":14,"title":73,"abstract":74,"tags":75},"H6",[],[],[29],[77,78],"土地利用 气候变化 水资源 遥感","土地利用 气候变化","土地利用气候变化水资源遥感-2514","10.54536\u002Fajgt.v5i1.8320",{"doi":80,"openalex_id":82,"authors":83,"venue":10,"cited_by_count":90,"oa_url":91,"card":92,"direction":98,"ingested_from":99},"W7212965144",[84,86,88],{"name":85,"orcid":9},"Pauline Angerlyline Wamalwa",{"name":87,"orcid":9},"Samson Mabwoga",{"name":89,"orcid":9},"Charity Konana",0,"https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fajgt\u002Farticle\u002Fdownload\u002F8320\u002F4014",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"基于Landsat影像评估肯尼亚Kiambu县2004-2024年土地利用变化对生态水文气候的影响。","Landsat影像监督分类、NDVI、地表温度与降雨数据统计分析。","建设用地扩张导致耕地、植被和水体减少，地表温度升高、降雨格局改变。","农业遥感与作物表型","可结合多源遥感与水文模型，量化城市化对农业区水热平衡的长期影响机制。","智慧农业 \u002F 农业物联网","openalex","2026-09-15T23:30:08.755171Z",{"total":59,"page":21,"page_size":59,"items":102},[103,171,235,288,339,402],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":117,"tags":119,"primary_topic":122,"secondary_topics":123,"classification":124,"search_phrases":148,"slug":151,"view_count":152,"doi":153,"paper":154,"created_at":170},4497,"A SwiGLU enhanced ConvNeXt framework for multispectral Sentinel-2 land use and land cover classification","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-67985-x","Land use and land cover (LCLU) classification from multispectral satellite imagery is important for applications such as urban planning, environmental monitoring, agriculture, and disaster management. Existing deep learning based approaches have improved LCLU; however, most of them utilize only the red, green, and blue (RGB) bands of multispectral sentinel-2 imagery, thereby underutilizing the complementary spectral information available in the near-infrared (NIR), short-wave infrared (SWIR), and derived spectral indices such as normalized difference built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). In this work, we propose a convolutional neural network (CNN), SwigNeXT, based on a modified ConvNeXt architecture for the classification of multispectral Sentinel-2 imagery using spectral band combinations which are RGB, NIR–Red–Green, SWIR–NIR–Red, and derived index combination NDBI–NDVI–NDWI rather than using only the RGB. The SwigNeXT incorporates SwiGLU-based gated feature modulation to improve spectral spatial feature representation. The final classification is obtained by ensembling the predictions of the three spectral band combinations and the derived index combination. Experimental results show that the proposed ensemble framework achieved an overall accuracy of 99.19% with a Kappa coefficient ( \\(\\kappa \\) ) of 0.9909, outperforming conventional CNNs, transfer learning based CNNs and transformer based approaches. A gradient based SHapley Additive exPlanations framework was further integrated to analyse spatial and spectral feature contributions. The proposed framework was further validated on Sentinel-2 imagery from the Delhi–NCR region, to examine its applicability under geographically distinct real world imaging conditions.","多光谱卫星影像的土地利用与土地覆盖（LCLU）分类对城市规划、环境监测、农业和灾害管理等应用具有重要意义。现有的基于深度学习的方法改进了LCLU分类；然而，大多数方法仅利用多光谱Sentinel-2影像的红、绿、蓝（RGB）波段，从而未能充分利用近红外（NIR）、短波红外（SWIR）以及归一化差异建成区指数（NDBI）、归一化差异植被指数（NDVI）和归一化差异水体指数（NDWI）等衍生光谱指数中可用的互补光谱信息。在本研究中，我们提出了一种基于改进ConvNeXt架构的卷积神经网络（CNN）——SwigNeXT，用于多光谱Sentinel-2影像分类，采用RGB、NIR–Red–Green、SWIR–NIR–Red等光谱波段组合以及NDBI–NDVI–NDWI衍生指数组合，而非仅使用RGB。SwigNeXT引入了基于SwiGLU的门控特征调制，以改进光谱空间特征表示。最终分类通过集成三种光谱波段组合和衍生指数组合的预测结果获得。实验结果表明，所提出的集成框架达到了99.19%的总体精度，Kappa系数（\\(\\kappa\\)）为0.9909，优于传统CNN、基于迁移学习的CNN以及基于Transformer的方法。进一步集成了基于梯度的SHapley加性解释框架，以分析空间和光谱特征贡献。所提出的框架在德里–NCR地区的Sentinel-2影像上进一步验证，以检验其在地理上不同的真实成像条件下的适用性。","Scientific Reports","2026-10-07T00:00:00Z",81,{"impact":18,"substance":113,"depth":18,"authority":114,"freshness":115,"relevant":21,"comment":116},22,14,9,"提出融合SwiGLU门控与多光谱波段组合的ConvNeXt集成框架，在Sentinel-2土地利用分类上达99.19%精度，方法新颖且对农业遥感监测有实用价值。",[118],{"name":109,"url":106},[120,121,27,28,29],"智慧农业","农业人工智能",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":125},{"candidates":126,"agricultural_evidence":45},[127,132,139,143],{"code":32,"score":20,"supported":45,"title":128,"abstract":130,"tags":131},[129],"multispectral",[129],[28,27],{"code":133,"score":52,"supported":45,"title":134,"abstract":135,"tags":138},"I2",[],[136,137],"深度学习","deep learning",[121],{"code":72,"score":21,"supported":14,"title":140,"abstract":141,"tags":142},[],[],[29],{"code":144,"score":21,"supported":14,"title":145,"abstract":146,"tags":147},"I1",[],[],[120],[149,150],"Sentinel-2 多光谱 土地覆盖分类","ConvNeXt SwiGLU 遥感分类","Sentinel-2多光谱土地覆盖分类-4497",2,"10.1038\u002Fs41598-026-67985-x",{"doi":153,"openalex_id":155,"authors":156,"venue":109,"cited_by_count":90,"oa_url":164,"card":165,"direction":98,"ingested_from":99},"W7220869344",[157,159,161],{"name":158,"orcid":9},"Neeraj Kumar",{"name":160,"orcid":9},"Navin Rajpal",{"name":162,"orcid":163},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":166,"method":167,"finding":168,"direction":96,"opportunity":169},"提出SwiGLU增强的ConvNeXt框架，融合多光谱波段与指数集成分类Sentinel-2土地利用","改进ConvNeXt加SwiGLU门控，用RGB、NIR、SWIR及NDVI\u002FN","集成框架总体精度99.19%、Kappa 0.9909，优于CNN、迁移学习和Transformer","可探索多光谱指数组合与门控机制在作物精细分类及跨区域迁移中的泛化能力。","2026-10-09T23:30:12.234854Z",{"id":172,"title":173,"url":174,"summary":175,"summary_zh":176,"content":9,"source_name":177,"source_url":174,"published_at":178,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":179,"score_detail":180,"sources":183,"tags":185,"primary_topic":189,"secondary_topics":190,"classification":191,"search_phrases":215,"slug":218,"view_count":90,"doi":219,"paper":220,"created_at":234},5391,"Spatio-temporal analysis and forecasting of land use\u002Fland cover change in East Medinipur district, India using CA-Markov model","https:\u002F\u002Fdoi.org\u002F10.3846\u002Fgac.2026.23669","Land Use\u002FLand Cover (LU\u002FLC) serve as essential indicators of global environmental changes. This study examined the LU\u002FLC change in the East Medinipur district of West Bengal, India, using remote sensing data and the CA-Markov modeling approach. Through GIS tools and satellite imagery, the study maps, monitors, and analyzes LU\u002FLC changes over a 20-year period (2001–2021), utilizing data from Landsat 4–5 and Landsat 8. Analytical tools including ArcGIS 9, IDRISI Terset, and ERDAS 2014 are employed to project LU\u002FLC trends up to 2041. The results highlight significant transformations, notable reductions in agriculture, vegetation, barren land, and water bodies, alongside substantial increases in aquaculture and built-up areas from 2001 to 2021. Projections indicates that these trends will continue through 2041, with further declines in agriculture, vegetation, barren land, and water bodies, accompanied by continued growth in aquaculture and built-up areas.","土地利用\u002F土地覆盖（Land Use\u002FLand Cover，LU\u002FLC）是全球环境变化的重要指标。本研究利用遥感数据和CA-Markov建模方法，考察了印度西孟加拉邦东梅迪尼普尔县的土地利用\u002F土地覆盖变化。通过GIS工具和卫星影像，研究利用Landsat 4–5和Landsat 8数据，对2001—2021年20年间的土地利用\u002F土地覆盖变化进行了制图、监测与分析。研究采用ArcGIS 9、IDRISI Terset和ERDAS 2014等分析工具，对至2041年的土地利用\u002F土地覆盖趋势进行预测。结果表明，2001年至2021年间发生了显著转变，农业、植被、裸地和水体明显减少，水产养殖和建成区则大幅增加。预测显示，这些趋势将持续至2041年，农业、植被、裸地和水体进一步减少，水产养殖和建成区持续增长。","Geodesy and Cartography","2026-10-19T00:00:00Z",61,{"impact":17,"substance":18,"depth":19,"authority":181,"freshness":17,"relevant":21,"comment":182},12,"该研究基于遥感与CA-Markov模型分析印度区域土地利用变化，方法规范、结论明确，但属区域性案例，对国内三农与农业信息化的直接参考价值有限，可作为遥感应用类论文收录，不宜进入每日精选。",[184],{"name":177,"url":174},[186,27,29,187,188],"水产养殖","耕地变化","CA-Markov",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":192},{"candidates":193,"agricultural_evidence":45},[194,201,205,211],{"code":195,"score":52,"supported":45,"title":196,"abstract":197,"tags":200},"D",[],[198,199],"水产","aquaculture",[186],{"code":32,"score":52,"supported":45,"title":202,"abstract":203,"tags":204},[],[27,48],[27],{"code":58,"score":206,"supported":45,"title":207,"abstract":208,"tags":210},3,[],[209],"GIS",[],{"code":72,"score":21,"supported":14,"title":212,"abstract":213,"tags":214},[],[],[29],[216,217],"Landsat 土地利用 遥感监测","土地利用 水产养殖 耕地变化 遥感","Landsat土地利用遥感监测-5391","10.3846\u002Fgac.2026.23669",{"doi":219,"openalex_id":221,"authors":222,"venue":177,"cited_by_count":90,"oa_url":228,"card":229,"direction":96,"ingested_from":99},"W7222963948",[223,226],{"name":224,"orcid":225},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":227,"orcid":9},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":230,"method":231,"finding":232,"direction":96,"opportunity":233},"用遥感与CA-Markov模型分析印度东梅迪尼普尔2001-2021年土地利用变化并预测至2041年","Landsat影像、GIS与IDRISI\u002FERDAS，CA-Markov模型做L","农业、植被、裸地和水体持续减少，水产养殖与建设用地显著扩张并将延续至2041年。","可将CA-Markov与作物适宜性\u002F粮食安全模型耦合，评估养殖与建设用地扩张对农业生产的长期威胁。","2026-10-20T23:30:19.057951Z",{"id":236,"title":237,"url":238,"summary":239,"summary_zh":240,"content":9,"source_name":241,"source_url":238,"published_at":242,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":243,"score_detail":244,"sources":247,"tags":249,"primary_topic":253,"secondary_topics":254,"classification":255,"search_phrases":267,"slug":270,"view_count":21,"doi":271,"paper":272,"created_at":287},4749,"Beyond site-specific archaeological prospection: Spectral-geometry reveals cross-site cropmark signals in satellite data","https:\u002F\u002Fdoi.org\u002F10.1093\u002Fpnasnexus\u002Fpgag336","Abstract Remote sensing has long supported archaeological prospection, mainly through the detection of cropmarks, a key archaeological proxy expressed as spectral vegetation anomalies over subsurface features. Yet, cropmark detection is still assumed in the literature to be highly site-specific, governed by local agronomic and environmental conditions. Using multispectral Sentinel-2 data from three European archaeological sites, we show that models trained in one landscape can recover meaningful archaeological proxy signals in other locations. By relying strictly on satellite spectral signatures for training, our models achieve measurable cross-location transferability without incorporating ancillary environmental data. We find that cross-site performance arises not from geographic proximity but from the spectral-geometric similarity of vegetation responses, and that transferability is strongly directional. We quantify this relationship through a Directional Strength Score (DSS), derived from monthly subcluster distances under multiple norms, which captures directional geometric similarity and correlates strongly with machine-learning performance. To keep the analysis grounded in the underlying spectral signal, we relied on architecturally restrained nonlinear classifiers (SVM and constrained XGBoost) and used NDVI-identified phenophases to align training and evaluation with comparable seasonal windows. Taken together, these results demonstrate that partial, spectrally mediated cross-location cropmark detection is feasible and that the underlying vegetation responses exhibit stable spectral structure, with spectral geometry offering a clear interpretive lens on why these signals can transfer across sites. Leveraging globally harmonized satellite data, this approach offers a scalable complement to uneven historical aerial archives and supports systematic regional archaeological prioritization to be deployed within integrated archaeological workflows.","摘要 遥感长期支持考古勘探，主要通过检测作物痕迹（cropmarks）——一种关键的考古代用指标，表现为地下特征上方植被的光谱异常。然而，文献中仍普遍认为作物痕迹检测高度依赖于具体遗址，受当地农艺和环境条件制约。利用来自三个欧洲考古遗址的多光谱Sentinel-2数据，我们表明在一个景观中训练的模型可以在其他地点恢复有意义的考古代用信号。通过严格依赖卫星光谱特征进行训练，我们的模型在不纳入辅助环境数据的情况下实现了可测量的跨地点迁移能力。我们发现，跨遗址性能并非源于地理邻近性，而是源于植被响应的光谱-几何相似性，并且迁移能力具有强烈的方向性。我们通过方向强度评分（Directional Strength Score, DSS）量化这一关系，该评分基于多种范数下的逐月子簇距离推导而来，捕捉方向性几何相似性并与机器学习性能强烈相关。为使分析立足于底层光谱信号，我们采用架构受限的非线性分类器（SVM和约束XGBoost），并使用NDVI识别的物候期来对齐训练和评估，使其处于可比较的季节窗口内。综合来看，这些结果表明，部分、以光谱为中介的跨地点作物痕迹检测是可行的，且底层植被响应表现出稳定的光谱结构，光谱几何为理解这些信号为何能够跨遗址迁移提供了清晰的解释视角。利用全球协调的卫星数据，该方法为不均衡的历史航空档案提供了可扩展的补充，并支持在综合考古工作流程中部署系统性的区域考古优先级排序。","PNAS Nexus","2026-10-10T00:00:00Z",82,{"impact":18,"substance":113,"depth":245,"authority":19,"freshness":17,"relevant":21,"comment":246},19,"PNAS Nexus 论文，用 Sentinel-2 多光谱数据证明作物标记信号可跨遗址迁移，方法新颖、结论可靠，对农业遥感与考古信息化有参考价值，但属细分领域突破，未达全国性产业事件层级。",[248],{"name":241,"url":238},[250,27,28,251,252],"Sentinel-2","考古遥感","作物标记",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":256},{"candidates":257,"agricultural_evidence":45},[258,262],{"code":32,"score":181,"supported":45,"title":259,"abstract":260,"tags":261},[],[27,48,129],[28,250,27],{"code":133,"score":206,"supported":45,"title":263,"abstract":264,"tags":266},[],[265],"机器学习",[],[268,269],"Sentinel-2 作物标记 考古","遥感 光谱几何 跨遗址","Sentinel-2作物标记考古-4749","10.1093\u002Fpnasnexus\u002Fpgag336",{"doi":271,"openalex_id":273,"authors":274,"venue":241,"cited_by_count":90,"oa_url":281,"card":282,"direction":96,"ingested_from":99},"W7221319130",[275,278],{"name":276,"orcid":277},"Elias Gravanis","https:\u002F\u002Forcid.org\u002F0000-0002-5331-6661",{"name":279,"orcid":280},"Άθως Αγαπίου","https:\u002F\u002Forcid.org\u002F0000-0001-9106-6766","https:\u002F\u002Facademic.oup.com\u002Fpnasnexus\u002Fadvance-article-pdf\u002Fdoi\u002F10.1093\u002Fpnasnexus\u002Fpgag336\u002F71838194\u002Fpgag336.pdf",{"tldr":283,"method":284,"finding":285,"direction":96,"opportunity":286},"用Sentinel-2多光谱数据证明作物标记模型可跨遗址迁移，且迁移性由光谱几何相似性决定。","Sentinel-2多光谱、SVM与约束XGBoost、NDVI物候对齐、方向强","跨遗址作物标记检测可行，迁移性源于植被光谱几何相似而非地理邻近，且具方向性。","可将光谱几何迁移性指标引入作物表型与病害遥感，构建跨区域可迁移的植被异常检测模型。","2026-10-12T23:30:24.489835Z",{"id":289,"title":290,"url":291,"summary":292,"summary_zh":293,"content":9,"source_name":294,"source_url":291,"published_at":242,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":298,"tags":300,"primary_topic":304,"secondary_topics":305,"classification":306,"search_phrases":322,"slug":325,"view_count":21,"doi":326,"paper":327,"created_at":338},4677,"Geospatial Analysis of Climate-Driven Landscape Change and Urban Expansion: Assessing Human–Environment Interactions, Resource Pressure, and Sustainable Spatial Planning","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280111","The increasing demands and changing features of rapidly expanding cities and the impacts on land cover and the surface energy balance are evident. Yet, urban planners need to differentiate between the changes that have occurred and those which have been predicted. This study offers another geospatial analysis of the Delhi National Capital Region. This study complements previously published studies by the use of remote sensing techniques to retrieve land surface temperatures and studies of urban resource pressures. This study also incorporates land use and land cover studies of the years 2003, 2013 and 2023, which are based on the analysis of the Landsat imagery. The primary land use and land cover study analyzed an area of 13,754 km2 that includes the entirety of the Delhi National Capital Region and portions of the districts of Gurugram, Faridabad, Sonipat, Jhajjar, Ghaziabad, Gautam Buddha Nagar and Ghaziabad. This study also carried out a reanalysis of published land use and land cover maps to compute the absolute and relative changes and the annual growth rates. The area of land cover of built-up land increased from 1,893.64 km2 (13.77%) in 2003 to 3,386.76 km2 (24.62%) in the year 2023, which is an increase of 1,493.12 km (78.85%. CAGR 2.95%). Decreases were observed in the land cover of agricultural land, forests, water bodies and open land. The rate of land cover of built-up land slowed from an annual rate of 4.37% during 2003 to 2013, to an annual rate of 1.55% during the 2013 to 2023 period. The peripheral Shannon Entropy increased from a value of 0.24 to 0.36 during the same periods. The average land surface temperature of built-up land in Delhi, as reported independently of this study, increased by 5.23°C from 36.58°C in the year 2001 to 41.81°C in the year 2021. The integrated findings indicate that land that was previously agricultural and ecological cover has been extensively converted into built-up areas, intensifying surface warming and resource pressure across the urban periphery and underscoring the urgent need for sustainable spatial planning.","快速扩张的城市日益增长的需求和不断变化的特征，以及对土地覆盖和地表能量平衡的影响显而易见。然而，城市规划者需要区分已经发生的变化和预测的变化。本研究提供了对德里国家首都辖区的另一项地理空间分析。本研究通过利用遥感技术反演地表温度以及研究城市资源压力，对先前发表的研究进行了补充。本研究还纳入了基于Landsat影像分析的2003年、2013年和2023年的土地利用与土地覆盖研究。主要的土地利用与土地覆盖研究分析了13,754 km²的区域，涵盖德里国家首都辖区全境以及古尔冈、法里达巴德、索尼帕特、贾贾尔、加济阿巴德、乔达摩菩提那加尔和加济阿巴德等地区的部分区域。本研究还对已发表的土地利用与土地覆盖图进行了再分析，以计算绝对变化量、相对变化量及年增长率。建设用地土地覆盖面积从2003年的1,893.64 km²（13.77%）增加至2023年的3,386.76 km²（24.62%），增加了1,493.12 km²（78.85%，复合年增长率2.95%）。农业用地、森林、水体和裸地的土地覆盖均有所减少。建设用地土地覆盖的增长速率从2003年至2013年的年均4.37%放缓至2013年至2023年的年均1.55%。同期，边缘香农熵从0.24增加至0.36。据独立于本研究的报道，德里建设用地平均地表温度从2001年的36.58°C上升至2021年的41.81°C，升高了5.23°C。综合研究结果表明，原有的农业和生态用地已被大量转化为建设用地，加剧了城市边缘的地表增温和资源压力，凸显了可持续空间规划的迫切需求。","Zenodo (CERN European Organization for Nuclear Research)",79,{"impact":18,"substance":113,"depth":18,"authority":20,"freshness":17,"relevant":21,"comment":297},"基于Landsat遥感数据量化德里首都圈2003-2023年建设用地扩张与地表升温，方法扎实、数据具体，对农业信息化中的遥感监测与城乡空间规划有参考价值。",[299],{"name":294,"url":291},[27,29,301,302,303],"地表温度","城市扩张","空间规划",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":307},{"candidates":308,"agricultural_evidence":45},[309,313,318],{"code":32,"score":17,"supported":45,"title":310,"abstract":311,"tags":312},[],[27,48],[301,27],{"code":58,"score":67,"supported":45,"title":314,"abstract":315,"tags":317},[],[316],"空间分析",[303],{"code":72,"score":21,"supported":14,"title":319,"abstract":320,"tags":321},[],[],[29],[323,324],"城市扩张 地表温度 遥感","土地利用 地表温度 城市扩张 空间规划","城市扩张地表温度遥感-4677","10.5281\u002Fzenodo.23280111",{"doi":326,"openalex_id":328,"authors":329,"venue":294,"cited_by_count":90,"oa_url":291,"card":333,"direction":96,"ingested_from":99},"W7221308370",[330],{"name":331,"orcid":332},"Amit Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0008-5573-0525",{"tldr":334,"method":335,"finding":336,"direction":96,"opportunity":337},"基于Landsat遥感分析德里首都区2003-2023年土地利用变化、地表温度与城市扩张，评估资源压","Landsat影像解译、土地利用\u002F覆盖分类、地表温度反演、Shannon熵与增长","建设用地增78.85%，农林业与水体减少，地表升温5.23°C，城市外围资源压力加剧。","可结合多源遥感与作物模型，量化城市扩张对周边农地生产力与粮食安全的时空影响。","2026-10-11T23:30:30.090512Z",{"id":340,"title":341,"url":342,"summary":343,"summary_zh":344,"content":9,"source_name":294,"source_url":342,"published_at":345,"category":12,"cover_url":9,"hotness":346,"is_selected":14,"score":347,"score_detail":348,"sources":352,"tags":356,"primary_topic":359,"secondary_topics":363,"classification":365,"search_phrases":380,"slug":383,"view_count":21,"doi":384,"paper":385,"created_at":401},3895,"Analysis Of Changes In Land Use And Land Cover In Pune City Over A Decade Using Geospatial Techniques.","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053479","Abstract Land use and land cover (LULC) changes play a significant role in reflecting natural and anthropogenic environmental changes. In this study, changes in 'Land Use and Land Cover' (LULC) in Pune city for the period 2015-2025 have been analysed using the techniques of 'Remote Sensing' and 'Geographic Information System' (GIS). The May 2015 and May 2025 30 m resolution Landsat 08 satellite images were used. For LULC classification, the 'Supervised Maximum Likelihood Classification' method was used, resulting in 5 major LULC classes: built-up land, barren land, and agricultural land, vegetation, and water bodies. More than 100 'training samples' were taken for each land cover type to improve classification accuracy. The findings of the study show that there was a considerable shift in the spatial distribution of LULC in this decade. Built-up land increased from 154.48 sq. km (30.48%) in 2015 to 292.26 sq. km (57.86%) in 2025, representing an increase of approximately 137.78 sq. km. Conversely, the area of barren land decreased from 203.94 sq. km (40.24%) to 106.93 sq. km (21.10%). The amount of agricultural land has reduced from 44.39 sq. km (8.75%) to 18.93 sq. km (3.72%), indicating that agricultural land has been converted for urbanisation on a large scale. Regarding vegetation cover, 85.91 sq. km showed a marginal decrease from 16.95% to 15.58%, whereas the area of water bodies decreased from 18.13 sq. km (3.58%) to 10.77 sq. km (2.12%). The results show the rapid growth of Pune city during the study period, which is mainly observed in the loss or transformation of barren land, agricultural land and water bodies. The study shows the usefulness of using Landsat data, ‘supervised maximum likelihood classification’, GIS and ‘change detection' techniques for tracking long-term urban growth and environmental changes. The results of this research can help urban planners and decision makers in formulating policies for sustainable land use and in safeguarding environmentally sensitive areas.","摘要 土地利用与土地覆盖（LULC）变化在反映自然与人为环境变化方面发挥着重要作用。本研究利用遥感（Remote Sensing）和地理信息系统（GIS）技术，分析了浦那市2015—2025年期间土地利用与土地覆盖（LULC）的变化。研究使用了2015年5月和2025年5月分辨率为30米的Landsat 08卫星影像。在LULC分类中，采用了监督最大似然分类（Supervised Maximum Likelihood Classification）方法，最终得到5个主要LULC类别：建设用地、裸地、农用地、植被和水体。为提高分类精度，每种土地覆盖类型均选取了100个以上的训练样本（training samples）。研究结果表明，在这十年间，LULC的空间分布发生了显著变化。建设用地从2015年的154.48平方公里（30.48%）增加到2025年的292.26平方公里（57.86%），增加了约137.78平方公里。相反，裸地面积从203.94平方公里（40.24%）减少到106.93平方公里（21.10%）。农用地面积从44.39平方公里（8.75%）减少到18.93平方公里（3.72%），表明农用地已大规模转化为城市化用地。植被覆盖方面，85.91平方公里略有下降，从16.95%降至15.58%；水体面积则从18.13平方公里（3.58%）减少到10.77平方公里（2.12%）。结果表明，研究期间浦那市快速扩张，主要体现在裸地、农用地和水体的减少或转变上。研究表明，利用Landsat数据、监督最大似然分类、GIS和变化检测（change detection）技术对于追踪长期城市增长与环境变化具有重要价值。本研究结果可帮助城市规划者和决策者制定可持续土地利用政策，并保护环境敏感区域。","2026-09-30T00:00:00Z",25,65,{"impact":17,"substance":349,"depth":350,"authority":181,"freshness":115,"relevant":21,"comment":351},20,16,"基于Landsat遥感与GIS的浦那市十年土地利用变化研究，数据翔实、结论明确，对农业用地流失与城市扩张监测有参考价值，但属区域案例研究，公共影响有限。",[353,354],{"name":294,"url":342},{"name":294,"url":355},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053480",[27,29,357,209,358],"Landsat","城市化",{"code":58,"slug":360,"name":361,"path":362,"parent_code":36},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[364],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":366},{"candidates":367,"agricultural_evidence":45},[368,372,376],{"code":58,"score":13,"supported":45,"title":369,"abstract":370,"tags":371},[],[62,209,63],[209],{"code":32,"score":17,"supported":45,"title":373,"abstract":374,"tags":375},[],[27,48],[357,27],{"code":72,"score":21,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[29],[381,382],"土地利用 城市化 遥感 Landsat","土地利用 城市化","土地利用城市化遥感Landsat-3895","10.5281\u002Fzenodo.23053479",{"doi":384,"openalex_id":386,"authors":387,"venue":294,"cited_by_count":90,"oa_url":342,"card":396,"direction":96,"ingested_from":99},"W7214889813",[388,390,392,394],{"name":389,"orcid":9},"Kalyani Machhindra sonawane",{"name":391,"orcid":9},"Sumitra Nandkumar Nikam",{"name":393,"orcid":9},"Sunil W. Gaikwad",{"name":395,"orcid":9},"Yogesh N. Pawar",{"tldr":397,"method":398,"finding":399,"direction":96,"opportunity":400},"用遥感与GIS分析印度浦那市2015-2025年土地利用变化，发现建设用地大幅扩张、农地水体锐减。","Landsat 8 30米影像，监督最大似然分类，5类LULC与变化检测。","建设用地从30.48%增至57.86%，农地由8.75%降至3.72%，裸地与水体同步减少。","可结合多时相高分辨率影像与CA-Markov模型，预测城市扩张对农地和水体的未来胁迫。","2026-10-01T23:30:28.617704Z",{"id":403,"title":404,"url":405,"summary":406,"summary_zh":407,"content":9,"source_name":408,"source_url":405,"published_at":409,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":410,"score_detail":411,"sources":413,"tags":415,"primary_topic":418,"secondary_topics":424,"classification":426,"search_phrases":443,"slug":446,"view_count":206,"doi":447,"paper":448,"created_at":458},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z",63,{"impact":17,"substance":349,"depth":350,"authority":13,"freshness":115,"relevant":21,"comment":412},"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[414],{"name":408,"url":405},[120,416,417,27,28],"产量预测","棉花",{"code":419,"slug":420,"name":421,"path":422,"parent_code":423},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[425],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":427},{"candidates":428,"agricultural_evidence":45},[429,434,439],{"code":419,"score":19,"supported":45,"title":430,"abstract":432,"tags":433},[431],"cotton",[417,431],[417],{"code":32,"score":435,"supported":45,"title":436,"abstract":437,"tags":438},11,[48],[],[28,416,27],{"code":144,"score":21,"supported":14,"title":440,"abstract":441,"tags":442},[],[],[120],[444,445],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838","10.52171\u002Fherald.342",{"doi":447,"openalex_id":449,"authors":450,"venue":408,"cited_by_count":90,"oa_url":405,"card":453,"direction":96,"ingested_from":99},"W7214748696",[451],{"name":452,"orcid":9},"B.H. Babashli",{"tldr":454,"method":455,"finding":456,"direction":96,"opportunity":457},"结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z"]