[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3362":3,"related-3362":86},{"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":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":85},3362,"Satellite-Based Delineation of the Urban–Rural Interface Using Landscape Heterogeneity Metrics: Evidence from Three Argentine Cities","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15101777","Urban–rural interfaces are transitional territories characterized by the coexistence of urban, agricultural, and natural land covers. Despite their relevance for territorial planning and environmental management, their spatial delineation remains methodologically challenging due to their dynamic and heterogeneous nature. This study proposes a reproducible remote sensing-based approach for delineating urban–rural interfaces and evaluating their spatial coherence through landscape heterogeneity metrics. The method was applied to three intermediate Argentine cities representing contrasting environmental contexts: Córdoba, Rosario, and Mendoza. Interface areas were identified by combining nighttime light intensity, the Normalized Built-up Area Index (NBAI), and the Normalized Difference Vegetation Index (NDVI) within Google Earth Engine. To evaluate the coherence of the resulting delineations, five alternative interface configurations were generated using different nighttime light thresholds and assessed through landscape metrics describing diversity, interspersion, fragmentation, and spatial structure. The workflow produced spatially coherent candidate interface areas in all three cities, producing spatial patterns consistent with local urban expansion processes. The highest heterogeneity values occurred at different interface extents among cities, with intermediate configurations generally showing the strongest responses. Among the evaluated metrics, Shannon Diversity Index (SHDI) and Interspersion and Juxtaposition Index (IJI) provided the most consistent results. The findings suggest that spatial heterogeneity constitutes a useful criterion for assessing the coherence of interface delineations, while highlighting that maximum heterogeneity does not necessarily correspond to the most operationally suitable boundary for territorial management.","城乡界面是以城市、农业和自然土地覆盖并存为特征的过渡性地域。尽管其对于国土规划与环境管理具有重要意义，但由于其动态性和异质性，空间划定在方法上仍具挑战性。本研究提出了一种可重复的基于遥感的方法，用于划定城乡界面，并通过景观异质性指标评估其空间一致性。该方法应用于三座代表不同环境背景的阿根廷中等城市：科尔多瓦、罗萨里奥和门多萨。界面区域通过在Google Earth Engine中结合夜间灯光强度、归一化建成区指数（NBAI）和归一化植被指数（NDVI）进行识别。为评估所得划定结果的一致性，利用不同的夜间灯光阈值生成了五种替代界面配置，并通过描述多样性、交错性、破碎化和空间结构的景观指标进行评估。该工作流程在三座城市中均生成了空间一致的候选界面区域，其空间格局与当地城市扩张过程相符。最高异质性值在各城市出现的界面范围不同，中间配置通常表现出最强的响应。在评估的指标中，香农多样性指数（SHDI）和交错与毗邻指数（IJI）提供了最为一致的结果。研究结果表明，空间异质性可作为评估界面划定一致性的有用标准，同时强调最大异质性并不一定对应于国土管理中最具操作适宜性的边界。",null,"Land","2026-09-23T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,17,13,1,"方法可复现、指标组合有新意，但研究对象为阿根廷城市，对国内三农与农业信息化的直接参考价值有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"城乡融合","遥感","土地利用","景观格局","阿根廷",{"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":46},[44,51,56],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},7,true,[],[27,49],"remote sensing",[27],{"code":52,"score":21,"supported":14,"title":53,"abstract":54,"tags":55},"G5",[],[],[26],{"code":57,"score":21,"supported":14,"title":58,"abstract":59,"tags":60},"H6",[],[],[28],[62,63],"阿根廷 城乡结合部 遥感","Google Earth Engine 夜间灯光 NDVI","阿根廷城乡结合部遥感-3362",3,"10.3390\u002Fland15101777",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":77,"oa_url":6,"card":78,"direction":82,"ingested_from":84},"W7214122245",[70,73,75],{"name":71,"orcid":72},"Yuliana Céliz","https:\u002F\u002Forcid.org\u002F0000-0003-3810-8490",{"name":74,"orcid":9},"Beatriz Giobellina",{"name":76,"orcid":9},"Ramiro Sarandón",0,{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"提出基于遥感与景观异质性指标划定城乡结合部的方法，并在三座阿根廷城市验证。","Google Earth Engine结合夜间灯光、NBAI和NDVI，用景观指","SHDI和IJI最一致，但最大异质性边界未必最适合管理。","农业遥感与作物表型","可探索将城乡界面异质性指标与农业多功能性、生态系统服务结合，优化国土空间规划。","openalex","2026-09-24T23:30:20.991175Z",{"total":87,"page":21,"page_size":87,"items":88},6,[89,155,209,278,357,420],{"id":90,"title":91,"url":92,"summary":93,"summary_zh":94,"content":9,"source_name":95,"source_url":92,"published_at":96,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":97,"score_detail":98,"sources":103,"tags":105,"primary_topic":109,"secondary_topics":110,"classification":111,"search_phrases":135,"slug":138,"view_count":77,"doi":139,"paper":140,"created_at":154},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":99,"depth":100,"authority":101,"freshness":17,"relevant":21,"comment":102},18,15,12,"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。",[104],{"name":95,"url":92},[106,27,28,107,108],"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":112},{"candidates":113,"agricultural_evidence":46},[114,121,125,131],{"code":115,"score":45,"supported":46,"title":116,"abstract":117,"tags":120},"D",[],[118,119],"水产","aquaculture",[106],{"code":32,"score":45,"supported":46,"title":122,"abstract":123,"tags":124},[],[27,49],[27],{"code":126,"score":65,"supported":46,"title":127,"abstract":128,"tags":130},"I4",[],[129],"GIS",[],{"code":57,"score":21,"supported":14,"title":132,"abstract":133,"tags":134},[],[],[28],[136,137],"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":139,"openalex_id":141,"authors":142,"venue":95,"cited_by_count":77,"oa_url":148,"card":149,"direction":82,"ingested_from":84},"W7222963948",[143,146],{"name":144,"orcid":145},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":147,"orcid":9},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":150,"method":151,"finding":152,"direction":82,"opportunity":153},"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":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":9,"source_name":161,"source_url":158,"published_at":162,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":163,"score_detail":164,"sources":167,"tags":169,"primary_topic":173,"secondary_topics":174,"classification":175,"search_phrases":192,"slug":195,"view_count":21,"doi":196,"paper":197,"created_at":208},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)","2026-10-10T00:00:00Z",79,{"impact":99,"substance":165,"depth":99,"authority":20,"freshness":17,"relevant":21,"comment":166},22,"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。",[168],{"name":161,"url":158},[27,28,170,171,172],"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":176},{"candidates":177,"agricultural_evidence":46},[178,182,188],{"code":32,"score":17,"supported":46,"title":179,"abstract":180,"tags":181},[],[27,49],[170,27],{"code":126,"score":183,"supported":46,"title":184,"abstract":185,"tags":187},4,[],[186],"空间分析",[172],{"code":57,"score":21,"supported":14,"title":189,"abstract":190,"tags":191},[],[],[28],[193,194],"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":196,"openalex_id":198,"authors":199,"venue":161,"cited_by_count":77,"oa_url":158,"card":203,"direction":82,"ingested_from":84},"W7221308370",[200],{"name":201,"orcid":202},"Amit Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0008-5573-0525",{"tldr":204,"method":205,"finding":206,"direction":82,"opportunity":207},"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":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":216,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":217,"score_detail":218,"sources":222,"tags":224,"primary_topic":228,"secondary_topics":229,"classification":230,"search_phrases":254,"slug":257,"view_count":258,"doi":259,"paper":260,"created_at":277},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":99,"substance":165,"depth":99,"authority":219,"freshness":220,"relevant":21,"comment":221},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。",[223],{"name":215,"url":212},[225,226,27,227,28],"智慧农业","农业人工智能","NDVI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":231},{"candidates":232,"agricultural_evidence":46},[233,238,245,249],{"code":32,"score":20,"supported":46,"title":234,"abstract":236,"tags":237},[235],"multispectral",[235],[227,27],{"code":239,"score":45,"supported":46,"title":240,"abstract":241,"tags":244},"I2",[],[242,243],"深度学习","deep learning",[226],{"code":57,"score":21,"supported":14,"title":246,"abstract":247,"tags":248},[],[],[28],{"code":250,"score":21,"supported":14,"title":251,"abstract":252,"tags":253},"I1",[],[],[225],[255,256],"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":259,"openalex_id":261,"authors":262,"venue":215,"cited_by_count":77,"oa_url":270,"card":271,"direction":276,"ingested_from":84},"W7220869344",[263,265,267],{"name":264,"orcid":9},"Neeraj Kumar",{"name":266,"orcid":9},"Navin Rajpal",{"name":268,"orcid":269},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":272,"method":273,"finding":274,"direction":82,"opportunity":275},"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。","智慧农业 \u002F 农业物联网","2026-10-09T23:30:12.234854Z",{"id":279,"title":280,"url":281,"summary":282,"summary_zh":283,"content":9,"source_name":284,"source_url":281,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":286,"score_detail":287,"sources":290,"tags":292,"primary_topic":296,"secondary_topics":297,"classification":298,"search_phrases":325,"slug":328,"view_count":258,"doi":329,"paper":330,"created_at":356},4082,"Dynamic Assessment and Synergistic Reconfiguration of Rural Territorial System Resilience: A Case Study of Quzhou County, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16192132","Rapid urbanization is widely regarded as a major pathway to rural development through improved market accessibility, infrastructure provision, and urban–rural functional linkages. Yet long-term empirical evidence has not sufficiently tested whether urban proximity enhances or erodes the resilience of rural territorial systems. Drawing on multisource remote sensing imagery, a harmonized nighttime light series, socioeconomic statistics, and key informant interviews spanning 1985–2025, this study reconstructs the spatiotemporal evolution of rural territorial system resilience in Quzhou County, a representative agricultural county on the North China Plain. An integrated element–structure–function framework conceptualizes rural resilience along three dimensions—element support capacity, structural resistance, and functional adaptability—extending the analytical focus from assessing system states to examining underlying mechanisms. The results show that the county-wide mean resilience index declined from 0.4832 to 0.2897, a cumulative decrease of 40%, with a 26.7% decline during 2015–2025 alone. The coefficient of variation across townships followed a nonlinear rise–fall–rise trajectory, indicating widening disparities, brief convergence, and renewed divergence, and reached a study-period maximum of 0.3859 in 2025. The combined area share of the low and relatively low resilience classes increased from 10.89% to 79.27%, whereas that of the high resilience class contracted from 17.50% to 7.82%, remained low, and ultimately fell to zero in 2025. Spatially, townships surrounding the county seat experienced the sharpest declines, contrary to the conventional expectation that proximity to urban centers strengthens rural resilience. Policy interventions yielded only temporary rebounds, and no township achieved systemic recovery during the study period. Instead, the system exhibited interconnected deterioration characterized by resource depletion, structural fragmentation, and functional homogenization. These findings motivate a “proximity paradox” hypothesis in urban–rural integration: when urban expansion erodes the resource base of surrounding rural areas, disrupts their spatial organization, and reduces their functional redundancy, geographical proximity to cities may shift from an enabling condition for resilience to a source of resilience loss. Accordingly, this study proposes a coordinated governance approach integrating the conservation of critical resources, the restructuring of settlements into a three-tier hierarchy, and spatial zoning into four functional categories. This approach provides an empirical basis for overcoming persistent rural decline and implementing differentiated spatial governance in agricultural plains undergoing rapid urbanization.","快速城镇化被广泛视为通过改善市场可达性、基础设施供给和城乡功能联系来推动农村发展的重要路径。然而，长期实证证据尚未充分检验城市邻近性究竟增强还是侵蚀了乡村地域系统韧性。本研究基于多源遥感影像、统一夜间灯光序列、社会经济统计数据以及1985—2025年关键知情人访谈，重建了华北平原典型农业县曲周县乡村地域系统韧性的时空演变。研究构建了要素—结构—功能一体化框架，从要素支撑能力、结构抵抗力和功能适应性三个维度界定乡村韧性，将分析重点从评估系统状态拓展至考察其内在机制。结果表明，全县平均韧性指数由0.4832降至0.2897，累计下降40%，其中2015—2025年 alone 下降26.7%。各乡镇变异系数呈非线性“升—降—升”轨迹，表明差距扩大、短暂收敛后又重新分化，并于2025年达到研究期最大值0.3859。低韧性和较低韧性等级的面积占比合计由10.89%增至79.27%，而高韧性等级面积占比由17.50%收缩至7.82%，持续处于低位，并最终在2025年降至零。空间上，县城周边乡镇韧性下降最为剧烈，这与靠近城市中心会增强乡村韧性的传统预期相反。政策干预仅带来暂时性回升，研究期内没有任何乡镇实现系统性恢复。相反，系统表现出以资源耗竭、结构碎片化和功能同质化为特征的联动性退化。这些发现促使我们提出城乡融合中的“邻近性悖论”假说：当城市扩张侵蚀周边乡村地区的资源基础、扰乱其空间组织并降低其功能冗余时，地理上靠近城市可能从韧性的有利条件转变为韧性丧失的来源。据此，本研究提出一种协同治理路径，整合关键资源保护、th","Agriculture","2026-10-02T00:00:00Z",78,{"impact":288,"substance":165,"depth":99,"authority":20,"freshness":220,"relevant":21,"comment":289},16,"基于1985—2025年多源遥感与统计数据的县域乡村韧性长时序研究，提出“邻近悖论”假说，数据扎实、结论有政策参考价值，值得进入每日精选。",[291],{"name":284,"url":281},[293,26,294,28,295],"乡村治理","遥感监测","乡村韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":299},{"candidates":300,"agricultural_evidence":46},[301,305,312,317,321],{"code":32,"score":45,"supported":46,"title":302,"abstract":303,"tags":304},[],[27,49],[294],{"code":306,"score":87,"supported":46,"title":307,"abstract":308,"tags":311},"G",[],[309,310],"农村发展","rural development",[],{"code":313,"score":65,"supported":46,"title":314,"abstract":315,"tags":316},"G6",[],[26],[],{"code":52,"score":258,"supported":14,"title":318,"abstract":319,"tags":320},[],[],[293,26],{"code":57,"score":21,"supported":14,"title":322,"abstract":323,"tags":324},[],[],[28],[326,327],"曲周县 乡村韧性 遥感","华北平原 城乡融合 乡村衰退","曲周县乡村韧性遥感-4082","10.3390\u002Fagriculture16192132",{"doi":329,"openalex_id":331,"authors":332,"venue":284,"cited_by_count":77,"oa_url":281,"card":350,"direction":82,"ingested_from":84},"W7215822322",[333,336,338,340,342,345,348],{"name":334,"orcid":335},"Yibin Zhang","https:\u002F\u002Forcid.org\u002F0009-0007-5358-407X",{"name":337,"orcid":9},"Limeng Jia",{"name":339,"orcid":9},"Xin Zhang",{"name":341,"orcid":9},"Yahui Duan",{"name":343,"orcid":344},"Mu Li","https:\u002F\u002Forcid.org\u002F0000-0002-2927-4756",{"name":346,"orcid":347},"Qiangqiang Sun","https:\u002F\u002Forcid.org\u002F0000-0003-2017-6743",{"name":349,"orcid":9},"HAO Jinmin",{"tldr":351,"method":352,"finding":353,"direction":354,"opportunity":355},"基于多源数据重建曲周县1985-2025年乡村地域系统韧性演变，提出‘邻近悖论’假说。","多源遥感、夜间灯光、社会经济统计与访谈，构建要素-结构-功能框架。","县域韧性均值下降40%，城郊乡镇降幅最大，政策仅带来短暂回升，无系统恢复。","数字乡村与农业信息化","可探究城乡融合中‘邻近悖论’的阈值与机制，并开发韧性动态监测与协同治理决策模型。","2026-10-03T23:30:32.229183Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":362,"content":9,"source_name":161,"source_url":360,"published_at":363,"category":12,"cover_url":9,"hotness":364,"is_selected":14,"score":365,"score_detail":366,"sources":368,"tags":372,"primary_topic":375,"secondary_topics":379,"classification":381,"search_phrases":398,"slug":401,"view_count":21,"doi":402,"paper":403,"created_at":419},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":18,"depth":288,"authority":101,"freshness":220,"relevant":21,"comment":367},"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。",[369,370],{"name":161,"url":360},{"name":161,"url":371},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053480",[27,28,373,129,374],"Landsat","城市化",{"code":126,"slug":376,"name":377,"path":378,"parent_code":36},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[380],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":382},{"candidates":383,"agricultural_evidence":46},[384,390,394],{"code":126,"score":13,"supported":46,"title":385,"abstract":386,"tags":389},[],[387,129,388],"地理信息","geographic information",[129],{"code":32,"score":17,"supported":46,"title":391,"abstract":392,"tags":393},[],[27,49],[373,27],{"code":57,"score":21,"supported":14,"title":395,"abstract":396,"tags":397},[],[],[28],[399,400],"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":402,"openalex_id":404,"authors":405,"venue":161,"cited_by_count":77,"oa_url":360,"card":414,"direction":82,"ingested_from":84},"W7214889813",[406,408,410,412],{"name":407,"orcid":9},"Kalyani Machhindra sonawane",{"name":409,"orcid":9},"Sumitra Nandkumar Nikam",{"name":411,"orcid":9},"Sunil W. Gaikwad",{"name":413,"orcid":9},"Yogesh N. Pawar",{"tldr":415,"method":416,"finding":417,"direction":82,"opportunity":418},"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":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":423,"published_at":427,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":428,"score_detail":429,"sources":431,"tags":433,"primary_topic":436,"secondary_topics":437,"classification":438,"search_phrases":464,"slug":467,"view_count":21,"doi":468,"paper":469,"created_at":484},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。","American Journal of Geospatial Technology","2026-09-14T00:00:00Z",62,{"impact":17,"substance":99,"depth":100,"authority":20,"freshness":17,"relevant":21,"comment":430},"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。",[432],{"name":426,"url":423},[434,27,227,28,435],"气候变化","水资源",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":439},{"candidates":440,"agricultural_evidence":46},[441,445,451,455,460],{"code":32,"score":17,"supported":46,"title":442,"abstract":443,"tags":444},[],[27,49],[227,27],{"code":446,"score":45,"supported":46,"title":447,"abstract":448,"tags":450},"H7",[],[434,449],"climate change",[434],{"code":126,"score":87,"supported":46,"title":452,"abstract":453,"tags":454},[],[387,388],[],{"code":456,"score":183,"supported":46,"title":457,"abstract":458,"tags":459},"H3",[],[435],[435],{"code":57,"score":21,"supported":14,"title":461,"abstract":462,"tags":463},[],[],[28],[465,466],"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":468,"openalex_id":470,"authors":471,"venue":426,"cited_by_count":77,"oa_url":478,"card":479,"direction":276,"ingested_from":84},"W7212965144",[472,474,476],{"name":473,"orcid":9},"Pauline Angerlyline Wamalwa",{"name":475,"orcid":9},"Samson Mabwoga",{"name":477,"orcid":9},"Charity Konana","https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fajgt\u002Farticle\u002Fdownload\u002F8320\u002F4014",{"tldr":480,"method":481,"finding":482,"direction":82,"opportunity":483},"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。","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。","2026-09-15T23:30:08.755171Z"]