[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4677":3,"related-4677":82},{"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":62,"slug":65,"view_count":21,"doi":66,"paper":67,"created_at":81},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。",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","土地利用","地表温度","城市扩张","空间规划",{"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],{"code":32,"score":20,"supported":45,"title":46,"abstract":47,"tags":49},true,[],[26,48],"remote sensing",[28,26],{"code":51,"score":52,"supported":45,"title":53,"abstract":54,"tags":56},"I4",4,[],[55],"空间分析",[30],{"code":58,"score":21,"supported":14,"title":59,"abstract":60,"tags":61},"H6",[],[],[27],[63,64],"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":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":73,"oa_url":6,"card":74,"direction":78,"ingested_from":80},"W7221308370",[70],{"name":71,"orcid":72},"Amit Kumar Singh","https:\u002F\u002Forcid.org\u002F0009-0008-5573-0525",0,{"tldr":75,"method":76,"finding":77,"direction":78,"opportunity":79},"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。","农业遥感与作物表型","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。","openalex","2026-10-11T23:30:30.090512Z",{"total":83,"page":21,"page_size":83,"items":84},6,[85,151,196,254,301,371],{"id":86,"title":87,"url":88,"summary":89,"summary_zh":90,"content":9,"source_name":91,"source_url":88,"published_at":92,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":93,"score_detail":94,"sources":98,"tags":100,"primary_topic":104,"secondary_topics":105,"classification":106,"search_phrases":131,"slug":134,"view_count":73,"doi":135,"paper":136,"created_at":150},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":20,"substance":17,"depth":95,"authority":96,"freshness":20,"relevant":21,"comment":97},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。",[99],{"name":91,"url":88},[101,26,27,102,103],"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":107},{"candidates":108,"agricultural_evidence":45},[109,117,121,127],{"code":110,"score":111,"supported":45,"title":112,"abstract":113,"tags":116},"D",7,[],[114,115],"水产","aquaculture",[101],{"code":32,"score":111,"supported":45,"title":118,"abstract":119,"tags":120},[],[26,48],[26],{"code":51,"score":122,"supported":45,"title":123,"abstract":124,"tags":126},3,[],[125],"GIS",[],{"code":58,"score":21,"supported":14,"title":128,"abstract":129,"tags":130},[],[],[27],[132,133],"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":135,"openalex_id":137,"authors":138,"venue":91,"cited_by_count":73,"oa_url":144,"card":145,"direction":78,"ingested_from":80},"W7222963948",[139,142],{"name":140,"orcid":141},"Parag Jyoti Kashyap","https:\u002F\u002Forcid.org\u002F0000-0003-4273-841X",{"name":143,"orcid":9},"Sahana Bose","https:\u002F\u002Fjournals.vilniustech.lt\u002Findex.php\u002FGAC\u002Farticle\u002Fdownload\u002F23669\u002F13567",{"tldr":146,"method":147,"finding":148,"direction":78,"opportunity":149},"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":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":159,"sources":161,"tags":163,"primary_topic":165,"secondary_topics":166,"classification":167,"search_phrases":178,"slug":181,"view_count":73,"doi":182,"paper":183,"created_at":195},5094,"An Investigation of Built-up Area Expansion in the Syrian Coastal Mountain Environment","https:\u002F\u002Fdoi.org\u002F10.14246\u002Firspsd.14.4_109","As built-up expansion in the Syrian coastal mountainous areas intensifies, its environmental impacts are becoming increasingly severe. Existing research, however, offers limited quantitative evidence to elucidate the dynamics of this phenomenon. This study addresses this gap by employing mixed-method techniques at the district level of Safita. Landsat satellite imagery, remote sensing, and ArcGIS software were used to classify and analyze geospatial data from 2010 to 2020. Statistical methodologies were then applied to investigate spatio-temporal built-up growth, identify the drivers of expansion, and evaluate resultant spatial patterns. The analysis reveals accelerated built-up expansion, with the most dramatic growth occurring in southern peripheral zones, recording increases of 150-169%. The study identifies a persistent trend of low-density, dispersed sprawl following a non-linear trajectory, evidenced by Shannon's entropy increasing steadily to 0.89 by 2020. Analysis of Urban Expansion Rate and Intensity indices shows a distinct shift from moderate, semi-efficient growth (2010–2015) to rapid and extensive sprawl (2015–2020). Furthermore, the Built-up Expansion Index demonstrates an evolution from initial economically driven leapfrog expansion through conflict-induced infilling consolidation to a final hybrid pattern combining edge-expansion and ribbon development. Kernel Density Estimation reveals a fundamental spatial transformation from semi-concentrated hotspots to widespread medium-density sprawl, strongly constrained by physical factors. Specifically, 93% of built-up areas concentrated on gentle slopes (0-15%), and 68% were located within 200 meters of road networks. These findings underscore the urgent need for planning decision-makers to implement strict regulatory measures that curb built-up sprawl, protect agricultural lands and natural resources, and guide growth through updated master plans to achieve sustainable development.","随着叙利亚沿海山区建成区扩张不断加剧，其环境影响日益严重。然而，现有研究在阐明这一现象的动态方面提供的定量证据有限。本研究通过在萨菲塔区级层面采用混合方法技术来填补这一空白。利用Landsat卫星影像、遥感技术和ArcGIS软件对2010年至2020年的地理空间数据进行分类和分析。随后应用统计方法研究建成区的时空增长，识别扩张的驱动因素，并评估由此产生的空间格局。分析揭示了建成区扩张的加速，最显著的增长发生在南部边缘地带，增幅达150%至169%。研究识别出低密度、分散式蔓延的持续趋势，呈非线性轨迹，香农熵（Shannon's entropy）到2020年稳步上升至0.89即为明证。城市扩张速率和强度指数的分析显示，从2010至2015年的温和、半高效增长明显转向2015至2020年的快速、广泛蔓延。此外，建成区扩张指数（Built-up Expansion Index）表明，扩张从最初经济驱动的跳跃式扩展，经过冲突引发的填充式整合，演变为边缘扩展与带状发展相结合的最终混合模式。核密度估计（Kernel Density Estimation）揭示了从半集中热点向广泛中密度蔓延的根本性空间转变，且受自然因素的强烈制约。具体而言，93%的建成区集中在缓坡（0-15%）上，68%位于道路网络200米范围内。这些发现凸显了规划决策者迫切需要实施严格的监管措施，以遏制建成区蔓延，保护农田和自然资源，并通过更新总体规划引导增长，以实现可持续发展。","International Review for Spatial Planning and Sustainable Development","2026-10-14T00:00:00Z",{"impact":73,"substance":73,"depth":73,"authority":73,"freshness":73,"relevant":73,"comment":160},"研究叙利亚沿海山区建成区扩张，属城市规划与环境领域，与三农、农业信息化、数字乡村无关。",[162],{"name":157,"url":154},[164,27,29],"遥感监测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":168},{"candidates":169,"agricultural_evidence":45},[170,174],{"code":32,"score":111,"supported":45,"title":171,"abstract":172,"tags":173},[],[26,48],[164],{"code":58,"score":21,"supported":14,"title":175,"abstract":176,"tags":177},[],[],[27],[179,180],"叙利亚沿海山区 建成区扩张","Safita 遥感 土地利用","叙利亚沿海山区建成区扩张-5094","10.14246\u002Firspsd.14.4_109",{"doi":182,"openalex_id":184,"authors":185,"venue":157,"cited_by_count":73,"oa_url":189,"card":190,"direction":78,"ingested_from":80},"W7221786958",[186],{"name":187,"orcid":188},"Tarek Rahmoun","https:\u002F\u002Forcid.org\u002F0000-0001-5112-2487","https:\u002F\u002Fwww.jstage.jst.go.jp\u002Farticle\u002Firspsd\u002F14\u002F4\u002F14_6\u002F_pdf",{"tldr":191,"method":192,"finding":193,"direction":78,"opportunity":194},"研究叙利亚沿海山区萨菲塔地区2010-2020年建设用地扩张动态、驱动因素与空间格局。","Landsat影像、遥感与ArcGIS分类，结合统计方法分析时空扩张。","建设用地加速低密度蔓延，93%集中于缓坡、68%靠近道路，熵值升至0.89。","可结合遥感与机器学习，量化山区建设用地扩张对农地流失和生态的长期影响。","2026-10-16T23:30:28.556800Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":203,"score_detail":204,"sources":210,"tags":212,"primary_topic":216,"secondary_topics":217,"classification":218,"search_phrases":225,"slug":228,"view_count":122,"doi":229,"paper":230,"created_at":253},5007,"Kernel-Driven Model Correction of Thermal Directionality: Pixel-Level Analysis Using Airborne Multi-Angular Observations","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18203525","Directional anisotropy (DA) in thermal infrared (TIR) remote sensing introduces significant variability in land surface temperature (LST) retrievals, particularly in high-resolution observations. This study presents the empirical pixel-level modelling of DA, by using multi-angular airborne data from the NETSense campaign over an agricultural area near Grosseto, Italy. Six kernel-driven models (KDMs) are evaluated for their ability to fit directional effects, with the Vinnikov–Roujean–Lagouarde (Vinnikov–RL) model achieving the highest accuracy (RMSE = 1.07 K). At 40 m resolution, modelled directional effects reach median deviations of +1.80 K at the hotspot position, −2.03 K opposite to the Sun, and −1.37 K perpendicular to the solar principal plane, with extremes up to ±6 K, highlighting their operational significance for missions such as LSTM, TRISHNA, and SBG. To improve operational feasibility, two simplified models—adapted RL (RLa) and Vinnikov–RLa—are introduced, leveraging a consistent inverse relationship between hotspot magnitude and width to reduce parameter complexity via a power-law formulation. The analysis reveals that directional effects vary with spatial resolution and are influenced by canopy structure and solar geometry, while biophysical parameters alone are insufficient for coefficient estimation. Validation using an independent flight day confirms the robustness of the simplified models under different acquisition geometries, surface types, and geophysical conditions. A tiered model strategy is proposed for future satellite missions, recommending model selection based on day or night observations and data availability. These findings support the integration of directional correction into operational pipelines, enabling consistent and accurate LST products across platforms, resolutions, and observation conditions.","热红外（TIR）遥感中的方向各向异性（DA）会导致地表温度（LST）反演出现显著变化，在高分辨率观测中尤为明显。本研究利用意大利格罗塞托附近农业区NETSense campaign的多角度机载数据，开展了像元尺度的DA经验建模。评估了六种核驱动模型（KDM）拟合方向效应的能力，其中Vinnikov–Roujean–Lagouarde（Vinnikov–RL）模型精度最高（RMSE = 1.07 K）。在40 m分辨率下，模拟的方向效应在热点位置达到中位偏差+1.80 K，背阳方向为−2.03 K，垂直于太阳主平面方向为−1.37 K，极值可达±6 K，凸显了其对LSTM、TRISHNA和SBG等任务的实际意义。为提高业务可行性，引入了两种简化模型——改进RL（RLa）和Vinnikov–RLa，利用热点幅度与宽度之间一致的负相关关系，通过幂律公式降低参数复杂度。分析表明，方向效应随空间分辨率变化，并受冠层结构和太阳几何影响，而仅凭生物物理参数不足以估计系数。利用独立飞行日的验证证实了简化模型在不同观测几何、地表类型和地球物理条件下的稳健性。提出了面向未来卫星任务的分层模型策略，建议根据白天或夜间观测及数据可用性选择模型。这些发现支持将方向校正集成到业务化流程中，从而在不同平台、分辨率和观测条件下实现一致且准确的LST产品。","Remote Sensing",83,{"impact":17,"substance":205,"depth":206,"authority":207,"freshness":208,"relevant":21,"comment":209},23,19,14,9,"基于航空多角度观测的热红外方向性建模研究，方法新颖、数据扎实，对LSTM、TRISHNA等农业遥感卫星任务的地表温度产品校正具有直接参考价值。",[211],{"name":202,"url":199},[213,26,28,214,215],"LSTM","热红外","TRISHNA",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":219},{"candidates":220,"agricultural_evidence":45},[221],{"code":32,"score":20,"supported":45,"title":222,"abstract":223,"tags":224},[],[26,48],[28,26],[226,227],"NETSense 航空 多角度 热红外","LSTM TRISHNA SBG 地表温度","NETSense航空多角度热红外-5007","10.3390\u002Frs18203525",{"doi":229,"openalex_id":231,"authors":232,"venue":202,"cited_by_count":73,"oa_url":199,"card":248,"direction":78,"ingested_from":80},"W7221818626",[233,236,239,242,245],{"name":234,"orcid":235},"Louis Snyders","https:\u002F\u002Forcid.org\u002F0000-0003-1093-2517",{"name":237,"orcid":238},"J. A. D. L. Blommaert","https:\u002F\u002Forcid.org\u002F0000-0002-5797-2439",{"name":240,"orcid":241},"J. León-Tavares","https:\u002F\u002Forcid.org\u002F0000-0002-6186-9936",{"name":243,"orcid":244},"Mary Langsdale","https:\u002F\u002Forcid.org\u002F0000-0002-6078-0702",{"name":246,"orcid":247},"Lorenzo Genesio","https:\u002F\u002Forcid.org\u002F0000-0001-9265-886X",{"tldr":249,"method":250,"finding":251,"direction":78,"opportunity":252},"基于机载多角度热红外数据，在像素尺度评估核驱动模型并简化，以校正地表温度方向性效应。","使用NETSense机载多角度热红外数据，评估六种核驱动模型并构建简化模型。","Vinnikov–RL模型精度最高，方向性偏差中值达+1.80K至-2.03K，极端达±6K。","可探索方向性校正与作物冠层结构参数耦合，提升卫星LST产品在农业监测中的一致性。","2026-10-15T23:30:29.753962Z",{"id":255,"title":256,"url":257,"summary":258,"summary_zh":259,"content":9,"source_name":10,"source_url":257,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":73,"score_detail":260,"sources":262,"tags":264,"primary_topic":265,"secondary_topics":266,"classification":267,"search_phrases":283,"slug":286,"view_count":21,"doi":287,"paper":288,"created_at":300},4679,"Spatio-temporal variation of urban sprawl in Tura town using the Shannon entropy model","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23277607","Abstract Urbanization is driving substantial transformation of cities worldwide, with unmanaged expansion often resulting in urban sprawl and the loss of agricultural land, forests, and environmentally sensitive areas. Tura Town, headquarters of West Garo Hills District, has experienced steady urban growth over the past two decades, yet few studies have examined its expansion pattern. This study aims to analyse the spatio-temporal variation and assessing the degree and directional patterns of urban sprawl from 2006 to 2026 using Remote Sensing, GIS, and Shannon's Entropy Index. Landsat imagery was classified into LULC categories, with accuracy validated through a confusion matrix, achieving satisfactory results (77–81%). Built-up area increased from 10 km² to 17 km²—a 70% rise, with faster growth occurring during 2006–2016 (40%) than 2016–2026 (21.4%). Shannon's Entropy Index revealed the compactness or dispersal of growth, while buffer and directional analyses identified expansion patterns. The findings offer valuable insights for sustainable urban planning and land-use management in Tura Town. Keyword: Urban sprawl, Urbanization, Land use\u002Fland cover (LULC), Remote Sensing, GIS, Shannon's Entropy Index, Built-up area, Tura Town, Spatio-temporal analysis, Sustainable urban planning","摘要 城市化正推动全球城市发生深刻转型，而无序扩张往往导致城市蔓延以及农田、森林和环境敏感区域的丧失。作为西加罗丘陵县首府的图拉镇，过去二十年经历了稳定的城市增长，但对其扩张模式的研究却很少。本研究旨在利用遥感、地理信息系统和香农熵指数，分析2006年至2026年城市蔓延的时空变化，并评估其程度与方向性模式。研究将Landsat影像分类为土地利用\u002F土地覆盖（LULC）类别，并通过混淆矩阵验证精度，取得了令人满意的结果（77%–81%）。建成区面积从10平方公里增至17平方公里，增长了70%，其中2006—2016年（40%）的增长快于2016—2026年（21.4%）。香农熵指数揭示了增长的紧凑或分散特征，而缓冲区和方向性分析则识别了扩张模式。研究结果为图拉镇的可持续城市规划和土地利用管理提供了有价值的见解。关键词：城市蔓延，城市化，土地利用\u002F土地覆盖（LULC），遥感，地理信息系统，香农熵指数，建成区，图拉镇，时空分析，可持续城市规划",{"impact":73,"substance":73,"depth":73,"authority":73,"freshness":73,"relevant":73,"comment":261},"该论文研究印度图拉镇城市扩张的遥感与熵模型分析，属城市地理与土地利用范畴，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[263],{"name":10,"url":257},[164,27,29],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":268},{"candidates":269,"agricultural_evidence":45},[270,274,279],{"code":32,"score":111,"supported":45,"title":271,"abstract":272,"tags":273},[],[26,48],[164],{"code":51,"score":83,"supported":45,"title":275,"abstract":276,"tags":278},[],[277,125],"地理信息",[],{"code":58,"score":21,"supported":14,"title":280,"abstract":281,"tags":282},[],[],[27],[284,285],"土地利用 城市扩张 遥感监测","土地利用 城市扩张","土地利用城市扩张遥感监测-4679","10.5281\u002Fzenodo.23277607",{"doi":287,"openalex_id":289,"authors":290,"venue":10,"cited_by_count":73,"oa_url":257,"card":295,"direction":78,"ingested_from":80},"W7221355035",[291,293],{"name":292,"orcid":9},"Kambai Ch. Marak",{"name":294,"orcid":9},"Bring Blessing L Ryntathiang",{"tldr":296,"method":297,"finding":298,"direction":78,"opportunity":299},"用遥感、GIS和香农熵分析印度图拉镇2006-2026年城市蔓延的时空变化。","Landsat影像分类LULC，混淆矩阵验证精度77-81%，结合香农熵、缓冲与","建成区从10增至17平方公里（增70%），2006-2016年增长快于2016-2026年，熵值揭示","可结合耕地流失与农业用地破碎化，评估城市蔓延对农业生态系统的长期影响。","2026-10-11T23:30:30.230545Z",{"id":302,"title":303,"url":304,"summary":305,"summary_zh":306,"content":9,"source_name":307,"source_url":304,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":313,"tags":315,"primary_topic":318,"secondary_topics":319,"classification":320,"search_phrases":346,"slug":349,"view_count":21,"doi":350,"paper":351,"created_at":370},4678,"Remote Sensing-based Integration of Land Use\u002FLand Cover, Vegetation Cover, and Land Surface Temperature with Hydrological Implications in Tikrit City, Iraq","https:\u002F\u002Fdoi.org\u002F10.14445\u002F23488352\u002Fijce-v13i9p115","This study examines the integrated relationship among Land Use\u002FLand Cover (LULC), Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST), with hydrological implications, in Tikrit City, Iraq, using Landsat data acquired in 2017 and 2025. LULC, NDVI, and LST layers were prepared using the same spatial boundary, and 4124 paired sample points were extracted from identical locations to support temporal and statistical comparison. The LULC results showed a clear reduction in Soil from 70.52% in 2017 to 49.88% in 2025, whereas Vegetation increased from 25.49% to 44.12%. Built Area also increased from 3.08% to 4.90%, indicating moderate urban expansion. The dominant land-cover transition was from Soil to Vegetation, covering 66,014.66 ha. Mean NDVI increased significantly from 0.061 to 0.075, while mean LST remained statistically stable, changing only from 41.992°C to 42.031°C. However, non-parametric tests indicated that the spatial distribution of LST changed significantly. The NDVI–LST relationship was positive in both years, with Pearson correlation increasing from 0.670 in 2017 to 0.709 in 2025. Regression analysis showed that NDVI explained 44.9% and 50.2% of LST variation in 2017 and 2025, respectively. Class-based analysis indicated that the dominant heat-contributing class shifted from Soil in 2017 to Built Area in 2025. These findings show that surface thermal behavior in Tikrit City cannot be explained by vegetation cover alone, but depends on land-cover composition, mixed pixels, agricultural activity, soil exposure, surface moisture, and seasonal conditions. From a hydrological perspective, the reduction of exposed soil, expansion of vegetation, and growth of built-up areas have implications for infiltration opportunity, runoff potential, evapotranspiration tendency, and surface moisture retention.","本研究利用2017年和2025年获取的Landsat数据，考察了伊拉克提克里特市土地利用\u002F土地覆盖（LULC）、归一化植被指数（NDVI）和地表温度（LST）之间的综合关系及其水文意义。LULC、NDVI和LST图层采用相同的空间边界制备，并从相同位置提取了4124对样本点，以支持时间与统计比较。LULC结果显示，土壤占比从2017年的70.52%明显下降至2025年的49.88%，而植被占比从25.49%上升至44.12%。建成区面积也从3.08%增至4.90%，表明存在中等程度的城市扩张。主导性土地覆盖转变为由土壤转为植被，覆盖面积达66,014.66公顷。平均NDVI从0.061显著增至0.075，而平均LST在统计上保持稳定，仅从41.992°C变为42.031°C。然而，非参数检验表明LST的空间分布发生了显著变化。NDVI与LST的关系在两年中均为正相关，Pearson相关系数从2017年的0.670增至2025年的0.709。回归分析表明，NDVI在2017年和2025年分别解释了LST变异的44.9%和50.2%。基于类别的分析表明，主要的热贡献类别从2017年的土壤转变为2025年的建成区。这些发现表明，提克里特市的地表热行为不能仅由植被覆盖来解释，而是取决于土地覆盖组成、混合像元、农业活动、土壤裸露、地表湿度和季节条件。从水文角度看，裸露土壤减少、植被扩张和建成区增长对下渗机会、径流潜力、蒸散发趋势和地表水分保持具有影响。","International Journal of Civil Engineering",68,{"impact":20,"substance":310,"depth":311,"authority":19,"freshness":13,"relevant":21,"comment":312},20,17,"基于Landsat双期影像量化伊拉克提克里特市土地利用、植被与地表温度耦合关系及其水文效应，数据扎实、结论明确，对农业遥感与土地变化研究有参考价值，但地域性强、国内关联度有限。",[314],{"name":307,"url":304},[164,27,28,316,317],"植被覆盖","水文响应",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":321},{"candidates":322,"agricultural_evidence":45},[323,328,335,342],{"code":32,"score":324,"supported":45,"title":325,"abstract":326,"tags":327},11,[48],[],[28,316,164],{"code":329,"score":83,"supported":45,"title":330,"abstract":331,"tags":334},"H2",[],[332,333],"土壤","soil",[],{"code":336,"score":83,"supported":45,"title":337,"abstract":338,"tags":341},"H3",[],[339,340],"蒸散发","evapotranspiration",[],{"code":58,"score":21,"supported":14,"title":343,"abstract":344,"tags":345},[],[],[27],[347,348],"Landsat 土地利用 植被覆盖 地表温度","土地利用 地表温度 植被覆盖 水文响应","Landsat土地利用植被覆盖地表温度-4678","10.14445\u002F23488352\u002Fijce-v13i9p115",{"doi":350,"openalex_id":352,"authors":353,"venue":307,"cited_by_count":73,"oa_url":364,"card":365,"direction":78,"ingested_from":80},"W7221352158",[354,356,358,361],{"name":355,"orcid":9},"Omar Taher Nafe'e",{"name":357,"orcid":9},"Dalia Shaker Mahdi",{"name":359,"orcid":360},"Lubna B. Mahmood","https:\u002F\u002Forcid.org\u002F0009-0000-4428-4891",{"name":362,"orcid":363},"Asmaa Abdul Jabbar Jamel","https:\u002F\u002Forcid.org\u002F0000-0001-7602-3426","https:\u002F\u002Fwww.internationaljournalssrg.org\u002F..\u002FIJCE\u002F2026\u002FVolume13-Issue9\u002FIJCE-V13I9P115.pdf",{"tldr":366,"method":367,"finding":368,"direction":78,"opportunity":369},"基于Landsat 2017与2025年数据，分析伊拉克提克里特市LULC、NDVI与LST的耦合关","Landsat影像、4124个配对样点、NDVI与LST反演、相关与回归分析。","植被增加、裸土减少，LST均值稳定但空间分布显著变化，NDVI-LST正相关增强，主导热贡献由裸土转","可结合土壤湿度与蒸散数据，探究NDVI-LST正相关机制及混合像元对城市农业水文过程的影响。","2026-10-11T23:30:30.165387Z",{"id":372,"title":373,"url":374,"summary":375,"summary_zh":376,"content":9,"source_name":377,"source_url":374,"published_at":378,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":379,"score_detail":380,"sources":382,"tags":384,"primary_topic":388,"secondary_topics":389,"classification":390,"search_phrases":414,"slug":417,"view_count":418,"doi":419,"paper":420,"created_at":437},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":17,"substance":18,"depth":17,"authority":207,"freshness":208,"relevant":21,"comment":381},"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。",[383],{"name":377,"url":374},[385,386,26,387,27],"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":391},{"candidates":392,"agricultural_evidence":45},[393,398,405,409],{"code":32,"score":19,"supported":45,"title":394,"abstract":396,"tags":397},[395],"multispectral",[395],[387,26],{"code":399,"score":111,"supported":45,"title":400,"abstract":401,"tags":404},"I2",[],[402,403],"深度学习","deep learning",[386],{"code":58,"score":21,"supported":14,"title":406,"abstract":407,"tags":408},[],[],[27],{"code":410,"score":21,"supported":14,"title":411,"abstract":412,"tags":413},"I1",[],[],[385],[415,416],"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":419,"openalex_id":421,"authors":422,"venue":377,"cited_by_count":73,"oa_url":430,"card":431,"direction":436,"ingested_from":80},"W7220869344",[423,425,427],{"name":424,"orcid":9},"Neeraj Kumar",{"name":426,"orcid":9},"Navin Rajpal",{"name":428,"orcid":429},"Pramod Kumar Soni","https:\u002F\u002Forcid.org\u002F0000-0003-0632-5044","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-67985-x_reference.pdf",{"tldr":432,"method":433,"finding":434,"direction":78,"opportunity":435},"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"]