[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1993":3,"related-1993":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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":70,"slug":73,"view_count":21,"doi":74,"paper":75,"created_at":100},1993,"Multi-scale OPGD uncovers spatial pattern and influencing factors of rural settlements in the Minjiang River Basin","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-69084-3","Rapid urbanization and industrialization are reshaping rural systems, necessitating a thorough analysis of their spatial patterns and driving forces. This study conducted cross-sectional analysis of rural settlement in the Minjiang River Basin (MRB) for the year 2020, using rural-settlement land data derived from annual China Land Cover Dataset (CLCD). Settlement scale was defined as the total area of rural settlement land within each analysis grid cell. was characterized using spatial analysis methods. Spatial analysis methods including Average Nearest Neighbor (ANN), Kernel Density Estimation (KDE), and Hot Spot Analysis applied to characterize the spatial pattern of rural settlements. An optimal analysis grid was identified through scale sensitivity testing, and the Optimal Parameter Geodetector (OPGD) was subsequently applied to examine the associated mechanisms of spatial differentiation in settlement scale. The results indicate that: (1) Rural settlements in the MRB exhibit a significant clustered distribution at the global scale (R = 0.698, Z = − 30.08), manifesting a \"one primary core and multiple sub-cores\" structure and a distinct Northwest-Southeast axial, with the spatial centroid notably shifted toward coastal lowlands. (2) A 4 km grid size produced the highest mean q-value among the six tested scales. (3) Factor detection reveals that the Normalized Difference Vegetation Index (NDVI, q = 0.116) and road network density ( q = 0.091) showed the strongest spatial stratification in driving settlement scale differentiation. (4) All factor interactions exhibit enhancement effects; notably, the nonlinear enhancement between road network density and topographic relief is the most significant ( q = 0.147). These findings provide decision support for differentiated rural planning in the MRB and offer a valuable reference for spatial analysis and influential factor research in regions with complex terrain.","快速城市化与工业化正在重塑乡村系统，亟需对其空间格局及驱动机制进行深入分析。本研究基于年度中国土地覆盖数据集（CLCD）提取的农村居民点用地数据，对2020年岷江流域（MRB）农村居民点进行了横截面分析。居民点规模定义为每个分析网格单元内农村居民点用地的总面积，并采用空间分析方法刻画其特征。运用平均最近邻指数（ANN）、核密度估计（KDE）和热点分析等空间分析方法表征农村居民点的空间格局。通过尺度敏感性测试确定最优分析网格，进而应用最优参数地理探测器（OPGD）探究居民点规模空间分异的关联机制。结果表明：（1）岷江流域农村居民点在全球尺度上呈现显著的集聚分布格局（R = 0.698，Z = −30.08），表现为“一主核多副核”的空间结构及明显的西北—东南轴向特征，空间重心显著偏向沿海低地。（2）在六种测试尺度中，4 km网格尺度产生的平均q值最高。（3）因子探测显示，归一化植被指数（NDVI，q = 0.116）和路网密度（q = 0.091）对居民点规模分异具有最强的空间分层解释力。（4）所有因子交互作用均呈现增强效应，其中路网密度与地形起伏度之间的非线性增强最为显著（q = 0.147）。上述发现为岷江流域差异化乡村规划提供了决策支持，也为复杂地形区域的空间格局及影响因素研究提供了有价值的参考。",null,"Scientific Reports","2026-09-08T00:00:00Z","论文",10,false,56,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,16,12,2,1,"论文聚焦流域尺度乡村聚落空间格局与影响因素，方法严谨，对复杂地形区乡村规划有参考价值，但时效性较低。",[25],{"name":10,"url":6},[27,28,29,30,31],"数字乡村","遥感","乡村聚落","空间格局","地理探测器",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,53,60,65],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":52},6,true,[],[50,51],"空间分析","spatial analysis",[],{"code":54,"score":55,"supported":47,"title":56,"abstract":57,"tags":59},"I2",3,[],[58],"decision support",[],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"I3",[],[],[28],{"code":66,"score":22,"supported":14,"title":67,"abstract":68,"tags":69},"I6",[],[],[27],[71,72],"地理探测器 乡村聚落 数字乡村 空间格局","地理探测器 乡村聚落","地理探测器乡村聚落数字乡村空间格局-1993","10.1038\u002Fs41598-026-69084-3",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":90,"oa_url":91,"card":92,"direction":98,"ingested_from":99},"W7211874041",[78,80,83,86,88],{"name":79,"orcid":9},"Shanghui Li",{"name":81,"orcid":82},"Zhipeng Zhu","https:\u002F\u002Forcid.org\u002F0000-0001-6825-3117",{"name":84,"orcid":85},"Xinxin Xia","https:\u002F\u002Forcid.org\u002F0009-0007-5767-3758",{"name":87,"orcid":9},"江道镨",{"name":89,"orcid":9},"Peng Yu",0,"https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-69084-3_reference.pdf",{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"利用多尺度最优参数地理探测器分析闽江流域农村居民点空间格局及影响因素。","使用CLCD数据，结合最邻近指数、核密度、热点分析和最优参数地理探测器。","农村居民点呈集聚分布，NDVI和路网密度为主要驱动因素，交互作用增强。","数字乡村与农业信息化","可延伸至山区农村居民点优化布局，结合多源数据与多尺度分析，探索地形与交通交互影响。","农业遥感与作物表型","openalex","2026-09-09T23:30:22.214502Z",{"total":46,"page":22,"page_size":46,"items":102},[103,166,240,305,378,459],{"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":115,"tags":117,"primary_topic":121,"secondary_topics":125,"classification":127,"search_phrases":149,"slug":152,"view_count":21,"doi":153,"paper":154,"created_at":165},4102,"Application of GIS and Remote Sensing Techniques in Geospatial Mapping of Gully Statuses of an Alfisols in Yola South Local Government Area, Adamawa State","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fwjimt.v10.no1.2026.pg155.165","This research work saddled to study the application of GIS and Remote Sensing (RS) Techniques in geospatial mapping of gully erosion statuses of an Alfisols in Yola South LGA. Bole farmland was selected where expansion and development of gully was geospatially mapped out for the period of 20 years (2004-2024) at interval of five (5) years using GIS and RS techniques. The results reveal a concerning trend of increasing gully erosion, with significant growth in affected areas over the years. The highest percentage of increase occurred at 2014-2019 with about 58,069.00 m2 (65.5 %) and in 2019-2024was increased with 14,154.00 m2 (15.97 %). The total area affected in the area within the study period was 88,627.00 m2 caused by heavy rainfall and deforestation. Therefore, there are needs for effective land management practices, policy interventions, and community engagement to mitigate the effects of gully erosion.","本研究旨在探讨GIS与遥感（RS）技术在约拉南部地方政府区淋溶土冲沟侵蚀状况地理空间制图中的应用。选取博莱农田为研究区，利用GIS与遥感技术，以5年为间隔，对2004—2024年共20年间冲沟的扩展与发育进行了地理空间制图。结果表明，冲沟侵蚀呈令人担忧的加剧趋势，受影响面积逐年显著增长。增幅最大的时段为2014—2019年，约增加58 069.00 m²（65.5%）；2019—2024年增加14 154.00 m²（15.97%）。研究期内该区域受影响总面积为88 627.00 m²，主要由强降雨和森林砍伐所致。因此，需要采取有效的土地管理措施、政策干预和社区参与，以减轻冲沟侵蚀的影响。","WORLD JOURNAL OF INNOVATION AND MODERN TECHNOLOGY","2026-09-30T00:00:00Z",54,{"impact":17,"substance":18,"depth":113,"authority":46,"freshness":17,"relevant":22,"comment":114},14,"基于GIS与遥感的20年沟蚀动态监测，数据具体、结论明确，但属区域案例研究，公共影响有限。",[116],{"name":109,"url":106},[27,28,118,119,120],"土壤侵蚀","GIS","水土保持",{"code":61,"slug":122,"name":123,"path":124,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[126],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":128},{"candidates":129,"agricultural_evidence":47},[130,136,140,145],{"code":61,"score":131,"supported":47,"title":132,"abstract":134,"tags":135},15,[133],"remote sensing",[28,133],[28],{"code":33,"score":20,"supported":47,"title":137,"abstract":138,"tags":139},[119],[119],[119],{"code":141,"score":22,"supported":14,"title":142,"abstract":143,"tags":144},"H6",[],[],[120],{"code":66,"score":22,"supported":14,"title":146,"abstract":147,"tags":148},[],[],[27],[150,151],"Yola South 沟蚀 遥感","Alfisols 沟蚀 GIS 制图","YolaSouth沟蚀遥感-4102","10.56201\u002Fwjimt.v10.no1.2026.pg155.165",{"doi":153,"openalex_id":155,"authors":156,"venue":109,"cited_by_count":90,"oa_url":159,"card":160,"direction":98,"ingested_from":99},"W7214920253",[157],{"name":158,"orcid":9},"Mohammed Pakra Umar","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FWJIMT\u002FVOL. 10 NO. 1 2026\u002FAPPLICATION OF GIS AND REMOTE 156-165.pdf",{"tldr":161,"method":162,"finding":163,"direction":98,"opportunity":164},"用GIS和遥感技术对尼日利亚Yola South地区20年沟蚀动态进行空间制图与监测。","基于GIS与遥感影像，按5年间隔提取2004-2024年沟蚀范围并计算面积变化。","沟蚀面积持续扩大，2014-2019年增幅最大达65.5%，20年累计影响88,627平方米。","可结合多源高分辨率遥感与机器学习，构建沟蚀风险预警模型并评估保护性耕作等治理措施效果。","2026-10-03T23:30:37.949646Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":110,"category":12,"cover_url":9,"hotness":173,"is_selected":14,"score":174,"score_detail":175,"sources":179,"tags":183,"primary_topic":187,"secondary_topics":191,"classification":193,"search_phrases":223,"slug":226,"view_count":227,"doi":228,"paper":229,"created_at":239},2333,"Empowering India's Digital Future through Artificial Intelligence in Geography","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705830","The convergence of Geographic Information Systems (GIS), high-resolution Earth observation (EO) systems, and Artificial Intelligence (AI) has established a new paradigm: Geospatial Artificial Intelligence (GeoAI). In the context of India's rapid digital transformation, GeoAI serves as a core technology driving sustainable urbanization, precision agriculture, disaster risk reduction, and multi-modal logistics planning. Anchored by strategic national frameworks such as the National Geospatial Policy (NGP), PM Gati Shakti National Master Plan, and the SVAMITVA scheme, this paper examines how AI-integrated spatial analysis accelerates India's digital governance. Through technical methodologies, statistical hypothesis testing, applied case studies, and systemic evaluation, this study demonstrates the pivotal role of GeoAI in building resilient spatial data infrastructure and offers strategic policy recommendations for scalable national adoption.","地理信息系统（GIS）、高分辨率对地观测（EO）系统与人工智能（AI）的融合，已确立了一种新范式：地理空间人工智能（GeoAI）。在印度快速数字化转型的背景下，GeoAI成为推动可持续城市化、精准农业、灾害风险降低和多式联运物流规划的核心技术。本文以国家地理空间政策（NGP）、PM Gati Shakti国家总体规划以及SVAMITVA计划等战略性国家框架为依托，考察了AI集成的空间分析如何加速印度的数字治理。通过技术方法、统计假设检验、应用案例研究和系统性评估，本研究表明GeoAI在构建韧性空间数据基础设施中发挥关键作用，并为全国范围的可扩展应用提供了战略性政策建议。","Zenodo (CERN European Organization for Nuclear Research)",25,72,{"impact":18,"substance":176,"depth":18,"authority":177,"freshness":55,"relevant":22,"comment":178},20,13,"论文系统阐述GeoAI在印度精准农业、数字治理中的应用与政策建议，方法扎实但发布日期在未来，时效性存疑。",[180,181],{"name":172,"url":169},{"name":172,"url":182},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22705829",[27,184,185,28,186],"智慧农业","农业人工智能","地理信息",{"code":54,"slug":188,"name":189,"path":190,"parent_code":37},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[192],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":194},{"candidates":195,"agricultural_evidence":47},[196,201,207,215,219],{"code":54,"score":20,"supported":47,"title":197,"abstract":199,"tags":200},[198],"artificial intelligence",[198],[185],{"code":33,"score":202,"supported":47,"title":203,"abstract":204,"tags":206},9,[],[186,50,119,51,205],"geographic information",[],{"code":208,"score":209,"supported":47,"title":210,"abstract":211,"tags":214},"I1",7,[],[212,213],"精准农业","precision agriculture",[184],{"code":61,"score":22,"supported":14,"title":216,"abstract":217,"tags":218},[],[],[28],{"code":66,"score":22,"supported":14,"title":220,"abstract":221,"tags":222},[],[],[27],[224,225],"农业人工智能 地理信息 数字乡村 智慧农业","农业人工智能 地理信息","农业人工智能地理信息数字乡村智慧农业-2333",4,"10.5281\u002Fzenodo.22705830",{"doi":228,"openalex_id":230,"authors":231,"venue":172,"cited_by_count":90,"oa_url":169,"card":234,"direction":96,"ingested_from":99},"W7212270621",[232],{"name":233,"orcid":9},"Lalitkumar G. Thakur",{"tldr":235,"method":236,"finding":237,"direction":98,"opportunity":238},"本文探讨地理空间人工智能（GeoAI）如何推动印度数字治理与可持续发展。","结合GIS、高分辨率对地观测与AI，采用案例研究、统计检验和政策评估。","GeoAI在精准农业、灾害减灾等领域关键，需国家政策框架支持规模化应用。","可研究GeoAI在印度小农精准农业中的适配性、数据鸿沟与政策落地机制。","2026-09-13T23:30:29.245252Z",{"id":241,"title":242,"url":243,"summary":244,"summary_zh":245,"content":9,"source_name":246,"source_url":243,"published_at":247,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":248,"score_detail":249,"sources":251,"tags":253,"primary_topic":255,"secondary_topics":256,"classification":257,"search_phrases":285,"slug":288,"view_count":90,"doi":289,"paper":290,"created_at":304},5572,"Potential erosion risk assessment of Kanak stream basin (Sivas, Sarkisla) with the ICONA model","https:\u002F\u002Fdoi.org\u002F10.51753\u002Fflsrt.1828829","The rapid development of technology following the Industrial Revolution has increased human domination over nature. Erosion, which plays a critical role in the process of natural shaping of the earth, can accelerate over time because of improper utilization of the natural environment by humans. The main objective of this study is to determine the erosion susceptibility of the Kanak Creek Basin, one of the sub-basins in the upper reaches of the Kizilirmak River, using the ICONA model, whose main parameters are slope, lithology, land use and vegetation cover, and Geographic Information Systems (GIS) and Remote Sensing techniques, and to list the measures that can be taken against erosion. According to the results of the ICONA model potential erosion level map, 48% of the study area has low erosion susceptibility. These areas largely correspond to the vicinity of Sama Mountain, which has volcanic rocks resistant to erosion, and the base of Sarkisla Plain where the slope is very low. The plain floor is largely covered with agricultural lands. The expansion of irrigated agriculture in this region is one of the main measures that can be taken against erosion. Erosion susceptibility increases around Turna Mountain on the northern border of the basin and Cal Mountain in the south due to both the increase in slope and the lack of resistance of the rocks in the region to erosion, and corresponds to 30% of the study area. In 22% of the basin, susceptibility to erosion is at a medium level. Most of these areas consist of natural meadows and pastures. It is essential to take measures against overgrazing in these areas to prevent the complete loss of the already sparse vegetation cover. Planning land use in the basin in a sustainable manner, arranging fields and layers perpendicular to the slope, expanding irrigated agriculture, and preventing overgrazing are of vital importance in terms of combating soil erosion.","工业革命以来技术的迅猛发展增强了人类对自然的支配能力。侵蚀在地球自然塑造过程中起着关键作用，而人类对自然环境的不当利用会加速侵蚀进程。本研究的主要目标是利用ICONA模型（其主要参数为坡度、岩性、土地利用和植被覆盖）以及地理信息系统（GIS）和遥感技术，确定克孜勒河上游子流域之一卡纳克溪流域的侵蚀敏感性，并列出可采取的侵蚀防治措施。根据ICONA模型潜在侵蚀等级图的结果，研究区48%的区域侵蚀敏感性较低。这些区域主要对应于具有抗侵蚀火山岩的萨马山附近以及坡度极低的萨尔基什拉平原底部。平原底部大部分为农业用地覆盖。在该地区扩大灌溉农业是防治侵蚀的主要措施之一。在流域北部边界的图尔纳山和南部的恰尔山周围，由于坡度增大以及该地区岩石抗侵蚀能力不足，侵蚀敏感性增加，占研究区面积的30%。流域22%的区域侵蚀敏感性为中等水平。这些区域大部分为天然草甸和牧场。在这些地区采取措施防止过度放牧至关重要，以避免本已稀疏的植被覆盖完全丧失。以可持续方式规划流域土地利用、沿垂直于坡向布置田块和土层、扩大灌溉农业以及防止过度放牧，对防治土壤侵蚀具有至关重要的意义。","Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z",61,{"impact":17,"substance":18,"depth":113,"authority":20,"freshness":202,"relevant":22,"comment":250},"该研究以ICONA模型结合GIS与遥感评估土耳其小流域侵蚀风险，方法规范、结论具体，但属区域性案例，公共影响有限，可作为专业参考而非每日精选。",[252],{"name":246,"url":243},[28,254,118,119,120],"土地利用",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":258},{"candidates":259,"agricultural_evidence":47},[260,264,268,275,281],{"code":33,"score":13,"supported":47,"title":261,"abstract":262,"tags":263},[],[186,119,205],[119],{"code":61,"score":209,"supported":47,"title":265,"abstract":266,"tags":267},[],[28,133],[28],{"code":269,"score":46,"supported":47,"title":270,"abstract":271,"tags":274},"H2",[],[272,273],"土壤","soil",[],{"code":276,"score":55,"supported":47,"title":277,"abstract":278,"tags":280},"H3",[],[279],"灌溉",[],{"code":141,"score":21,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[254,120],[286,287],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572","10.51753\u002Fflsrt.1828829",{"doi":289,"openalex_id":291,"authors":292,"venue":246,"cited_by_count":90,"oa_url":9,"card":298,"direction":98,"ingested_from":99},"W7223117733",[293,295],{"name":294,"orcid":9},"MERİÇ KURT",{"name":296,"orcid":297},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":299,"method":300,"finding":301,"direction":302,"opportunity":303},"用ICONA模型评估土耳其Kanak流域潜在侵蚀风险并列出防治措施。","ICONA模型结合GIS与遥感，输入坡度、岩性、土地利用与植被覆盖。","48%区域低侵蚀风险，30%高敏感，22%中等，主因坡度与岩性。","农业绿色发展与碳","可将ICONA与遥感时序数据结合，动态监测流域侵蚀并评估农业管理措施效果。","2026-10-22T11:20:39.475037Z",{"id":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":312,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":313,"score_detail":314,"sources":316,"tags":318,"primary_topic":322,"secondary_topics":323,"classification":325,"search_phrases":354,"slug":357,"view_count":90,"doi":358,"paper":359,"created_at":377},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex","2026-10-18T00:00:00Z",60,{"impact":17,"substance":18,"depth":131,"authority":13,"freshness":202,"relevant":22,"comment":315},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[317],{"name":311,"url":308},[28,119,319,320,321],"农用地适宜性","土地覆被","孟加拉农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[324],{"code":61,"slug":122,"name":123,"path":124,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":326},{"candidates":327,"agricultural_evidence":47},[328,332,336,343,350],{"code":33,"score":18,"supported":47,"title":329,"abstract":330,"tags":331},[119],[186,119,205],[119],{"code":61,"score":131,"supported":47,"title":333,"abstract":334,"tags":335},[133],[28,133],[28],{"code":337,"score":46,"supported":47,"title":338,"abstract":339,"tags":342},"A1",[],[340,341],"稻米","rice",[],{"code":344,"score":46,"supported":47,"title":345,"abstract":346,"tags":349},"G",[],[347,348],"农村发展","rural development",[],{"code":269,"score":46,"supported":47,"title":351,"abstract":352,"tags":353},[],[272,273],[],[355,356],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":358,"openalex_id":360,"authors":361,"venue":371,"cited_by_count":90,"oa_url":308,"card":372,"direction":98,"ingested_from":99},"W7222895468",[362,364,366,368],{"name":363,"orcid":9},"Raju Ahmed",{"name":365,"orcid":9},"Shaikh Mehdee Mohammad",{"name":367,"orcid":9},"Ummul Momanin Coalee",{"name":369,"orcid":370},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":373,"method":374,"finding":375,"direction":98,"opportunity":376},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z",{"id":379,"title":380,"url":381,"summary":382,"summary_zh":383,"content":9,"source_name":384,"source_url":381,"published_at":385,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":387,"sources":390,"tags":392,"primary_topic":395,"secondary_topics":396,"classification":402,"search_phrases":444,"slug":447,"view_count":21,"doi":9,"paper":448,"created_at":458},5013,"REMOTE SENSING AND GIS-BASED ASSESSMENT OF SOIL SALINITY IN IRRIGATED LANDS: METHODS, CHALLENGES AND PROSPECTS FOR PRECISION AGRICULTURE","http:\u002F\u002Fwww.arabianjournalofscience.com\u002Findex.php\u002Findex\u002Farticle\u002Fview\u002F1223","Soil salinization is a major land degradation process affecting the sustainability of irrigated agriculture, particularly in arid and semi-arid regions. Conventional soil sampling provides essential information about salinity levels but may be insufficient for continuous monitoring of large agricultural territories because of spatial variability, sampling costs, and temporal limitations. Remote sensing (RS) and geographic information systems (GIS) offer complementary approaches for identifying spatial patterns of salinity, monitoring land conditions, and supporting agricultural management. This article examines the scientific and methodological foundations of integrating multispectral satellite imagery, spectral indices, geospatial analysis, and machine learning for soil salinity assessment in irrigated lands. Particular attention is given to Landsat and Sentinel-2 imagery, the interpretation of vegetation and soil reflectance indicators, the role of electrical conductivity measurements in model calibration, and the limitations associated with vegetation cover, soil moisture, and seasonal variability. The analysis indicates that reliable salinity assessment requires the integration of satellite observations with field measurements and environmental variables rather than the isolated application of spectral indices. The article proposes a methodological framework for developing spatially explicit salinity assessment systems applicable to irrigated agricultural landscapes, including those of Central Asia. The framework provides a basis for improving monitoring strategies and supporting evidence-based land management without assuming unverified local measurement results.","土壤盐渍化是影响灌溉农业可持续性的主要土地退化过程，在干旱和半干旱地区尤为突出。传统土壤采样可提供盐分水平的基本信息，但由于空间变异性、采样成本和时间限制，可能不足以对大面积农业区域进行持续监测。遥感（RS）和地理信息系统（GIS）为识别盐渍化空间格局、监测土地状况和支持农业管理提供了互补性方法。本文探讨了整合多光谱卫星影像、光谱指数、地理空间分析和机器学习进行灌溉土地土壤盐渍化评估的科学与方法学基础。文章特别关注Landsat和Sentinel-2影像、植被与土壤反射率指标的解译、电导率测量在模型校准中的作用，以及与植被覆盖、土壤湿度和季节变异性相关的局限性。分析表明，可靠的盐渍化评估需要将卫星观测与野外测量和环境变量相结合，而非孤立地应用光谱指数。本文提出了一个方法学框架，用于开发适用于灌溉农业景观（包括中亚地区）的空间显式盐渍化评估系统。该框架为改进监测策略和支持基于证据的土地管理提供了基础，且不以未经核实的当地测量结果为假设前提。","Arabian journal of science.","2026-10-13T00:00:00Z",74,{"impact":19,"substance":176,"depth":388,"authority":177,"freshness":17,"relevant":22,"comment":389},17,"系统综述遥感与GIS在灌溉农田盐渍化评估中的方法框架与挑战，对盐碱地治理和精准农业有参考价值，但属综述性论文，非突破性成果。",[391],{"name":384,"url":381},[184,212,28,393,394],"盐碱地治理","土壤监测",{"code":61,"slug":122,"name":123,"path":124,"parent_code":37},[397,398],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":269,"slug":273,"name":399,"path":400,"parent_code":401},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":40,"locked":14,"version":41,"method":42,"evidence":403},{"candidates":404,"agricultural_evidence":47},[405,410,414,418,422,428,432,438],{"code":61,"score":18,"supported":47,"title":406,"abstract":407,"tags":409},[133],[28,133,408],"multispectral",[28],{"code":33,"score":388,"supported":47,"title":411,"abstract":412,"tags":413},[119],[186,50,119,205],[],{"code":269,"score":131,"supported":47,"title":415,"abstract":416,"tags":417},[273],[272,273],[393],{"code":208,"score":13,"supported":47,"title":419,"abstract":420,"tags":421},[213],[],[184,212],{"code":54,"score":46,"supported":47,"title":423,"abstract":424,"tags":427},[],[425,426],"机器学习","machine learning",[],{"code":276,"score":55,"supported":47,"title":429,"abstract":430,"tags":431},[],[279],[],{"code":433,"score":55,"supported":47,"title":434,"abstract":435,"tags":437},"H7",[],[436],"干旱",[],{"code":439,"score":55,"supported":47,"title":440,"abstract":441,"tags":443},"I7",[],[442],"methodological framework",[],[445,446],"遥感 GIS 土壤盐渍化","Landsat Sentinel-2 盐分监测","遥感GIS土壤盐渍化-5013",{"doi":9,"openalex_id":449,"authors":450,"venue":384,"cited_by_count":90,"oa_url":381,"card":453,"direction":98,"ingested_from":99},"W7221843179",[451],{"name":452,"orcid":9},"Sadullayev Sardor Zokirjon o'g'li",{"tldr":454,"method":455,"finding":456,"direction":98,"opportunity":457},"综述遥感与GIS评估灌溉地土壤盐渍化的方法、挑战与前景，并提出空间显式评估框架。","整合多光谱卫星影像、光谱指数、GIS空间分析与机器学习，结合电导率校准。","可靠盐渍化评估需卫星观测与实地测量及环境变量融合，而非孤立使用光谱指数。","可探索多源遥感与机器学习融合的盐渍化动态监测，并验证于中亚等干旱灌区。","2026-10-15T23:30:32.401626Z",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":172,"source_url":462,"published_at":465,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":386,"score_detail":466,"sources":469,"tags":471,"primary_topic":476,"secondary_topics":477,"classification":479,"search_phrases":503,"slug":506,"view_count":90,"doi":507,"paper":508,"created_at":518},5008,"ASSESSING THE GEOECOLOGICAL IMPACT OF LARGE HYDRAULIC STRUCTURES IN ARID REGIONS USING REMOTE SENSING AND GIS TECHNOLOGIES (A CASE STUDY OF THE CENTRAL FERGANA RESERVOIR)","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23359723","This article presents a comprehensive study of the geoecological and ameliorative impacts of large hydraulic structures built under arid subtropical conditions on adjacent agricultural landscapes, using the Central Fergana Reservoir (CFR) as a case study. The main objective of the study is to assess landscape changes, the spatial dynamics of vegetation cover, and secondary soil salinization processes across 29,330 hectares within the reservoir’s impact zone between 2010 and 2025 using remote sensing and GIS technologies. The study calculates the Normalized Difference Vegetation Index (NDVI) based on Landsat satellite imagery (Landsat 5 TM, Landsat 8 OLI\u002FTIRS, and Landsat 9 OLI-2) and analyzes long-term changes. The results show that the water surface area expanded from 1% (293.3 ha) in 2010 to 15% (4,399.5 ha) by 2025. Furthermore, analyses of 27 soil profiles and laboratory measurements of electrical conductivity (\\(EC_e\\)) and moisture content confirmed the formation of localized waterlogged areas and highly saline solonchaks in the 500-meter coastal buffer zone as a result of a rise in the groundwater table to 1.0–1.5 m. Environmental stress factors identified through comparative analyses with a control (background) site were scientifically substantiated, and practical recommendations for sustainable regional management were developed.","本文以中央费尔干纳水库（Central Fergana Reservoir, CFR）为例，对干旱亚热带条件下大型水利设施对邻近农业景观的地质生态与土壤改良影响进行了综合研究。研究的主要目标在于利用遥感与GIS技术，评估2010年至2025年间水库影响区内29330公顷土地上的景观变化、植被覆盖空间动态以及次生土壤盐渍化过程。研究基于Landsat卫星影像（Landsat 5 TM、Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2）计算归一化植被指数（Normalized Difference Vegetation Index, NDVI），并分析其长期变化。结果表明，水域面积从2010年的1%（293.3公顷）扩大至2025年的15%（4399.5公顷）。此外，对27个土壤剖面的分析以及电导率（\\(EC_e\\)）和含水量的实验室测定证实，由于地下水位上升至1.0–1.5米，在500米沿岸缓冲带内形成了局部渍水区和重度盐化盐土。通过与对照（背景）样地的比较分析，科学地证实了所识别的环境胁迫因子，并提出了区域可持续管理的实践建议。","2026-10-14T00:00:00Z",{"impact":20,"substance":467,"depth":18,"authority":177,"freshness":202,"relevant":22,"comment":468},22,"基于Landsat长时序遥感与GIS评估干旱区大型水利工程对灌区植被与次生盐渍化的影响，方法规范、数据翔实，对农业遥感与灌区生态监测有参考价值，但属区域案例研究，公共影响有限。",[470],{"name":172,"url":462},[472,28,473,474,475],"农业遥感","水资源管理","灌溉农业","盐渍化",{"code":61,"slug":122,"name":123,"path":124,"parent_code":37},[478],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":480},{"candidates":481,"agricultural_evidence":47},[482,486,491,495,499],{"code":61,"score":19,"supported":47,"title":483,"abstract":484,"tags":485},[133],[28,133],[472,28],{"code":33,"score":487,"supported":47,"title":488,"abstract":489,"tags":490},11,[119],[119],[],{"code":269,"score":209,"supported":47,"title":492,"abstract":493,"tags":494},[],[272,273],[475],{"code":433,"score":55,"supported":47,"title":496,"abstract":497,"tags":498},[],[436],[],{"code":276,"score":21,"supported":14,"title":500,"abstract":501,"tags":502},[],[],[473,474],[504,505],"费尔干纳水库 遥感 盐渍化","Landsat NDVI 灌区 土壤盐渍化","费尔干纳水库遥感盐渍化-5008","10.5281\u002Fzenodo.23359723",{"doi":507,"openalex_id":509,"authors":510,"venue":172,"cited_by_count":90,"oa_url":462,"card":513,"direction":98,"ingested_from":99},"W7221819099",[511],{"name":512,"orcid":9},"Akbarov Sarvarbek",{"tldr":514,"method":515,"finding":516,"direction":98,"opportunity":517},"用遥感和GIS评估费尔干纳中部水库对周边农业景观的生态影响。","Landsat影像NDVI分析、27个土壤剖面及ECe与含水量测定。","水库水面从1%扩至15%，500米缓冲带地下水位升至1.0–1.5米，形成盐渍化。","可结合多源遥感与土壤盐分模型，构建干旱区水库周边盐渍化预警与灌溉调控方案。","2026-10-15T23:30:29.814507Z"]