[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5321":3,"related-5321":110},{"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":43,"search_phrases":83,"slug":86,"view_count":87,"doi":88,"paper":89,"created_at":109},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）",null,"OpenAlex","2026-10-18T00:00:00Z","论文",10,false,60,{"impact":17,"substance":18,"depth":19,"authority":13,"freshness":20,"relevant":21,"comment":22},8,18,15,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感","GIS","农用地适宜性","土地覆被","孟加拉农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":44,"locked":14,"version":45,"method":46,"evidence":47},"classified","2026-10-22.2","rules",{"candidates":48,"agricultural_evidence":50},[49,56,61,69,76],{"code":32,"score":18,"supported":50,"title":51,"abstract":52,"tags":55},true,[27],[53,27,54],"地理信息","geographic information",[27],{"code":39,"score":19,"supported":50,"title":57,"abstract":59,"tags":60},[58],"remote sensing",[26,58],[26],{"code":62,"score":63,"supported":50,"title":64,"abstract":65,"tags":68},"A1",6,[],[66,67],"稻米","rice",[],{"code":70,"score":63,"supported":50,"title":71,"abstract":72,"tags":75},"G",[],[73,74],"农村发展","rural development",[],{"code":77,"score":63,"supported":50,"title":78,"abstract":79,"tags":82},"H2",[],[80,81],"土壤","soil",[],[84,85],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321",0,"10.3329\u002Fbrds.v26i1.94265",{"doi":88,"openalex_id":90,"authors":91,"venue":101,"cited_by_count":87,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7222895468",[92,94,96,98],{"name":93,"orcid":9},"Raju Ahmed",{"name":95,"orcid":9},"Shaikh Mehdee Mohammad",{"name":97,"orcid":9},"Ummul Momanin Coalee",{"name":99,"orcid":100},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"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","农业遥感与作物表型","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。","openalex","2026-10-19T23:30:22.906704Z",{"total":63,"page":21,"page_size":63,"items":111},[112,190,278,338,420,492],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":126,"tags":128,"primary_topic":132,"secondary_topics":133,"classification":141,"search_phrases":169,"slug":172,"view_count":87,"doi":173,"paper":174,"created_at":189},4753,"Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04943-7","Groundwater is a crucial source of freshwater in semi-arid regions, particularly in water-stressed areas, such as northern Uganda, where the surface water is scarce and unreliable precipitation patterns changes. This province lacks sufficient clean water for human consumption and only 20% of the water for agriculture extracted from the ground due to inaccessibility and insufficient information regarding the groundwater. With regard to these difficulties in water that are faced by the local communities, it has not been yet studied the distribution of groundwater in Kitgum and Pader districts. Therefore, the result of this research contributes preliminary groundwater investigation for contacting this indispensable source of water. The study employed a geospatial approach integrated with multi-criteria analysis to identify groundwater potential zones in Kitgum and Pader districts of Acholi subregion. The Utilization of Geographic Information Systems (GIS), remote sensing, and the Analytic Hierarchy Process (AHP) and six thematic layers; rainfall, drainage density, slope, soil type, geology, and land use\u002Fland cover were selected based on their hydrogeological influence and the accessibility of the data. After standardizing, and assigning normalized weights,a weighted overlay analysis in ArcGIS Pro version 3.1.0 was used to synthesize these layers into a comprehensive groundwater potential zonation map. The result categorized the study area into five zones; very low, low, medium, high, and very high potential. The analysis revealed that Kitgum District is predominantly characterized by very low to low groundwater potential zones of 67.24% when it is combined both, constrained by factors such as higher drainage density, and steeper slopes. Conversely, Pader District showed favorable conditions, with 85.5% of its area classified as high to very high groundwater potential, attributed to gentle slopes, and higher rainfall, that enhance infiltration capacity. Validation against yield data from 18 existing boreholes demonstrated a 75.0% alignment between the model and actual well performance, confirming the model’s reliability at acceptable result. This study demonstrated the effectiveness of GIS-based and AHP as a cost-effective tool for preliminary groundwater exploration in data-scarce environments. The resulting groundwater potential map provides a vital scientific foundation for sustainable water resource management, enabling targeted borehole siting, informed aquifer development, and resilience-building strategies to enhance water security for communities and livelihoods in the Acholi sub-region.","地下水是半干旱地区重要的淡水来源，尤其是在乌干达北部等水资源紧张地区，那里地表水稀缺且降水模式变化不可靠。该地区缺乏足够的清洁饮用水供人类消费，由于地下水难以获取且信息不足，仅有20%的农业用水来自地下开采。鉴于当地社区面临这些水资源困难，基特古姆和帕德尔地区的地下水分布尚未得到研究。因此，本研究结果为接触这一不可或缺的水源提供了初步的地下水调查。本研究采用地理空间方法与多准则分析相结合，识别阿乔利次区域基特古姆和帕德尔地区的地下水潜力区。利用地理信息系统（GIS）、遥感技术和层次分析法（AHP），基于其水文地质影响和数据可获取性，选取了六个主题图层：降雨量、河网密度、坡度、土壤类型、地质和土地利用\u002F土地覆盖。经过标准化和归一化权重赋值后，使用ArcGIS Pro 3.1.0版本中的加权叠加分析将这些图层综合成一张全面的地下水潜力分区图。结果将研究区划分为五个区域：极低、低、中等、高和极高潜力。分析表明，基特古姆区主要以极低至低地下水潜力区为主，两者合计占67.24%，受较高河网密度和较陡坡度等因素制约。相反，帕德尔区表现出有利条件，其85.5%的面积被归类为高至极高地下水潜力，这归因于平缓的坡度和较高的降雨量，增强了入渗能力。利用18口现有钻孔的出水量数据进行验证，模型与实际井表现之间的一致率达到75.0%，证实了模型在可接受结果范围内的可靠性。本研究证明了基于GIS和AHP的方法作为数据稀缺环境中初步地下水勘探的成本效益工具的有效性。所生成的地下水潜力图为可持续水资源管理提供了重要的科学基础，有助于有针对性地选址钻孔，为知情决策提供依据。","Discover Sustainability","2026-10-10T00:00:00Z",67,{"impact":17,"substance":122,"depth":123,"authority":124,"freshness":20,"relevant":21,"comment":125},20,17,13,"基于GIS、遥感与AHP的乌干达北部地下水潜力制图研究，方法成熟、验证可靠，对缺水地区农业水资源规划有参考价值，但属区域性初步研究，公共影响有限。",[127],{"name":118,"url":115},[26,129,27,130,131],"水资源管理","地下水潜力","乌干达农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[134,135],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":136,"slug":137,"name":138,"path":139,"parent_code":140},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":44,"locked":14,"version":45,"method":46,"evidence":142},{"candidates":143,"agricultural_evidence":50},[144,148,152,158,162],{"code":32,"score":18,"supported":50,"title":145,"abstract":146,"tags":147},[27],[53,27,54],[27],{"code":39,"score":19,"supported":50,"title":149,"abstract":150,"tags":151},[58],[26,58],[26],{"code":136,"score":17,"supported":50,"title":153,"abstract":154,"tags":157},[],[155,156],"水资源","water resource",[130,129],{"code":77,"score":63,"supported":50,"title":159,"abstract":160,"tags":161},[],[80,81],[],{"code":163,"score":164,"supported":50,"title":165,"abstract":166,"tags":168},"H7",3,[],[167],"干旱",[],[170,171],"乌干达 Kitgum Pader 地下水","GIS AHP 地下水潜力","乌干达KitgumPader地下水-4753","10.1007\u002Fs43621-026-04943-7",{"doi":173,"openalex_id":175,"authors":176,"venue":118,"cited_by_count":87,"oa_url":115,"card":184,"direction":106,"ingested_from":108},"W7221339030",[177,180,182],{"name":178,"orcid":179},"Ahmed Abdirizak Dirie","https:\u002F\u002Forcid.org\u002F0009-0002-8109-0724",{"name":181,"orcid":9},"Abdulkadir Ahmed Mohamed",{"name":183,"orcid":9},"Ambiga Kannapiran",{"tldr":185,"method":186,"finding":187,"direction":106,"opportunity":188},"用GIS、遥感和AHP制图评估乌干达两区地下水潜力并验证。","GIS与遥感结合AHP，加权叠加6个主题图层，用18口井数据验证。","Kitgum区67.24%为低潜力，Pader区85.5%为高潜力，模型与井数据吻合75%。","可将该地下水潜力制图方法迁移到其他数据稀缺区，并结合农业灌溉需求优化井位。","2026-10-12T23:30:24.761508Z",{"id":191,"title":192,"url":193,"summary":194,"summary_zh":195,"content":9,"source_name":196,"source_url":193,"published_at":197,"category":12,"cover_url":9,"hotness":198,"is_selected":14,"score":199,"score_detail":200,"sources":204,"tags":208,"primary_topic":212,"secondary_topics":213,"classification":220,"search_phrases":251,"slug":254,"view_count":87,"doi":255,"paper":256,"created_at":277},4294,"GIS- and Remote Sensing-Based Identification of Suitable Agricultural Waste Landfill Sites for Climate-Smart Agriculture in Ilorin, Nigeria","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23168428","Abstract Lack of a designated landfill for agricultural waste management (AWM) in Ilorin municipalities led to indiscriminate disposal of agricultural waste within the city and the only existing Sokoto-Ayekale general solid waste dumpsite, posing environmental and public health risks while undermining climate-smart agriculture (CSA). The study identifies suitable landfill sites for AWM by integrating Geographic Information System (GIS), Remote Sensing (RS), and Multi-Criteria Decision Analysis (MCDA) to locate environmentally and climate-resilient sites. Spatial data employed for the study include Shuttle Radar Topography Mission (SRTM), Landsat 9 imagery, Digital Elevation Model (DEM), road networks, waterbodies, built-up areas, soil quality, slope, and geology, which were processed in ArcGIS 10.3 to develop constraint thematic suitability maps. The Analytical Hierarchy Process (AHP) was employed to derive criterion weights, and the Weighted Linear Combination (WLC) was used to generate the map. AHP consistency ratio (CR) was used to validate the model’s consistency (CR=0.016), which falls within the acceptable CR \u003C0.1. The AHP weighted results were 0.41, 0.2, 0.14, 0.10, 0.08, and 0.07 for waterbodies, built-up area, slope, soil quality, geology, and road network, respectively. Maps were created for each criterion, reclassified, and integrated using the weighted linear combination method to generate an overall AWM suitability map. The study identified four potential locations for AWM. Approximately 507.23 km² are unsuitable, 1204.21 km² are marginally suitable, 4899.28 km² are moderately suitable, and 2021.7 km² are highly suitable for AWM landfill development. The findings present a scientifically based decision-support framework to guide policymakers, environmental agencies, and urban planners in developing sustainable AWM strategies that minimize environmental risk while advancing climate-smart agriculture. Keywords: Agricultural waste, Climate-smart agriculture, Landfill, Suitability, Sustainability","摘要 伊洛林各市镇缺乏用于农业废弃物管理（AWM）的指定填埋场，导致农业废弃物在城市内部及唯一现有的索科托-阿耶卡莱一般固体废弃物倾倒场被随意处置，既构成环境和公共健康风险，也削弱了气候智慧型农业（CSA）。本研究通过整合地理信息系统（GIS）、遥感（RS）和多准则决策分析（MCDA），识别适用于AWM的填埋场选址，以确定具有环境韧性和气候韧性的场址。研究采用的空间数据包括航天飞机雷达地形测绘任务（SRTM）数据、Landsat 9影像、数字高程模型（DEM）、道路网络、水体、建成区、土壤质量、坡度和地质，并在ArcGIS 10.3中进行处理以生成约束性专题适宜性图。采用层次分析法（AHP）推导准则权重，并运用加权线性组合（WLC）生成地图。使用AHP一致性比率（CR）验证模型的一致性（CR=0.016），该值处于可接受的CR\u003C0.1范围内。AHP加权结果分别为：水体0.41、建成区0.2、坡度0.14、土壤质量0.10、地质0.08、道路网络0.07。针对每个准则生成地图，经重分类后采用加权线性组合方法进行整合，生成总体AWM适宜性图。研究确定了四个潜在的AWM选址。约507.23 km²不适宜，1204.21 km²勉强适宜，4899.28 km²中等适宜，2021.7 km²高度适宜建设AWM填埋场。研究结果为政策制定者、环境机构和城市规划者提供了基于科学的决策支持框架，以指导制定可持续的AWM策略，在降低环境风险的同时推进气候智慧型农业。关键词：农业废弃物，气候智慧型农业，填埋场，适宜性，可持续性","Zenodo (CERN European Organization for Nuclear Research)","2026-10-05T00:00:00Z",25,72,{"impact":201,"substance":202,"depth":123,"authority":124,"freshness":20,"relevant":21,"comment":203},12,21,"GIS与遥感结合MCDA为农业废弃物填埋选址提供决策框架，方法规范、数据翔实，但属区域性案例研究，公共影响有限。",[205,206],{"name":196,"url":193},{"name":196,"url":207},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23168429",[26,209,210,27,211],"农业废弃物","气候智慧农业","选址评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[214,219],{"code":215,"slug":216,"name":217,"path":218,"parent_code":140},"H5","waste","面源污染与废弃物利用","\u002Ftopic\u002Fenvironment\u002Fwaste",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":221},{"candidates":222,"agricultural_evidence":50},[223,227,232,236,243,247],{"code":32,"score":18,"supported":50,"title":224,"abstract":225,"tags":226},[27],[53,27,54],[27],{"code":215,"score":19,"supported":50,"title":228,"abstract":230,"tags":231},[229],"agricultural waste",[209,229],[209],{"code":39,"score":19,"supported":50,"title":233,"abstract":234,"tags":235},[58],[26,58],[26],{"code":237,"score":238,"supported":50,"title":239,"abstract":241,"tags":242},"I1",11,[240],"smart agriculture",[240],[],{"code":77,"score":63,"supported":50,"title":244,"abstract":245,"tags":246},[],[80,81],[],{"code":163,"score":21,"supported":14,"title":248,"abstract":249,"tags":250},[],[],[210],[252,253],"Ilorin 农业废弃物 填埋场 选址","GIS 遥感 农业废弃物 填埋场","Ilorin农业废弃物填埋场选址-4294","10.5281\u002Fzenodo.23168428",{"doi":255,"openalex_id":257,"authors":258,"venue":196,"cited_by_count":87,"oa_url":193,"card":270,"direction":276,"ingested_from":108},"W7220389353",[259,261,263,265,268],{"name":260,"orcid":9},"Saheed Mohammed Ishaq",{"name":262,"orcid":9},"Alamin Adedeji Adenekan",{"name":264,"orcid":9},"Ibrahim Dauda",{"name":266,"orcid":267},"Afeez Oladeji Amoo","https:\u002F\u002Forcid.org\u002F0000-0002-0412-9333",{"name":269,"orcid":9},"Nureni Babatunde Amoo",{"tldr":271,"method":272,"finding":273,"direction":274,"opportunity":275},"用GIS、遥感和多准则决策为尼日利亚Ilorin筛选农业废弃物填埋场适宜选址。","GIS\u002FRS结合AHP与加权线性组合，基于DEM、Landsat 9等空间数据。","识别出4处潜在选址，约2021.7 km²高度适宜，模型一致性良好。","农业绿色发展与碳","可将该选址框架与温室气体排放、运输成本及气候情景耦合，拓展到其他区域。","智慧农业 \u002F 农业物联网","2026-10-06T23:30:07.715522Z",{"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":299,"search_phrases":320,"slug":323,"view_count":324,"doi":325,"paper":326,"created_at":337},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":288,"authority":63,"freshness":17,"relevant":21,"comment":289},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。",[291],{"name":284,"url":281},[293,26,294,27,295],"数字乡村","土壤侵蚀","水土保持",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[298],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":300},{"candidates":301,"agricultural_evidence":50},[302,306,310,315],{"code":39,"score":19,"supported":50,"title":303,"abstract":304,"tags":305},[58],[26,58],[26],{"code":32,"score":201,"supported":50,"title":307,"abstract":308,"tags":309},[27],[27],[27],{"code":311,"score":21,"supported":14,"title":312,"abstract":313,"tags":314},"H6",[],[],[295],{"code":316,"score":21,"supported":14,"title":317,"abstract":318,"tags":319},"I6",[],[],[293],[321,322],"Yola South 沟蚀 遥感","Alfisols 沟蚀 GIS 制图","YolaSouth沟蚀遥感-4102",2,"10.56201\u002Fwjimt.v10.no1.2026.pg155.165",{"doi":325,"openalex_id":327,"authors":328,"venue":284,"cited_by_count":87,"oa_url":331,"card":332,"direction":106,"ingested_from":108},"W7214920253",[329],{"name":330,"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":333,"method":334,"finding":335,"direction":106,"opportunity":336},"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":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":350,"tags":352,"primary_topic":356,"secondary_topics":357,"classification":360,"search_phrases":384,"slug":387,"view_count":388,"doi":389,"paper":390,"created_at":419},4085,"Seasonal Snow Water Resources and Spring Runoff Harvesting Potential in the North Kazakhstan Region: An Integrated Remote Sensing and GIS-Based Multi-Criteria Assessment","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fearth7050163","Seasonal snow is a critical water resource in Northern Kazakhstan, where spring snowmelt dominates surface runoff and agricultural water supply. However, traditional rainwater harvesting frameworks often neglect snow water equivalent (SWE), limiting their applicability in cold, snow-dominated steppe environments. This study develops an integrated remote sensing and GIS-based approach to identify potential sites for spring runoff harvesting. Using Sentinel-2 imagery, snow cover fraction (SCF) was derived and combined with a digital elevation model (DEM) to estimate snow depth through a quadratic regression model, validated against in situ data from 23 meteorological stations. Snow water equivalent was then calculated by incorporating field-measured snow density. Alongside SWE, six additional parameters—slope, drainage density, land use\u002Fland cover (LULC), and soil texture (sand, silt, clay)—were integrated within an Analytical Hierarchy Process (AHP) multi-criteria decision analysis. Results show that SWE, drainage density, and LULC collectively contributed 75% of the weight in site suitability, underscoring their dominant role in snowmelt-driven hydrology. The final suitability map indicates that 28.03% of the region falls into the “Very High” class, concentrated in central and northwestern districts. Developing just 30% of these potential zones could yield approximately 4.2 × 108 m3 of usable meltwater, supporting up to 105,000 ha under full irrigation or 280,000 ha under deficit irrigation. The study demonstrates that integrating high-resolution SWE with MCDA provides a scalable framework for enhancing agricultural resilience, mitigating floods, and strengthening water security in cold, semi-arid landscapes.","季节性积雪是哈萨克斯坦北部的重要水资源，春季融雪主导着地表径流和农业供水。然而，传统的雨水收集框架往往忽视雪水当量（SWE），限制了其在寒冷、以积雪为主的草原环境中的适用性。本研究开发了一种集成遥感和GIS的方法，用于识别春季径流收集的潜在场址。利用Sentinel-2影像提取积雪覆盖率（SCF），并结合数字高程模型（DEM）通过二次回归模型估算雪深，利用23个气象站的实地数据进行验证。随后，结合实地测量的雪密度计算雪水当量。除SWE外，还将坡度、河网密度、土地利用\u002F土地覆盖（LULC）以及土壤质地（砂、粉砂、黏土）等六个附加参数纳入层次分析法（AHP）多准则决策分析。结果表明，SWE、河网密度和LULC在场地适宜性评价中合计贡献了75%的权重，凸显了它们在融雪驱动水文中的主导作用。最终适宜性图显示，该区域28.03%的面积属于“极高”等级，集中在中部和西北部地区。仅开发这些潜在区域的30%，即可产生约4.2 × 10⁸ m³的可用融水，可支持多达105,000公顷的充分灌溉或280,000公顷的亏缺灌溉。本研究表明，将高分辨率SWE与多准则决策分析相结合，为增强农业韧性、减轻洪水和加强寒冷半干旱地区的水安全提供了一个可扩展的框架。","Earth","2026-10-02T00:00:00Z",77,{"impact":348,"substance":202,"depth":18,"authority":124,"freshness":20,"relevant":21,"comment":349},16,"该研究将遥感与GIS多准则评估结合，量化了北哈萨克斯坦融雪水资源与灌溉潜力，方法新颖、数据扎实，对寒区农业水资源管理有参考价值，但地域性较强，属细分领域进展。",[351],{"name":344,"url":341},[353,354,26,27,355],"农业水资源","哈萨克斯坦","融雪灌溉",{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},[358,359],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":136,"slug":137,"name":138,"path":139,"parent_code":140},{"status":44,"locked":14,"version":45,"method":46,"evidence":361},{"candidates":362,"agricultural_evidence":50},[363,367,371,376,380],{"code":39,"score":19,"supported":50,"title":364,"abstract":365,"tags":366},[58],[26,58],[26],{"code":32,"score":201,"supported":50,"title":368,"abstract":369,"tags":370},[27],[27],[27],{"code":136,"score":13,"supported":50,"title":372,"abstract":373,"tags":375},[],[374,155,137,156],"灌溉",[353],{"code":77,"score":63,"supported":50,"title":377,"abstract":378,"tags":379},[],[80,81],[],{"code":163,"score":164,"supported":50,"title":381,"abstract":382,"tags":383},[],[167],[],[385,386],"北哈萨克斯坦 融雪 灌溉潜力","Sentinel-2 雪水当量 遥感","北哈萨克斯坦融雪灌溉潜力-4085",5,"10.3390\u002Fearth7050163",{"doi":389,"openalex_id":391,"authors":392,"venue":344,"cited_by_count":87,"oa_url":341,"card":414,"direction":106,"ingested_from":108},"W7216430083",[393,396,399,401,403,405,408,411],{"name":394,"orcid":395},"Zhanassyl Teleubay","https:\u002F\u002Forcid.org\u002F0000-0003-4783-5489",{"name":397,"orcid":398},"Erkin Ussalinov","https:\u002F\u002Forcid.org\u002F0000-0003-1907-9532",{"name":400,"orcid":9},"Laura Ryskulbekova",{"name":402,"orcid":9},"Asset Arystanov",{"name":404,"orcid":9},"Ibragim Seitassanov",{"name":406,"orcid":407},"Улжан Онласын","https:\u002F\u002Forcid.org\u002F0000-0001-5789-5464",{"name":409,"orcid":410},"Nuray Kutymova","https:\u002F\u002Forcid.org\u002F0009-0000-7214-1708",{"name":412,"orcid":413},"Farabi Yermekov","https:\u002F\u002Forcid.org\u002F0000-0002-0290-3866",{"tldr":415,"method":416,"finding":417,"direction":106,"opportunity":418},"结合遥感与GIS评估哈萨克斯坦北部季节性雪水资源及春季径流收集潜力。","Sentinel-2与DEM估算雪深和SWE，结合AHP多准则决策分析选址。","SWE、河网密度和土地利用贡献75%权重，28%区域为极高适宜区。","可将SWE驱动的选址框架推广至其他寒旱区，并耦合气候情景与灌溉效率优化。","2026-10-03T23:30:32.427699Z",{"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":435,"secondary_topics":436,"classification":438,"search_phrases":466,"slug":469,"view_count":470,"doi":471,"paper":472,"created_at":491},4027,"Groundwater Potential Zonation of Unchehra Watershed, Central India using AHP, Remote Sensing, and GIS Techniques","https:\u002F\u002Fdoi.org\u002F10.17491\u002Fjgsi.2026.174637","ABSTRACT Groundwater is the primary source of domestic and agricultural water in the semi-arid regions of central India. Despite receiving moderate annual rainfall, Unchehra watershed experiences seasonal water shortages, especially during summer, highlighting the need for scientific assessment of groundwater resources. This study delineates groundwater potential zones (GWPZs) in the Unchehra watershed using an integrated Remote Sensing (RS), Geographic Information System (GIS), and Analytical Hierarchy Process (AHP) approach. Seven major factors influencing groundwater occurrence lineament density, geology, land use and land cover (LULC), drainage density, geomorphology, slope and soil type were considered in the analysis. Spatial thematic layers for these parameters were prepared from satellite imagery and Digital Elevation Model (DEM) data using ArcGIS. The relative importance of each factor and its classes was determined through AHP, providing a systematic weighting scheme for groundwater potential assessment. The weighted layers were integrated to generate a groundwater potential zonation map. The resulting map classified the watershed into five groundwater potential zones: very low, low, moderate, high, and very high. The analysis revealed that 7.88% of the watershed falls under very low potential, while significant portions of the area exhibit moderate (28.07%) to very high (22.61%) groundwater potential. To assess the reliability of the generated model, validation was carried out using 41 randomly selected dug wells and Receiver Operating Characteristic (ROC) curve analysis. The near-zero Consistency Ratio (CR) confirms the reliability of the AHP weighting scheme, while the Area Under the Curve (AUC) value of 0.763 indicates good predictive accuracy of the groundwater potential model. The identified high and very high groundwater potential zones are suitable for groundwater development and artificial recharge measures such as check dams, percolation tanks, and recharge wells. The integrated RS-GIS-AHP framework developed in this study provides a reliable decision-support tool for groundwater exploration, recharge planning, and sustainable groundwater management in Unchehra watershed and similar hydrogeological settings.","摘要 地下水是印度中部半干旱地区生活用水和农业用水的主要来源。尽管Unchehra流域年降水量适中，但仍面临季节性缺水问题，尤其是在夏季，这凸显了对地下水资源进行科学评估的必要性。本研究采用遥感（RS）、地理信息系统（GIS）和层次分析法（AHP）相结合的方法，对Unchehra流域的地下水潜力区（GWPZs）进行了划分。分析中考虑了影响地下水赋存的七个主要因素：线性构造密度、地质、土地利用与土地覆盖（LULC）、河网密度、地貌、坡度和土壤类型。利用ArcGIS从卫星影像和数字高程模型（DEM）数据中制备了这些参数的空间专题图层。通过AHP确定各因素及其类别的相对重要性，为地下水潜力评估提供了系统化的权重方案。将加权图层进行叠加整合，生成了地下水潜力分区图。所得图件将流域划分为五个地下水潜力区：极低、低、中等、高和极高。分析表明，流域面积的7.88%属于极低潜力区，而相当部分区域表现出中等（28.07%）至极高（22.61%）的地下水潜力。为评估所生成模型的可靠性，利用41口随机选取的民井和受试者工作特征（ROC）曲线分析进行了验证。接近零的一致性比率（CR）证实了AHP权重方案的可靠性，而曲线下面积（AUC）值为0.763表明地下水潜力模型具有良好的预测精度。所识别的高和极高地下水潜力区适宜进行地下水开发和人工补给措施，如拦水坝、渗滤池和回灌井。本研究开发的RS-GIS-AHP集成框架为Unchehra流域及类似水文地质条件下的地下水勘探、补给规划和可持续地下水管理提供了可靠的决策支持工具。","Journal of the Geological Society of India","2026-10-01T00:00:00Z",66,{"impact":17,"substance":122,"depth":123,"authority":124,"freshness":17,"relevant":21,"comment":430},"方法扎实、验证可靠的区域地下水潜力评估论文，对农业水资源管理有参考价值，但属印度区域研究，公共影响有限。",[432],{"name":426,"url":423},[26,129,27,434,130],"印度农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[437],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":439},{"candidates":440,"agricultural_evidence":50},[441,445,449,453,457,461],{"code":32,"score":18,"supported":50,"title":442,"abstract":443,"tags":444},[27],[53,27,54],[27],{"code":39,"score":19,"supported":50,"title":446,"abstract":447,"tags":448},[58],[26,58],[26],{"code":77,"score":63,"supported":50,"title":450,"abstract":451,"tags":452},[],[80,81],[],{"code":136,"score":388,"supported":50,"title":454,"abstract":455,"tags":456},[],[155],[130,129],{"code":163,"score":164,"supported":50,"title":458,"abstract":459,"tags":460},[],[167],[],{"code":462,"score":21,"supported":14,"title":463,"abstract":464,"tags":465},"J3",[],[],[434],[467,468],"地下水潜力 水资源管理 印度农业 遥感","地下水潜力 水资源管理","地下水潜力水资源管理印度农业遥感-4027",4,"10.17491\u002Fjgsi.2026.174637",{"doi":471,"openalex_id":473,"authors":474,"venue":426,"cited_by_count":87,"oa_url":9,"card":486,"direction":106,"ingested_from":108},"W7215068088",[475,477,479,481,483],{"name":476,"orcid":9},"Rabindra Nath Tiwari",{"name":478,"orcid":9},"Brahmanand Sharma",{"name":480,"orcid":9},"Sarvesh Kumar Patel",{"name":482,"orcid":9},"Aditya Singh Baghel",{"name":484,"orcid":485},"Ashish Kumar Mishra","https:\u002F\u002Forcid.org\u002F0000-0002-9953-4576",{"tldr":487,"method":488,"finding":489,"direction":106,"opportunity":490},"用RS、GIS与AHP集成方法划分印度Unchehra流域地下水潜力区并验证精度。","基于遥感与DEM提取7个因子，AHP加权叠加，41口井与ROC验证。","流域分五级潜力区，高与极高潜力占22.61%，模型AUC达0.763，精度良好。","可将该框架与实时灌溉需水、作物布局耦合，支撑半干旱区精准补灌与地下水回补选址。","2026-10-02T23:30:24.566714Z",{"id":493,"title":494,"url":495,"summary":496,"summary_zh":497,"content":9,"source_name":10,"source_url":495,"published_at":285,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":498,"score_detail":499,"sources":502,"tags":504,"primary_topic":508,"secondary_topics":509,"classification":512,"search_phrases":555,"slug":558,"view_count":21,"doi":559,"paper":560,"created_at":571},3901,"Geospatial analysis of irrigation suitability in Nigeria using remote sensing and GIS","https:\u002F\u002Fdoi.org\u002F10.53030\u002Ftjags.1937471","This research offers a comprehensive geospatial evaluation of land suitability for irrigation across Nigeria. It leverages 2020 Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, processed within the Google Earth Engine (GEE) cloud-based computational environment. Given growing concerns about food security and climate variability, ascertaining areas amenable to irrigation is paramount for fostering sustainable agricultural development within Nigeria. This investigation integrated multi-temporal remote sensing observations, including the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water Index - NDWI, to construct an Irrigation Suitability Index (ISI). These datasets underwent analysis at a 500-meter spatial resolution, encompassing the entirety of the nation. Weighted overlay analysis, performed within the GEE environment, generated a nationwide map delineating irrigation potential across five distinct categories: Very Low, Low, Moderate, High, and Very High suitability. The findings reveal suitability scores ranging from a minimum of 0.11 (very low) to a maximum of 1 (very high). Specifically, northern Nigerian states such as Sokoto, Kebbi, Yobe, and Borno demonstrated high to very high suitability. In contrast, southern states, including Lagos, Rivers, Cross River, and Delta, were characterized by very low suitability. The study underscores significant geographical disparities, particularly in arid northern regions which experience greater constraints due to lower precipitation and elevated evapotranspiration. These findings offer actionable directives for agricultural planners, policymakers, and stakeholders seeking to enhance irrigation capacity and strengthen food security resilience amidst evolving climatic conditions.","本研究对尼日利亚全国灌溉土地适宜性进行了综合地理空间评估。研究利用2020年中分辨率成像光谱仪（MODIS）卫星数据，在谷歌地球引擎（GEE）云端计算环境中进行处理。鉴于粮食安全和气候变率日益受到关注，确定适宜灌溉的区域对于促进尼日利亚可持续农业发展至关重要。本研究整合了多时相遥感观测数据，包括归一化植被指数（NDVI）、地表温度（LST）和归一化水体指数（NDWI），以构建灌溉适宜性指数（ISI）。这些数据集以500米空间分辨率进行分析，覆盖全国范围。在GEE环境中进行的加权叠加分析生成了全国灌溉潜力分布图，划分为五个类别：极低、低、中等、高和极高适宜性。研究结果显示，适宜性评分从最低0.11（极低）到最高1（极高）不等。具体而言，尼日利亚北部各州如索科托、凯比、约贝和博尔诺表现出高至极高适宜性。相比之下，南部各州包括拉各斯、河流州、十字河州和三角洲州则以极低适宜性为特征。研究强调了显著的地理差异，特别是干旱的北部地区因降水量较低和蒸散量较高而面临更大限制。这些发现为农业规划者、政策制定者和利益相关者提供了可操作的指导，以在气候条件不断变化的情况下增强灌溉能力并提升粮食安全韧性。",78,{"impact":18,"substance":500,"depth":123,"authority":124,"freshness":17,"relevant":21,"comment":501},22,"基于MODIS与GEE的全国尺度灌溉适宜性制图，方法规范、结论具体，对农业规划有参考价值，但属他国案例，国内落地性有限。",[503],{"name":10,"url":495},[505,506,26,27,507],"数字农业","粮食安全","灌溉适宜性",{"code":136,"slug":137,"name":138,"path":139,"parent_code":140},[510,511],{"code":39,"slug":40,"name":41,"path":42,"parent_code":36},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":44,"locked":14,"version":45,"method":46,"evidence":513},{"candidates":514,"agricultural_evidence":50},[515,520,524,528,535,539,545,551],{"code":136,"score":123,"supported":50,"title":516,"abstract":517,"tags":519},[137],[374,137,518],"evapotranspiration",[],{"code":39,"score":19,"supported":50,"title":521,"abstract":522,"tags":523},[58],[26,58],[26],{"code":32,"score":20,"supported":50,"title":525,"abstract":526,"tags":527},[27],[],[27],{"code":529,"score":530,"supported":50,"title":531,"abstract":532,"tags":534},"A8",7,[],[506,533],"food security",[506],{"code":163,"score":164,"supported":50,"title":536,"abstract":537,"tags":538},[],[167],[],{"code":540,"score":164,"supported":50,"title":541,"abstract":542,"tags":544},"H8",[],[543],"可持续农业",[],{"code":546,"score":164,"supported":50,"title":547,"abstract":548,"tags":550},"J1",[],[549],"农业规划",[],{"code":237,"score":21,"supported":14,"title":552,"abstract":553,"tags":554},[],[],[505],[556,557],"尼日利亚 灌溉适宜性 遥感","Google Earth Engine 灌溉","尼日利亚灌溉适宜性遥感-3901","10.53030\u002Ftjags.1937471",{"doi":559,"openalex_id":561,"authors":562,"venue":9,"cited_by_count":87,"oa_url":9,"card":566,"direction":106,"ingested_from":108},"W7214955379",[563],{"name":564,"orcid":565},"Emmanuel M. Menegbo","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3226",{"tldr":567,"method":568,"finding":569,"direction":106,"opportunity":570},"用遥感与GIS评估尼日利亚全国灌溉适宜性并绘制五级分区图。","2020年MODIS数据在GEE中计算NDVI、LST、NDWI并加权叠加。","北部各州适宜性高，南部低，全国适宜性得分0.11至1。","可结合土壤、地下水与作物需水数据，将静态适宜性图升级为动态灌溉决策支持系统。","2026-10-01T23:30:29.159371Z"]