[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4316":3,"related-4316":91},{"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":9,"secondary_topics":31,"classification":32,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":90},4316,"GIS-BASED ASSESSMENT OF SOIL EROSION RISK IN GUBI DAM WATERSHED, NORTHEASTERN NIGERIA, USING THE REVISED UNIVERSAL SOIL LOSS EQUATION (RUSLE)","https:\u002F\u002Fdoi.org\u002F10.70382\u002Fbejemcr.v13i4.040","Soil erosion within reservoir watersheds can degrade land resources and mobilise sediment through drainage networks, making spatial identification of erosion-prone areas important for watershed management. This study assessed the magnitude and spatial distribution of soil erosion in the Gubi Dam watershed, northeastern Nigeria, using the Revised Universal Soil Loss Equation (RUSLE) integrated with Geographic Information System (GIS) and remote-sensing techniques. A 30m Shuttle Radar Topography Mission (SRTM) digital elevation model was used for watershed delineation and terrain analysis, while rainfall records, FAO soil information and September 2021 Landsat 8 OLI imagery were used to derive rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover-management (C), and support-practice (P) factors. Vegetation protection was represented using a continuous NDVI-derived C-factor surface. The delineated watershed covered approximately 18,067.39 ha (180.67 km²). RUSLE estimated soil loss ranged from approximately 0 to 715.3 t ha⁻¹ yr⁻¹, with a watershed-wide mean of 5.590t ha⁻¹ yr⁻¹. The archived study-specific classification indicated that 98.6% of the watershed occurred within the two lower soil loss classes, whereas the three upper classes collectively occupied approximately 252.94 ha (1.4%). Elevated estimates were spatially concentrated, particularly in areas associated with steep terrain and major drainage pathways. The assessment provides a spatial screening basis for prioritising field investigation and soil conservation planning. The estimates represent potential sheet and rill soil loss rather than sediment delivery to Gubi Reservoir, and field validation is recommended.","水库流域内的土壤侵蚀会退化土地资源，并通过排水网络搬运泥沙，因此对易侵蚀区域进行空间识别对流域管理具有重要意义。本研究采用修订通用土壤流失方程（RUSLE），结合地理信息系统（GIS）和遥感技术，评估了尼日利亚东北部古比大坝流域土壤侵蚀的强度与空间分布。研究使用30 m航天飞机雷达地形测绘任务（SRTM）数字高程模型进行流域划分和地形分析，并利用降雨记录、FAO土壤信息和2021年9月Landsat 8 OLI影像提取降雨侵蚀力（R）、土壤可蚀性（K）、坡长与坡度（LS）、植被覆盖与管理（C）以及水土保持措施（P）因子。植被保护状况通过基于NDVI生成的连续C因子表面来表示。划分出的流域面积约为18 067.39 ha（180.67 km²）。RUSLE估算的土壤流失量约为0至715.3 t ha⁻¹ yr⁻¹，全流域平均值为5.590 t ha⁻¹ yr⁻¹。本研究采用的分类结果表明，流域内98.6%的区域属于两个较低土壤流失等级，而三个较高等级合计约占252.94 ha（1.4%）。高估算值在空间上较为集中，尤其出现在陡峭地形和主要排水通道相关区域。该评估为优先开展野外调查和水土保持规划提供了空间筛查依据。这些估算值代表潜在的片蚀和细沟土壤流失，而非输移至古比水库的泥沙量，建议进行野外验证。",null,"Journal of Environmental Management and Construction Research","2026-10-04T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,12,1,"基于RUSLE与GIS\u002F遥感的流域土壤侵蚀定量评估，方法规范、数据具体，但属区域性案例研究，对国内三农信息化仅有方法借鉴价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感监测","土壤侵蚀","GIS","水土保持","流域治理",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":14,"reason":66},[38,46,54,60],{"code":39,"score":18,"supported":40,"title":41,"abstract":42,"tags":45},"I4",true,[28],[43,28,44],"地理信息","geographic information",[28],{"code":47,"score":48,"supported":40,"title":49,"abstract":51,"tags":53},"H2",14,[50],"soil",[52,50],"土壤",[],{"code":55,"score":56,"supported":40,"title":57,"abstract":58,"tags":59},"H6",4,[],[29],[29],{"code":61,"score":56,"supported":40,"title":62,"abstract":63,"tags":65},"I3",[],[64],"遥感",[26],"农业相关性需要复核",[68,69],"Gubi Dam 流域 RUSLE","尼日利亚 土壤侵蚀 GIS","GubiDam流域RUSLE-4316",0,"10.70382\u002Fbejemcr.v13i4.040",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":71,"oa_url":82,"card":83,"direction":87,"ingested_from":89},"W7219911927",[76,78,80],{"name":77,"orcid":9},"AHMED BABAYO",{"name":79,"orcid":9},"ABDULKADIR ABUBAKAR",{"name":81,"orcid":9},"ZAKARI DANLADI","https:\u002F\u002Fberkeleypublications.com\u002Fbjemcr\u002Farticle\u002Fdownload\u002F895\u002F790",{"tldr":84,"method":85,"finding":86,"direction":87,"opportunity":88},"用RUSLE结合GIS和遥感评估尼日利亚Gubi水库流域土壤侵蚀风险并制图。","RUSLE模型、30m SRTM DEM、Landsat 8 OLI、NDVI与","流域年均土壤流失5.59 t\u002Fha\u002Fyr，高侵蚀区集中于陡坡和主排水路径，仅1.4%面积属高侵蚀等级","农业遥感与作物表型","可结合泥沙输移比与实地验证，将潜在侵蚀估算转化为水库淤积风险与保护措施优先区研究。","openalex","2026-10-06T23:30:21.805554Z",{"total":92,"page":21,"page_size":92,"items":93},6,[94,165,228,286,350,423],{"id":95,"title":96,"url":97,"summary":98,"summary_zh":99,"content":9,"source_name":100,"source_url":97,"published_at":101,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":102,"sources":105,"tags":107,"primary_topic":109,"secondary_topics":114,"classification":115,"search_phrases":145,"slug":148,"view_count":71,"doi":149,"paper":150,"created_at":164},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",{"impact":17,"substance":18,"depth":48,"authority":20,"freshness":103,"relevant":21,"comment":104},9,"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。",[106],{"name":100,"url":97},[64,108,27,28,29],"土地利用",{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":116,"locked":14,"version":34,"method":35,"evidence":117},"classified",{"candidates":118,"agricultural_evidence":40},[119,123,129,133,140],{"code":39,"score":13,"supported":40,"title":120,"abstract":121,"tags":122},[],[43,28,44],[28],{"code":61,"score":124,"supported":40,"title":125,"abstract":126,"tags":128},7,[],[64,127],"remote sensing",[64],{"code":47,"score":92,"supported":40,"title":130,"abstract":131,"tags":132},[],[52,50],[],{"code":134,"score":135,"supported":40,"title":136,"abstract":137,"tags":139},"H3",3,[],[138],"灌溉",[],{"code":55,"score":141,"supported":14,"title":142,"abstract":143,"tags":144},2,[],[],[108,29],[146,147],"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":149,"openalex_id":151,"authors":152,"venue":100,"cited_by_count":71,"oa_url":9,"card":158,"direction":87,"ingested_from":89},"W7223117733",[153,155],{"name":154,"orcid":9},"MERİÇ KURT",{"name":156,"orcid":157},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"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":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":9,"source_name":171,"source_url":168,"published_at":172,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":173,"score_detail":174,"sources":179,"tags":181,"primary_topic":183,"secondary_topics":184,"classification":185,"search_phrases":200,"slug":203,"view_count":141,"doi":204,"paper":205,"created_at":227},4596,"Spatio-temporal analysis of land use\u002Fland cover (LULC) changes using GIS techniques in Upper Bhavani River Basin, Nilgiri Hills, India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-00915-w","The study investigates the land use and land cover (LULC) changes in Upper Bhavani River basin of the Western Ghats over the past four decades (1989–2019) using remote sensing (RS) and geographic information system (GIS) techniques. It also explores the correlation between LULC changes and population growth during this period. The study highlights the importance of mapping and monitoring LULC changes in developing better management strategies for the sustainable development of natural resources. The iterative self-organising data analysis technique was used for unsupervised classification to create LULC maps of the riverbasin.The study identified and categorised seven LULC categories, namely water bodies, deciduous forest, evergreen forest, agriculture, plantation, barren land, and settlement. The results of the study showed a complex series of modifications in the LULC in the study basin over the past forty years due to policy changes and conventional agroforestry systems. The study revealed a decrease in agriculture, evergreen forest, and deciduous forest by − 6.88%, − 31.42%, and − 9.88%, respectively, while water bodies, plantations, barren land, and settlement area increased by 7.06%, 17.64%, 13.29%, and 10.23%, respectively. The findings of the study provide insights into specific changes in LULC and help predict which land uses will be impacted in the future. The overall accuracy of the classification was0.73.Overall, this study emphasised the importance of monitoring and mapping LULC changes using RS and GIS techniques to develop better management strategies for the sustainable development of natural resources. The findings of the study were useful for policymakers, land-use planners, and conservationists in developing effective strategies to maintain the ecological balance of the Western Ghats.","本研究利用遥感（RS）和地理信息系统（GIS）技术，探讨了过去四十年（1989—2019年）西高止山脉上巴瓦尼河流域的土地利用与土地覆盖（LULC）变化，并分析了该时期LULC变化与人口增长之间的相关性。研究强调了制图和监测LULC变化对于制定自然资源可持续发展管理策略的重要性。采用迭代自组织数据分析技术进行非监督分类，以生成该流域的LULC图。研究识别并划分了七类LULC，分别为水体、落叶林、常绿林、农业、种植园、裸地和居民点。研究结果表明，受政策变化和传统农林业系统的影响，过去四十年研究流域的LULC经历了一系列复杂的改变。研究揭示，农业、常绿林和落叶林分别减少了−6.88%、−31.42%和−9.88%，而水体、种植园、裸地和居民点面积分别增加了7.06%、17.64%、13.29%和10.23%。研究结果揭示了LULC的具体变化，有助于预测未来哪些土地利用类型将受到影响。分类总体精度为0.73。总体而言，本研究强调了利用RS和GIS技术监测和制图LULC变化对于制定自然资源可持续发展管理策略的重要性。研究结果有助于政策制定者、土地利用规划者和保护主义者制定有效策略，以维持西高止山脉的生态平衡。","Discover Environment","2026-10-08T00:00:00Z",70,{"impact":20,"substance":175,"depth":176,"authority":177,"freshness":17,"relevant":21,"comment":178},20,17,13,"基于遥感与GIS的四十年土地利用变化实证研究，数据翔实、结论可靠，对流域生态管理与土地规划有参考价值，但属区域案例研究，公共影响层级有限。",[180],{"name":171,"url":168},[26,108,28,30,182],"西高止山",{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},[],{"status":116,"locked":14,"version":34,"method":35,"evidence":186},{"candidates":187,"agricultural_evidence":40},[188,192,196],{"code":39,"score":18,"supported":40,"title":189,"abstract":190,"tags":191},[28],[43,28,44],[28],{"code":61,"score":124,"supported":40,"title":193,"abstract":194,"tags":195},[],[64,127],[26],{"code":55,"score":21,"supported":14,"title":197,"abstract":198,"tags":199},[],[],[108],[201,202],"土地利用 流域治理 西高止山 遥感监测","土地利用 流域治理","土地利用流域治理西高止山遥感监测-4596","10.1007\u002Fs44274-026-00915-w",{"doi":204,"openalex_id":206,"authors":207,"venue":171,"cited_by_count":71,"oa_url":221,"card":222,"direction":87,"ingested_from":89},"W7221028855",[208,210,213,215,217,219],{"name":209,"orcid":9},"Sapna Kinattinkara",{"name":211,"orcid":212},"Thangavelu Arumugam","https:\u002F\u002Forcid.org\u002F0000-0002-2984-283X",{"name":214,"orcid":9},"Karpagamani Vellingiri",{"name":216,"orcid":9},"Manoj Kumar Patley",{"name":218,"orcid":9},"Manimaran Arumugam",{"name":220,"orcid":9},"Nigatu Wondrade","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-00915-w.pdf",{"tldr":223,"method":224,"finding":225,"direction":87,"opportunity":226},"用遥感与GIS分析印度上巴瓦尼河流域1989-2019年土地利用变化及其与人口增长的关系。","遥感与GIS、ISODATA非监督分类，划分七类LULC，总体精度0.73。","农业、常绿林和落叶林分别减少6.88%、31.42%、9.88%，水体、种植园、裸地和居民地增加。","可结合高精度分类与人口、政策数据，构建LULC变化驱动力模型并预测未来情景。","2026-10-10T23:30:30.955766Z",{"id":229,"title":230,"url":231,"summary":232,"summary_zh":233,"content":9,"source_name":234,"source_url":231,"published_at":235,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":236,"score_detail":237,"sources":239,"tags":241,"primary_topic":243,"secondary_topics":247,"classification":249,"search_phrases":269,"slug":272,"view_count":141,"doi":273,"paper":274,"created_at":285},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":48,"authority":92,"freshness":17,"relevant":21,"comment":238},"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。",[240],{"name":234,"url":231},[242,64,27,28,29],"数字乡村",{"code":61,"slug":244,"name":245,"path":246,"parent_code":113},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[248],{"code":39,"slug":110,"name":111,"path":112,"parent_code":113},{"status":116,"locked":14,"version":34,"method":35,"evidence":250},{"candidates":251,"agricultural_evidence":40},[252,256,260,264],{"code":61,"score":19,"supported":40,"title":253,"abstract":254,"tags":255},[127],[64,127],[64],{"code":39,"score":20,"supported":40,"title":257,"abstract":258,"tags":259},[28],[28],[28],{"code":55,"score":21,"supported":14,"title":261,"abstract":262,"tags":263},[],[],[29],{"code":265,"score":21,"supported":14,"title":266,"abstract":267,"tags":268},"I6",[],[],[242],[270,271],"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":273,"openalex_id":275,"authors":276,"venue":234,"cited_by_count":71,"oa_url":279,"card":280,"direction":87,"ingested_from":89},"W7214920253",[277],{"name":278,"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":281,"method":282,"finding":283,"direction":87,"opportunity":284},"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":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":292,"source_url":289,"published_at":293,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":294,"score_detail":295,"sources":297,"tags":299,"primary_topic":9,"secondary_topics":301,"classification":302,"search_phrases":323,"slug":326,"view_count":124,"doi":327,"paper":328,"created_at":349},3363,"Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02576-x","Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India。Journal of the Indian Society of Remote Sensing","基于RUSLE、遥感和情景机器学习的土壤侵蚀驱动因素综合评估——以印度东部乔塔纳格普尔高原数据稀缺流域为例。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-23T00:00:00Z",63,{"impact":17,"substance":18,"depth":19,"authority":177,"freshness":103,"relevant":21,"comment":296},"方法组合有新意但属区域案例研究，影响范围有限，可作为遥感与水土保持主题的补充素材。",[298],{"name":292,"url":289},[300,26,27,29],"机器学习",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":303},{"candidates":304,"agricultural_evidence":14,"reason":66},[305,311,315,319],{"code":306,"score":19,"supported":40,"title":307,"abstract":309,"tags":310},"I2",[308],"machine learning",[300,308],[300],{"code":61,"score":19,"supported":40,"title":312,"abstract":313,"tags":314},[127],[64,127],[26],{"code":47,"score":48,"supported":40,"title":316,"abstract":317,"tags":318},[50],[52,50],[],{"code":55,"score":21,"supported":14,"title":320,"abstract":321,"tags":322},[],[],[29],[324,325],"Chota Nagpur Plateau 土壤侵蚀","RUSLE 遥感 机器学习","ChotaNagpurPlateau土壤侵蚀-3363","10.1007\u002Fs12524-026-02576-x",{"doi":327,"openalex_id":329,"authors":330,"venue":292,"cited_by_count":71,"oa_url":9,"card":344,"direction":87,"ingested_from":89},"W7214122637",[331,334,337,339,341],{"name":332,"orcid":333},"Mukesh Kumar Tiwari","https:\u002F\u002Forcid.org\u002F0000-0003-0385-4426",{"name":335,"orcid":336},"Prabhat Kumar Guru","https:\u002F\u002Forcid.org\u002F0000-0002-9294-2091",{"name":338,"orcid":9},"Sanjeet Kumar",{"name":340,"orcid":9},"Yogesh A. Rajwade",{"name":342,"orcid":343},"Narendra Singh Chandel","https:\u002F\u002Forcid.org\u002F0000-0003-4850-4702",{"tldr":345,"method":346,"finding":347,"direction":87,"opportunity":348},"结合RUSLE、遥感与情景机器学习，评估印度Chota Nagpur高原缺数据流域的土壤侵蚀驱动因素","RUSLE模型、遥感数据与情景机器学习集成分析。","在数据稀缺流域识别出土壤侵蚀关键驱动因子并预测不同情景下的侵蚀风险。","可探索缺数据区多源遥感与机器学习融合的土壤侵蚀动态监测与情景预警方法。","2026-09-24T23:30:21.065678Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":355,"content":9,"source_name":356,"source_url":353,"published_at":357,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":358,"score_detail":359,"sources":361,"tags":363,"primary_topic":9,"secondary_topics":364,"classification":365,"search_phrases":388,"slug":391,"view_count":141,"doi":392,"paper":393,"created_at":422},2425,"GIS-Based Multi-Criteria Modelling of Soil Erosion Susceptibility Using AHP in Ikere-Ekiti, Southwestern Nigeria","https:\u002F\u002Fdoi.org\u002F10.54536\u002Fajgt.v5i1.8103","Soil erosion remains a critical environmental challenge in tropical regions, particularly in areas experiencing rapid land-use change and geomorphic instability. This study presents a GIS-based multi-criteria modelling approach for assessing soil erosion susceptibility in Ikere-Ekiti, Southwestern Nigeria. Twelve conditioning factors-land use\u002Fland cover (LULC), slope, aspect, elevation, drainage density, soil type, rainfall, Normalized Difference Vegetation Index (NDVI), geology, rainfall erosivity (R-factor), soil erodibility (K-factor), and Topographic Wetness Index (TWI)-were integrated using an Analytic Hierarchy Process (AHP) framework. Spatial datasets derived from ASTER DEM, Landsat 8 imagery, soil and geological maps, and rainfall records were standardized and weighted using pairwise comparison, with model consistency validated (CR = 0.06). A weighted overlay model was employed to generate erosion susceptibility zones. Results indicate that low and moderate susceptibility zones dominate (90.48%), while high and very high-risk areas account for 4.50% and 0.03%, respectively. High-risk zones are strongly associated with steep slopes, high drainage density, sparse vegetation, and erodible soils. The study demonstrates the robustness of GIS-based multi-criteria modelling in erosion prediction and provides a decision-support framework for sustainable land management in tropical environments.","土壤侵蚀仍然是热带地区面临的严峻环境挑战，尤其是在土地利用快速变化和地貌不稳定的区域。本研究提出了一种基于GIS的多准则建模方法，用于评估尼日利亚西南部伊凯雷-埃基蒂（Ikere-Ekiti）地区的土壤侵蚀敏感性。研究整合了12个条件因子——土地利用\u002F土地覆盖（LULC）、坡度、坡向、高程、河网密度、土壤类型、降雨量、归一化植被指数（NDVI）、地质、降雨侵蚀力（R因子）、土壤可蚀性（K因子）和地形湿度指数（TWI），并采用层次分析法（AHP）框架进行分析。来自ASTER DEM、Landsat 8影像、土壤与地质图及降雨记录的空间数据集经过标准化处理，并通过成对比较法赋权，模型一致性得到验证（CR = 0.06）。采用加权叠加模型生成侵蚀敏感性分区。结果表明，低度和中度敏感区占主导地位（90.48%），而高风险区和极高风险区分别占4.50%和0.03%。高风险区与陡坡、高河网密度、稀疏植被和易蚀土壤密切相关。本研究证明了基于GIS的多准则建模在侵蚀预测中的稳健性，并为热带环境下的可持续土地管理提供了决策支持框架。","American Journal of Geospatial Technology","2026-09-12T00:00:00Z",62,{"impact":17,"substance":18,"depth":19,"authority":177,"freshness":17,"relevant":21,"comment":360},"基于GIS与AHP的土壤侵蚀敏感性建模研究，方法规范、结论可靠，对热带地区土地管理与农业生态保护有参考价值，但属区域性案例研究，公共影响有限。",[362],{"name":356,"url":353},[242,64,27,28,29],[],{"status":33,"locked":14,"version":34,"method":35,"evidence":366},{"candidates":367,"agricultural_evidence":14,"reason":66},[368,372,376,380,384],{"code":47,"score":48,"supported":40,"title":369,"abstract":370,"tags":371},[50],[52,50],[],{"code":39,"score":20,"supported":40,"title":373,"abstract":374,"tags":375},[28],[28],[28],{"code":55,"score":21,"supported":14,"title":377,"abstract":378,"tags":379},[],[],[29],{"code":61,"score":21,"supported":14,"title":381,"abstract":382,"tags":383},[],[],[64],{"code":265,"score":21,"supported":14,"title":385,"abstract":386,"tags":387},[],[],[242],[389,390],"土壤侵蚀 数字乡村 水土保持 遥感","土壤侵蚀 数字乡村","土壤侵蚀数字乡村水土保持遥感-2425","10.54536\u002Fajgt.v5i1.8103",{"doi":392,"openalex_id":394,"authors":395,"venue":356,"cited_by_count":71,"oa_url":416,"card":417,"direction":87,"ingested_from":89},"W7212388696",[396,398,401,404,407,410,412,414],{"name":397,"orcid":9},"Samuel Fatile",{"name":399,"orcid":400},"Elochukwu Moka","https:\u002F\u002Forcid.org\u002F0000-0001-6089-1392",{"name":402,"orcid":403},"Olaniran Emmanuel Aluko","https:\u002F\u002Forcid.org\u002F0000-0002-8750-1742",{"name":405,"orcid":406},"Ugonna C. Nkwunonwo","https:\u002F\u002Forcid.org\u002F0000-0002-6944-0675",{"name":408,"orcid":409},"Seyi F. Olatoyinbo","https:\u002F\u002Forcid.org\u002F0000-0002-6859-9350",{"name":411,"orcid":9},"Ndukwe Emmanuel Chiemelu",{"name":413,"orcid":9},"Peter Damulak DAKUNG",{"name":415,"orcid":9},"Mark Fredrick OCHOLI","https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fajgt\u002Farticle\u002Fdownload\u002F8103\u002F4012",{"tldr":418,"method":419,"finding":420,"direction":87,"opportunity":421},"用GIS与AHP多准则模型评估尼日利亚Ikere-Ekiti地区土壤侵蚀敏感性并划分风险区。","整合12个因子，基于AHP加权叠加，数据来自ASTER DEM、Landsat ","低与中度敏感区占90.48%，高与极高风险区仅4.53%，高风险与陡坡、高排水密度、稀疏植被和易蚀土","可引入机器学习或时序遥感提升AHP权重客观性，并验证侵蚀风险与作物产量的空间关联。","2026-09-14T23:30:24.740002Z",{"id":424,"title":425,"url":426,"summary":427,"summary_zh":428,"content":9,"source_name":429,"source_url":426,"published_at":430,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":431,"score_detail":432,"sources":435,"tags":437,"primary_topic":438,"secondary_topics":442,"classification":443,"search_phrases":468,"slug":471,"view_count":71,"doi":472,"paper":473,"created_at":503},5487,"Quantification of soil loss by using RUSLE model under different land use and land cover conditions in AKANYARU catchment in Rwanda","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44378-026-00347-1","This study quantified annual soil loss across the Akanyaru Catchment (3,402 km², Rwanda) under four land use and land cover types forest, agriculture, residential, and bare soil using the RUSLE model integrated with GIS. The Rainfall erosivity (R) is derived from 20 years of daily rainfall data obtained from the NASA database. The Soil erodibility (K) used is from global soil texture data; slope length and steepness (LS) from a 30 m DEM; and cover management (C) from Landsat 8 NDVI. The support practice factor (P) was set to 1.0, reflecting the absence of formal conservation structures in the catchment. The Soil loss range from 0 to 51.4391 t\u002Fha\u002Fyear . Near zero soil loss occurred under dense forest cover, while the highest rates were concentrated in bare and intensively farmed areas on steep slopes particularly in the northeastern and southwestern sub-catchments. The three RUSLE factors showing greatest spatial variationwere steep topography (LS: 0–1.678), degraded or absent vegetation cover (C: 0.0004–0.524), and high rainfall erosivity (R: 527.507–888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹). Findings call for prioritizing terracing and contour farming on steep agricultural slopes, expanding agroforestry and cover crops to reduce surface exposure, and improving soil organic matter in erodible sandy-silty soils. The spatially explicit outputs directly support evidence based targeting of conservation resources by Rwanda’s land and water management authorities.","本研究采用RUSLE模型并结合GIS，对阿卡尼亚鲁流域（面积3 402 km²，位于卢旺达）在森林、农业、居民区和裸地四种土地利用与土地覆盖类型下的年土壤流失量进行了定量评估。降雨侵蚀力（R）因子基于从NASA数据库获取的20年日降雨数据推算得出；土壤可蚀性（K）因子采用全球土壤质地数据；坡长与坡度（LS）因子基于30 m数字高程模型（DEM）提取；覆盖管理（C）因子由Landsat 8 NDVI数据获得。支持措施因子（P）设为1.0，以反映该流域缺乏正式的水土保持措施。土壤流失量范围为0～51.4391 t\u002Fha\u002F年。茂密森林覆盖下土壤流失量接近于零，而流失率最高的区域集中在陡坡上的裸地和集约耕作区，尤其是东北部和西南部子流域。空间变异最大的三个RUSLE因子分别为陡峭地形（LS：0～1.678）、退化或缺失的植被覆盖（C：0.0004～0.524）以及高降雨侵蚀力（R：527.507～888.624 MJ·mm·ha⁻¹·h⁻¹·yr⁻¹）。研究结果呼吁优先在陡坡农地实施梯田与等高耕作，扩大农林复合经营和覆盖作物以减少地表裸露，并改善易侵蚀砂质粉砂土壤的有机质含量。空间显式输出结果可直接为卢旺达土地与水资源管理部门基于证据的水土保持资源靶向配置提供支持。","Discover Soil.","2026-10-20T00:00:00Z",69,{"impact":20,"substance":175,"depth":433,"authority":177,"freshness":17,"relevant":21,"comment":434},16,"基于RUSLE与GIS的流域土壤流失量化研究，方法规范、数据扎实，对水土保持与遥感应用有参考价值，但属区域性案例，公共影响有限。",[436],{"name":429,"url":426},[64,108,27,29],{"code":47,"slug":50,"name":439,"path":440,"parent_code":441},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":116,"locked":14,"version":34,"method":35,"evidence":444},{"candidates":445,"agricultural_evidence":40},[446,450,455,460,464],{"code":47,"score":48,"supported":40,"title":447,"abstract":448,"tags":449},[50],[52,50],[],{"code":55,"score":451,"supported":40,"title":452,"abstract":453,"tags":454},5,[],[29],[108,29],{"code":134,"score":135,"supported":40,"title":456,"abstract":457,"tags":459},[],[458],"水资源",[],{"code":39,"score":135,"supported":40,"title":461,"abstract":462,"tags":463},[],[28],[],{"code":61,"score":21,"supported":14,"title":465,"abstract":466,"tags":467},[],[],[64],[469,470],"RUSLE 模型 卢旺达","Akanyaru 流域 土壤流失","RUSLE模型卢旺达-5487","10.1007\u002Fs44378-026-00347-1",{"doi":472,"openalex_id":474,"authors":475,"venue":429,"cited_by_count":71,"oa_url":497,"card":498,"direction":87,"ingested_from":89},"W7223058039",[476,479,482,485,488,491,494],{"name":477,"orcid":478},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":480,"orcid":481},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":483,"orcid":484},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":486,"orcid":487},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":489,"orcid":490},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":492,"orcid":493},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":495,"orcid":496},"Judith Uwihirwe","https:\u002F\u002Forcid.org\u002F0000-0002-0351-5726","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44378-026-00347-1.pdf",{"tldr":499,"method":500,"finding":501,"direction":162,"opportunity":502},"用RUSLE与GIS量化卢旺达Akanyaru流域四类土地利用的年土壤流失量。","RUSLE模型结合GIS，采用NASA降雨、全球土壤、DEM与Landsat 8","土壤流失0–51.4 t\u002Fha\u002F年，裸地与陡坡集约农地最高，密林近零。","可结合遥感时序与保护措施情景，评估梯田与农林复合的减蚀效益及碳协同。","2026-10-21T23:30:23.420269Z"]