[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5187":3,"related-5187":78},{"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":18,"tags":20,"primary_topic":9,"secondary_topics":24,"classification":25,"search_phrases":54,"slug":57,"view_count":15,"doi":58,"paper":59,"created_at":77},5187,"Determination of Erosion Potential of Rioni River Basin Using GIS and RUSLE Model","https:\u002F\u002Fdoi.org\u002F10.47191\u002Fetj\u002Fv11i10.07","This study focuses on determining the soil erosion potential within the Rioni River basin using the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) technologies. A 90-meter resolution Digital Elevation Model (DEM) from the Shuttle Radar Topographic Mission (SRTM) was utilized to delineate the basin boundaries and hydrological network. The RUSLE model, an empirical approach, was applied to estimate the long-term average annual soil loss. Individual RUSLE factors were computed using specific methodologies: the rainfall-runoff factor (R) was derived from a linear empirical relationship based on average atmospheric precipitation (1989-2021); the soil erodibility factor (K) was calculated using the Sharpley–Williams equation, yielding a value of 0.0184 for the prevalent soil type; the topographic factor (LS) was determined using a RUSLE-adapted approach; the cover-management factor (C) was calculated using the Durigon equation from NDVI values; and the support practice factor (P) was assigned based on slope and cultivation practices. Results indicate that the R-factor ranges from 313.65 to 473.89 MJ×mm\u002F(ha×hours), with an average of 391.02 MJ×mm\u002F(ha×hours). The maximum LS-factor reached 7137.810. The C-factor values (0.104 to 0.769) suggest a significant risk of water erosion across the basin. Overall, the study concludes that riverbed erosion predominantly occurs in the Rioni River basin, primarily influenced by flow velocity and slope.","本研究聚焦于利用修正通用土壤流失方程（RUSLE）与地理信息系统（GIS）技术，确定里奥尼河流域的土壤侵蚀潜力。研究采用航天飞机雷达地形测绘任务（SRTM）提供的90米分辨率数字高程模型（DEM）来划定流域边界与水文网络。RUSLE模型作为一种经验方法，被用于估算长期年均土壤流失量。各RUSLE因子采用特定方法计算：降雨-径流因子（R）基于1989—2021年平均大气降水量，通过线性经验关系推导得出；土壤可蚀性因子（K）采用Sharpley–Williams方程计算，针对主要土壤类型所得值为0.0184；地形因子（LS）采用RUSLE适配方法确定；覆盖管理因子（C）利用NDVI值，通过Durigon方程计算；支持措施因子（P）则依据坡度与耕作方式赋值。结果表明，R因子范围为313.65至473.89 MJ×mm\u002F（ha×小时），平均值为391.02 MJ×mm\u002F（ha×小时）。LS因子最大值达到7137.810。C因子值（0.104至0.769）表明全流域存在显著的水蚀风险。总体而言，研究认为里奥尼河流域的侵蚀主要发生在河床，主要受流速与坡度影响。",null,"Engineering and Technology Journal","2026-10-15T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究以格鲁吉亚里奥尼河流域为对象，属于水土保持与遥感应用类论文，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[19],{"name":10,"url":6},[21,22,23],"遥感","土壤侵蚀","GIS",[],{"status":26,"locked":14,"version":27,"method":28,"evidence":29},"needs_review","2026-10-22.2","rules",{"candidates":30,"agricultural_evidence":14,"reason":53},[31,39,47],{"code":32,"score":33,"supported":34,"title":35,"abstract":36,"tags":38},"I4",15,true,[23],[37,23],"地理信息",[23],{"code":40,"score":41,"supported":34,"title":42,"abstract":43,"tags":46},"H2",6,[],[44,45],"土壤","soil",[],{"code":48,"score":49,"supported":14,"title":50,"abstract":51,"tags":52},"I3",1,[],[],[21],"农业相关性需要复核",[55,56],"RUSLE GIS 土壤侵蚀","土壤侵蚀 遥感 GIS","RUSLEGIS土壤侵蚀-5187","10.47191\u002Fetj\u002Fv11i10.07",{"doi":58,"openalex_id":60,"authors":61,"venue":10,"cited_by_count":15,"oa_url":68,"card":69,"direction":75,"ingested_from":76},"W7222052256",[62,64,66],{"name":63,"orcid":9},"GIORGI CHITIASHVILI",{"name":65,"orcid":9},"IRMA INASHVILI",{"name":67,"orcid":9},"IRINA KLIMIASHVILI","https:\u002F\u002Fwww.everant.org\u002Findex.php\u002Fetj\u002Farticle\u002Fdownload\u002F2054\u002F2236",{"tldr":70,"method":71,"finding":72,"direction":73,"opportunity":74},"用GIS和RUSLE模型评估里奥尼河流域土壤侵蚀潜力。","RUSLE模型结合SRTM DEM、NDVI与降水数据计算各侵蚀因子。","流域以河床侵蚀为主，主要受流速和坡度影响，存在显著水蚀风险。","农业遥感与作物表型","可引入高分辨率遥感与时序NDVI改进C因子，并耦合水文模型动态评估侵蚀。","数字乡村与农业信息化","openalex","2026-10-17T23:30:38.427888Z",{"total":41,"page":49,"page_size":41,"items":79},[80,159,217,291,362,442],{"id":81,"title":82,"url":83,"summary":84,"summary_zh":85,"content":9,"source_name":86,"source_url":83,"published_at":87,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":88,"score_detail":89,"sources":96,"tags":98,"primary_topic":101,"secondary_topics":106,"classification":107,"search_phrases":139,"slug":142,"view_count":15,"doi":143,"paper":144,"created_at":158},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":90,"substance":91,"depth":92,"authority":93,"freshness":94,"relevant":49,"comment":95},8,18,14,12,9,"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。",[97],{"name":86,"url":83},[21,99,22,23,100],"土地利用","水土保持",{"code":32,"slug":102,"name":103,"path":104,"parent_code":105},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":108,"locked":14,"version":27,"method":28,"evidence":109},"classified",{"candidates":110,"agricultural_evidence":34},[111,116,122,126,133],{"code":32,"score":13,"supported":34,"title":112,"abstract":113,"tags":115},[],[37,23,114],"geographic information",[23],{"code":48,"score":117,"supported":34,"title":118,"abstract":119,"tags":121},7,[],[21,120],"remote sensing",[21],{"code":40,"score":41,"supported":34,"title":123,"abstract":124,"tags":125},[],[44,45],[],{"code":127,"score":128,"supported":34,"title":129,"abstract":130,"tags":132},"H3",3,[],[131],"灌溉",[],{"code":134,"score":135,"supported":14,"title":136,"abstract":137,"tags":138},"H6",2,[],[],[99,100],[140,141],"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":143,"openalex_id":145,"authors":146,"venue":86,"cited_by_count":15,"oa_url":9,"card":152,"direction":73,"ingested_from":76},"W7223117733",[147,149],{"name":148,"orcid":9},"MERİÇ KURT",{"name":150,"orcid":151},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":153,"method":154,"finding":155,"direction":156,"opportunity":157},"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":160,"title":161,"url":162,"summary":163,"summary_zh":164,"content":9,"source_name":165,"source_url":162,"published_at":166,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":167,"score_detail":168,"sources":170,"tags":172,"primary_topic":174,"secondary_topics":178,"classification":180,"search_phrases":200,"slug":203,"view_count":135,"doi":204,"paper":205,"created_at":216},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":90,"substance":91,"depth":92,"authority":41,"freshness":90,"relevant":49,"comment":169},"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。",[171],{"name":165,"url":162},[173,21,22,23,100],"数字乡村",{"code":48,"slug":175,"name":176,"path":177,"parent_code":105},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[179],{"code":32,"slug":102,"name":103,"path":104,"parent_code":105},{"status":108,"locked":14,"version":27,"method":28,"evidence":181},{"candidates":182,"agricultural_evidence":34},[183,187,191,195],{"code":48,"score":33,"supported":34,"title":184,"abstract":185,"tags":186},[120],[21,120],[21],{"code":32,"score":93,"supported":34,"title":188,"abstract":189,"tags":190},[23],[23],[23],{"code":134,"score":49,"supported":14,"title":192,"abstract":193,"tags":194},[],[],[100],{"code":196,"score":49,"supported":14,"title":197,"abstract":198,"tags":199},"I6",[],[],[173],[201,202],"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":204,"openalex_id":206,"authors":207,"venue":165,"cited_by_count":15,"oa_url":210,"card":211,"direction":73,"ingested_from":76},"W7214920253",[208],{"name":209,"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":212,"method":213,"finding":214,"direction":73,"opportunity":215},"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":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":225,"score_detail":226,"sources":229,"tags":231,"primary_topic":9,"secondary_topics":232,"classification":233,"search_phrases":256,"slug":259,"view_count":135,"doi":260,"paper":261,"created_at":290},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":90,"substance":91,"depth":33,"authority":227,"freshness":90,"relevant":49,"comment":228},13,"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。",[230],{"name":223,"url":220},[173,21,22,23,100],[],{"status":26,"locked":14,"version":27,"method":28,"evidence":234},{"candidates":235,"agricultural_evidence":14,"reason":53},[236,240,244,248,252],{"code":40,"score":92,"supported":34,"title":237,"abstract":238,"tags":239},[45],[44,45],[],{"code":32,"score":93,"supported":34,"title":241,"abstract":242,"tags":243},[23],[23],[23],{"code":134,"score":49,"supported":14,"title":245,"abstract":246,"tags":247},[],[],[100],{"code":48,"score":49,"supported":14,"title":249,"abstract":250,"tags":251},[],[],[21],{"code":196,"score":49,"supported":14,"title":253,"abstract":254,"tags":255},[],[],[173],[257,258],"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":260,"openalex_id":262,"authors":263,"venue":223,"cited_by_count":15,"oa_url":284,"card":285,"direction":73,"ingested_from":76},"W7212388696",[264,266,269,272,275,278,280,282],{"name":265,"orcid":9},"Samuel Fatile",{"name":267,"orcid":268},"Elochukwu Moka","https:\u002F\u002Forcid.org\u002F0000-0001-6089-1392",{"name":270,"orcid":271},"Olaniran Emmanuel Aluko","https:\u002F\u002Forcid.org\u002F0000-0002-8750-1742",{"name":273,"orcid":274},"Ugonna C. Nkwunonwo","https:\u002F\u002Forcid.org\u002F0000-0002-6944-0675",{"name":276,"orcid":277},"Seyi F. Olatoyinbo","https:\u002F\u002Forcid.org\u002F0000-0002-6859-9350",{"name":279,"orcid":9},"Ndukwe Emmanuel Chiemelu",{"name":281,"orcid":9},"Peter Damulak DAKUNG",{"name":283,"orcid":9},"Mark Fredrick OCHOLI","https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fajgt\u002Farticle\u002Fdownload\u002F8103\u002F4012",{"tldr":286,"method":287,"finding":288,"direction":73,"opportunity":289},"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":292,"title":293,"url":294,"summary":295,"summary_zh":296,"content":9,"source_name":297,"source_url":294,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":299,"score_detail":300,"sources":304,"tags":306,"primary_topic":310,"secondary_topics":314,"classification":315,"search_phrases":341,"slug":344,"view_count":15,"doi":345,"paper":346,"created_at":361},5495,"Artificial Neural Network-Based Prediction and Mapping of Elephant Corridors in West Bengal Using Geospatial and Environmental Variables","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23496876","The fragmentation of forest habitats and increasing anthropogenic pressures have significantly altered elephant movementpatternsinWestBengal,India,resultinginanescalatinghuman-elephantconflict.Accurateidentification and mapping of elephant corridors are essential for landscape-level conservation planning and mitigation.Traditional corridor mapping methods, including expert-based delineation and least-cost path analyses, oftenstruggle to capture the complex and nonlinear relationships between environmental variables influencing elephant movement. This study proposes an Artificial Neural Network (ANN)-based framework to predict potential elephant corridors across West Bengal using remotely sensed and geographic information system (GIS) datasets. The predictor variables included forest cover, normalized difference vegetation index (NDVI), elevation, slope,proximity to water sources, distance from roads, railways, settlements, agricultural land, and historical elephant occurrence records. The ANN model was trained using known elephant movement locations and validated using independent datasets. The predicted suitable surface was integrated into a GIS environment to identify probable movement corridors and prioritize areas for conservation. The proposed approach demonstrates the ability of machine learning to improve corridor prediction compared with conventional GIS-based techniques and provides a decision support tool for forest managers and policymakers.","森林栖息地的破碎化和日益增加的人为压力显著改变了印度西孟加拉邦大象的活动模式，导致人象冲突不断加剧。准确识别和绘制大象廊道对于景观层面的保护规划和冲突缓解至关重要。传统的廊道制图方法，包括基于专家的 delineation（边界划定）和最小成本路径分析，往往难以捕捉影响大象活动的环境变量之间复杂的非线性关系。本研究提出了一种基于人工神经网络（Artificial Neural Network, ANN）的框架，利用遥感数据和地理信息系统（Geographic Information System, GIS）数据集预测西孟加拉邦潜在的大象廊道。预测变量包括森林覆盖、归一化植被指数（Normalized Difference Vegetation Index, NDVI）、海拔、坡度、距水源的邻近度、距道路、铁路、居民点、农田的距离以及历史大象出现记录。ANN模型利用已知的大象活动位置进行训练，并使用独立数据集进行验证。将预测得到的适宜性表面整合到GIS环境中，以识别可能的迁移廊道并确定保护优先区域。所提出的方法展示了机器学习相较于传统基于GIS的技术在改进廊道预测方面的能力，并为森林管理者和政策制定者提供了决策支持工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-20T00:00:00Z",70,{"impact":93,"substance":301,"depth":302,"authority":93,"freshness":94,"relevant":49,"comment":303},20,17,"该研究用人工神经网络结合遥感与GIS预测大象迁徙走廊，方法新颖、数据扎实，对生态保护与乡村人兽冲突治理有参考价值，但属区域性研究，公共影响有限。",[305],{"name":297,"url":294},[307,21,23,308,309],"农业人工智能","野生动物保护","人兽冲突",{"code":105,"slug":311,"name":312,"path":313,"parent_code":9},"digital-agriculture","数字农业与前沿技术","\u002Ftopic\u002Fdigital-agriculture",[],{"status":316,"locked":14,"version":27,"method":28,"evidence":317},"partial",{"candidates":318,"agricultural_evidence":34,"reason":340},[319,327,331,336],{"code":320,"score":13,"supported":34,"title":321,"abstract":322,"tags":326},"I2",[],[323,324,325],"机器学习","machine learning","decision support",[307],{"code":32,"score":13,"supported":34,"title":328,"abstract":329,"tags":330},[],[37,23,114],[23],{"code":48,"score":332,"supported":34,"title":333,"abstract":334,"tags":335},4,[],[21],[21],{"code":134,"score":49,"supported":14,"title":337,"abstract":338,"tags":339},[],[],[308],"一级领域明确，二级方向需要细分",[342,343],"西孟加拉邦 大象走廊","人工神经网络 大象走廊","西孟加拉邦大象走廊-5495","10.5281\u002Fzenodo.23496876",{"doi":345,"openalex_id":347,"authors":348,"venue":297,"cited_by_count":15,"oa_url":294,"card":355,"direction":360,"ingested_from":76},"W7222999966",[349,351,353],{"name":350,"orcid":9},"Bhutia Dechen Doma",{"name":352,"orcid":9},"Swarup Das",{"name":354,"orcid":9},"Mandal Rakesh Kumar",{"tldr":356,"method":357,"finding":358,"direction":73,"opportunity":359},"用人工神经网络结合遥感与GIS变量预测并制图西孟加拉邦大象迁徙廊道。","ANN模型，输入遥感与GIS环境变量及历史象群出现记录，GIS集成制图。","ANN能刻画环境变量非线性关系，廊道预测优于传统GIS方法，可作决策支持工具。","可将ANN廊道预测与人类活动、农田冲突热点耦合，探索农业景观中野生动物通道与作物保护的协同规划。","农业人工智能与决策模型","2026-10-21T23:30:43.936453Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":367,"content":9,"source_name":368,"source_url":365,"published_at":298,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":369,"score_detail":370,"sources":373,"tags":375,"primary_topic":376,"secondary_topics":380,"classification":381,"search_phrases":406,"slug":409,"view_count":15,"doi":410,"paper":411,"created_at":441},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.",69,{"impact":93,"substance":301,"depth":371,"authority":227,"freshness":90,"relevant":49,"comment":372},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。",[374],{"name":368,"url":365},[21,99,22,100],{"code":40,"slug":45,"name":377,"path":378,"parent_code":379},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":108,"locked":14,"version":27,"method":28,"evidence":382},{"candidates":383,"agricultural_evidence":34},[384,388,393,398,402],{"code":40,"score":92,"supported":34,"title":385,"abstract":386,"tags":387},[45],[44,45],[],{"code":134,"score":389,"supported":34,"title":390,"abstract":391,"tags":392},5,[],[100],[99,100],{"code":127,"score":128,"supported":34,"title":394,"abstract":395,"tags":397},[],[396],"水资源",[],{"code":32,"score":128,"supported":34,"title":399,"abstract":400,"tags":401},[],[23],[],{"code":48,"score":49,"supported":14,"title":403,"abstract":404,"tags":405},[],[],[21],[407,408],"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":410,"openalex_id":412,"authors":413,"venue":368,"cited_by_count":15,"oa_url":435,"card":436,"direction":73,"ingested_from":76},"W7223058039",[414,417,420,423,426,429,432],{"name":415,"orcid":416},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":418,"orcid":419},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":421,"orcid":422},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":424,"orcid":425},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":427,"orcid":428},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":430,"orcid":431},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":433,"orcid":434},"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":437,"method":438,"finding":439,"direction":156,"opportunity":440},"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",{"id":443,"title":444,"url":445,"summary":446,"summary_zh":447,"content":9,"source_name":448,"source_url":445,"published_at":449,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":450,"score_detail":451,"sources":453,"tags":455,"primary_topic":459,"secondary_topics":460,"classification":461,"search_phrases":496,"slug":499,"view_count":15,"doi":500,"paper":501,"created_at":512},5403,"RS-GIS Based Dss (Decision Support System) for Land evaluation and Land resource management – (case study Amol District)","https:\u002F\u002Fdoi.org\u002F10.71500\u002Fthesis.2026.519222","Creating a proper planting pattern according to land talent identification, cannot be achieved without considering socioeconomic conditions and detailed holistic. This issue is considered by development of rice which is one of the main world’s most important agricultural products in rural areas and ensures their food security. In the country rice is considered as a strategic plant. This is among sustainable resources of area and attention to its development and expanding its cultivation causes farmers’ confidence and ensures economic stability of the region. On the other hand, crop insurance is same for all land, because statistical information on agricultural lands are most obtained through traditional methods based on the opinion of experts and individuals and collect this information always accompanied with error. Accordingly annually huge costs are paid for insured loss of agricultural lands Thus land evaluation, identification and resolution of land based on its capabilities by taking the capacity of the soil and environmental risks into account is essential for land management. This study presents a spatial decision support system (SDSS) to land evaluation and identification of suitable areas for rice cultivation in the studied area. After gathering data and field studies, various layers of criteria affecting decisions were created in GIS environment. Development of multi-criteria decision-making techniques (MCDM) is done using Analytical Hierarchy Process AHP, fuzzy logic and artificial neural networks. The comparison and integration of models (model compilation and standardization of fuzzy AHP, FAHP, fuzzy standardization and FAHP) was also carried out. To review and matching between fitness maps obtained from different models and the observed value of kappa coefficient was calculated. Neural network model with kappa coefficient 0.65 has highest fitness coefficient among land suitability class and the harvest amount and this due to the performance value in determining the classes is predictable in this model. After that, Fuzzy standardization compilation models and FAHP and Fuzzy standardization and AHP with Kappa coefficient of 0.54 and 0.51 respectively showed highest agreement between the land suitability classes and performance. The majority of lands located on non-prone class have high slope and if cultivation increase risk of soil erosion. Investigating area under rice plantation (paddy fields) with very prone and prone lands in different styles showed that almost the majority of this area is currently devoted to rice cultivation. Although neural network model showed higher accuracy than other models, but this assessment model requires data on rice yield and collecting performance rate required accuracy, cost and much time and sometimes land management and environmental hazards affects it.","根据土地适宜性识别来制定合理的种植模式，离不开对社会经济条件和详细整体性因素的考量。水稻作为世界最重要的农产品之一，其发展在农村地区受到重视，并保障了当地的粮食安全。在我国，水稻被视为战略作物。水稻是区域可持续资源之一，关注其发展并扩大种植面积能够增强农民信心，保障区域经济稳定。另一方面，作物保险对所有土地采取统一标准，因为农业用地的统计信息大多基于专家和个人意见通过传统方法获取，且此类信息的采集始终伴随着误差。因此，每年为农业用地保险损失支付巨额费用。有鉴于此，土地评价、识别及基于土地能力的土地问题解决，必须将土壤容量和环境风险纳入考量，这对土地管理至关重要。本研究提出了一种空间决策支持系统（SDSS），用于研究区域的土地评价和水稻适宜种植区识别。在收集数据和实地调研之后，于GIS环境中创建了影响决策的各准则图层。多准则决策技术（MCDM）的开发采用了层次分析法（AHP）、模糊逻辑和人工神经网络。同时进行了模型的比较与集成（模型编制与模糊AHP标准化、FAHP、模糊标准化与FAHP）。为审查和匹配不同模型获得的适宜性图件，计算了观测值与kappa系数。神经网络模型的kappa系数为0.65，在土地适宜性等级与产量之间具有最高的拟合系数，这归因于该模型在确定等级方面的性能值具有可预测性。其后，模糊标准化编制模型与FAHP、模糊标准化与AHP的kappa系数分别为0.54和0.51，在土地适宜性等级与产量之间表现出最高的一致性。位于非适宜等级的大多数土地具有高坡度，若扩大种植将增加土壤侵蚀风险。对不同适宜性等级下水稻种植区（稻田）的调查……","Test","2026-10-19T00:00:00Z",50,{"impact":90,"substance":371,"depth":33,"authority":128,"freshness":90,"relevant":49,"comment":452},"案例研究有一定方法参考价值，但来源不明、地域局限，不足以进入每日精选。",[454],{"name":448,"url":445},[456,21,23,457,458],"决策支持系统","水稻种植","土地评价",{"code":105,"slug":311,"name":312,"path":313,"parent_code":9},[],{"status":316,"locked":14,"version":27,"method":28,"evidence":462},{"candidates":463,"agricultural_evidence":34,"reason":340},[464,468,472,481,488,492],{"code":320,"score":93,"supported":34,"title":465,"abstract":466,"tags":467},[325],[325],[456],{"code":32,"score":93,"supported":34,"title":469,"abstract":470,"tags":471},[23],[23],[23],{"code":473,"score":13,"supported":34,"title":474,"abstract":475,"tags":480},"A1",[],[476,477,478,479],"水稻","稻田","rice","paddy",[457],{"code":482,"score":41,"supported":34,"title":483,"abstract":484,"tags":487},"A8",[],[485,486],"粮食安全","food security",[],{"code":40,"score":41,"supported":34,"title":489,"abstract":490,"tags":491},[],[44,45],[],{"code":48,"score":49,"supported":14,"title":493,"abstract":494,"tags":495},[],[],[21],[497,498],"Amol District 水稻 土地适宜性","RS-GIS 决策支持系统 土地评价","AmolDistrict水稻土地适宜性-5403","10.71500\u002Fthesis.2026.519222",{"doi":500,"openalex_id":502,"authors":503,"venue":448,"cited_by_count":15,"oa_url":445,"card":507,"direction":360,"ingested_from":76},"W7222963628",[504],{"name":505,"orcid":506},"Zeinab Maddahi","https:\u002F\u002Forcid.org\u002F0000-0002-2824-2736",{"tldr":508,"method":509,"finding":510,"direction":73,"opportunity":511},"构建基于RS-GIS的空间决策支持系统，评估伊朗Amol地区水稻种植适宜性。","GIS叠加多准则决策、AHP、模糊逻辑与人工神经网络，用Kappa系数验证。","神经网络模型Kappa系数0.65最高，土地适宜性与产量吻合最好。","可将该SDSS与实时遥感、作物保险定损结合，发展动态土地适宜性监测。","2026-10-20T23:30:40.596410Z"]