[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2425":3},{"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,"view_count":31,"doi":32,"paper":33,"created_at":64},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。",null,"American Journal of Geospatial Technology","2026-09-12T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,13,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"数字乡村","遥感","土壤侵蚀","GIS","水土保持",0,"10.54536\u002Fajgt.v5i1.8103",{"doi":32,"openalex_id":34,"authors":35,"venue":10,"cited_by_count":31,"oa_url":56,"card":57,"direction":61,"ingested_from":63},"W7212388696",[36,38,41,44,47,50,52,54],{"name":37,"orcid":9},"Samuel Fatile",{"name":39,"orcid":40},"Elochukwu Moka","https:\u002F\u002Forcid.org\u002F0000-0001-6089-1392",{"name":42,"orcid":43},"Olaniran Emmanuel Aluko","https:\u002F\u002Forcid.org\u002F0000-0002-8750-1742",{"name":45,"orcid":46},"Ugonna C. Nkwunonwo","https:\u002F\u002Forcid.org\u002F0000-0002-6944-0675",{"name":48,"orcid":49},"Seyi F. Olatoyinbo","https:\u002F\u002Forcid.org\u002F0000-0002-6859-9350",{"name":51,"orcid":9},"Ndukwe Emmanuel Chiemelu",{"name":53,"orcid":9},"Peter Damulak DAKUNG",{"name":55,"orcid":9},"Mark Fredrick OCHOLI","https:\u002F\u002Fjournals.e-palli.com\u002Fhome\u002Findex.php\u002Fajgt\u002Farticle\u002Fdownload\u002F8103\u002F4012",{"tldr":58,"method":59,"finding":60,"direction":61,"opportunity":62},"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","农业遥感与作物表型","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。","openalex","2026-09-14T23:30:24.740002Z"]