[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"topic-RUSLE":3},{"name":4,"kind":5,"tokens":6,"total":7,"page":7,"page_size":8,"items":9},"RUSLE","tag",[4],1,100,[10],{"id":11,"title":12,"url":13,"summary":14,"summary_zh":15,"content":16,"source_name":17,"source_url":13,"published_at":18,"category":19,"cover_url":16,"hotness":20,"is_selected":21,"score":22,"score_detail":23,"sources":30,"tags":32,"primary_topic":37,"secondary_topics":43,"classification":44,"search_phrases":93,"slug":96,"view_count":77,"doi":97,"paper":98,"created_at":119},3902,"ҚАЗАҚСТАННЫҢ ОҢТҮСТІК-ШЫҒЫСЫНДАҒЫ ТӘЛІМДІ ЖЕРЛЕРДІҢ ЭРОЗИЯЛЫҚ ҚАУІПТІЛІГІН ҚАШЫҚТАН ЗОНДТАУ ДЕРЕКТЕРІ МЕН RUSLE МОДЕЛІ НЕГІЗІНДЕ БАҒАЛАУ","https:\u002F\u002Fdoi.org\u002F10.52578\u002F3134-8556-2026-3-14-23","This study assesses potential soil erosion on arable land in the foothill zone of South-East Kazakhstan using the Revised Universal Soil Loss Equation (RUSLE), remote sensing data, and Geographic Information Systems (GIS). The research focuses on a 17.6-hectare agricultural plot located in the Almaty region. Sentinel-2 satellite imagery and the FABDEM_V1 global digital elevation model were used as the primary data sources for spatial analysis. The methodology involved the calculation and integration of the main RUSLE factors, including rainfall erosivity (R), soil erodibility (K), topographic factor (LS), cover-management factor (C), and support practice factor (P). The LS factor was derived from the FABDEM digital elevation model, while the vegetation cover factor (C) was estimated using the Normalized Difference Vegetation Index (NDVI) for two key agricultural periods: April (pre-growing season) and July (post-harvest period). Based on soil mechanical composition and humus content, the K factor was determined as 0.26. The results show that the average annual soil loss amounted to 10.01 t\u002Fha\u002Fyear in April and increased to 13.67 t\u002Fha\u002Fyear in July. The analysis revealed that erosion risk rises by approximately 36% after harvesting due to reduced vegetation cover. The maximum soil loss reached 55.46 t\u002Fha\u002Fyear in the most erosion-prone areas. The findings highlight that the post-harvest period represents the most critical phase for soil degradation in the study area. The study emphasizes the importance of implementing soil conservation measures and demonstrates the effectiveness of integrating RUSLE modeling with remote sensing and GIS technologies for sustainable land management.","本研究利用修正通用土壤流失方程（RUSLE）、遥感数据和地理信息系统（GIS），评估了哈萨克斯坦东南部山麓地带耕地的潜在土壤侵蚀。研究聚焦于阿拉木图地区一块17.6公顷的农业地块。采用Sentinel-2卫星影像和FABDEM_V1全球数字高程模型作为空间分析的主要数据来源。研究方法包括计算并整合RUSLE的主要因子，包括降雨侵蚀力（R）、土壤可蚀性（K）、地形因子（LS）、植被覆盖与管理因子（C）以及水土保持措施因子（P）。LS因子由FABDEM数字高程模型提取，植被覆盖因子（C）则利用归一化植被指数（NDVI）在两个关键农业时期进行估算：4月（生长季前）和7月（收获后）。根据土壤机械组成和腐殖质含量，确定K因子为0.26。结果表明，4月年均土壤流失量为10.01吨\u002F公顷\u002F年，7月增至13.67吨\u002F公顷\u002F年。分析显示，由于植被覆盖减少，收获后侵蚀风险上升约36%。在侵蚀最严重的区域，最大土壤流失量达到55.46吨\u002F公顷\u002F年。研究结果突出表明，收获后时期是研究区土壤退化最为关键的阶段。本研究强调了实施土壤保持措施的重要性，并证明了将RUSLE模型与遥感和GIS技术相结合用于可持续土地管理的有效性。",null,"Agricultural Systems and Natural Resources","2026-09-30T00:00:00Z","论文",10,false,72,{"impact":24,"substance":25,"depth":26,"authority":27,"freshness":28,"relevant":7,"comment":29},12,21,17,13,9,"基于RUSLE模型与遥感数据评估哈萨克斯坦东南部耕地侵蚀风险，方法规范、数据具体，对干旱区数字农业与水土保持有参考价值，但属区域案例研究，公共影响有限。",[31],{"name":17,"url":13},[33,34,35,36,4],"数字农业","遥感","土壤侵蚀","水土保持",{"code":38,"slug":39,"name":40,"path":41,"parent_code":42},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":45,"locked":21,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":51},[50,60,67,75,81,88],{"code":38,"score":28,"supported":51,"title":52,"abstract":53,"tags":59},true,[],[54,55,56,57,58],"地理信息","空间分析","GIS","spatial analysis","geographic information",[],{"code":61,"score":62,"supported":51,"title":63,"abstract":64,"tags":66},"I3",7,[],[34,65],"remote sensing",[34],{"code":68,"score":69,"supported":51,"title":70,"abstract":71,"tags":74},"H2",6,[],[72,73],"土壤","soil",[],{"code":76,"score":77,"supported":51,"title":78,"abstract":79,"tags":80},"H6",4,[],[36],[36],{"code":82,"score":83,"supported":51,"title":84,"abstract":85,"tags":87},"I7",3,[],[86],"研究方法",[],{"code":89,"score":7,"supported":21,"title":90,"abstract":91,"tags":92},"I1",[],[],[33],[94,95],"哈萨克斯坦 阿拉木图 RUSLE 土壤侵蚀","Sentinel-2 NDVI 土壤流失","哈萨克斯坦阿拉木图RUSLE土壤侵蚀-3902","10.52578\u002F3134-8556-2026-3-14-23",{"doi":97,"openalex_id":99,"authors":100,"venue":17,"cited_by_count":111,"oa_url":13,"card":112,"direction":116,"ingested_from":118},"W7214985337",[101,103,105,107,109],{"name":102,"orcid":16},"Д. Кайнушева",{"name":104,"orcid":16},"Р. Жапаев",{"name":106,"orcid":16},"Е. Кенжехан",{"name":108,"orcid":16},"Д. Кыдырбаева",{"name":110,"orcid":16},"Ә. Мақұлбек",0,{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"用RUSLE模型和遥感数据评估哈萨克斯坦东南部耕地土壤侵蚀风险。","RUSLE模型、Sentinel-2影像、FABDEM高程数据与GIS空间分析。","年均土壤流失量4月为10.01 t\u002Fha，7月增至13.67 t\u002Fha，收获后侵蚀风险上升约36%。","农业遥感与作物表型","可结合多时相遥感与机器学习，动态预测收获后侵蚀热点，优化保护性耕作措施。","openalex","2026-10-01T23:30:29.218572Z"]