[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5572":3,"related-5572":103},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":80,"slug":83,"view_count":84,"doi":85,"paper":86,"created_at":102},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。",null,"Frontiers in Life Sciences and Related Technologies","2026-10-21T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,18,14,12,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感","土地利用","土壤侵蚀","GIS","水土保持",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,52,59,67,74],{"code":33,"score":13,"supported":46,"title":47,"abstract":48,"tags":51},true,[],[49,30,50],"地理信息","geographic information",[30],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":58},"I3",7,[],[27,57],"remote sensing",[27],{"code":60,"score":61,"supported":46,"title":62,"abstract":63,"tags":66},"H2",6,[],[64,65],"土壤","soil",[],{"code":68,"score":69,"supported":46,"title":70,"abstract":71,"tags":73},"H3",3,[],[72],"灌溉",[],{"code":75,"score":76,"supported":14,"title":77,"abstract":78,"tags":79},"H6",2,[],[],[28,31],[81,82],"ICONA 模型 侵蚀风险","Kanak 流域 侵蚀","ICONA模型侵蚀风险-5572",0,"10.51753\u002Fflsrt.1828829",{"doi":85,"openalex_id":87,"authors":88,"venue":10,"cited_by_count":84,"oa_url":9,"card":94,"direction":100,"ingested_from":101},"W7223117733",[89,91],{"name":90,"orcid":9},"MERİÇ KURT",{"name":92,"orcid":93},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"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。","农业遥感与作物表型","openalex","2026-10-22T11:20:39.475037Z",{"total":61,"page":22,"page_size":61,"items":104},[105,164,234,311,369,427],{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":116,"tags":118,"primary_topic":120,"secondary_topics":124,"classification":126,"search_phrases":147,"slug":150,"view_count":76,"doi":151,"paper":152,"created_at":163},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":19,"authority":61,"freshness":17,"relevant":22,"comment":115},"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。",[117],{"name":111,"url":108},[119,27,29,30,31],"数字乡村",{"code":53,"slug":121,"name":122,"path":123,"parent_code":37},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[125],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":127},{"candidates":128,"agricultural_evidence":46},[129,134,138,142],{"code":53,"score":130,"supported":46,"title":131,"abstract":132,"tags":133},15,[57],[27,57],[27],{"code":33,"score":20,"supported":46,"title":135,"abstract":136,"tags":137},[30],[30],[30],{"code":75,"score":22,"supported":14,"title":139,"abstract":140,"tags":141},[],[],[31],{"code":143,"score":22,"supported":14,"title":144,"abstract":145,"tags":146},"I6",[],[],[119],[148,149],"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":151,"openalex_id":153,"authors":154,"venue":111,"cited_by_count":84,"oa_url":157,"card":158,"direction":100,"ingested_from":101},"W7214920253",[155],{"name":156,"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":159,"method":160,"finding":161,"direction":100,"opportunity":162},"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":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":177,"tags":179,"primary_topic":182,"secondary_topics":183,"classification":184,"search_phrases":203,"slug":206,"view_count":22,"doi":207,"paper":208,"created_at":233},4585,"Integrated multi-criteria decision analysis and bivariate statistical modeling for landslide susceptibility zonation in the northwestern Ethiopian highlands","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44475-026-00073-w","Landslides represent a critical geo-environmental hazard in the Ethiopian highlands, specifically within the Telemt, Janamora, and Beyeda districts, where they frequently result in fatalities and the destruction of vital infrastructure and agricultural land. This study integrated Geographic Information Systems (GIS) and remote sensing data to perform landslide susceptibility mapping (LSM) using two distinct modeling approaches: the Frequency Ratio (FR) and the Analytical Hierarchy Process (AHP). A total of 140 data points (98 for training (70%) and 42 for testing (30%)) were considered for modeling and validation. Eleven geo-environmental conditioning factors: Slope, rainfall, lithology, density of lineaments, curvature, aspect, normalized difference vegetation index, land use\u002Fland cover, distance to drainage, drainage density, and soil were analyzed to evaluate their influence on landslide occurrence. According to the AHP model, 13.69% and 26.91% of the study area were classified as very high and high susceptibility zones, respectively. In comparison, the FR model identified 12.09% as very high and 23.78% as high susceptibility zones. Both models underscored that steep slopes, porphyritic basalt unit, high lineament density, proximity to drainage systems, and concave terrain curvature are the primary drivers of slope instability. Conversely, gentle slopes, sandstone unit, and dense vegetation cover were strongly associated with low-susceptibility regions. Model performance was validated using the Receiver Operating Characteristic (ROC) curve, yielding an Area Under the Curve (AUC) value of 0.812 for the AHP model and 0.793 for the FR model, alongside a prediction rate AUC value of 77.4% (0.774) for the AHP model and 70.4% for the FR model, confirming the predictive reliability of both methods. These results provide essential geospatial intelligence for policy-makers and stakeholders to guide disaster risk management, sustainable land-use planning, and targeted rehabilitation efforts in the region.","滑坡是埃塞俄比亚高地，特别是Telemt、Janamora和Beyeda地区面临的严重地质环境灾害，频繁造成人员伤亡以及重要基础设施和农田的破坏。本研究综合地理信息系统（GIS）和遥感数据，采用频率比（FR）和层次分析法（AHP）两种不同的建模方法进行滑坡易发性制图（LSM）。建模与验证共考虑了140个数据点（98个用于训练（70%），42个用于测试（30%））。分析了十一个地质环境条件因子：坡度、降雨量、岩性、线理密度、曲率、坡向、归一化植被指数、土地利用\u002F土地覆被、距水系距离、水系密度和土壤，以评估它们对滑坡发生的影响。根据AHP模型，研究区分别有13.69%和26.91%的区域被划分为极高和高易发区。相比之下，FR模型将12.09%的区域划为极高易发区，23.78%划为高易发区。两种模型均强调，陡坡、斑状玄武岩单元、高线理密度、邻近水系以及凹形地形曲率是斜坡失稳的主要驱动因素。相反，缓坡、砂岩单元和茂密植被覆盖与低易发区密切相关。采用受试者工作特征（ROC）曲线对模型性能进行验证，AHP模型的曲线下面积（AUC）值为0.812，FR模型为0.793，同时AHP模型的预测率AUC值为77.4%（0.774），FR模型为70.4%，证实了两种方法的预测可靠性。这些结果为政策制定者和利益相关者提供了重要的地理空间情报，可指导该地区的灾害风险管理、可持续土地利用规划和有针对性的修复工作。","Discover Hazards","2026-10-09T00:00:00Z",64,{"impact":17,"substance":174,"depth":175,"authority":20,"freshness":17,"relevant":22,"comment":176},20,16,"GIS与遥感结合的滑坡易发性制图研究，方法规范、数据扎实，对农业灾害风险管理和土地规划有参考价值，但属区域性案例，公共影响有限。",[178],{"name":170,"url":167},[27,28,30,180,181],"灾害风险","滑坡易发性",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":185},{"candidates":186,"agricultural_evidence":46},[187,191,195,199],{"code":33,"score":13,"supported":46,"title":188,"abstract":189,"tags":190},[],[49,30,50],[30],{"code":53,"score":54,"supported":46,"title":192,"abstract":193,"tags":194},[],[27,57],[27],{"code":60,"score":61,"supported":46,"title":196,"abstract":197,"tags":198},[],[64,65],[],{"code":75,"score":22,"supported":14,"title":200,"abstract":201,"tags":202},[],[],[28],[204,205],"埃塞俄比亚高原 滑坡易发性","GIS 遥感 滑坡风险制图","埃塞俄比亚高原滑坡易发性-4585","10.1007\u002Fs44475-026-00073-w",{"doi":207,"openalex_id":209,"authors":210,"venue":170,"cited_by_count":84,"oa_url":167,"card":228,"direction":100,"ingested_from":101},"W7221122595",[211,214,216,218,220,222,224,226],{"name":212,"orcid":213},"Engdaw Gulbet Tebege","https:\u002F\u002Forcid.org\u002F0009-0008-4252-1249",{"name":215,"orcid":9},"Belete Getahun",{"name":217,"orcid":9},"Yohannes Dessalegn Girma",{"name":219,"orcid":9},"Yibeltal Awoke Temeche",{"name":221,"orcid":9},"Worku Birhan Fenklew",{"name":223,"orcid":9},"Ayalnesh Mihrete Tagele",{"name":225,"orcid":9},"Gashaw Tesfaw Chekole",{"name":227,"orcid":9},"Muluken Mintesinot",{"tldr":229,"method":230,"finding":231,"direction":100,"opportunity":232},"用GIS与遥感结合FR和AHP模型，对埃塞俄比亚高原滑坡易发性进行分区制图与验证。","GIS与遥感数据，11个孕灾因子，频率比与层次分析法，ROC曲线验证。","AHP精度略高（AUC 0.812），陡坡、玄武岩、高线密度与近水系为主要致灾因子。","可将滑坡易发性与农田地块、作物分布叠加，评估农业用地风险并支撑山区土地整治决策。","2026-10-10T23:30:27.880607Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":239,"content":9,"source_name":240,"source_url":237,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":247,"tags":249,"primary_topic":252,"secondary_topics":253,"classification":254,"search_phrases":287,"slug":290,"view_count":272,"doi":291,"paper":292,"created_at":310},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。","Agricultural Systems and Natural Resources",72,{"impact":20,"substance":243,"depth":244,"authority":245,"freshness":21,"relevant":22,"comment":246},21,17,13,"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。",[248],{"name":240,"url":237},[250,27,29,31,251],"数字农业","RUSLE",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":255},{"candidates":256,"agricultural_evidence":46},[257,263,267,271,276,282],{"code":33,"score":21,"supported":46,"title":258,"abstract":259,"tags":262},[],[49,260,30,261,50],"空间分析","spatial analysis",[],{"code":53,"score":54,"supported":46,"title":264,"abstract":265,"tags":266},[],[27,57],[27],{"code":60,"score":61,"supported":46,"title":268,"abstract":269,"tags":270},[],[64,65],[],{"code":75,"score":272,"supported":46,"title":273,"abstract":274,"tags":275},4,[],[31],[31],{"code":277,"score":69,"supported":46,"title":278,"abstract":279,"tags":281},"I7",[],[280],"研究方法",[],{"code":283,"score":22,"supported":14,"title":284,"abstract":285,"tags":286},"I1",[],[],[250],[288,289],"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":291,"openalex_id":293,"authors":294,"venue":240,"cited_by_count":84,"oa_url":237,"card":305,"direction":100,"ingested_from":101},"W7214985337",[295,297,299,301,303],{"name":296,"orcid":9},"Д. Кайнушева",{"name":298,"orcid":9},"Р. Жапаев",{"name":300,"orcid":9},"Е. Кенжехан",{"name":302,"orcid":9},"Д. Кыдырбаева",{"name":304,"orcid":9},"Ә. Мақұлбек",{"tldr":306,"method":307,"finding":308,"direction":100,"opportunity":309},"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%。","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。","2026-10-01T23:30:29.218572Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":9,"source_name":317,"source_url":314,"published_at":112,"category":12,"cover_url":9,"hotness":318,"is_selected":14,"score":319,"score_detail":320,"sources":322,"tags":326,"primary_topic":329,"secondary_topics":330,"classification":332,"search_phrases":347,"slug":350,"view_count":22,"doi":351,"paper":352,"created_at":368},3895,"Analysis Of Changes In Land Use And Land Cover In Pune City Over A Decade Using Geospatial Techniques.","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053479","Abstract Land use and land cover (LULC) changes play a significant role in reflecting natural and anthropogenic environmental changes. In this study, changes in 'Land Use and Land Cover' (LULC) in Pune city for the period 2015-2025 have been analysed using the techniques of 'Remote Sensing' and 'Geographic Information System' (GIS). The May 2015 and May 2025 30 m resolution Landsat 08 satellite images were used. For LULC classification, the 'Supervised Maximum Likelihood Classification' method was used, resulting in 5 major LULC classes: built-up land, barren land, and agricultural land, vegetation, and water bodies. More than 100 'training samples' were taken for each land cover type to improve classification accuracy. The findings of the study show that there was a considerable shift in the spatial distribution of LULC in this decade. Built-up land increased from 154.48 sq. km (30.48%) in 2015 to 292.26 sq. km (57.86%) in 2025, representing an increase of approximately 137.78 sq. km. Conversely, the area of barren land decreased from 203.94 sq. km (40.24%) to 106.93 sq. km (21.10%). The amount of agricultural land has reduced from 44.39 sq. km (8.75%) to 18.93 sq. km (3.72%), indicating that agricultural land has been converted for urbanisation on a large scale. Regarding vegetation cover, 85.91 sq. km showed a marginal decrease from 16.95% to 15.58%, whereas the area of water bodies decreased from 18.13 sq. km (3.58%) to 10.77 sq. km (2.12%). The results show the rapid growth of Pune city during the study period, which is mainly observed in the loss or transformation of barren land, agricultural land and water bodies. The study shows the usefulness of using Landsat data, ‘supervised maximum likelihood classification’, GIS and ‘change detection' techniques for tracking long-term urban growth and environmental changes. The results of this research can help urban planners and decision makers in formulating policies for sustainable land use and in safeguarding environmentally sensitive areas.","摘要 土地利用与土地覆盖（LULC）变化在反映自然与人为环境变化方面发挥着重要作用。本研究利用遥感（Remote Sensing）和地理信息系统（GIS）技术，分析了浦那市2015—2025年期间土地利用与土地覆盖（LULC）的变化。研究使用了2015年5月和2025年5月分辨率为30米的Landsat 08卫星影像。在LULC分类中，采用了监督最大似然分类（Supervised Maximum Likelihood Classification）方法，最终得到5个主要LULC类别：建设用地、裸地、农用地、植被和水体。为提高分类精度，每种土地覆盖类型均选取了100个以上的训练样本（training samples）。研究结果表明，在这十年间，LULC的空间分布发生了显著变化。建设用地从2015年的154.48平方公里（30.48%）增加到2025年的292.26平方公里（57.86%），增加了约137.78平方公里。相反，裸地面积从203.94平方公里（40.24%）减少到106.93平方公里（21.10%）。农用地面积从44.39平方公里（8.75%）减少到18.93平方公里（3.72%），表明农用地已大规模转化为城市化用地。植被覆盖方面，85.91平方公里略有下降，从16.95%降至15.58%；水体面积则从18.13平方公里（3.58%）减少到10.77平方公里（2.12%）。结果表明，研究期间浦那市快速扩张，主要体现在裸地、农用地和水体的减少或转变上。研究表明，利用Landsat数据、监督最大似然分类、GIS和变化检测（change detection）技术对于追踪长期城市增长与环境变化具有重要价值。本研究结果可帮助城市规划者和决策者制定可持续土地利用政策，并保护环境敏感区域。","Zenodo (CERN European Organization for Nuclear Research)",25,65,{"impact":17,"substance":174,"depth":175,"authority":20,"freshness":21,"relevant":22,"comment":321},"基于Landsat遥感与GIS的浦那市十年土地利用变化研究，数据翔实、结论明确，对农业用地流失与城市扩张监测有参考价值，但属区域案例研究，公共影响有限。",[323,324],{"name":317,"url":314},{"name":317,"url":325},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053480",[27,28,327,30,328],"Landsat","城市化",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[331],{"code":53,"slug":121,"name":122,"path":123,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":333},{"candidates":334,"agricultural_evidence":46},[335,339,343],{"code":33,"score":13,"supported":46,"title":336,"abstract":337,"tags":338},[],[49,30,50],[30],{"code":53,"score":17,"supported":46,"title":340,"abstract":341,"tags":342},[],[27,57],[327,27],{"code":75,"score":22,"supported":14,"title":344,"abstract":345,"tags":346},[],[],[28],[348,349],"土地利用 城市化 遥感 Landsat","土地利用 城市化","土地利用城市化遥感Landsat-3895","10.5281\u002Fzenodo.23053479",{"doi":351,"openalex_id":353,"authors":354,"venue":317,"cited_by_count":84,"oa_url":314,"card":363,"direction":100,"ingested_from":101},"W7214889813",[355,357,359,361],{"name":356,"orcid":9},"Kalyani Machhindra sonawane",{"name":358,"orcid":9},"Sumitra Nandkumar Nikam",{"name":360,"orcid":9},"Sunil W. Gaikwad",{"name":362,"orcid":9},"Yogesh N. Pawar",{"tldr":364,"method":365,"finding":366,"direction":100,"opportunity":367},"用遥感与GIS分析印度浦那市2015-2025年土地利用变化，发现建设用地大幅扩张、农地水体锐减。","Landsat 8 30米影像，监督最大似然分类，5类LULC与变化检测。","建设用地从30.48%增至57.86%，农地由8.75%降至3.72%，裸地与水体同步减少。","可结合多时相高分辨率影像与CA-Markov模型，预测城市扩张对农地和水体的未来胁迫。","2026-10-01T23:30:28.617704Z",{"id":370,"title":371,"url":372,"summary":373,"summary_zh":374,"content":9,"source_name":317,"source_url":372,"published_at":375,"category":12,"cover_url":9,"hotness":318,"is_selected":14,"score":376,"score_detail":377,"sources":379,"tags":383,"primary_topic":386,"secondary_topics":387,"classification":388,"search_phrases":403,"slug":406,"view_count":22,"doi":407,"paper":408,"created_at":426},3068,"MAPPING LAND USE OF BENUE STATE UNIVERSITY, MAKURDI MAIN CAMPUS, BENUE STATE, NIGERIA USING GEOSPATIAL TECHNIQUES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865603","This study examined the spatial distribution and extent of land use types within Benue State University (BSU), Makurdi Main Campus, using Geographic Information System (GIS) and remote sensing techniques. Satellite imagery obtained from the National Space Research and Development Agency (NASRDA) was processed and analyzed in ArcGIS 10.8. Land use classification was achieved through on-screen digitization supported by intensive field survey and ground-truthing. The results reveal a heterogeneous land use structure with a total mapped area of 166.69 hectares. Agricultural land use dominates with 20.35%, followed by residential (15.24%) and forest cover (12.29%). Environmentally sensitive land uses, including forestry, green areas, and marshlands, collectively account for 29.18% of the total mapped area, indicating significant ecological value. Institutional land uses (administrative, educational, and religious) account for 15.50%, while commercial and mixed uses constitute 9.29%. The findings highlight a semi-urban institutional landscape undergoing gradual transformation, with increasing development pressures. The study demonstrates the effectiveness of GIS in land use planning and recommends sustainable land management strategies to balance development with environmental conservation.","本研究利用地理信息系统（GIS）和遥感技术，考察了马库尔迪贝努埃州立大学（BSU）主校区内土地利用类型的空间分布与范围。研究对来自国家空间研究与发展局（NASRDA）的卫星影像在ArcGIS 10.8中进行了处理与分析。土地利用分类通过屏幕数字化完成，并辅以密集的实地调查与地面验证。结果显示，该区域土地利用结构具有异质性，总制图面积为166.69公顷。农业用地占主导地位，为20.35%，其次为住宅用地（15.24%）和林地覆盖（12.29%）。环境敏感型土地利用类型，包括林业用地、绿地和沼泽地，合计占总制图面积的29.18%，表明其具有显著的生态价值。机构用地（行政、教育和宗教）占15.50%，而商业和混合用途占9.29%。研究结果凸显出一个正处于逐步转型中的半城市机构景观，其面临日益增大的开发压力。本研究证明了GIS在土地利用规划中的有效性，并建议采取可持续土地管理策略，以平衡开发与环境保护。","2026-09-21T00:00:00Z",56,{"impact":61,"substance":175,"depth":245,"authority":20,"freshness":21,"relevant":22,"comment":378},"基于GIS与遥感的校园土地利用分类研究，方法规范、数据具体，但属尼日利亚个案，对国内三农与农业信息化参考价值有限。",[380,381],{"name":317,"url":372},{"name":317,"url":382},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22865602",[27,28,30,384,385],"土地分类","校园规划",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":389},{"candidates":390,"agricultural_evidence":46},[391,395,399],{"code":33,"score":13,"supported":46,"title":392,"abstract":393,"tags":394},[],[49,30,50],[30],{"code":53,"score":54,"supported":46,"title":396,"abstract":397,"tags":398},[],[27,57],[27],{"code":75,"score":22,"supported":14,"title":400,"abstract":401,"tags":402},[],[],[28],[404,405],"土地分类 土地利用 校园规划 遥感","土地分类 土地利用","土地分类土地利用校园规划遥感-3068","10.5281\u002Fzenodo.22865603",{"doi":407,"openalex_id":409,"authors":410,"venue":317,"cited_by_count":84,"oa_url":372,"card":421,"direction":100,"ingested_from":101},"W7213752705",[411,413,415,417,419],{"name":412,"orcid":9},"Alex S. Ortese",{"name":414,"orcid":9},"Innocent E. Bello",{"name":416,"orcid":9},"I. K. SAMAILA",{"name":418,"orcid":9},"M. ALKALI",{"name":420,"orcid":9},"A. Mahmud",{"tldr":422,"method":423,"finding":424,"direction":100,"opportunity":425},"用GIS和遥感技术绘制尼日利亚贝努埃州立大学主校区土地利用分布图。","NASRDA卫星影像，ArcGIS 10.8屏幕数字化，结合实地调查与地面验证。","农业用地占20.35%居首，环境敏感用地共占29.18%，校园呈半城市化转型压力。","可延伸至校园及城郊农业用地动态监测与生态敏感区保护规划研究。","2026-09-21T23:30:23.914355Z",{"id":428,"title":429,"url":430,"summary":431,"summary_zh":432,"content":9,"source_name":433,"source_url":430,"published_at":434,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":435,"score_detail":436,"sources":438,"tags":440,"primary_topic":442,"secondary_topics":447,"classification":449,"search_phrases":487,"slug":490,"view_count":76,"doi":491,"paper":492,"created_at":509},2000,"ANALYSIS OF LAND USE LAND COVER CHANGE IN THE GLEN VALLEY IRRIGATION SCHEME OF SOUTH-EASTERN BOTSWANA","https:\u002F\u002Fdoi.org\u002F10.30574\u002Fgjeta.2026.28.3.0228","This study focused on assessing land use land cover (LULC) changes in the Glen Valley Irrigation Scheme of South Eastern Botswana between 2002 and 2022. The study employed remote sensing (RS) and geographic information systems (GIS) tools for analysing LULC changes in the study area during the study period. Landsat 7 ETM+ and Landsat 8 OLI images of 6 February 2002 and 21 February 2022, respectively, were used to detect LULC changes. Image classification was done using a supervised classification approach based on a Maximum Likelihood Classifier. Seven LULC types, namely, Built up, Cultivation, Water body, Sparse vegetation, Moderately dense vegetation, Dense vegetation, and Bare ground, were identified in the area. Post Classification Comparison (PCC) approach was used to detect LULC change during the study period. A pronounced 250 ha increase of cultivated land resulted from 13.2% conversion of sparse vegetation, with adverse implication on greenhouse gas (GHG) emissions and global warming. A noticeable 105 ha increase of Built-up area in the City of Gaborone resulted from 4.3% conversion of sparse vegetation, with implications on land planning process related to rapid urbanization. All changes of LULC types relied on availability of sparse vegetation as a key carbon sink and stabilizer of GHG emissions. Mitigation strategies including conservation agriculture, efficient irrigation and renewable energy are considered necessary.","本研究旨在评估2002年至2022年间博茨瓦纳东南部格伦谷灌溉计划区土地利用\u002F土地覆盖（LULC）的变化。研究采用遥感（RS）和地理信息系统（GIS）工具，对研究期间该区域的土地利用\u002F土地覆盖变化进行分析。分别利用2002年2月6日的Landsat 7 ETM+影像和2022年2月21日的Landsat 8 OLI影像来检测土地利用\u002F土地覆盖变化。影像分类采用基于最大似然分类器的监督分类方法。研究区域内识别出七种土地利用\u002F土地覆盖类型，即建设用地、耕地、水体、稀疏植被、中等密度植被、高密度植被和裸地。采用分类后比较（PCC）方法检测研究期间的土地利用\u002F土地覆盖变化。耕地面积显著增加250公顷，其中13.2%来自稀疏植被的转化，这对温室气体（GHG）排放和全球变暖产生了不利影响。哈博罗内市建设用地面积明显增加105公顷，其中4.3%来自稀疏植被的转化，这对与快速城市化相关的土地规划过程产生了影响。所有土地利用\u002F土地覆盖类型的变化均依赖于稀疏植被作为关键碳汇和温室气体排放稳定器的可用性。研究认为，采取保护性农业、高效灌溉和可再生能源等减缓策略十分必要。","Global Journal of Engineering and Technology Advances","2026-09-08T00:00:00Z",58,{"impact":20,"substance":18,"depth":175,"authority":13,"freshness":76,"relevant":22,"comment":437},"研究利用遥感和GIS分析博茨瓦纳灌溉区土地利用变化，对农业规划有参考价值，但地域偏远且时效性低。",[439],{"name":433,"url":430},[27,28,30,441],"灌溉农业",{"code":68,"slug":443,"name":444,"path":445,"parent_code":446},"irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[448],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":450},{"candidates":451,"agricultural_evidence":46},[452,456,460,464,471,477,483],{"code":68,"score":130,"supported":46,"title":453,"abstract":454,"tags":455},[443],[72,443],[441],{"code":33,"score":13,"supported":46,"title":457,"abstract":458,"tags":459},[],[49,30,50],[30],{"code":53,"score":54,"supported":46,"title":461,"abstract":462,"tags":463},[],[27,57],[27],{"code":465,"score":61,"supported":46,"title":466,"abstract":467,"tags":470},"B7",[],[468,469],"温室","greenhouse",[],{"code":472,"score":69,"supported":46,"title":473,"abstract":474,"tags":476},"H4",[],[475],"碳汇",[],{"code":478,"score":69,"supported":46,"title":479,"abstract":480,"tags":482},"H8",[],[481],"conservation agriculture",[],{"code":75,"score":22,"supported":14,"title":484,"abstract":485,"tags":486},[],[],[28],[488,489],"土地利用 灌溉农业 遥感 GIS","土地利用 灌溉农业","土地利用灌溉农业遥感GIS-2000","10.30574\u002Fgjeta.2026.28.3.0228",{"doi":491,"openalex_id":493,"authors":494,"venue":433,"cited_by_count":84,"oa_url":503,"card":504,"direction":100,"ingested_from":101},"W7211969418",[495,497,499,501],{"name":496,"orcid":9},"Lucia Moroka",{"name":498,"orcid":9},"Benedict Kayombo",{"name":500,"orcid":9},"Matapa Tapela",{"name":502,"orcid":9},"Thembeka Mpuisang","https:\u002F\u002Fgjeta.com\u002Fsites\u002Fdefault\u002Ffiles\u002Ffulltext_pdf\u002FGJETA-2026-0228.pdf",{"tldr":505,"method":506,"finding":507,"direction":100,"opportunity":508},"利用遥感与GIS分析博茨瓦纳Glen Valley灌区2002-2022年土地利用变化，发现耕地和建","采用Landsat影像、监督分类（最大似然法）和分类后比较法分析LULC变化。","耕地增加250公顷，建设用地增加105公顷，主要侵占稀疏植被，影响碳汇和温室气体排放。","可延伸研究灌溉农业扩张对碳汇的影响，结合高分辨率影像和碳模型量化区域碳排放，探索可持续灌溉与土地利用优化策略。","2026-09-09T23:30:22.675859Z"]