[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1436":3,"related-1436":73},{"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":22,"secondary_topics":28,"classification":29,"search_phrases":50,"slug":51,"view_count":15,"doi":52,"paper":53,"created_at":72},1436,"Integrated GIS–AHP modelling for flood susceptibility assessment in Makurdi Metropolis, Nigeria","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44288-026-00714-z","Flooding is one of the most severe environmental hazards affecting riverine urban centers, particularly in developing countries characterized by rapid urbanization, inadequate drainage systems, and climate variability. Makurdi metropolis, located along the floodplain of the River Benue in North-Central Nigeria, experiences recurrent flood events that threaten lives, infrastructure, agricultural land, and socio-economic activities. This study assessed flood susceptibility in Makurdi metropolis using an integrated Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) approach. Ten flood-conditioning factors, including elevation, slope, rainfall, drainage density, distance to streams, Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), geomorphology, curvature, and land use\u002Fland cover (LULC), were analyzed within the ArcGIS Pro 3.4.0 environment using datasets derived from ASTER DEM, Landsat 8 imagery, Sentinel-2 land cover data, rainfall records, and field observations. The AHP analysis identified elevation and slope as the most influential conditioning factors, with normalized weights of 19.43% and 18.21%, respectively, followed by distance to streams (14.22%), drainage density (13.43%), precipitation (8.73%), LULC (8.25%), TWI (6.98%), NDVI (4.89%), geomorphology (3.34%), and curvature (2.52%).The resulting flood susceptibility map classified Makurdi metropolis into four susceptibility classes comprising low (44.07 km²; 5.34%), moderate (546.02 km²; 66.13%), high (229.62 km²; 27.81%), and very high susceptibility (5.96 km²; 0.72%). Moderate susceptibility occupied the largest proportion of the study area, while very high susceptibility was concentrated within the River Benue floodplain and adjacent low-lying communities. Highly susceptible zones were concentrated around low-lying floodplain communities and areas adjacent to the River Benue. Validation using an independent binary validation dataset comprising historical flood-occurrence points and non-flood reference points yielded an Area Under the Receiver Operating Characteristic Curve (ROC–AUC) value of 0.94, suggesting good predictive performance of the GIS–AHP model. Although this result demonstrates a strong discriminatory capability of the GIS–AHP model due to the limited data points considered, Hence, validation using larger independent datasets and complementary uncertainty analyses would provide additional confidence in the model’s robustness.","洪水是影响河流沿岸城市中心最严重的环境灾害之一，尤其是在以快速城市化、排水系统不足和气候多变性为特征的发展中国家。马库尔迪大都市区位于尼日利亚中北部贝努埃河泛滥平原沿岸，频繁遭受洪水事件，威胁着生命、基础设施、农田和社会经济活动。本研究采用地理信息系统（GIS）与层次分析法（AHP）相结合的方法，对马库尔迪大都市区的洪水敏感性进行了评估。在ArcGIS Pro 3.4.0环境中，利用ASTER DEM、Landsat 8影像、Sentinel-2土地覆盖数据、降雨记录及实地观测所得数据集，分析了包括高程、坡度、降雨量、排水密度、距河流距离、地形湿度指数（TWI）、归一化植被指数（NDVI）、地貌、曲率和土地利用\u002F土地覆盖（LULC）在内的十个洪水形成因子。层次分析法识别出高程和坡度是最具影响力的形成因子，其归一化权重分别为19.43%和18.21%，其次是距河流距离（14.22%）、排水密度（13.43%）、降水量（8.73%）、土地利用\u002F土地覆盖（8.25%）、地形湿度指数（6.98%）、归一化植被指数（4.89%）、地貌（3.34%）和曲率（2.52%）。由此生成的洪水敏感性图将马库尔迪大都市区划分为四个敏感性等级，包括低敏感性（44.07平方公里；5.34%）、中敏感性（546.02平方公里；66.13%）、高敏感性（229.62平方公里；27.81%）和极高敏感性（5.96平方公里；0.72%）。中敏感性区域占研究区面积比例最大，而极高敏感性区域集中在贝努埃河泛滥平原及邻近的低洼社区。高敏感性区域集中在低洼泛滥平原社区和贝努埃河邻近地区。使用包含历史洪水发生点和非洪水参考点的独立二元验证数据集进行验证，得出受试者工作特征曲线下面积（ROC–AUC）值为0.94，表明GIS–AHP模型具有良好的预测性能。尽管这一结果因所考虑的数据点有限而显示出GIS–AHP模型较强的判别能力，但使用更大的独立数据集进行验证并辅以不确定性分析，将为模型的稳健性提供额外信心。",null,"Discover Geoscience","2026-09-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该研究聚焦尼日利亚城市洪水风险，与三农、农业信息化等主题无关。",[19],{"name":10,"url":6},[21],"洪水风险评估",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":30,"locked":14,"version":31,"method":32,"evidence":33},"classified","2026-10-22.2","rules",{"candidates":34,"agricultural_evidence":37},[35,44],{"code":23,"score":36,"supported":37,"title":38,"abstract":40,"tags":43},17,true,[39],"GIS",[41,39,42],"地理信息","geographic information",[],{"code":45,"score":46,"supported":14,"title":47,"abstract":48,"tags":49},"H7",1,[],[],[21],[],"IntegratedGIS–AHPmodellingforfloodsuscep-1436","10.1007\u002Fs44288-026-00714-z",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":15,"oa_url":63,"card":64,"direction":70,"ingested_from":71},"W7204861452",[56,59,61],{"name":57,"orcid":58},"Oloche Robert Ekwule","https:\u002F\u002Forcid.org\u002F0000-0002-4835-3951",{"name":60,"orcid":9},"Gariel Delian Akpen",{"name":62,"orcid":9},"Matthew Igbalumun Aho","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44288-026-00714-z.pdf",{"tldr":65,"method":66,"finding":67,"direction":68,"opportunity":69},"集成GIS与AHP评估尼日利亚Makurdi城市洪水易发性，绘制易发性图并验证模型。","GIS与AHP集成，分析10个洪水因子，使用ArcGIS Pro和遥感数据。","高程和坡度影响最大，中等易发区占66%，模型AUC达0.94。","其他","可扩展至农业区域洪水风险评估，结合作物损失模型，并采用更大数据集和不确定性分析提升稳健性。","农业遥感与作物表型","openalex","2026-09-02T23:30:51.359941Z",{"total":74,"page":46,"page_size":74,"items":75},6,[76,144,202,271,338,394],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":9,"source_name":82,"source_url":79,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":84,"sources":86,"tags":88,"primary_topic":92,"secondary_topics":97,"classification":104,"search_phrases":126,"slug":129,"view_count":15,"doi":130,"paper":131,"created_at":143},5326,"Flood Hazard and Vulnerability Assessment of Chitrakoot District, Uttar Pradesh: An Integrated Geographical, GIS and Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.36948\u002Fijfmr.2026.v08i05.89708","Flood is one of the most important hydro-meteorological hazards affecting human settlements, agriculture, infrastructure and regional economies. Chitrakoot district of Uttar Pradesh presents a distinctive case of hydro-climatic vulnerability because it belongs to the drought-prone Bundelkhand region but simultaneously experiences recurrent riverine and flash-flood conditions during intense monsoon rainfall. The present study assesses the spatial characteristics and determinants of flood hazard and vulnerability in Chitrakoot district using an integrated geographical, GIS and remote sensing approach. The analysis incorporates the Digital Elevation Model (DEM), drainage network, physiography, river proximity, historical flood information, satellite-based flood observations and socio-economic exposure. The district's DEM indicates a marked elevation gradient, approximately from 66 m to 451 m, with relatively higher terrain in the southern part and lower terrain towards the northern Yamuna valley. This topographic configuration facilitates rapid surface runoff from elevated southern terrain towards comparatively low-lying riverine areas. The drainage system is dominated by the Yamuna and Mandakini\u002FPaisuni systems along with Baghain, Gunta, Bardaha and smaller tributaries. The Uttar Pradesh Flood Hazard Atlas identifies 5,114 ha of flood-hazard area in Chitrakoot for 1998–2020, of which 4,129 ha (81%) falls in the very-low hazard category and 985 ha (19%) in the low-hazard category. However, the historical classification does not fully represent the potential consequences of extreme rainfall events. The August 2026 flood provides compelling evidence: the Mandakini at Chitrakoot reached a reported new high flood level of 145.60 m on 29 August 2026, approximately 2.95 m above the previous reported high level, while the water level rose by about 7.5 m within six hours. The study concludes that flood risk in Chitrakoot is spatially concentrated around river corridors, low-elevation areas, floodplains, agricultural land, settlements and tourism infrastructure. A shift from conventional relief-oriented flood management towards integrated floodplain regulation, watershed management, GIS-based risk zonation, real-time monitoring and community-based preparedness is therefore essential.","洪水是影响人类住区、农业、基础设施和区域经济的最重要的水文气象灾害之一。北方邦吉德古德（Chitrakoot）县在水文气候脆弱性方面呈现出独特案例，因为它属于易受干旱影响的本德尔坎德（Bundelkhand）地区，但同时在强季风降雨期间反复遭遇河流洪水和山洪状况。本研究采用地理学、地理信息系统（GIS）和遥感相结合的综合方法，评估吉德古德县洪水灾害与脆弱性的空间特征及其决定因素。分析纳入了数字高程模型（DEM）、河网、地貌、距河流远近、历史洪水信息、基于卫星的洪水观测以及社会经济暴露度。该县的DEM显示出明显的高程梯度，大约从66米到451米，南部地形相对较高，向北部的亚穆纳（Yamuna）河谷地势降低。这种地形格局促使地表径流从较高的南部地形快速流向相对低洼的河流区域。排水系统以亚穆纳河和曼达基尼\u002F派苏尼（Mandakini\u002FPaisuni）水系为主，另有巴盖恩（Baghain）、贡塔（Gunta）、巴尔达哈（Bardaha）及较小支流。北方邦洪水灾害图集确认，1998—2020年吉德古德县洪水灾害面积为5，114公顷，其中4，129公顷（81%）属于极低灾害类别，985公顷（19%）属于低灾害类别。然而，历史分类并不能充分反映极端降雨事件的潜在后果。2026年8月的洪水提供了有力证据：据报道，2026年8月29日吉德古德的曼达基尼河达到145.60米的新高洪水位，比此前报告的高水位高出约2.95米，而水位在六小时内上升了约7.5米。研究得出结论，吉德古德的洪水风险在空间上集中于河流廊道、低海拔地区、洪泛平原、农业用地、住区和旅游基础设施周围。因此，必须从传统的以救济为导向的洪水管理转向洪泛平原综合调控、流域管理、基于GIS的风险区划、实时监测和社区为基础的备灾。","International Journal For Multidisciplinary Research","2026-10-17T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":85},"该研究聚焦印度北方邦的洪水灾害评估，虽涉及GIS与遥感方法，但与三农、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。",[87],{"name":82,"url":79},[89,90,21,91],"防灾减灾","遥感监测","GIS应用",{"code":93,"slug":94,"name":95,"path":96,"parent_code":27},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[98,99],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"code":45,"slug":100,"name":101,"path":102,"parent_code":103},"climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",{"status":30,"locked":14,"version":31,"method":32,"evidence":105},{"candidates":106,"agricultural_evidence":37},[107,114,119],{"code":93,"score":108,"supported":37,"title":109,"abstract":111,"tags":113},15,[110],"remote sensing",[112,110],"遥感",[90],{"code":23,"score":115,"supported":37,"title":116,"abstract":117,"tags":118},14,[39],[41,39],[],{"code":45,"score":120,"supported":37,"title":121,"abstract":122,"tags":125},8,[],[123,124],"干旱","drought",[21,89],[127,128],"Chitrakoot 洪水风险 GIS","Yamuna 洪水脆弱性 遥感","Chitrakoot洪水风险GIS-5326","10.36948\u002Fijfmr.2026.v08i05.89708",{"doi":130,"openalex_id":132,"authors":133,"venue":82,"cited_by_count":15,"oa_url":137,"card":138,"direction":70,"ingested_from":71},"W7222885988",[134],{"name":135,"orcid":136},"Birendra Yadav","https:\u002F\u002Forcid.org\u002F0009-0007-0864-474X","https:\u002F\u002Fwww.ijfmr.com\u002Fpapers\u002F2026\u002F5\u002F89708.pdf",{"tldr":139,"method":140,"finding":141,"direction":70,"opportunity":142},"用GIS与遥感综合评估Chitrakoot区洪水灾害与脆弱性空间分布。","DEM、河网、历史洪水与卫星观测及社会经济数据叠加分析。","洪水风险集中于河谷低地、农田与居民点，历史分级低估极端降雨风险。","可结合时序遥感与水文模型，构建极端降雨情景下的农田洪涝风险动态制图与预警。","2026-10-19T23:30:24.356743Z",{"id":145,"title":146,"url":147,"summary":148,"summary_zh":149,"content":9,"source_name":150,"source_url":147,"published_at":151,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":152,"score_detail":153,"sources":157,"tags":159,"primary_topic":161,"secondary_topics":162,"classification":164,"search_phrases":185,"slug":188,"view_count":176,"doi":189,"paper":190,"created_at":201},2529,"Flood Risk Assessment and Environmental Change Analysis Using GIS and Remote Sensing","https:\u002F\u002Fdoi.org\u002F10.34248\u002Fbsengineering.1994338","Floods can be very destructive natural hazard; it causes significant environmental, social, and economic impacts. Havza is a district in Northern part of Türkiye, experienced major flood events in May 1998 and May 2026, which emphasize the need of identify flood-prone areas and analyze the factors that influence flood vulnerability. The aim of this study is to asses flood risk and analyzes land use\u002Fland cover (LULC) monitoring derived from remote sensing data in Havza. Furthermore, topographic characteristics were also obtained from Shuttle Radar Topography Mission (SRTM) and Digital Elevation Model (DEM) using Geographic Information Systems (GIS). Hydrological analyses include slope and flow accumulation models, and these analyses were used to identify susceptible areas. It has been observed that the mean NDVI value increased from 0.4239 in 1998 to 0.7447 in 2026, indicating a substantial increase in vegetation cover. LULC classification results also showed that urban areas increased from 16% to 20% while agricultural land decreased 54% to 50% in approximately last 3 decades. 3-Dimensional (3D) terrain visualization and river corridor-based flood risk mapping revealed that low elevation valley floors and settlements located very near to stream channels, which is the most vulnerable areas of floods. The findings of this study demonstrate the effectiveness of integration of GIS and remote sensing, topographic analysis, and LULC change detection within a single framework for flood risk assessment and sustainable land management. The results provide valuable support for disaster risk reduction, urban planning, and future development strategies in Havza and similar watershed environments.","洪水是一种极具破坏性的自然灾害，会对环境、社会和经济造成显著影响。哈夫扎（Havza）是土耳其北部的一个区，1998年5月和2026年5月经历了重大洪水事件，这凸显了识别洪水易发区域并分析影响洪水脆弱性因素的必要性。本研究旨在评估哈夫扎的洪水风险，并分析基于遥感数据获得的土地利用\u002F土地覆盖（LULC）监测结果。此外，还利用地理信息系统（GIS）从航天飞机雷达地形测绘任务（SRTM）和数字高程模型（DEM）中获取了地形特征。水文分析包括坡度和汇流累积模型，这些分析用于识别易受洪水影响的区域。研究发现，NDVI均值从1998年的0.4239上升至2026年的0.7447，表明植被覆盖显著增加。LULC分类结果还显示，在近三十年间，城市面积从16%增加至20%，而农业用地从54%减少至50%。三维（3D）地形可视化和基于河流廊道的洪水风险制图表明，低海拔河谷底部和紧邻河道的居民点是洪水最脆弱的区域。本研究结果表明，将GIS与遥感、地形分析以及LULC变化检测整合于同一框架内，对于洪水风险评估和可持续土地管理具有有效性。研究结果为哈夫扎及类似流域环境的减灾、城市规划和未来发展战略提供了有价值的支持。","Black Sea Journal of Engineering and Science","2026-09-14T00:00:00Z",61,{"impact":120,"substance":154,"depth":108,"authority":155,"freshness":120,"relevant":46,"comment":156},18,12,"以GIS与遥感融合评估土耳其流域洪水风险与土地利用变化，方法框架对农业防灾与乡村空间规划有参考价值，但属区域案例研究，公共影响有限。",[158],{"name":150,"url":147},[89,112,21,39,160],"土地利用变化",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[163],{"code":93,"slug":94,"name":95,"path":96,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":165},{"candidates":166,"agricultural_evidence":37},[167,171,175,180],{"code":23,"score":154,"supported":37,"title":168,"abstract":169,"tags":170},[39],[41,39,42],[39],{"code":93,"score":108,"supported":37,"title":172,"abstract":173,"tags":174},[110],[112,110],[112],{"code":45,"score":176,"supported":14,"title":177,"abstract":178,"tags":179},2,[],[],[21,89],{"code":181,"score":46,"supported":14,"title":182,"abstract":183,"tags":184},"H6",[],[],[160],[186,187],"土地利用变化 洪水风险评估 防灾减灾 遥感","土地利用变化 洪水风险评估","土地利用变化洪水风险评估防灾减灾遥感-2529","10.34248\u002Fbsengineering.1994338",{"doi":189,"openalex_id":191,"authors":192,"venue":150,"cited_by_count":15,"oa_url":147,"card":196,"direction":70,"ingested_from":71},"W7212642487",[193],{"name":194,"orcid":195},"Sibel Canaz","https:\u002F\u002Forcid.org\u002F0000-0001-5552-6067",{"tldr":197,"method":198,"finding":199,"direction":70,"opportunity":200},"结合GIS与遥感评估土耳其Havza地区洪水风险，并分析近30年土地利用与植被变化。","使用SRTM\u002FDEM地形分析、坡度与汇流模型、NDVI及LULC分类。","低海拔河谷与近河道居民点最易受灾；城区扩张、农地减少、植被覆盖显著增加。","可引入时序遥感与机器学习，构建流域尺度洪水风险动态预警与土地利用协同优化模型。","2026-09-15T23:30:20.567355Z",{"id":203,"title":204,"url":205,"summary":206,"summary_zh":207,"content":9,"source_name":208,"source_url":205,"published_at":209,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":152,"score_detail":210,"sources":213,"tags":215,"primary_topic":219,"secondary_topics":220,"classification":221,"search_phrases":251,"slug":254,"view_count":15,"doi":255,"paper":256,"created_at":270},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":120,"substance":154,"depth":115,"authority":155,"freshness":211,"relevant":46,"comment":212},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。",[214],{"name":208,"url":205},[112,216,217,39,218],"土地利用","土壤侵蚀","水土保持",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":222},{"candidates":223,"agricultural_evidence":37},[224,228,233,240,247],{"code":23,"score":13,"supported":37,"title":225,"abstract":226,"tags":227},[],[41,39,42],[39],{"code":93,"score":229,"supported":37,"title":230,"abstract":231,"tags":232},7,[],[112,110],[112],{"code":234,"score":74,"supported":37,"title":235,"abstract":236,"tags":239},"H2",[],[237,238],"土壤","soil",[],{"code":241,"score":242,"supported":37,"title":243,"abstract":244,"tags":246},"H3",3,[],[245],"灌溉",[],{"code":181,"score":176,"supported":14,"title":248,"abstract":249,"tags":250},[],[],[216,218],[252,253],"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":255,"openalex_id":257,"authors":258,"venue":208,"cited_by_count":15,"oa_url":9,"card":264,"direction":70,"ingested_from":71},"W7223117733",[259,261],{"name":260,"orcid":9},"MERİÇ KURT",{"name":262,"orcid":263},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":265,"method":266,"finding":267,"direction":268,"opportunity":269},"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":272,"title":273,"url":274,"summary":275,"summary_zh":276,"content":9,"source_name":277,"source_url":274,"published_at":278,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":279,"sources":281,"tags":283,"primary_topic":286,"secondary_topics":291,"classification":293,"search_phrases":315,"slug":318,"view_count":15,"doi":319,"paper":320,"created_at":337},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":280},"研究聚焦埃塞俄比亚城市扩张与土地利用变化，属城市地理与遥感应用，与三农、农业信息化主题无直接关联，不建议进入每日精选。",[282],{"name":277,"url":274},[284,90,216,285],"机器学习","城市扩张",{"code":287,"slug":288,"name":289,"path":290,"parent_code":27},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[292],{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":294},{"candidates":295,"agricultural_evidence":37},[296,301,307,311],{"code":287,"score":108,"supported":37,"title":297,"abstract":299,"tags":300},[298],"machine learning",[284,298],[284],{"code":23,"score":211,"supported":37,"title":302,"abstract":303,"tags":306},[],[304,39,305],"空间分析","spatial analysis",[],{"code":93,"score":229,"supported":37,"title":308,"abstract":309,"tags":310},[],[112,110],[90],{"code":181,"score":46,"supported":14,"title":312,"abstract":313,"tags":314},[],[],[216],[316,317],"Gambella Town 城市扩张","Random Forest 土地利用分类","GambellaTown城市扩张-5485","10.1371\u002Fjournal.pone.0360419",{"doi":319,"openalex_id":321,"authors":322,"venue":277,"cited_by_count":15,"oa_url":274,"card":332,"direction":70,"ingested_from":71},"W7223022155",[323,326,328,330],{"name":324,"orcid":325},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":327,"orcid":9},"Goanar Jal Dup",{"name":329,"orcid":9},"Weyesa Merga Chemeda",{"name":331,"orcid":9},"Shankar Karuppannan",{"tldr":333,"method":334,"finding":335,"direction":70,"opportunity":336},"用机器学习与遥感分析埃塞俄比亚甘贝拉镇2005-2025年城市扩张与土地利用变化。","Landsat多时相影像、随机森林分类与GIS空间分析，结合277份问卷。","建成区从93公顷增至1956公顷，灌草地大幅转为建成区和农地。","可将随机森林时序分类与驱动力调查结合，扩展到更多非洲小城镇并耦合城市增长模拟。","2026-10-21T23:30:23.271293Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":83,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":345,"score_detail":346,"sources":348,"tags":350,"primary_topic":353,"secondary_topics":354,"classification":355,"search_phrases":370,"slug":373,"view_count":15,"doi":374,"paper":375,"created_at":393},5323,"Impact of Land Use and Land Cover Change on Vegetation Cover Loss in Rwanda: Evidence from Muhoza Sector, Musanze District (2015–2025)","https:\u002F\u002Fdoi.org\u002F10.38124\u002Fijisrt\u002F26oct696","This study analyzed the impact of land use and land cover (LULC) change on vegetation cover loss in Muhoza Sector, Musanze District, Rwanda, between 2015 and 2025. A quantitative geospatial research design was adopted using multi-temporal Landsat and Sentinel-2 satellite imagery, field-based GPS validation, Geographic Information Systems, and Normalized Difference Vegetation Index (NDVI) analysis. Six land-cover classes—forestland, grassland, cropland, urbanized land, wetland, and water bodies—were mapped using supervised classification and post-classification change detection.","本研究分析了2015年至2025年间卢旺达穆桑泽区穆霍扎地区土地利用与土地覆盖（LULC）变化对植被覆盖丧失的影响。研究采用定量地理空间研究设计，运用多时相Landsat和Sentinel-2卫星影像、基于实地GPS验证、地理信息系统以及归一化植被指数（NDVI）分析。通过监督分类和分类后变化检测，对林地、草地、耕地、城市化用地、湿地和水体六类土地覆盖类型进行了制图。","International Journal of Innovative Science and Research Technology (IJISRT)",55,{"impact":120,"substance":154,"depth":115,"authority":74,"freshness":211,"relevant":46,"comment":347},"基于Landsat与Sentinel-2多时相影像的卢旺达局部土地利用变化与植被损失实证研究，方法规范但属区域案例，国际参考价值有限。",[349],{"name":344,"url":341},[90,216,351,352],"植被覆盖","卢旺达农业",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[],{"status":30,"locked":14,"version":31,"method":32,"evidence":356},{"candidates":357,"agricultural_evidence":37},[358,362,366],{"code":23,"score":74,"supported":37,"title":359,"abstract":360,"tags":361},[],[41,42],[],{"code":93,"score":176,"supported":14,"title":363,"abstract":364,"tags":365},[],[],[351,90],{"code":181,"score":46,"supported":14,"title":367,"abstract":368,"tags":369},[],[],[216],[371,372],"Muhoza Sector 土地利用","卢旺达 NDVI 植被覆盖","MuhozaSector土地利用-5323","10.38124\u002Fijisrt\u002F26oct696",{"doi":374,"openalex_id":376,"authors":377,"venue":344,"cited_by_count":15,"oa_url":387,"card":388,"direction":70,"ingested_from":71},"W7222817253",[378,380,382,384],{"name":379,"orcid":9},"Vincent Hategekimana",{"name":381,"orcid":9},"Richard Mind J. E.",{"name":383,"orcid":9},"Aboubakar Gasirabo",{"name":385,"orcid":386},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":389,"method":390,"finding":391,"direction":70,"opportunity":392},"分析卢旺达Muhoza地区2015–2025年土地利用变化对植被覆盖丧失的影响。","多时相Landsat与Sentinel-2影像、GPS实地验证、GIS与NDVI","六类地物制图揭示建设用地扩张等LULC变化是植被覆盖减少的主要驱动。","可结合高分辨率时序遥感与驱动因子模型，量化小农户尺度城镇化对耕地与植被的胁迫。","2026-10-19T23:30:23.040351Z",{"id":395,"title":396,"url":397,"summary":398,"summary_zh":399,"content":9,"source_name":400,"source_url":397,"published_at":401,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":402,"score_detail":403,"sources":405,"tags":407,"primary_topic":411,"secondary_topics":412,"classification":414,"search_phrases":443,"slug":446,"view_count":15,"doi":447,"paper":448,"created_at":466},5321,"GIS and remote sensing based multicriteria analysis for identifying potential agricultural land: A case study on Savar Upazila, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbrds.v26i1.94265","Despite of gradually decreasing its contribution to gross domestic product (GDP), agriculture, however, is the backbone of the Bangladeshi economy. Farming is the primary mean of rural livelihoods in Bangladesh but agricultural land is slowly declining day by day. Although, at present, Bangladesh is self-sufficient in terms of producing food, especially rice and some other major agricultural products, it is predicted that, in near future, the country is going to face immense challenges due to climate related threats. The research aims to identify the potential lands for suitable farming in Savar Upazila (sub-district) under Dhaka district applying a geographic information system (GIS) and remote sensing (RS) based multicriteria analysis. Relevant biophysical characteristics of soil, terrain, and landuse and land cover (LULC) were considered. Soil and evelvation data was collected from Soil Resource Development Institute (SRDI)and Shuttle Radar Topography Mission (SRTM), respectively, whereas satellite images were collected from Landsat-8. The maximum likelihood supervised classification technique was used for LULC classification, and the multicriteria evaluation (MCE) and analytical hierarchy process (AHP) approach was employed to identify suitable areas for farming. The research finds the LULC classes are agricultural land, built-up area, fallow land, vegetation cover, waterbodies, and wetland are 12.42%, 31.01%, 25.69%, 25.98%, 4.81%, and 0.09%, respectively. This research also finds that agricultural land suitability was classified as highly suitable, moderately suitable, marginally suitable, and not suitable are 24.28%, 29.53%, 24.66%, and 21.53%, respectively. In contrast, the current agricultural land usage accounts for just 12.42% of the total land area in this research area. The Kappa accuracy has been measured at a value of 0.88 and an overall accuracy value of 0.90 which indicates that the LULC classification is done almost perfectly. This study offered information at the local level that farmers may utilize to decide on cropping patterns and appropriateness. Bangladesh Rural Development Studies, 26(1), 2023 (13-26)","尽管农业在国内生产总值（GDP）中的贡献逐渐下降，但它仍是孟加拉国经济的支柱。农耕是孟加拉国农村生计的主要方式，但农业用地正日益减少。虽然目前孟加拉国在粮食生产方面，尤其是稻米和其他一些主要农产品方面已实现自给自足，但据预测，在不久的将来，该国将因气候相关威胁而面临巨大挑战。本研究旨在运用地理信息系统（GIS）和遥感（RS）基础上的多准则分析，确定达卡县萨瓦尔乡（分区）适宜耕作的土地潜力。研究考虑了土壤、地形以及土地利用与土地覆盖（LULC）等相关生物物理特征。土壤数据来自土壤资源开发研究所（SRDI），高程数据来自航天飞机雷达地形测绘任务（SRTM），卫星影像则来自Landsat-8。土地利用与土地覆盖分类采用最大似然监督分类技术，适宜耕作区的识别则运用多准则评价（MCE）和层次分析法（AHP）。研究发现，土地利用与土地覆盖类别中，农业用地、建成区、休耕地、植被覆盖、水体及湿地分别占12.42%、31.01%、25.69%、25.98%、4.81%和0.09%。研究还发现，农业用地适宜性分为高度适宜、中度适宜、勉强适宜和不适宜，分别占24.28%、29.53%、24.66%和21.53%。相比之下，当前农业用地仅占本研究区土地总面积的12.42%。Kappa精度值为0.88，总体精度值为0.90，表明土地利用与土地覆盖分类近乎完美。本研究提供了地方层面的信息，可供农民在决定种植模式和适宜性时参考。《孟加拉国农村发展研究》，第26卷第1期，2023年（第13-26页）","OpenAlex","2026-10-18T00:00:00Z",60,{"impact":120,"substance":154,"depth":108,"authority":13,"freshness":211,"relevant":46,"comment":404},"方法规范、数据可靠的区域农用地适宜性案例研究，但属孟加拉地方尺度，对国内三农信息化仅有方法借鉴价值，不宜进入每日精选。",[406],{"name":400,"url":397},[112,39,408,409,410],"农用地适宜性","土地覆被","孟加拉农业",{"code":23,"slug":24,"name":25,"path":26,"parent_code":27},[413],{"code":93,"slug":94,"name":95,"path":96,"parent_code":27},{"status":30,"locked":14,"version":31,"method":32,"evidence":415},{"candidates":416,"agricultural_evidence":37},[417,421,425,432,439],{"code":23,"score":154,"supported":37,"title":418,"abstract":419,"tags":420},[39],[41,39,42],[39],{"code":93,"score":108,"supported":37,"title":422,"abstract":423,"tags":424},[110],[112,110],[112],{"code":426,"score":74,"supported":37,"title":427,"abstract":428,"tags":431},"A1",[],[429,430],"稻米","rice",[],{"code":433,"score":74,"supported":37,"title":434,"abstract":435,"tags":438},"G",[],[436,437],"农村发展","rural development",[],{"code":234,"score":74,"supported":37,"title":440,"abstract":441,"tags":442},[],[237,238],[],[444,445],"Savar Upazila 农用地适宜性","Landsat-8 土地覆被分类","SavarUpazila农用地适宜性-5321","10.3329\u002Fbrds.v26i1.94265",{"doi":447,"openalex_id":449,"authors":450,"venue":460,"cited_by_count":15,"oa_url":397,"card":461,"direction":70,"ingested_from":71},"W7222895468",[451,453,455,457],{"name":452,"orcid":9},"Raju Ahmed",{"name":454,"orcid":9},"Shaikh Mehdee Mohammad",{"name":456,"orcid":9},"Ummul Momanin Coalee",{"name":458,"orcid":459},"Khandakar Hasan Mahmud","https:\u002F\u002Forcid.org\u002F0000-0003-1222-0791","The Bangladesh Rural Development Studies",{"tldr":462,"method":463,"finding":464,"direction":70,"opportunity":465},"用GIS与遥感多准则分析评估孟加拉Savar地区潜在农业用地适宜性。","Landsat-8监督分类提取土地利用，结合土壤、地形数据用AHP与MCE做适宜","仅12.42%为农地，高、中、边缘、不适宜分别占24.28%、29.53%、24.66%、21.53","可引入时序遥感与气候情景，动态评估适宜性变化并耦合作物种植决策支持。","2026-10-19T23:30:22.906704Z"]