[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4067":3,"related-4067":87},{"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":25,"classification":26,"search_phrases":56,"slug":59,"view_count":60,"doi":61,"paper":62,"created_at":86},4067,"From pixels to runoff: integrating high-resolution remote sensing and deep learning for hydrologically meaningful urban land-use classification","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.cacint.2026.100489","Accurate urban land-use classification is essential for hydrological modeling, yet most studies evaluate classification schemes solely by pixel-level accuracy rather than their hydrological consequences. This study examines whether conventional classification accuracy is sufficient to identify land-use maps suitable for hydrological modeling. To address this question, high-resolution (8 cm), 3-band aerial imagery was classified using two machine-learning classifiers, random forest (RF) and support vector machine (SVM), and three deep-learning semantic-segmentation models (U-Net, PSP-Net, DeepLabV3). Classification accuracy was evaluated against reference points using Overall Accuracy (OA), Kappa, and class-specific accuracies. Hydrological performance of the resulting maps was assessed with NSE, R 2 , KGE, and RMSE. U-Net achieved the highest macro-IoU (63.1%) and stronger spatial agreement for Pavement and Sidewalk, indicating better preservation of hydrologically influential impervious features. Although postprocessed RF and U-Net achieved equivalent OA values of 84.7%, DL-based classifications generally produced stronger hydrological performance, with U-Net frequently among the strongest-performing models. Design-storm analysis revealed that the type of classification altered peak flow by 1.54 m 3 \u002Fs, demonstrating the sensitivity of runoff predictions to the classified land-use input. These findings demonstrate that overall pixel-level accuracy alone is insufficient to determine hydrological suitability and that class-specific representation of hydrologically important surfaces should also be considered. This study highlights the importance of evaluating remote sensing-based classification with respect to hydrological outcomes, with implications for stormwater infrastructure design and urban flood resilience.","准确的城市土地利用分类对水文建模至关重要，然而多数研究仅以像元级精度评价分类方案，而未考察其水文后果。本研究探讨传统分类精度是否足以识别适用于水文建模的土地利用图。针对这一问题，采用随机森林（RF）和支持向量机（SVM）两种机器学习分类器，以及三种深度学习语义分割模型（U-Net、PSP-Net、DeepLabV3），对高分辨率（8 cm）三波段航空影像进行分类。以参考点为基础，采用总体精度（OA）、Kappa系数及各类别精度评价分类精度。以NSE、R²、KGE和RMSE评估所得图件的水文性能。U-Net取得了最高的宏平均IoU（63.1%），并在铺装路面和人行道类别上表现出更强的空间一致性，表明其更好地保留了具有水文影响的不透水面特征。尽管经后处理的RF和U-Net取得了相同的OA值（84.7%），但基于深度学习的分类总体上产生了更强的水文性能，其中U-Net频繁位列表现最优的模型之列。设计暴雨分析表明，分类类型可使峰值流量改变1.54 m³\u002Fs，证明径流预测对分类土地利用输入具有敏感性。这些发现表明，仅凭总体像元级精度不足以判定水文适用性，还应考虑水文重要地表的类别特异性表征。本研究强调了从水文结果角度评价遥感分类的重要性，对雨水基础设施设计和城市防洪韧性具有启示意义。",null,"City and Environment Interactions","2026-10-01T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究聚焦城市土地利用遥感分类与水文建模，属城市环境与遥感领域，与三农、农业信息化、智慧农业无直接关联，未达相关性门槛。",[19],{"name":10,"url":6},[21,22,23,24],"深度学习","土地利用","遥感分类","城市水文",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":55},[32,41,49],{"code":33,"score":34,"supported":35,"title":36,"abstract":38,"tags":40},"I2",15,true,[37],"deep learning",[21,39],"机器学习",[21],{"code":42,"score":43,"supported":35,"title":44,"abstract":46,"tags":48},"I3",14,[45],"remote sensing",[47,45],"遥感",[],{"code":50,"score":51,"supported":14,"title":52,"abstract":53,"tags":54},"H6",1,[],[],[22],"农业相关性需要复核",[57,58],"高分辨率遥感 城市土地利用 分类","U-Net 城市径流 水文建模","高分辨率遥感城市土地利用分类-4067",2,"10.1016\u002Fj.cacint.2026.100489",{"doi":61,"openalex_id":63,"authors":64,"venue":10,"cited_by_count":15,"oa_url":77,"card":78,"direction":84,"ingested_from":85},"W7215729157",[65,68,70,72,74],{"name":66,"orcid":67},"Ahmed Wageeh","https:\u002F\u002Forcid.org\u002F0000-0001-5353-7862",{"name":69,"orcid":9},"Alaya Albalushi",{"name":71,"orcid":9},"Fatema Alnuaimi",{"name":73,"orcid":9},"Maitha Aldarmaki",{"name":75,"orcid":76},"Mohamed A. Hamouda","https:\u002F\u002Forcid.org\u002F0000-0003-1797-4904","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS2590252026001960\u002Fpdf",{"tldr":79,"method":80,"finding":81,"direction":82,"opportunity":83},"比较高分辨率遥感分类精度与水文模拟效果，发现像素精度不足以判断土地利用图的水文适用性。","8厘米3波段航空影像，用RF、SVM及U-Net、PSP-Net、DeepLab","U-Net宏观IoU最高且水文表现强；分类方式使峰值流量差1.54 m³\u002Fs，总精度相同但水文效果不","农业遥感与作物表型","可将分类评价从像素精度转向水文\u002F生态过程指标，探索面向应用目标的遥感分类优化与不确定性传播。","智慧农业 \u002F 农业物联网","openalex","2026-10-03T23:30:13.261481Z",{"total":88,"page":51,"page_size":88,"items":89},6,[90,146,233,305,387,447],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":9,"source_name":96,"source_url":93,"published_at":97,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":98,"sources":100,"tags":102,"primary_topic":105,"secondary_topics":110,"classification":111,"search_phrases":128,"slug":131,"view_count":132,"doi":133,"paper":134,"created_at":145},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。","Journal of Landscape Ecology","2026-09-19T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":99},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[101],{"name":96,"url":93},[21,103,22,104],"遥感监测","城市扩张",{"code":33,"slug":106,"name":107,"path":108,"parent_code":109},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":112,"locked":14,"version":28,"method":29,"evidence":113},"classified",{"candidates":114,"agricultural_evidence":35},[115,119,124],{"code":33,"score":34,"supported":35,"title":116,"abstract":117,"tags":118},[37],[21,37],[21],{"code":42,"score":120,"supported":35,"title":121,"abstract":122,"tags":123},7,[],[47,45],[103],{"code":50,"score":51,"supported":14,"title":125,"abstract":126,"tags":127},[],[],[22],[129,130],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015",4,"10.2478\u002Fjlecol-2026-0037",{"doi":133,"openalex_id":135,"authors":136,"venue":96,"cited_by_count":15,"oa_url":93,"card":140,"direction":82,"ingested_from":85},"W7213670913",[137],{"name":138,"orcid":139},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":141,"method":142,"finding":143,"direction":82,"opportunity":144},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z",{"id":147,"title":148,"url":149,"summary":150,"summary_zh":151,"content":9,"source_name":152,"source_url":149,"published_at":153,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":154,"score_detail":155,"sources":162,"tags":164,"primary_topic":9,"secondary_topics":168,"classification":169,"search_phrases":203,"slug":206,"view_count":15,"doi":207,"paper":208,"created_at":232},5577,"Assessing Land Use and Land Cover Change in the Jamanxim River Region (Brazilian Amazon) from 2015 to 2025 Using CBERS-4\u002FPAN Imagery","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i4.269068","Land use and land cover (LULC) changes in the Amazon are among the main drivers of biodiversity loss, ecosystem degradation, and the intensification of climate change. This study aimed to analyze the multitemporal dynamics of LULC around the Jamanxim River, Pará, Brazilian Amazon, between 2015 and 2025, using high-resolution imagery from the CBERS-4\u002FPAN satellite. The study area covers 492.74 km², including part of the Jamanxim National Forest, a conservation unit under strong pressure from agricultural expansion and logging activities. The methodology included geometric correction of the images, Gram–Schmidt pansharpening, and supervised classification with the Random Forest algorithm, validated with historical imagery from Google Earth Pro. Thematic maps of land use and cover were generated, and performance metrics such as overall accuracy, omission and commission errors, and F1-scores were assessed. The results indicated a significant reduction in forest cover, from 412.28 km² in 2015 to 355.47 km² in 2025 (–13.78%). Non-vegetated areas increased by 132.47%, while agricultural areas expanded by 54.97%. The forest and water body classes showed high accuracy (F1-score > 0.95), while confusion persisted between agricultural and bare soil classes. Overall accuracy remained above 90% across all years, with the highest performance recorded in 2020 (96.31%). These results highlight the potential of CBERS-4\u002FPAN imagery for monitoring multitemporal changes in tropical forest landscapes. At the same time, they reveal the intensification of deforestation and the growing threats to conservation in the Amazon, reinforcing the importance of remote sensing for environmental management and policy support.","亚马逊地区的土地利用与土地覆盖（LULC）变化是生物多样性丧失、生态系统退化以及气候变化加剧的主要驱动因素之一。本研究旨在利用CBERS-4\u002FPAN卫星的高分辨率影像，分析2015年至2025年间巴西亚马孙州雅曼辛河（Jamanxim River）周边土地利用与土地覆盖的多时相动态。研究区面积为492.74 km²，包括雅曼辛国家森林（Jamanxim National Forest）的一部分，该保护区正面临农业扩张和采伐活动的强烈压力。研究方法包括影像几何校正、Gram–Schmidt全色锐化以及基于随机森林（Random Forest）算法的监督分类，并利用Google Earth Pro的历史影像进行验证。研究生成了土地利用与覆盖专题图，并评估了总体精度、漏分误差与错分误差以及F1分数等性能指标。结果表明，森林覆盖显著减少，从2015年的412.28 km²降至2025年的355.47 km²（–13.78%）。无植被区域增加了132.47%，农业区域扩张了54.97%。森林和水体类别表现出较高精度（F1分数 > 0.95），而农业类别与裸土类别之间仍存在混淆。各年份总体精度均保持在90%以上，其中2020年性能最高（96.31%）。这些结果凸显了CBERS-4\u002FPAN影像在监测热带森林景观多时相变化方面的潜力，同时揭示了亚马逊地区森林砍伐的加剧以及保护面临的日益增长的威胁，进一步强调了遥感在环境管理和政策支持中的重要性。","Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z",78,{"impact":156,"substance":157,"depth":158,"authority":159,"freshness":160,"relevant":51,"comment":161},18,22,17,12,9,"该研究利用CBERS-4\u002FPAN影像量化亚马逊保护区十年间森林减少13.78%，方法规范、结论可靠，对遥感监测与生态保护有参考价值，但地域性强、与国内三农直接关联有限。",[163],{"name":152,"url":149},[103,22,165,166,167],"森林保护","亚马逊","CBERS-4",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":170},{"candidates":171,"agricultural_evidence":35,"reason":202},[172,178,182,189,196],{"code":50,"score":120,"supported":35,"title":173,"abstract":174,"tags":177},[],[175,176],"生物多样性","biodiversity",[22],{"code":42,"score":120,"supported":35,"title":179,"abstract":180,"tags":181},[],[47,45],[103],{"code":183,"score":88,"supported":35,"title":184,"abstract":185,"tags":188},"H7",[],[186,187],"气候变化","climate change",[],{"code":190,"score":191,"supported":35,"title":192,"abstract":193,"tags":195},"H2",3,[],[194],"soil",[],{"code":197,"score":191,"supported":35,"title":198,"abstract":199,"tags":201},"I7",[],[200],"研究方法",[],"多个产业证据相当，需要确认主归属",[204,205],"CBERS-4 亚马逊 土地利用","Jamanxim 森林覆盖 遥感","CBERS-4亚马逊土地利用-5577","10.29150\u002Fjhrs.v16i4.269068",{"doi":207,"openalex_id":209,"authors":210,"venue":152,"cited_by_count":15,"oa_url":149,"card":227,"direction":82,"ingested_from":85},"W7223141286",[211,214,216,219,222,225],{"name":212,"orcid":213},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":215,"orcid":9},"Gabriel Henrique Santos Silva",{"name":217,"orcid":218},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":220,"orcid":221},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":223,"orcid":224},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":226,"orcid":9},"Sylvana Melo dos Santos",{"tldr":228,"method":229,"finding":230,"direction":82,"opportunity":231},"用CBERS-4\u002FPAN影像分析亚马逊Jamanxim河流域2015-2025年土地利用变化。","几何校正、Gram-Schmidt融合、随机森林监督分类，Google Eart","森林覆盖减少13.78%，农业扩张54.97%，裸地增132.47%，总体精度超90%。","可探索中巴地球资源卫星在热带森林退化监测中的业务化应用与多源数据融合。","2026-10-22T11:20:42.901761Z",{"id":234,"title":235,"url":236,"summary":237,"summary_zh":238,"content":9,"source_name":239,"source_url":236,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":241,"score_detail":242,"sources":245,"tags":247,"primary_topic":251,"secondary_topics":256,"classification":257,"search_phrases":285,"slug":288,"view_count":15,"doi":289,"paper":290,"created_at":304},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":243,"substance":156,"depth":43,"authority":159,"freshness":160,"relevant":51,"comment":244},8,"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。",[246],{"name":239,"url":236},[47,22,248,249,250],"土壤侵蚀","GIS","水土保持",{"code":252,"slug":253,"name":254,"path":255,"parent_code":109},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",[],{"status":112,"locked":14,"version":28,"method":29,"evidence":258},{"candidates":259,"agricultural_evidence":35},[260,266,270,275,281],{"code":252,"score":13,"supported":35,"title":261,"abstract":262,"tags":265},[],[263,249,264],"地理信息","geographic information",[249],{"code":42,"score":120,"supported":35,"title":267,"abstract":268,"tags":269},[],[47,45],[47],{"code":190,"score":88,"supported":35,"title":271,"abstract":272,"tags":274},[],[273,194],"土壤",[],{"code":276,"score":191,"supported":35,"title":277,"abstract":278,"tags":280},"H3",[],[279],"灌溉",[],{"code":50,"score":60,"supported":14,"title":282,"abstract":283,"tags":284},[],[],[22,250],[286,287],"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":289,"openalex_id":291,"authors":292,"venue":239,"cited_by_count":15,"oa_url":9,"card":298,"direction":82,"ingested_from":85},"W7223117733",[293,295],{"name":294,"orcid":9},"MERİÇ KURT",{"name":296,"orcid":297},"Celal ŞENOL","https:\u002F\u002Forcid.org\u002F0000-0003-0857-866X",{"tldr":299,"method":300,"finding":301,"direction":302,"opportunity":303},"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":306,"title":307,"url":308,"summary":309,"summary_zh":310,"content":9,"source_name":311,"source_url":308,"published_at":153,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":312,"score_detail":313,"sources":318,"tags":320,"primary_topic":321,"secondary_topics":325,"classification":326,"search_phrases":351,"slug":354,"view_count":15,"doi":355,"paper":356,"created_at":386},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":159,"substance":314,"depth":315,"authority":316,"freshness":243,"relevant":51,"comment":317},20,16,13,"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。",[319],{"name":311,"url":308},[47,22,248,250],{"code":190,"slug":194,"name":322,"path":323,"parent_code":324},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",[],{"status":112,"locked":14,"version":28,"method":29,"evidence":327},{"candidates":328,"agricultural_evidence":35},[329,333,338,343,347],{"code":190,"score":43,"supported":35,"title":330,"abstract":331,"tags":332},[194],[273,194],[],{"code":50,"score":334,"supported":35,"title":335,"abstract":336,"tags":337},5,[],[250],[22,250],{"code":276,"score":191,"supported":35,"title":339,"abstract":340,"tags":342},[],[341],"水资源",[],{"code":252,"score":191,"supported":35,"title":344,"abstract":345,"tags":346},[],[249],[],{"code":42,"score":51,"supported":14,"title":348,"abstract":349,"tags":350},[],[],[47],[352,353],"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":355,"openalex_id":357,"authors":358,"venue":311,"cited_by_count":15,"oa_url":380,"card":381,"direction":82,"ingested_from":85},"W7223058039",[359,362,365,368,371,374,377],{"name":360,"orcid":361},"Ntirenganya Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0001-5785-2875",{"name":363,"orcid":364},"Eric Derrick Bugenimana","https:\u002F\u002Forcid.org\u002F0000-0002-0363-6915",{"name":366,"orcid":367},"Rose Niyonkuru","https:\u002F\u002Forcid.org\u002F0009-0009-3460-9731",{"name":369,"orcid":370},"Aphrodice Iragena","https:\u002F\u002Forcid.org\u002F0009-0001-7747-244X",{"name":372,"orcid":373},"Pierre Celestin Uwamahoro","https:\u002F\u002Forcid.org\u002F0009-0009-8925-2493",{"name":375,"orcid":376},"Tuyisabe Jean Bosco","https:\u002F\u002Forcid.org\u002F0009-0002-7381-3013",{"name":378,"orcid":379},"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":382,"method":383,"finding":384,"direction":302,"opportunity":385},"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":388,"title":389,"url":390,"summary":391,"summary_zh":392,"content":9,"source_name":393,"source_url":390,"published_at":153,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":394,"sources":396,"tags":398,"primary_topic":399,"secondary_topics":400,"classification":402,"search_phrases":424,"slug":427,"view_count":15,"doi":428,"paper":429,"created_at":446},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",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":395},"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。",[397],{"name":393,"url":390},[39,103,22,104],{"code":33,"slug":106,"name":107,"path":108,"parent_code":109},[401],{"code":252,"slug":253,"name":254,"path":255,"parent_code":109},{"status":112,"locked":14,"version":28,"method":29,"evidence":403},{"candidates":404,"agricultural_evidence":35},[405,410,416,420],{"code":33,"score":34,"supported":35,"title":406,"abstract":408,"tags":409},[407],"machine learning",[39,407],[39],{"code":252,"score":160,"supported":35,"title":411,"abstract":412,"tags":415},[],[413,249,414],"空间分析","spatial analysis",[],{"code":42,"score":120,"supported":35,"title":417,"abstract":418,"tags":419},[],[47,45],[103],{"code":50,"score":51,"supported":14,"title":421,"abstract":422,"tags":423},[],[],[22],[425,426],"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":428,"openalex_id":430,"authors":431,"venue":393,"cited_by_count":15,"oa_url":390,"card":441,"direction":82,"ingested_from":85},"W7223022155",[432,435,437,439],{"name":433,"orcid":434},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":436,"orcid":9},"Goanar Jal Dup",{"name":438,"orcid":9},"Weyesa Merga Chemeda",{"name":440,"orcid":9},"Shankar Karuppannan",{"tldr":442,"method":443,"finding":444,"direction":82,"opportunity":445},"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":448,"title":449,"url":450,"summary":451,"summary_zh":9,"content":9,"source_name":452,"source_url":450,"published_at":153,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":453,"sources":455,"tags":457,"primary_topic":9,"secondary_topics":459,"classification":460,"search_phrases":471,"slug":474,"view_count":15,"doi":475,"paper":476,"created_at":490},5484,"Spatiotemporal Dynamics and Assess of Ecological Quality in Sylhet Metropolitan Area amid Rapid Urbanization: A Remote Sensing Approach","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-11422676\u002Fv1","Spatiotemporal Dynamics and Assess of Ecological Quality in Sylhet Metropolitan Area amid Rapid Urbanization: A Remote Sensing Approach。Research Square","Research Square",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":454},"研究孟加拉国锡尔赫特市生态质量，与三农及农业信息化无关，不予精选。",[456],{"name":452,"url":450},[103,22,458],"城市生态",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":461},{"candidates":462,"agricultural_evidence":14,"reason":55},[463,467],{"code":42,"score":159,"supported":35,"title":464,"abstract":465,"tags":466},[45],[45],[103],{"code":50,"score":60,"supported":14,"title":468,"abstract":469,"tags":470},[],[],[22,458],[472,473],"Sylhet 城市扩张 遥感","土地利用 城市生态 遥感监测","Sylhet城市扩张遥感-5484","10.21203\u002Frs.3.rs-11422676\u002Fv1",{"doi":475,"openalex_id":477,"authors":478,"venue":452,"cited_by_count":15,"oa_url":489,"card":9,"direction":82,"ingested_from":85},"W7223013355",[479,481,483,486],{"name":480,"orcid":9},"Md Saikoth Jahan",{"name":482,"orcid":9},"Mst Arjumoni Anu",{"name":484,"orcid":485},"Md. Inzamul Haque","https:\u002F\u002Forcid.org\u002F0000-0003-1756-5561",{"name":487,"orcid":488},"Most. Atia Parvin","https:\u002F\u002Forcid.org\u002F0000-0002-2718-9259","https:\u002F\u002Fwww.researchsquare.com\u002Farticle\u002Frs-11422676\u002Flatest.pdf","2026-10-21T23:30:23.223087Z"]