[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4584":3,"related-4584":98},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":9,"secondary_topics":31,"classification":32,"search_phrases":73,"slug":76,"view_count":77,"doi":78,"paper":79,"created_at":97},4584,"Farmland Transformation Reshapes Ecosystem Services Through Multi-Level Landscape Reconfiguration: Evidence from China’s Songnen Plain","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagriculture16202188","Global agricultural expansion has profoundly reshaped terrestrial landscapes, yet the ecological consequences of farmland transformation across multiple spatial scales remain insufficiently understood. This study investigates how farmland dynamics influence ecosystem service (ES) provision in China’s Songnen Plain by integrating long-term land-use trajectories, remote sensing data, and landscape ecological analysis. Using annual 30 m land-use\u002Fland-cover datasets from 2000 to 2020, we identified diverse farmland transformation pathways, including stable cultivation, reclamation, abandonment, fallow, afforestation, and urban encroachment. The InVEST model was applied to quantify three key ecosystem services: food supply, soil conservation, and carbon storage, while grid-level Partial Least Squares Regression (PLSR) was employed to identify their major drivers. Results revealed substantial spatial and temporal heterogeneity in ecosystem services. Food supply and soil conservation generally increased during the study period, whereas carbon storage exhibited a declining trend. Landscape-level characteristics, particularly Shannon diversity index (SHDI), aggregation index (AI), and patch density (PD), were identified as dominant factors regulating ecosystem service variation. Class-level indicators, including farmland area (FA) and mean patch area (MPA), also showed significant but comparatively weaker effects. This study provides new insights into the ecological consequences of intensive farmland transformation and highlights the importance of optimizing multi-scale landscape configurations to balance agricultural production and ecosystem sustainability.","全球农业扩张深刻重塑了陆地景观格局，但农田转型在多个空间尺度上的生态效应仍缺乏充分认识。本研究通过整合长期土地利用轨迹、遥感数据和景观生态分析，探讨了农田动态如何影响中国松嫩平原的生态系统服务（ES）供给。基于2000—2020年逐年30 m土地利用\u002F土地覆盖数据集，我们识别了多种农田转型路径，包括稳定耕作、开垦、撂荒、休耕、退耕还林和城市侵占。应用InVEST模型量化了粮食供给、土壤保持和碳储量三项关键生态系统服务，并采用格网级偏最小二乘回归（PLSR）识别其主要驱动因子。结果表明，生态系统服务存在显著的空间和时间异质性。粮食供给和土壤保持总体上在研究期间呈增加趋势，而碳储量则表现出下降趋势。景观尺度特征，特别是香农多样性指数（SHDI）、聚集度指数（AI）和斑块密度（PD），被识别为调控生态系统服务变化的主导因子。类型尺度指标，包括农田面积（FA）和平均斑块面积（MPA），也表现出显著但相对较弱的影响。本研究为集约化农田转型的生态后果提供了新见解，并强调了优化多尺度景观格局以平衡农业生产与生态系统可持续性的重要性。",null,"Agriculture","2026-10-09T00:00:00Z","论文",10,false,79,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,8,1,"基于2000—2020年30米土地利用数据与InVEST模型，揭示松嫩平原农田转型对生态系统服务的多尺度影响，方法与数据扎实，对黑土地保护与农业可持续布局有参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","黑土地保护","遥感监测","土地利用","生态系统服务",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":36},"needs_review","2026-10-22.2","rules",{"candidates":37,"agricultural_evidence":40,"reason":72},[38,45,52,60,67],{"code":39,"score":20,"supported":40,"title":41,"abstract":42,"tags":44},"H6",true,[],[30,43],"ecosystem service",[29,30],{"code":46,"score":20,"supported":40,"title":47,"abstract":48,"tags":51},"I3",[],[49,50],"遥感","remote sensing",[26,28],{"code":53,"score":54,"supported":40,"title":55,"abstract":56,"tags":59},"H2",6,[],[57,58],"土壤","soil",[],{"code":61,"score":62,"supported":40,"title":63,"abstract":64,"tags":66},"J3",3,[],[65],"全球农业",[],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"H1",[],[],[27],"多个产业证据相当，需要确认主归属",[74,75],"松嫩平原 农田转型 生态系统服务","InVEST模型 碳储量 土壤保持","松嫩平原农田转型生态系统服务-4584",0,"10.3390\u002Fagriculture16202188",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":77,"oa_url":6,"card":89,"direction":95,"ingested_from":96},"W7221099965",[82,84,87],{"name":83,"orcid":9},"Ruhao Xue",{"name":85,"orcid":86},"Bonoua Faye","https:\u002F\u002Forcid.org\u002F0000-0003-4465-6792",{"name":88,"orcid":9},"Hu Xie",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"基于松嫩平原2000-2020年土地利用数据，揭示耕地转型通过多尺度景观格局重塑生态系统服务。","30米土地利用数据、InVEST模型与网格级偏最小二乘回归（PLSR）。","食物供给与土壤保持增加而碳储量下降，景观格局指数是主导驱动因子。","农业绿色发展与碳","可探究不同耕地转型路径对碳储量的非线性影响及多尺度景观优化配置阈值。","农业遥感与作物表型","openalex","2026-10-10T23:30:27.812095Z",{"total":54,"page":21,"page_size":54,"items":99},[100,181,250,330,396,441],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":113,"tags":115,"primary_topic":117,"secondary_topics":122,"classification":123,"search_phrases":163,"slug":166,"view_count":62,"doi":167,"paper":168,"created_at":180},3846,"Integrating Earth Observation Data for Assessing the Impact of Mining Ponds on Land Use and Land Cover Change","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.11.no10.2025.pg136.153","This study examines the relationship between mining ponds and land use and land cover (LULC) dynamics, emphasizing both the environmental challenges and the socio-economic opportunities they present. Earth observation data from Landsat and questionnaire administration were used to evaluate the spatial and socio-environmental impacts of mining activities in Jos South Local Government Area (LGA). Supervised Maximum Likelihood Classification was applied to multitemporal satellite imagery to determine LULC changes, while nearest neighborhood analysis assessed the spatial distribution of mining ponds. A stratified random sampling method was used to collect responses from five districts Du, Zawan, Kuru, Vwang, and Gyel. Results revealed that mining ponds accounted for approximately 2.03% (10.20 km²) of the total land area in 1990, showing a slight decline to 2.00% (10.04 km²) by 2021. Despite their negative impacts, such as land degradation, erosion, and pollution, mining ponds also contribute positively to local livelihoods. An all-year-round supply of water by these ponds makes activities such as irrigation farming, fish farming, block making, recreation and livestock husbandry possible at all times. These benefits generate a wide range of opportunities, which attract investments from private companies, all channeled towards the socio-economic development of Jos-south. We recommended prioritizing the adoption of modern technologies such as GIS and remote sensing by the government for consistent supervision of the expansion of these mining ponds, towards sustainable environmental development of the Jos-South local government area.","本研究考察了矿坑与土地利用和土地覆盖（LULC）动态之间的关系，重点关注其带来的环境挑战与社会经济机遇。研究利用Landsat地球观测数据和问卷调查，评估了乔斯南部地方政府辖区（LGA）采矿活动的空间及社会环境的影响。研究对多时相卫星影像采用监督最大似然分类法以确定土地利用和土地覆盖变化，同时运用最近邻分析评估矿坑的空间分布。采用分层随机抽样方法，从Du、Zawan、Kuru、Vwang和Gyel五个区收集了调查问卷。结果显示，1990年矿坑约占土地总面积的2.03%（10.20 km²），到2021年略微下降至2.00%（10.04 km²）。尽管矿坑带来了土地退化、侵蚀和污染等负面影响，但也对当地生计作出了积极贡献。这些矿坑全年供水，使得灌溉农业、养鱼、制砖、休闲娱乐和畜牧等活动得以常年开展。这些效益产生了广泛的机遇，吸引了私营企业的投资，全部用于乔斯南部的社会经济发展。我们建议政府优先采用地理信息系统（GIS）和遥感等现代技术，对这些矿坑的扩张进行持续监管，以促进乔斯南部地方政府辖区的环境可持续发展。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-28T00:00:00Z",60,{"impact":20,"substance":17,"depth":110,"authority":13,"freshness":111,"relevant":21,"comment":112},15,9,"基于Landsat遥感与问卷评估尼日利亚矿区池塘对土地利用变化的影响，方法规范但属区域案例，对国内三农信息化参考价值有限。",[114],{"name":106,"url":103},[26,28,29,116],"矿区生态",{"code":46,"slug":118,"name":119,"path":120,"parent_code":121},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":124,"locked":14,"version":34,"method":35,"evidence":125},"classified",{"candidates":126,"agricultural_evidence":40},[127,131,138,145,152,158],{"code":46,"score":20,"supported":40,"title":128,"abstract":129,"tags":130},[],[49,50],[26,28],{"code":132,"score":54,"supported":40,"title":133,"abstract":134,"tags":137},"C",[],[135,136],"畜牧","livestock",[],{"code":139,"score":54,"supported":40,"title":140,"abstract":141,"tags":144},"H3",[],[142,143],"灌溉","irrigation",[],{"code":146,"score":54,"supported":40,"title":147,"abstract":148,"tags":151},"I4",[],[149,150],"地理信息","GIS",[],{"code":153,"score":62,"supported":40,"title":154,"abstract":155,"tags":157},"D",[],[156],"fish farming",[],{"code":39,"score":159,"supported":14,"title":160,"abstract":161,"tags":162},2,[],[],[29,116],[164,165],"Jos South 矿区 土地利用","Landsat 遥感 土地覆盖","JosSouth矿区土地利用-3846","10.56201\u002Fijgem.vol.11.no10.2025.pg136.153",{"doi":167,"openalex_id":169,"authors":170,"venue":106,"cited_by_count":77,"oa_url":174,"card":175,"direction":95,"ingested_from":96},"W7214571622",[171],{"name":172,"orcid":173},"Momoh Khalifa Adeiza","https:\u002F\u002Forcid.org\u002F0009-0000-8594-7372","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 11 NO. 10 2025\u002FIntegrating Earth Observation 136-153.pdf",{"tldr":176,"method":177,"finding":178,"direction":95,"opportunity":179},"用Landsat影像与问卷评估矿区池塘对土地利用变化及社会经济的影响。","Landsat多时相影像、最大似然分类、最近邻分析与分层随机抽样问卷。","矿塘面积占比由1990年2.03%微降至2021年2.00%，但带来退化与生计双重效应。","可结合时序遥感与农户调查，量化矿塘对灌溉农业与生计的长期耦合影响。","2026-09-30T23:30:29.520262Z",{"id":182,"title":183,"url":184,"summary":185,"summary_zh":186,"content":9,"source_name":187,"source_url":188,"published_at":189,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":190,"score_detail":191,"sources":193,"tags":195,"primary_topic":198,"secondary_topics":199,"classification":204,"search_phrases":230,"slug":233,"view_count":234,"doi":235,"paper":236,"created_at":249},3187,"Spatio-temporal analysis of Land Use and Land Cover (LULC) change using high resolution satellite data: A case study of Nagaon, Assam","https:\u002F\u002Fdoi.org\u002F10.31018\u002Fjans.v18i3.7774","Geographic information systems (GIS) coupled with satellite remote sensing (RS) have had a significant impact on the assessment and mapping of land surface dynamics, specifically the analysis of land-use land-cover (LULC) change. This study uses high-resolution multispectral LISS-IV (Linear Imaging Self-Scanning Sensor-IV) imagery with a 5.8-meter resolution to evaluate LULC trends in the Nagaon district of Assam between 2015 and 2024. Land use classes in a variety of categories, including vegetation, water bodies, agricultural land, built-up areas, scrubland, sandbars, tea plantations, and trees outside forests (TOF), were identified applying the maximum likelihood classification algorithm. Overall, The accuracy was 85.7% in 2015 and 90.3% in 2024, with respective Kappa Coefficients of 0.836 and 0.889, respectively. The results showed that between 2015 and 2024, the areas of tea plantations, natural vegetation, scrubland, and agricultural land fell by 0.26%, 6.14%, 0.26%, and 9.83%, respectively. Conversely, the waterbody, sandbar, built-up, and TOF have all increased by 0.29%, 0.91%, 0.82%, and 14.46%, respectively. This noticeable shift from conventional agricultural and natural vegetation landscapes toward tree-based systems and urban expansion on the study area, emphasize on matters concerning climate stress, declining soil fertility, economic benefits, policy support, and the need for resilient livelihoods.","地理信息系统（GIS）与卫星遥感（RS）相结合，对地表动态的评估与制图产生了显著影响，尤其是土地利用与土地覆盖（LULC）变化分析。本研究利用分辨率为5.8米的高分辨率多光谱LISS-IV（线性成像自扫描传感器-IV）影像，评估了阿萨姆邦纳冈县2015年至2024年间的LULC变化趋势。采用最大似然分类算法，识别了植被、水体、农地、建设用地、灌丛地、沙洲、茶园及林外树木（TOF）等多种土地利用类别。总体而言，2015年分类精度为85.7%，2024年为90.3%，Kappa系数分别为0.836和0.889。结果表明，2015年至2024年间，茶园、自然植被、灌丛地和农地面积分别减少了0.26%、6.14%、0.26%和9.83%。相反，水体、沙洲、建设用地和TOF分别增加了0.29%、0.91%、0.82%和14.46%。研究区从传统农业和自然植被景观向林基系统和城市扩张的显著转变，凸显了气候胁迫、土壤肥力下降、经济效益、政策支持以及韧性生计需求等相关问题。","Journal of Applied and Natural Science","https:\u002F\u002Fjournals.ansfoundation.org\u002Findex.php\u002Fjans\u002Farticle\u002Fview\u002F7774","2026-09-20T00:00:00Z",62,{"impact":20,"substance":17,"depth":110,"authority":19,"freshness":20,"relevant":21,"comment":192},"基于高分辨率遥感的区域土地利用变化实证研究，方法规范、数据翔实，对农业遥感监测有参考价值，但属地方性案例，公共影响有限。",[194],{"name":187,"url":188},[26,28,29,196,197],"印度农业","植被覆盖",{"code":46,"slug":118,"name":119,"path":120,"parent_code":121},[200],{"code":146,"slug":201,"name":202,"path":203,"parent_code":121},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":124,"locked":14,"version":34,"method":35,"evidence":205},{"candidates":206,"agricultural_evidence":40},[207,213,218,222,226],{"code":46,"score":208,"supported":40,"title":209,"abstract":210,"tags":212},12,[],[49,50,211],"multispectral",[26,197,28],{"code":146,"score":111,"supported":40,"title":214,"abstract":215,"tags":217},[],[149,150,216],"geographic information",[],{"code":53,"score":54,"supported":40,"title":219,"abstract":220,"tags":221},[],[57,58],[],{"code":39,"score":21,"supported":14,"title":223,"abstract":224,"tags":225},[],[],[29],{"code":61,"score":21,"supported":14,"title":227,"abstract":228,"tags":229},[],[],[196],[231,232],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187",5,"10.31018\u002Fjans.v18i3.7774",{"doi":235,"openalex_id":237,"authors":238,"venue":187,"cited_by_count":77,"oa_url":188,"card":244,"direction":95,"ingested_from":96},"W7213942371",[239,241],{"name":240,"orcid":9},"J. C. Das",{"name":242,"orcid":243},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":245,"method":246,"finding":247,"direction":95,"opportunity":248},"利用高分辨率卫星影像分析印度阿萨姆邦纳冈地区2015-2024年土地利用\u002F覆盖变化。","LISS-IV 5.8米多光谱影像，最大似然分类，精度与Kappa系数评估。","农业用地和自然植被分别减少9.83%和6.14%，而林外树木和建设用地分别增加14.46%和0.82","可结合时序高分辨率影像与农户调查，探究林外树木扩张对农业韧性和碳汇的驱动机制。","2026-09-22T23:30:25.539323Z",{"id":251,"title":252,"url":253,"summary":254,"summary_zh":255,"content":9,"source_name":256,"source_url":253,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":258,"score_detail":259,"sources":262,"tags":264,"primary_topic":9,"secondary_topics":268,"classification":269,"search_phrases":300,"slug":303,"view_count":77,"doi":304,"paper":305,"created_at":329},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":17,"substance":18,"depth":260,"authority":208,"freshness":111,"relevant":21,"comment":261},17,"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。",[263],{"name":256,"url":253},[28,29,265,266,267],"森林保护","亚马逊","CBERS-4",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":270},{"candidates":271,"agricultural_evidence":40,"reason":72},[272,279,283,290,294],{"code":39,"score":273,"supported":40,"title":274,"abstract":275,"tags":278},7,[],[276,277],"生物多样性","biodiversity",[29],{"code":46,"score":273,"supported":40,"title":280,"abstract":281,"tags":282},[],[49,50],[28],{"code":284,"score":54,"supported":40,"title":285,"abstract":286,"tags":289},"H7",[],[287,288],"气候变化","climate change",[],{"code":53,"score":62,"supported":40,"title":291,"abstract":292,"tags":293},[],[58],[],{"code":295,"score":62,"supported":40,"title":296,"abstract":297,"tags":299},"I7",[],[298],"研究方法",[],[301,302],"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":304,"openalex_id":306,"authors":307,"venue":256,"cited_by_count":77,"oa_url":253,"card":324,"direction":95,"ingested_from":96},"W7223141286",[308,311,313,316,319,322],{"name":309,"orcid":310},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":312,"orcid":9},"Gabriel Henrique Santos Silva",{"name":314,"orcid":315},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":317,"orcid":318},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":320,"orcid":321},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":323,"orcid":9},"Sylvana Melo dos Santos",{"tldr":325,"method":326,"finding":327,"direction":95,"opportunity":328},"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":331,"title":332,"url":333,"summary":334,"summary_zh":335,"content":9,"source_name":336,"source_url":333,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":77,"score_detail":337,"sources":339,"tags":341,"primary_topic":344,"secondary_topics":349,"classification":351,"search_phrases":373,"slug":376,"view_count":77,"doi":377,"paper":378,"created_at":395},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":77,"substance":77,"depth":77,"authority":77,"freshness":77,"relevant":77,"comment":338},"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。",[340],{"name":336,"url":333},[342,28,29,343],"机器学习","城市扩张",{"code":345,"slug":346,"name":347,"path":348,"parent_code":121},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[350],{"code":146,"slug":201,"name":202,"path":203,"parent_code":121},{"status":124,"locked":14,"version":34,"method":35,"evidence":352},{"candidates":353,"agricultural_evidence":40},[354,359,365,369],{"code":345,"score":110,"supported":40,"title":355,"abstract":357,"tags":358},[356],"machine learning",[342,356],[342],{"code":146,"score":111,"supported":40,"title":360,"abstract":361,"tags":364},[],[362,150,363],"空间分析","spatial analysis",[],{"code":46,"score":273,"supported":40,"title":366,"abstract":367,"tags":368},[],[49,50],[28],{"code":39,"score":21,"supported":14,"title":370,"abstract":371,"tags":372},[],[],[29],[374,375],"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":377,"openalex_id":379,"authors":380,"venue":336,"cited_by_count":77,"oa_url":333,"card":390,"direction":95,"ingested_from":96},"W7223022155",[381,384,386,388],{"name":382,"orcid":383},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":385,"orcid":9},"Goanar Jal Dup",{"name":387,"orcid":9},"Weyesa Merga Chemeda",{"name":389,"orcid":9},"Shankar Karuppannan",{"tldr":391,"method":392,"finding":393,"direction":95,"opportunity":394},"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":397,"title":398,"url":399,"summary":400,"summary_zh":9,"content":9,"source_name":401,"source_url":399,"published_at":257,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":77,"score_detail":402,"sources":404,"tags":406,"primary_topic":9,"secondary_topics":408,"classification":409,"search_phrases":421,"slug":424,"view_count":77,"doi":425,"paper":426,"created_at":440},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":77,"substance":77,"depth":77,"authority":77,"freshness":77,"relevant":77,"comment":403},"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。",[405],{"name":401,"url":399},[28,29,407],"城市生态",[],{"status":33,"locked":14,"version":34,"method":35,"evidence":410},{"candidates":411,"agricultural_evidence":14,"reason":420},[412,416],{"code":46,"score":208,"supported":40,"title":413,"abstract":414,"tags":415},[50],[50],[28],{"code":39,"score":159,"supported":14,"title":417,"abstract":418,"tags":419},[],[],[29,407],"农业相关性需要复核",[422,423],"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":425,"openalex_id":427,"authors":428,"venue":401,"cited_by_count":77,"oa_url":439,"card":9,"direction":95,"ingested_from":96},"W7223013355",[429,431,433,436],{"name":430,"orcid":9},"Md Saikoth Jahan",{"name":432,"orcid":9},"Mst Arjumoni Anu",{"name":434,"orcid":435},"Md. Inzamul Haque","https:\u002F\u002Forcid.org\u002F0000-0003-1756-5561",{"name":437,"orcid":438},"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",{"id":442,"title":443,"url":444,"summary":445,"summary_zh":446,"content":9,"source_name":447,"source_url":444,"published_at":448,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":77,"score_detail":449,"sources":451,"tags":453,"primary_topic":456,"secondary_topics":457,"classification":458,"search_phrases":478,"slug":481,"view_count":77,"doi":482,"paper":483,"created_at":511},5390,"Land use and socio-economic changes caused by riverbank erosion - case study: Obrenovac municipality, Serbia","https:\u002F\u002Fdoi.org\u002F10.59954\u002Fstnv.797","Lateral bank erosion of meandering rivers represents a significant geomorphological process in the lower part of the Kolubara River Basin in Serbia. This process directly impacts land-use and agricultural productivity and lead to broader environmental, socioeconomic, and demographic changes. Historically, the Kolubara riverbanks have been intensively used for agriculture. Today, lateral channel migration and riverbank erosion, driven by the interaction between natural process and human activities, directly threaten traditionally active land plots along the river. This study investigates the long-term dynamics of riverbank erosion along the four active meanders of the Kolubara River in Obrenovac Municipality over a 97-year period (1930–2026). Main tasks include quantification of lateral channel migration and land loss rates, analysis of land-use changes within the endangered plots, and an investigation of socioeconomic consequences and demographic dynamics in affected settlements. The methodological framework integrates cadastral maps analysis, remote sensing techniques (UAVs), and GIS software, with analysis of demographic and land-use data. The results indicate that maximum lateral channel migration reached 259.3 m during the observed period, while the total land loss amounted to 17.6 ha, consisting mostly of arable land. This reduction has directly affected the local agricultural sector, resulting in an estimated land loss value of approximately 24,122 USD. Demographic analysis reveals population decline exceeding 40% in the analysed settlements (Draževac, Konatice and Poljane) between 1961 and 2022, from over 90% of the economically active population in 1961 to less than 2% in 2022. Ultimately, by highlighting the socioeconomic impacts of riverbank erosion such as irreversible agricultural land loss and rural livelihood disruption, these findings offer valuable insights for spatial planning and sustainable land management.","蜿蜒河流的侧向河岸侵蚀是塞尔维亚科卢巴拉河（Kolubara River）流域下游一个重要的地貌过程。该过程直接影响土地利用和农业生产力，并引发更广泛的环境、社会经济和人口变化。历史上，科卢巴拉河岸曾被密集用于农业生产。如今，在自然过程与人类活动相互作用驱动下的侧向河道迁移和河岸侵蚀，直接威胁着沿河传统上活跃的地块。本研究考察了奥布雷诺瓦茨市（Obrenovac Municipality）科卢巴拉河四个活跃曲流段在97年期间（1930—2026年）河岸侵蚀的长期动态。主要任务包括：量化侧向河道迁移和土地流失速率，分析受威胁地块内的土地利用变化，以及调查受影响居民点的社会经济后果和人口动态。方法框架整合了地籍图分析、遥感技术（无人机，UAVs）和GIS软件，并结合人口与土地利用数据分析。结果表明，在观测期内侧向河道迁移最大距离达259.3 m，土地流失总量为17.6 ha，其中大部分为耕地。这一减少直接影响了当地农业部门，估计土地流失价值约为24,122美元。人口分析显示，1961年至2022年间，所分析居民点（Draževac、Konatice和Poljane）的人口下降幅度超过40%，经济活动人口比例从1961年的超过90%降至2022年的不足2%。最终，通过突出河岸侵蚀的社会经济影响，如不可逆的农业土地流失和农村生计破坏，这些发现为空间规划和可持续土地管理提供了有价值的见解。","Stanovnistvo","2026-10-19T00:00:00Z",{"impact":77,"substance":77,"depth":77,"authority":77,"freshness":77,"relevant":77,"comment":450},"塞尔维亚河岸侵蚀案例研究，属地理与土地利用领域，与三农及农业信息化主题关联弱，不建议进入每日精选。",[452],{"name":447,"url":444},[28,29,454,455],"土壤侵蚀","乡村人口",{"code":46,"slug":118,"name":119,"path":120,"parent_code":121},[],{"status":124,"locked":14,"version":34,"method":35,"evidence":459},{"candidates":460,"agricultural_evidence":40},[461,465,469,474],{"code":46,"score":273,"supported":40,"title":462,"abstract":463,"tags":464},[],[49,50],[28],{"code":146,"score":62,"supported":40,"title":466,"abstract":467,"tags":468},[],[150],[],{"code":295,"score":62,"supported":40,"title":470,"abstract":471,"tags":473},[],[472],"methodological framework",[],{"code":39,"score":21,"supported":14,"title":475,"abstract":476,"tags":477},[],[],[29],[479,480],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":482,"openalex_id":484,"authors":485,"venue":447,"cited_by_count":77,"oa_url":444,"card":505,"direction":95,"ingested_from":96},"W7222945959",[486,489,492,494,497,500,503],{"name":487,"orcid":488},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":490,"orcid":491},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":493,"orcid":9},"Милан Радовић",{"name":495,"orcid":496},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":498,"orcid":499},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":501,"orcid":502},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":504,"orcid":9},"Manojlović Stefan",{"tldr":506,"method":507,"finding":508,"direction":509,"opportunity":510},"研究塞尔维亚Kolubara河岸侵蚀97年间的河道迁移、耕地损失及社会经济影响。","结合地籍图、UAV遥感、GIS与人口土地利用数据分析四条曲流。","最大侧向迁移259.3米，损失17.6公顷耕地约2.4万美元，村落人口降超40%。","其他","可将河岸侵蚀遥感监测与农户生计、土地价值评估耦合，构建侵蚀风险预警与补偿决策模型。","2026-10-20T23:30:18.982943Z"]