[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-5577":3,"related-5577":110},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":77,"slug":80,"view_count":81,"doi":82,"paper":83,"created_at":109},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。",null,"Journal of Hyperspectral Remote Sensing","2026-10-20T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,17,12,9,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"遥感监测","土地利用","森林保护","亚马逊","CBERS-4",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":42,"reason":76},[39,48,55,63,70],{"code":40,"score":41,"supported":42,"title":43,"abstract":44,"tags":47},"H6",7,true,[],[45,46],"生物多样性","biodiversity",[28],{"code":49,"score":41,"supported":42,"title":50,"abstract":51,"tags":54},"I3",[],[52,53],"遥感","remote sensing",[27],{"code":56,"score":57,"supported":42,"title":58,"abstract":59,"tags":62},"H7",6,[],[60,61],"气候变化","climate change",[],{"code":64,"score":65,"supported":42,"title":66,"abstract":67,"tags":69},"H2",3,[],[68],"soil",[],{"code":71,"score":65,"supported":42,"title":72,"abstract":73,"tags":75},"I7",[],[74],"研究方法",[],"多个产业证据相当，需要确认主归属",[78,79],"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",0,"10.29150\u002Fjhrs.v16i4.269068",{"doi":82,"openalex_id":84,"authors":85,"venue":10,"cited_by_count":81,"oa_url":6,"card":102,"direction":106,"ingested_from":108},"W7223141286",[86,89,91,94,97,100],{"name":87,"orcid":88},"Ubiratan Joaquim da Silva","https:\u002F\u002Forcid.org\u002F0000-0001-7995-6416",{"name":90,"orcid":9},"Gabriel Henrique Santos Silva",{"name":92,"orcid":93},"Juarez Antonio da Silva","https:\u002F\u002Forcid.org\u002F0000-0002-2898-0309",{"name":95,"orcid":96},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":98,"orcid":99},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998",{"name":101,"orcid":9},"Sylvana Melo dos Santos",{"tldr":103,"method":104,"finding":105,"direction":106,"opportunity":107},"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%。","农业遥感与作物表型","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。","openalex","2026-10-22T11:20:42.901761Z",{"total":57,"page":22,"page_size":57,"items":111},[112,204,263,329,373,447],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":126,"tags":128,"primary_topic":9,"secondary_topics":130,"classification":131,"search_phrases":164,"slug":167,"view_count":81,"doi":168,"paper":169,"created_at":203},4674,"Impact assessment of co-management strategies on forest ecosystem in protected areas of Bangladesh using remote sensing and GIS based analysis","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01052-0","Co-management of Protected Areas (PAs) has emerged as a strategy to make forest restoration and biodiversity conservation more effective by encouraging the active participation of local people. This study analyzed the co-management effects in two protected areas i.e. Dharmapur Shal Forest and Nawabganj National Park in Dinajpur District, Bangladesh. Land use and land cover change were monitored between 1990 and 2022 using Landsat images. The study considered four main categories: forests, encroached areas, barren lands and water bodies. The findings indicated that forest cover diminished in both forests between 1990 and 2003, but experienced a significant recovery by 2022. The expanse of Dharmapur Shal forest diminished from 552.05 hectares in 1990 to 469.95 hectares in 2003, then rising to 550.41 hectares in 2022. While the forest cover in Nawabganj National Park declined from 454.18 hectares to 393.72 hectares, it recovered to 432.36 hectares in 2022, suggesting a positive association between co-management implementation and forest recovery. However, 2023 ground-truth data indicates that while native Shorea robusta remains dominant (60–65% in Dharmapur; 75–80% in Nawabganj), the forest recovery includes a significant component of exotic plantations ( Acacia and Eucalyptus ) established in previously degraded areas. This suggests a shift in forest composition alongside overall increase in canopy cover. Classification accuracy for 2022 was assessed using a confusion matrix against 120 ground-truth points, achieving an overall accuracy of 86.67% (Kappa = 0.82). Producer's and user's accuracies ranged from 78.57 to 93.33%, with the primary confusion observed between Barren Land and Encroached Area classes. Barren land initially increased and decreased in both areas, while water bodies remained relatively stable. Ecosystem service values (ESVs) related to carbon sequestration, biodiversity and water regulation also improved, based on global valuation coefficients rather than site-specific estimates. Stakeholder perception analysis, including forest officials and local communities, revealed strong support for co-management, citing enhanced forest cover, increased native species and reduced illegal extraction. However, challenges remain in achieving historical biodiversity levels. This study highlights the potential of co-management in forest restoration and sustainable land governance in developing countries and provides evidence-based insights to inform future policy and planning.","保护区（Protected Areas, PAs）共管已成为一种策略，通过鼓励当地民众积极参与，使森林恢复和生物多样性保护更加有效。本研究分析了孟加拉国迪纳杰布尔县两个保护区的共管效果，即达尔马普尔娑罗林和纳瓦布根杰国家公园。利用Landsat影像监测了1990年至2022年间的土地利用与土地覆盖变化。研究考虑了四个主要类别：森林、侵占区、裸地和水体。研究结果表明，1990年至2003年间，两处森林的森林覆盖均有所减少，但到2022年经历了显著恢复。达尔马普尔娑罗林面积从1990年的552.05公顷减少至2003年的469.95公顷，随后在2022年增至550.41公顷。纳瓦布根杰国家公园的森林覆盖从454.18公顷下降至393.72公顷，但在2022年恢复至432.36公顷，表明共管实施与森林恢复之间存在正相关关系。然而，2023年地面实况数据表明，尽管本地娑罗双树（Shorea robusta）仍占主导地位（达尔马普尔为60–65%；纳瓦布根杰为75–80%），但森林恢复中包含了大量在先前退化区域建立的外来人工林（金合欢属和桉属）。这表明森林组成发生了转变，同时冠层覆盖总体增加。利用混淆矩阵对2022年分类精度进行了评估，以120个地面实况点为参照，总体精度达到86.67%（Kappa = 0.82）。生产者精度和用户精度范围为78.57%至93.33%，主要混淆出现在裸地和侵占区类别之间。两处区域的裸地均呈现先增后减的趋势，而水体保持相对稳定。基于全球估值系数而非特定地点估算，与碳固存、生物多样性和水资源调节相关的生态系统服务价值（ESVs）也有所改善。利益相关者感知分析（包括林业官员和当地社区）显示，各方对共管给予强烈支持，理由包括森林覆盖增加、本地物种增多以及非法采伐减少。然而，在达到历史生物多样性水平方面仍存在挑战。本研究凸显了共管在发展中国家森林恢复和可持续土地治理中的潜力，并提供了基于证据的见解，以期为未来","Discover Environment","2026-10-09T00:00:00Z",71,{"impact":20,"substance":122,"depth":19,"authority":123,"freshness":124,"relevant":22,"comment":125},21,13,8,"基于Landsat遥感与GIS的孟加拉国保护区共管成效评估，方法规范、数据翔实，对发展中国家森林治理有参考价值，但与国内三农及农业信息化直接关联较弱。",[127],{"name":118,"url":115},[45,27,28,29,129],"社区共管",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":132},{"candidates":133,"agricultural_evidence":14,"reason":163},[134,141,145,151,157],{"code":40,"score":135,"supported":42,"title":136,"abstract":137,"tags":140},11,[],[45,138,46,139],"生态系统服务","ecosystem service",[28,45],{"code":49,"score":21,"supported":42,"title":142,"abstract":143,"tags":144},[53],[],[27],{"code":146,"score":124,"supported":42,"title":147,"abstract":149,"tags":150},"I4",[148],"GIS",[],[],{"code":152,"score":65,"supported":42,"title":153,"abstract":154,"tags":156},"H3",[],[155],"水资源",[],{"code":158,"score":65,"supported":42,"title":159,"abstract":160,"tags":162},"H4",[],[161],"carbon sequestration",[],"农业相关性需要复核",[165,166],"孟加拉国 保护区 森林共管","Landsat 遥感 森林覆盖变化","孟加拉国保护区森林共管-4674","10.1007\u002Fs44274-026-01052-0",{"doi":168,"openalex_id":170,"authors":171,"venue":118,"cited_by_count":81,"oa_url":196,"card":197,"direction":202,"ingested_from":108},"W7221074353",[172,174,177,179,182,185,187,189,191,194],{"name":173,"orcid":9},"Md Manik Ali",{"name":175,"orcid":176},"Sudipto Saha Dipto","https:\u002F\u002Forcid.org\u002F0000-0001-6479-9593",{"name":178,"orcid":9},"Md Shafiqul Bari",{"name":180,"orcid":181},"Mahmuod Abubakar Bashir","https:\u002F\u002Forcid.org\u002F0000-0002-6296-8522",{"name":183,"orcid":184},"Nazmin Akter","https:\u002F\u002Forcid.org\u002F0009-0006-7337-2798",{"name":186,"orcid":9},"Sujan Kumar Shil",{"name":188,"orcid":9},"Md Farid Hasan",{"name":190,"orcid":9},"Md Asif Adnan Prince",{"name":192,"orcid":193},"Muhammad Madnee","https:\u002F\u002Forcid.org\u002F0000-0002-1362-7473",{"name":195,"orcid":9},"Abdullahi Omar Mohamud","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01052-0.pdf",{"tldr":198,"method":199,"finding":200,"direction":106,"opportunity":201},"评估孟加拉国两个保护区共管策略对森林生态的影响，发现森林覆盖先减后增但树种组成改变。","用1990-2022年Landsat影像做土地利用分类，结合地面验证与利益相关者","共管后森林覆盖显著恢复，但恢复中混入大量外来树种，原生生物多样性未完全恢复。","可延伸研究共管政策下外来树种扩张对碳汇与生物多样性的长期权衡，并开发本地化生态服务估值模型。","智慧农业 \u002F 农业物联网","2026-10-11T23:30:17.636276Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":211,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":212,"score_detail":213,"sources":216,"tags":218,"primary_topic":221,"secondary_topics":226,"classification":227,"search_phrases":246,"slug":249,"view_count":242,"doi":250,"paper":251,"created_at":262},2528,"Assessment of Deforestation in Mbaav 1 Forest Reserve in Gwer East Local Government Area of Benue State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no5.2025.pg28.42","The study investigates the ongoing issue of deforestation in Mbaav1 Forest Reserve, located in Gwer East Local Government Area (LGA), Benue State, Nigeria. Deforestation in the reserve is exacerbated by socio-economic factors such as agricultural expansion, population growth, logging, and development projects. Understanding these drivers is crucial for effective policy formulation and sustainable forest management. The primary aim of this study is to assess changes in forest cover in Mbaav1 Forest Reserve between 1989 and 2019, with particular emphasis on the socio-economic factors driving deforestation. The study specifically examines the socio-economic characteristics of local communities, land use changes, the extent of deforestation, and the key drivers of these changes. Data were collected using both primary and secondary methods. Primary data were gathered through semi-structured questionnaires and interviews with 387 community members, while secondary data were obtained from Landsat satellite imagery (1989, 2009, and 2019), For socio-economic analysis, simple descriptive statistics were used to present data on respondents' characteristics, including income, age, education, occupation, and household size. To examine the influence of socioeconomic factors on deforestation, a binary logistic regression model was adopted. Landsat satellite imageries were analyzed using Geographic Information Systems (GIS) and remote sensing tools. The supervised and unsupervised classification methods were used to detect and classify land cover changes. The Normalized Difference Vegetation Index (NDVI) was also used to map vegetation changes. The results of analysis revealed significant relationships between deforestation and factors such as income, population growth, developmental projects, and land-use activities. Notably, income had the highest influence on deforestation, with a unit increase in income leading to a 1.35 increase in deforestation (β = 1.35, p\u003C0.05). Population growth (β = 1.02, p\u003C0.05) and development projects (β = 0.77, p\u003C0.05) also significantly contributed to forest loss. Logging (β = 0.10, p\u003C0.05) and farming (β = 0.39, p\u003C0.05) further exacerbated deforestation, although to a lesser degree. The percentage of land cover change between 1989, 2009, and 2019 revealed a consistent decline in forest area. In 1989, the forest area covered 75.56% of the total land, but by 2019, this had dropped to 36.25%. Meanwhile, farmland increased from 5.86% in 1989 to 29.83% in 2019, reflecting the expansion of agricultural activities. The study concludes that the rapid deforestation in Mbaav1 Forest Reserve is driven by human activities, particularly agricultural expansion and logging, compounded by socio-economic pressures such as income and population growth. The study recommends that addressing these drivers through sustainable practices like agroforestry, improving forest management, and implementing policies that integrate forest conservation with agricultural development are essential. Furthermore, public awareness campaigns on the environmental and socio-economic consequences of deforestation are crucial for fostering community participation in conservation efforts.","本研究探讨了尼日利亚贝努埃州格韦尔东地方政府区（LGA）内Mbaav1森林保护区持续存在的毁林问题。农业扩张、人口增长、伐木和开发项目等社会经济因素加剧了该保护区的毁林。理解这些驱动因素对于有效制定政策和可持续森林管理至关重要。本研究的主要目的是评估1989年至2019年间Mbaav1森林保护区森林覆盖的变化，特别关注驱动毁林的社会经济因素。研究具体考察了当地社区的社会经济特征、土地利用变化、毁林程度以及这些变化的主要驱动因素。数据采用一手和二手方法收集。一手数据通过半结构化问卷和对387名社区成员的访谈获取，二手数据来自Landsat卫星影像（1989年、2009年和2019年）。在社会经济分析方面，采用简单描述性统计来呈现受访者特征数据，包括收入、年龄、教育程度、职业和家庭规模。为考察社会经济因素对毁林的影响，采用了二元逻辑回归模型。Landsat卫星影像使用地理信息系统（GIS）和遥感工具进行分析。采用监督分类和非监督分类方法检测和分类土地覆盖变化。归一化植被指数（NDVI）也被用于绘制植被变化图。分析结果显示，毁林与收入、人口增长、开发项目和土地利用活动等因素之间存在显著关系。值得注意的是，收入对毁林的影响最大，收入每增加一个单位，毁林增加1.35（β = 1.35，p\u003C0.05）。人口增长（β = 1.02，p\u003C0.05）和开发项目（β = 0.77，p\u003C0.05）也显著导致森林丧失。伐木（β = 0.10，p\u003C0.05）和耕作（β = 0.39，p\u003C0.05）进一步加剧了毁林，但程度较低。1989年、2009年和2019年间的土地覆盖变化百分比显示森林面积持续下降。1989年，森林面积占总土地的75.56%，但到2019年，这一比例降至36.25%。与此同时，农田从1989年的5.86%增加至","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-14T00:00:00Z",61,{"impact":124,"substance":17,"depth":214,"authority":20,"freshness":124,"relevant":22,"comment":215},15,"基于Landsat与NDVI的30年森林覆盖变化实证研究，数据扎实但属尼日利亚地方案例，对国内三农信息化仅有方法借鉴价值。",[217],{"name":210,"url":207},[27,28,219,29,220],"农业扩张","毁林治理",{"code":146,"slug":222,"name":223,"path":224,"parent_code":225},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":228,"locked":14,"version":35,"method":36,"evidence":229},"classified",{"candidates":230,"agricultural_evidence":42},[231,237,241],{"code":146,"score":21,"supported":42,"title":232,"abstract":233,"tags":236},[],[234,148,235],"地理信息","geographic information",[],{"code":49,"score":41,"supported":42,"title":238,"abstract":239,"tags":240},[],[52,53],[27],{"code":40,"score":242,"supported":14,"title":243,"abstract":244,"tags":245},2,[],[],[28,220],[247,248],"农业扩张 土地利用 森林保护 毁林治理","农业扩张 土地利用","农业扩张土地利用森林保护毁林治理-2528","10.56201\u002Fijaes.vol.11.no5.2025.pg28.42",{"doi":250,"openalex_id":252,"authors":253,"venue":210,"cited_by_count":81,"oa_url":256,"card":257,"direction":106,"ingested_from":108},"W7212586088",[254],{"name":255,"orcid":9},"I. Verinumbe","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJAES\u002FVOL. 11 NO. 5 2025\u002FASSESSMENT OF DEFORESTATION 28-42.pdf",{"tldr":258,"method":259,"finding":260,"direction":106,"opportunity":261},"评估尼日利亚Mbaav1森林保护区1989-2019年森林砍伐，并分析其社会经济驱动因素。","Landsat影像、GIS与遥感分类、NDVI，结合387份问卷和二元逻辑回归。","森林覆盖率从75.56%降至36.25%，收入、人口增长和开发项目是主要驱动因素。","可结合多源遥感与农户调查，在热带森林区构建社会经济-遥感耦合的砍伐预警模型。","2026-09-15T23:30:20.501919Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":269,"source_url":266,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":270,"sources":272,"tags":274,"primary_topic":277,"secondary_topics":282,"classification":284,"search_phrases":306,"slug":309,"view_count":81,"doi":310,"paper":311,"created_at":328},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":81,"substance":81,"depth":81,"authority":81,"freshness":81,"relevant":81,"comment":271},"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。",[273],{"name":269,"url":266},[275,27,28,276],"机器学习","城市扩张",{"code":278,"slug":279,"name":280,"path":281,"parent_code":225},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[283],{"code":146,"slug":222,"name":223,"path":224,"parent_code":225},{"status":228,"locked":14,"version":35,"method":36,"evidence":285},{"candidates":286,"agricultural_evidence":42},[287,292,298,302],{"code":278,"score":214,"supported":42,"title":288,"abstract":290,"tags":291},[289],"machine learning",[275,289],[275],{"code":146,"score":21,"supported":42,"title":293,"abstract":294,"tags":297},[],[295,148,296],"空间分析","spatial analysis",[],{"code":49,"score":41,"supported":42,"title":299,"abstract":300,"tags":301},[],[52,53],[27],{"code":40,"score":22,"supported":14,"title":303,"abstract":304,"tags":305},[],[],[28],[307,308],"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":310,"openalex_id":312,"authors":313,"venue":269,"cited_by_count":81,"oa_url":266,"card":323,"direction":106,"ingested_from":108},"W7223022155",[314,317,319,321],{"name":315,"orcid":316},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":318,"orcid":9},"Goanar Jal Dup",{"name":320,"orcid":9},"Weyesa Merga Chemeda",{"name":322,"orcid":9},"Shankar Karuppannan",{"tldr":324,"method":325,"finding":326,"direction":106,"opportunity":327},"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":330,"title":331,"url":332,"summary":333,"summary_zh":9,"content":9,"source_name":334,"source_url":332,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":335,"sources":337,"tags":339,"primary_topic":9,"secondary_topics":341,"classification":342,"search_phrases":353,"slug":356,"view_count":81,"doi":357,"paper":358,"created_at":372},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":81,"substance":81,"depth":81,"authority":81,"freshness":81,"relevant":81,"comment":336},"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。",[338],{"name":334,"url":332},[27,28,340],"城市生态",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":343},{"candidates":344,"agricultural_evidence":14,"reason":163},[345,349],{"code":49,"score":20,"supported":42,"title":346,"abstract":347,"tags":348},[53],[53],[27],{"code":40,"score":242,"supported":14,"title":350,"abstract":351,"tags":352},[],[],[28,340],[354,355],"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":357,"openalex_id":359,"authors":360,"venue":334,"cited_by_count":81,"oa_url":371,"card":9,"direction":106,"ingested_from":108},"W7223013355",[361,363,365,368],{"name":362,"orcid":9},"Md Saikoth Jahan",{"name":364,"orcid":9},"Mst Arjumoni Anu",{"name":366,"orcid":367},"Md. Inzamul Haque","https:\u002F\u002Forcid.org\u002F0000-0003-1756-5561",{"name":369,"orcid":370},"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":374,"title":375,"url":376,"summary":377,"summary_zh":378,"content":9,"source_name":379,"source_url":376,"published_at":380,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":81,"score_detail":381,"sources":383,"tags":385,"primary_topic":388,"secondary_topics":392,"classification":393,"search_phrases":413,"slug":416,"view_count":81,"doi":417,"paper":418,"created_at":446},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":81,"substance":81,"depth":81,"authority":81,"freshness":81,"relevant":81,"comment":382},"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。",[384],{"name":379,"url":376},[27,28,386,387],"土壤侵蚀","乡村人口",{"code":49,"slug":389,"name":390,"path":391,"parent_code":225},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[],{"status":228,"locked":14,"version":35,"method":36,"evidence":394},{"candidates":395,"agricultural_evidence":42},[396,400,404,409],{"code":49,"score":41,"supported":42,"title":397,"abstract":398,"tags":399},[],[52,53],[27],{"code":146,"score":65,"supported":42,"title":401,"abstract":402,"tags":403},[],[148],[],{"code":71,"score":65,"supported":42,"title":405,"abstract":406,"tags":408},[],[407],"methodological framework",[],{"code":40,"score":22,"supported":14,"title":410,"abstract":411,"tags":412},[],[],[28],[414,415],"Kolubara 河岸侵蚀","Obrenovac 土地利用","Kolubara河岸侵蚀-5390","10.59954\u002Fstnv.797",{"doi":417,"openalex_id":419,"authors":420,"venue":379,"cited_by_count":81,"oa_url":376,"card":440,"direction":106,"ingested_from":108},"W7222945959",[421,424,427,429,432,435,438],{"name":422,"orcid":423},"Slavoljub Dragićević","https:\u002F\u002Forcid.org\u002F0000-0002-1417-5405",{"name":425,"orcid":426},"Marko Langović","https:\u002F\u002Forcid.org\u002F0000-0002-4614-7265",{"name":428,"orcid":9},"Милан Радовић",{"name":430,"orcid":431},"Radislav Tošić","https:\u002F\u002Forcid.org\u002F0000-0002-3994-125X",{"name":433,"orcid":434},"Milica Langović","https:\u002F\u002Forcid.org\u002F0000-0002-6979-377X",{"name":436,"orcid":437},"Novica Lovrić","https:\u002F\u002Forcid.org\u002F0009-0006-0838-4370",{"name":439,"orcid":9},"Manojlović Stefan",{"tldr":441,"method":442,"finding":443,"direction":444,"opportunity":445},"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",{"id":448,"title":449,"url":450,"summary":451,"summary_zh":452,"content":9,"source_name":453,"source_url":450,"published_at":454,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":455,"score_detail":456,"sources":459,"tags":461,"primary_topic":464,"secondary_topics":465,"classification":466,"search_phrases":481,"slug":484,"view_count":81,"doi":485,"paper":486,"created_at":504},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)","2026-10-17T00:00:00Z",55,{"impact":124,"substance":17,"depth":457,"authority":57,"freshness":21,"relevant":22,"comment":458},14,"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。",[460],{"name":453,"url":450},[27,28,462,463],"植被覆盖","卢旺达农业",{"code":146,"slug":222,"name":223,"path":224,"parent_code":225},[],{"status":228,"locked":14,"version":35,"method":36,"evidence":467},{"candidates":468,"agricultural_evidence":42},[469,473,477],{"code":146,"score":57,"supported":42,"title":470,"abstract":471,"tags":472},[],[234,235],[],{"code":49,"score":242,"supported":14,"title":474,"abstract":475,"tags":476},[],[],[462,27],{"code":40,"score":22,"supported":14,"title":478,"abstract":479,"tags":480},[],[],[28],[482,483],"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":485,"openalex_id":487,"authors":488,"venue":453,"cited_by_count":81,"oa_url":498,"card":499,"direction":106,"ingested_from":108},"W7222817253",[489,491,493,495],{"name":490,"orcid":9},"Vincent Hategekimana",{"name":492,"orcid":9},"Richard Mind J. E.",{"name":494,"orcid":9},"Aboubakar Gasirabo",{"name":496,"orcid":497},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":500,"method":501,"finding":502,"direction":106,"opportunity":503},"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"]