[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2528":3,"related-2528":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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":67,"slug":70,"view_count":63,"doi":71,"paper":72,"created_at":86},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",null,"INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-14T00:00:00Z","论文",10,false,61,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,18,15,12,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"遥感监测","土地利用","农业扩张","森林保护","毁林治理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,61],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":52},9,true,[],[49,50,51],"地理信息","GIS","geographic information",[],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":60},"I3",7,[],[58,59],"遥感","remote sensing",[26],{"code":62,"score":63,"supported":14,"title":64,"abstract":65,"tags":66},"H6",2,[],[],[27,30],[68,69],"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":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":77,"oa_url":78,"card":79,"direction":83,"ingested_from":85},"W7212586088",[75],{"name":76,"orcid":9},"I. Verinumbe",0,"https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJAES\u002FVOL. 11 NO. 5 2025\u002FASSESSMENT OF DEFORESTATION 28-42.pdf",{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"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。","农业遥感与作物表型","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。","openalex","2026-09-15T23:30:20.501919Z",{"total":88,"page":21,"page_size":88,"items":89},6,[90,157,215,279,351,420],{"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":77,"score_detail":98,"sources":100,"tags":102,"primary_topic":105,"secondary_topics":110,"classification":112,"search_phrases":134,"slug":137,"view_count":77,"doi":138,"paper":139,"created_at":156},5485,"Integrating machine learning and geospatial technologies for spatio-temporal analysis of urban expansion and land use transformation in Gambella Town, Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1371\u002Fjournal.pone.0360419","Rapid urbanization and the associated expansion of built-up areas are transforming land resources, ecosystem functions, and the spatial structure of cities. Distinguishing urban growth, urban expansion, and urban sprawl is essential for understanding the processes and patterns of urban transformation and supporting sustainable urban planning. This study integrates machine learning and geospatial technologies to analyze spatio-temporal land use and land cover (LULC) dynamics and urban expansion in Gambella Town, Ethiopia. Multi-temporal Landsat satellite images from 2005, 2015, and 2025 were processed using remote sensing techniques and a supervised machine learning approach based on the Random Forest (RF) classifier. Six major LULC classes were identified: Agricultural Land, Bush with Grass, Built-Up Area, Bare Land, Forest Land, and Water Bodies. The classification achieved overall accuracies of 90%, 90%, and 89% for 2005, 2015, and 2025, respectively. The results revealed substantial land transformation and rapid physical urban expansion, with the built-up area increasing from 93 ha (2.0%) in 2005–1,956 ha (41.3%) in 2025. This expansion was accompanied by major transformations of surrounding land resources, particularly the conversion of Bush with Grass and other open areas to built-up land. Agricultural land increased from 634 ha (13.4%) to 1,750 ha (36.9%), whereas Bush with Grass declined from 3,375 ha (71.1%) to 652 ha (13.7%). Socio-economic information collected from 277 respondents indicated that population growth, rural–urban migration, housing demand, employment opportunities, and infrastructure development were major drivers of urban expansion. The study distinguishes urban expansion, represented by the increase in built-up land, from urban sprawl, which refers to the spatial pattern and form of outward urban development. The integration of Random Forest classification and GIS-based spatial analysis provides valuable evidence of land transformation, vegetation loss, and increasing pressure on urban services, supporting more sustainable urban planning and land management in Gambella Town.","快速城市化及与之相关的建成区扩张正在改变土地资源、生态系统功能和城市空间结构。区分城市增长（urban growth）、城市扩张（urban expansion）和城市蔓延（urban sprawl），对于理解城市转型的过程与格局、支撑可持续城市规划至关重要。本研究融合机器学习与地理空间技术，分析埃塞俄比亚甘贝拉镇（Gambella Town）土地利用与土地覆盖（LULC）的时空动态及城市扩张。采用遥感技术和基于随机森林（RF）分类器的监督机器学习方法，处理了2005年、2015年和2025年的多时相Landsat卫星影像。研究识别出六类主要LULC类型：农业用地、灌草地、建成区、裸地、林地和水体。2005年、2015年和2025年的分类总体精度分别为90%、90%和89%。结果表明，土地发生了显著转变，城市实体空间快速扩张，建成区面积从2005年的93公顷（2.0%）增至2025年的1,956公顷（41.3%）。这一扩张伴随着周边土地资源的重大转变，尤其是灌草地及其他开阔地被转化为建成用地。农业用地从634公顷（13.4%）增至1,750公顷（36.9%），而灌草地则从3,375公顷（71.1%）降至652公顷（13.7%）。从277名受访者收集的社会经济信息表明，人口增长、城乡迁移、住房需求、就业机会和基础设施建设是城市扩张的主要驱动因素。本研究将城市扩张（以建成用地增加为表征）与城市蔓延（指城市向外发展的空间格局与形态）加以区分。随机森林分类与基于GIS的空间分析的融合，为土地转变、植被丧失及城市服务压力加剧提供了有价值的证据，有助于甘贝拉镇实现更可持续的城市规划与土地管理。","PLoS ONE","2026-10-20T00:00:00Z",{"impact":77,"substance":77,"depth":77,"authority":77,"freshness":77,"relevant":77,"comment":99},"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。",[101],{"name":96,"url":93},[103,26,27,104],"机器学习","城市扩张",{"code":106,"slug":107,"name":108,"path":109,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[111],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":113},{"candidates":114,"agricultural_evidence":46},[115,120,126,130],{"code":106,"score":19,"supported":46,"title":116,"abstract":118,"tags":119},[117],"machine learning",[103,117],[103],{"code":32,"score":45,"supported":46,"title":121,"abstract":122,"tags":125},[],[123,50,124],"空间分析","spatial analysis",[],{"code":54,"score":55,"supported":46,"title":127,"abstract":128,"tags":129},[],[58,59],[26],{"code":62,"score":21,"supported":14,"title":131,"abstract":132,"tags":133},[],[],[27],[135,136],"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":138,"openalex_id":140,"authors":141,"venue":96,"cited_by_count":77,"oa_url":93,"card":151,"direction":83,"ingested_from":85},"W7223022155",[142,145,147,149],{"name":143,"orcid":144},"Kanenus Fufa Dararo","https:\u002F\u002Forcid.org\u002F0009-0007-5747-1558",{"name":146,"orcid":9},"Goanar Jal Dup",{"name":148,"orcid":9},"Weyesa Merga Chemeda",{"name":150,"orcid":9},"Shankar Karuppannan",{"tldr":152,"method":153,"finding":154,"direction":83,"opportunity":155},"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":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":165,"score_detail":166,"sources":169,"tags":171,"primary_topic":174,"secondary_topics":175,"classification":176,"search_phrases":191,"slug":194,"view_count":77,"doi":195,"paper":196,"created_at":214},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":17,"substance":18,"depth":167,"authority":88,"freshness":45,"relevant":21,"comment":168},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。",[170],{"name":163,"url":160},[26,27,172,173],"植被覆盖","卢旺达农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":177},{"candidates":178,"agricultural_evidence":46},[179,183,187],{"code":32,"score":88,"supported":46,"title":180,"abstract":181,"tags":182},[],[49,51],[],{"code":54,"score":63,"supported":14,"title":184,"abstract":185,"tags":186},[],[],[172,26],{"code":62,"score":21,"supported":14,"title":188,"abstract":189,"tags":190},[],[],[27],[192,193],"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":195,"openalex_id":197,"authors":198,"venue":163,"cited_by_count":77,"oa_url":208,"card":209,"direction":83,"ingested_from":85},"W7222817253",[199,201,203,205],{"name":200,"orcid":9},"Vincent Hategekimana",{"name":202,"orcid":9},"Richard Mind J. E.",{"name":204,"orcid":9},"Aboubakar Gasirabo",{"name":206,"orcid":207},"Didier Ntwali","https:\u002F\u002Forcid.org\u002F0000-0002-6914-6652","https:\u002F\u002Fwww.ijisrt.com\u002Fassets\u002Fupload\u002Ffiles\u002FIJISRT26OCT696.pdf",{"tldr":210,"method":211,"finding":212,"direction":83,"opportunity":213},"分析卢旺达Muhoza地区2015–2025年土地利用变化对植被覆盖丧失的影响。","多时相Landsat与Sentinel-2影像、GPS实地验证、GIS与NDVI","六类地物制图揭示建设用地扩张等LULC变化是植被覆盖减少的主要驱动。","可结合高分辨率时序遥感与驱动因子模型，量化小农户尺度城镇化对耕地与植被的胁迫。","2026-10-19T23:30:23.040351Z",{"id":216,"title":217,"url":218,"summary":219,"summary_zh":220,"content":9,"source_name":221,"source_url":218,"published_at":222,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":223,"score_detail":224,"sources":229,"tags":231,"primary_topic":234,"secondary_topics":235,"classification":236,"search_phrases":251,"slug":254,"view_count":63,"doi":255,"paper":256,"created_at":278},4596,"Spatio-temporal analysis of land use\u002Fland cover (LULC) changes using GIS techniques in Upper Bhavani River Basin, Nilgiri Hills, India","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-00915-w","The study investigates the land use and land cover (LULC) changes in Upper Bhavani River basin of the Western Ghats over the past four decades (1989–2019) using remote sensing (RS) and geographic information system (GIS) techniques. It also explores the correlation between LULC changes and population growth during this period. The study highlights the importance of mapping and monitoring LULC changes in developing better management strategies for the sustainable development of natural resources. The iterative self-organising data analysis technique was used for unsupervised classification to create LULC maps of the riverbasin.The study identified and categorised seven LULC categories, namely water bodies, deciduous forest, evergreen forest, agriculture, plantation, barren land, and settlement. The results of the study showed a complex series of modifications in the LULC in the study basin over the past forty years due to policy changes and conventional agroforestry systems. The study revealed a decrease in agriculture, evergreen forest, and deciduous forest by − 6.88%, − 31.42%, and − 9.88%, respectively, while water bodies, plantations, barren land, and settlement area increased by 7.06%, 17.64%, 13.29%, and 10.23%, respectively. The findings of the study provide insights into specific changes in LULC and help predict which land uses will be impacted in the future. The overall accuracy of the classification was0.73.Overall, this study emphasised the importance of monitoring and mapping LULC changes using RS and GIS techniques to develop better management strategies for the sustainable development of natural resources. The findings of the study were useful for policymakers, land-use planners, and conservationists in developing effective strategies to maintain the ecological balance of the Western Ghats.","本研究利用遥感（RS）和地理信息系统（GIS）技术，探讨了过去四十年（1989—2019年）西高止山脉上巴瓦尼河流域的土地利用与土地覆盖（LULC）变化，并分析了该时期LULC变化与人口增长之间的相关性。研究强调了制图和监测LULC变化对于制定自然资源可持续发展管理策略的重要性。采用迭代自组织数据分析技术进行非监督分类，以生成该流域的LULC图。研究识别并划分了七类LULC，分别为水体、落叶林、常绿林、农业、种植园、裸地和居民点。研究结果表明，受政策变化和传统农林业系统的影响，过去四十年研究流域的LULC经历了一系列复杂的改变。研究揭示，农业、常绿林和落叶林分别减少了−6.88%、−31.42%和−9.88%，而水体、种植园、裸地和居民点面积分别增加了7.06%、17.64%、13.29%和10.23%。研究结果揭示了LULC的具体变化，有助于预测未来哪些土地利用类型将受到影响。分类总体精度为0.73。总体而言，本研究强调了利用RS和GIS技术监测和制图LULC变化对于制定自然资源可持续发展管理策略的重要性。研究结果有助于政策制定者、土地利用规划者和保护主义者制定有效策略，以维持西高止山脉的生态平衡。","Discover Environment","2026-10-08T00:00:00Z",70,{"impact":20,"substance":225,"depth":226,"authority":227,"freshness":17,"relevant":21,"comment":228},20,17,13,"基于遥感与GIS的四十年土地利用变化实证研究，数据翔实、结论可靠，对流域生态管理与土地规划有参考价值，但属区域案例研究，公共影响层级有限。",[230],{"name":221,"url":218},[26,27,50,232,233],"流域治理","西高止山",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":237},{"candidates":238,"agricultural_evidence":46},[239,243,247],{"code":32,"score":18,"supported":46,"title":240,"abstract":241,"tags":242},[50],[49,50,51],[50],{"code":54,"score":55,"supported":46,"title":244,"abstract":245,"tags":246},[],[58,59],[26],{"code":62,"score":21,"supported":14,"title":248,"abstract":249,"tags":250},[],[],[27],[252,253],"土地利用 流域治理 西高止山 遥感监测","土地利用 流域治理","土地利用流域治理西高止山遥感监测-4596","10.1007\u002Fs44274-026-00915-w",{"doi":255,"openalex_id":257,"authors":258,"venue":221,"cited_by_count":77,"oa_url":272,"card":273,"direction":83,"ingested_from":85},"W7221028855",[259,261,264,266,268,270],{"name":260,"orcid":9},"Sapna Kinattinkara",{"name":262,"orcid":263},"Thangavelu Arumugam","https:\u002F\u002Forcid.org\u002F0000-0002-2984-283X",{"name":265,"orcid":9},"Karpagamani Vellingiri",{"name":267,"orcid":9},"Manoj Kumar Patley",{"name":269,"orcid":9},"Manimaran Arumugam",{"name":271,"orcid":9},"Nigatu Wondrade","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-00915-w.pdf",{"tldr":274,"method":275,"finding":276,"direction":83,"opportunity":277},"用遥感与GIS分析印度上巴瓦尼河流域1989-2019年土地利用变化及其与人口增长的关系。","遥感与GIS、ISODATA非监督分类，划分七类LULC，总体精度0.73。","农业、常绿林和落叶林分别减少6.88%、31.42%、9.88%，水体、种植园、裸地和居民地增加。","可结合高精度分类与人口、政策数据，构建LULC变化驱动力模型并预测未来情景。","2026-10-10T23:30:30.955766Z",{"id":280,"title":281,"url":282,"summary":283,"summary_zh":284,"content":9,"source_name":285,"source_url":282,"published_at":286,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":287,"score_detail":288,"sources":290,"tags":292,"primary_topic":295,"secondary_topics":301,"classification":303,"search_phrases":332,"slug":335,"view_count":21,"doi":336,"paper":337,"created_at":350},3903,"An Analysis of Land Use and Land Cover Change Detection Using Geospatial Technology-A Case Study of Phalsoond Tehsil in Jaisalmer District, Rajasthan, India","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.84983","Analysis of Land Use and Land Cover (LULC) change is significant indicators of environmental changes, resource utilization and sustainable development, especially in arid regions where land and water resources are very fragile. The present study has investigated the spatio-temporal analysis of LULC changes in Phascoond Tehsil of Jaisalmer District of Rajasthan, India from the year 2017 to 2024 using Remote sensing and Geographic Information System (GIS) techniques. To classify the study area, multi-temporal Sentinel-2 data with 10 m spatial resolution were provided by the Esri Sentinel-2 Land Cover Explorer, which was used to separate the area into five categories i.e. water bodies, agriculture land, built-up areas, bare ground and wasteland. LULC changes were quantified in terms of magnitude and direction using post-classification comparison and GIS-based change detection techniques. The findings show significant change in landscape over the year 2017 to 2024. Wasteland remained the dominant land-cover category but declined from 779.07 km² (78.28%) in 2017 to 726.86 km² (71.41%) in 2024. Agricultural area grew by 86.62 km² (+19.48% to 280.52 km²), adding 94.76 km² of wasteland. Gradual settlement and infrastructure development also resulted in an increase in built-up area from 3.89 km2 to 7.73 km2 . The water bodies were small in spatial extent and slight shifts happened between bare ground and other land-cover classes. The results clearly show that the Sentinel-2 data and GIS-based analysis is useful in monitoring LULC dynamics in data-sparse arid regions. The present extent of agriculture and development has occurred at a time when there is a need to manage the land and groundwater resources sustainably, monitor environment, and plan land use with climate change in Phalsoond tehsil of Jaisalmer District in western Rajasthan.","土地利用与土地覆盖（LULC）变化分析是环境变化、资源利用和可持续发展的重要指标，在土地与水资源极为脆弱的干旱地区尤为如此。本研究利用遥感与地理信息系统（GIS）技术，对印度拉贾斯坦邦杰伊瑟尔梅尔县Phascoond乡2017年至2024年间的LULC变化进行了时空分析。为对研究区进行分类，采用了Esri Sentinel-2土地覆盖浏览器提供的10 m空间分辨率多时相Sentinel-2数据，将区域划分为五个类别，即水体、农用地、建设用地、裸地和荒地。通过分类后比较和基于GIS的变化检测技术，从幅度和方向两方面对LULC变化进行了量化。研究结果表明，2017年至2024年间景观发生了显著变化。荒地仍为优势土地覆盖类型，但从2017年的779.07 km²（78.28%）下降至2024年的726.86 km²（71.41%）。农业面积增加了86.62 km²（增幅19.48%，达到280.52 km²），占用了94.76 km²的荒地。逐步的聚落与基础设施建设也导致建设用地从3.89 km²增加至7.73 km²。水体空间范围较小，裸地与其他土地覆盖类别之间发生了轻微转换。结果清楚表明，Sentinel-2数据与基于GIS的分析对于监测数据稀缺干旱地区的LULC动态具有实用价值。当前农业与开发的程度出现在这样一个时期：拉贾斯坦邦西部杰伊瑟尔梅尔县Phalsoond乡亟需可持续管理土地与地下水资源、监测环境，并在气候变化背景下进行土地利用规划。","International Journal for Research in Applied Science and Engineering Technology","2026-09-30T00:00:00Z",52,{"impact":88,"substance":18,"depth":167,"authority":88,"freshness":17,"relevant":21,"comment":289},"基于Sentinel-2与GIS的印度干旱区土地利用变化研究，方法规范但属区域性案例，对国内三农信息化参考价值有限。",[291],{"name":285,"url":282},[26,27,293,294],"干旱区农业","GIS应用",{"code":296,"slug":297,"name":298,"path":299,"parent_code":300},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[302],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":304},{"candidates":305,"agricultural_evidence":46},[306,313,317,321,328],{"code":296,"score":13,"supported":46,"title":307,"abstract":308,"tags":312},[],[309,310,311],"气候变化","干旱","climate change",[293],{"code":32,"score":45,"supported":46,"title":314,"abstract":315,"tags":316},[],[49,50,51],[],{"code":54,"score":55,"supported":46,"title":318,"abstract":319,"tags":320},[],[58,59],[26],{"code":322,"score":323,"supported":46,"title":324,"abstract":325,"tags":327},"H3",3,[],[326],"水资源",[],{"code":62,"score":21,"supported":14,"title":329,"abstract":330,"tags":331},[],[],[27],[333,334],"Jaisalmer 土地利用变化","Sentinel-2 遥感 干旱区","Jaisalmer土地利用变化-3903","10.22214\u002Fijraset.2026.84983",{"doi":336,"openalex_id":338,"authors":339,"venue":285,"cited_by_count":77,"oa_url":282,"card":345,"direction":83,"ingested_from":85},"W7214996325",[340,342],{"name":341,"orcid":9},"Deendayal .",{"name":343,"orcid":344},"Om Prakash","https:\u002F\u002Forcid.org\u002F0000-0003-1239-445X",{"tldr":346,"method":347,"finding":348,"direction":83,"opportunity":349},"基于Sentinel-2和GIS分析印度拉贾斯坦邦干旱区2017-2024年土地利用\u002F覆盖变化。","Sentinel-2 10m影像与GIS分类，采用分类后比较法检测LULC变化。","荒地减少52.21 km²，农业增加86.62 km²，建设用地翻倍，水资源面积小。","可结合多源遥感与机器学习，在数据稀疏干旱区开展高精度LULC动态监测与驱动机制研究。","2026-10-01T23:30:29.277138Z",{"id":352,"title":353,"url":354,"summary":355,"summary_zh":356,"content":9,"source_name":357,"source_url":354,"published_at":358,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":359,"score_detail":360,"sources":363,"tags":365,"primary_topic":369,"secondary_topics":370,"classification":371,"search_phrases":399,"slug":402,"view_count":63,"doi":403,"paper":404,"created_at":419},3849,"Land Use\u002F Land Cover Dynamics and the Decline of Grazing Resources in Dauchi Grazing Reserve, Adamawa State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.33003\u002Ffjs-2026-1018-6083","Land use and land cover (LULC) change is a major driver of rangeland degradation, threatening grazing resources and pastoral livelihoods in the Guinea and Sudan savanna ecosystems of northeastern Nigeria. This study evaluates the impact of LULC change on grazing resources in Dauchi Grazing Reserve, Song LGA, Adamawa State, Nigeria, using multi-temporal satellite imagery covering forty years. Satellite data were processed and classified using supervised classification techniques within a Geographic Information System (GIS) and remote sensing framework. Six LULC classes—grassland, shrubland, cropland, bare surface, built-up areas, and water body—were mapped and analysed to assess spatial and temporal transformations. Change detection analysis was employed to quantify transitions among land cover classes and assess implications for pasture availability. Results revealed a substantial decline in natural grazing resources, particularly grassland, which decreased from 32.77 km² (54.05%) in 1984 to 5.36 km² (8.84%) in 2024, representing a loss of 27.41 km². Conversely, cultivated land expanded considerably, increasing from 21.47 km² in 1984 to 46.35 km² in 2024. These changes reduced the availability and spatial continuity of natural grazing areas, potentially constraining forage availability and grazing capacity. The findings demonstrate that sustained LULC transformation has significantly altered the spatial distribution of grazing resources in Dauchi Grazing Reserve, creating challenges for sustainable livestock production and ecosystem resilience. The study recommends rangeland restoration, controlled grazing, integrated land-use planning, and protection of remaining natural vegetation to conserve grazing resources and support pastoral livelihoods.","土地利用与土地覆盖（LULC）变化是牧场退化的主要驱动因素，威胁着尼日利亚东北部几内亚和苏丹萨瓦纳生态系统的放牧资源和牧民生计。本研究利用覆盖四十年的多时相卫星影像，评估了尼日利亚阿达马瓦州松地方政府区道奇放牧保护区内土地利用与土地覆盖变化对放牧资源的影响。卫星数据在地理信息系统（GIS）和遥感框架内采用监督分类技术进行处理和分类。研究绘制并分析了六个土地利用与土地覆盖类别——草地、灌丛地、耕地、裸地、建设用地和水体——以评估其空间和时间变化。采用变化检测分析量化土地覆盖类别之间的转换，并评估其对牧草可利用性的影响。结果表明，天然放牧资源大幅减少，尤其是草地，从1984年的32.77平方公里（54.05%）下降至2024年的5.36平方公里（8.84%），减少了27.41平方公里。相反，耕地显著扩张，从1984年的21.47平方公里增加至2024年的46.35平方公里。这些变化减少了天然放牧区的可利用性和空间连续性，可能制约牧草供应和放牧承载力。研究结果表明，持续的土地利用与土地覆盖变化显著改变了道奇放牧保护区内放牧资源的空间分布，对可持续畜牧生产和生态系统恢复力构成了挑战。研究建议开展牧场恢复、实施控制性放牧、进行综合土地利用规划，并保护剩余天然植被，以保育放牧资源并支持牧民生计。","FUDMA Journal of Sciences","2026-09-28T00:00:00Z",65,{"impact":17,"substance":225,"depth":361,"authority":20,"freshness":45,"relevant":21,"comment":362},16,"基于四十年多时相遥感的牧区草地退化实证研究，数据扎实但属区域案例，对国内农业信息化参考价值有限。",[364],{"name":357,"url":354},[26,27,366,367,368],"尼日利亚","农牧交错带","草地退化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":372},{"candidates":373,"agricultural_evidence":46},[374,378,382,389,395],{"code":32,"score":45,"supported":46,"title":375,"abstract":376,"tags":377},[],[49,50,51],[],{"code":54,"score":55,"supported":46,"title":379,"abstract":380,"tags":381},[],[58,59],[26],{"code":383,"score":88,"supported":46,"title":384,"abstract":385,"tags":388},"C",[],[386,387],"畜牧","livestock",[],{"code":390,"score":323,"supported":46,"title":391,"abstract":392,"tags":394},"C4",[],[393],"forage",[],{"code":62,"score":21,"supported":14,"title":396,"abstract":397,"tags":398},[],[],[27],[400,401],"Dauchi 放牧保护区 土地利用","Adamawa 草地退化 遥感","Dauchi放牧保护区土地利用-3849","10.33003\u002Ffjs-2026-1018-6083",{"doi":403,"openalex_id":405,"authors":406,"venue":357,"cited_by_count":77,"oa_url":413,"card":414,"direction":83,"ingested_from":85},"W7214627198",[407,409,411],{"name":408,"orcid":9},"Nagarta Jamala Gailyson",{"name":410,"orcid":9},"Andrew Egba Ubogu",{"name":412,"orcid":9},"Yaro Abdu","https:\u002F\u002Ffjs.fudutsinma.edu.ng\u002Findex.php\u002Ffjs\u002Farticle\u002Fdownload\u002F6083\u002F3992",{"tldr":415,"method":416,"finding":417,"direction":83,"opportunity":418},"用40年遥感影像评估尼日利亚Dauchi放牧保护区土地利用变化对放牧资源的影响。","多时相卫星影像监督分类与GIS变化检测，划分六类地物。","草地从54.05%降至8.84%，耕地大幅扩张，放牧资源显著萎缩。","可结合多源遥感与牧草生产力模型，量化放牧承载力并预警退化风险。","2026-09-30T23:30:31.022343Z",{"id":421,"title":422,"url":423,"summary":424,"summary_zh":425,"content":9,"source_name":426,"source_url":427,"published_at":428,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":429,"score_detail":430,"sources":432,"tags":434,"primary_topic":437,"secondary_topics":441,"classification":443,"search_phrases":471,"slug":474,"view_count":475,"doi":476,"paper":477,"created_at":490},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":17,"substance":18,"depth":19,"authority":227,"freshness":17,"relevant":21,"comment":431},"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。",[433],{"name":426,"url":427},[435,26,27,436,172],"农业遥感","印度农业",{"code":54,"slug":438,"name":439,"path":440,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[442],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":444},{"candidates":445,"agricultural_evidence":46},[446,451,455,462,466],{"code":54,"score":20,"supported":46,"title":447,"abstract":448,"tags":450},[],[58,59,449],"multispectral",[435,172,26],{"code":32,"score":45,"supported":46,"title":452,"abstract":453,"tags":454},[],[49,50,51],[],{"code":456,"score":88,"supported":46,"title":457,"abstract":458,"tags":461},"H2",[],[459,460],"土壤","soil",[],{"code":62,"score":21,"supported":14,"title":463,"abstract":464,"tags":465},[],[],[27],{"code":467,"score":21,"supported":14,"title":468,"abstract":469,"tags":470},"J3",[],[],[436],[472,473],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187",5,"10.31018\u002Fjans.v18i3.7774",{"doi":476,"openalex_id":478,"authors":479,"venue":426,"cited_by_count":77,"oa_url":427,"card":485,"direction":83,"ingested_from":85},"W7213942371",[480,482],{"name":481,"orcid":9},"J. C. Das",{"name":483,"orcid":484},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":486,"method":487,"finding":488,"direction":83,"opportunity":489},"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"]