[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3187":3,"related-3187":54},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":11,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":16,"score_detail":17,"sources":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":53},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。",null,"Journal of Applied and Natural Science","https:\u002F\u002Fjournals.ansfoundation.org\u002Findex.php\u002Fjans\u002Farticle\u002Fview\u002F7774","2026-09-20T00:00:00Z","论文",10,false,62,{"impact":18,"substance":19,"depth":20,"authority":21,"freshness":18,"relevant":22,"comment":23},8,18,15,13,1,"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。",[25],{"name":10,"url":11},[27,28,29,30,31],"农业遥感","遥感监测","土地利用","印度农业","植被覆盖",[33,34],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187",0,"10.31018\u002Fjans.v18i3.7774",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":11,"card":46,"direction":50,"ingested_from":52},"W7213942371",[41,43],{"name":42,"orcid":9},"J. C. Das",{"name":44,"orcid":45},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":47,"method":48,"finding":49,"direction":50,"opportunity":51},"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","农业遥感与作物表型","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。","openalex","2026-09-22T23:30:25.539323Z",{"total":55,"page":22,"page_size":55,"items":56},6,[57,91,140,176,221,263],{"id":58,"title":59,"url":60,"summary":61,"summary_zh":62,"content":9,"source_name":63,"source_url":60,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":64,"score_detail":65,"sources":69,"tags":71,"search_phrases":74,"slug":77,"view_count":36,"doi":78,"paper":79,"created_at":90},3183,"Integrated Remote Sensing and Time Series Analysis for Long-Term Assessment of Vegetation Resilience: Synthesizing Climatic Variables and Land-Use\u002FLand-Cover Trajectories in Al-Ahsa Oasis, Saudi Arabia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091758","Continuous monitoring of land-use\u002Fland-cover (LULC) change is essential in the Al-Ahsa Oasis, an arid region characterized by a sensitive and fragile environment. This study is the first to integrate vegetation, climate, and land-use frameworks by combining vegetation indicators, climatic variables, and LULC trajectory classification over a 41-year period (1985–2025). Data from three Landsat sensors (Landsat 5-TM, 7-ETM+, and 8-OLI) were processed to derive vegetation indicators and an LULC trajectory classification, while ERA5-Land and CHIRPS data provided surface temperature and rainfall information, respectively. LULC trajectory classification was implemented using a random forest classifier, generating six LULC trajectory classes: stable barren, stable urban, stable water, urban expansion, transition lands, and stable vegetation. Pearson correlation analysis was used to assess the relationships between the vegetation indicators (normalized difference vegetation index [NDVI] and soil-adjusted vegetation index [SAVI]) and climatic variables (temperature and rainfall) at different time lags (0–3 years) and after detrending the time series. The detrending analysis showed no statistically significant associations between the vegetation indicators (NDVI and SAVI) and the climatic variables (temperature and rainfall) across the four examined lags, whereas the original, non-detrended time series showed positive associations that were mainly attributable to long-term trends rather than to interannual climate–vegetation covariation. The accuracy of LULC trajectory classification was high, with an overall accuracy of 0.939 and a kappa coefficient of 0.924, indicating robust mapping performance. Overall vegetation cover improved over the study period. The degradation occurred in the central part of the oasis (stable urban, urban expansion, and stable water classes, accounting for 4.506%, 5.179%, and 41.773% of each trajectory class, respectively), whereas the recovery was observed in the northern, southern, and eastern parts (stable vegetation and transition lands, accounting for 37.656% and 52.891% of each trajectory class, respectively). These findings provide long-term insights into vegetation resilience in the Al-Ahsa Oasis over 41 years, supporting sustainable urban planning and vegetation and agricultural management.","在环境敏感而脆弱的干旱区——哈萨绿洲，持续监测土地利用\u002F土地覆盖（LULC）变化至关重要。本研究首次在41年（1985—2025年）时间跨度内，通过整合植被指标、气候变量与LULC轨迹分类，构建了植被、气候与土地利用的综合框架。研究处理了三颗Landsat传感器（Landsat 5-TM、7-ETM+和8-OLI）的数据，以提取植被指标并进行LULC轨迹分类，同时分别利用ERA5-Land和CHIRPS数据获取地表温度和降雨信息。LULC轨迹分类采用随机森林分类器实现，生成了六类LULC轨迹：稳定裸地、稳定城市、稳定水体、城市扩张、过渡土地和稳定植被。采用Pearson相关分析评估植被指标（归一化差异植被指数[NDVI]和土壤调节植被指数[SAVI]）与气候变量（温度和降雨）在不同时间滞后（0—3年）及时间序列去趋势后的关系。去趋势分析表明，在四个考察滞后下，植被指标（NDVI和SAVI）与气候变量（温度和降雨）之间均无统计学显著关联，而原始未去趋势时间序列则显示出正相关，这主要归因于长期趋势而非年际气候—植被协变。LULC轨迹分类精度较高，总体精度为0.939，kappa系数为0.924，表明制图性能稳健。研究期间整体植被覆盖有所改善。退化发生在绿洲中部（稳定城市、城市扩张和稳定水体类别，分别占各轨迹类别的4.506%、5.179%和41.773%），而恢复则出现在北部、南部和东部（稳定植被和过渡土地，分别占各轨迹类别的37.656%和52.891%）。这些发现为哈萨绿洲41年来的植被恢复力提供了长期见解，可支持可持续城市规划以及植被与农业管理。","Land",73,{"impact":66,"substance":67,"depth":19,"authority":21,"freshness":18,"relevant":22,"comment":68},12,22,"41年长时序遥感与气候变量整合分析，方法扎实、结论可靠，对干旱区绿洲农业可持续管理有参考价值，但属区域性案例研究，公共影响有限。",[70],{"name":63,"url":60},[72,28,29,31,73],"气候变化","绿洲农业",[75,76],"Al-Ahsa Oasis 遥感 植被","Landsat NDVI 土地利用变化","Al-AhsaOasis遥感植被-3183","10.3390\u002Fland15091758",{"doi":78,"openalex_id":80,"authors":81,"venue":63,"cited_by_count":36,"oa_url":60,"card":85,"direction":50,"ingested_from":52},"W7213862324",[82],{"name":83,"orcid":84},"Amal H. Aljaddani","https:\u002F\u002Forcid.org\u002F0000-0003-1171-8416",{"tldr":86,"method":87,"finding":88,"direction":50,"opportunity":89},"整合41年Landsat植被指数、气候变量与LULC轨迹，评估沙特Al-Ahsa绿洲植被恢复力。","Landsat 5\u002F7\u002F8时序、ERA5-Land与CHIRPS气候数据、随机森","去趋势后植被与气候无显著相关，绿洲整体植被改善，中部退化、南北东恢复。","可引入非线性\u002F因果推断与高分辨率数据，区分灌溉、城市化与气候对干旱区绿洲植被恢复力的驱动。","2026-09-22T23:30:25.304168Z",{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":99,"score_detail":100,"sources":102,"tags":104,"search_phrases":108,"slug":111,"view_count":36,"doi":112,"paper":113,"created_at":139},3189,"Influence of Drought Events on Vegetation and Land Use Dynamics Utilising Orbital Data: A Case Study in the Pajeú River Basin, Pernambuco","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i03.269030","Monitoring drought in the northeastern Semi-Arid region is a challenge compounded by high rainfall variability and a scarcity of in situ monitoring networks, which makes the use of geotechnologies for water resource management essential. This study aimed to analyze land use and occupation dynamics from 2002 to 2022 in the Pajeú River basin in Pernambuco, Brazil, and their relationship with drought events, using indices based on remote sensing. The Standardized Precipitation Index (SPI) was applied to characterize meteorological drought, while the Vegetation Health Index (VHI) and Normalized Vegetation Water Supply Index (NVSWI) were used to assess the ecosystem's response. The results indicated that the period from 2011 to 2016 was the most severe and prolonged drought, which was reinforced by low VHI and NVSWI values, indicating water stress on vegetation during the same period. The correlation analysis revealed that land use classes, such as vegetation and water, responded directly to drought, whereas degraded anthropogenic areas, including pasture and exposed soil, exhibited the opposite behavior. The joint analysis of the three indices and land use and occupation data provided a comprehensive understanding of the evolution of drought in the study area, highlighting the importance of a multiple approach to monitoring complex environments.","监测半干旱东北部地区的干旱是一项挑战，降雨变率大且原位监测网络稀缺使这一挑战更加复杂，因此利用地理技术进行水资源管理至关重要。本研究旨在利用基于遥感的指数，分析2002年至2022年巴西伯南布哥州帕热乌河流域的土地利用与覆盖动态及其与干旱事件的关系。采用标准化降水指数（SPI）表征气象干旱，同时使用植被健康指数（VHI）和归一化植被供水指数（NVSWI）评估生态系统的响应。结果表明，2011年至2016年是最严重且持续时间最长的干旱期，同期较低的VHI和NVSWI值进一步证实了这一点，表明植被在此期间受到水分胁迫。相关性分析显示，植被和水体等土地利用类别对干旱有直接响应，而牧场和裸露土壤等退化的人为区域则表现出相反的行为。三个指数与土地利用及覆盖数据的联合分析为理解研究区域干旱演变提供了全面的认识，凸显了多方法途径在监测复杂环境中的重要性。","Journal of Hyperspectral Remote Sensing","2026-09-19T00:00:00Z",61,{"impact":18,"substance":19,"depth":20,"authority":66,"freshness":18,"relevant":22,"comment":101},"该研究利用遥感指数分析巴西半干旱流域干旱与植被动态，方法扎实但属区域性案例，对国内三农信息化参考价值有限。",[103],{"name":97,"url":94},[105,28,29,106,107],"水资源管理","干旱监测","植被指数",[109,110],"Pajeú River basin 干旱 遥感","SPI VHI NVSWI 植被","PajeúRiverbasin干旱遥感-3189","10.29150\u002Fjhrs.v16i03.269030",{"doi":112,"openalex_id":114,"authors":115,"venue":97,"cited_by_count":36,"oa_url":133,"card":134,"direction":50,"ingested_from":52},"W7213792298",[116,119,122,125,128,130],{"name":117,"orcid":118},"Juliana Farias Santos de Moraes","https:\u002F\u002Forcid.org\u002F0000-0002-3241-844X",{"name":120,"orcid":121},"Estephania Silva Jovino","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3533",{"name":123,"orcid":124},"Alex Vinícius de Melo Vieira","https:\u002F\u002Forcid.org\u002F0009-0002-5204-3734",{"name":126,"orcid":127},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":129,"orcid":9},"Sylvana Sylvana Melo dos Santos",{"name":131,"orcid":132},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998","https:\u002F\u002Fperiodicos.ufpe.br\u002Frevistas\u002Fjhrs\u002Farticle\u002Fdownload\u002F269030\u002F52814",{"tldr":135,"method":136,"finding":137,"direction":50,"opportunity":138},"利用遥感指数分析巴西Pajeú河流域2002-2022年干旱对植被与土地利用的影响。","采用SPI、VHI和NVSWI指数，结合遥感数据与土地利用分类。","2011-2016年干旱最严重，植被和水体响应直接，而牧场和裸地呈相反趋势。","可融合多源遥感与机器学习，构建半干旱区干旱-植被-土地利用耦合预警模型。","2026-09-22T23:30:26.557816Z",{"id":141,"title":142,"url":143,"summary":144,"summary_zh":145,"content":9,"source_name":146,"source_url":143,"published_at":98,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":36,"score_detail":147,"sources":149,"tags":151,"search_phrases":154,"slug":157,"view_count":36,"doi":158,"paper":159,"created_at":175},3071,"Urban Influence on Landscape Transformation and Land Cover Interactions During Urban Expansion (1972–2022): A Remote Sensing and GIS-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0038","Abstract This study investigates how urban expansion influences land cover dynamics. An interactive analytical approach was adopted to examine the transitions among built-up areas, cropland, and bare land throughout the urbanization process. Remote Sensing and GIS techniques, in conjunction with the Land Transfer Matrix (LTM), Landscape Metrics (LM), Multi-buffer Ring (MBR) and complementary spatial tools, were employed to analyze the city of Tiaret in Algeria over the past half century (1972-2022). The results reveal a marked intensification of urbanization, with an Overall Average Annual Urban Expansion Rate (OA-AUER) of 28.3 ha and a concurrent rise in bare land driven by cropland conversion under urban pressure. Notably, 89.4 % of cropland loss initially transitions into bare land, while subsequent urban development originates from cropland (52 %) and bare land (47 %). The observed density patterns in peripheral areas confirm the significant influence of the urban core on surrounding land-cover transformations. These findings elucidate the urbanization process while considering the transitional role of bare land and further explain diffusion coalescence process. Further empirical research is recommended to refine theoretical frameworks on landscape transformation dynamics and to inform policies aimed at mitigating bare-land expansion in peri-urban areas, controlling cropland urbanization, and planning sustainable urban growth.","摘要 本研究探讨城市扩张如何影响土地覆盖动态。采用交互式分析方法，考察城市化进程中建成区、耕地和裸地之间的转换。运用遥感与地理信息系统技术，结合土地转移矩阵（LTM）、景观格局指数（LM）、多缓冲环（MBR）及辅助空间工具，对阿尔及利亚提亚雷特市过去半个世纪（1972—2022年）的变化进行了分析。结果表明，城市化显著加剧，年均城市扩张总面积率（OA-AUER）为28.3公顷，同时在城市压力下耕地转换导致裸地同步增加。值得注意的是，89.4%的耕地损失最初转变为裸地，而随后的城市发展来源于耕地（52%）和裸地（47%）。外围区域观测到的密度格局证实了城市核心对周边土地覆盖变化的显著影响。这些发现阐明了城市化过程，同时考虑了裸地的过渡性作用，并进一步解释了扩散聚合过程。建议开展进一步实证研究，以完善景观转型动态的理论框架，并为旨在减缓城郊地区裸地扩张、控制耕地城市化以及规划可持续城市增长的政策提供依据。","Journal of Landscape Ecology",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":148},"该论文聚焦阿尔及利亚Tiaret市城市扩张与土地覆盖变化，属城市地理与遥感应用研究，与三农、农业信息化、智慧农业主题无直接关联，不建议进入每日精选。",[150],{"name":146,"url":143},[152,28,29,153],"耕地保护","城市扩张",[155,156],"Tiaret 城市扩张 遥感","耕地 建设用地 转移矩阵","Tiaret城市扩张遥感-3071","10.2478\u002Fjlecol-2026-0038",{"doi":158,"openalex_id":160,"authors":161,"venue":146,"cited_by_count":36,"oa_url":169,"card":170,"direction":50,"ingested_from":52},"W7213652939",[162,164,166],{"name":163,"orcid":9},"Amina Kalbaza",{"name":165,"orcid":9},"Belkacem Marir",{"name":167,"orcid":168},"Farida Naceur","https:\u002F\u002Forcid.org\u002F0009-0004-1962-4937","https:\u002F\u002Freference-global.com\u002Fdownload\u002Farticle\u002F10.2478\u002Fjlecol-2026-0038.pdf",{"tldr":171,"method":172,"finding":173,"direction":50,"opportunity":174},"基于遥感与GIS分析阿尔及利亚提亚雷特1972—2022年城市扩张对景观与土地覆盖的影响。","遥感、GIS、土地转移矩阵、景观格局指数与多缓冲环分析。","耕地先转为裸地再被城市占用，城市核心显著驱动外围土地覆盖转变。","可延伸研究裸地作为耕地—城市转换中间态的机制，并构建城郊耕地流失预警模型。","2026-09-21T23:30:24.332177Z",{"id":177,"title":178,"url":179,"summary":180,"summary_zh":181,"content":9,"source_name":182,"source_url":179,"published_at":12,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":183,"score_detail":184,"sources":187,"tags":189,"search_phrases":193,"slug":196,"view_count":36,"doi":197,"paper":198,"created_at":220},3069,"Impact of Urbanization on Agriculture and Wetland Management in Makurdi LGA of Benue State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.65757\u002Fhjev1n22","Wetlands are among the most productive ecosystems, providing essential ecological, hydrological, and socio-economic services, including flood regulation, groundwater recharge, water purification, biodiversity conservation, and agricultural support. However, rapid urbanization has accelerated the conversion of wetlands to built-up areas and agricultural land, threatening their ecological integrity. This study assessed the impact of urbanization on agriculture and wetland management in Makurdi Local Government Area, Benue State, over a twenty-year period (2004–2024). The specific objectives were to examine the spatial distribution and extent of wetlands, evaluate the relationship between urban growth and wetland dynamics, and determine the extent of wetland encroachment resulting from urban development. The study employed Geographic Information Systems (GIS) and Remote Sensing techniques using Landsat 7 ETM+, Landsat 8 OLI\u002FTIRS, and Landsat 9 OLI-2\u002FTIRS-2 satellite imagery acquired for 2004, 2014, and 2024. Image preprocessing, supervised classification, change detection, cross-tabulation matrix analysis, and accuracy assessment were performed in ArcGIS and ERDAS Imagine, while field observations and GPS data were used for ground-truthing and validation. Seven land-use\u002Fland-cover classes were identified: wetlands, farmland, shrubland, forest, water bodies, sandbars, and settlements. The findings revealed significant land-use and land-cover changes over the study period. Wetland coverage declined from 4,478.76 ha (5.37%) in 2004 to 2,664.36 ha (2.39%) in 2024, while settlements expanded from 1,182.60 ha (1.42%) to 5,119.22 ha (4.61%) during the same period. Farmland increased from 39.23% to 41.09%, indicating continued conversion of natural ecosystems to agricultural and urban uses. Cross-tabulation analysis further demonstrated substantial transitions of wetlands and farmlands into settlement areas, confirming that urban expansion has become a major driver of wetland degradation in Makurdi. Classification accuracy assessments yielded overall accuracies ranging from 72.15% to 75.93%, with Kappa coefficients between 0.60 and 0.68, indicating that the classified maps were sufficiently reliable for land-use change analysis. The study concludes that rapid urbanization has significantly contributed to wetland loss and fragmentation and has increasingly encroached on agricultural lands, posing serious threats to ecosystem sustainability, biodiversity conservation, flood regulation, and long-term food security. To address these challenges, the study recommends strengthening wetland protection policies, implementing continuous GIS-based monitoring, promoting sustainable urban development, and establishing institutional frameworks to conserve and effectively manage wetlands and agricultural lands in Makurdi","湿地是最具生产力的生态系统之一，提供重要的生态、水文和社会经济服务，包括洪水调节、地下水补给、水质净化、生物多样性保护和农业支持。然而，快速城市化加速了湿地向建成区和农业用地的转化，威胁其生态完整性。本研究评估了贝努埃州马库尔迪地方政府区二十年期间（2004—2024年）城市化对农业和湿地管理的影响。具体目标包括：考察湿地的空间分布与范围，评估城市增长与湿地动态之间的关系，并确定城市发展导致的湿地侵占程度。研究采用地理信息系统（GIS）和遥感技术，使用2004年、2014年和2024年的Landsat 7 ETM+、Landsat 8 OLI\u002FTIRS和Landsat 9 OLI-2\u002FTIRS-2卫星影像。在ArcGIS和ERDAS Imagine中进行了影像预处理、监督分类、变化检测、交叉列联表矩阵分析和精度评估，同时利用实地观测和GPS数据进行地面验证与确认。研究识别出七类土地利用\u002F土地覆盖类型：湿地、农田、灌丛、森林、水体、沙洲和居民点。研究结果显示，研究期间土地利用和土地覆盖发生了显著变化。湿地覆盖面积从2004年的4,478.76公顷（5.37%）下降至2024年的2,664.36公顷（2.39%），而居民点面积同期从1,182.60公顷（1.42%）扩张至5,119.22公顷（4.61%）。农田面积从39.23%增至41.09%，表明自然生态系统持续向农业和城市用途转化。交叉列联表分析进一步表明，湿地和农田大量转变为居民点用地，证实城市扩张已成为马库尔迪湿地退化的主要驱动因素。分类精度评估的总体精度为72.15%至75.93%，Kappa系数在0.60至0.68之间，表明分类图对于土地利用变化分析具有足够的可靠性。研究认为，快速城市化显著加剧了湿地丧失和破碎化，并日益侵占农业用地，对生态系统可持续性、生物多样性保护","Hensard Journal of Environment",56,{"impact":18,"substance":19,"depth":20,"authority":55,"freshness":185,"relevant":22,"comment":186},9,"基于Landsat遥感与GIS的尼日利亚湿地退化实证研究，数据扎实但属区域案例，公共影响有限。",[188],{"name":182,"url":179},[190,28,29,191,192],"粮食安全","城市化","湿地保护",[194,195],"Makurdi 湿地 城市化","Benue State 遥感","Makurdi湿地城市化-3069","10.65757\u002Fhjev1n22",{"doi":197,"openalex_id":199,"authors":200,"venue":182,"cited_by_count":36,"oa_url":179,"card":215,"direction":50,"ingested_from":52},"W7213745048",[201,203,205,207,209,211,213],{"name":202,"orcid":9},"Joy Iganya Agene",{"name":204,"orcid":9},"Onoja Sunday",{"name":206,"orcid":9},"Akintunde Elijah",{"name":208,"orcid":9},"Ugese Ayangeaor Andrew",{"name":210,"orcid":9},"Kebiru Umoru",{"name":212,"orcid":9},"Aku Umaku Esther",{"name":214,"orcid":9},"Ombugu Gideon",{"tldr":216,"method":217,"finding":218,"direction":50,"opportunity":219},"用遥感与GIS评估尼日利亚马库尔迪20年间城市化对湿地和农业用地的侵占影响。","Landsat 7\u002F8\u002F9影像，监督分类、变化检测与交叉表分析，GPS实地验证。","湿地占比由5.37%降至2.39%，建设用地由1.42%升至4.61%，城市化是湿地退化主因。","可结合多源高分辨率遥感与CA-Markov模型，预测湿地-农田-城市冲突并支撑国土空间优化。","2026-09-21T23:30:24.084401Z",{"id":222,"title":223,"url":224,"summary":225,"summary_zh":226,"content":9,"source_name":227,"source_url":224,"published_at":98,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":228,"score_detail":229,"sources":233,"tags":235,"search_phrases":239,"slug":242,"view_count":36,"doi":243,"paper":244,"created_at":262},3060,"Geospatial Intelligence for Peri-Urban Land-Use Conflicts: Evaluating Agricultural Suitability against Rapid Urbanisation using the Analytical Hierarchy Process and Cloud Computing","https:\u002F\u002Fdoi.org\u002F10.59543\u002F6mpcwr41","This paper presents a geospatial multi-criteria evaluation of agricultural potential in the suburban region of Bapatla using eight physical and land-use characteristics: elevation, slope, road accessibility, proximity to water bodies, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Land Use\u002FLand Cover (LULC), and soil texture, processed using Google Earth Engine. The Analytic Hierarchy Process (AHP) was used to determine the relative weights of each criterion. NDVI received the highest weight (26.33%), followed by LST, slope, and proximity to water bodies (14.96% each), while elevation received the lowest weight (4.6%) due to the region's flat terrain. Weighted overlay analysis classified the 142.25 km² study area into Suitable (108.90 km²; 76.55%), Not Suitable (32.60 km²; 22.92%), and Highly Suitable (0.75 km²; 0.53%) categories. Suitable areas are mainly distributed across the southern and peripheral agricultural zones, whereas unsuitable areas are concentrated within Bapatla Urban and its surroundings. The limited extent of highly suitable land highlights the scarcity of optimal agricultural sites. The results reveal land-use conflicts driven primarily by urbanisation rather than environmental constraints. The AHP-weighted suitability map provides an evidence-based tool for agricultural land conservation, water-resource management, and sustainable urban expansion.","本文基于八项自然与土地利用特征，对巴帕特拉（Bapatla）郊区农业潜力进行了地理空间多准则评价，这些特征包括：海拔、坡度、道路可达性、距水体远近、地表温度（LST）、归一化植被指数（NDVI）、土地利用\u002F土地覆盖（LULC）以及土壤质地，并利用Google Earth Engine进行处理。采用层次分析法（AHP）确定各准则的相对权重。NDVI权重最高（26.33%），其次为LST、坡度和距水体远近（均为14.96%），而海拔因该地区地形平坦权重最低（4.6%）。加权叠加分析将142.25 km²的研究区划分为适宜（108.90 km²；76.55%）、不适宜（32.60 km²；22.92%）和高适宜（0.75 km²；0.53%）三类。适宜区主要分布于南部及外围农业区，而不适宜区集中于巴帕特拉城区及其周边。高适宜土地面积有限，凸显了优质农业用地的稀缺性。结果表明，土地利用冲突主要由城市化驱动，而非环境限制。基于AHP的适宜性地图为农业用地保护、水资源管理和可持续城市扩张提供了循证工具。","Journal of Urban Intelligence and Smart Systems",70,{"impact":66,"substance":230,"depth":231,"authority":21,"freshness":18,"relevant":22,"comment":232},20,17,"该论文利用遥感与AHP方法评估城郊农业用地冲突，方法新颖、数据详实，对农业土地保护有参考价值，但属细分领域研究，影响范围有限。",[234],{"name":227,"url":224},[236,27,237,238,29],"智慧农业","农业信息化","遥感",[240,241],"Bapatla 农业用地 城市化","Google Earth Engine 农业适宜性","Bapatla农业用地城市化-3060","10.59543\u002F6mpcwr41",{"doi":243,"openalex_id":245,"authors":246,"venue":227,"cited_by_count":36,"oa_url":255,"card":256,"direction":261,"ingested_from":52},"W7213644047",[247,249,251,253],{"name":248,"orcid":9},"Sreerama Naik Naik S R",{"name":250,"orcid":9},"T K Prasad",{"name":252,"orcid":9},"Feba Jose Jasmine",{"name":254,"orcid":9},"Jayapal G","https:\u002F\u002Fjuiss.org\u002Findex.php\u002Fjuiss\u002Farticle\u002Fdownload\u002F366\u002F231",{"tldr":257,"method":258,"finding":259,"direction":50,"opportunity":260},"用AHP与云平台评估印度Bapatla城郊农业适宜性，揭示城市化引发的土地利用冲突。","Google Earth Engine处理8个因子，AHP加权叠加分析142.2","76.55%区域适宜农业，但高度适宜仅0.53%，冲突主因是城市化而非环境限制。","可引入时序遥感与动态城市扩张模拟，构建城郊农业保护与城市增长协同优化模型。","智慧农业 \u002F 农业物联网","2026-09-21T23:30:09.448414Z",{"id":264,"title":265,"url":266,"summary":267,"summary_zh":268,"content":9,"source_name":146,"source_url":266,"published_at":98,"category":13,"cover_url":9,"hotness":14,"is_selected":15,"score":36,"score_detail":269,"sources":271,"tags":273,"search_phrases":275,"slug":278,"view_count":36,"doi":279,"paper":280,"created_at":291},3015,"Forecasting Urbanization Dynamics On İstanbul’s European Side Using Deep Learning And Extrapolation Techniques","https:\u002F\u002Fdoi.org\u002F10.2478\u002Fjlecol-2026-0037","Abstract Rapid urbanization in metropolitan regions poses significant environmental, social, and infrastructural challenges, necessitating advanced analytical approaches to monitor and predict urban growth. This study investigates the spatio-temporal dynamics of urbanization on the European side of Istanbul from 2013 to 2024 using Landsat 8 imagery and a deep learning (DL)–based Land Cover Classification model integrated within ArcGIS Pro. The U-Net–based pre-trained model generated 15-class Land Use\u002FLand Cover (LULC) maps, which were validated against the Urban Atlas dataset, resulting in high classification accuracies for forest and water classes (PA: 0.84–0.94; UA: 0.87–0.87) and an overall binary urban\u002Fnon-urban accuracy of 87 %, confirming the robustness of the employed DL approach. Spatio-temporal analyses of LULC data were conducted using both Ordinary Least Squares (OLS) and nonlinear regression functions to examine urban growth trends and project future development for 2025, 2026, and 2027. The results indicate a strong linear increase in urbanized areas across most districts, with total developed area on the European side projected to reach approximately 807 km² by 2027, representing a nearly 50% increase compared to 2013. These findings highlight the significant pressure of urban expansion on natural and agricultural lands and emphasize the need for informed planning strategies. By integrating remote sensing, deep learning, and predictive modeling, this study provides actionable insights for sustainable urban development, offering a replicable framework for monitoring rapid urbanization and supporting policy decisions to mitigate environmental and socio-spatial impacts in rapidly growing metropolitan regions.","摘要 大都市区域的快速城市化带来了显著的环境、社会和基础设施挑战，亟需先进的分析方法来监测和预测城市增长。本研究利用Landsat 8影像和集成于ArcGIS Pro中的基于深度学习（DL）的土地覆盖分类模型，研究了2013年至2024年伊斯坦布尔欧洲一侧城市化的时空动态。基于U-Net的预训练模型生成了15类土地利用\u002F土地覆盖（LULC）地图，并依据Urban Atlas数据集进行了验证，森林和水体类别的分类精度较高（生产者精度PA：0.84–0.94；用户精度UA：0.87–0.87），城市\u002F非城市二分类总体精度达87%，证实了所采用深度学习方法稳健可靠。研究采用普通最小二乘法（OLS）和非线性回归函数对LULC数据进行时空分析，以考察城市增长趋势并预测2025年、2026年和2027年的未来发展。结果表明，大多数区域的城市化面积呈显著线性增长，预计到2027年欧洲一侧的总建成区面积将达到约807 km²，较2013年增长近50%。这些发现凸显了城市扩张对自然和农业用地的巨大压力，并强调了科学规划策略的必要性。通过整合遥感、深度学习和预测建模，本研究为可持续城市发展提供了可操作的见解，为监测快速城市化提供了一个可复制的框架，并支持旨在缓解快速增长的都市区域中环境和社会空间影响的政策决策。",{"impact":36,"substance":36,"depth":36,"authority":36,"freshness":36,"relevant":36,"comment":270},"研究伊斯坦布尔城市扩张与土地覆盖预测，属城市遥感与景观生态领域，与三农、农业信息化、智慧农业无直接关联，不建议进入每日精选。",[272],{"name":146,"url":266},[274,28,29,153],"深度学习",[276,277],"伊斯坦布尔 城市扩张 遥感","U-Net 土地覆盖分类","伊斯坦布尔城市扩张遥感-3015","10.2478\u002Fjlecol-2026-0037",{"doi":279,"openalex_id":281,"authors":282,"venue":146,"cited_by_count":36,"oa_url":266,"card":286,"direction":50,"ingested_from":52},"W7213670913",[283],{"name":284,"orcid":285},"Gizem Dinç","https:\u002F\u002Forcid.org\u002F0000-0003-2406-604X",{"tldr":287,"method":288,"finding":289,"direction":50,"opportunity":290},"用Landsat 8影像和U-Net深度学习模型分析伊斯坦布尔欧洲侧2013-2024年城市化动态并","Landsat 8影像、ArcGIS Pro中U-Net预训练模型生成15类LU","城市面积呈强线性增长，2027年预计达807 km²，较2013年增长近50%，挤压自然与农业用地。","可借鉴该遥感+深度学习+外推框架，研究快速城市化对城郊农业用地与耕地保护的时空影响。","2026-09-20T23:30:21.335156Z"]