[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3519":3,"related-3519":45},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"search_phrases":25,"slug":28,"view_count":15,"doi":29,"paper":30,"created_at":44},3519,"Spatial Analysis of Pipeline Fire Incidents and their Effects on Vegetation Health Across Buffer Zones in Ogba\u002FEgbema\u002FNdoni, Rivers State, Nigeria","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.12.no8.2026.pg243.260","This research focused on analyzing the geographical distribution of pipeline fire occurrences and their impact on the health of vegetation in the buffer zones of the Ogba\u002FEgbema\u002FNdoni Local Government Area in Rivers State, Nigeria. A cross-sectional survey approach was utilized alongside a quasi-experimental design to evaluate the environmental differences between sites that were affected and those that served as control. Primary data was gathered through soil samples and structured surveys administered to 400 participants, while secondary sources included records of pipeline fire occurrences from 2010 to 2019, Landsat 8 satellite images, MODIS datasets, and organizational reports. The health of vegetation was measured with the Normalised Difference Vegetation Index (NDVI), and analyses of spatial data such as buffer zones, overlays, and hotspot mapping were executed using GIS methods. Soil and environmental factors were evaluated using established laboratory techniques, with inferential statistical methods such as ANOVA and regression analyses applied. Results illustrated a distinct spatial trend in vegetation health in relation to distance from the pipeline. Average values for vegetation health showed an increase from 0.5087 at a 50 m distance to 0.5619 at 1000 m, suggesting that ecological conditions improve with greater distance from pipeline fire incidents. The ANOVA outcomes indicated that the observed differences were statistically significant (F = 11.69, p = 0.024), validating that the proximity to pipeline fire events has a quantifiable impact on vegetation. Additional post-hoc analysis revealed that the most notable enhancement in vegetation health was found within the 1000 m buffer zone. Regression analysis indicated that distance was responsible for 35.2% of the variation in vegetation health (R² = 0.352); however, the separate effect of distance was not statistically significant, implying that recovery patterns may not follow a linear trajectory. A comparative assessment between areas that were affected and those that were not indicated slightly diminished vegetation health in the impacted regions (mean = 0.4585) versus the control regions (mean = 0.4652). The research concludes that incidences of pipeline fires considerably harm vegetation health nearby, with evidence of gradual ecological recovery as distance increases.These results highlight the necessity of conducting spatially informed environmental monitoring and the implementation of intervention strategies in regions involved in oil production","本研究聚焦于分析尼日利亚河流州奥格巴\u002F埃格贝马\u002F恩多尼地方政府区管道火灾发生的地理分布及其对缓冲区植被健康的影响。研究采用横断面调查方法与准实验设计相结合，以评估受影响地点与对照地点之间的环境差异。原始数据通过土壤样本和对400名参与者进行的结构化问卷调查收集，二手数据来源包括2010年至2019年管道火灾发生记录、Landsat 8卫星影像、MODIS数据集及组织机构报告。植被健康状况采用归一化植被指数（NDVI）进行测量，缓冲区分析、叠加分析和热点制图等空间数据分析通过GIS方法执行。土壤和环境因子采用既定实验室技术进行评估，并应用方差分析（ANOVA）和回归分析等推断统计方法。结果表明，植被健康与距管道距离之间呈现明显的空间趋势。植被健康平均值从50米距离处的0.5087增至1000米处的0.5619，表明距管道火灾事件越远，生态条件越好。方差分析结果表明，所观察到的差异具有统计学显著性（F = 11.69，p = 0.024），证实了距管道火灾事件的邻近程度对植被具有可量化的影响。进一步的事后分析显示，植被健康最显著的改善出现在1000米缓冲区内。回归分析表明，距离解释了植被健康35.2%的变异（R² = 0.352）；然而，距离的单独效应未达到统计学显著性，暗示恢复模式可能并非呈线性轨迹。受影响区域与未受影响区域的比较评估显示，受影响区域的植被健康（均值 = 0.4585）略低于对照区域（均值 = 0.4652）。研究结论认为，管道火灾事件对附近植被健康造成显著损害，并有证据表明随着距离增加，生态呈逐步恢复趋势。这些结果凸显了开展空间信息化环境监测的必要性以及实施相关",null,"IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-22T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"研究主题为尼日利亚油气管道火灾对植被的影响，属环境遥感领域，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。",[19],{"name":10,"url":6},[21,22,23,24],"遥感监测","植被健康","油气管道","生态环境",[26,27],"尼日利亚 管道火灾 植被","NDVI 缓冲区 植被健康","尼日利亚管道火灾植被-3519","10.56201\u002Fijgem.vol.12.no8.2026.pg243.260",{"doi":29,"openalex_id":31,"authors":32,"venue":10,"cited_by_count":15,"oa_url":35,"card":36,"direction":42,"ingested_from":43},"W7214036751",[33],{"name":34,"orcid":9},"Y. Y. Okatahi","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 12 NO. 8 2026\u002FSpatial Analysis of Pipeline Fire Incidents 243-260.pdf",{"tldr":37,"method":38,"finding":39,"direction":40,"opportunity":41},"分析尼日利亚管道火灾空间分布及其对缓冲区植被健康的影响。","用NDVI、Landsat 8、MODIS与GIS缓冲区及热点分析，结合ANOV","距管道越远植被健康越好，1000米缓冲区恢复最明显，距离解释35.2%变异。","农业遥感与作物表型","可结合时序遥感与机器学习，量化管道火灾后植被非线性恢复轨迹及驱动因子。","农业绿色发展与碳","openalex","2026-09-25T23:31:11.991798Z",{"total":46,"page":47,"page_size":46,"items":48},6,1,[49,93,137,174,204,249],{"id":50,"title":51,"url":52,"summary":53,"summary_zh":54,"content":9,"source_name":55,"source_url":52,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":56,"score_detail":57,"sources":64,"tags":66,"search_phrases":71,"slug":74,"view_count":15,"doi":75,"paper":76,"created_at":92},3514,"Artificial Intelligence for Climate Adaptation Decision Support in Data-Poor Developing Regions","https:\u002F\u002Fdoi.org\u002F10.22541\u002Fessoar.15009304\u002Fv1","Climate adaptation is a sequence of decisions taken under uncertainty, and the regions where climate risk is rising fastest are those with the least information to guide them. Only about 10 per cent of deaths are registered in the WHO African Region; nearly 90 per cent of required surface weather observations are missing across least developed countries and small island states; and only 40 per cent of African countries have multi-hazard early warning systems. This report examines whether artificial intelligence — machine learning, remote sensing and predictive analytics — can close these information gaps and improve adaptation decisions in data-poor developing regions. The report organises the problem as a decision chain with three information gaps — observation, prediction and decision — followed by an action gap that AI cannot close. It finds that AI has advanced fastest on prediction: AI weather models became operational at ECMWF in 2025, AI flood forecasts now cover 100 countries and about 700 million people, satellite nowcasts reach a continent with little radar, and AI monsoon-onset forecasts reached 38 million Indian farmers in 2025. On observation, satellite machine learning explains around 70 per cent of the variation in village wealth but only up to about half of the variation in changes over time. On decision, evidence from Togo, Bangladesh and Kenya shows that AI-assisted targeting, forecast-based triggers and satellite index insurance can deliver assistance faster and better, within clear limits. The report's central argument is the ground-truth paradox: AI stretches scarce observations further, but every AI product must be trained and verified against ground truth, so reliance on AI raises the value of each remaining station, survey and label. The 2025 interruption of FEWS NET and termination of the DHS Program show how fragile that foundation is. Because the value of information is the product of skill, lead time, reach, trust and the means to act, the highest returns usually lie not in more skilful models but in dissemination, institutions and prearranged finance. The report sets out a risk register, a six-principle policy framework, actions by actor and a roadmap to 2030.","气候适应是在不确定性下做出的一系列决策，而气候风险上升最快的地区恰恰是指导信息最匮乏的地区。世卫组织非洲区域仅登记了约10%的死亡病例；最不发达国家和小岛屿国家缺失了近90%所需的地面天气观测数据；仅有40%的非洲国家拥有多灾种早期预警系统。本报告考察人工智能——机器学习、遥感和预测分析——能否弥合这些信息缺口，改善数据匮乏的发展中地区的适应决策。报告将这一问题组织为一条决策链，包含三个信息缺口——观测、预测和决策——以及一个人工智能无法弥合的行动缺口。报告发现，人工智能在预测方面进展最快：人工智能天气模型于2025年在欧洲中期天气预报中心（ECMWF）投入业务运行，人工智能洪水预报现已覆盖100个国家和约7亿人口，卫星临近预报覆盖了一个几乎没有雷达的大陆，人工智能季风爆发预报于2025年惠及3800万印度农民。在观测方面，卫星机器学习可解释村庄财富约70%的变异，但对时间变化的解释力仅约一半。在决策方面，来自多哥、孟加拉国和肯尼亚的证据表明，人工智能辅助的目标定位、基于预报的触发机制和卫星指数保险能够在明确限度内更快、更好地提供援助。报告的核心论点是地面真值悖论：人工智能能够将稀缺的观测数据发挥更大效用，但每个人工智能产品都必须依据地面真值进行训练和验证，因此对人工智能的依赖提升了每一个剩余站点、调查和标注数据的价值。2025年FEWS NET的中断和DHS项目的终止表明这一基础何等脆弱。由于信息的价值是技能、提前期、覆盖面、信任和行动手段的乘积，最高回报通常不在于更精密的模型，而在于传播、制度和预先安排的融资。报告提出了风险登记册、六项原则的政策框架、各行为主体的行动以及到2030年的路线图。","OpenAlex",86,{"impact":58,"substance":59,"depth":60,"authority":61,"freshness":62,"relevant":47,"comment":63},22,24,19,13,8,"系统梳理AI在数据匮乏地区气候适应决策中的观测、预测与决策三类信息缺口，提出“地面真值悖论”，数据与结论扎实，对农业信息化与智慧农业有较强参考价值。",[65],{"name":55,"url":52},[67,68,69,21,70],"智慧农业","农业人工智能","气候适应","早期预警",[72,73],"AI 气候适应 决策支持","数据匮乏地区 农业预警","AI气候适应决策支持-3514","10.22541\u002Fessoar.15009304\u002Fv1",{"doi":75,"openalex_id":77,"authors":78,"venue":9,"cited_by_count":15,"oa_url":84,"card":85,"direction":91,"ingested_from":43},"W7214097088",[79,81],{"name":80,"orcid":9},"H Heuristics",{"name":82,"orcid":83},"Hunter Hughes","https:\u002F\u002Forcid.org\u002F0009-0002-6161-9387","https:\u002F\u002Fessopenarchive.org\u002Fdoi\u002Fpdf\u002F10.22541\u002Fessoar.15009304\u002Fv1",{"tldr":86,"method":87,"finding":88,"direction":89,"opportunity":90},"评估AI能否弥补数据匮乏地区气候适应决策的信息缺口，并提出地面真值悖论。","梳理观测、预测、决策三环节，结合AI天气模型、卫星ML与多国案例证据。","AI预测进展最快，但依赖地面真值；最高回报常在传播、制度与预置资金而非模型。","农业人工智能与决策模型","可研究AI辅助农业气候适应中地面真值稀缺下的验证与信任机制，及预置资金触发设计。","数字乡村与农业信息化","2026-09-25T23:30:46.008325Z",{"id":94,"title":95,"url":96,"summary":97,"summary_zh":98,"content":9,"source_name":99,"source_url":96,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":100,"sources":102,"tags":104,"search_phrases":107,"slug":110,"view_count":15,"doi":111,"paper":112,"created_at":136},3511,"Advancing spatio-temporal fusion with spectral super-resolution for enhancing remote sensing of marine debris and landfills","https:\u002F\u002Fdoi.org\u002F10.1080\u002F22797254.2026.2718171","Monitoring environmental challenges such as landfill expansion and marine debris accumulation requires high-resolution satellite imagery with enhanced spectral, spatial, and temporal details. However, remote sensing systems often face trade-offs between these resolutions, limiting their effectiveness in detecting and analyzing dynamic environmental changes. To overcome this limitation, we propose a novel Spatio-Temporal Fusion (STF) framework that integrates Sentinel-2 and PlanetScope satellite datasets, improving image quality through spectral super-resolution. This study systematically evaluates deep learning-based STF methods, focusing on Conditional Generative Adversarial Networks (cGANs) and Multi-Stage Spectral-wise Transformers (MST++). Results indicate that instance normalization-based cGANs enhance spectral fidelity but introduce noise, while MST++ models offer superior structural reconstruction and generalization. By retraining models with an expanded dataset, their robustness significantly improved, with MST++ v2 emerging as the most reliable. The integration of STF methodologies enhances environmental monitoring by enabling more precise and frequent observations of critical issues such as plastic pollution, landfill management, and marine debris detection. The ability to generate high-resolution, multi-sensor fused images provides a powerful tool for sustainable resource management and environmental decision-making. This research highlights the potential of STF frameworks to advance Earth Observation applications and support global sustainability efforts, offering innovative solutions for addressing pressing environmental concerns.","监测垃圾填埋场扩张和海洋废弃物堆积等环境挑战，需要具有增强光谱、空间和时间细节的高分辨率卫星影像。然而，遥感系统往往在这些分辨率之间面临权衡，限制了其探测和分析动态环境变化的能力。为克服这一局限，我们提出了一种新颖的时空融合（STF）框架，该框架整合了Sentinel-2和PlanetScope卫星数据集，通过光谱超分辨率提升影像质量。本研究系统评估了基于深度学习的STF方法，重点关注条件生成对抗网络（cGANs）和多阶段光谱变换器（MST++）。结果表明，基于实例归一化的cGANs提升了光谱保真度，但引入了噪声，而MST++模型则提供了更优越的结构重建和泛化能力。通过使用扩展数据集重新训练模型，其鲁棒性显著提高，其中MST++ v2成为最可靠的模型。STF方法的整合通过实现对塑料污染、垃圾填埋场管理和海洋废弃物检测等关键问题的更精确、更频繁观测，增强了环境监测能力。生成高分辨率、多传感器融合影像的能力为可持续资源管理和环境决策提供了强大工具。本研究凸显了STF框架在推动地球观测应用和支持全球可持续发展努力方面的潜力，为解决紧迫的环境问题提供了创新解决方案。","European Journal of Remote Sensing",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":101},"该论文聚焦遥感时空融合与光谱超分辨率用于海洋垃圾和填埋场监测，属环境遥感领域，与三农、农业信息化、智慧农业无直接关联，相关性门槛不通过。",[103],{"name":99,"url":96},[105,21,106],"卫星影像","海洋垃圾",[108,109],"Sentinel-2 PlanetScope 时空融合","MST 光谱超分辨率","Sentinel-2PlanetScope时空融合-3511","10.1080\u002F22797254.2026.2718171",{"doi":111,"openalex_id":113,"authors":114,"venue":99,"cited_by_count":15,"oa_url":130,"card":131,"direction":40,"ingested_from":43},"W7214001390",[115,118,121,124,127],{"name":116,"orcid":117},"Maria Kremezi","https:\u002F\u002Forcid.org\u002F0000-0002-1579-3520",{"name":119,"orcid":120},"Vassilia Karathanassi","https:\u002F\u002Forcid.org\u002F0000-0002-8834-4734",{"name":122,"orcid":123},"Nicolò Taggio","https:\u002F\u002Forcid.org\u002F0009-0003-6392-3099",{"name":125,"orcid":126},"Antonello Aiello","https:\u002F\u002Forcid.org\u002F0000-0003-4446-0191",{"name":128,"orcid":129},"Giulio Ceriola","https:\u002F\u002Forcid.org\u002F0009-0009-9093-4088","https:\u002F\u002Fwww.tandfonline.com\u002Fdoi\u002Fpdf\u002F10.1080\u002F22797254.2026.2718171?needAccess=true",{"tldr":132,"method":133,"finding":134,"direction":40,"opportunity":135},"提出融合Sentinel-2与PlanetScope的时空融合框架，结合光谱超分辨提升海洋垃圾与填埋","基于cGAN与MST++的深度学习时空融合，并用扩展数据集重训练。","MST++结构重建与泛化更优，实例归一化cGAN光谱保真但引入噪声。","可将该时空融合与光谱超分辨框架迁移至农田地块尺度作物长势与地膜\u002F塑料污染监测。","2026-09-25T23:30:35.443965Z",{"id":138,"title":139,"url":140,"summary":141,"summary_zh":142,"content":9,"source_name":55,"source_url":140,"published_at":143,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":144,"sources":146,"tags":148,"search_phrases":152,"slug":155,"view_count":15,"doi":156,"paper":157,"created_at":173},3506,"Urban Sprawl and Traffic-Related Air Pollution in Greater Gaborone: A Spatial Analysis of the Land Use–Transport–Environment Relationship","https:\u002F\u002Fdoi.org\u002F10.31223\u002Fx5v22m","Rapid urban sprawl in many Sub-Saharan African cities has significantly reshaped urban mobility patterns, resulting in longer commuting distances, increased private vehicle dependence, and growing pressure on major transport corridors. These trends have important implications for environmental quality, urban sustainability, and climate resilience. This paper investigates the spatial relationship between urban sprawl and traffic-related air pollution (TRAP) in Greater Gaborone, Botswana, using Geographic Information Systems (GIS) and remote sensing techniques. Landsat imagery from the United States Geological Survey (USGS), accessed via Google Earth Engine, is used to map land use and land cover change between 2006 and 2026, and Sentinel-5P TROPOMI tropospheric NO2 data are used to map TRAP hotspots for 2026 using the Getis-Ord Gi* statistic. Built-up land area in Greater Gaborone grew by 79.6% over the study period, spreading outward from the historic core in multiple directions. TRAP hotspots are concentrated in this core and extend along the city's major roads into nearby growth areas, while more distant peripheral growth remains in the coldspot zone. This suggests that pollution has not spread uniformly with the built-up footprint, and that Greater Gaborone's transport corridors may play a role in shaping which growth areas experience elevated pollution and which do not. These findings offer evidence-based insights for land use planning and air quality management within Botswana's urban development framework, and highlight that Greater Gaborone spans multiple local government jurisdictions with no single authority responsible for the metropolitan area as a whole.","许多撒哈拉以南非洲城市的快速城市蔓延显著重塑了城市出行模式，导致通勤距离延长、私人车辆依赖加剧以及对主要交通走廊的压力不断增大。这些趋势对环境质量、城市可持续性和气候韧性具有重要影响。本文利用地理信息系统（GIS）和遥感技术，研究了博茨瓦纳大哈博罗内地区城市蔓延与交通相关空气污染（TRAP）之间的空间关系。研究使用通过Google Earth Engine获取的美国地质调查局（USGS）Landsat影像，绘制了2006年至2026年间的土地利用与土地覆盖变化，并利用Sentinel-5P TROPOMI对流层NO2数据，采用Getis-Ord Gi*统计量绘制了2026年TRAP热点分布。研究期间，大哈博罗内的建成区面积增长了79.6%，从历史核心区向多个方向向外扩展。TRAP热点集中在该核心区，并沿城市主要道路延伸至邻近的增长区域，而更远的外围增长区仍处于冷点区。这表明污染并未随建成区足迹均匀扩散，大哈博罗内的交通走廊可能在决定哪些增长区出现污染升高、哪些不升高方面发挥了作用。这些发现为博茨瓦纳城市发展框架内的土地利用规划和空气质量管理提供了基于证据的见解，并凸显出大哈博罗内跨越多个地方政府辖区，没有任何单一机构对整个大都市区负责。","2026-09-23T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":145},"研究博茨瓦纳哈博罗内城市扩张与交通空气污染，属城市环境与遥感领域，与三农、农业信息化、智慧农业无关，不建议进入每日精选。",[147],{"name":55,"url":140},[21,149,150,151],"土地利用","城市扩张","交通空气污染",[153,154],"Greater Gaborone 城市扩张","Sentinel-5P NO2 遥感","GreaterGaborone城市扩张-3506","10.31223\u002Fx5v22m",{"doi":156,"openalex_id":158,"authors":159,"venue":9,"cited_by_count":15,"oa_url":167,"card":168,"direction":40,"ingested_from":43},"W7214110791",[160,163,165],{"name":161,"orcid":162},"Keone Kelobonye","https:\u002F\u002Forcid.org\u002F0000-0002-6652-6262",{"name":164,"orcid":9},"Chenesani Sithole",{"name":166,"orcid":9},"Orateng Amos","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15173\u002Fdownload\u002F26308\u002F",{"tldr":169,"method":170,"finding":171,"direction":40,"opportunity":172},"用GIS与遥感分析大哈博罗内城市蔓延与交通相关空气污染的空间关系。","Landsat与Sentinel-5P NO2数据，结合Getis-Ord Gi","建设用地增79.6%，污染热点集中于核心区并沿主干道延伸，外围增长区仍为冷点。","可延伸至城乡交错带交通污染暴露与土地利用协同治理，关注跨辖区空气质量管理机制。","2026-09-25T23:30:33.701778Z",{"id":175,"title":176,"url":177,"summary":178,"summary_zh":179,"content":9,"source_name":180,"source_url":177,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":182,"sources":184,"tags":186,"search_phrases":190,"slug":193,"view_count":15,"doi":194,"paper":195,"created_at":203},3502,"Diagnosis of climate condition variations and their socio-environmental impacts in the Ogou prefecture (Southeast of the Plateaux Region - Togo)","https:\u002F\u002Fdoi.org\u002F10.24214\u002Fjcbps.d.16.4.68398","This study is conducted within the context of increasingly variable climate conditions across the world, which are affecting socioeconomic activities.The study aims to assess climate changes and identify their socio-environmental effects in the Ogou Prefecture.Data from ANAMET and Power NASA (1990-2024) enabled a frequency analysis of climate indices and the five-year spatio-temporal mapping of temperatures.Landsat imagery from 2003 and 2023, as well as MODIS data (temperature and NDVI) from 2000 to 2024 were used to study changes in vegetation cover in relation to temperature.From the survey of 369 participants, socioeconomic impacts have been identified.The results show a decline in rainy seasons, a recurrence of dry spells, and a rise in temperatures along a northwest-southeast gradient, along with values exceeding 26°C.The five-year period from 2015 to 2019 and the period from 2020 to 2024 are the most affected by global warming.A Diachronic analysis of land-cover maps reveals a degradation of vegetation cover; Diagnosis of …Faya Lemou et al.","本研究是在全球气候条件日益多变、并影响社会经济活动的背景下开展的。研究旨在评估气候变化并识别其在奥古省（Ogou Prefecture）产生的社会环境效应。利用ANAMET和Power NASA（1990—2024年）的数据，对气候指数进行了频率分析，并绘制了五年期气温时空分布图。研究还使用了2003年和2023年的Landsat影像，以及2000—2024年的MODIS数据（温度和NDVI），以探讨植被覆盖随温度变化的情况。通过对369名受访者的调查，识别出了社会经济影响。结果表明，雨季缩短、干旱期反复出现，气温沿西北—东南梯度上升，且数值超过26°C。2015—2019年的五年期以及2020—2024年期间受全球变暖影响最为严重。土地覆盖图的历时分析揭示了植被覆盖退化；对……Faya Lemou等人的诊断","Journal of Chemical Biological and Physical Sciences","2026-09-24T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":183},"研究多哥Ogou地区气候变化与社会环境影响，属气候环境科学，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。",[185],{"name":180,"url":177},[187,21,188,189],"气候变化","植被覆盖","多哥",[191,192],"植被覆盖 气候变化 遥感监测 多哥","植被覆盖 气候变化","植被覆盖气候变化遥感监测多哥-3502","10.24214\u002Fjcbps.d.16.4.68398",{"doi":194,"openalex_id":196,"authors":197,"venue":180,"cited_by_count":15,"oa_url":177,"card":198,"direction":40,"ingested_from":43},"W7214205919",[],{"tldr":199,"method":200,"finding":201,"direction":40,"opportunity":202},"评估多哥Ogou省气候变化及其对社会环境的影响。","用ANAMET\u002FNASA气象数据、Landsat与MODIS影像及369份问卷分","雨季减少、干旱频发、气温升高，植被退化，2015年后变暖加剧。","可结合多源遥感与农户调查，量化气候变暖对西非农业生计的级联影响。","2026-09-25T23:30:31.156223Z",{"id":205,"title":206,"url":207,"summary":208,"summary_zh":209,"content":9,"source_name":210,"source_url":207,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":211,"score_detail":212,"sources":217,"tags":219,"search_phrases":223,"slug":226,"view_count":15,"doi":227,"paper":228,"created_at":248},3499,"Tillage Practice Discrimination Using High-Resolution PlanetScope and Sentinel-2 Imagery","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02580-1","Abstract Accurate and timely assessment of soil tillage practices is crucial for monitoring sustainability in agriculture. To achieve more site-specific discrimination, it is necessary to understand the spectral and temporal properties of tillage practices across seasons. This study presents developments in tillage-practice discrimination by comparing two high-resolution remote sensing datasets, PlanetScope and Sentinel-2, to characterise and discriminate fields under intensive tillage (IT) and conservation tillage (CT) in the winter and spring seasons. A field experiment was conducted at an experimental site in the United Kingdom, collecting data on tillage practices in 2022–23 and 2023–24. We analysed the spectral and temporal characteristics of two tillage types and subsequently classified them using the random forest (RF) algorithm. Results showed reflectance differences between the two tillage treatments during the early period in both seasons. We also revealed that green, red-edge, and near-infrared wavelengths were relevant for the classification. PlanetScope showed greater potential for classifying tillage (OA = 70–80%), whereas Sentinel-2 exhibited lower performance (OA = 53–73%). Models from the winter achieved higher accuracy scores than those from the spring period, suggesting a seasonal variation in tillage discrimination. The findings highlight the utility of high-resolution satellite-based data, combined with machine learning, for mapping tillage practices and advancing precision agriculture.","准确的土壤耕作方式评估对于监测农业可持续性至关重要。为了实现更具针对性的区分，有必要了解不同季节耕作方式的光谱和时间特征。本研究通过比较两套高分辨率遥感数据集PlanetScope和Sentinel-2，在冬季和春季对集约耕作（IT）和保护性耕作（CT）田块进行表征与区分，从而推动耕作方式判别研究的发展。在英国一个试验站点开展了田间试验，收集了2022—23年和2023—24年的耕作方式数据。我们分析了两种耕作类型的光谱和时间特征，随后使用随机森林（RF）算法对其进行分类。结果表明，在兩個季节的早期阶段，两种耕作处理之间存在反射率差异。我们还发现，绿光、红边和近红外波段与分类相关。PlanetScope在耕作分类方面表现出更大的潜力（总体精度OA = 70–80%），而Sentinel-2的表现较低（OA = 53–73%）。冬季模型获得的精度评分高于春季模型，表明耕作判别存在季节性变化。研究结果凸显了高分辨率卫星数据结合机器学习在耕作方式制图和推进精准农业方面的实用性。","Journal of the Indian Society of Remote Sensing",67,{"impact":62,"substance":213,"depth":214,"authority":61,"freshness":215,"relevant":47,"comment":216},20,17,9,"基于高分辨率卫星影像与随机森林识别耕作方式的实证研究，方法清晰、结论具体，对精准农业与耕地监测有参考价值，但属细分领域学术进展，公共影响有限。",[218],{"name":210,"url":207},[67,220,221,21,222],"机器学习","保护性耕作","土壤耕作",[224,225],"PlanetScope Sentinel-2 耕作识别","保护性耕作 遥感 分类","PlanetScopeSentinel-2耕作识别-3499","10.1007\u002Fs12524-026-02580-1",{"doi":227,"openalex_id":229,"authors":230,"venue":210,"cited_by_count":15,"oa_url":207,"card":243,"direction":40,"ingested_from":43},"W7214193864",[231,234,237,240],{"name":232,"orcid":233},"Vidya Nahdhiyatul Fikriyah","https:\u002F\u002Forcid.org\u002F0000-0003-2869-3657",{"name":235,"orcid":236},"Roshanak Darvishzadeh","https:\u002F\u002Forcid.org\u002F0000-0001-7512-0574",{"name":238,"orcid":239},"Stephan M. Haefele","https:\u002F\u002Forcid.org\u002F0000-0003-0389-8373",{"name":241,"orcid":242},"Andrew Nelson","https:\u002F\u002Forcid.org\u002F0000-0002-7249-3778",{"tldr":244,"method":245,"finding":246,"direction":40,"opportunity":247},"对比PlanetScope与Sentinel-2影像，用随机森林区分冬春两季的集约与保护性耕作。","英国田间试验，2022-24年光谱时序数据，随机森林分类。","PlanetScope分类精度70-80%优于Sentinel-2，冬季模型精度高于春季，绿、红边和","可探索多源高分辨率影像融合与时序特征优化，提升不同季节和区域耕作分类的泛化能力。","2026-09-25T23:30:30.866355Z",{"id":250,"title":251,"url":252,"summary":253,"summary_zh":254,"content":9,"source_name":255,"source_url":252,"published_at":181,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":256,"sources":258,"tags":260,"search_phrases":263,"slug":266,"view_count":15,"doi":267,"paper":268,"created_at":282},3497,"Evaluating the Dynamics of Urban Expansion and Land Use-Land Cover Change in the Inter-Urban Areas of Barasat and Barrackpore of West Bengal, India","https:\u002F\u002Fdoi.org\u002F10.11648\u002Fj.ajrs.20261402.16","Urban expansion has predominantly occurred in peri-urban areas rather than within the urban core. It is mainly determined by unorganised growth, increased immigration, and rapid population growth in the urban fringe areas. The present paper analyses the trend of urbanisation, the land use and land cover change (LULC), and growth of built-up area in the Barasat-Barrackpore urban areas over the last four decades since 1990. The LULC change was prepared by using the Landsat images for the years of 1990, 2000, 2010, and 2020 by applying the maximum likelihood algorithm of supervised classification with the help of Remote Sensing and GIS techniques in ArcGIS. The four urban landscapes viz. urban core area, peri-urban area, scatter settlements, and open spaces were categorised using the NDBI sprawl matrix for analysing the magnitude and direction of urban growth. It is found that the urban population, urban area and urban units increased by 54%, 32%, and 42%, respectively in the period of 2001- 2011. The built-up area increased in 102.50%, while agricultural, vegetation cover, fallow, and wetlands decreased in 23%, 59%, 60%, and 57%, accordingly. It is also known, by conversion matrix, that agricultural lands have been converted into built-up and fallow lands, vegetative cover into built-up, fallow lands and agriculture, fallow lands into built-up and agricultural, wetlands into fallow lands, agricultural and built-up area, respectively. This study may help the planners and policy-makers for sustainable urban development.","城市扩张主要发生在城市边缘区而非城市核心区，其驱动因素主要包括无序增长、移民增加以及城市边缘区人口的快速增长。本文分析了自1990年以来近四十年间Barasat-Barrackpore城市地区的城市化趋势、土地利用与土地覆盖变化（LULC）以及建成区增长情况。利用1990年、2000年、2010年和2020年的Landsat影像，在ArcGIS中借助遥感与GIS技术，采用监督分类中的最大似然算法制备了LULC变化图。运用NDBI蔓延矩阵将四种城市景观类型——城市核心区、城市边缘区、分散聚落和开放空间——进行分类，以分析城市增长的程度和方向。研究发现，2001—2011年间，城市人口、城市面积和城市单元分别增长了54%、32%和42%。建成区面积增加了102.50%，而农业用地、植被覆盖、休耕地和湿地分别减少了23%、59%、60%和57%。通过转移矩阵还发现，农业用地分别转化为建成区和休耕地，植被覆盖分别转化为建成区、休耕地和农业用地，休耕地分别转化为建成区和农业用地，湿地分别转化为休耕地、农业用地和建成区。本研究可为规划者和政策制定者提供可持续城市发展的参考。","American Journal of Remote Sensing",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":257},"研究印度西孟加拉邦城市边缘区土地利用变化，属城市地理与遥感应用，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。",[259],{"name":255,"url":252},[21,261,150,262],"土地利用变化","印度西孟加拉邦",[264,265],"Barasat Barrackpore 城市扩张","Landsat 土地利用变化 印度","BarasatBarrackpore城市扩张-3497","10.11648\u002Fj.ajrs.20261402.16",{"doi":267,"openalex_id":269,"authors":270,"venue":255,"cited_by_count":15,"oa_url":252,"card":277,"direction":40,"ingested_from":43},"W7214169212",[271,274],{"name":272,"orcid":273},"Madhusudan Pramanick","https:\u002F\u002Forcid.org\u002F0009-0006-6499-5635",{"name":275,"orcid":276},"Lakshminarayan Satpati","https:\u002F\u002Forcid.org\u002F0000-0002-6920-9588",{"tldr":278,"method":279,"finding":280,"direction":40,"opportunity":281},"分析印度Barasat-Barrackpore地区1990-2020年城市扩张与土地利用覆被变化。","Landsat影像监督分类、NDBI蔓延矩阵与ArcGIS空间分析。","建设用地增102.5%，农业、植被、休耕和湿地分别减少23%、59%、60%、57%。","可结合多源遥感与机器学习预测城郊农地转换，服务耕地保护与可持续规划。","2026-09-25T23:30:30.714101Z"]