[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3676":3,"related-3676":59},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":58},3676,"Assessing Land Use, Land Cover Changes, and Urbanization Impacts on Water Stress in West African Watersheds: A Systematic Methodological Review","https:\u002F\u002Fdoi.org\u002F10.3791\u002F72287","Water stress is one of the major environmental and socio-economic constraints in West Africa, where population growth, agricultural expansion, soil sealing, hydroclimatic variability, and rising pressure on surface water and groundwater rapidly transform watershed functioning. Evaluating the impacts of land use and land cover (LULC) changes and urbanization requires methodological approaches that link the spatial dynamics of landscapes to observed or simulated hydrological responses. This review provides a comparative and critical analysis of the main techniques used in the region: instrumented watersheds and in situ observations, controlled field and laboratory experiments, satellite remote sensing and multi-temporal analysis, groundwater-oriented methods, conceptual, semi-distributed, physically based, and integrated surface-subsurface hydrological modeling, statistical and machine-learning approaches, and hybrid methods coupling land-use and climate scenarios. The search and selection process followed a transparent, PRISMA-based protocol and yielded 51 included records, of which 35 are anchored in West Africa. Hydrological modeling driven by remote sensing dominates the corpus, accounting for 15 of the 35 West African records (43%), particularly with SWAT, ACRU, WaSiM, CEQUEAU, SHETRAN, HydroGeoSphere, and ParFlow-CLM; reported Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and coefficient of determination generally range from about 0.6 to 0.9, which is satisfactory to very good against standard evaluation guidelines. Statistical and machine-learning studies account for 8 of 35 records, whereas urban- and groundwater-focused studies remain scarce. Moreover, 8 of 35 records originate from a single research network, so several regional conclusions rest on a narrow and partly non-independent evidence base. In situ networks remain essential for process understanding but are constrained by cost and low density; purely satellite-based approaches suffer from cloud cover and spectral confusion in subhumid zones. The most promising advances lie in multi-source, multi-scale frameworks combining targeted instrumentation, satellite time series, integrated hydrological modeling, territorial scenarios, systematic uncertainty analyses, and co-construction with water-management and planning actors.","水资源压力是西非主要的环境和社会经济制约因素之一。在该地区，人口增长、农业扩张、土壤封闭、水文气候变率以及地表水和地下水压力的不断上升，正迅速改变着流域功能。评估土地利用与土地覆盖（LULC）变化及城市化带来的影响，需要将景观的空间动态与观测或模拟的水文响应相联系的方法学途径。本综述对该地区所采用的主要技术进行了比较性和批判性分析：受控流域与实地观测、受控野外与实验室实验、卫星遥感与多时相分析、面向地下水的方法、概念性、半分布式、基于物理过程及地表-地下耦合的综合水文模型、统计与机器学习方法，以及耦合土地利用与气候情景的混合方法。检索与筛选过程遵循透明、基于PRISMA的协议，最终纳入51篇文献，其中35篇以西非为研究区。由遥感驱动的水文建模在文献主体中占主导地位，在35篇西非文献中占15篇（43%），尤以SWAT、ACRU、WaSiM、CEQUEAU、SHETRAN、HydroGeoSphere和ParFlow-CLM为主；所报告的纳什-萨特克利夫效率（NSE）、克林-古普塔效率（KGE）和决定系数通常约在0.6至0.9之间，对照标准评价指南属于满意至很好水平。统计与机器学习研究在35篇文献中占8篇，而聚焦城市和地下水的研究仍然稀少。此外，35篇文献中有8篇来自同一研究网络，因此若干区域性结论建立在狭窄且部分非独立的证据基础之上。实地观测网络对于过程理解仍然不可或缺，但受成本和低密度制约；纯卫星方法在半湿润区则受云覆盖和光谱混淆的影响。最有前景的进展在于多源、多尺度框架，其结合了针对性观测、卫星时间序列、综合水文建模、区域情景、系统不确定性分析，以及与水资源管理和规划主体的共同构建。",null,"Journal of Visualized Experiments","2026-09-25T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,17,13,1,"系统综述方法学扎实、数据翔实，但聚焦西非流域且与国内农业信息化关联较弱，仅具方法参考价值。",[24],{"name":10,"url":6},[26,27,28,29,30],"机器学习","遥感监测","土地利用","水文模型","西非流域",[32,33],"西非流域 土地利用 水文模型","SWAT 遥感 水资源压力","西非流域土地利用水文模型-3676",0,"10.3791\u002F72287",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":9,"card":51,"direction":55,"ingested_from":57},"W7214394296",[40,42,45,47,49],{"name":41,"orcid":9},"Valère-Carin Jofack Sokeng",{"name":43,"orcid":44},"Nakouana Timité","https:\u002F\u002Forcid.org\u002F0000-0002-3894-1450",{"name":46,"orcid":9},"Toto Marc Zahui",{"name":48,"orcid":9},"Kouamé Koffi Fernand",{"name":50,"orcid":9},"Koné Tiémoman",{"tldr":52,"method":53,"finding":54,"direction":55,"opportunity":56},"系统综述西非流域土地利用变化与城市化对水资源压力的评估方法。","PRISMA系统综述，纳入51篇文献，比较遥感、水文模型与机器学习等方法。","遥感驱动水文模型占主导（43%），城市与地下水研究稀缺，证据基础偏窄。","农业遥感与作物表型","可构建多源多尺度框架，融合实地观测、遥感时序与集成水文模型，并开展不确定性分析与利益相关者协同。","openalex","2026-09-28T23:30:31.949583Z",{"total":60,"page":21,"page_size":60,"items":61},6,[62,102,147,184,227,265],{"id":63,"title":64,"url":65,"summary":66,"summary_zh":67,"content":9,"source_name":68,"source_url":65,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":69,"score_detail":70,"sources":75,"tags":77,"search_phrases":81,"slug":84,"view_count":35,"doi":85,"paper":86,"created_at":101},3568,"Interpretable multi-index framework for extracting dry-season built-up areas: benchmarking machine learning with Sentinel-2","https:\u002F\u002Fdoi.org\u002F10.4995\u002Fraet.2027.25885","Spectral similarity between built-up surfaces and exposed dry soil significantly constrains built-up area extraction during dry seasons. Although machine-learning techniques can address this challenge by modeling complex spectral relationships, they generally require sufficiently large and representative labeled training datasets. This study therefore investigates a less data-demanding, rule-based multi-index approach for dry-season built-up mapping, aiming to reduce reliance on large labeled training datasets while maintaining effective classification performance. Sentinel-2A imagery acquired in April 2024 was analyzed, and temporal validation was conducted for 2020, 2022, and 2024. Among the tested combinations, the Built-up Area Extraction Index (BAEI), Dry BareSoil Index (DBSI), and Normalized Difference Vegetation Index (NDVI) achieved the highest performance, yielding 95 % overall accuracy and a Kappa coefficient of 0.89. This represents a substantial improvement over singleindex BAEI (80 % overall accuracy; Kappa 0.55), increasing the built-up user’s accuracy from 61 % to 92 %. Multitemporal validation confirmed that the optimized BAEI–DBSI–NDVI decision rules remained stable across the 2020–2024 dry-season images without recalibration, achieving 93–96 % overall accuracy and a built-up F1-score of 84–93 %. Spatial analysis demonstrated robust performance across peripheral and agricultural zones, with moderate variability in dense urban cores due to spectral heterogeneity. To evaluate the operational robustness of the proposed framework, it was benchmarked against machine-learning classifiers, specifically Support Vector Machine and Random Forest (RF). Under the specific conditions of this study, the proposed rule-based method (95 % accuracy) marginally outperformed both SVM (93 %) and RF (92 %), while offering greater transparency and reducing dependency on large, labeled training datasets. Furthermore, feature importance analysis confirmed the critical role of these selected indices in resolving spectral confusion. These findings suggest that the proposed framework offers a transparent and computationally efficient approach for dry-season urban monitoring in tropical coastal environments similar to Visakhapatnam.","建成表面与裸露干土之间的光谱相似性显著制约了旱季建成区提取。尽管机器学习技术可通过建模复杂光谱关系来应对这一挑战，但其通常需要足够大且具有代表性的标记训练数据集。因此，本研究探讨了一种对数据需求较低、基于规则的多指数方法用于旱季建成区制图，旨在减少对大规模标记训练数据集的依赖，同时保持有效的分类性能。研究分析了2024年4月获取的Sentinel-2A影像，并对2020年、2022年和2024年进行了时间验证。在测试的组合中，建成区提取指数（BAEI）、干裸土指数（DBSI）和归一化差异植被指数（NDVI）表现最佳，总体精度达95%，Kappa系数为0.89。相较于单一指数BAEI（总体精度80%；Kappa 0.55），这一结果有显著提升，建成区用户精度从61%提高至92%。多时相验证证实，优化后的BAEI–DBSI–NDVI决策规则在2020—2024年旱季影像上无需重新校准即可保持稳定，总体精度达93%—96%，建成区F1分数为84%—93%。空间分析表明，该方法在外围和农业区域表现稳健，而在密集城市核心区由于光谱异质性存在中等程度变异。为评估所提框架的业务化稳健性，将其与机器学习分类器进行了基准比较，具体为支持向量机（SVM）和随机森林（RF）。在本研究的特定条件下，所提出的基于规则的方法（95%精度）略优于SVM（93%）和RF（92%），同时具有更高的透明性并减少了对大规模标记训练数据集的依赖。此外，特征重要性分析证实了所选指数在解决光谱混淆方面的关键作用。这些发现表明，所提框架为类似维沙卡帕特南的热带沿海环境旱季城市监测提供了一种透明且计算高效的方法。","Revista de teledetección: Revista de la Asociación Española de Teledetección",72,{"impact":71,"substance":72,"depth":19,"authority":20,"freshness":73,"relevant":21,"comment":74},12,21,9,"该研究提出可解释的多指数规则框架，在旱季建成区提取上以更少标注数据达到95%精度并优于SVM\u002FRF，方法新颖、验证充分，对农业遥感与乡村土地利用监测有参考价值。",[76],{"name":68,"url":65},[78,26,79,27,80],"Sentinel-2","NDVI","建成区提取",[82,83],"Sentinel-2 建成区提取","BAEI DBSI NDVI 旱季","Sentinel-2建成区提取-3568","10.4995\u002Fraet.2027.25885",{"doi":85,"openalex_id":87,"authors":88,"venue":68,"cited_by_count":35,"oa_url":65,"card":95,"direction":100,"ingested_from":57},"W7214385607",[89,92],{"name":90,"orcid":91},"Sarah Sejari","https:\u002F\u002Forcid.org\u002F0009-0002-7300-9084",{"name":93,"orcid":94},"Vazeer Mahammood","https:\u002F\u002Forcid.org\u002F0000-0003-1667-5232",{"tldr":96,"method":97,"finding":98,"direction":55,"opportunity":99},"提出可解释多指数规则框架，用Sentinel-2提取旱季建成区，性能优于机器学习。","Sentinel-2A影像，BAEI、DBSI、NDVI组合规则，对比SVM与随","BAEI-DBSI-NDVI规则达95%精度、Kappa 0.89，优于单指数及SVM\u002FRF且无需重","可探索该规则框架向其他气候带与传感器迁移，并结合少量样本的半监督学习提升城市核心区精度。","农业人工智能与决策模型","2026-09-26T23:30:53.200163Z",{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":109,"score_detail":110,"sources":114,"tags":116,"search_phrases":120,"slug":123,"view_count":35,"doi":124,"paper":125,"created_at":146},3553,"Advances and gaps in mitigation and management practices across critical landscapes and scales","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jenvman.2026.131013","Environmental systems are increasingly stressed by the interacting effects of climate change, land-use change, agricultural intensification, urbanization, and emerging contaminants. This paper synthesizes contributions to the Journal of Environmental Management special collection, “Advances and gaps in mitigation and management practices across critical landscapes and scales,” which examines current progress and remaining challenges in environmental mitigation and best management practices. The reviewed studies span diverse landscapes, scales, and management contexts, with emphasis on water quality, water quantity, pollutant transport, ecological resilience, and sustainable agricultural systems. Four major themes emerge: the combined pressures of climate and human activity on environmental systems; innovations in modeling, monitoring, remote sensing, and statistical forecasting; evaluation of management interventions and mitigation strategies; and cross-cutting insights related to spatial variability, extreme events, long-term dynamics, and implementation gaps. Collectively, the studies demonstrate that mitigation effectiveness is highly context-dependent and influenced by hydrologic connectivity, land use, climate variability, management history, and social factors. Advances in process-based models, machine learning, geospatial technologies, and monitoring, reporting, and verification frameworks are improving the ability to predict environmental responses and evaluate intervention outcomes. However, persistent gaps remain in long-term monitoring, maintenance of management practices, adaptation to extreme events, and translation of scientific findings into policy and practice. This synthesis highlights the need for integrated, adaptive, and stakeholder-informed approaches to strengthen environmental resilience across critical landscapes.","环境系统日益受到气候变化、土地利用变化、农业集约化、城市化以及新兴污染物相互作用的影响。本文综合了《环境管理杂志》特刊“关键景观与尺度下减缓与管理实践的进展与差距”的贡献，该特刊审视了环境减缓与最佳管理实践的当前进展及剩余挑战。所综述的研究涵盖多样化的景观、尺度和管理情境，重点关注水质、水量、污染物迁移、生态韧性和可持续农业系统。由此浮现出四大主题：气候与人类活动对环境系统的复合压力；建模、监测、遥感与统计预测方面的创新；管理干预与减缓策略的评估；以及关于空间变异性、极端事件、长期动态与实施差距的交叉性见解。总体而言，这些研究表明，减缓效果高度依赖于具体情境，并受水文连通性、土地利用、气候变异性、管理历史和社会因素的影响。基于过程的模型、机器学习、地理空间技术以及监测、报告与核查框架的进步，正在提升预测环境响应和评估干预结果的能力。然而，在长期监测、管理实践的维持、极端事件的适应以及科学发现向政策与实践的转化方面，仍存在持续性的差距。本综述强调，需要采取综合性、适应性且利益相关者知情的方法，以增强关键景观的环境韧性。","Journal of Environmental Management",78,{"impact":111,"substance":18,"depth":111,"authority":112,"freshness":17,"relevant":21,"comment":113},18,14,"核心期刊综述，系统梳理环境缓解与管理实践进展与缺口，对农业面源污染与水质管理有参考价值，但非农业信息化直接落地成果。",[115],{"name":108,"url":105},[26,117,27,118,119],"农业面源污染","农业可持续发展","水质管理",[121,122],"Journal of Environmental Management 缓解措施 管理实践","农业可持续发展 农业面源污染 机器学习 水质管理","JournalofEnvironmentalManagement缓解措施管理实践-3553","10.1016\u002Fj.jenvman.2026.131013",{"doi":124,"openalex_id":126,"authors":127,"venue":108,"cited_by_count":35,"oa_url":105,"card":140,"direction":55,"ingested_from":57},"W7214310594",[128,131,134,137],{"name":129,"orcid":130},"Fouad H. Jaber","https:\u002F\u002Forcid.org\u002F0000-0001-8643-8668",{"name":132,"orcid":133},"Latif Kalin","https:\u002F\u002Forcid.org\u002F0000-0001-9562-8834",{"name":135,"orcid":136},"Soni  Mulmi Pradhanang","https:\u002F\u002Forcid.org\u002F0000-0002-1142-9457",{"name":138,"orcid":139},"Aleksey Y. Sheshukov","https:\u002F\u002Forcid.org\u002F0000-0002-4842-908X",{"tldr":141,"method":142,"finding":143,"direction":144,"opportunity":145},"综述环境减缓与管理实践进展，指出成效高度依赖情境且存在实施缺口。","综合多景观尺度研究，涵盖过程模型、机器学习、遥感与MRV框架。","减缓效果受水文连通、土地利用、气候与历史管理影响，长期监测与政策转化仍不足。","农业绿色发展与碳","可研究农业景观中管理实践的长期维持机制与极端事件下的适应性策略。","2026-09-26T23:30:29.702279Z",{"id":148,"title":149,"url":150,"summary":151,"summary_zh":152,"content":9,"source_name":153,"source_url":150,"published_at":154,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":155,"sources":157,"tags":159,"search_phrases":162,"slug":165,"view_count":35,"doi":166,"paper":167,"created_at":183},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。","OpenAlex","2026-09-23T00:00:00Z",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":156},"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。",[158],{"name":153,"url":150},[27,28,160,161],"城市扩张","交通空气污染",[163,164],"Greater Gaborone 城市扩张","Sentinel-5P NO2 遥感","GreaterGaborone城市扩张-3506","10.31223\u002Fx5v22m",{"doi":166,"openalex_id":168,"authors":169,"venue":9,"cited_by_count":35,"oa_url":177,"card":178,"direction":55,"ingested_from":57},"W7214110791",[170,173,175],{"name":171,"orcid":172},"Keone Kelobonye","https:\u002F\u002Forcid.org\u002F0000-0002-6652-6262",{"name":174,"orcid":9},"Chenesani Sithole",{"name":176,"orcid":9},"Orateng Amos","https:\u002F\u002Feartharxiv.org\u002Frepository\u002Fobject\u002F15173\u002Fdownload\u002F26308\u002F",{"tldr":179,"method":180,"finding":181,"direction":55,"opportunity":182},"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":185,"title":186,"url":187,"summary":188,"summary_zh":189,"content":9,"source_name":190,"source_url":187,"published_at":191,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":192,"score_detail":193,"sources":195,"tags":197,"search_phrases":201,"slug":204,"view_count":35,"doi":205,"paper":206,"created_at":226},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","2026-09-24T00:00:00Z",67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":73,"relevant":21,"comment":194},"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。",[196],{"name":190,"url":187},[198,26,199,27,200],"智慧农业","保护性耕作","土壤耕作",[202,203],"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":205,"openalex_id":207,"authors":208,"venue":190,"cited_by_count":35,"oa_url":187,"card":221,"direction":55,"ingested_from":57},"W7214193864",[209,212,215,218],{"name":210,"orcid":211},"Vidya Nahdhiyatul Fikriyah","https:\u002F\u002Forcid.org\u002F0000-0003-2869-3657",{"name":213,"orcid":214},"Roshanak Darvishzadeh","https:\u002F\u002Forcid.org\u002F0000-0001-7512-0574",{"name":216,"orcid":217},"Stephan M. Haefele","https:\u002F\u002Forcid.org\u002F0000-0003-0389-8373",{"name":219,"orcid":220},"Andrew Nelson","https:\u002F\u002Forcid.org\u002F0000-0002-7249-3778",{"tldr":222,"method":223,"finding":224,"direction":55,"opportunity":225},"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":228,"title":229,"url":230,"summary":231,"summary_zh":232,"content":9,"source_name":233,"source_url":230,"published_at":191,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":234,"sources":236,"tags":238,"search_phrases":240,"slug":243,"view_count":35,"doi":244,"paper":245,"created_at":264},3496,"Cartographic Analysis of the Spatial Changes in the Tourist Resort of Saly (Senegal) Between 1988 and 2024","https:\u002F\u002Fdoi.org\u002F10.11648\u002Fj.ajep.20261505.12","The creation of the seaside resort of Saly in Senegal in 1984 marked the beginning of a transition from a village to an urban center. However, the development of a designated area is often marked by a context of urban transformation and expansion, giving rise to various territorial and societal challenges. Hence, the objective of this study is to assess the spatial dynamics of the Saly tourist resort between 1988 and 2024. The methodological approach was based on the analysis of satellite imagery and the application of remote sensing techniques, notably the NDBI index and the calculation of the average annual growth rate of spatial units. The results revealed that from 1988 to 2002), the built-up area increased significantly (from 38 to 534 ha), while agricultural land (from 963 to 613 ha), bare land (from 315 to 78 ha), and beaches (from 67 to 52 ha) declined. Between 2002 and 2014, built-up areas increased (from 534 to 903 ha), followed by a decrease in vegetation (from 421 to 349 ha) and agricultural land (from 613 to 345 ha). During the period from 2014 to 2024, built-up areas saw a slight increase (from 903 to 950 ha) and a shift in trends regarding vegetation (from 349 to 460 ha) and open land (from 64 to 235 ha), while agricultural land (from 345 to 13 ha) and beaches (from 47 to 44 ha) decreased further. These trends show that 86.29% of the territory underwent land-use conversion, compared with 13.71% that remained stable over these 36 years. The built-up area shows an estimated average annual growth rate of 9.4%, while vegetation increased slightly by 0.99%. In contrast, agricultural land declined by 11.20%, as did beaches and bare ground, which decreased by 1.31% and 0.82%, respectively. These trends reflect three phases of spatial dynamics: rapid expansion (1988-2002), consolidation (2002-2014), and spatial restructuring (2014-2024). Analysis of the NDBI index from 1988 shows values ranging from generally low to slightly negative (-0.42 and 0.02) to a higher value (0.21) in 2024. Spatial dynamics reveal rapid urbanization in Saly, requiring sustainable land-use policies that take natural areas into account and continuous monitoring of land use to better manage urban growth.","1984年塞内加尔萨利海滨度假胜地的创建，标志着该地区从村庄向城市中心的转型开端。然而，指定区域的发展往往伴随着城市转型与扩张的背景，由此引发各种领土与社会挑战。因此，本研究旨在评估1988年至2024年间萨利旅游度假区的空间动态。研究方法基于卫星影像分析和遥感技术的应用，特别是NDBI指数（归一化建筑指数）以及空间单元年均增长率的计算。结果显示，1988年至2002年间，建成区面积显著增加（从38公顷增至534公顷），而农业用地（从963公顷降至613公顷）、裸地（从315公顷降至78公顷）和海滩（从67公顷降至52公顷）则有所减少。2002年至2014年间，建成区面积继续增加（从534公顷增至903公顷），随后植被（从421公顷降至349公顷）和农业用地（从613公顷降至345公顷）减少。2014年至2024年间，建成区面积略有增加（从903公顷增至950公顷），植被（从349公顷增至460公顷）和空地（从64公顷增至235公顷）趋势发生转变，而农业用地（从345公顷降至13公顷）和海滩（从47公顷降至44公顷）进一步减少。这些趋势表明，在这36年间，86.29%的领土发生了土地利用转换，而13.71%保持稳定。建成区年均增长率估计为9.4%，植被略有增加，为0.99%。相比之下，农业用地下降了11.20%，海滩和裸地分别下降了1.31%和0.82%。这些趋势反映了三个空间动态阶段：快速扩张期（1988-2002年）、巩固期（2002-2014年）和空间重组期（2014-2024年）。对1988年以来NDBI指数的分析显示，数值从普遍较低至略负（-0.42和0.02），到2024年升至较高值（0.21）。空间动态揭示了萨利的快速城市化，需要制定考虑自然区域的可持续土地利用政策，并持续监测土地利用，以更好地管理城市增长。","American Journal of Environmental Protection",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":235},"研究塞内加尔海滨度假区1988-2024年土地利用变化，属城市与旅游地理遥感范畴，与三农、农业信息化、数字乡村主题无关，不予入选。",[237],{"name":233,"url":230},[27,28,239],"滨海旅游",[241,242],"Saly Senegal 遥感 土地利用","土地利用 滨海旅游 遥感监测","SalySenegal遥感土地利用-3496","10.11648\u002Fj.ajep.20261505.12",{"doi":244,"openalex_id":246,"authors":247,"venue":233,"cited_by_count":35,"oa_url":230,"card":259,"direction":55,"ingested_from":57},"W7214164135",[248,250,253,256],{"name":249,"orcid":9},"Moctar Badji",{"name":251,"orcid":252},"Hyacinthe Sambou","https:\u002F\u002Forcid.org\u002F0009-0000-5049-3550",{"name":254,"orcid":255},"Cheikh Mbow","https:\u002F\u002Forcid.org\u002F0000-0003-4620-4490",{"name":257,"orcid":258},"Dramé Amata Fodé","https:\u002F\u002Forcid.org\u002F0009-0007-1428-6061",{"tldr":260,"method":261,"finding":262,"direction":55,"opportunity":263},"基于遥感影像分析塞内加尔Saly旅游度假区1988-2024年土地利用变化。","卫星影像与NDBI指数，计算空间单元年均增长率。","建设用地年均增长9.4%，农地减少11.20%，86.29%土地发生转换。","可结合多源遥感与机器学习，监测旅游区扩张对农地与生态的长期影响。","2026-09-25T23:30:30.648444Z",{"id":266,"title":267,"url":268,"summary":269,"summary_zh":270,"content":9,"source_name":153,"source_url":268,"published_at":191,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":35,"score_detail":271,"sources":273,"tags":275,"search_phrases":277,"slug":280,"view_count":35,"doi":281,"paper":282,"created_at":306},3493,"Urbanization and Land Use\u002FLand Cover Change in Jalingo Local Government Area, Taraba State, Nigeria: A Multi-Temporal Remote Sensing and Socio-Economic Assessment","https:\u002F\u002Fdoi.org\u002F10.54117\u002Fcanhm718","Rapid urbanization is a major driver of land use and land cover (LULC) transformation, particularly in rapidly developing cities of sub-Saharan Africa. This study examined the impact of urbanization on LULC in Jalingo Local Government Area of Taraba State, Nigeria, with particular emphasis on the period 1994–2024. The study integrated multi-temporal Landsat satellite imagery, Geographic Information Systems (GIS), post-classification change detection and questionnaire data to assess the spatial and temporal dynamics of urban growth and its environmental and socio-economic implications. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI\u002FTIRS imagery for 1994, 2004, 2014 and 2024 were classified into four major LULC categories: built-up area, vegetation, bare surface and water body. A questionnaire survey involving 384 respondents was also used to assess perceived drivers and consequences of urbanization. The results revealed substantial transformation of the Jalingo landscape. Vegetation declined from 170.56 km² (87.47%) in 1994 to 50.39 km² (25.84%) in 2024, representing a net loss of 120.17 km². In contrast, built-up land increased from 4.30 km² (2.20%) to 18.06 km² (9.26%), while bare surfaces expanded from 18.60 km² (9.54%) to 124.68 km² (63.94%). The most rapid transformation occurred between 2004 and 2014, when vegetation declined by 81.57 km² while bare surfaces increased by 71.79 km² and built-up land by 10.46 km². Survey results indicated that government policies and initiatives, economic opportunities, population growth and infrastructure development were important perceived drivers of urbanization. Most respondents also perceived significant negative effects on agricultural land, livelihoods, food security and access to essential natural resources. The study concludes that urban growth in Jalingo has generated important economic and infrastructural benefits but has also produced substantial environmental degradation and socio-economic pressures. Strengthened land-use planning, protection of agricultural and ecological areas, urban greening, sustainable livelihood programmes, community participation and continuous GIS-based monitoring are recommended.","快速城市化是土地利用与土地覆盖(LULC)变化的主要驱动力，在撒哈拉以南非洲快速发展的城市中尤为突出。本研究考察了尼日利亚塔拉巴州贾林戈地方政府辖区城市化对土地利用与土地覆盖的影响，重点关注1994—2024年这一时期。研究综合运用多时相Landsat卫星影像、地理信息系统(GIS)、分类后变化检测和问卷调查数据，评估城市增长的空间与时间动态及其环境和社会经济影响。将1994年、2004年、2014年和2024年的Landsat 5 TM、Landsat 7 ETM+和Landsat 8 OLI\u002FTIRS影像分为四个主要土地利用与土地覆盖类别：建设用地、植被、裸地表和水体。同时开展了一项涉及384名受访者的问卷调查，以评估城市化的感知驱动因素及其后果。结果表明，贾林戈景观发生了显著转变。植被从1994年的170.56 km²(87.47%)下降至2024年的50.39 km²(25.84%)，净损失120.17 km²。相比之下，建设用地从4.30 km²(2.20%)增至18.06 km²(9.26%)，而裸地表从18.60 km²(9.54%)扩展至124.68 km²(63.94%)。变化最快的时期为2004—2014年，期间植被减少81.57 km²，裸地表增加71.79 km²，建设用地增加10.46 km²。调查结果表明，政府政策和举措、经济机会、人口增长和基础设施建设是城市化的重要感知驱动因素。大多数受访者还认为，城市化对农业用地、生计、粮食安全和基本自然资源的获取产生了显著的负面影响。研究认为，贾林戈的城市增长带来了重要的经济和基础设施效益，但也造成了严重的环境退化和社会经济压力。建议加强土地利用规划、保护农业和生态区域、推进城市绿化、实施可持续生计项目、促进社区参与以及持续开展基于GIS的监测。",{"impact":35,"substance":35,"depth":35,"authority":35,"freshness":35,"relevant":35,"comment":272},"研究对象为尼日利亚Jalingo地区城市化与土地利用变化，属国际区域地理研究，与三农、农业信息化、智慧农业等主题无直接关联，不建议进入每日精选。",[274],{"name":153,"url":268},[27,28,276],"城市化",[278,279],"尼日利亚 Jalingo 土地利用","Landsat 遥感 城市化","尼日利亚Jalingo土地利用-3493","10.54117\u002Fcanhm718",{"doi":281,"openalex_id":283,"authors":284,"venue":9,"cited_by_count":35,"oa_url":268,"card":301,"direction":55,"ingested_from":57},"W7214145360",[285,288,291,293,295,297,299],{"name":286,"orcid":287},"Mohammed Bakoji Yusuf","https:\u002F\u002Forcid.org\u002F0000-0002-8719-2396",{"name":289,"orcid":290},"Ambrose Audu Zemba","https:\u002F\u002Forcid.org\u002F0000-0002-7692-9911",{"name":292,"orcid":9},"Huzaima Abubakar Bakoji",{"name":294,"orcid":9},"AUWAL ADAMU BAKAKU",{"name":296,"orcid":9},"UMAR ABBA JAURO",{"name":298,"orcid":9},"Aliyu Mohammed Jingudo",{"name":300,"orcid":9},"Abubakar Isa",{"tldr":302,"method":303,"finding":304,"direction":55,"opportunity":305},"用多时相遥感和问卷评估尼日利亚Jalingo 1994–2024年城市化驱动的土地利用变化。","Landsat 5\u002F7\u002F8影像分类、GIS变化检测与384份问卷。","植被减少120.17 km²，建设用地和裸地大幅扩张，2004–2014变化最快。","可结合多源遥感与农户调查，研究西非城市扩张对农地流失和粮食安全的驱动机制。","2026-09-25T23:30:30.441601Z"]