[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2799":3,"related-2799":102},{"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":74,"slug":77,"view_count":69,"doi":78,"paper":79,"created_at":101},2799,"Multi-Temporal Assessment of Bimodal Monsoon Flood Dynamics and Agricultural Exposure Using Integrated Sentinel-1 SAR and Sentinel-2 Optical Data in Punjab, Pakistan","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fgeohazards7040114","Floods in monsoon-dominated river basins exhibit high spatio-temporal variability, necessitating high-resolution, multi-sensor approaches for reliable monitoring and impact assessment. In flood-prone agricultural regions, continuous monitoring using optical remote sensing is frequently hindered by dense monsoon cloud cover. This study establishes a comprehensive multi-sensor framework within the Google Earth Engine (GEE) to examine the spatio-temporal dynamics and land surface impacts of the 2025 monsoon floods in Punjab, Pakistan. Flood inundation mapping was executed using a 12-day Sentinel-1 Synthetic Aperture Radar (SAR) time series via a dual-threshold change detection methodology. Concurrently, Sentinel-2 imagery facilitated the derivation of land use\u002Fland cover (LULC) changes and vegetation dynamics using a Random Forest classifier, achieving overall accuracy of 93% (pre-flood), 91% (during flood), and 94% (post-flood). These accuracy levels were consistent across all three phases despite spectral confusion between water, saturated soil, and vegetation during peak inundation, indicating consistent classification performance under monsoon conditions. The analysis revealed a distinct bimodal flooding regime, characterized by an early monsoon peak in July–August and a more severe late monsoon peak in August-September. The cumulative maximum flood extent reached 9495.33 km2, with peak single-date inundation reaching 5449 km2. Mapped cropland declined by 6.7% (8181 km2) during peak flooding, with 3.9% (4796 km2) remaining non-cropland by the end of the observation period; 5892 km2 of pre-flood cropland was identified as inundated through spatial intersection. In addition, the Normalized Difference Vegetation Index (NDVI) declined by 28.6%, from 0.28 to 0.20, indicating a substantial reduction in vegetation greenness. Spatial consistency was checked with the United Nations Satellite Centre (UNOSAT) and the Food and Agriculture Organization (FAO), independently collected data showing moderate spatial agreement. The proposed framework is highly scalable for continuous flood monitoring, offering critical insights for disaster management and climate adaptation planning in monsoon regions plagued by data scarcity and persistent cloudiness. The approach is particularly relevant for near-real-time operational monitoring, given its reliance on freely available Sentinel data and cloud-based processing that requires no specialized ground infrastructure.","在季风主导的流域，洪水表现出高度的时空变异性，因此需要高分辨率、多传感器方法来进行可靠监测和影响评估。在易受洪水影响的农业区域，利用光学遥感进行连续监测常常受到季风期密集云层的阻碍。本研究在Google Earth Engine（GEE）中建立了一个综合多传感器框架，以考察2025年巴基斯坦旁遮普省季风洪水的时空动态及其对地表的影 响。利用12天Sentinel-1合成孔径雷达（SAR）时间序列，通过双阈值变化检测方法进行洪水淹没制图。同时，利用Sentinel-2影像，通过随机森林分类器提取土地利用\u002F土地覆盖（LULC）变化和植被动态，总体精度分别达到93%（洪水前）、91%（洪水期间）和94%（洪水后）。尽管在淹没峰值期水体、饱和土壤和植被之间存在光谱混淆，这三个阶段的精度水平仍保持一致，表明在季风条件下分类性能稳定。分析揭示出明显的双峰洪水情势，其特征是7—8月季风早期峰值和8—9月更为严重的季风晚期峰值。累计最大洪水范围达到9495.33 km²，单日最大淹没面积达到5449 km²。在洪水峰值期，制图耕地减少6.7%（8181 km²），到观测期末仍有3.9%（4796 km²）为非耕地；通过空间交集识别出5892 km²的洪水前耕地被淹没。此外，归一化植被指数（NDVI）下降28.6%，从0.28降至0.20，表明植被绿度显著降低。利用联合国卫星中心（UNOSAT）和联合国粮食及农业组织（FAO）独立收集的数据进行了空间一致性检验，结果显示具有中等空间一致性。所提出的框架对于连续洪水监测具有高度可扩展性，为受数据稀缺和持续多云困扰的季风地区的灾害管理和气候适应规划提供了关键见解。鉴于该方法依赖免费可用的Sentinel数据和无需专门地面基础设施的云端处理，它尤其适用于近实时业务化监测。",null,"GeoHazards","2026-09-16T00:00:00Z","论文",10,false,80,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,22,13,9,1,"基于Sentinel-1\u002F2多源遥感与GEE平台的洪水-农业暴露评估，方法可迁移、数据详实，对农业灾害遥感监测有参考价值，但属区域性案例研究，非国内三农政策或产业级事件。",[24],{"name":10,"url":6},[26,27,28,29,30],"农业遥感","遥感监测","洪涝灾害","作物受灾评估","巴基斯坦",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,52,60,67],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},8,true,[],[49,50],"遥感","remote sensing",[26,27],{"code":53,"score":54,"supported":46,"title":55,"abstract":56,"tags":59},"H2",6,[],[57,58],"土壤","soil",[],{"code":61,"score":62,"supported":46,"title":63,"abstract":64,"tags":66},"H7",4,[],[65],"气候适应",[28],{"code":68,"score":69,"supported":46,"title":70,"abstract":71,"tags":73},"B4",3,[],[72],"月季",[],[75,76],"作物受灾评估 农业遥感 巴基斯坦 洪涝灾害","作物受灾评估 农业遥感","作物受灾评估农业遥感巴基斯坦洪涝灾害-2799","10.3390\u002Fgeohazards7040114",{"doi":78,"openalex_id":80,"authors":81,"venue":10,"cited_by_count":93,"oa_url":6,"card":94,"direction":98,"ingested_from":100},"W7213432591",[82,84,87,90],{"name":83,"orcid":9},"Nida Khursheed",{"name":85,"orcid":86},"Asif Sajjad","https:\u002F\u002Forcid.org\u002F0000-0003-1921-8213",{"name":88,"orcid":89},"Mazhar Iqbal","https:\u002F\u002Forcid.org\u002F0000-0001-5891-9798",{"name":91,"orcid":92},"Rana Waqar Aslam","https:\u002F\u002Forcid.org\u002F0000-0002-8711-8700",0,{"tldr":95,"method":96,"finding":97,"direction":98,"opportunity":99},"基于GEE融合Sentinel-1\u002F2监测2025年巴基斯坦旁遮普季风洪水双峰动态及农田暴露。","GEE平台、Sentinel-1 SAR双阈值变化检测、Sentinel-2随机","洪水呈7-8月与8-9月双峰，最大淹没9495平方公里，耕地减少6.7%，NDVI降28.6%。","农业遥感与作物表型","可延伸至多云区近实时洪涝-作物损失耦合评估与灾后恢复监测模型构建。","openalex","2026-09-17T23:30:37.558541Z",{"total":54,"page":21,"page_size":54,"items":103},[104,166,226,300,374,444],{"id":105,"title":106,"url":107,"summary":108,"summary_zh":109,"content":9,"source_name":110,"source_url":107,"published_at":111,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":112,"score_detail":113,"sources":116,"tags":118,"primary_topic":122,"secondary_topics":123,"classification":124,"search_phrases":143,"slug":146,"view_count":147,"doi":148,"paper":149,"created_at":165},4588,"Integrating terrestrial and marine remote sensing indicators to estimate subnational economic output","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2743115","Subnational economic data remain scarce across much of the Global South, particularly in fragmented island and coastal geographies, thereby constraining the capacity of researchers and policymakers to monitor local economic conditions. This study proposes and empirically evaluates a four-proxy remote sensing framework for subnational gross domestic product (GDP) estimation. The framework extends the established Night-time Lights (NTL)–Normalised Difference Vegetation Index (NDVI)–Forest Loss (FL) approach by incorporating sea surface Chlorophyll-a (CHLA) as a marine fisheries proxy. This achieves full proxy coverage across the non-agriculture, forestry, and fisheries sector through NTL, agriculture through NDVI, forestry through FL, and marine fisheries through CHLA. The empirical analysis employs Multiscale Geographically Weighted Regression (MGWR) and pooled ordinary least squares (OLS) across 102 regencies and cities in Sulawesi and Maluku, eastern Indonesia, over 2015–2024, with a province-level panel extending to 2002–2024. The stepwise MGWR R2 rises from 0.468 (NTL only) to 0.543 (NTL, NDVI, and FL) to 0.561 (full model), with a pooled Pearson correlation of 0.760. CHLA is positive and significant at both spatial scales after conditioning on all terrestrial proxies, confirmed by OLS coefficients of 0.029 at the regency\u002Fcity level and 0.148 at the province level, significant at the 0.1% and 1% levels respectively. All proxy relationships replicate in direction and significance across scales and over the 23-year panel. The framework provides a freely replicable tool for subnational economic monitoring in data-scarce coastal and island settings, with direct applicability to marine-dependent primary sector economies globally.","在全球南方的大部分地区，尤其是碎片化的岛屿和沿海地理区域，地方经济数据仍然稀缺，从而制约了研究者和政策制定者监测地方经济状况的能力。本研究提出并实证评估了一个用于地方国内生产总值（GDP）估算的四代理变量遥感框架。该框架在已有的夜间灯光（NTL）—归一化植被指数（NDVI）—森林损失（FL）方法基础上，引入海表叶绿素a（CHLA）作为海洋渔业代理变量。由此实现了对非农、林业和渔业部门的代理变量全覆盖：NTL对应非农部门，NDVI对应农业，FL对应林业，CHLA对应海洋渔业。实证分析采用多尺度地理加权回归（MGWR）和混合普通最小二乘法（OLS），覆盖印度尼西亚东部苏拉威西和马鲁古的102个县和市，时间范围为2015—2024年，并辅以延伸至2002—2024年的省级面板数据。逐步MGWR的R²从0.468（仅NTL）升至0.543（NTL、NDVI和FL）再升至0.561（全模型），混合皮尔逊相关系数为0.760。在控制所有陆地代理变量后，CHLA在两个空间尺度上均为正向且显著，OLS系数在县\u002F市级为0.029、省级为0.148，分别在0.1%和1%水平上显著。所有代理变量关系在不同尺度和23年面板期间在方向和显著性上均得到重复验证。该框架为数据稀缺的沿海和岛屿地区的地方经济监测提供了一个可自由复制的工具，并可直接适用于全球依赖海洋的初级产业经济体。","International Journal of Remote Sensing","2026-10-09T00:00:00Z",81,{"impact":17,"substance":18,"depth":17,"authority":114,"freshness":20,"relevant":21,"comment":115},14,"将叶绿素a纳入遥感代理框架估算次国家级GDP，方法新颖、数据扎实，对数据稀缺沿海岛屿地区的农业与渔业经济监测有直接参考价值。",[117],{"name":110,"url":107},[119,26,27,120,121],"数字乡村","渔业经济","GDP估算",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":125},{"candidates":126,"agricultural_evidence":46},[127,132,138],{"code":32,"score":128,"supported":46,"title":129,"abstract":130,"tags":131},16,[50],[49,50],[26,27],{"code":133,"score":69,"supported":46,"title":134,"abstract":135,"tags":137},"D",[],[136],"渔业",[],{"code":139,"score":21,"supported":14,"title":140,"abstract":141,"tags":142},"I6",[],[],[119],[144,145],"苏拉威西 马鲁古 遥感 GDP","叶绿素a 海洋渔业 遥感","苏拉威西马鲁古遥感GDP-4588",2,"10.1080\u002F01431161.2026.2743115",{"doi":148,"openalex_id":150,"authors":151,"venue":110,"cited_by_count":93,"oa_url":9,"card":160,"direction":98,"ingested_from":100},"W7221189467",[152,155,157],{"name":153,"orcid":154},"Laurenz Thiele","https:\u002F\u002Forcid.org\u002F0009-0009-9294-1337",{"name":156,"orcid":9},"Amanus Khalifafilardy Yunus",{"name":158,"orcid":159},"Rahmatia","https:\u002F\u002Forcid.org\u002F0009-0009-7550-9243",{"tldr":161,"method":162,"finding":163,"direction":98,"opportunity":164},"提出融合陆地与海洋遥感指标的四代理框架，估算印尼东部次国家级GDP。","用夜间灯光、NDVI、森林损失与叶绿素a，结合MGWR和OLS分析102个县市。","加入叶绿素a后模型R²从0.543升至0.561，海洋渔业代理显著提升经济估算精度。","可将海洋遥感代理拓展至其他沿海地区，并耦合渔业产量与贸易数据验证经济监测普适性。","2026-10-10T23:30:28.117529Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":171,"content":9,"source_name":172,"source_url":169,"published_at":173,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":174,"score_detail":175,"sources":178,"tags":180,"primary_topic":183,"secondary_topics":184,"classification":185,"search_phrases":206,"slug":209,"view_count":62,"doi":210,"paper":211,"created_at":225},3907,"Land use and land cover change in the Okavango Delta, Botswana: integrating remote sensing and community perspectives on change drivers","https:\u002F\u002Fdoi.org\u002F10.1080\u002F0035919x.2026.2731206","Land use and land cover change is a complex process triggered by either natural or anthropogenic factors. It has important implications for socioeconomic development and environmental sustainability. The present study assessed the dynamics of LULC in Shakawe and Gumare villages of northern Botswana from 1990 to 2023. The study also analysed stakeholders’ perceptions regarding the causes of LULC transformations. The drivers and patterns of land use and land cover change (LULCC) were identified by adopting satellite imagery analysis and a survey approach. The study employed supervised digital image classification and change detection techniques in combination with structured surveys to capture stakeholders’ perceptions. The results show urbanisation and agriculture are the dominant drivers of LULCC, leading to notable alterations in built-up areas, vegetation cover, and infrastructure development. The growth of population and economic activities play a role in shaping LULC dynamics in the study area. Findings also show that the built-up areas in Shakawe increased from 0.97 km2 to 2.02 km2 and from 0.26 km2 to 1.30 km2 in Gumare. Barren land was reduced from 10.9 km2 in 1990 to 4.02 km2 in 2023, and shrubs and trees decreased from 20.53 km2 in 1990 to 16.21 km2 in 2023. A Kruskal–Wallis test revealed a statistically significant difference (X2 = 28.4; p \u003C 0.001) in the way people from different ethnic groups perceived factors influencing LULC. The results highlight the interplay between environmental change and human activities, necessitating sustainable land management practices and highly informed decision-making. The research further highlights the importance of local-level analysis of how LULC patterns and change drivers are understood, particularly in regions experiencing high population growth and resource utilisation. By presenting temporal and spatial patterns of LULC, this research paper contributes to the broader knowledge of land use change processes and policy development aimed at environmental resilience and community well-being.","土地利用与土地覆盖变化是由自然或人为因素触发的复杂过程，对社会经济发展和环境可持续性具有重要影响。本研究评估了博茨瓦纳北部沙卡韦和古马雷村庄1990年至2023年土地利用与土地覆盖（LULC）的动态变化，同时分析了利益相关者对土地利用与土地覆盖变化（LULCC）驱动因素的认知。研究采用卫星影像分析和问卷调查方法，识别了LULCC的驱动因素和变化模式。研究运用监督式数字图像分类和变化检测技术，结合结构化问卷调查，以获取利益相关者的认知。结果表明，城市化和农业是LULCC的主要驱动因素，导致建成区、植被覆盖和基础设施建设发生显著变化。人口增长和经济活动在塑造研究区LULC动态中发挥了重要作用。研究结果还显示，沙卡韦的建成区面积从0.97 km²增至2.02 km²，古马雷从0.26 km²增至1.30 km²。裸地从1990年的10.9 km²减少至2023年的4.02 km²，灌木和乔木从1990年的20.53 km²减少至2023年的16.21 km²。Kruskal–Wallis检验表明，不同族群对影响LULC因素的认知存在统计学显著差异（X² = 28.4；p \u003C 0.001）。研究结果凸显了环境变化与人类活动之间的相互作用，亟需可持续的土地管理实践和高度科学的决策。本研究进一步强调了地方层面分析LULC格局及变化驱动因素认知的重要性，尤其是在人口快速增长和资源利用强度较高的地区。通过呈现LULC的时间和空间格局，本文为土地利用变化过程的广泛知识体系以及旨在增强环境韧性和社区福祉的政策制定作出了贡献。","Transactions of the Royal Society of South Africa","2026-09-29T00:00:00Z",65,{"impact":45,"substance":176,"depth":128,"authority":19,"freshness":45,"relevant":21,"comment":177},20,"遥感与社区调查结合的土地利用变化研究，方法扎实、数据具体，但属境外区域案例，对国内三农信息化参考价值有限。",[179],{"name":172,"url":169},[119,26,181,27,182],"可持续农业","土地利用变化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":186},{"candidates":187,"agricultural_evidence":46},[188,192,197,202],{"code":32,"score":13,"supported":46,"title":189,"abstract":190,"tags":191},[50],[],[26,27],{"code":193,"score":21,"supported":14,"title":194,"abstract":195,"tags":196},"H6",[],[],[182],{"code":198,"score":21,"supported":14,"title":199,"abstract":200,"tags":201},"H8",[],[],[181],{"code":139,"score":21,"supported":14,"title":203,"abstract":204,"tags":205},[],[],[119],[207,208],"Okavango Delta 土地利用变化","博茨瓦纳 遥感 社区感知","OkavangoDelta土地利用变化-3907","10.1080\u002F0035919x.2026.2731206",{"doi":210,"openalex_id":212,"authors":213,"venue":172,"cited_by_count":93,"oa_url":169,"card":220,"direction":98,"ingested_from":100},"W7214955668",[214,217],{"name":215,"orcid":216},"Reniko Gondo","https:\u002F\u002Forcid.org\u002F0000-0002-0957-0179",{"name":218,"orcid":219},"Oluwatoyin Dare Kolawole","https:\u002F\u002Forcid.org\u002F0000-0002-3977-8747",{"tldr":221,"method":222,"finding":223,"direction":98,"opportunity":224},"结合遥感与社区调查，分析博茨瓦纳奥卡万戈三角洲1990-2023年土地利用变化及驱动因素。","监督分类与变化检测遥感影像，结合结构化问卷调查和Kruskal-Wallis检验","城市化和农业是主要驱动力，建设用地增加、林灌减少，不同族群对驱动因素感知有显著差异。","可探索多源遥感与社区感知融合的LULC驱动机制建模，并关注族群差异对土地管理的影响。","2026-10-01T23:30:30.841433Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":231,"content":9,"source_name":232,"source_url":229,"published_at":233,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":234,"score_detail":235,"sources":238,"tags":240,"primary_topic":243,"secondary_topics":244,"classification":245,"search_phrases":282,"slug":285,"view_count":69,"doi":286,"paper":287,"created_at":299},3846,"Integrating Earth Observation Data for Assessing the Impact of Mining Ponds on Land Use and Land Cover Change","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijgem.vol.11.no10.2025.pg136.153","This study examines the relationship between mining ponds and land use and land cover (LULC) dynamics, emphasizing both the environmental challenges and the socio-economic opportunities they present. Earth observation data from Landsat and questionnaire administration were used to evaluate the spatial and socio-environmental impacts of mining activities in Jos South Local Government Area (LGA). Supervised Maximum Likelihood Classification was applied to multitemporal satellite imagery to determine LULC changes, while nearest neighborhood analysis assessed the spatial distribution of mining ponds. A stratified random sampling method was used to collect responses from five districts Du, Zawan, Kuru, Vwang, and Gyel. Results revealed that mining ponds accounted for approximately 2.03% (10.20 km²) of the total land area in 1990, showing a slight decline to 2.00% (10.04 km²) by 2021. Despite their negative impacts, such as land degradation, erosion, and pollution, mining ponds also contribute positively to local livelihoods. An all-year-round supply of water by these ponds makes activities such as irrigation farming, fish farming, block making, recreation and livestock husbandry possible at all times. These benefits generate a wide range of opportunities, which attract investments from private companies, all channeled towards the socio-economic development of Jos-south. We recommended prioritizing the adoption of modern technologies such as GIS and remote sensing by the government for consistent supervision of the expansion of these mining ponds, towards sustainable environmental development of the Jos-South local government area.","本研究考察了矿坑与土地利用和土地覆盖（LULC）动态之间的关系，重点关注其带来的环境挑战与社会经济机遇。研究利用Landsat地球观测数据和问卷调查，评估了乔斯南部地方政府辖区（LGA）采矿活动的空间及社会环境的影响。研究对多时相卫星影像采用监督最大似然分类法以确定土地利用和土地覆盖变化，同时运用最近邻分析评估矿坑的空间分布。采用分层随机抽样方法，从Du、Zawan、Kuru、Vwang和Gyel五个区收集了调查问卷。结果显示，1990年矿坑约占土地总面积的2.03%（10.20 km²），到2021年略微下降至2.00%（10.04 km²）。尽管矿坑带来了土地退化、侵蚀和污染等负面影响，但也对当地生计作出了积极贡献。这些矿坑全年供水，使得灌溉农业、养鱼、制砖、休闲娱乐和畜牧等活动得以常年开展。这些效益产生了广泛的机遇，吸引了私营企业的投资，全部用于乔斯南部的社会经济发展。我们建议政府优先采用地理信息系统（GIS）和遥感等现代技术，对这些矿坑的扩张进行持续监管，以促进乔斯南部地方政府辖区的环境可持续发展。","IIARD INTERNATIONAL JOURNAL OF GEOGRAPHY AND ENVIRONMENTAL MANAGEMENT","2026-09-28T00:00:00Z",60,{"impact":45,"substance":17,"depth":236,"authority":13,"freshness":20,"relevant":21,"comment":237},15,"基于Landsat遥感与问卷评估尼日利亚矿区池塘对土地利用变化的影响，方法规范但属区域案例，对国内三农信息化参考价值有限。",[239],{"name":232,"url":229},[26,27,241,242],"土地利用","矿区生态",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":246},{"candidates":247,"agricultural_evidence":46},[248,252,259,266,273,278],{"code":32,"score":45,"supported":46,"title":249,"abstract":250,"tags":251},[],[49,50],[26,27],{"code":253,"score":54,"supported":46,"title":254,"abstract":255,"tags":258},"C",[],[256,257],"畜牧","livestock",[],{"code":260,"score":54,"supported":46,"title":261,"abstract":262,"tags":265},"H3",[],[263,264],"灌溉","irrigation",[],{"code":267,"score":54,"supported":46,"title":268,"abstract":269,"tags":272},"I4",[],[270,271],"地理信息","GIS",[],{"code":133,"score":69,"supported":46,"title":274,"abstract":275,"tags":277},[],[276],"fish farming",[],{"code":193,"score":147,"supported":14,"title":279,"abstract":280,"tags":281},[],[],[241,242],[283,284],"Jos South 矿区 土地利用","Landsat 遥感 土地覆盖","JosSouth矿区土地利用-3846","10.56201\u002Fijgem.vol.11.no10.2025.pg136.153",{"doi":286,"openalex_id":288,"authors":289,"venue":232,"cited_by_count":93,"oa_url":293,"card":294,"direction":98,"ingested_from":100},"W7214571622",[290],{"name":291,"orcid":292},"Momoh Khalifa Adeiza","https:\u002F\u002Forcid.org\u002F0009-0000-8594-7372","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJGEM\u002FVOL. 11 NO. 10 2025\u002FIntegrating Earth Observation 136-153.pdf",{"tldr":295,"method":296,"finding":297,"direction":98,"opportunity":298},"用Landsat影像与问卷评估矿区池塘对土地利用变化及社会经济的影响。","Landsat多时相影像、最大似然分类、最近邻分析与分层随机抽样问卷。","矿塘面积占比由1990年2.03%微降至2021年2.00%，但带来退化与生计双重效应。","可结合时序遥感与农户调查，量化矿塘对灌溉农业与生计的长期耦合影响。","2026-09-30T23:30:29.520262Z",{"id":301,"title":302,"url":303,"summary":304,"summary_zh":305,"content":9,"source_name":306,"source_url":303,"published_at":307,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":308,"score_detail":309,"sources":311,"tags":313,"primary_topic":317,"secondary_topics":322,"classification":324,"search_phrases":349,"slug":352,"view_count":69,"doi":353,"paper":354,"created_at":373},3550,"Asymmetric Vegetation Responses to Flood Exposure in the Chi River Basin: A Multi-Temporal Remote Sensing and Machine Learning Investigation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym18101606","Flooding and drought alternate in many tropical floodplains, but how vegetation responds to both hazards over multiple years remains poorly quantified. We examined vegetation–flood associations in the Chi River Basin, Northeast Thailand, drawing on Sentinel-2 imagery from 2020, 2023, and 2024, flood records from 2017, 2018, 2021, and 2022, and machine learning methods across 230,911 point-based spatial units. Nine forms of asymmetry emerged from the analysis. The most striking was recovery asymmetry: areas that flooded at least twice between 2017 and 2022 gained vegetation during 2020–2023 (mean NDVI change = +0.0270), whereas areas with little or no flood exposure lost vegetation (mean change = −0.0430). This gap was statistically significant (Cohen’s d = 0.5586, p \u003C 0.001) and suggests that repeated flooding may buffer vegetation against subsequent drought. Agricultural land declined less (−0.0316) than forest (−0.0937; ANOVA F = 1717.7, p \u003C 0.001). Baseline vegetation condition, measured as NDVI in 2020, contributed 32.9% to model importance, more than twice the contribution of elevation (14.5%). Spectral indices together accounted for 76.6% of importance, compared with 23.5% for topographic variables. The 2023 El Niño year produced the largest difference between high-flood and low-flood areas (+0.0364); because only one year per ENSO phase was available, we treat this as a case-based comparison rather than a general ENSO response. Threshold analysis identified two distinct values: an operational cut-off at NDVI = 0.05 (overall accuracy 82.67%) and an ecological transition around 0.25–0.30. Spatial clustering was weak but significant (Moran’s I = 0.2179, p \u003C 0.001). Spatial block cross-validation gave lower accuracy (0.597) than random cross-validation (0.627), pointing to spatial autocorrelation in the data. High-flood areas had 1.67 times the vulnerability index of low-flood areas (0.4306 vs. 0.2573). These patterns support differentiated management: elevation-based zoning, warning systems calibrated to local flood regimes, and focused interventions at hotspots.","在许多热带洪泛平原，洪水与干旱交替发生，但植被如何在多年间同时响应这两种灾害，仍缺乏充分的定量研究。我们以泰国东北部栖河（Chi River）流域为研究区，考察植被与洪水的关联，数据来源包括2020年、2023年和2024年的Sentinel-2影像，2017年、2018年、2021年和2022年的洪水记录，并采用机器学习方法，覆盖230 911个基于点的空间单元。分析中出现了九种不对称形式。其中最显著的是恢复不对称：2017—2022年间至少发生两次洪水的区域，在2020—2023年间植被增加（NDVI平均变化=+0.0270），而洪水暴露很少或没有洪水暴露的区域则植被减少（平均变化=−0.0430）。这一差异具有统计显著性（Cohen’s d=0.5586，p\u003C0.001），表明反复洪水可能缓冲植被对随后干旱的响应。农田的下降幅度（−0.0316）小于森林（−0.0937；ANOVA F=1717.7，p\u003C0.001）。以2020年NDVI衡量的基线植被状况对模型重要性的贡献为32.9%，是海拔贡献（14.5%）的两倍多。光谱指数合计占重要性的76.6%，而地形变量占23.5%。2023年厄尔尼诺年（El Niño year）高洪水区与低洪水区之间的差异最大（+0.0364）；由于每个ENSO相位仅有1年数据，我们将其视为基于个案的比较，而非一般性的ENSO响应。阈值分析识别出两个不同的值：操作性截断值为NDVI=0.05（总体精度82.67%），生态过渡值约为0.25~0.30。空间聚类较弱但显著（Moran’s I=0.2179，p\u003C0.001）。空间分块交叉验证的精度（0.597）低于随机交叉验证（0.627），表明数据中存在空间自相关。高洪水区的脆弱性指数是低洪水区的1.67倍（0.4306对0.2573）。这些格局支持差异化治理：基于海拔的分区、根据当地洪水情势校准的预警系统，以及在热点区域的重点干预。","Symmetry","2026-09-25T00:00:00Z",70,{"impact":45,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":310},"基于Sentinel-2与机器学习的多时相洪涝-植被响应研究，数据规模大、结论具体，对农业遥感与灾害风险管理有参考价值，但属区域案例研究，公共影响有限。",[312],{"name":306,"url":303},[314,315,27,28,316],"智慧农业","农业人工智能","植被恢复",{"code":318,"slug":319,"name":320,"path":321,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[323],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":325},{"candidates":326,"agricultural_evidence":46},[327,333,337,344],{"code":318,"score":236,"supported":46,"title":328,"abstract":330,"tags":332},[329],"machine learning",[331,329],"机器学习",[315],{"code":32,"score":13,"supported":46,"title":334,"abstract":335,"tags":336},[50],[],[316,27],{"code":61,"score":338,"supported":46,"title":339,"abstract":340,"tags":343},7,[],[341,342],"干旱","drought",[28],{"code":345,"score":21,"supported":14,"title":346,"abstract":347,"tags":348},"I1",[],[],[314],[350,351],"湄公河支流 流域 遥感 洪水","Sentinel-2 NDVI 洪涝 植被","湄公河支流流域遥感洪水-3550","10.3390\u002Fsym18101606",{"doi":353,"openalex_id":355,"authors":356,"venue":306,"cited_by_count":93,"oa_url":303,"card":368,"direction":98,"ingested_from":100},"W7214283372",[357,359,362,365],{"name":358,"orcid":9},"Jiradech Majandang",{"name":360,"orcid":361},"Patiwat Littidej","https:\u002F\u002Forcid.org\u002F0000-0002-1024-547X",{"name":363,"orcid":364},"Benjamabhorn Pumhirunroj","https:\u002F\u002Forcid.org\u002F0009-0009-6607-5594",{"name":366,"orcid":367},"D. C. Slack","https:\u002F\u002Forcid.org\u002F0000-0003-0324-2163",{"tldr":369,"method":370,"finding":371,"direction":98,"opportunity":372},"利用多时相遥感和机器学习揭示泰国湄公河支流流域植被对洪水的非对称响应。","Sentinel-2影像、洪水记录与机器学习，分析23万个空间单元。","重复洪水区植被增加，低洪水区减少，表明洪水可缓冲干旱影响。","可探索洪水-干旱交替下植被恢复机制，并开发基于阈值的差异化预警系统。","2026-09-26T23:30:29.514361Z",{"id":375,"title":376,"url":377,"summary":378,"summary_zh":379,"content":9,"source_name":380,"source_url":381,"published_at":382,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":383,"score_detail":384,"sources":386,"tags":388,"primary_topic":391,"secondary_topics":392,"classification":397,"search_phrases":424,"slug":427,"view_count":428,"doi":429,"paper":430,"created_at":443},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":45,"substance":17,"depth":236,"authority":19,"freshness":45,"relevant":21,"comment":385},"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。",[387],{"name":380,"url":381},[26,27,241,389,390],"印度农业","植被覆盖",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[393],{"code":267,"slug":394,"name":395,"path":396,"parent_code":36},"gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"status":39,"locked":14,"version":40,"method":41,"evidence":398},{"candidates":399,"agricultural_evidence":46},[400,406,411,415,419],{"code":32,"score":401,"supported":46,"title":402,"abstract":403,"tags":405},12,[],[49,50,404],"multispectral",[26,390,27],{"code":267,"score":20,"supported":46,"title":407,"abstract":408,"tags":410},[],[270,271,409],"geographic information",[],{"code":53,"score":54,"supported":46,"title":412,"abstract":413,"tags":414},[],[57,58],[],{"code":193,"score":21,"supported":14,"title":416,"abstract":417,"tags":418},[],[],[241],{"code":420,"score":21,"supported":14,"title":421,"abstract":422,"tags":423},"J3",[],[],[389],[425,426],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187",5,"10.31018\u002Fjans.v18i3.7774",{"doi":429,"openalex_id":431,"authors":432,"venue":380,"cited_by_count":93,"oa_url":381,"card":438,"direction":98,"ingested_from":100},"W7213942371",[433,435],{"name":434,"orcid":9},"J. C. Das",{"name":436,"orcid":437},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":439,"method":440,"finding":441,"direction":98,"opportunity":442},"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",{"id":445,"title":446,"url":447,"summary":448,"summary_zh":449,"content":9,"source_name":450,"source_url":447,"published_at":451,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":452,"sources":454,"tags":456,"primary_topic":459,"secondary_topics":465,"classification":472,"search_phrases":503,"slug":506,"view_count":93,"doi":507,"paper":508,"created_at":534},5573,"Improving alfalfa nitrogen estimation with canopy structural priors from DSSAT–PROSAIL and UAV hyperspectral data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.eja.2026.128375","Accurate estimation of nitrogen (N) concentration in alfalfa ( Medicago sativa L .) supports forage quality assessment and harvest scheduling. Empirical remote sensing models often overfit because of confounding effects of three-dimensional canopy structure. Incorporating structural parameters can stabilize biochemical inversions, but concurrent in situ morphological measurements are difficult to acquire at scale. We developed the Morphology-Aware Latent Embedding Network (MALENet), a hybrid framework integrating physiological and morphological mechanisms with hyperspectral data. The Decision Support System for Agrotechnology Transfer (DSSAT), coupled with the PROSAIL radiative transfer model, generated a structurally rich synthetic hyperspectral prior. A convolutional neural network (CNN) with a Squeeze-and-Excitation (SE) attention mechanism, was pre-trained on these data to distill ecophysiological knowledge into a dense latent representation. This mechanistically derived embedding was then fused with empirical hyperspectral measurements collected under diverse field conditions. Evaluation using multi-year field quadrats in the Hexi Corridor showed improved predictive performance ( R² = 0.71, RMSE = 0.30%) relative to conventional machine learning and deep learning models (LASSO-ANN: R² = 0.61; CNN: R² = 0.66). Feature attribution indicated that MALENet used near-infrared multiple scattering (58.3% attribution) as a structural proxy to calibrate nitrogen concentration retrieval against the confounding effects of canopy architecture. Despite constraints imposed by sample size and PROSAIL's 1D canopy assumption, these findings demonstrate the potential of physically guided deep learning to use structural information for agricultural trait retrieval in precision forage management.","准确估算紫花苜蓿（Medicago sativa L.）的氮浓度有助于饲草品质评估和收获期安排。经验性遥感模型常因三维冠层结构的混杂效应而出现过拟合。引入结构参数可以稳定生化反演，但同步开展原位形态学测量难以大规模获取。我们开发了形态感知潜在嵌入网络（Morphology-Aware Latent Embedding Network，MALENet），这是一个将生理与形态机制同高光谱数据相结合的混合框架。农业技术转移决策支持系统（Decision Support System for Agrotechnology Transfer，DSSAT）与PROSAIL辐射传输模型耦合，生成了结构信息丰富的合成高光谱先验数据。在此基础上，预训练了一个带有挤压—激励（Squeeze-and-Excitation，SE）注意力机制的卷积神经网络（convolutional neural network，CNN），将生态生理学知识提炼为稠密潜在表征。随后，将这一机制性导出的嵌入与在不同田间条件下采集的经验性高光谱测量数据相融合。利用河西走廊多年田间样方进行的评估显示，相较于传统机器学习和深度学习模型（LASSO-ANN：R² = 0.61；CNN：R² = 0.66），预测性能有所提升（R² = 0.71，RMSE = 0.30%）。特征归因表明，MALENet利用近红外多次散射（归因占比58.3%）作为结构代理，以校准氮浓度反演，抵御冠层结构的混杂效应。尽管受样本量和PROSAIL一维冠层假设的限制，这些发现表明，物理引导的深度学习有潜力利用结构信息，在精准饲草管理中进行农业性状反演。","European Journal of Agronomy","2026-10-21T00:00:00Z",{"impact":128,"substance":18,"depth":17,"authority":114,"freshness":13,"relevant":21,"comment":453},"该研究将作物模型与辐射传输模型耦合生成结构先验，并结合无人机高光谱数据反演苜蓿氮素，方法新颖、田间验证扎实，对精准饲草管理有参考价值，值得进入每日精选。",[455],{"name":450,"url":447},[314,315,26,457,458],"氮素监测","紫花苜蓿",{"code":460,"slug":461,"name":462,"path":463,"parent_code":464},"A7","forage-cropping","饲草与耕作制度","\u002Ftopic\u002Ffield-crops\u002Fforage-cropping","A",[466,467],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":468,"slug":469,"name":470,"path":471,"parent_code":253},"C4","feed","饲料与饲草","\u002Ftopic\u002Flivestock\u002Ffeed",{"status":39,"locked":14,"version":40,"method":41,"evidence":473},{"candidates":474,"agricultural_evidence":46},[475,480,486,492,499],{"code":32,"score":17,"supported":46,"title":476,"abstract":478,"tags":479},[477],"hyperspectral",[49,50,477],[26],{"code":460,"score":236,"supported":46,"title":481,"abstract":483,"tags":485},[482],"alfalfa",[484,482],"苜蓿",[458],{"code":468,"score":13,"supported":46,"title":487,"abstract":488,"tags":491},[],[489,484,490],"饲草","forage",[458],{"code":318,"score":13,"supported":46,"title":493,"abstract":494,"tags":498},[],[495,331,329,496,497],"深度学习","deep learning","decision support",[315],{"code":345,"score":21,"supported":14,"title":500,"abstract":501,"tags":502},[],[],[314],[504,505],"DSSAT PROSAIL 苜蓿 氮素","无人机 高光谱 苜蓿","DSSATPROSAIL苜蓿氮素-5573","10.1016\u002Fj.eja.2026.128375",{"doi":507,"openalex_id":509,"authors":510,"venue":450,"cited_by_count":93,"oa_url":447,"card":529,"direction":98,"ingested_from":100},"W7223154576",[511,513,515,517,519,522,524,527],{"name":512,"orcid":9},"Shuai Fu",{"name":514,"orcid":9},"Yunhao Li",{"name":516,"orcid":9},"Chunli Miao",{"name":518,"orcid":9},"Jie Liu",{"name":520,"orcid":521},"Tiangang Liang","https:\u002F\u002Forcid.org\u002F0000-0001-5599-6587",{"name":523,"orcid":9},"Yongjia Hu",{"name":525,"orcid":526},"Qisheng Feng","https:\u002F\u002Forcid.org\u002F0009-0000-1391-9591",{"name":528,"orcid":9},"吴彩霞",{"tldr":530,"method":531,"finding":532,"direction":98,"opportunity":533},"构建MALENet融合DSSAT-PROSAIL结构先验与无人机高光谱，提升苜蓿氮浓度估算精度。","DSSAT耦合PROSAIL生成合成高光谱先验，预训练CNN-SE网络，再融合田","MALENet精度优于传统机器学习和深度学习模型，R²达0.71，近红外多次散射作为结构代理贡献58","可探索将作物模型结构先验迁移至其他牧草或作物生化参数反演，并缓解PROSAIL一维冠层假设局限。","2026-10-22T11:20:39.622844Z"]