[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3840":3,"related-3840":97},{"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,"primary_topic":9,"secondary_topics":26,"classification":27,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":96},3840,"Open-Source GIS Applications for Flood and Landslide Assessment: Comparative Cases from Poland and Malaysia","https:\u002F\u002Fdoi.org\u002F10.36777\u002Fjag2026.5.2.6","Research on natural disasters requires analytical procedures that are rapid, reproducible, and based on sound scientific principles. Such procedures should also be accessible to institutions responsible for environmental monitoring, land-use planning, and disaster risk reduction. This study evaluated the practical use of publicly available geospatial data and open-source GIS tools for the preliminary assessment of two contrasting natural hazards: the September 2024 flood in Lower Silesia, Poland, with particular focus on the urban floodplain of Wrocław, and the December 2022 landslide in Batang Kali, Selangor, Malaysia. The study utilised Copernicus Sentinel-2 MSI Level-2A satellite imagery, digital elevation model (DEM) data from the Copernicus programme, OpenStreetMap vector layers, and spatial processing in QGIS. The methodology employed a pre- and post-event change-detection approach based on spectral indices. The Normalized Difference Water Index (NDWI) was used to map the extent of flooding, while the Normalized Difference Vegetation Index (NDVI) was used to identify vegetation disturbances associated with the landslide. Raster classification, elevation analysis, vector-layer overlay, and thematic mapping were subsequently used to delineate disaster-affected areas and examine their spatial relationships with built-up areas, hydrographic features, and terrain morphology. The results show that the Copernicus Data Space Ecosystem and QGIS provide a transparent and cost-effective analytical environment for the preliminary assessment of natural disaster impacts. In the Wrocław case, the NDWI helped identify flooded areas and infrastructure potentially exposed to flooding. In the Batang Kali case, differences in NDVI helped identify patterns of vegetation disturbance associated with the landslide-affected area. The study demonstrates that open-source geospatial tools can support repeatable natural disaster analysis, geospatial education, and early-stage decision support for disaster risk reduction.","自然灾害研究需要快速、可重复且基于可靠科学原理的分析程序。此类程序还应能为负责环境监测、土地利用规划和灾害风险减少的机构所使用。本研究评估了公开地理空间数据和开源GIS工具在两类不同自然灾害初步评估中的实际应用：2024年9月波兰下西里西亚洪水，重点关注弗罗茨瓦夫的城市洪泛区；以及2022年12月马来西亚雪兰莪州巴当加里滑坡。研究使用了哥白尼Sentinel-2 MSI Level-2A卫星影像、哥白尼计划的数字高程模型（DEM）数据、OpenStreetMap矢量图层，并在QGIS中进行空间处理。方法采用基于光谱指数的灾前灾后变化检测方法。归一化差异水体指数（NDWI）用于制图洪水范围，归一化差异植被指数（NDVI）用于识别与滑坡相关的植被扰动。随后利用栅格分类、高程分析、矢量图层叠加和专题制图来圈定受灾区域，并考察其与建成区、水系特征和地形形态的空间关系。结果表明，哥白尼数据空间生态系统和QGIS为自然灾害影响的初步评估提供了透明且具有成本效益的分析环境。在弗罗茨瓦夫案例中，NDWI有助于识别淹没区和可能受洪水影响的基础设施。在巴当加里案例中，NDVI差异有助于识别与滑坡影响区相关的植被扰动格局。研究表明，开源地理空间工具能够支持可重复的自然灾害分析、地理空间教育以及灾害风险减少的早期决策支持。",null,"Journal of Asian Geography","2026-09-29T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文聚焦开源GIS与遥感在洪涝滑坡灾害评估中的应用，属地理信息与灾害风险领域，与三农、农业信息化、智慧农业无直接关联，故不进入每日精选。",[19],{"name":10,"url":6},[21,22,23,24,25],"NDVI","遥感监测","灾害评估","NDWI","开源GIS",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":31},"needs_review","2026-10-22.2","rules",{"candidates":32,"agricultural_evidence":14,"reason":60},[33,41,48,54],{"code":34,"score":35,"supported":36,"title":37,"abstract":39,"tags":40},"I4",11,true,[38],"GIS",[38],[],{"code":42,"score":43,"supported":36,"title":44,"abstract":45,"tags":47},"I2",3,[],[46],"decision support",[],{"code":49,"score":50,"supported":14,"title":51,"abstract":52,"tags":53},"I3",2,[],[],[21,22],{"code":55,"score":56,"supported":14,"title":57,"abstract":58,"tags":59},"H7",1,[],[],[23],"农业相关性需要复核",[62,63],"QGIS 洪水 滑坡 评估","Sentinel-2 灾害 遥感","QGIS洪水滑坡评估-3840",4,"10.36777\u002Fjag2026.5.2.6",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":15,"oa_url":88,"card":89,"direction":93,"ingested_from":95},"W7214781133",[70,72,74,76,78,80,83,85],{"name":71,"orcid":9},"Oliwia Szweda",{"name":73,"orcid":9},"Wazif Akmal Bin Hassdi",{"name":75,"orcid":9},"Muhammad Naqib Akmal Bin Hasbullah",{"name":77,"orcid":9},"Kacper Kaczmarski",{"name":79,"orcid":9},"Divbya A.P. Lachimanan",{"name":81,"orcid":82},"K. Tomiczek","https:\u002F\u002Forcid.org\u002F0000-0001-9227-310X",{"name":84,"orcid":9},"Hareyani Zabidi",{"name":86,"orcid":87},"Monika Żogała","https:\u002F\u002Forcid.org\u002F0000-0002-2309-6493","https:\u002F\u002Fejournal.usm.my\u002Fjag\u002Farticle\u002Fdownload\u002F6257\u002F9385",{"tldr":90,"method":91,"finding":92,"direction":93,"opportunity":94},"用开源GIS与Sentinel-2数据对比评估波兰洪水与马来西亚滑坡灾害影响。","Sentinel-2影像、DEM、OpenStreetMap与QGIS，NDWI","开源工具可低成本、可重复地识别洪水范围与滑坡植被扰动，支持早期决策。","农业遥感与作物表型","可将该开源遥感流程迁移至农田洪涝与滑坡风险监测，结合作物损失评估与预警。","openalex","2026-09-30T23:30:28.033994Z",{"total":98,"page":56,"page_size":98,"items":99},6,[100,151,209,276,324,390],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":9,"content":9,"source_name":105,"source_url":103,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":114,"tags":116,"primary_topic":9,"secondary_topics":119,"classification":120,"search_phrases":132,"slug":135,"view_count":15,"doi":136,"paper":137,"created_at":150},3180,"Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis","https:\u002F\u002Fdoi.org\u002F10.21203\u002Frs.3.rs-10690691\u002Fv1","Satellite-Based Assessment of Vegetation and Hydrological Dynamics at the Garâa of Sejnane Ramsar Wetland (Tunisia): A Multi-Temporal NDVI and NDWI Analysis。Research Square","Research Square","2026-09-21T00:00:00Z",56,{"impact":109,"substance":110,"depth":111,"authority":98,"freshness":112,"relevant":56,"comment":113},8,18,15,9,"基于多时相NDVI\u002FNDWI的湿地植被与水文动态遥感评估，方法规范但属区域性案例研究，公共影响有限。",[115],{"name":105,"url":103},[21,117,22,118,24],"农业生态","湿地保护",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":121},{"candidates":122,"agricultural_evidence":14,"reason":60},[123,128],{"code":124,"score":50,"supported":14,"title":125,"abstract":126,"tags":127},"H6",[],[],[117,118],{"code":49,"score":50,"supported":14,"title":129,"abstract":130,"tags":131},[],[],[21,22],[133,134],"Sejnane 湿地 NDVI NDWI","突尼斯 拉姆萨尔湿地 遥感","Sejnane湿地NDVINDWI-3180","10.21203\u002Frs.3.rs-10690691\u002Fv1",{"doi":136,"openalex_id":138,"authors":139,"venue":105,"cited_by_count":15,"oa_url":103,"card":9,"direction":93,"ingested_from":95},"W7213955587",[140,142,144,147],{"name":141,"orcid":9},"Imen Khemiri",{"name":143,"orcid":9},"Chahida Chemingui",{"name":145,"orcid":146},"Alaeddine Rouissi","https:\u002F\u002Forcid.org\u002F0009-0000-9319-5182",{"name":148,"orcid":149},"Sahar Abidi","https:\u002F\u002Forcid.org\u002F0000-0003-3355-6876","2026-09-22T23:30:23.965045Z",{"id":152,"title":153,"url":154,"summary":155,"summary_zh":156,"content":9,"source_name":157,"source_url":154,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":159,"sources":161,"tags":163,"primary_topic":9,"secondary_topics":165,"classification":166,"search_phrases":184,"slug":187,"view_count":15,"doi":188,"paper":189,"created_at":208},5488,"Application of radar remote sensing for cyclone damage mapping in Bangladesh: a coherence-based approach","https:\u002F\u002Fdoi.org\u002F10.5194\u002Fnhess-26-5103-2026","Abstract. Rapid and reliable assessment of cyclone-induced damage remains a major challenge in coastal regions where cloud cover and accessibility limit conventional approaches. This study presents a coherence-based Synthetic Aperture Radar (SAR) framework for identifying as well as quantifying cyclone-induced surface changes in coastal districts in Bangladesh. Multi-temporal Sentinel-1 data were analyzed using interferometric coherence to capture spatial and temporal variations associated with five major cyclones: Amphan (2020), Sitrang (2022), Mocha (2023), Hamoon (2023), and Remal (2024). A time-series approach incorporating multiple pre-, during-, and post-event image pairs was implemented to distinguish cyclone-driven changes from natural surface variability and to improve detection reliability. The results reveal a consistent decline in coherence during cyclone events followed by gradual recovery. Spatial analysis indicates that areas of significant coherence loss correspond to zones affected by flooding, vegetation damage, and structural disruption. Estimated damage extents varied across cyclones, with Remal (2024) showing the largest affected area (~601 km²), followed by Mocha (~474 km²), Amphan (~288 km²), Sitrang (~122 km²), and Hamoon (~117 km²). Validation using field observations and secondary data yielded an overall classification accuracy of 94 %, confirming the robustness of the coherence-based approach. Complementary inundation mapping further demonstrated that coherence captures a broader spectrum of cyclone impacts beyond flood extent alone, including wind-induced damage and geomorphic changes. This study establishes multi-temporal SAR coherence analysis as a scalable, cloud-independent, and operationally effective tool for rapid post-cyclone damage assessment. The proposed framework enhances the reliability of disaster monitoring in complex coastal environments and offers strong potential for integration into near-real-time disaster response systems. Future research should focus on integrating multi-sensor datasets and long-term coherence time series to further refine damage characterization and reduce uncertainty.","摘要：在云覆盖和可达性限制传统方法的沿海地区，快速可靠地评估气旋所致损害仍是一项重大挑战。本研究提出了一种基于相干性的合成孔径雷达（SAR）框架，用于识别并量化孟加拉国沿海地区气旋引起的地表变化。利用干涉相干性对多时相Sentinel-1数据进行分析，以捕捉与五次主要气旋相关的时间和空间变化：安潘（Amphan，2020年）、西特朗（Sitrang，2022年）、穆查（Mocha，2023年）、哈蒙（Hamoon，2023年）和雷马尔（Remal，2024年）。研究采用了一种整合多个事件前、事件期间和事件后影像对的时间序列方法，以将气旋驱动变化与自然地表的变异性区分开来，并提高检测可靠性。结果表明，气旋事件期间相干性持续下降，随后逐渐恢复。空间分析表明，相干性显著丧失的区域与受洪水、植被破坏和结构损毁影响的区域相对应。各气旋的估计受损范围有所不同，其中雷马尔（2024年）受影响面积最大（约601 km²），其次为穆查（约474 km²）、安潘（约288 km²）、西特朗（约122 km²）和哈蒙（约117 km²）。利用实地观测和二手数据进行的验证获得了94%的总体分类精度，证实了基于相干性方法的稳健性。补充的淹没制图进一步表明，相干性捕捉到了除洪水范围之外的更广泛的气旋影响，包括风致损害和地貌变化。本研究确立了多时相SAR相干性分析作为一种可扩展、不依赖云覆盖且业务上有效的工具，用于气旋后快速损害评估。所提出的框架提高了复杂沿海环境中灾害监测的可靠性，并具有将其集成到近实时灾害响应系统中的强大潜力。未来研究应侧重于整合多传感器数据集和长期相干性时间序列，以进一步完善损害表征并减少不确定性。","Natural hazards and earth system sciences","2026-10-19T00:00:00Z",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":160},"该研究聚焦孟加拉国海岸带气旋灾害的SAR相干性制图，虽涉及遥感技术，但与三农、农业信息化、数字乡村等主题无直接关联，不宜进入每日精选。",[162],{"name":157,"url":154},[164,22,23],"SAR遥感",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":167},{"candidates":168,"agricultural_evidence":14,"reason":60},[169,174,180],{"code":49,"score":112,"supported":36,"title":170,"abstract":172,"tags":173},[171],"remote sensing",[],[22],{"code":34,"score":98,"supported":36,"title":175,"abstract":176,"tags":179},[],[177,178],"空间分析","spatial analysis",[],{"code":55,"score":56,"supported":14,"title":181,"abstract":182,"tags":183},[],[],[23],[185,186],"Sentinel-1 气旋 灾害制图","雷达遥感 孟加拉湾 气旋","Sentinel-1气旋灾害制图-5488","10.5194\u002Fnhess-26-5103-2026",{"doi":188,"openalex_id":190,"authors":191,"venue":157,"cited_by_count":15,"oa_url":202,"card":203,"direction":93,"ingested_from":95},"W7160271999",[192,195,198,200],{"name":193,"orcid":194},"Shadmin Islam Prova","https:\u002F\u002Forcid.org\u002F0000-0001-8549-1758",{"name":196,"orcid":197},"Md. Shahoriar Sarker","https:\u002F\u002Forcid.org\u002F0009-0006-5131-1214",{"name":199,"orcid":9},"Rosdania Islam",{"name":201,"orcid":9},"Ridad Ibne Mahfuz","https:\u002F\u002Fnhess.copernicus.org\u002Farticles\u002F26\u002F5103\u002F2026\u002Fnhess-26-5103-2026.pdf",{"tldr":204,"method":205,"finding":206,"direction":93,"opportunity":207},"用Sentinel-1多时相干涉相干性识别并量化孟加拉湾五次气旋的地表破坏。","Sentinel-1多时相InSAR相干性时间序列，结合实地与二手数据验证。","气旋期间相干性显著下降后逐步恢复，分类精度94%，Remal受灾面积最大约601km²。","可将相干性框架迁移至农田风灾与洪涝受损制图，融合多源SAR与长时序相干性降低不确定性。","2026-10-21T23:30:23.509482Z",{"id":210,"title":211,"url":212,"summary":213,"summary_zh":214,"content":9,"source_name":215,"source_url":212,"published_at":158,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":216,"score_detail":217,"sources":220,"tags":222,"primary_topic":9,"secondary_topics":226,"classification":227,"search_phrases":254,"slug":257,"view_count":15,"doi":258,"paper":259,"created_at":275},5481,"Comparison of Satellite-Based Drought Monitoring Indicators (NDVI, NDMI, and LST): The Case of the Northern Aegean Drainage Basin","https:\u002F\u002Fdoi.org\u002F10.30910\u002Fturkjans.2010067","Thanks to advancements in technology, satellite remote sensing provides a valuable means of monitoring the spatial and temporal responses of vegetation, surface moisture conditions, and land surface temperature to drought. Its capacity to monitor large areas with high temporal continuity significantly eliminates the spatial limitations of ground observations. This allows for the simultaneous and comparable analysis of key variables such as vegetation dynamics, soil moisture, and land surface temperature. Indicators such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST) provide complementary information on vegetation condition, surface moisture, and thermal stress, thereby supporting drought monitoring. This integrated monitoring framework supports the sustainable management of water resources while strengthening the scientific basis of decision-making processes, significantly contributing to disaster risk reduction. This study comparatively evaluates NDVI, NDMI, and LST values derived from Landsat 8 data obtained in March, April, and May between 2019 and 2022, and examines the relationships between these variables across the Northern Aegean Drainage Basin (NADB). The results show increasingly strong negative relationships between LST and the spectral indices from March to May, with the strongest correlations observed in May for NDMI–LST (r = −0.78) and NDVI–LST (r = −0.74). The NDMI-LST relationship can be said to be slightly better than the NDVI-LST relationship. The correlation coefficient between NDMI and NDVI was calculated to be lowest in March at r=0.54 and highest in May at r=0.90. It was determined that areas with high LST values had lower NDVI and NDMI values. Drought monitoring using remote sensing methods is important in terms of time and accuracy, but it is not sufficient on its own. In addition to problems arising from satellite imagery, land surfaces are affected by short-term climatic factors. Therefore, the indices obtained from satellite images must be evaluated together with meteorological and terrestrial data. However, it is thought that satellite-based drought monitoring will produce more reliable results under current technological and methodological conditions.","得益于技术进步，卫星遥感为监测植被、地表水分状况和地表温度对干旱的时空响应提供了重要手段。其大范围、高时间连续性的监测能力显著消除了地面观测的空间局限性，使得植被动态、土壤水分和地表温度等关键变量能够被同步、可比地分析。归一化差异植被指数（NDVI）、归一化差异水分指数（NDMI）和地表温度（LST）等指标提供了关于植被状况、地表水分和热胁迫的互补信息，从而支持干旱监测。这一综合监测框架既支持水资源的可持续管理，又增强了决策过程的科学基础，对减少灾害风险具有重要贡献。本研究对2019年至2022年3月、4月和5月获取的Landsat 8数据所衍生的NDVI、NDMI和LST值进行了比较评估，并考察了北爱琴海流域（NADB）内这些变量之间的关系。结果表明，从3月到5月，LST与光谱指数之间的负相关关系日益增强，5月观测到最强相关性，NDMI–LST（r = −0.78）和NDVI–LST（r = −0.74）。可以认为NDMI-LST关系略优于NDVI-LST关系。NDMI与NDVI之间的相关系数在3月最低（r=0.54），5月最高（r=0.90）。研究确定，LST值较高的区域其NDVI和NDMI值较低。利用遥感方法进行干旱监测在时间和精度方面具有重要意义，但仅靠其自身并不充分。除卫星影像带来的问题外，地表还受到短期气候因素的影响。因此，从卫星影像获得的指数必须与气象和地面数据结合评估。然而，可以认为在当前技术和方法条件下，基于卫星的干旱监测将产生更可靠的结果。","Türk Tarım ve Doğa Bilimleri Dergisi",61,{"impact":109,"substance":110,"depth":111,"authority":218,"freshness":109,"relevant":56,"comment":219},12,"方法对比扎实、结论明确，但属区域案例研究，公共影响有限，适合作为遥感干旱监测主题的补充素材而非头条精选。",[221],{"name":215,"url":212},[21,22,223,224,225],"干旱监测","NDMI","LST",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":228},{"candidates":229,"agricultural_evidence":14,"reason":60},[230,236,241,248],{"code":55,"score":111,"supported":36,"title":231,"abstract":233,"tags":235},[232],"drought",[234,232],"干旱",[223],{"code":49,"score":109,"supported":36,"title":237,"abstract":238,"tags":240},[],[239,171],"遥感",[21,22],{"code":242,"score":98,"supported":36,"title":243,"abstract":244,"tags":247},"H2",[],[245,246],"土壤","soil",[],{"code":249,"score":43,"supported":36,"title":250,"abstract":251,"tags":253},"H3",[],[252],"水资源",[],[255,256],"北爱琴海流域 干旱监测","干旱监测 遥感监测 NDMI NDVI","北爱琴海流域干旱监测-5481","10.30910\u002Fturkjans.2010067",{"doi":258,"openalex_id":260,"authors":261,"venue":215,"cited_by_count":15,"oa_url":268,"card":269,"direction":274,"ingested_from":95},"W7222954725",[262,265],{"name":263,"orcid":264},"Arman Monshizadeh Karimi","https:\u002F\u002Forcid.org\u002F0000-0002-2945-5346",{"name":266,"orcid":267},"İ̇smail Taş","https:\u002F\u002Forcid.org\u002F0000-0003-0872-2529","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F6296754",{"tldr":270,"method":271,"finding":272,"direction":93,"opportunity":273},"比较Landsat 8的NDVI、NDMI与LST三种遥感干旱指标在北爱琴海流域的相关性。","用2019-2022年3-5月Landsat 8数据计算NDVI、NDMI、LS","LST与NDMI、NDVI负相关随春季增强，5月最强（r=-0.78、-0.74），NDMI略优。","可融合气象与地面数据构建多源干旱指数，并验证NDMI-LST关系在其他气候区的普适性。","智慧农业 \u002F 农业物联网","2026-10-21T23:30:10.758669Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":282,"source_url":279,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":287,"tags":289,"primary_topic":9,"secondary_topics":293,"classification":294,"search_phrases":307,"slug":310,"view_count":15,"doi":311,"paper":312,"created_at":323},5182,"Land Use Land Cover (LULC) Change Detection in Delhi NCT Using Multi-Sensor Satellite Data and Google Earth Engine","https:\u002F\u002Fdoi.org\u002F10.21275\u002Fsr26903183702","Land Use Land Cover (LULC) change detection is essential for understanding the dynamics of human modified landscapes and for supporting effective land management and urban planning. This study analyses LULC change across the Delhi National Capital Territory over a fifteen-year period, from 2010 to 2025, to assess urban sprawl and its environmental impact in one of India's fastest growing metropolitan regions. Multi temporal Landsat imagery was used as the primary data source, processed on Google Earth Engine, the cloud-based platform that enabled efficient handling and visualisation of large geospatial datasets. Five classes, built up area, agricultural land, vegetation, water, and barren land, were mapped and monitored, and classification accuracy was assessed for both dates. The Normalised Difference Vegetation Index was additionally employed to track vegetation vigour and ecological change. Results reveal a marked expansion of built-up areas alongside a substantial decline in agricultural and vegetated land, driven by peri urban growth along the northwestern and southwestern fringe. NDVI analysis shows rising greenness across the outer belt, reflecting more intensive cultivation on land that remains under crops, a pattern that complements rather than contradicts the measured loss of vegetated area. Combining classification based and an index-based approach provides a robust, scalable framework for LULC monitoring. By revealing where and how urban growth is reshaping the landscape, the study offers a transferable basis for environmental assessment, resource management, and sustainable urban planning in rapidly urbanising regions worldwide.","土地利用\u002F土地覆盖（LULC）变化检测对于理解人类改造景观的动态以及支撑有效的土地管理和城市规划至关重要。本研究分析了德里国家首都辖区在2010年至2025年十五年间的土地利用\u002F土地覆盖变化，以评估印度增长最快的大都市区之一的城市蔓延及其环境影响。研究以多时相Landsat影像作为主要数据源，在Google Earth Engine这一基于云的平台上进行处理，该平台能够高效处理和可视化大型地理空间数据集。研究对建成区、农业用地、植被、水体与裸地五个类别进行了制图和监测，并对两个时相的分类精度进行了评估。此外，研究采用归一化植被指数（NDVI）追踪植被长势与生态变化。结果表明，建成区显著扩张，农业用地和植被用地大幅减少，这一变化主要由西北部和西南部边缘的近郊增长所驱动。NDVI分析显示外围地带的绿度上升，反映出仍处于耕作状态的土地上集约化种植程度提高，这一格局与所测得的植被面积损失相辅相成而非相互矛盾。将基于分类的方法与基于指数的方法相结合，为土地利用\u002F土地覆盖监测提供了一个稳健且可扩展的框架。通过揭示城市增长在何处以及如何重塑景观，本研究为全球快速城市化地区的环境评估、资源管理和可持续城市规划提供了可迁移的基础。","International Journal of Science and Research (IJSR)","2026-10-15T00:00:00Z",62,{"impact":218,"substance":110,"depth":111,"authority":109,"freshness":112,"relevant":56,"comment":286},"方法可迁移的遥感LULC变化检测研究，对快速城市化地区土地管理有参考价值，但属区域案例、非三农核心议题。",[288],{"name":282,"url":279},[21,22,290,291,292],"土地利用","Google Earth Engine","城市化",[],{"status":295,"locked":14,"version":29,"method":30,"evidence":296},"unclassified",{"candidates":297,"agricultural_evidence":36,"reason":306},[298,302],{"code":49,"score":43,"supported":14,"title":299,"abstract":300,"tags":301},[],[],[291,21,22],{"code":124,"score":56,"supported":14,"title":303,"abstract":304,"tags":305},[],[],[290],"缺少足够的主题证据",[308,309],"德里 土地利用 遥感","Google Earth Engine 城市扩张","德里土地利用遥感-5182","10.21275\u002Fsr26903183702",{"doi":311,"openalex_id":313,"authors":314,"venue":282,"cited_by_count":15,"oa_url":317,"card":318,"direction":93,"ingested_from":95},"W7222341300",[315],{"name":316,"orcid":9},"Neil Rohatgi","https:\u002F\u002Fwww.ijsr.net\u002Farchive\u002Fv15i10\u002FSR26903183702.pdf",{"tldr":319,"method":320,"finding":321,"direction":93,"opportunity":322},"基于多时相Landsat与GEE分析德里国家首都辖区2010-2025年土地利用变化。","多时相Landsat影像、Google Earth Engine平台、五类分类与","建设用地显著扩张，农业与植被用地大幅减少，城郊西北和西南边缘增长驱动。","可结合分类与指数方法构建可迁移框架，拓展至城乡交错带耕地保护与可持续规划研究。","2026-10-17T23:30:31.767409Z",{"id":325,"title":326,"url":327,"summary":328,"summary_zh":329,"content":9,"source_name":330,"source_url":327,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":331,"score_detail":332,"sources":337,"tags":339,"primary_topic":9,"secondary_topics":342,"classification":343,"search_phrases":358,"slug":361,"view_count":56,"doi":362,"paper":363,"created_at":389},5177,"Long-term associations between land-surface indicators and dust-storm occurrence along the margins of the Taklimakan Desert","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75251-3","Land-surface change and atmospheric variability jointly shape dust activity in arid regions. We examined land-cover transitions, station dust-storm records, a Sentinel-1 backscatter index, meteorological variables from the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) and dust diagnostics from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) along the margins of the Taklimakan Desert. Within the mapped domain, grassland increased from 9.45% in 1990 to 10.59% in 2023, while barren land decreased from 86.45% to 82.30%. Annual dust-storm-day trends were negative at 21 of 30 stations over available 1990–2024 records, and Sentinel-1 index trends were positive at 22 stations during 2015–2024. Mixed-effects path analysis of 3,804 station-month observations showed that the Sentinel-1 index was negatively associated with modelled dust emission (standardized coefficient = − 0.191; 95% confidence interval: −0.289 to − 0.043). The normalized difference vegetation index (NDVI) and friction velocity had the highest training-set feature importance in the random forest analysis.","地表变化与大气变率共同塑造了干旱地区的沙尘活动。我们沿塔克拉玛干沙漠边缘，研究了土地覆盖转变、台站沙尘暴记录、Sentinel-1后向散射指数、来自欧洲中期天气预报中心第五代再分析资料（ERA5）的气象变量，以及来自现代研究与应用回顾分析第2版（MERRA-2）的沙尘诊断量。在制图范围内，草地面积从1990年的9.45%增加到2023年的10.59%，而裸地面积从86.45%减少到82.30%。在1990—2024年现有记录中，30个台站中有21个台站的年沙尘暴日数呈下降趋势；2015—2024年间，22个台站的Sentinel-1指数呈上升趋势。对3804个台站—月观测值的混合效应路径分析表明，Sentinel-1指数与模拟沙尘排放呈负相关（标准化系数=−0.191；95%置信区间：−0.289至−0.043）。在随机森林分析中，归一化植被指数（NDVI）和摩擦速度在训练集中的特征重要性最高。","Scientific Reports",78,{"impact":333,"substance":334,"depth":110,"authority":335,"freshness":109,"relevant":56,"comment":336},16,22,14,"基于多源遥感与30站长期记录，量化地表指标与沙尘暴关联，方法扎实、数据规模可观，对干旱区生态监测有参考价值。",[338],{"name":330,"url":327},[21,340,22,290,341],"荒漠化","沙尘暴",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":344},{"candidates":345,"agricultural_evidence":14,"reason":60},[346,350,354],{"code":55,"score":43,"supported":36,"title":347,"abstract":348,"tags":349},[],[234],[],{"code":124,"score":50,"supported":14,"title":351,"abstract":352,"tags":353},[],[],[290,340],{"code":49,"score":50,"supported":14,"title":355,"abstract":356,"tags":357},[],[],[21,22],[359,360],"塔克拉玛干沙漠 沙尘暴 遥感","Sentinel-1 土地覆盖 沙尘","塔克拉玛干沙漠沙尘暴遥感-5177","10.1038\u002Fs41598-026-75251-3",{"doi":362,"openalex_id":364,"authors":365,"venue":330,"cited_by_count":15,"oa_url":383,"card":384,"direction":93,"ingested_from":95},"W7222086957",[366,368,371,374,376,378,380],{"name":367,"orcid":9},"Yifei Wang",{"name":369,"orcid":370},"Wen Huo","https:\u002F\u002Forcid.org\u002F0000-0002-7861-2424",{"name":372,"orcid":373},"Mayibaier Maihamuti","https:\u002F\u002Forcid.org\u002F0009-0007-4084-5891",{"name":375,"orcid":9},"Fan Yang",{"name":377,"orcid":9},"Chenglong Zhou",{"name":379,"orcid":9},"Xinghua Yang",{"name":381,"orcid":382},"Ali Mamtimin","https:\u002F\u002Forcid.org\u002F0000-0001-7335-801X","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75251-3_reference.pdf",{"tldr":385,"method":386,"finding":387,"direction":93,"opportunity":388},"分析塔克拉玛干沙漠边缘地表指标与沙尘暴发生的长期关联。","结合土地覆盖、站点沙尘记录、Sentinel-1后向散射、ERA5与MERRA-","站点沙尘日数多呈下降趋势，Sentinel-1指数与模拟沙尘排放显著负相关。","可引入多源遥感与机器学习量化地表变化对沙尘的因果贡献，并拓展至绿洲农业防护。","2026-10-17T23:30:30.235613Z",{"id":391,"title":392,"url":393,"summary":394,"summary_zh":395,"content":9,"source_name":396,"source_url":393,"published_at":397,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":398,"score_detail":399,"sources":404,"tags":406,"primary_topic":9,"secondary_topics":409,"classification":410,"search_phrases":427,"slug":430,"view_count":56,"doi":431,"paper":432,"created_at":456},5012,"Evaluating Multi-Index Remote Sensing Frameworks for Drought Monitoring in Seasonally Dry Dipterocarp Deciduous Forests: A Case Study of Yok Don National Park, Vietnam","https:\u002F\u002Fdoi.org\u002F10.5755\u002Fj01.erem.82.3.44241","This study examined the effectiveness of the normalized difference vegetation index (NDVI), land surface temperature (LST), and their integrated indices in capturing dry-season drought dynamics in the dipterocarp deciduous forest ecosystems of Yok Don National Park from 2014 to 2024. Results indicate that individual remote-sensing indices captured drought processes but reflected different dimensions of drought conditions, with NDVI indicating canopy greenness and LST detecting early thermal stress. Integrated indices partially mitigated the limitations of single indicators during confirmed drought years by showing stronger correlations with reference data. The effectiveness of the integrated indices varied significantly depending on the integration framework. The spatial-normalization–based integration of NDVI and LST showed consistently strong correlations with surface evapotranspiration (ET), with correlation coefficients of approximately 0.8 across years, and was less sensitive to land-cover heterogeneity. In contrast, the triangular NDVI–LST space integration exhibited variable relationships with ET due to its sensitivity to edge definition and phenological shifts in dipterocarp deciduous forests. Phase-dependent behavior was observed, with integrated indices maintaining more stable relationships with ET during drying periods, while NDVI showed greater sensitivity under wetter conditions. The results also indicate that VHI demonstrated a more robust and stable performance than TVDI, suggesting its suitability as a primary indicator in heterogeneous forest environments. These findings provide practical guidance for drought monitoring in heterogeneous forest landscapes by identifying adaptive integration strategies tailored to different hydroclimatic conditions.","本研究考察了归一化差异植被指数（NDVI）、地表温度（LST）及其综合指数在捕捉2014至2024年 Yok Don 国家公园龙脑香落叶林生态系统旱季干旱动态方面的有效性。结果表明，单一遥感指数能够捕捉干旱过程，但反映的是干旱状况的不同维度，其中NDVI指示冠层绿度，LST探测早期热胁迫。综合指数在已确认的干旱年份中通过与参考数据表现出更强的相关性，部分缓解了单一指标的局限性。综合指数的有效性因集成框架的不同而差异显著。基于空间标准化的NDVI与LST集成与地表蒸散发（ET）表现出持续较强的相关性，各年份相关系数约为0.8，且对土地覆盖异质性较不敏感。相比之下，三角NDVI–LST空间集成与ET的关系多变，这是由于其边缘定义及龙脑香落叶林物候变化较为敏感。研究观察到相位依赖行为，综合指数在干旱期与ET保持更稳定的关系，而NDVI在较湿润条件下表现出更高的敏感性。结果还表明，VHI比TVDI表现出更稳健和稳定的性能，表明其适合作为异质森林环境中的主要指标。这些发现通过识别适应不同水文气候条件的自适应集成策略，为异质森林景观的干旱监测提供了实践指导。","Environmental Research Engineering and Management","2026-10-13T00:00:00Z",67,{"impact":109,"substance":400,"depth":401,"authority":402,"freshness":112,"relevant":56,"comment":403},20,17,13,"越南干旱区遥感监测方法学研究，方法新颖数据扎实，对农业遥感干旱监测有参考价值，但属区域性案例，公共影响有限。",[405],{"name":396,"url":393},[407,21,22,223,408],"农业遥感","森林生态",[],{"status":28,"locked":14,"version":29,"method":30,"evidence":411},{"candidates":412,"agricultural_evidence":14,"reason":60},[413,417,421],{"code":55,"score":111,"supported":36,"title":414,"abstract":415,"tags":416},[232],[234,232],[223],{"code":49,"score":335,"supported":36,"title":418,"abstract":419,"tags":420},[171],[239],[21,407,22],{"code":249,"score":98,"supported":36,"title":422,"abstract":423,"tags":426},[],[424,425],"蒸散发","evapotranspiration",[],[428,429],"Yok Don 国家公园 遥感 干旱","NDVI LST 干旱监测 森林","YokDon国家公园遥感干旱-5012","10.5755\u002Fj01.erem.82.3.44241",{"doi":431,"openalex_id":433,"authors":434,"venue":396,"cited_by_count":15,"oa_url":450,"card":451,"direction":93,"ingested_from":95},"W7221717828",[435,438,441,443,445,447],{"name":436,"orcid":437},"Mai Son Le","https:\u002F\u002Forcid.org\u002F0000-0002-5360-7119",{"name":439,"orcid":440},"Ha Thu Nguyen","https:\u002F\u002Forcid.org\u002F0000-0002-4490-308X",{"name":442,"orcid":9},"Hung Manh Nguyen",{"name":444,"orcid":9},"Huy Xuan Chu",{"name":446,"orcid":9},"Hoa Thanh Nguyen",{"name":448,"orcid":449},"Duy-Toan Dao","https:\u002F\u002Forcid.org\u002F0009-0003-6057-4436","https:\u002F\u002Ferem.ktu.lt\u002Findex.php\u002Ferem\u002Farticle\u002Fdownload\u002F44241\u002F23724",{"tldr":452,"method":453,"finding":454,"direction":93,"opportunity":455},"评估NDVI、LST及其集成指数在越南干旱季风落叶林干旱监测中的效果。","利用2014-2024年NDVI、LST遥感数据，构建空间归一化和三角空间集成指","空间归一化集成指数与ET相关性稳定（约0.8），VHI比TVDI更稳健，集成效果依赖框架。","可探索自适应集成框架在不同水热条件和异质景观下的干旱监测普适性。","2026-10-15T23:30:32.336137Z"]