[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3183":3,"related-3183":52},{"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":24,"tags":26,"search_phrases":32,"slug":35,"view_count":36,"doi":37,"paper":38,"created_at":51},3183,"Integrated Remote Sensing and Time Series Analysis for Long-Term Assessment of Vegetation Resilience: Synthesizing Climatic Variables and Land-Use\u002FLand-Cover Trajectories in Al-Ahsa Oasis, Saudi Arabia","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091758","Continuous monitoring of land-use\u002Fland-cover (LULC) change is essential in the Al-Ahsa Oasis, an arid region characterized by a sensitive and fragile environment. This study is the first to integrate vegetation, climate, and land-use frameworks by combining vegetation indicators, climatic variables, and LULC trajectory classification over a 41-year period (1985–2025). Data from three Landsat sensors (Landsat 5-TM, 7-ETM+, and 8-OLI) were processed to derive vegetation indicators and an LULC trajectory classification, while ERA5-Land and CHIRPS data provided surface temperature and rainfall information, respectively. LULC trajectory classification was implemented using a random forest classifier, generating six LULC trajectory classes: stable barren, stable urban, stable water, urban expansion, transition lands, and stable vegetation. Pearson correlation analysis was used to assess the relationships between the vegetation indicators (normalized difference vegetation index [NDVI] and soil-adjusted vegetation index [SAVI]) and climatic variables (temperature and rainfall) at different time lags (0–3 years) and after detrending the time series. The detrending analysis showed no statistically significant associations between the vegetation indicators (NDVI and SAVI) and the climatic variables (temperature and rainfall) across the four examined lags, whereas the original, non-detrended time series showed positive associations that were mainly attributable to long-term trends rather than to interannual climate–vegetation covariation. The accuracy of LULC trajectory classification was high, with an overall accuracy of 0.939 and a kappa coefficient of 0.924, indicating robust mapping performance. Overall vegetation cover improved over the study period. The degradation occurred in the central part of the oasis (stable urban, urban expansion, and stable water classes, accounting for 4.506%, 5.179%, and 41.773% of each trajectory class, respectively), whereas the recovery was observed in the northern, southern, and eastern parts (stable vegetation and transition lands, accounting for 37.656% and 52.891% of each trajectory class, respectively). These findings provide long-term insights into vegetation resilience in the Al-Ahsa Oasis over 41 years, supporting sustainable urban planning and vegetation and agricultural management.","在环境敏感而脆弱的干旱区——哈萨绿洲，持续监测土地利用\u002F土地覆盖（LULC）变化至关重要。本研究首次在41年（1985—2025年）时间跨度内，通过整合植被指标、气候变量与LULC轨迹分类，构建了植被、气候与土地利用的综合框架。研究处理了三颗Landsat传感器（Landsat 5-TM、7-ETM+和8-OLI）的数据，以提取植被指标并进行LULC轨迹分类，同时分别利用ERA5-Land和CHIRPS数据获取地表温度和降雨信息。LULC轨迹分类采用随机森林分类器实现，生成了六类LULC轨迹：稳定裸地、稳定城市、稳定水体、城市扩张、过渡土地和稳定植被。采用Pearson相关分析评估植被指标（归一化差异植被指数[NDVI]和土壤调节植被指数[SAVI]）与气候变量（温度和降雨）在不同时间滞后（0—3年）及时间序列去趋势后的关系。去趋势分析表明，在四个考察滞后下，植被指标（NDVI和SAVI）与气候变量（温度和降雨）之间均无统计学显著关联，而原始未去趋势时间序列则显示出正相关，这主要归因于长期趋势而非年际气候—植被协变。LULC轨迹分类精度较高，总体精度为0.939，kappa系数为0.924，表明制图性能稳健。研究期间整体植被覆盖有所改善。退化发生在绿洲中部（稳定城市、城市扩张和稳定水体类别，分别占各轨迹类别的4.506%、5.179%和41.773%），而恢复则出现在北部、南部和东部（稳定植被和过渡土地，分别占各轨迹类别的37.656%和52.891%）。这些发现为哈萨绿洲41年来的植被恢复力提供了长期见解，可支持可持续城市规划以及植被与农业管理。",null,"Land","2026-09-20T00:00:00Z","论文",10,false,73,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,22,18,13,8,1,"41年长时序遥感与气候变量整合分析，方法扎实、结论可靠，对干旱区绿洲农业可持续管理有参考价值，但属区域性案例研究，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"气候变化","遥感监测","土地利用","植被覆盖","绿洲农业",[33,34],"Al-Ahsa Oasis 遥感 植被","Landsat NDVI 土地利用变化","Al-AhsaOasis遥感植被-3183",0,"10.3390\u002Fland15091758",{"doi":37,"openalex_id":39,"authors":40,"venue":10,"cited_by_count":36,"oa_url":6,"card":44,"direction":48,"ingested_from":50},"W7213862324",[41],{"name":42,"orcid":43},"Amal H. Aljaddani","https:\u002F\u002Forcid.org\u002F0000-0003-1171-8416",{"tldr":45,"method":46,"finding":47,"direction":48,"opportunity":49},"整合41年Landsat植被指数、气候变量与LULC轨迹，评估沙特Al-Ahsa绿洲植被恢复力。","Landsat 5\u002F7\u002F8时序、ERA5-Land与CHIRPS气候数据、随机森","去趋势后植被与气候无显著相关，绿洲整体植被改善，中部退化、南北东恢复。","农业遥感与作物表型","可引入非线性\u002F因果推断与高分辨率数据，区分灌溉、城市化与气候对干旱区绿洲植被恢复力的驱动。","openalex","2026-09-22T23:30:25.304168Z",{"total":53,"page":22,"page_size":53,"items":54},6,[55,91,128,168,217,270],{"id":56,"title":57,"url":58,"summary":59,"summary_zh":60,"content":9,"source_name":61,"source_url":62,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":63,"score_detail":64,"sources":67,"tags":69,"search_phrases":72,"slug":75,"view_count":36,"doi":76,"paper":77,"created_at":90},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",62,{"impact":21,"substance":19,"depth":65,"authority":20,"freshness":21,"relevant":22,"comment":66},15,"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。",[68],{"name":61,"url":62},[70,28,29,71,30],"农业遥感","印度农业",[73,74],"Nagaon Assam LULC 遥感","LISS-IV 土地利用变化","NagaonAssamLULC遥感-3187","10.31018\u002Fjans.v18i3.7774",{"doi":76,"openalex_id":78,"authors":79,"venue":61,"cited_by_count":36,"oa_url":62,"card":85,"direction":48,"ingested_from":50},"W7213942371",[80,82],{"name":81,"orcid":9},"J. C. Das",{"name":83,"orcid":84},"Prodyut Bhattacharya","https:\u002F\u002Forcid.org\u002F0000-0002-4294-5585",{"tldr":86,"method":87,"finding":88,"direction":48,"opportunity":89},"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":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":105,"tags":107,"search_phrases":110,"slug":113,"view_count":36,"doi":114,"paper":115,"created_at":127},2678,"Simulating the Impact of Land Use and Land Cover Change on Surface Air Temperature Trends in South-Central Vietnam","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2515-7620\u002Faea745","Abstract The decline of vegetation, especially due to deforestation and urbanization, has significantly increased the temperature at local and regional scales in many places. In contrast, the development of water resources, increased investment in fertilizers for agricultural production, and afforestation to cover bare land have contributed to reducing temperature increases in many areas. This study aims to quantify the impact of land use and land cover change (LUCC) on temperature trends in South Central Vietnam. The data used are monthly averages over the past 24 years, including the Normalized Difference Vegetation Index (NDVI) and air temperature at 2 meters above the earth's surface. The methods used in the study include statistical analysis and ANN simulation. The main influencing variables included in the analysis are NDVI and its slope in buffer zones around weather stations with diameters ranging from 1 to 20 km. The results of the study show that the closer NDVI changes to the weather station, especially within a range of less than 2 km, the greater the impact on temperature trends. The variables in the buffer zone around the weather station where NDVI changes the most also have the best relationship with temperature trends. Using NDVI-derived variables, the ANN model reliably reproduced the temperature trend and clarified the contribution of LUCC. The NDVI trend variables contribute 46% to the simulation accuracy and 50% to the temperature trend differences between weather stations. This research direction can be used to assess the environmental impacts of LUCC, and to separate temperature trends due to global climate change from local causes.","植被退化，尤其是森林砍伐和城市化导致的植被减少，在许多地区显著加剧了局地和区域尺度的升温。与之相对，水资源开发、农业生产中肥料投入的增加以及裸露土地造林等措施，在许多地区有助于减缓温度上升。本研究旨在量化土地利用与土地覆被变化（LUCC）对越南中南部温度趋势的影响。所用数据为过去24年的月平均值，包括归一化植被指数（NDVI）和距地表2米高度的气温。研究方法包括统计分析和人工神经网络（ANN）模拟。分析中纳入的主要影响变量为气象站周围缓冲区内（直径1至20公里）的NDVI及其变化斜率。研究结果表明，NDVI变化距气象站越近，尤其是在2公里以内的范围内，对温度趋势的影响越大。气象站周围缓冲区内NDVI变化最大的变量与温度趋势的关系也最为密切。利用NDVI衍生的变量，ANN模型可靠地再现了温度趋势，并阐明了LUCC的贡献。NDVI趋势变量对模拟精度的贡献率为46%，对气象站间温度趋势差异的贡献率为50%。这一研究方向可用于评估LUCC的环境影响，并将全球气候变化引起的温度趋势与局地成因区分开来。","Environmental Research Communications","2026-09-14T00:00:00Z",75,{"impact":101,"substance":102,"depth":103,"authority":20,"freshness":21,"relevant":22,"comment":104},16,21,17,"以NDVI与ANN量化越南中南部落LUCC对气温趋势的影响，方法新颖、结论可靠，对农业遥感与气候适应研究有参考价值。",[106],{"name":97,"url":94},[108,27,109,29,30],"农业人工智能","遥感",[111,112],"农业人工智能 土地利用 植被覆盖 气候变化","农业人工智能 土地利用","农业人工智能土地利用植被覆盖气候变化-2678","10.1088\u002F2515-7620\u002Faea745",{"doi":114,"openalex_id":116,"authors":117,"venue":97,"cited_by_count":36,"oa_url":121,"card":122,"direction":48,"ingested_from":50},"W7212835908",[118],{"name":119,"orcid":120},"Luong Van Viet","https:\u002F\u002Forcid.org\u002F0000-0003-4416-7200","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faea745\u002Fpdf",{"tldr":123,"method":124,"finding":125,"direction":48,"opportunity":126},"量化越南中南部24年土地利用\u002F覆盖变化对气温趋势的影响。","用NDVI与2米气温月均数据，结合统计分析和ANN模拟。","站点2公里内NDVI变化对气温趋势影响最大，NDVI趋势变量贡献46%模拟精度。","可结合多源遥感与深度学习，分离全球变暖与局地LUCC对气温的贡献。","2026-09-16T23:30:32.318792Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":9,"source_name":134,"source_url":131,"published_at":135,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":136,"score_detail":137,"sources":139,"tags":141,"search_phrases":144,"slug":147,"view_count":36,"doi":148,"paper":149,"created_at":167},2175,"Human-induced land use change and population pressure as drivers of landslide occurrence during Cyclone Ditwah in Sri Lanka","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.rineng.2026.112851","Landslides are primarily triggered by earthquakes and extreme rainfall events, although anthropogenic factors such as vegetation change, deforestation, and unplanned land development can amplify their occurrence and severity. Landslides are catastrophic disasters that lead to fatalities, infrastructure damage, and economic disruption. Sri Lanka recently experienced the impact of Cyclone Ditwah in November 2025; the disaster resulted in 643 confirmed deaths, with 183 individuals missing. More than 6000 houses were completely destroyed, and nearly 114,000 sustained damage. However, it was observed that population density and land use and land cover (LULC) changes have some relationship to this damage. With the advancement of remote sensing technology, multispectral images were analyzed to assess how LULC affected natural disasters, alongside 2024 census data. The results of this study demonstrate that built-up and crop land areas have increased, while vegetation cover has decreased from 2017 to 2024. Furthermore, the findings indicate that landslide occurrence is associated with anthropogenic activities, such as urbanization and crop land development near the slope, which may contributed to reduced slope stability. The landslide-prone areas are located near hilly slopes with gradients greater than 30°. The Ududumbara–Minipe and Rideegama–Lunugala case studies demonstrate that landslides occurred more frequently on destabilized gentle slopes than on undisturbed steep slopes in these specific case-study areas, a pattern likely reflecting local anthropogenic disturbance rather than a general slope-stability principle. These findings highlight the importance of measuring and tailoring land-use policies related to urbanization, crop land, and settlement development in mountain regions to mitigate landslide risk.","滑坡主要由地震和极端降雨事件触发，尽管植被变化、森林砍伐和无序土地开发等人为因素会加剧其发生频率和严重程度。滑坡是导致人员伤亡、基础设施破坏和经济损失的重大灾害。斯里兰卡于2025年11月遭遇了气旋迪特瓦（Cyclone Ditwah）的冲击；该灾害造成643人确认死亡，183人失踪。超过6000栋房屋完全损毁，近114000栋房屋受损。然而，观测发现人口密度与土地利用和土地覆盖（LULC）变化与此次损害存在一定关联。随着遥感技术的进步，本研究利用多光谱影像分析了LULC如何影响自然灾害，并结合2024年人口普查数据。研究结果表明，2017年至2024年间，建成区和农田面积有所增加，而植被覆盖有所减少。此外，研究结果显示滑坡的发生与人为活动有关，如坡体附近的城市化和农田开发，这些活动可能降低了边坡稳定性。滑坡易发区位于坡度大于30°的丘陵斜坡附近。Ududumbara–Minipe和Rideegama–Lunugala案例研究表明，在这些特定研究区域内，滑坡在失稳的缓坡上比在未受扰动的陡坡上发生更为频繁，这一模式可能反映的是局部人为扰动，而非普遍的边坡稳定性规律。这些发现凸显了衡量并制定与山区城市化、农田和聚落开发相关的土地利用政策的必要性，以降低滑坡风险。","Results in Engineering","2026-09-08T00:00:00Z",76,{"impact":101,"substance":102,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":138},"基于多光谱遥感与人口普查数据揭示斯里兰卡气旋滑坡的人为驱动机制，对山地乡村土地规划与灾害风险治理有参考价值，但属境外案例、非农业信息化核心议题。",[140],{"name":134,"url":131},[27,28,29,142,143],"滑坡灾害","乡村规划",[145,146],"乡村规划 土地利用 气候变化 滑坡灾害","乡村规划 土地利用","乡村规划土地利用气候变化滑坡灾害-2175","10.1016\u002Fj.rineng.2026.112851",{"doi":148,"openalex_id":150,"authors":151,"venue":134,"cited_by_count":36,"oa_url":161,"card":162,"direction":48,"ingested_from":50},"W7211923709",[152,155,158],{"name":153,"orcid":154},"Janith De Silva","https:\u002F\u002Forcid.org\u002F0009-0002-5124-1018",{"name":156,"orcid":157},"Shanika Arachchi","https:\u002F\u002Forcid.org\u002F0000-0002-3128-1947",{"name":159,"orcid":160},"Upaka Rathnayake","https:\u002F\u002Forcid.org\u002F0000-0002-7341-9078","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS259012302603865X\u002Fpdf",{"tldr":163,"method":164,"finding":165,"direction":48,"opportunity":166},"分析斯里兰卡气旋Ditwah期间土地利用变化与人口压力对滑坡发生的影响。","多光谱遥感影像与2024年人口普查数据，分析2017-2024年LULC变化。","建设用地和耕地增加、植被减少，人类活动降低边坡稳定性，加剧滑坡风险。","可结合多时相遥感与人口数据，构建山区土地利用变化驱动的滑坡风险预警模型。","2026-09-11T23:30:33.053909Z",{"id":169,"title":170,"url":171,"summary":172,"summary_zh":173,"content":9,"source_name":174,"source_url":171,"published_at":175,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":176,"score_detail":177,"sources":179,"tags":181,"search_phrases":185,"slug":188,"view_count":36,"doi":189,"paper":190,"created_at":216},3189,"Influence of Drought Events on Vegetation and Land Use Dynamics Utilising Orbital Data: A Case Study in the Pajeú River Basin, Pernambuco","https:\u002F\u002Fdoi.org\u002F10.29150\u002Fjhrs.v16i03.269030","Monitoring drought in the northeastern Semi-Arid region is a challenge compounded by high rainfall variability and a scarcity of in situ monitoring networks, which makes the use of geotechnologies for water resource management essential. This study aimed to analyze land use and occupation dynamics from 2002 to 2022 in the Pajeú River basin in Pernambuco, Brazil, and their relationship with drought events, using indices based on remote sensing. The Standardized Precipitation Index (SPI) was applied to characterize meteorological drought, while the Vegetation Health Index (VHI) and Normalized Vegetation Water Supply Index (NVSWI) were used to assess the ecosystem's response. The results indicated that the period from 2011 to 2016 was the most severe and prolonged drought, which was reinforced by low VHI and NVSWI values, indicating water stress on vegetation during the same period. The correlation analysis revealed that land use classes, such as vegetation and water, responded directly to drought, whereas degraded anthropogenic areas, including pasture and exposed soil, exhibited the opposite behavior. The joint analysis of the three indices and land use and occupation data provided a comprehensive understanding of the evolution of drought in the study area, highlighting the importance of a multiple approach to monitoring complex environments.","监测半干旱东北部地区的干旱是一项挑战，降雨变率大且原位监测网络稀缺使这一挑战更加复杂，因此利用地理技术进行水资源管理至关重要。本研究旨在利用基于遥感的指数，分析2002年至2022年巴西伯南布哥州帕热乌河流域的土地利用与覆盖动态及其与干旱事件的关系。采用标准化降水指数（SPI）表征气象干旱，同时使用植被健康指数（VHI）和归一化植被供水指数（NVSWI）评估生态系统的响应。结果表明，2011年至2016年是最严重且持续时间最长的干旱期，同期较低的VHI和NVSWI值进一步证实了这一点，表明植被在此期间受到水分胁迫。相关性分析显示，植被和水体等土地利用类别对干旱有直接响应，而牧场和裸露土壤等退化的人为区域则表现出相反的行为。三个指数与土地利用及覆盖数据的联合分析为理解研究区域干旱演变提供了全面的认识，凸显了多方法途径在监测复杂环境中的重要性。","Journal of Hyperspectral Remote Sensing","2026-09-19T00:00:00Z",61,{"impact":21,"substance":19,"depth":65,"authority":17,"freshness":21,"relevant":22,"comment":178},"该研究利用遥感指数分析巴西半干旱流域干旱与植被动态，方法扎实但属区域性案例，对国内三农信息化参考价值有限。",[180],{"name":174,"url":171},[182,28,29,183,184],"水资源管理","干旱监测","植被指数",[186,187],"Pajeú River basin 干旱 遥感","SPI VHI NVSWI 植被","PajeúRiverbasin干旱遥感-3189","10.29150\u002Fjhrs.v16i03.269030",{"doi":189,"openalex_id":191,"authors":192,"venue":174,"cited_by_count":36,"oa_url":210,"card":211,"direction":48,"ingested_from":50},"W7213792298",[193,196,199,202,205,207],{"name":194,"orcid":195},"Juliana Farias Santos de Moraes","https:\u002F\u002Forcid.org\u002F0000-0002-3241-844X",{"name":197,"orcid":198},"Estephania Silva Jovino","https:\u002F\u002Forcid.org\u002F0000-0002-6694-3533",{"name":200,"orcid":201},"Alex Vinícius de Melo Vieira","https:\u002F\u002Forcid.org\u002F0009-0002-5204-3734",{"name":203,"orcid":204},"Anderson Luiz Ribeiro de Paiva","https:\u002F\u002Forcid.org\u002F0000-0003-3475-1454",{"name":206,"orcid":9},"Sylvana Sylvana Melo dos Santos",{"name":208,"orcid":209},"Leidjane Maria Maciel de Oliveira","https:\u002F\u002Forcid.org\u002F0000-0003-1251-6998","https:\u002F\u002Fperiodicos.ufpe.br\u002Frevistas\u002Fjhrs\u002Farticle\u002Fdownload\u002F269030\u002F52814",{"tldr":212,"method":213,"finding":214,"direction":48,"opportunity":215},"利用遥感指数分析巴西Pajeú河流域2002-2022年干旱对植被与土地利用的影响。","采用SPI、VHI和NVSWI指数，结合遥感数据与土地利用分类。","2011-2016年干旱最严重，植被和水体响应直接，而牧场和裸地呈相反趋势。","可融合多源遥感与机器学习，构建半干旱区干旱-植被-土地利用耦合预警模型。","2026-09-22T23:30:26.557816Z",{"id":218,"title":219,"url":220,"summary":221,"summary_zh":222,"content":9,"source_name":223,"source_url":220,"published_at":224,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":225,"score_detail":226,"sources":230,"tags":232,"search_phrases":236,"slug":239,"view_count":36,"doi":240,"paper":241,"created_at":269},3169,"Climate change and the rising threat of Macrophomina phaseolina: implications for food security, food safety, and sustainable crop health management","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10725-026-01525-5","Abstract Macrophomina phaseolina is a destructive soil-borne necrotrophic fungal pathogen that causes substantial yield losses in a wide range of economically important crops worldwide. Disease severity is strongly enhanced under drought, high temperature, and salinity stress, conditions that are becoming increasingly prevalent under current climate change scenarios. This review examines the interactions between climate-driven abiotic stress, host physiological regulation, and pathogen aggressiveness, highlighting how stress-induced disruptions in hormonal signalling, reactive oxygen species homeostasis, antioxidant defence systems, and plant metabolism collectively increase susceptibility to M. phaseolina . Recent advances in understanding pathogen virulence mechanisms, plant immune responses, and resistance-associated molecular pathways are synthesised together with emerging evidence from transcriptomics, proteomics, metabolomics, and comparative genomics. The review further evaluates current management strategies, including host resistance, biological control, plant growth-promoting microorganisms, stress priming, and integrated disease management, while discussing their limitations under field conditions. Emerging technologies such as precision agriculture, remote sensing, artificial intelligence-assisted disease forecasting, and multi-omics approaches are highlighted as promising tools for improving early diagnosis, risk prediction, and climate-resilient disease management. By integrating advances in plant physiology, molecular biology, and sustainable crop protection, this review provides a comprehensive framework for understanding M. phaseolina pathogenesis under changing environmental conditions. It identifies key research priorities to improve crop resilience and safeguard global food security.","摘要 菜豆壳球孢（Macrophomina phaseolina）是一种具有破坏性的土传死体营养型真菌病原菌，在全球范围内对多种具有重要经济价值的作物造成严重产量损失。在干旱、高温和盐胁迫条件下，病害严重程度显著加剧，而这些条件在当前气候变化情景下正变得越来越普遍。本文综述了气候驱动的非生物胁迫、寄主生理调控与病原菌致病力之间的相互作用，重点阐述了胁迫诱导的激素信号传导紊乱、活性氧稳态失衡、抗氧化防御系统受损以及植物代谢改变如何共同增加对菜豆壳球孢的易感性。本文综合了病原菌毒力机制、植物免疫反应及抗性相关分子通路方面的最新研究进展，并结合转录组学、蛋白质组学、代谢组学和比较基因组学的新兴证据。本文进一步评估了当前的管理策略，包括寄主抗性、生物防治、植物促生微生物、胁迫 priming 和病害综合管理，同时讨论了这些策略在田间条件下的局限性。精准农业、遥感、人工智能辅助病害预测和多组学方法等新兴技术被重点介绍为改善早期诊断、风险预测和气候韧性病害管理的有前景的工具。通过整合植物生理学、分子生物学和可持续作物保护方面的进展，本文为理解变化环境条件下菜豆壳球孢的致病机制提供了综合框架，并确定了提高作物韧性和保障全球粮食安全的关键研究优先方向。","Plant Growth Regulation","2026-09-22T00:00:00Z",80,{"impact":19,"substance":227,"depth":19,"authority":228,"freshness":13,"relevant":22,"comment":229},20,14,"核心期刊综述，系统梳理气候胁迫下土传病害机制与AI遥感等智慧防控手段，对农业信息化与粮食安全主题有聚合价值。",[231],{"name":223,"url":220},[233,234,235,27,28],"智慧农业","粮食安全","植物病害",[237,238],"Macrophomina phaseolina 病害 防控","气候变暖 土传病害 粮食安全","Macrophominaphaseolina病害防控-3169","10.1007\u002Fs10725-026-01525-5",{"doi":240,"openalex_id":242,"authors":243,"venue":223,"cited_by_count":36,"oa_url":220,"card":263,"direction":48,"ingested_from":50},"W7213972258",[244,246,248,251,254,257,260],{"name":245,"orcid":9},"Sindiswa Khawula",{"name":247,"orcid":9},"Siyabonga Ntshalitshali",{"name":249,"orcid":250},"Arun Gokul","https:\u002F\u002Forcid.org\u002F0000-0003-1575-0632",{"name":252,"orcid":253},"Lee‐Ann Niekerk","https:\u002F\u002Forcid.org\u002F0000-0002-9788-3131",{"name":255,"orcid":256},"Ashwil Klein","https:\u002F\u002Forcid.org\u002F0000-0002-5606-886X",{"name":258,"orcid":259},"Marshall Keyster","https:\u002F\u002Forcid.org\u002F0000-0002-8718-736X",{"name":261,"orcid":262},"Mbukeni Nkomo","https:\u002F\u002Forcid.org\u002F0000-0002-7652-1588",{"tldr":264,"method":265,"finding":266,"direction":267,"opportunity":268},"综述气候变化下干旱高温盐胁迫加剧菜豆壳球孢菌病害的机制与可持续防控策略。","整合转录组、蛋白组、代谢组、比较基因组及精准农业、遥感、AI预测等技术。","气候胁迫破坏激素与ROS平衡降低作物抗性，需多组学与智能技术实现早期预警和抗性管理。","农业人工智能与决策模型","可构建融合多组学与气象遥感的AI病害预警模型，并研发胁迫 priming 与生防协同的田间方案。","2026-09-22T23:30:22.790623Z",{"id":271,"title":272,"url":273,"summary":274,"summary_zh":275,"content":9,"source_name":276,"source_url":273,"published_at":277,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":278,"score_detail":279,"sources":281,"tags":283,"search_phrases":286,"slug":289,"view_count":36,"doi":290,"paper":291,"created_at":333},3083,"Multi-index analysis reveals complexity of tundra greening and shrubification on Yamal Peninsula","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2752-664x\u002Faea99e","Abstract Arctic vegetation cover is undergoing rapid structural and spatial change under a warming climate. Shrubification is a major component of these changes, whereby shrubs grow outwards and upwards, infilling existing patches and spreading into new areas. Satellite-derived vegetation indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), show increasing trends across decades in many regions of the Arctic, a phenomenon referred to as greening, and interpreted as an indicator of compositional, structural, spatial and functional changes in vegetation. However, the direct contribution of shrubification on satellite-level greening remains poorly understood, partly due to the spectral limitations of single-index methods and the coarse spatial resolution of multi-decadal satellite records. Here we examine and compare the spatial patterns and magnitude of greening on Yamal Peninsula, Arctic Russia, measured with four commonly used vegetation indices derived from Landsat imagery (1987-2023): NDVI, Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI), and kernel NDVI (kNDVI). We also examine how willow (Salix lanata) cover (%) relates to greening, using cover data derived from field observations, and Unoccupied Aerial Vehicle (UAV) and Very High Resolution WorldView 3 satellite imagery. Vegetation greened in 12–25% of the study area, driven primarily by vegetation regeneration on cryogenic landslides and possibly due to shrub expansion. However, greening magnitude was dependent on the VI selected, as they showed only a maximum of 54% similarity in spatial distribution. Uncertainty in the relationship between spectral greening and shrubification adds complexity to interpreting drivers of vegetation change, including how herbivory, for example by reindeer (Rangifer tarandusi), inhibits shrub expansion and how abiotic and other biotic influences may be detected by different or some combination of VIs. We demonstrate that decadal vegetation greenness changes have not been homogenous on Yamal Peninsula, with contrasting Salix-dominated areas responding differently to shared changes in climate, likely due to herbivore dynamics, soil effects, as well as microclimatic or topographical differences in the area.","摘要 在气候变暖背景下，北极植被覆盖正经历快速的结构和空间变化。灌木化是这些变化的主要组成部分，表现为灌木向外和向上生长，填充现有斑块并向新区域扩散。基于卫星的植被指数（VIs），如归一化差异植被指数（NDVI），在北极许多地区显示出数十年间的增长趋势，这一现象被称为绿化，并被解释为植被组成、结构、空间和功能变化的指标。然而，灌木化对卫星尺度绿化的直接贡献仍知之甚少，部分原因在于单一指数方法的光谱局限性以及多年代际卫星记录的空间分辨率较粗。本研究利用1987—2023年Landsat影像衍生的四种常用植被指数——NDVI、增强植被指数2（EVI2）、土壤调节植被指数（SAVI）和核NDVI（kNDVI）——考察并比较了俄罗斯北极亚马尔半岛绿化的空间格局和幅度。我们还利用野外观测、无人驾驶飞行器（UAV）和甚高分辨率WorldView 3卫星影像获得的覆盖度数据，研究了柳树（Salix lanata）覆盖度（%）与绿化的关系。研究区12%—25%的区域出现植被绿化，主要由低温滑坡上的植被再生驱动，也可能源于灌木扩张。然而，绿化幅度取决于所选用的植被指数，因为它们在空间分布上的相似性最高仅为54%。光谱绿化与灌木化之间关系的不确定性增加了解释植被变化驱动因素的复杂性，包括植食作用（例如驯鹿Rangifer tarandus的啃食）如何抑制灌木扩张，以及非生物和其他生物影响如何通过不同植被指数或其组合被检测到。我们证明，亚马尔半岛数十年尺度的植被绿度变化并非均一，以柳树为主的对比区域对共同的气候变化响应不同，这可能归因于植食动物动态、土壤效应以及该地区微气候或地形的差异。","Environmental Research Ecology","2026-09-18T00:00:00Z",69,{"impact":21,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":280},"多指数遥感揭示北极苔原绿化与灌木扩张复杂性，方法新颖数据扎实，但属基础生态研究，与三农信息化关联间接，公共价值有限。",[282],{"name":276,"url":273},[27,28,184,284,285],"北极苔原","灌木扩张",[287,288],"北极苔原 灌木扩张 遥感","北极苔原 植被指数 气候变化 灌木扩张","北极苔原灌木扩张遥感-3083","10.1088\u002F2752-664x\u002Faea99e",{"doi":290,"openalex_id":292,"authors":293,"venue":276,"cited_by_count":36,"oa_url":327,"card":328,"direction":48,"ingested_from":50},"W7213598351",[294,297,300,302,304,307,310,313,316,319,321,324],{"name":295,"orcid":296},"Elias Koivisto","https:\u002F\u002Forcid.org\u002F0009-0007-6204-0963",{"name":298,"orcid":299},"Anton Kuzmin","https:\u002F\u002Forcid.org\u002F0000-0001-5066-5535",{"name":301,"orcid":9},"Logan Berner",{"name":303,"orcid":9},"Jeff T Kerby",{"name":305,"orcid":306},"Mariana Verdonen","https:\u002F\u002Forcid.org\u002F0000-0001-9780-0052",{"name":308,"orcid":309},"Anna Skarin","https:\u002F\u002Forcid.org\u002F0000-0003-3221-1024",{"name":311,"orcid":312},"Tiina H. M. Kolari","https:\u002F\u002Forcid.org\u002F0000-0003-0955-2402",{"name":314,"orcid":315},"Teemu Tahvanainen","https:\u002F\u002Forcid.org\u002F0000-0002-7856-299X",{"name":317,"orcid":318},"Pasi Korpelainen","https:\u002F\u002Forcid.org\u002F0009-0005-9956-6016",{"name":320,"orcid":9},"Miguel Villosada",{"name":322,"orcid":323},"Bruce C. Forbes","https:\u002F\u002Forcid.org\u002F0000-0002-4593-5083",{"name":325,"orcid":326},"Timo Kumpula","https:\u002F\u002Forcid.org\u002F0000-0002-2716-7420","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2752-664X\u002Faea99e\u002Fpdf",{"tldr":329,"method":330,"finding":331,"direction":48,"opportunity":332},"用四种植被指数分析亚马尔半岛苔原绿化，揭示灌木扩张与光谱绿化的复杂关系。","Landsat 1987-2023年NDVI、EVI2、SAVI、kNDVI及无","绿化面积12-25%，但不同指数空间分布相似度仅54%，灌木扩张与绿化关系不确定。","多指数遥感可揭示植被变化的异质性，需结合地面与高分辨率数据解析生物与非生物驱动机制。","2026-09-21T23:30:27.685080Z"]