[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2678":3},{"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,"view_count":32,"doi":33,"paper":34,"created_at":48},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。",null,"Environmental Research Communications","2026-09-14T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},16,21,17,13,8,1,"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。",[25],{"name":10,"url":6},[27,28,29,30,31],"农业人工智能","气候变化","遥感","土地利用","植被覆盖",0,"10.1088\u002F2515-7620\u002Faea745",{"doi":33,"openalex_id":35,"authors":36,"venue":10,"cited_by_count":32,"oa_url":40,"card":41,"direction":45,"ingested_from":47},"W7212835908",[37],{"name":38,"orcid":39},"Luong Van Viet","https:\u002F\u002Forcid.org\u002F0000-0003-4416-7200","https:\u002F\u002Fiopscience.iop.org\u002Farticle\u002F10.1088\u002F2515-7620\u002Faea745\u002Fpdf",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"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。","农业遥感与作物表型","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。","openalex","2026-09-16T23:30:32.318792Z"]