西藏地表温度时空演变特征及影响因子  

Spatiotemporal evolution characteristics and influencing factors of land surface temperature(LST)in Tibet

作　　者：伍健恒 孙彩歌 樊风雷[1,2] WU Jianheng;SUN Caige;FAN Fenglei(School of Geography,South China Normal University,Guangzhou 510631,China;Joint Laboratory of Plateau Surface Remote Sensing,Tibet University,Lhasa 850000,China)

机构地区：[1]华南师范大学地理科学学院,广东广州510631 [2]西藏大学高原地表遥感监测联合实验室,西藏拉萨850000

出　　处：《冰川冻土》2022年第5期1523-1538,共16页Journal of Glaciology and Geocryology

基　　金：广东省基础与应用基础研究基金项目(2021A1515011411)资助。

摘　　要：地表温度(land surface temperature,LST)是反映生态环境状况的重要指标。西藏作为气候变化的敏感地区,掌握其LST的时空变化有利于深入了解西藏热环境演化过程,为长期监测高原基础生态变化提供帮助。研究基于谷歌地球引擎获取西藏2000—2020年的MODIS LST数据,采用归一化分级方法对LST进行5个等级的划分,利用趋势分析、热力空间分析以及重心迁移等方法分析了研究区近20年来的LST时空演变特征。同时,选取归一化植被指数(normalized difference vegetation index,NDVI)、裸土指数(bare soil index,BI)、垂直不透水面指数(perpendicular impervious surface index,PISI)、湿度(WET)以及高程(digital elevation model,DEM)等5个影响LST的地表参数,结合多尺度地理加权回归,探讨了LST影响因子的作用尺度与作用效力。结果表明:2000—2020年,西藏LST均值由18.72℃上升至20.28℃,年均增长0.09℃,LST呈现微弱上升态势。20年来,LST在所有年份皆具有西北高、东南低的空间分布格局,LST增温趋势亦表现为西北高、东南低的分布特征。低温区和高温区空间分布聚集,形状简单、规则;次低温区、中温区以及次高温区空间分布破碎,形状复杂。2000—2020年各温区重心分布具有明显的方向性,且各温区重心迁移轨迹具有显著差异。特别是,低温区重心与高温区重心迁移轨迹呈现出由相向而行到背向而行的转变,反映出研究区东西部区域LST差距经历了由缩小到扩大的过程。DEM和WET对LST具有负向影响,BI、PISI和NDVI具有正向影响,常数项在不同生态区具有不同的影响性质。DEM具有较小的作用尺度以及最强的作用效力,常数项具有最小的作用尺度以及仅次于DEM的作用效力。Land surface temperature(LST)is one of the important indicators reflecting the ecological environ⁃ment and has a strong correlation with air temperature.Against the backdrop of rising global air temperature,Ti⁃bet,a sensitive area for climate change,is worthy of being monitored the spatiotemporal change for LST by re⁃mote sensing technology on a large scale,which is helpful for gaining insight into the evolutionary process of the Tibetan thermal environment and for long-term monitoring of basic ecological change in plateau areas.Howev⁃er,remote sensing images in plateau areas generally face the problem of cloud shading,with the annual average cloud amount exceeding 50%,which greatly reduces the availability of remote sensing data and makes it difficult to effectively monitor the LST in plateau areas over a large range and long-time span.In addition,the research on LST and surface parameters is mainly based on classical linear regression and geographically weighted regres⁃sion,and the scale effects of different surface parameters on the spatial differentiation pattern of LST are not ful⁃ly considered,which results in an unstable regression.In this paper,with the help of Google Earth Engine(GEE),the MODIS LST data of Tibet without cloud cover from 2000 to 2020 were obtained by the methods of cloud identification,could masking,image overlay and mean value composite to address the problem of cloud cover in the plateau region.LST was classified into 5 classes by normalized classification method,and then the spatiotemporal distribution characteristics of Tibet’s LST is explored using trend analysis,landscape pattern in⁃dexes and movement analysis of the center of gravity.Moreover,the surface parameters which effect LST such as normalized difference vegetation index(NDVI),bare soil index(BI),perpendicular impervious surface in⁃dex(PISI),humidity(WET)are retrieved and digital elevation model(DEM)is attained.In view of the fact that traditional regression methods cannot take into account the spatial scal

关 键 词：地表温度 多尺度地理加权回归 时空演化 MODIS 西藏 

分 类 号：P423.7[天文地球—大气科学及气象学] TP79[自动化与计算机技术—检测技术与自动化装置]