Jiang Fugen, Deng Muli, Long Yi, Sun Hua
Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Changsha, China.
Key Laboratory of Forestry Remote Sensing Based Big Data and Ecological Security for Hunan Province, Changsha, China.
Front Plant Sci. 2022 Apr 25;13:892625. doi: 10.3389/fpls.2022.892625. eCollection 2022.
Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil-Sen median method and Mann-Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.
由于寒冷的气候和剧烈起伏的海拔高度，识别动态植被趋势和主要驱动因素对于维持西藏的生态平衡至关重要。归一化植被指数（NDVI）作为最常用的绿度指数，能够有效评估植被健康状况和空间格局。获取了2001年至2020年西藏的MODIS-NDVI（中分辨率成像光谱仪-NDVI）数据，并在谷歌地球引擎（GEE）云平台上进行了预处理。采用泰尔-森中位数法和曼-肯德尔检验法研究NDVI的动态变化，并利用赫斯特指数预测未来植被趋势。此外，还分析了NDVI变化的主要驱动因素。结果表明：（1）2001年至2020年西藏植被NDVI显著增加，年平均NDVI值在0.31至0.34之间波动，年增长率为0.0007；（2）植被改善面积占研究区域的比例最大，为56.6%，其次是稳定不变和退化区域，比例分别为27.5%和15.9%。西藏NDVI的总体变异系数较低，平均值为0.13；（3）赫斯特指数的平均值为0.53，持续改善区域面积占研究区域的41.2%，表明大多数地区植被变化趋势具有连续性；（4）西藏的NDVI呈现出高度的空间集聚性。然而，NDVI集聚区域的空间分布存在明显差异，集聚类型主要包括高高型和低低型；（5）降水和人口增长对西藏西部植被覆盖的改善有显著贡献。此外，利用GEE获取遥感数据并结合时间序列数据分析，为快速获取大规模植被变化趋势提供了可能。
