基于RSEI的阿克苏河流域生态环境质量变化及驱动力分析

Ecological Environment Quality Changes and Drivers in Aksu River Basin Basis on Remote Sensing Ecological Index

摘要: 阿克苏河流域是中国西北地区典型的干旱区绿洲，是塔里木河重要源流，生态系统相对脆弱。长期监测和评价阿克苏河流域生态环境状况为实现区域可持续发展提供重要科学依据。基于Google Earth Engine (GEE)平台的海量数据，构建了1990~2020年的遥感生态指数(RSEI)。采用Spearman秩相关分析、确定阿克苏河流域RSEI指数的适用性。使用Sen’s slope分析、Moran’s I指数和地理探测器等方法，分析1990~2020年阿克苏河流域生态环境质量的时空分布特征及变化趋势，探讨RSEI空间异质性的自然和人为因素。结果表明：(1) 1990~2020年阿克苏河流域RSEI高值集中在北部山区，低值集中在南部沙漠和荒漠。RSEI均值表明生态环境质量处于差和较差等级；(2) 近30年阿克苏河流域生态环境质量处于下降趋势，但北部和中部地区生态环境质量有所改善；(3) 阿克苏河流域生态环境质量大部分保持稳定发展趋势，尤其是近10年稳定区域占比达84%以上；(4) Moran’I值表现出正空间相关性。H-H聚集区在北部山区，L-L聚集区主要分布在南部沙漠和荒漠地区；(5) 阿克苏河流域生态环境质量受自然因素和人类活动共同影响。其中，土地利用和覆盖变化(LUCC)是主导因子，年均气温(Tem)、太阳辐射(Sr)、年均降水量(Pre)、国内生产总值(GDP)、人口密度(Pop)和高程(DEM)是主要驱动因素。各因子交互作用对RSEI的影响远大于单因子的作用。

Abstract: The ecosystem of the Aksu River Basin (ARB) has been adversely affected by prolonged arid climate conditions and land reclamation, leading to a decline in environmental quality. Therefore, long-term ecological monitoring and assessment of the ARB is needed to maintain the ecological sustainability of the basin. On the basis of the Landsat data of the Google Earth Engine platform, a remote sensing ecological index (RSEI) that covered the period 1990 to 2020 was constructed. Spearman rank correlation analysis was conducted to determine the applicability of RSEI to ARB. Sen’s slope analysis, Moran’s I index and a geodetector were used to analyse the spatiotemporal distribution characteristics and change trends of RSEI in ARB from 1990 to 2020 and explore the natural and human factors that affect RSEI spatial heterogeneity. The results show that:(1) The high values of RSEI in ARB from 1990 to 2020 were concentrated in the northern mountainous areas, and the low values were concentrated in the southern deserts and wilderness. The mean values of RSEI indicated that eco-environment quality (EEQ) was poor or fair. (2) The changes in ecological quality in ARB tended to decrease more than the area tended to increase. The northern mountainous and central areas increased. (3) Most of the EEQ values of ARB maintained a stable development trend, especially in the past 10 years, when the proportion of stable areas exceeded 84%. (4) The Moran’s I values showed a spatial positive correlation. The high-high concentration area was in the northern mountainous region, and the low-low concentration area was mainly distributed in the southern desert and wilderness. (5) The EEQ of ARB was influenced by natural and human activities. Land use and cover change was the dominant factor, and annual mean temperature (Tem), solar radiation (Sr), annual mean precipitation (Pre), gross domestic product (GDP), population density (Pop) and digital elevation model (DEM) were the main driving factors. The interactive effects of various factors on RSEI are far greater than the effects of individual factors.