基于遥感生态指数(RSEI)改进模型的祁连山国家级自然保护区生态环境质量评价

doi:10.19741/j.issn.1673-4831.2022.1054

摘要/Abstract

摘要： 针对遥感生态指数（RSEI）模型特征向量方向不唯一性及光学影像存在坏死像元的问题，基于Google Earth Engine平台选择祁连山国家级自然保护区1986-2021年489景Landsat TM/SR可用性遥感影像，采用RSEI改进模型进行生态环境质量评价，并引入地理探测器中单因子分析和交互式探测分析对RSEI的绿度、湿度、热度、干度、土地利用类型、DEM和人口密度7个影响因子进行成因分析。结果表明：（1）与RSEI模型相比，改进模型避免了特征向量方向的干扰，可以较好地反映生态环境质量变化。1986-2021年保护区生态环境质量呈现先下降后上升的恢复趋势，空间分布呈现东高西低。（2）时空差异分析表明，35 a来保护区生态环境质量以轻度恶化、不变和轻度改善为主，恶化区域主要分布在张掖市肃南裕固族自治县北部以及张掖市与武威市交界处等地区。轻度改善区域在保护区分布零散，其中在核心区分布相对较多。（3）从生态环境质量成因分析来看，7个影响因子中绿度对RSEI的空间分异特征解释力最强，交互式探测结果表明绿度和干度是研究区生态环境质量的关键驱动因素。研究结果表明祁连山国家级自然保区生态环境质量近年来逐步改善，保护区的一系列环境保护举措是有效的。

关键词: 遥感生态指数改进模型, 祁连山国家级自然保护区, 遥感动态监测, 地理探测器, 影像可用性分析

Abstract: Aiming at the non uniqueness of feature vector direction of remote sensing ecological index(RSEI) model and the existence of necrotic pixels in optical images, 489 Landsat TM/SR availability remote sensing images of Qilian Mountain National Nature Reserve from 1986 to 2021 were selected in view of Google Earth Engine platform, and the improved remote sensing ecological index model was utilized to assess the ecological environment quality of the reserve. The single factor analysis and interactive detection analysis in geographical detector were applied to analyze the mechanisms of the seven influencing factors (NDVI, WET, LST, NDBSI, land use type, DEM, and population density) of RSEI. The results show that:(1) Compared with remote sensing ecological index model, the improved remote sensing ecological index model avoided the interference of feature vector direction, and could better reflect the changes of ecological environment quality. The ecological environment quality from 1986 to 2021 demonstrated a recovery trend of "first decreasing and then increasing", with a spatial distribution of "high in the east and low in the west". (2)The spatio-temporal differences in the reserve indicate that the ecological environment quality slightly deteriorated, unchanged or slightly improved in the past 35 years. The distribution of slightly deteriorated areas located mainly in the northern part of Sunan Yugur Autonomous County in Zhangye City and the junction of Zhangye City and Wuwei City. The slightly improved areas were scattered in the reserve, while the core areas were relatively more distributed in the reserve. (3) From the analysis of the causes of ecological environment quality, among the seven influencing factors, greenness (NDVI) had the strongest explanatory power on the spatial differentiation characteristics of RSEI. Considering the synergistic effect of multiple factors, the NDVI and NDBSI were the key driving indicators of ecological environment quality. The results above show that the ecological environment quality in Qilian Mountain Nature Reserve has been gradually improved in recent years, and a series of environment protection measures were effective.

Key words: improved remote sensing ecological index, Qilian Mountain National Nature Reserve, remote sensing dynamic monitoring, geographical detector, image availability analysis

中图分类号:

X826

汪孝贤, 张秀霞, 李旺平, 程小强, 凌晴, 周兆叶, 郝君明, 林庆润, 陈璐. 基于遥感生态指数(RSEI)改进模型的祁连山国家级自然保护区生态环境质量评价[J]. 生态与农村环境学报, 2023, 39(7): 853-863.

WANG Xiao-xian, ZHANG Xiu-xia, LI Wang-ping, CHENG Xiao-qiang, LING Qing, ZHOU Zhao-ye, HAO Jun-ming, LIN Qing-run, CHEN Lu. Assessment of Ecological Environment Quality in Qilian Mountain National Nature Reserve Based on Improved RSEI Model[J]. Journal of Ecology and Rural Environment, 2023, 39(7): 853-863.

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