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2021 , Vol. 44 >Issue 2: 460 - 470

DOI: https://doi.org/10.12118/j.issn.1000–6060.2021.02.17

地球信息科学

新疆生态系统健康遥感评估及关键驱动因子研究

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  • 1.山东科技大学测绘科学与工程学院,山东 青岛 266000
    2.生态环境部卫星环境应用中心,北京 100094
    3.中国科学院空天信息创新研究院,北京 100094
李灏欣（1994-）,男,在读硕士研究生,主要从事生态遥感方向研究. E-mail:1197665475@qq.com

收稿日期: 2020-11-10

  修回日期: 2021-02-07

  网络出版日期: 2021-04-14

基金资助

国家重点研发计划项目(2018YFC0507201);国家自然科学基金青年科学基金资助(41801366)

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Remote sensing assessment and key driving factors of ecosystem health in Xinjiang

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  • 1. Shandong University of Science and Technology College of Geomatics, Qingdao 266000, Shandong, China
    2. Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment, Beijing 100094, China
    3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

Received date: 2020-11-10

  Revised date: 2021-02-07

  Online published: 2021-04-14

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摘要

作为我国陆地面积最大的省级行政区,新疆维吾尔自治区具有多样的生态系统结构和丰富的动植物物种资源,是我国西北地区重要的生物多样性热点研究区域。通过以新疆维吾尔自治区为研究对象,结合区域特点和定量遥感数据实时、易获取、周期性等优势,选取归一化植被指数、动物物种丰富度、干旱度、人类扰动指数四类单因子指标,依据“活力-组织-恢复力”生态健康评估模型,定量化的构建了新疆以遥感数据为主导的区域综合生态健康遥感评估指数,并进一步基于地理探测器探明不同环境驱动因子对新疆生态健康的影响程度,在县级行政区层面对该方法进行了长时间序列的应用分析和验证。结果表明：（1） 新疆生态健康水平以塔里木河—叶尔羌河流域为分界线,北疆生态健康水平明显高于南疆,具有显著的空间分异性和集聚特征。（2） 各环境因子对新疆生态健康的影响程度依次为：归一化植被指数（0.645）>人类扰动指数（0.512）>动物物种丰富度（0.414）>干旱度（0.116）。（3） 2000—2018年新疆整体生态健康水平呈现逐步攀升的趋势,变化最为显著的区域是在昌吉回族自治州的玛纳斯县、呼图壁县以及昌吉市;阿勒泰地区的福海县和富蕴县。

关键词： 生态健康评估; 单因子指标; 地理探测器; 驱动要素; 动态分析; 新疆

本文引用格式

李灏欣,万华伟,孙林,刘玉平,李利平,王永财 . 新疆生态系统健康遥感评估及关键驱动因子研究[J]. 干旱区地理, 2021 , 44(2) : 460 -470 . DOI: 10.12118/j.issn.1000–6060.2021.02.17

Abstract

As the largest provincial administrative region with land area in China, the Xinjiang Uygur Autonomous Region has a diversified ecosystem structure and abundant animal and plant resources, and is an important biodiversity hotspot in northwest China. The Xinjiang Uygur Autonomous Region was selected as the research object combined with regional characteristics and quantitative remote sensing data in real-time. Advantages of this evaluation are the facile obtainment, normalized difference vegetation index, species richness, drought degree, and human disturbance index of four types of single-factor index. According to the “vigor, organization, resilience” ecological health assessment model, the construction of a quantitative remote sensing data of the Xinjiang regional comprehensive ecosystem health assessment index, comprising remote sensing, and further environmental driving factors in the geographical detector, is based on the proved influence degree of the Xinjiang ecological health, and level of this method in the county administrative region for application of long time series analysis and verification. The remote sensing data used in this study were mainly provided by the national aeronautics and space administration (NASA) through the MODIS vegetation index and temperature series products. The species used data is based on the data reported in the literature. Compared to traditional ecological health assessment methods, this presents a wide coverage, convenient data acquisition, and updated cycle in a short time, providing a more refined evaluation of the results. The results showed that: (1) the ecological health level of Xinjiang considers the Tarim and Yarkant River Basins as the boundaries; moreover, the ecological health level of northern Xinjiang is significantly higher than that of southern Xinjiang, with substantial spatial differentiation and agglomeration characteristics. (2) The impact degree of each environmental factor on Xinjiang ecological health was as follows: normalized difference vegetation index (0.645)>human disturbance index (0.512)>animal species richness (0.414)>drought (0.116). (3) From 2000 to 2018, the overall ecological health level of Xinjiang demonstrated a gradually increasing trend, with the most significant changes occurring in the Manas County, Hutubi County, and Changji City in Changji Hui Autonomous Prefecture; and the Altay Prefecture including Fuhai County and Fuyun County.

Key words： ecological health assessment; single factor index; geographic detector; driving factors; a dynamic analysis; Xinjiang

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