新疆生态系统健康遥感评估及关键驱动因子研究

doi:10.12118/j.issn.1000–6060.2021.02.17

摘要/Abstract

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

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

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.

LI Haoxin,WAN Huawei,SUN Lin,LIU Yuping,LI Liping,WANG Yongcai. Remote sensing assessment and key driving factors of ecosystem health in Xinjiang[J]. Arid Land Geography, 2021, 44(2): 460-470.

图/表 12

图1

表1

表2

表3

表4

表5

自变量因子对因变量交互作用的类型"

判据	交互作用
$q (X 1 ⋂ X 2) < min [q X 1, q (X 2)]$	非线性减弱
$min [q X 1, q (X 2)] < q (X 1 ⋂ X 2) < max [q (X 1 ⋂ X 2)]$	单因子非线性减弱
$q (X 1 ⋂ X 2) > max [q (X 1 ⋂ X 2)]$	双因子增强
$q (X 1 ⋂ X 2$ )= $q X 1 + q X 2$	独立
$q (X 1 ⋂ X 2) > q X 1 + q X 2$	非线性增强

表5

图2

图3

表6

表7

交互作用探测分析结果"

交互指标	交互关系
NDVI $⋂$ 动物物种丰富度	双因子增强
NDVI $⋂$ 人类扰动指数	双因子增强
NDVI $⋂$ 干旱度	双因子增强
人类扰动指数 $⋂$ 动物物种丰富度	双因子增强
人类扰动指数 $⋂$ 干旱度	双因子增强
干旱度 $⋂$ 动物物种丰富度	非线性增强

表7

图4

表8

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基础数据	数据来源	空间分辨率	合成方式	时间
归一化植被指数	MOD13 A2产品	1 km	16 d合成	2000、2010、2018年6—9月
地表温度	MOD11 A2产品	1 km	8 d合成	2000、2010、2018年6—9月
土地覆盖类型	2000、2010、2018年全国30 m分辨率土地覆盖分类数据	30 km	最邻近内插法合成	2000、2010、2018年6—9月
动物物种丰富度	文献资料查询	10 km	统计合成	2010年

指标	方差膨胀因子（VIF）	容忍度（TOL）
归一化植被指数	1.918	0.521
动物物种丰富度	1.242	0.805
人类扰动指数	1.521	0.657
干旱度	1.269	0.788

土地利用/覆被类型	CI值	土地利用/覆被类型	CI值
耕地	0.200	低覆盖改良草地	0.067
水田	0.200	水库池塘	0.600
旱地	0.200	城镇用地	1.000
高覆盖改良草地	0.067	农村居民点	1.000
中覆盖改良草地	0.067	建设用地	1.000

目标层	准则层	指标层	属性
新疆生态健康（EH）	活力（EV）	归一化植被指数（NDVI）	正向
	组织力（EO）	动物物种丰富度（ASR）	正向
	恢复力（ER）	人类扰动指数（HDI）	负向
	恢复力（ER）	干旱度（DI）	负向

年份	归一化植被指数	动物物种丰富度	干旱度	人类扰动指数	生态健康评估指数
2000	0.20	0.55	0.64	0.25	0.027
2010	0.34	0.55	0.63	0.28	0.035
2018	0.32	0.55	0.60	0.26	0.037