Spatial-temporal evolution of habitat quality and its influencing factors in the Yellow River Basin based on InVEST model and GeoDetector

doi:10.7522/j.issn.1000-694X.2021.00026

Journal of Desert Research ›› 2021, Vol. 41 ›› Issue (4): 12-22.DOI: 10.7522/j.issn.1000-694X.2021.00026

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Spatial-temporal evolution of habitat quality and its influencing factors in the Yellow River Basin based on InVEST model and GeoDetector

Jie Yang^a(), Baopeng Xie^b, Degang Zhang^a()

^a.College of Pratacultural Science /, Gansu Agricultural University，Lanzhou 730070，China
^b.College of Management, Gansu Agricultural University，Lanzhou 730070，China

Received:2020-12-18 Revised:2021-03-12 Online:2021-07-27 Published:2021-07-27
Contact: Degang Zhang

Abstract

Abstract:

The Yellow River Basin is an important ecological region in China， and it is of great significance to explore the spatio-temporal variation characteristics of biodiversity in this basin for regional ecological protection and restoration. In this study， the InVEST model is used to evaluate the habitat quality of the Yellow River Basin from 2000 to 2018， combined with the spatial autocorrelation model to explore its temporal and spatial changes characteristics. The driving factors of spatial differentiation characteristics of habitat quality are analyzed by using the single factor detection and interactive detection methods of geographic detectors. The results show that： from 2000 to 2018， the average habitat quality index of the Yellow River Basin was 0.631， with a decline rate of 0.16%， and the habitat quality index was high in the west and low in the east. The habitat quality index of the Yellow River Basin exhibits a highly positive spatial correlation characteristic， which shows that the similar values of habitat quality gather in space. The low-value areas of habitat quality are mainly concentrated in the lower reaches of the Yellow River Basin， Guanzhong Plain and Fenhe Valley， while the high-value areas of habitat quality are mainly concentrated in the Qinghai-Tibet Plateau in the upper reaches of the Yellow River. Land use cover type is the most important driving factor for the spatial differentiation of habitat quality， with q value of 0.5560. Moreover， the interaction between any two driving factors on spatial differentiation of habitat quality is greater than that of one driving factor alone， and the interaction factors between land use cover types and temperature， elevation， rainfall， slope and NDVI are all greater than 0.5.

Key words: habitat quality, InVEST model, GeoDetector, spatio-temporal evolution, Yellow River Basin

CLC Number:

X171

Jie Yang, Baopeng Xie, Degang Zhang. Spatial-temporal evolution of habitat quality and its influencing factors in the Yellow River Basin based on InVEST model and GeoDetector[J]. Journal of Desert Research, 2021, 41(4): 12-22.

Figures/Tables 12

Fig.1 Range of Yellow River Basin and distribution of river system

Table 1 The weight and the maximum influence distance of the threat source

威胁因子	最大影响距离/km	权重	衰退类型
城镇用地	10	1.0	指数
农村居民地	8	0.8	指数
其他建设用地	9	0.9	指数
耕地	6	0.6	线性
未利用地	4	0.4	线性

Table 2 Sensitivity of land use type to habitat threat factors

土地利用/覆被	生境适宜度	敏感度
土地利用/覆被	生境适宜度	城镇用地	农村居民点	其他建设用地	耕地	未利用地
耕地	0.3	0.8	0.6	0.7	0.0	0.4
林地	1.0	0.8	0.7	0.7	0.6	0.2
草地	1.0	0.7	0.5	0.6	0.5	0.6
水域	0.9	0.7	0.6	0.7	0.4	0.4
建设用地	0.0	0.0	0.0	0.0	0.0	0.0
未利用地	0.6	0.6	0.5	0.6	0.4	0.0

Table 3 Types of two-factor interaction result

判断依据	交互作用类型
$q$ ( $X 1$ ∩ $X 2$ )<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( $q$ ( $X 1$ ), $? q$ ( $X 2$ ))	单因子非线性减弱
$q$ ( $X 1$ ∩ $X 2$ )>max( $q$ ( $X 1$ ), $? q$ ( $X 2$ ))	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

Table 3 Types of two-factor interaction result

判断依据	交互作用类型
$q$ ( $X 1$ ∩ $X 2$ )<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( $q$ ( $X 1$ ), $? q$ ( $X 2$ ))	单因子非线性减弱
$q$ ( $X 1$ ∩ $X 2$ )>max( $q$ ( $X 1$ ), $? q$ ( $X 2$ ))	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

Fig.2 Spatial distribution of habitat quality in 2000 （A）， 2005 （B）， 2010 （C）， 2018 （D）

Table 4 Proportion of habitat quality of different grades in the Yellow River Basin from 2000 to 2018

等级	生境质量指数	各质量等级生境在不同年份所占的比例/%
等级	生境质量指数	2000年	2005年	2010年	2018年
低	0.0—0.2	2.31	2.31	2.56	3.02
较低	0.2—0.4	27.05	27.05	26.51	26.31
中等	0.4—0.6	8.96	8.96	9.11	8.93
较高	0.6—0.8	0.00	0.00	0.00	0.00
高	0.8—1.0	61.68	61.68	61.82	61.74

Fig.3 Spatial Lisa cluster of habitat quality in 2000 （A）， 2005 （B）， 2010 （C）， 2018 （D）

Fig.4 Spatial distribution of habitat quality degradation in 2000 （A）， 2005 （B）， 2010 （C）， 2018 （D）

Table 5 Proportion of habitat quality of different degradation grades in the Yellow River Basin from 2000 to 2018

等级	生境退化度	各退化度等级生境在不同年份所占的比例/%
等级	生境退化度	2000年	2005年	2010年	2018年
低	0.00	3.225	3.233	3.532	3.493
较低	0.00—0.03	43.438	43.560	43.514	43.472
中等	0.03—0.06	27.833	28.099	28.097	28.394
较高	0.06—0.09	16.052	15.808	15.702	15.675
高	≥0.09	9.452	9.300	9.155	8.966

Table 6 GeoDetector of habitat quality factors

影响因子	NDVI	人口密度	GDP	土地利用/覆被	降雨量	气温	坡度	海拔
决定力	0.3500	0.2012	0.2000	0.5560	0.2066	0.4394	0.1541	0.3430

Table 7 Interactive detection of habitat quality factors

影响因子	NDVI	人口密度	GDP	土地利用/覆被	降雨量	气温	坡度	海拔
NDVI	0.3500
人口密度	0.3913	0.2012
GDP	0.3906	0.2020	0.2000
土地利用/覆被	0.6764	0.5647	0.5634	0.5560
降雨量	0.4709	0.2725	0.2789	0.6131	0.2066
气温	0.6659	0.5082	0.5083	0.7012	0.4934	0.4394
坡度	0.5096	0.3147	0.3133	0.6986	0.4042	0.5159	0.1541
海拔	0.6488	0.5091	0.5086	0.7021	0.4773	0.4634	0.3964	0.3430

Fig.5 Transition matrix of land-use in the Yellow River Basin in 2000—2018

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Spatial-temporal evolution of habitat quality and its influencing factors in the Yellow River Basin based on InVEST model and GeoDetector

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