Spatiotemporal Changes and Driving Factors of Ecosystem Health in the Qinling-Daba Mountains

Abstract

Rapid industrialization and urbanization have accelerated land-use changes in mountainous areas, with dramatic impacts on ecosystem health. In particular, the Qinling-Daba Mountains, as China’s central water tower, ecological green lung, and biological gene bank, have rich resource endowments and extremely high ecological value and are an important protective wall to China’s ecological security. Therefore, understanding the level of ecosystem health and its drivers in the research area contributes to the conservation and restoration of the mountain ecosystem. Based on remote sensing image data and land-use data from 2000 to 2020, we explored the spatial characteristics of ecosystem health, and supplemented with socio-economic data to explore its driving factors. The results show that (1) the ecosystem health in the study area has been continuously improved during the study period, and the regional differences in ecological organization are the most prominent; (2) the level of ecosystem health in the Qinling-Daba Mountains has been spatially improved from the peripheral areas to the central area, showing significant spatial autocorrelation and local spatial aggregation; (3) the ecosystem health is influenced by a combination of natural and anthropogenic factors, among which the negative effect of GRDP is mainly concentrated in the eastern region, the negative effect of the proportion of built-up land gradually spreads to the western region, and the positive effect of the proportion of forest land has a large scale. This study contributes to a better understanding of ecosystem health in mountainous counties in China and provides useful information for policymakers to formulate ecological and environmental management policies.

Full text

Citation: Xiang, T.; Meng, X.; Wang,
X.; Xiong, J.; Xu, Z. Spatiotemporal
Changes and Driving Factors of
Ecosystem Health in the
Qinling-Daba Mountains. ISPRS Int.
J. Geo-Inf. 2022,11, 600. https://
doi.org/10.3390/ijgi11120600
Academic Editors: Wolfgang Kainz,
Maurizio Pollino and
Giuseppe Modica
Received: 9 October 2022
Accepted: 24 November 2022
Published: 29 November 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Geo-Information
Article
Spatiotemporal Changes and Driving Factors of Ecosystem
Health in the Qinling-Daba Mountains
Ting Xiang 1, Xiaoliang Meng 1,*, Xinshuang Wang 2, Jing Xiong 3and Zelin Xu 4
1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2Shaanxi Geomatics Center, Ministry of Natural Resources, Xi’an 710054, China
3Hubei Environmental Monitoring Central Station, Wuhan 430072, China
4Department of Geoscience & Remote Sensing, Delft University of Technology, P.O. Box 5048,
2600 Delft, The Netherlands
*Correspondence: xmeng@whu.edu.cn
Abstract: Rapid industrialization and urbanization have accelerated land-use changes in mountain-
ous areas, with dramatic impacts on ecosystem health. In particular, the Qinling-Daba Mountains,
as China’s central water tower, ecological green lung, and biological gene bank, have rich resource
endowments and extremely high ecological value and are an important protective wall to China’s
ecological security. Therefore, understanding the level of ecosystem health and its drivers in the
research area contributes to the conservation and restoration of the mountain ecosystem. Based
on remote sensing image data and land-use data from 2000 to 2020, we explored the spatial char-
acteristics of ecosystem health, and supplemented with socio-economic data to explore its driving
factors. The results show that (1) the ecosystem health in the study area has been continuously
improved during the study period, and the regional differences in ecological organization are the
most prominent; (2) the level of ecosystem health in the Qinling-Daba Mountains has been spatially
improved from the peripheral areas to the central area, showing significant spatial autocorrelation
and local spatial aggregation; (3) the ecosystem health is influenced by a combination of natural
and anthropogenic factors, among which the negative effect of GRDP is mainly concentrated in the
eastern region, the negative effect of the proportion of built-up land gradually spreads to the western
region, and the positive effect of the proportion of forest land has a large scale. This study contributes
to a better understanding of ecosystem health in mountainous counties in China and provides useful
information for policymakers to formulate ecological and environmental management policies.
Keywords:
ecosystem health; spatiotemporal characteristics; driving factors; GWR; Qinling-Daba Moun-
tains
1. Introduction
China’s massive and rapid urbanization has accelerated national economic growth at
great costs of resource use and environmental pollution, causing many ecological conse-
quences, such as farmland occupation [
1
], landscape fragmentation [
2
], energy shortages [
3
],
air pollution [
4
], and reduced biodiversity [
5
], which have threatened the sustainable devel-
opment of the whole country. Ecosystems are facing unprecedented shocks, and ecosystem
health issues [
6
] have become a serious challenge to achieve green and sustainable develop-
ment. Therefore, it is necessary to conduct a systematic assessment of ecosystem health to
provide a scientific basis for managers to formulate policies.
A healthy ecosystem provides the material foundation and ecological services for hu-
man survival [
7
], and it also plays a critical role in maintaining and improving urban
ecosystem function [
8
]. Ecosystems are large and complex, and a systematic assessment
of ecosystem health requires a comprehensive consideration of multiple aspects. Based
on the statistics of physical, ecological, and socioeconomic, pressure–state–response [
9
–
11
],
driver–pressure–state–impact [
12
], driver–pressure–state–impact–response [
13
], and other
ISPRS Int. J. Geo-Inf. 2022,11, 600. https://doi.org/10.3390/ijgi11120600 https://www.mdpi.com/journal/ijgi

ISPRS Int. J. Geo-Inf. 2022,11, 600 2 of 18
methods to construct a system of indicators, and the analytic hierarchy process is often used
to give weight to the indicators. These models are not good measures of land-use/land-
cover (LULC) changes and shifts in landscape patterns, which not only affect the provision
of essential ecosystem services for human health and well-being but can also misestimate
the self-mitigation capacity of ecosystems. The vigor–organization–resilience (VOR) model
is widely accepted for its simplicity of calculation and focus on LULC changes. Ecosystem
vigor, ecosystem organization, and ecosystem resilience evaluate the health level of ecosys-
tems in a comprehensive manner in terms of ecosystem primary productivity, structural
stability, and recovery ability, respectively [
14
–
16
]. In a comprehensive measure of the
natural condition of the ecosystem, we used the VOR model to quantitatively evaluate the
level of ecosystem health in the Qinling-Daba Mountains.
Exploring the factors influencing ecosystem health is also a pressing issue. Methods
such as correlation analysis, principal component analysis, regression analysis, and geo-
graphical detectors have been applied to discuss the relationship between ecosystem health
and its drivers, especially the impact of human activities on ecosystem health at the regional
scale [
17
–
20
]. However, traditional statistical and spatial analysis methods do not reflect
the spatiotemporal variability of different drivers of geographic processes, which may limit
practical decision-making for ecological environment management policies [
21
]. Compared
with traditional statistical analysis methods, geographically weighted regression (GWR)
models can not only analyze the relative importance of drivers but also graphically repre-
sent the spatial pattern generated under parameter estimation and represent the spatial
pattern of the intensity of each driver’s influence in the form of a map [22,23].
The Qinling-Daba Mountains are key areas for biodiversity protection and mainte-
nance of ecological function [
24
]. With an ecological red line area of nearly 70%, the high
forest cover provides a high-quality habitat for biodiversity [
25
]. As the north–south bound-
ary of China, it has a unique geographical location and ancient geological history [
26
].
Studies of vegetation dynamics [
27
,
28
], forest structure [
29
], climate [
30
,
31
], and ecosystem
services [
32
] in the region found an increase in vegetation coverage and significant spatial
variation in ecosystem services in the Qinling-Daba Mountains. However, there is a lack
of understanding of the study area ecosystems at the macro level, and few studies have
analyzed ecosystem health and influencing factors in the region.
This paper analyzes the ecosystem health and its influencing factors in 120 counties
in the Qinling-Daba Mountains from 2000 to 2020 using 30 m resolution remote sensing
imagery and LULC data. This study aims to explore the following: (1) spatial characteristics
of ecosystem health, mainly spatial correlation and spatial heterogeneity: (2) analysis of the
factors influencing ecosystem health at the county scale. Our study will help managers to
develop ecological conservation policies.
2. Materials and Methods
2.1. Study Area
The Qinling-Daba Mountains are in the central part of China, spanning 308,093 km
2
,
crossing Gansu, Shaanxi, Henan, Sichuan, Hubei, and Chongqing Municipality. It runs
across five provinces and one municipality, including 120 counties (Figure 1). The topog-
raphy of the Qinling-Daba Mountains is characterized by mountains and hills, gradually
rising from east to west, and the altitude is very different. The southern part of the study
area has a subtropical humid climate with an annual precipitation of 820 mm and an
annual average temperature of 14
◦
C, while the northern part belongs to a warm temper-
ate semi-humid climate with an annual precipitation of 520 mm and an annual average
temperature of 10 ◦C [33]. The vegetation types are diverse and the zoning characteristics
are obvious. It is a transition from warm-temperate deciduous broadleaf forests in the
north to subtropical mixed broadleaf evergreen and deciduous forests in the south, with
both northern and southern Chinese plant species. The study area is an interactive zone
between China’s humans, geography, climate, and biology, and it is also a fragile area of
the ecological environment.

ISPRS Int. J. Geo-Inf. 2022,11, 600 3 of 18
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 3 of 18
to subtropical mixed broadleaf evergreen and deciduous forests in the south, with both
northern and southern Chinese plant species. The study area is an interactive zone be-
tween China’s humans, geography, climate, and biology, and it is also a fragile area of the
ecological environment.
Figure 1. Geographical location map of the Qinling-Daba Mountains. (a) The location of the Qinling-
Daba Mountains in China (b) The location and elevation of 120 counties and the corresponding six
provinces.
2.2. Data Source and Preprocessing
Given the advantages of Google Earth Engine (GEE), such as free, easy data access
and fast and batch processing of a huge number of images, this study obtained the nor-
malized difference vegetation index (NDVI) through the rapid calculation of GEE. The
data were obtained from the Landsat dataset provided by the Google Earth Engine (GEE)
platform with a spatial resolution of 30 m. Image pre-processing needs to use GEE plat-
form coding to search all the images in the study area in 2000, 2010 and 2020, and then
project the data to the “WGS 84/UTM zone 48 N” coordinate system and normalize the
data. Finally, the annual data are obtained by using the maximum synthesis method. The
LULC data were derived from GlobeLand30 (http://www.globallandcover.com accessed
on 9 May 2021), with a spatial resolution of 30 m. In this study, we merged woodlands
and shrublands into forests and divided the land-use types in the Qinling-Daba Moun-
tains into eight categories: farmland, forest, grassland, wetland, water body, impervious
surface, glacier and snow, and bare land. Digital elevation model (DEM) data with 30 m
spatial resolution come from ASTER GDEM (https://yceo.yale.edu/aster-gdem-global-el-
evation-data accessed on 15 May 2021).
The statistics of gross regional domestic product (GRDP) and population size (POP)
of the research area for 2000, 2010, and 2020 are acquired from “Shaanxi Statistical Year-
book (2000–2021)”, “Gansu Statistical Yearbook (2000–2021)”, “Sichuan Statistical Year-
book (2000–2021)”, “Henan Statistical Yearbook (2000–2021)”, “Chongqing Mongolia Sta-
tistical Yearbook (2000–2021)”, “Hubei Statistical Yearbook (2000–2021)” (National Bu-
reau of Statistics, 2000–2021), the official websites of local governments, and other statis-
tical outlets, with the county as a basic statistical unit. The proportion of built-up land
(PBL) and the proportion of forest land (PFL) are calculated from LULC. Given the differ-
ences in the dimensions and magnitudes of the selected indicators, the data needed to be
standardized before analysis using the following equations:
For positive indicators:
Figure 1.
Geographical location map of the Qinling-Daba Mountains. (
a
) The location of the Qinling-
Daba Mountains in China (
b
) The location and elevation of 120 counties and the corresponding
six provinces.
2.2. Data Source and Preprocessing
Given the advantages of Google Earth Engine (GEE), such as free, easy data access and
fast and batch processing of a huge number of images, this study obtained the normalized
difference vegetation index (NDVI) through the rapid calculation of GEE. The data were
obtained from the Landsat dataset provided by the Google Earth Engine (GEE) platform
with a spatial resolution of 30 m. Image pre-processing needs to use GEE platform coding
to search all the images in the study area in 2000, 2010 and 2020, and then project the data
to the “WGS 84/UTM zone 48 N” coordinate system and normalize the data. Finally, the
annual data are obtained by using the maximum synthesis method. The LULC data were
derived from GlobeLand30 (http://www.globallandcover.com accessed on 9 May 2021),
with a spatial resolution of 30 m. In this study, we merged woodlands and shrublands into
forests and divided the land-use types in the Qinling-Daba Mountains into eight categories:
farmland, forest, grassland, wetland, water body, impervious surface, glacier and snow,
and bare land. Digital elevation model (DEM) data with 30 m spatial resolution come
from ASTER GDEM (https://yceo.yale.edu/aster-gdem-global-elevation-data accessed on
15 May 2021).
The statistics of gross regional domestic product (GRDP) and population size (POP) of
the research area for 2000, 2010, and 2020 are acquired from “Shaanxi Statistical Yearbook
(2000–2021)”, “Gansu Statistical Yearbook (2000–2021)”, “Sichuan Statistical Yearbook
(2000–2021)”, “Henan Statistical Yearbook (2000–2021)”, “Chongqing Mongolia Statistical
Yearbook (2000–2021)”, “Hubei Statistical Yearbook (2000–2021)” (National Bureau of
Statistics, 2000–2021), the official websites of local governments, and other statistical outlets,
with the county as a basic statistical unit. The proportion of built-up land (PBL) and the
proportion of forest land (PFL) are calculated from LULC. Given the differences in the
dimensions and magnitudes of the selected indicators, the data needed to be standardized
before analysis using the following equations:
For positive indicators:
Xij =xi j −min(xj)
max(xj)−min(xj)(1)
For negative indicators:
Xij =max(xj)−xi j
max(xj)−min(xj)(2)

ISPRS Int. J. Geo-Inf. 2022,11, 600 4 of 18
where
Xij
is the normalized value of indicator jin year i,
xij
represents the value of indicator
jin year i;
max(xj)
and
min(xj)
, respectively, denote the maximum and minimum value of
indicator jin all years. All the index values are in the range of [0, 1].
2.3. Methodology
This study assessed the ecosystem health level of the Qinling-Daba Mountains at the
county scale. The vigor–organization–resilience model and Moran’s I index were used to
comprehensively evaluate the ecosystem health level and its spatial pattern of the study
area, and the GWR model was used to analyze the impact of different factors on the
ecosystem health. The specific research process is shown in Figure 2.
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 4 of 18
𝑋

=
𝑥

−
𝑚𝑖𝑛
(
𝑥

)
𝑚𝑎𝑥
(
𝑥

)
−
𝑚𝑖𝑛
(
𝑥

)
(1
)
For negative indicators:
𝑋

=
𝑚𝑎𝑥
(
𝑥

)
−
𝑥

𝑚𝑎𝑥
(
𝑥

)
−
𝑚𝑖𝑛
(
𝑥

)
(2
)
where 𝑋 is the normalized value of indicator j in year i, 𝑥 represents the value of in-
dicator j in year i; 𝑚𝑎𝑥(𝑥) and 𝑚𝑖𝑛(𝑥), respectively, denote the maximum and mini-
mum value of indicator j in all years. All the index values are in the range of [0, 1].
2.3. Methodology
This study assessed the ecosystem health level of the Qinling-Daba Mountains at the
county scale. The vigor–organization–resilience model and Moran’s I index were used to
comprehensively evaluate the ecosystem health level and its spatial pattern of the study
area, and the GWR model was used to analyze the impact of different factors on the eco-
system health. The specific research process is shown in Figure 2.
Figure 2. Overall framework of the research.
2.3.1. Vigor–Organization–Resilience Model
Ecosystem health depends on three traditional indicators: ecosystem vigor, organi-
zation, and resilience [34]. The ecosystem health index is assessed based on the VOR
model. Ecosystem vigor refers to the metabolic capacity or primary productivity of the
ecosystem. NDVI is widely proven to be an effective indicator for evaluating vegetation
primary productivity [35,36]. NDVI is calculated using Landsat satellite images of a dif-
ferent time and multispectral bands [37–39].
Ecosystem organization (O) refers to the structural stability of an ecosystem and is
related to spatial patterns. The landscape pattern index reflects landscape heterogeneity
and landscape connectivity to measure spatial patterns. Landscape heterogeneity mainly
studies spatial heterogeneity, measured by landscape diversity and landscape fractal di-
mension, corresponding to Shannon’s diversity index and area-weighted mean patch frac-
tal dimension index in this paper, respectively. Connectivity was measured by fragmen-
tation and patch cohesion. Landscape connectivity is quantified by the landscape frag-
mentation index and landscape contagion index. As the dominant land-use type in moun-
tainous areas, forest connectivity is determined by the fragmentation index and patch co-
hesion index of forest. It is calculated as follows:
Figure 2. Overall framework of the research.
2.3.1. Vigor–Organization–Resilience Model
Ecosystem health depends on three traditional indicators: ecosystem vigor, organiza-
tion, and resilience [
34
]. The ecosystem health index is assessed based on the VOR model.
Ecosystem vigor refers to the metabolic capacity or primary productivity of the ecosystem.
NDVI is widely proven to be an effective indicator for evaluating vegetation primary
productivity [
35
,
36
]. NDVI is calculated using Landsat satellite images of a different time
and multispectral bands [37–39].
Ecosystem organization (O) refers to the structural stability of an ecosystem and is
related to spatial patterns. The landscape pattern index reflects landscape heterogeneity
and landscape connectivity to measure spatial patterns. Landscape heterogeneity mainly
studies spatial heterogeneity, measured by landscape diversity and landscape fractal dimen-
sion, corresponding to Shannon’s diversity index and area-weighted mean patch fractal
dimension index in this paper, respectively. Connectivity was measured by fragmentation
and patch cohesion. Landscape connectivity is quantified by the landscape fragmentation
index and landscape contagion index. As the dominant land-use type in mountainous
areas, forest connectivity is determined by the fragmentation index and patch cohesion
index of forest. It is calculated as follows:
O=0.25 ×SH D I +0.1 ×AMFR AC +0.25 ×F N1+0.1 ×CONTAG +0.2 ×FN2+0.1 ×COHESIO N (3)
where O represents the ecosystem organization of spatial entities, SHDI is Shannon’s
diversity index, AMFRAC is area-weighted mean patch fractal dimension index, FN1 is the
index of landscape fragmentation, CONTAG is the landscape contagion index, FN2 is the
landscape fragmentation index of forest land, and COHESION is the index of the patch
cohesion of forest land. These landscape indexes were obtained by Fragstats 4.2.

ISPRS Int. J. Geo-Inf. 2022,11, 600 5 of 18
The ecosystem elasticity coefficient score (EC) of each LULC type in the Qinling-Daba
Mountains developed as a weighted combination of resilience and resistance, given as [
40
]:
EC =0.7 ×resil +0.3 ×resist (4)
In Table 1, The ecosystem elasticity coefficient (EC) of each land-use type in the study
area is equal to 30% of the resilience coefficient (
resil
) plus 70% of the resistance coefficient
(
resist
). The coefficients of resilience and resistance are determined according to the relevant
papers [
15
,
16
] and the actual situation of the research area. Coefficients are assigned for
the eight land-use types, with coefficients ranging from 0 to 1. Resilience refers to the
ability of the ecosystem to return to its original state after being damaged by external
disturbance factors, while resistance refers to the ability of an ecosystem to resist external
disturbances and keep its own structure and function intact. Resistance and resilience
are related, and typically, ecosystems with high resistance have low resilience. Compared
with artificial ecosystems, natural ecosystems have stronger resistance and recovery ability.
The resistance and resilience of human-dominated ecosystems are weak, and it is difficult
to resist external disturbances and restore them to an original state, so the resilience and
resistance coefficients of impervious surfaces and farmland are small. Water bodies and
wetland ecosystems are more self-regulating and have a higher resistance and resilience
capacity with larger coefficients. Bare land ecosystems are simple, and it is difficult to
resist external disturbances, but they are easy to recover. Forests with complex species,
strong biological chains, and tight ecosystems are resilient to external disturbances, while
restoration to complex ecosystem structure and function takes more time. The ecosystem
resilience (R) is defined as follows:
R=∑m
k=1Pk×ECk(5)
where R is the ecosystem resilience of spatial entities,
Pk
is the proportion of the area of the
land-use type k,
ECk
is the ecosystem elasticity coefficient of land-use type k, and m is the
number of the land-use types.
Table 1. Ecosystem elasticity coefficient of land-use types in the Qinling-Daba Mountains.
LULC type Resilience Resistance EC
Farmland 0.30 0.60 0.51
Forest 0.50 1.00 0.85
Grassland 0.80 0.70 0.73
Impervious surface 0.20 0.30 0.27
Water body 0.70 0.90 0.84
Wetland 0.60 0.80 0.74
Bare land 1.00 0.20 0.44
Glacier and snow 0.10 0.10 0.10
Since each indicator can be quantified by different factors, the value of each indicator
needs to be normalized. To avoid magnification when multiplying the indicators during
calculation, it is necessary to use a root sign to neutralize the order of magnitude:
EH =3
√V×O×R(6)
where
EH
is the regional ecosystem health index of spatial entities,
V
is the regional ecosys-
tem vigor,
O
is the regional ecosystem organization,
R
is the regional ecosystem resilience.

ISPRS Int. J. Geo-Inf. 2022,11, 600 6 of 18
2.3.2. Moran’s I Index
Affected by spatial interaction and spatial diffusion, there is a spatial correlation
between any entities, but the nearby things are more related to each other [
41
]. The global
Moran’s I index is used to measuring the degree of dependence between spatial entities in
the whole region, while the local indicator of spatial association (LISA) is used to detect the
extent and location of outliers or agglomerations.
I=n
∑n
i=1∑n
j=1wij ×∑n
i=1∑n
j=1wij (yi−y)yj−y
∑n
i=1(yi−y)2(7)
Ii=yi−y,
1
n∑(yi−y)2
n
∑
j6=1
wij yj−y(8)
Z=I−E(I)
pVar(I)(9)
E(I) = −1
n−1(10)
Var(I) = EI2−E(I)2(11)
where Iis the global Moran’s I value; n is the number of spatial entities;
yi
and
yj
represent
the attribute values of the ith and jth space entities, respectively;
y
is the mean value of all
spatial entities attribute values;
wij
is weight between spatial entities i and j;
Ii
represents
the LISA value of the ith spatial entity;
Z
is the threshold for normalized statistics;
E(I)
is
expected autocorrelation; and Var(I)is variance.
2.3.3. Selection of Driving Factors
Ecosystem health is influenced by a combination of natural and anthropogenic factors,
and seven drivers were selected based on previous studies [
42
,
43
]. Natural factors include
annual mean precipitation (AMP), annual mean temperature (AMT), elevation (expressed
in DEM), and proportion of forest (PFL), which are related to climate, topography, and
vegetation and directly determine the external conditions that affect the ecosystem.
Anthropogenic factors mainly include human-led socioeconomic activities and their
altered land use, which indirectly change the corresponding ecological structure and affect
ecosystem health. Three main indicators are included: population size (POP), gross regional
domestic product (GRDP), and proportion of built-up land (PBL). POP can reflect the
intensity of human activities, GRDP can evaluate regional economic development, and PBL
can intuitively reflect the expansion of urban space. However, when the seven dependent
variables were tested for multicollinearity, it was found that the variance inflation factor
(VIF) of AMP and AMT was greater than 10, and there was strong multicollinearity with
other variables, so these two variables were excluded. In order to eliminate the influence of
units and dimensions, five independent variables were standardized.
2.3.4. Geographically Weighted Regression Model
GWR is widely used in geography and related disciplines involving spatial pattern
analysis [
44
,
45
] and can be used to quantitatively reflect spatial heterogeneity, as well as
to explore geographic variation between response and explanatory variables. The model
structure is as follows:
yi=β0(ui,vi)+
p
∑
l=1
βl(ui,vi)xil +εi(12)

ISPRS Int. J. Geo-Inf. 2022,11, 600 7 of 18
where yi is the fitted value of the ecosystem health for spatial entity i,
β0(ui,vi)
is the
coordinates of spatial entity i,
βl(ui,vi)xil
is the lth regression parameter of spatial entity i,
εiis the error correction term, and pis the number of explanatory variables.
ˆ
βk(ui,vi)=hXTW(ui,vi)Xi−1XTW(ui,vi)Y(13)
where
W(ui,vi)
is an n
×
n matrix whose diagonal Wij denotes the weight of the influence
weight of the jth entity on the ith entity, while the non-diagonal elements are all 0. Wij is
generally obtained through the kernel function based on the distance, and the Gaussian
kernel function is used in this study. The dependent variable in this study is ecosystem
health index, and the independent variable is the corresponding natural and anthropogenic
factors. R square (R2), adjusted R2, and Akaike information criterion (AICc) are used to
measure the performance of the model. In general, the higher the R2 and adjusted R2
values, and the lower the AICc values, the better the performance of the model.
3. Results
3.1. Spatiotemporal Differentiation of Land Use/Land Cover in the Qinling-Daba Mountains
Significant land-use changes have taken place in the study area (Figure A1). In the
Qinling-Daba Mountains, the forest is dominant, accounting for more than 60% of the total
area. Forest has increased by 1.9553% (6024 km
2
) since 2000 (Table 2), which is the land-use
type with the most growth. The impervious surface has also increased significantly, an
increase of 1.0566%, which is mainly due to the conversion of farmland to impervious
surface. The water area of Danjiangkou has significantly expanded, which is the main
source of the increase in the proportion of water body. The grassland area shrank the
most, with a decrease of 2.8928% (8913 km
2
), especially in the research area in Gansu
Province, which was mainly converted into farmland and forest. In 2000 (Figure A1a),
28.6544% (7266 km
2
) of the total area was used for agricultural activities, which dropped to
approximately 28% in 2020 (Figure A1c), mainly due to the occupation of agricultural land
by construction land. However, only slight changes have taken place in wetlands, glacier
and snow, and bare land.
Table 2.
Percentage of LULC changes in the Qinling-Daba Mountains in 2000, 2010, and 2020 (Unit: km
2
).
LULC 2000 2010 2020 Percentage Change
Farmland 28.6544 28.5291 28.0391 −0.6153
Forest 62.3893 64.7448 64.3446 1.9553
Grassland 7.2783 4.5099 4.3855 −2.8928
Wetland 0.0677 0.0764 0.0945 0.0268
Water body 0.6182 0.7990 1.0791 0.4609
Impervious surface 0.9920 1.3407 2.0486 1.0566
Glacier and snow 0.0001 0.0001 0.0003 0.0002
Bare land 0 0 0.0083 0.0083
3.2. Spatiotemporal Changes in Ecosystem Health in the Qinling-Daba Mountains
In this study, the five levels of weak, slightly weak, ordinary, slightly well, and well
were classified in equal intervals according to the ascending order of the values. The
most pronounced changes were in ecosystem vigor levels and the changes in the level of
ecological resilience were the least (Figure 3).

ISPRS Int. J. Geo-Inf. 2022,11, 600 8 of 18
There was an overall upward trend in ecosystem vigor. In 2000, approximately 70% (83)
of the counties did not reach a relatively well level, while only one county (Langzhong)
was at a relatively weak level in 2020. Among them, Weiyuan and Xichuan crossed two
levels from weak to ordinary ecological vigor level in 2020. Compared with ecosystem
vigor and ecosystem resilience, the overall level of ecosystem organization in the Qinling-
Daba Mountains is slightly lower. The part with weak ecological organization is mainly
concentrated within the central Shaanxi and Hubei provinces, with significant regional
differences. From 2000 to 2020, the ecological resilience has changed slightly, and more
than 70% of the districts and counties have reached a slightly better or above level, but
the ecological resilience of Yexian, Wolong, Fancheng, and Xiangzhou has decreased from
weak to weak. The change in ecosystem resilience is relatively insignificant. Over 70%
of the counties reached slightly well and above levels, but Yexian, Wolong, Fancheng,
and Xiangzhou showed a decrease in ecosystem resilience, changing from slightly weak
to weak.
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 8 of 18
two levels from weak to ordinary ecological vigor level in 2020. Compared with ecosystem
vigor and ecosystem resilience, the overall level of ecosystem organization in the Qinling-
Daba Mountains is slightly lower. The part with weak ecological organization is mainly
concentrated within the central Shaanxi and Hubei provinces, with significant regional
differences. From 2000 to 2020, the ecological resilience has changed slightly, and more
than 70% of the districts and counties have reached a slightly better or above level, but the
ecological resilience of Yexian, Wolong, Fancheng, and Xiangzhou has decreased from
weak to weak. The change in ecosystem resilience is relatively insignificant. Over 70% of
the counties reached slightly well and above levels, but Yexian, Wolong, Fancheng, and
Xiangzhou showed a decrease in ecosystem resilience, changing from slightly weak to
weak.
Figure 3. Changes in ecosystem indicators for each county in the Qinling-Daba Mountains from
2000 to 2020. (a–c) Ecosystem vigor for each county (d–f) Ecosystem organization for each county
(g–i) Ecosystem resilience for each county.
The changes in ecosystem health in the central and western parts of the study area
are significantly greater than in the eastern parts, and the ecosystems in the central and
western parts are healthier (Figure 4). From 2000 to 2020, the overall ecosystem health of
the research area has improved year by year, and by 2020, 62% (72) of counties had EH
values greater than 0.6. The value of ecosystem health in Fancheng and Wolong in the
eastern part of the study area showed a significant decline, with Wolong having the lowest
level (0.27) among all counties in 2020 (Figure 4c). The ecosystem health value of Xichuan
has increased the most, with an increase of 0.18 over 20 years. In 2020, ecosystems in Shi-
quan, Lushi, Yunyang_HB, and Hanbin are relatively healthier, all reaching 0.7. The eco-
system health at the county level shows a spatial pattern of patchy distribution. The coun-
ties with values of 0.5–0.6 in 2000 (Figure 4a) and 2010 (Figure 4b) and 0.6–0.7 in 2010 and
2020 show a clear spatial dependence.
Figure 3.
Changes in ecosystem indicators for each county in the Qinling-Daba Mountains from
2000 to 2020. (
a
–
c
) Ecosystem vigor for each county (
d
–
f
) Ecosystem organization for each county
(g–i) Ecosystem resilience for each county.
The changes in ecosystem health in the central and western parts of the study area
are significantly greater than in the eastern parts, and the ecosystems in the central and
western parts are healthier (Figure 4). From 2000 to 2020, the overall ecosystem health of the
research area has improved year by year, and by 2020, 62% (72) of counties had EH values
greater than 0.6. The value of ecosystem health in Fancheng and Wolong in the eastern
part of the study area showed a significant decline, with Wolong having the lowest level
(0.27) among all counties in 2020 (Figure 4c). The ecosystem health value of Xichuan has
increased the most, with an increase of 0.18 over 20 years. In 2020, ecosystems in Shiquan,
Lushi, Yunyang_HB, and Hanbin are relatively healthier, all reaching 0.7. The ecosystem
health at the county level shows a spatial pattern of patchy distribution. The counties with
values of 0.5–0.6 in 2000 (Figure 4a) and 2010 (Figure 4b) and 0.6–0.7 in 2010 and 2020 show
a clear spatial dependence.

ISPRS Int. J. Geo-Inf. 2022,11, 600 9 of 18
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 9 of 18
Figure 4. Spatial distribution of comprehensive ecosystem health index at the county level in the
Qinling-Daba Mountains from 2000 to 2020.
3.3. Spatial Dependency of Ecosystem Health
This study uses the global Moran’s I index to quantitatively measure the spatial au-
tocorrelation of ecosystem health in the entire region. Using GeoDa software, weights
based on Rook contiguity were obtained, and the results of 999 random inspections are
shown in Appendix B. Rook contiguity refers to when only the common edges of polygons
are considered to define the adjacency relationship (common vertices are ignored). The
global Moran’s I values for 2000, 2010, and 2020 all ranged from 0.38 to 0.42 (Table A1),
which were positive and passed the significance test at the 1% level. This indicates that
the ecosystem health level of the Qinling-Daba Mountains has significant spatial autocor-
relation throughout the region, that is, the level of ecosystem health in each county is more
positively influenced by the neighboring counties. In terms of temporal dynamics, the
global Moran’s I index showed an inverted V-shaped increase. The value increased from
2000 to 2010, indicating that the spatial dependence of ecosystem health has increased,
and the ecological pattern tends to be aggregated. From 2010 to 2020, the value slightly
decreased, implying that the spatial aggregation of ecosystem health in the study area
weakened and the ecological layout showed a trend of diffusion, reflecting the efficacy of
promoting the regional synergistic development strategy.
To further explore the local spatial autocorrelation, this paper uses the local indica-
tors of spatial association (LISA) to analyze the spatial aggregation that exists in counties
(Figure 5). The High–High type is the main agglomeration type within the Qinling-Daba
Mountains and is mainly distributed in the eastern part, indicating that the eastern part
gathers counties with high levels of ecosystem health. The High–High type counties in
2000 (Figure 5a) and 2020 (Figure 5c) were distributed in a block-like pattern near the
Qinling Mountains, while in 2010 (Figure 5b) they showed a strip-like distribution along
the Qinling Mountains, indicating the increased radiation capacity of the eastern region
during this period. The Low–Low type counties are mainly distributed in the southwest-
ern and eastern edges of the study area, with small spatial extent variation, indicating that
the improvement of counties with poor health is not significant. Low–High and High–
Low type counties are extremely rare in the study area, existing between the High–High
and Low–Low catchment areas.
Figure 4.
Spatial distribution of comprehensive ecosystem health index at the county level in the
Qinling-Daba Mountains from 2000 to 2020.
3.3. Spatial Dependency of Ecosystem Health
This study uses the global Moran’s I index to quantitatively measure the spatial au-
tocorrelation of ecosystem health in the entire region. Using GeoDa software, weights
based on Rook contiguity were obtained, and the results of 999 random inspections are
shown in Appendix B. Rook contiguity refers to when only the common edges of polygons
are considered to define the adjacency relationship (common vertices are ignored). The
global Moran’s I values for 2000, 2010, and 2020 all ranged from 0.38 to 0.42 (Table A1),
which were positive and passed the significance test at the 1% level. This indicates that the
ecosystem health level of the Qinling-Daba Mountains has significant spatial autocorrela-
tion throughout the region, that is, the level of ecosystem health in each county is more
positively influenced by the neighboring counties. In terms of temporal dynamics, the
global Moran’s I index showed an inverted V-shaped increase. The value increased from
2000 to 2010, indicating that the spatial dependence of ecosystem health has increased,
and the ecological pattern tends to be aggregated. From 2010 to 2020, the value slightly
decreased, implying that the spatial aggregation of ecosystem health in the study area
weakened and the ecological layout showed a trend of diffusion, reflecting the efficacy of
promoting the regional synergistic development strategy.
To further explore the local spatial autocorrelation, this paper uses the local indicators
of spatial association (LISA) to analyze the spatial aggregation that exists in counties
(Figure 5). The High–High type is the main agglomeration type within the Qinling-Daba
Mountains and is mainly distributed in the eastern part, indicating that the eastern part
gathers counties with high levels of ecosystem health. The High–High type counties in
2000 (Figure 5a) and 2020 (Figure 5c) were distributed in a block-like pattern near the
Qinling Mountains, while in 2010 (Figure 5b) they showed a strip-like distribution along
the Qinling Mountains, indicating the increased radiation capacity of the eastern region
during this period. The Low–Low type counties are mainly distributed in the southwestern
and eastern edges of the study area, with small spatial extent variation, indicating that the
improvement of counties with poor health is not significant. Low–High and High–Low
type counties are extremely rare in the study area, existing between the High–High and
Low–Low catchment areas.

ISPRS Int. J. Geo-Inf. 2022,11, 600 10 of 18
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 10 of 18
Figure 5. The spatial agglomeration of ecosystem health based on Rook contiguity from 2000 to 2020.
3.4. Analysis of Driving Factors for Ecosystem Health
GWR not only reveals the effects of the five explanatory variables on ecosystem
health but also exhibits significant spatial heterogeneity (Figure 6). The regression coeffi-
cients of the five explanatory variables for each county were obtained through GWR, and
the results showed that the five explanatory variables had both positive and negative ef-
fects on ecosystem health. GRDP had a significant inhibitory effect on the eastern part of
the Qinling-Daba Mountains, shifting from negative in the northeast to positive in the
southwest. In 2000, the impact of GRDP on ecosystem health dominated, especially in
Shaanxi and Hubei provinces in the east-central region. However, the negative impact of
GRDP diminished over time, and by 2020, the positive and negative effects were similar
in the study area. The positive impact of POP appears to decrease from the east to the two
sides, and the southwest and northeast of research area have negative effects. It is note-
worthy that the positive impact of the central region keeps spreading to the northwest.
The negative effect of PBL on EH gradually spreads to the southwest, with a positive effect
on only five counties by 2020. The growth of anthropogenic factors (GRDP and PBL) sug-
gests that urbanization is accelerating, but for them, both have varying degrees of inhibi-
tion on ecosystem health.
From 2000 to 2020, PFL promoted ecosystem health in the majority of the Qinling-
Daba Mountains, while DEM had an inhibitory effect on a wide range of research areas.
The positive effect of PFL gradually decreases from southwest to northeast, with a nega-
tive effect especially in some counties within Shaanxi Province. The high values of PFL
coefficients spread to the northeast, indicating that PFL has an increasingly strong pro-
moting effect on EH. At the same time, the negative effect of PFL on some central regions
is also increasing. The high value of the PFL coefficient spread to the northeast, indicating
that PFL has become more and more effective in promoting ecosystem health. At the same
time, the negative effect of PFL on central regions is also increasing. The positive effect of
DEM on ecosystem health is mainly concentrated in the northwest and northeast of the
study area and has a negative impact on the rest of the area. The inhibitory effect of DEM
on the study area is increasing with time, especially for the southeastern part.
Overall, anthropogenic factors (GRDP and PBL) and the natural factor (DEM) had a
strong negative impact on ecosystem health in the Qinling-Daba Mountains, while the
natural factor (PFL) had a more positive effect.
Figure 5.
The spatial agglomeration of ecosystem health based on Rook contiguity from 2000 to 2020.
3.4. Analysis of Driving Factors for Ecosystem Health
GWR not only reveals the effects of the five explanatory variables on ecosystem health
but also exhibits significant spatial heterogeneity (Figure 6). The regression coefficients
of the five explanatory variables for each county were obtained through GWR, and the
results showed that the five explanatory variables had both positive and negative effects
on ecosystem health. GRDP had a significant inhibitory effect on the eastern part of
the Qinling-Daba Mountains, shifting from negative in the northeast to positive in the
southwest. In 2000, the impact of GRDP on ecosystem health dominated, especially in
Shaanxi and Hubei provinces in the east-central region. However, the negative impact of
GRDP diminished over time, and by 2020, the positive and negative effects were similar
in the study area. The positive impact of POP appears to decrease from the east to the
two sides, and the southwest and northeast of research area have negative effects. It is
noteworthy that the positive impact of the central region keeps spreading to the northwest.
The negative effect of PBL on EH gradually spreads to the southwest, with a positive
effect on only five counties by 2020. The growth of anthropogenic factors (GRDP and
PBL) suggests that urbanization is accelerating, but for them, both have varying degrees of
inhibition on ecosystem health.
From 2000 to 2020, PFL promoted ecosystem health in the majority of the Qinling-Daba
Mountains, while DEM had an inhibitory effect on a wide range of research areas. The
positive effect of PFL gradually decreases from southwest to northeast, with a negative effect
especially in some counties within Shaanxi Province. The high values of PFL coefficients
spread to the northeast, indicating that PFL has an increasingly strong promoting effect on
EH. At the same time, the negative effect of PFL on some central regions is also increasing.
The high value of the PFL coefficient spread to the northeast, indicating that PFL has
become more and more effective in promoting ecosystem health. At the same time, the
negative effect of PFL on central regions is also increasing. The positive effect of DEM on
ecosystem health is mainly concentrated in the northwest and northeast of the study area
and has a negative impact on the rest of the area. The inhibitory effect of DEM on the study
area is increasing with time, especially for the southeastern part.
Overall, anthropogenic factors (GRDP and PBL) and the natural factor (DEM) had
a strong negative impact on ecosystem health in the Qinling-Daba Mountains, while the
natural factor (PFL) had a more positive effect.

ISPRS Int. J. Geo-Inf. 2022,11, 600 11 of 18
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 11 of 18
Figure 6. Spatial distribution patterns of correlation coefficients between ecosystem health and driv-
ing factors from 2000 to 2020. (a–c) GRDP: Gross regional domestic product. (d–f) POP: Population
size. (g–i) PBL: Proportion of built-up land. (j–l) PFL: Proportion of forest land. (m–o) DEM: Digital
elevation model, reflecting elevation.
4. Discussion
4.1. Methodological Advantages of GWR
Goodness of fit tests for comparing the ordinary least square (OLS) model and GWR
model are evaluated by using AICc, R2, and adjusted R2 (Table 3). Compared with the
Figure 6.
Spatial distribution patterns of correlation coefficients between ecosystem health and driv-
ing factors from 2000 to 2020. (
a
–
c
) GRDP: Gross regional domestic product. (
d
–
f
) POP: Population
size. (
g
–
i
) PBL: Proportion of built-up land. (
j
–
l
) PFL: Proportion of forest land. (
m
–
o
) DEM: Digital
elevation model, reflecting elevation.

ISPRS Int. J. Geo-Inf. 2022,11, 600 12 of 18
4. Discussion
4.1. Methodological Advantages of GWR
Goodness of fit tests for comparing the ordinary least square (OLS) model and GWR
model are evaluated by using AICc, R2, and adjusted R2 (Table 3). Compared with the
results of the OLS model, the R2 and adjusted R2 of the GWR model are larger than those
of the OLS model in 2000, 2010, and 2020, indicating that the GWR better explains the
spatial relationship between factors and ecosystem health. Moreover, the GWR model has
a smaller AICc than the OLS model, which indicates that the GWR model is better than the
OLS model. The adjusted R2 values were all above 0.58%, and the R2 values increased as
the years went by, indicating that the GWR model could be used to analyze the effects of
each variable on ecosystem health.
Table 3. Statistical results of GWR and OLS in 2000, 2010, and 2020 in the Qinling-Daba Mountains.
R2Adjusted R2AICc
OLS GWR OLS GWR OLS GWR
2000 0.3883 0.6901 0.3549 0.5802 −310.9931 −340.4735
2010 0.4742 0.7524 0.4455 0.6593 −302.9470 −337.7385
2020 0.4089 0.7830 0.3766 0.6951 −288.1240 −346.3527
The spatial variation of the local R2 of the GWR model from 2000 to 2020, reflecting
the spatial variation of the county-level fit (Figure A2). Provinces such as Henan, Hubei,
and Sichuan located in the east and southwest have a higher local R2, reaching above 0.7,
indicating that the local linear regression model performs well in this region. However, in
Gansu and Shaanxi, which are located in the north, the local R2 is relatively low. Especially
in 2000 (Figure A2a), there were 32 counties with a local R2 of less than 0.5, while Gansu
and Shaanxi provinces occupied 31 counties. In general, the local R2 of the GWR model
has improved, and the overall fitting effect tends to be good, which can explain the spatial
correlation between ecological health and GRDP, POP, PFL, PBL, and DEM.
4.2. Comparison with Previous Studies
The trends in ecosystem health are the result of a combination of natural and anthro-
pogenic factors, and different drivers play different roles at different times of ecosystem
evolution [
46
]. Anthropogenic factors are more active and dramatic at specific temporal
and spatial scales, especially with the increasing level of urbanization [47].
In terms of anthropogenic factors, the transformation of urban land is the main driver
of ecosystem health deterioration, which works in conjunction with rapid urbanization
to cause the degradation of ecosystem health [
14
]. However, this study shows that the
drivers are not absolutely positive or negative influences, and they have significant regional
differences. For example, the negative effect of PBL gradually increases and expands
westward, and eventually, its positive effect only exists in the five southern counties. Built-
up land is most affected by anthropogenic activities, and the expansion of built-up land
leads to a reduction in ecosystem services and ultimately damages ecosystem health [
48
].
Similar studies have shown that the expansion of urban space can lead to the degradation
of ecosystem health [
49
,
50
]. GRDP has a dual perturbative effect on ecosystems, manifested
as a positive contribution through the implementation of ecological conservation projects
and a negative inhibition by rapid urbanization [
51
]. In the western and eastern parts of
the study area, close to urban areas, POP has a negative effect on ecosystem health, which
is due to the concentration of population in areas close to cities and more interference from
human activities in areas close to cities. In contrast, the central part of the study area is far
away from the city, and appropriate human activities are beneficial to ecosystem health.

ISPRS Int. J. Geo-Inf. 2022,11, 600 13 of 18
In terms of natural factors, topography is the basic physical element that affects
human life patterns and landscape patterns [
50
]. Ecosystems at different altitudes have
different ecosystem structures [
52
]. In the Qinling-Daba Mountains, altitude has a negative
effect on ecosystem health in most areas. In mountainous ecosystems, where human
activities are relatively low and vegetation cover is extensive, ecosystem health degradation
is relatively low [
53
]. Although woodlands are less disturbed by humans, it cannot be
ignored that the reduction of vegetation cover also has a negative impact on ecosystem
health. The significant and positive effect of PFL on ecosystem health is apparent in
most areas, but its negative impact is gradually deepening and spreading. The high
vegetation cover in mountainous ecosystems and its effect on ecosystem improvement
decreases when vegetation cover reaches a certain level [
54
]. Similar studies have also found
that meteorological factors such as AMP and AMT have significant effects on ecosystem
health in regional ecosystem evolution [
55
,
56
]. However, meteorological factors were
not considered in this study because they failed the multiple covariance tests and were,
therefore, not considered as influencing factors, probably due to differences in study areas
and time periods.
4.3. Policy Implications
The analysis of county-level ecosystem health and its drivers contribute to the de-
velopment of ecological health at small scales from a scientific perspective and provide a
scientific basis for policymakers to develop relevant policies [
57
,
58
]. Based on the findings,
we identified regional differences in ecosystem health and its key drivers, so ecological
conservation measures taken in a specific area should be adjusted according to the ecosys-
tem health level and its drivers. (1) For some regions on the eastern and southwestern
fringes, the level of ecological health is poor, and anthropogenic factors have a negative
impact. The level of spatial expansion in these areas exceeds the local ecological carrying
level, and the population should be appropriately evacuated, the amount of construction
land exploitation should be reduced, green industries should be encouraged, and the
ecological environment should be restored. (2) For areas at moderate ecosystem health
levels, mainly attributed to low ecosystem organization and human factors (GRDP, PBL),
sustainable development needs to be maintained to balance economic growth and ecologi-
cal conservation. For example, in some marginal counties in the northwest and southeast,
it is necessary to optimize the land-use structure and avoid unreasonable reclamation and
construction to increase ecosystem organization. At the same time, it is necessary to control
the scale of urban and rural areas, implement more ecological protection and restoration
projects, focus on the layout of green low-impact industries, and carry out green industrial
development within the environmental carrying capacity. (3) For most areas with a high
level of ecosystem health in the western and central regions, high forest coverage has a
promoting effect. These counties have high ecological value and high ecological sensitivity
and need to implement strict ecological protection.
The empirical results of our study suggest that ecosystem health is influenced by
both natural and anthropogenic factors, as well as by spatial context. Ecosystem health
in a county is influenced not only by elements in that county but also by elements in
neighboring counties [
59
]. Areas with healthy ecosystems may promote the improvement
of areas with deteriorating ecosystems, and conversely, areas with deteriorating ecosystems
may delay the optimization of areas with healthy ecosystems, leading to a significant spatial
correlation in ecosystem health. Therefore, the formulation and implementation of policies
on regional economic layout and territorial spatial planning cannot be limited to individual
administrative units [
60
] but require a deeper understanding of the spatial interactions
of ecosystems.

ISPRS Int. J. Geo-Inf. 2022,11, 600 14 of 18
4.4. Limitations and Future Work
The study area is dominated by mountainous hills, which can be representative of
mountainous areas in China, but there is an insufficient explanation for other topographical
features. The ecosystem is a complex system. Ecosystem vigor, ecosystem organization,
and ecosystem resilience are its three very important aspects, but there are still some other
aspects that deserve attention, such as ecosystem services, ecosystem values, etc. Compared
to others’ studies, this study analyzed the influencing factors of ecosystem health, but the
selection of anthropogenic factors was somewhat limited due to the different statistical
indicators in each county. In the future, a detailed analysis of soils and climates could
help to elucidate the deep mechanisms of change in ecosystem health and the joint effects
of changes.
Despite the limitations, we still believe this study is meaningful. While the number of
metrics is somewhat small, the quantification of data processing is by no means arbitrary.
Geographically weighted regression models can not only study the importance of drivers
and their interactions on ecosystem health but also visualize spatial heterogeneity in the
form of maps. The results can help researchers understand the spatial pattern of changes
in ecosystem health and provide a scientific basis for ecological protection and make
comments and suggestions to the relevant government agencies.
5. Conclusions
In this paper, we analyzed the dynamics of the ecosystem health of 120 districts and
counties within the Qinling-Daba Mountains in 2000, 2010, and 2020 and analyzed the
driving factors in different regions using the GWR model. From the results, we obtained
the following conclusions.
(1)
Land-use changes are obvious in the Qinling-Daba Mountains, with the largest in-
creases in forest and impervious surface and large decreases in agricultural land
and grassland. This result indicates that afforestation efforts in the study area were
effective, and the expansion of forest and urban space has taken up a large amount of
farmland and grassland.
(2)
The overall ecological health of the Qinling-Daba Mountains is on the rise, with the
best ecosystem health status emerging in Shiquan, Lushi, Yunyang_HB, and Hanbin.
However, ecosystem health deteriorated in some areas, with Fancheng and Wolong
being the most obvious.
(3)
There is a clear spatial correlation in ecosystem health. The High–High type coun-
ties with healthy ecosystems are mainly concentrated near the Qinling Mountains,
indicating the overall high level of ecological health in this region.
(4)
Anthropogenic and natural factors have a bidirectional effect on ecosystem health
in the Qinling-Daba Mountains. The positive effect of GRDP gradually shifts to the
central region, while the negative effect of PBL spreads to the west, indicating that
the urban space in the western region is expanding. PFL has a catalytic effect on the
ecosystem health in most areas, so afforestation is an effective measure to protect
the ecology.
Research on ecosystem health can help protect the ecological environment, and identi-
fying the contribution of different factors can help decision-makers to make better develop
management plans and promote the coordination between regional development and
ecological protection.
Author Contributions:
Ting Xiang: conceptualization, methodology, software, writing—original
draft and revision. Xiaoliang Meng: writing—reviewing and editing. Xinshuang Wang: resources,
data curation. Jing Xiong: resources, data curation. Zelin Xu: conceptualization, formal analysis. All
authors have read and agreed to the published version of the manuscript.

ISPRS Int. J. Geo-Inf. 2022,11, 600 15 of 18
Funding:
This research was funded by National Natural Science Foundation of China (NSFC) under
grant number 41971352. The authors are very grateful to the many people who helped to comment
on the article, and the Large Scale Environment Remote Sensing Platform (Facility No. 16000009,
16000011, 16000012) provided by Wuhan University.
Data Availability Statement: Not applicable.
Acknowledgments:
Special thanks to the editors and reviewers for providing valuable insight into
this article.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 15 of 18
Appendix A
Figure A1. Land-use patterns in the Qinling-Daba Mountains in 2000, 2010, and 2020.
Appendix B
Table A1. Global Moran’s I statistics of ecosystem health.
Year
Moran’s I
Z
P
2000 0.3787 7.0974 0.001
2010
0.4119
7.5573
0.001
2020
0.4007
7.4086
0.001
Appendix C
Figure A2. Spatial mapping of the locally weighted coefficient of determination (R2) between the
observed and fitted values is performed by GWR modeling at the county level.
Figure A1. Land-use patterns in the Qinling-Daba Mountains in 2000, 2010, and 2020.
Appendix B
Table A1. Global Moran’s I statistics of ecosystem health.
Year Moran’s I Z P
2000 0.3787 7.0974 0.001
2010 0.4119 7.5573 0.001
2020 0.4007 7.4086 0.001
Appendix C
ISPRS Int. J. Geo-Inf. 2022, 11, x FOR PEER REVIEW 15 of 18
Appendix A
Figure A1. Land-use patterns in the Qinling-Daba Mountains in 2000, 2010, and 2020.
Appendix B
Table A1. Global Moran’s I statistics of ecosystem health.
Year
Moran’s I
Z
P
2000 0.3787 7.0974 0.001
2010
0.4119
7.5573
0.001
2020
0.4007
7.4086
0.001
Appendix C
Figure A2. Spatial mapping of the locally weighted coefficient of determination (R2) between the
observed and fitted values is performed by GWR modeling at the county level.
Figure A2.
Spatial mapping of the locally weighted coefficient of determination (R2) between the
observed and fitted values is performed by GWR modeling at the county level.

ISPRS Int. J. Geo-Inf. 2022,11, 600 16 of 18
References
1.
Huang, Z.; Du, X.; Castillo, C.S.Z. How does urbanization affect farmland protection? Evidence from China. Resour. Conserv.
Recycl. 2019,145, 139–147. [CrossRef]
2.
Dadashpoor, H.; Azizi, P.; Moghadasi, M. Land use change, urbanization, and change in landscape pattern in a metropolitan area.
Sci. Total Environ. 2019,655, 707–719. [CrossRef] [PubMed]
3.
Franco, S.; Mandla, V.R.; Rao, K.R.M. Urbanization, energy consumption and emissions in the Indian context A review. Renew.
Sustain. Energy Rev. 2017,71, 898–907. [CrossRef]
4.
Wang, S.; Gao, S.; Li, S.; Feng, K. Strategizing the relation between urbanization and air pollution: Empirical evidence from global
countries. J. Clean. Prod. 2020,243, 118615. [CrossRef]
5.
Sanderson, E.W.; Walston, J.; Robinson, J.G. From bottleneck to breakthrough: Urbanization and the future of biodiversity
conservation. BioScience. 2018,68, 412. [CrossRef]
6.
Su, M.; Fath, B.D.; Yang, Z. Urban ecosystem health assessment: A review. Sci. Total Environ.
2010
,408, 2425–2434. [CrossRef]
[PubMed]
7. Rapport, D.J.; Maffi, L. Eco-cultural health, global health, and sustainability. Ecol. Res. 2011,26, 1039–1049. [CrossRef]
8.
Douglas, I. Urban ecology and urban ecosystems: Understanding the links to human health and well-being. Curr. Opin. Environ.
Sustain. 2012,4, 385–392. [CrossRef]
9.
Yu, G.; Yu, Q.; Hu, L.; Zhang, S.; Fu, T.; Zhou, X.; He, X.; Liu, Y.; Wang, S.; Jia, H. Ecosystem health assessment based on analysis
of a land use database. Appl. Geogr. 2013,44, 154–164. [CrossRef]
10.
Liu, D.; Hao, S. Ecosystem health assessment at county-scale using the pressure-state-response framework on the Loess Plateau,
China. Int. J. Environ. Res. Public Health 2017,14, 2. [CrossRef]
11.
Sun, B.; Tang, J.; Yu, D.; Song, Z.; Wang, P. Ecosystem health assessment: A PSR analysis combining AHP and FCE methods for
Jiaozhou Bay, China1. Ocean Coast Manage 2019,168, 41–50. [CrossRef]
12.
Jafary, P.; Sarab, A.A.; Tehrani, N.A. Ecosystem health assessment using a fuzzy spatial decision support system in Taleghan
watershed before and after dam construction. Environ. Process. 2018,5, 807–831. [CrossRef]
13.
Malekmohammadi, B.; Jahanishakib, F. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-
state-impact-response (DPSIR) model. Ecol. Indic. 2017,82, 293–303. [CrossRef]
14.
Peng, J.; Liu, Y.; Wu, J.; Lv, H.; Hu, X. Linking ecosystem services and landscape patterns to assess urban ecosystem health: A
case study in Shenzhen City, China. Landscape Urban Plan. 2015,143, 56–68. [CrossRef]
15.
Xiao, R.; Liu, Y.; Fei, X.; Yu, W.; Zhang, Z.; Meng, Q. Ecosystem health assessment: A comprehensive and detailed analysis of the
case study in coastal metropolitan region, eastern China. Ecol. Indic. 2019,98, 363–376. [CrossRef]
16.
Das, M.; Das, A.; Mandal, A. Research note: Ecosystem Health (EH) assessment of a rapidly urbanizing metropolitan city region
of eastern India–A study on Kolkata Metropolitan Area. Landsc. Urban Plan. 2020,204, 103938. [CrossRef]
17.
Bae, D.Y.; Kumar, H.K.; Han, J.H.; Kim, J.Y.; Kim, K.W.; Kwon, Y.H.; An, K.G. Integrative ecological health assessments of an acid
mine stream and in situ pilot tests for wastewater treatments. Ecol. Eng. 2010,36, 653–663. [CrossRef]
18.
Bebianno, M.J.; Pereira, C.G.; Rey, F.; Cravo, A.; Duarte, D.; d’Errico, G.; Regoli, F. Integrated approach to assess ecosystem health
in harbor areas. Sci. Total Environ. 2015,514, 92–107. [CrossRef]
19.
Cheng, X.; Chen, L.; Sun, R.; Kong, P. Land use changes and socio-economic development strongly deteriorate river ecosystem
health in one of the largest basins in China. Sci. Total Environ. 2018,616, 376–385. [CrossRef]
20.
He, J.; Pan, Z.; Liu, D.; Guo, X. Exploring the regional differences of ecosystem health and its driving factors in China. Sci. Total
Environ. 2019,673, 553–564. [CrossRef]
21.
Xie, G.; Zhang, C.; Zhen, L.; Zhang, L. Dynamic changes in the value of China’s ecosystem services. Ecosyst. Serv.
2017
,26, 146–154.
[CrossRef]
22.
Wang, S.; Fang, C.; Ma, H.; Wang, Y.; Qin, J. Spatial differences and multi-mechanism of carbon footprint based on GWR model in
provincial China. J. Geogr. Sci. 2014,24, 612–630. [CrossRef]
23.
Zhu, C.; Zhang, X.; Zhou, M.; He, S.; Gan, M.; Yang, L.; Wang, K. Impacts of urbanization and landscape pattern on habitat
quality using OLS and GWR models in Hangzhou, China. Ecol. Indic. 2020,117, 106654. [CrossRef]
24.
Zhang, H.; Gao, Y.; Hua, Y.; Zhang, Y.; Liu, K. Assessing and mapping recreationists’ perceived social values for ecosystem
services in the Qinling Mountains, China. Ecosyst. Serv. 2019,39, 101006. [CrossRef]
25.
Fan, J.; Li, J.; Xia, R.; Hu, L.; Wu, X.; Li, G. Assessing the impact of climate change on the habitat distribution of the giant panda in
the Qinling Mountains of China. Ecol. Model. 2014,274, 12–20. [CrossRef]
26.
Lu, C.Y.; Gu, W.; Dai, A.H.; Wei, H.Y. Assessing habitat suitability based on geographic information system (GIS) and fuzzy: A
case study of Schisandra sphenanthera Rehd. et Wils. in Qinling Mountains, China. Ecol. Model. 2012,242, 105–115. [CrossRef]
27.
Liu, X.; Zhu, X.; Pan, Y.; Li, S.; Ma, Y.; Nie, J. Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors
between 2000 and 2014. J. Geogr. Sci. 2016,26, 45–58. [CrossRef]
28.
Bai, Y. Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data. Ecol. Indic.
2021
,
129, 108029. [CrossRef]
29.
Dang, H.; Zhang, Y.; Zhang, K.; Jiang, M.; Zhang, Q. Age structure and regeneration of subalpine fir (Abies fargesii) forests across
an altitudinal range in the Qinling Mountains, China. For. Ecol. Manag. 2010,259, 547–554. [CrossRef]

ISPRS Int. J. Geo-Inf. 2022,11, 600 17 of 18
30.
Shao, Y.; Mu, X.; He, Y.; Sun, W.; Zhao, G.; Gao, P. Spatiotemporal variations of extreme precipitation events at multi-time scales
in the Qinling-Daba mountains region, China. Quatern Int. 2019,525, 89–102. [CrossRef]
31.
Hu, Y.; Yao, Y.; Kou, Z. Exploring on the climate regionalization of Qinling-Daba mountains based on Geodetector-SVM model.
PLoS ONE 2020,15, e0241047. [CrossRef]
32.
Yu, Y.; Li, J.; Zhou, Z.; Ma, X.; Zhang, X. Response of multiple mountain ecosystem services on environmental gradients: How to
respond, and where should be priority conservation? J. Clean. Prod. 2021,278, 123264. [CrossRef]
33.
Li, J.; Zhang, L.; Ye, H. Spatiotemporal Change Analysis of Annual Average NDVI in Qinling Mountains as Ecological Security
Barrier and Dividing Line between Geography and Climate. IOP Conf. Ser. Earth Environ. Sci. 2021,804, 022108. [CrossRef]
34.
Costanza, R. Toward an operational definition of ecosystem health. In Ecosystem Health: New Goals for Environmental
Management. In Frontiers in Ecological Economics; Edward Elgar Publishing: Cheltenham, UK, 1997; pp. 239–269.
35.
Nemani, R.R.; Keeling, C.D.; Hashimoto, H.; Jolly, W.M.; Piper, S.C.; Tucker, C.J.; Myneni, R.B.; Running, S.W. Climate-driven
increases in global terrestrial net primary production from 1982 to 1999. Science 2003,300, 1560–1563. [CrossRef]
36.
Li, Y.; Cao, Z.; Long, H.; Liu, Y.; Li, W. Dynamic analysis of ecological environment combined with land cover and NDVI changes
and implications for sustainable urban–rural development: The case of Mu Us Sandy Land, China. J. Clean. Prod.
2017
,142,
697–715. [CrossRef]
37.
Carlson, T.N.; Ripley, D.A. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ.
1997,62, 241–252. [CrossRef]
38.
Pettorelli, N.; Vik, J.O.; Mysterud, A.; Gaillard, J.M.; Tucker, C.J.; Stenseth, N.C. Using the satellite-derived NDVI to assess
ecological responses to environmental change. Trends Ecol. Evol. 2005,20, 503–510. [CrossRef] [PubMed]
39.
Jiang, Z.; Huete, A.R.; Chen, J.; Chen, Y.; Li, J.; Yan, G.; Zhang, X. Analysis of NDVI and scaled difference vegetation index
retrievals of vegetation fraction. Remote Sens Environ. 2006,101, 366–378. [CrossRef]
40.
Peng, J.; Liu, Y.; Li, T.; Wu, J. Regional ecosystem health response to rural land use change: A case study in Lijiang City, China.
Ecol. Indic. 2017,72, 399–410. [CrossRef]
41.
Tobler, W.R. A computer movie simulating urban growth in the Detroit region. Econ. Geogr.
1970
,46 (Suppl. S1), 234–240.
[CrossRef]
42.
Fang, L.; Wang, L.; Chen, W.; Sun, J.; Cao, Q.; Wang, S.; Wang, L. Identifying the impacts of natural and human factors on
ecosystem service in the Yangtze and Yellow River Basins. J. Clean. Prod. 2021,314, 127995. [CrossRef]
43.
Chen, S.; Feng, Y.; Tong, X.; Liu, S.; Xie, H.; Gao, C.; Lei, Z. Modeling ESV losses caused by urban expansion using cellular
automata and geographically weighted regression. Sci. Total Environ. 2020,712, 136509. [CrossRef] [PubMed]
44.
Yu, H.; Gong, H.; Chen, B.; Liu, K.; Gao, M. Analysis of the influence of groundwater on land subsidence in Beijing based on the
geographical weighted regression (GWR) model. Sci. Total Environ. 2020,738, 139405. [CrossRef] [PubMed]
45.
Wang, Q.; Feng, H.; Feng, H.; Yu, Y.; Li, J.; Ning, E. The impacts of road traffic on urban air quality in Jinan based GWR and
remote sensing. Sci. Rep. 2021,11, 15512. [CrossRef]
46.
Wu, X.; Liu, S.; Zhao, S.; Hou, X.; Xu, J.; Dong, S.; Liu, G. Quantification and driving force analysis of ecosystem services supply,
demand and balance in China. Sci. Total Environ. 2019,652, 1375–1386. [CrossRef] [PubMed]
47.
Zhang, D.; Huang, Q.; He, C.; Wu, J. Impacts of urban expansion on ecosystem services in the Beijing-Tianjin-Hebei urban
agglomeration, China: A scenario analysis based on the shared socioeconomic pathways. Resour. Conserv. Recycl.
2017
,
125, 115–130. [CrossRef]
48.
Zhang, Y.; Yin, H.; Zhu, L.; Miao, C. Landscape Fragmentation in Qinling–Daba Mountains Nature Reserves and Its Influencing
Factors. Land 2021,10, 1124. [CrossRef]
49.
Mitchell, M.G.; Suarez-Castro, A.F.; Martinez-Harms, M.; Maron, M.; McAlpine, C.; Gaston, K.J.; Johansen, K.; Rhodes, J.R.
Reframing landscape fragmentation’s effects on ecosystem services. Trends Ecol. Evol. 2015,30, 190–198. [CrossRef] [PubMed]
50.
Chen, W.; Gu, T.; Zeng, J. Urbanisation and ecosystem health in the Middle Reaches of the Yangtze River urban agglomerations,
China: A U-curve relationship. J. Environ. Manag. 2022,318, 115565. [CrossRef] [PubMed]
51.
Zhang, Z.; Gao, J.; Fan, X.; Lan, Y.; Zhao, M. Response of ecosystem services to socioeconomic development in the Yangtze River
Basin, China. Ecol. Indic. 2017,72, 481–493. [CrossRef]
52.
Locatelli, B.; Lavorel, S.; Sloan, S.; Tappeiner, U.; Geneletti, D. Characteristic trajectories of ecosystem services in mountains. Front.
Ecol. Environ. 2017,15, 150–159. [CrossRef]
53.
Yin, L.; Dai, E.; Zheng, D.; Wang, Y.; Ma, L.; Tong, M. What drives the vegetation dynamics in the Hengduan Mountain region,
southwest China: Climate change or human activity? Ecol. Indic. 2020,112, 106013. [CrossRef]
54.
Ma, S.; Wang, L.J.; Jiang, J.; Chu, L.; Zhang, J.C. Threshold effect of ecosystem services in response to climate change and
vegetation coverage change in the Qinghai-Tibet Plateau ecological shelter. J. Clean. Prod. 2021,318, 128592. [CrossRef]
55.
Fu, Q.; Li, B.; Hou, Y.; Bi, X.; Zhang, X. Effects of land use and climate change on ecosystem services in Central Asia’s arid regions:
A case study in Altay Prefecture, China. Sci. Total Environ. 2017,607, 633–646. [CrossRef] [PubMed]
56.
Hao, R.; Yu, D.; Liu, Y.; Liu, Y.; Qiao, J.; Wang, X.; Du, J. Impacts of changes in climate and landscape pattern on ecosystem
services. Sci. Total Environ. 2017,579, 718–728. [CrossRef] [PubMed]
57.
Kang, P.; Chen, W.; Hou, Y.; Li, Y. Linking ecosystem services and ecosystem health to ecological risk assessment: A case study of
the Beijing-Tianjin-Hebei urban agglomeration. Sci. Total Environ. 2018,636, 1442–1454. [CrossRef]

ISPRS Int. J. Geo-Inf. 2022,11, 600 18 of 18
58.
Tang, D.; Liu, X.; Zou, X. An improved method for integrated ecosystem health assessments based on the structure and function
of coastal ecosystems: A case study of the Jiangsu coastal area, China. Ecol. Indic. 2018,84, 82–95. [CrossRef]
59.
Chi, G.; Marcouiller, D.W. Natural amenities and their effects on migration along the urban–rural continuum. Ann. Reg. Sci.
2013
,
50, 861–883. [CrossRef]
60.
Chi, G.; Ho, H.C. Population stress: A spatiotemporal analysis of population change and land development at the county level in
the contiguous United States, 2001–2011. Land Use Policy 2018,70, 128–137. [CrossRef]