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Journal of Arid Land
Journal of Arid Land  2026, Vol. 18 Issue (4): 608-631    DOI: 10.1016/j.jaridl.2026.04.004     CSTR: 32276.14.JAL.20250401
Research article     
Evaluation of ecological environmental quality and its driving factors in a mountain basin: A case study of the Manas River Basin, China
QI Wenwen1,2, LI Yuanyuan1,2,*(), SHI Xiang1,2
1 Department of Forestry and Ecological Environment, College of Urban and Environmental Sciences, Shihezi University, Shihezi 832000, China
2 Department of Forestry, Agricultural College, Shihezi University, Shihezi 832000, China
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Abstract  

In recent years, intensified land use change driven by climate change and human activities have markedly impacted the ecological environmental quality of the arid inland river basins. The implementation of forestry projects, coupled with continuous population growth, has increased the need for systematic assessments of ecological effects to ensure sustainable development in arid inland river basins. This study generated a 22-a (2000-2021) remote sensing ecological index (RSEI) data series for the Manas River Basin, a typical arid inland river basin in China, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Google Earth Engine (GEE) platform. We examined the spatiotemporal patterns of ecological environmental quality in the Manas River Basin through the Theil-Sen estimator, Mann-Kendall trend test, coefficient of variation (CV), and Hurst index. Furthermore, we employed the Optimal Parameter-based Geographical Detector (OPGD) method to quantify the influence of seven key drivers: elevation, slope, temperature, precipitation, gross domestic product (GDP), population density, and land use change. The key findings revealed that the basin's ecological environmental quality showed significant improvement (mean RSEI of 0.38, with a range of 0.34-0.41), with areas exhibiting good and excellent grades increasing by 16.71%, particularly in the midstream oasis region and upstream mountainous region, while areas exhibiting poor and relatively poor grades decreased by 11.52% in the downstream desert region. Spatial heterogeneity of ecological environmental quality was pronounced, with 32.23% of the areas showing localized degradation, the midstream oasis region exhibiting sustainable recovery potential (Hurst index>0.50), and only 36.67% of the areas maintaining stable and highly stable conditions (primarily in the upstream mountainous region). The OPGD analysis revealed that temperature (q-value=0.496-0.780), land use change (q-value=0.705-0.782), and elevation (q-value=0.245-0.637) were dominant factors, with the influence of land use change increasing during 2000-2020. Strong interaction effects emerged between land use change and temperature (q-value>0.705) and between land use change and elevation (q-value=0.751 in 2020), highlighting intensified human-nature coupling. These findings provide vital perspectives for ecosystem management in arid inland river basins under both climate and anthropogenic pressures.

Key words： ecological environment quality      land use change      remote sensing ecological index (RSEI)      Google Earth Engine (GEE)      Optimal Parameter-based Geographical Detector (OPGD)      mountain-oasis-desert system     
Received: 26 August 2025      Published: 30 April 2026
Corresponding Authors: *LI Yuanyuan (E-mail: 2004liyy@163.com)
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Cite this article:

QI Wenwen, LI Yuanyuan, SHI Xiang. Evaluation of ecological environmental quality and its driving factors in a mountain basin: A case study of the Manas River Basin, China. Journal of Arid Land, 2026, 18(4): 608-631.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.04.004     OR     http://jal.xjegi.com/Y2026/V18/I4/608

Fig. 1 Overview of the Manas River Basin. The image was accessed on 10 January 2024 from National Platform for Common GeoSpatial Information Services (https://www.tianditu.gov.cn/).
Remote sensing data Time resolution (d) Spatial resolution (m) Product level Variable Access to indicator
MOD13A1 16 500 L3 NDVI Greenness
MOD11A2 8 1000 L3 LST Heat
MOD9A1 8 500 L3 1-7 bands Wetness and dryness
Table 1 Details of remote sensing data
Data type Data name Data source
Natural factor Elevation (X1) Geospatial Data Cloud (https://www.gscloud.cn)
Temperature (X7) National Earth System Science Data Center (http://www.geodata.cn)
Precipitation (X6)
Slope (X4) Elevation extraction
Human factor Population density (X5) Resources and Environmental Science Data Platform (https://www.resdc.cn)
Land use change (X3)
GDP (X2)
Table 2 Details of natural factor data and human factor data
Indicator Formula Clarification
Greenness (NDVI) $\text{NDVI}=\left({\rho }_{\text{NIR}}-{\rho }_{\text{red}}\right)/\left({\rho }_{\text{NIR}}+{\rho }_{\text{red}}\right)$ NDVI is the normalized difference vegetation index; ρNIR represents the near-infrared band (nm); and ρred represents the red-light band (nm).
Wetness
(WET)		 $\begin{array}{l}\text{WET}=0.1147{\rho }_{\text{red}}+0.2489{\rho }_{\text{blue}}+0.3132{\rho }_{\text{green}}-\\ \text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }\text{ }0.3122{\rho }_{\text{NIR}}-0.6416{\rho }_{\text{SWIR1}}-0.5087{\rho }_{\text{SWIR2}}\end{array}$ WET is the wetness component; ρblue, ρgreen, ρSWIR1, and ρSWIR2 denote the blue-light band (nm), green-light band (nm), short-wave infrared 1 band (nm), and short-wave infrared 2 band (nm), respectively.
Dryness (NDBSI) $\begin{array}{l}\text{NDBSI}=\left(\text{SI}+\text{IBI}\right)/2\\ \text{SI}=\frac{\left({\rho }_{\text{SWIR1}}+{\rho }_{\text{red}}\right)-\left({\rho }_{\text{NIR}}+{\rho }_{\text{blue}}\right)}{\left({\rho }_{\text{SWIR1}}+{\rho }_{\text{red}}\right)+\left({\rho }_{\text{NIR}}+{\rho }_{\text{blue}}\right)}\\ \text{IBI}=\frac{2{\rho }_{\text{SWIR1}}/\left({\rho }_{\text{SWIR1}}+{\rho }_{\text{NIR}}\right)-\left[{\rho }_{\text{NIR}}/\left({\rho }_{\text{NIR}}+{\rho }_{\text{red}}\right)+{\rho }_{\text{green}}/\left({\rho }_{\text{green}}+{\rho }_{\text{SWIR1}}\right)\right]}{2{\rho }_{\text{SWIR1}}/\left({\rho }_{\text{SWIR1}}+{\rho }_{\text{NIR}}\right)+\left[{\rho }_{\text{NIR}}/\left({\rho }_{\text{NIR}}+{\rho }_{\text{red}}\right)+{\rho }_{\text{green}}/\left({\rho }_{\text{green}}+{\rho }_{\text{SWIR1}}\right)\right]}\end{array}$ NDBSI is the normalized difference bare soil index; SI denotes the soil index; and IBI denotes the index-based built-up index.
Heat (LST) $\mathrm{LST}=0.02 D N-273.15$ LST is the land surface temperature (°C); and DN denotes the MOD11A2 image grayscale value.
Table 3 Formulas for the four indicators of the remote sensing ecological index (RSEI)
SRSEI Z value Trend in RSEI SRSEI Z value Trend in RSEI
≥0.0005 ≥1.96 Significant improvement < -0.0005 -1.96-1.96 Slight degradation
≥0.0005 -1.96-1.96 Slight improvement < -0.0005 < -1.96 Significant degradation
-0.0005-0.0005 -1.96-1.96 Stable
Table 4 Significance and trend test categories
Interaction type Criteria Ecological interpretation
Nonlinear attenuation q(X1∩X2)<Min[q(X1), q(X2)] The combined effect of two factors shows weaker explanatory power than either factor alone, indicating inhibitory or redundant effects.
Unilinear attenuation Min[q(X1), q(X2)]<q(X1∩X2)<Max[q(X1), q(X2)] The interaction is primarily driven by one
dominant factor, with the second factor
contributing minimally.
Two-factor enhancement q(X1∩X2)>Max[q(X1), q(X2)] The combined effect exceeds individual factors but remains subadditive.
Independent q(X1∩X2)=q(X1)+q(X2) Factors affect ecological processes
independently without interaction.
Nonlinear enhancement q(X1∩X2)>q(X1)+q(X2) Factors exhibit superadditive effects,
dramatically amplifying ecological impacts.
Table 5 Interaction types and their ecological interpretation in Optimal Parameter-based Geographical Detector (OPGD) analysis
Year Indicator PC1 PC2 PC3 PC4
2000 NDVI 0.39 0.90 -0.06 0.18
LST -0.86 0.36 0.33 0.16
WET 0.25 -0.23 0.51 0.79
NDBSI -0.23 -0.06 -0.79 0.56
Eigenvalue 0.05 0.03 0.01 0.00
Eigenvalue contribution (%) 52.02 31.81 14.32 1.85
2005 NDVI 0.45 -0.88 -0.04 -0.14
LST -0.83 -0.43 -0.35 -0.05
WET 0.29 0.17 0.84 -0.43
NDBSI -0.16 0.08 -0.41 -0.89
Eigenvalue 0.06 0.03 0.01 0.00
Eigenvalue contribution (%) 58.27 35.85 5.43 0.46
2010 NDVI 0.57 0.76 0.26 0.18
LST -0.69 0.60 -0.36 0.17
WET 0.30 -0.20 -0.57 0.73
NDBSI -0.33 -0.14 0.69 0.63
Eigenvalue 0.06 0.04 0.01 0.00
Eigenvalue contribution (%) 56.40 36.49 6.15 0.97
2015 NDVI 0.54 -0.71 -0.41 0.19
LST -0.55 -0.66 0.49 0.17
WET 0.26 0.24 0.33 0.88
NDBSI -0.58 0.08 -0.70 0.41
Eigenvalue 0.08 0.05 0.01 0.00
Eigenvalue contribution (%) 57.15 32.43 9.55 0.87
2020 NDVI 0.69 -0.50 -0.49 0.18
LST -0.34 -0.81 0.49 0.22
WET 0.23 0.31 0.33 0.86
NDBSI -0.59 -0.00 -0.68 0.42
Eigenvalue 0.09 0.04 0.01 0.00
Eigenvalue contribution (%) 64.37 26.80 7.88 0.96
2021 NDVI 0.62 0.60 0.48 0.16
LST -0.53 0.76 -0.33 0.20
WET 0.25 -0.21 -0.36 0.87
NDBSI -0.52 -0.15 0.73 0.42
Eigenvalue 0.08 0.05 0.01 0.00
Eigenvalue contribution (%) 56.79 35.31 7.10 0.80
Table 6 Principial Component Analysis (PCA) results for the four ecological indicators in the Manas River Basin during 2000-2021
RSEI grade Land cover type NDVI WET LST Spatial zone and key characteristics
Poor
(0.00-0.20)		 Gobi and sandy land 0.29±0.03 0.17±0.04 0.87±0.04 Core area in the downstream desert region: sparse vegetation and high surface temperatures
Relatively poor (0.20-0.40) Low-coverage
grassland		 0.40±0.06 0.19±0.03 0.72±0.07 Oasis-desert ecotone: fragile ecosystem vulnerable to disturbance
Moderate
(0.40-0.60)		 Moderate-coverage
grassland		 0.51±0.16 0.27±0.05 0.62±0.15 Midstream alluvial plain and upstream slopes: including natural grassland and irrigated farmland with moderate ecological function
Good
(0.60-0.80)		 Farmland and
high-coverage grassland		 0.70±0.22 0.42±0.10 0.52±0.17 Piedmont alluvial fan: excellent water conditions supporting lush vegetation growth
Excellent
(0.80-1.00)		 Forest and water
body		 0.80±0.21 0.67±0.17 0.15±0.13 Upstream water source area: dominated by forests and glaciers, ensuring high soil moisture and stable ecological function
Table 7 Ecological characteristics of each RSEI grade in the Manas River Basin
Fig. 2 Interannual variation in the remote sensing ecological index (RSEI) and area for each RSEI grade in the Manas River Basin during 2000-2021
Fig. 3 Spatial distribution of RSEI grade within the Manas River Basin in 2000 (a), 2005 (b), 2010 (c), 2015 (d), 2020 (e), and 2021 (f)
Fig. 4 Change trend (a) and future spatial distribution (b) of ecological environmental quality in the Manas River Basin
RSEI development direction Future change trend Area (km2) Area percentage (%)
Strong anti-persistent degradation Significantly degraded in the past and may be improved in the future 6459.68 20.54
Weak anti-persistent degradation Slightly degraded in the past and may be improved in the future 8100.36 25.76
Weak anti-persistent stability Slightly stable in the past and may be changed in the future 5746.52 18.28
Weak anti-persistent improvement Slightly improved in the past and may be degraded in the future 6763.30 21.51
Weak persistent improvement Slightly improved in the past and may be continuously improved in the future 3728.86 11.86
Strong persistent improvement Significantly improved in the past and may be continuously improved in the future 643.26 2.05
Table 8 Area and area percentage of RSEI development direction and future change trend in the Manas River Basin
Fig. 5 Spatial distribution of integrated future trend (a) and coefficient of variation (CV; b) for ecological environmental quality in the Manas River Basin
Year Project Natural factor Human factor
X1 X4 X7 X6 X2 X5 X3
2000 q-value 0.637 0.464 0.780 0.624 0.227 0.064 0.757
q-value sorting 3 5 1 4 6 7 2
2005 q-value 0.593 0.399 0.774 0.537 0.292 0.118 0.782
q-value sorting 3 5 2 4 6 7 1
2010 q-value 0.360 0.184 0.636 0.307 0.315 0.187 0.753
q-value sorting 3 7 2 4 5 6 1
2015 q-value 0.307 0.125 0.570 0.309 0.277 0.198 0.711
q-value sorting 4 7 2 3 5 6 1
2020 q-value 0.245 0.021 0.496 0.046 0.296 0.316 0.705
q-value sorting 5 7 2 6 4 3 1
Table 9 Single-factor detection results of the influencing factors on the ecological environmental quality in the Manas River Basin during 2000-2020
Fig. 6 Heatmap of interaction q-values among driving factors of ecological environmental quality in the Manas River Basin in 2000 (a), 2005 (b), 2010 (c), 2015 (d), and 2020 (e). X1, elevation; X2, gross domestic production (GDP); X3, land use change; X4, slope; X5, population density; X6, precipitation; X7, temperature. q is the explanatory power indicator of the influencing factors on the ecological environmental quality.
Fig. 7 Land cover types of the Manas River Basin in 2000 (a), 2005 (b), 2010 (c), 2015 (d), and 2020 (e)
Land cover type Area (km2) Combined dynamic change (%)
2000 2005 2010 2015 2020
Farmland 4503.73 4914.37 7315.37 7412.46 7804.26 +9.69
Forest 1265.45 1240.26 497.96 498.14 455.43 -2.38
Grassland 11,380.47 11,005.52 10,127.56 10,044.77 9663.94 -5.04
Built-up area 2125.07 2109.12 937.07 942.02 1049.05 -3.16
Water body 322.82 347.09 443.01 505.62 542.70 +0.65
Unused land 14,453.74 14,434.74 14,730.45 14,648.33 14,536.07 +0.24
Table 10 Area statistics and combined dynamic change of land cover types in the Manas River Basin during 2000-2020
Fig. 8 Spatial distribution of land use change in the Manas River Basin from 2000 to 2020. ''-'' means the transition from one land cover type to another land cover type.
Direction of conversion Land cover type transition 2000-2020
Area (km2) Area percentage (%)
Conversion to farmland Grassland-farmland 2168.38 27.79
Built-up area-farmland 116.27 1.49
Forest-farmland 280.99 3.60
Water body-farmland 6.61 0.08
Unused land-farmland 1081.69 13.86
Total 3653.95 46.82
Conversion from farmland Farmland-grassland 150.17 3.33
Farmland-built-up area 172.30 3.83
Farmland-forest 13.21 0.29
Farmland‒water body 6.07 0.13
Farmland-unused land 11.91 0.26
Total 353.67 7.85
Table 11 Transition matrix of farmland with other land cover types during 2000-2020
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