Please wait a minute...
Journal of Arid Land
Journal of Arid Land  2025, Vol. 17 Issue (7): 958-978    DOI: 10.1007/s40333-025-0104-9     CSTR: 32276.14.JAL.02501049
Research article     
Spatiotemporal dynamic and drivers of ecological environmental quality on the Chinese Loess Plateau: Insights from kRSEI model and climate-human interaction analysis
XI Ruiyun1, PEI Tingting1, CHEN Ying1,*(), XIE Baopeng1, HOU Li1, WANG Wen2,3
1College of Management, Gansu Agricultural University, Lanzhou 730070, China
2Gansu Natural Resources Planning and Research Institute, Lanzhou 730070, China
3Gansu Branch of the Key Laboratory of Land Use, Ministry of Natural Resources of the People's Republic of China, Lanzhou 730070, China
Download: HTML     PDF(5796KB)
Export: BibTeX | EndNote (RIS)      

Abstract  

The Loess Plateau (LP), one of the most ecologically fragile regions in China, is affected by severe soil erosion and environmental degradation. Despite large-scale ecological restoration efforts made by Chinese government in recent years, the region continues to face significant ecological challenges due to the combined impact of climate change and human activities. In this context, we developed a kernal Remote Sensing Ecological Index (kRSEI) using Moderate Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine (GEE) platform to analyze the spatiotemporal patterns and trends in ecological environmental quality (EEQ) across the LP from 2000 to 2022 and project future trajectories. Then, we applied partial correlation analysis and multivariate regression residual analysis to further quantify the relative contributions of climate change and human activities to EEQ. During the study period, the kRSEI values exhibited significant spatial heterogeneity, with a stepwise degradation pattern in the southeast to northwest across the LP. The maximum (0.51) and minimum (0.46) values of the kRSEI were observed in 2007 and 2021, respectively. Trend analyses revealed a decline in EEQ across the LP. Hurst exponent analysis predicted a trend of weak anti-persistent development in most of the plateau areas in the future. A positive correlation was identified between kRSEI and precipitation, particularly in the central and western regions; although, improvements were limited by a precipitation threshold of 837.66 mm/a. A moderate increase in temperature was shown to potentially benefit the ecological environment within a certain range; however, temperature of -1.00°C-7.95°C often had a negative impact on the ecosystem. Climate change and human activities jointly influenced 65.78% of LP area on EEQ, primarily having a negative impact. In terms of contribution, human activities played a dominant role in driving changes in EEQ across the plateau. These findings provide crucial insights for accurately assessing the ecological state of the LP and suggest the design of future restoration strategies.

Key words： ecological environmental quality      Remote Sensing Ecological Index (RSEI)      kernel Normalized Difference Vegetation Index (kNDVI)      climate change      human activities      ecological restoration      Loess Plateau     
Received: 23 February 2025      Published: 31 July 2025
Corresponding Authors: *CHEN Ying (E-mail: cheny@gsau.edu)
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
XI Ruiyun
PEI Tingting
CHEN Ying
XIE Baopeng
HOU Li
WANG Wen
Cite this article:

XI Ruiyun, PEI Tingting, CHEN Ying, XIE Baopeng, HOU Li, WANG Wen. Spatiotemporal dynamic and drivers of ecological environmental quality on the Chinese Loess Plateau: Insights from kRSEI model and climate-human interaction analysis. Journal of Arid Land, 2025, 17(7): 958-978.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0104-9     OR     http://jal.xjegi.com/Y2025/V17/I7/958

Fig. 1 Elevation (a) and land cover of the Loess Plateau (LP). Note that the figures of the LP region were sourced from the Global Change Research Data Publishing & Repository (https://www.geodoi.ac.cn/WebCn/doi.aspx?Id=199), and the boundary has not been modified.
Slope value Z value Change category Trend characteristic
Slope>0.0000 2.58<Z 4 Extremely significant increase
1.96<Z≤2.58 3 Significant increase
1.65<Z≤1.96 2 Slight increase
Z≤1.65 1 No significant increase
Slope=0.0000 - 0 No change
Slope<0.0000 Z≤1.65 -1 No significant decrease
1.65<Z≤1.96 -2 Slight decrease
1.96<Z≤2.58 -3 Significant decrease
2.58<Z -4 Extremely significant decrease
Table 1 Change category of ecological environmental quality (EEQ) variation based on Sen's slope analysis and Mann-Kendall (MK) test
Slope value Hurst index (H) Future trend category Trend characteristic
Slope< -0.0005 0.65<H<1.00 1 Strong sustained degradation
Slope< -0.0005 0.50<H<0.65 2 Weak sustained degradation
Slope≥0.0005 0.00<H<0.35 3 Reversed strong sustained improvement
Slope≥0.0005 0.35<H<0.50 4 Reversed weak sustained improvement
Slope< -0.0005 0.35<H<0.50 5 Reversed weak sustained degradation
Slope< -0.0005 0.00<H<0.35 6 Reversed strong sustained degradation
Slope≥0.0005 0.50<H<0.65 7 Weak sustained improvement
Slope≥0.0005 0.65<H<1.00 8 Strong sustained improvement
-0.0005≤Slope<0.0005 - 9 No change
Table 2 Classification of future trends in EEQ based on Hurst exponent analysis
Slope(kRSEIobs) Driver Criteria for determining driver Contribution rate
Slope(kRSEICC) Slope(kRSEIHA) Climate change Human activities
>0.0000 Climate change and human activities >0.0000 >0.0000 $\frac{\text { Slpoe }\left(\mathrm{kRSEI}_{\mathrm{CC}}\right)}{\text { Slpoe }\left(\mathrm{kRSEI}_{\text {obs }}\right)} \times 100 \%$ $\frac{\text { Slpoe }\left(\text { kRSEI }_{\text {HA }}\right)}{\text { Slpoe }\left(\text { kRSEI }_{\text {obs }}\right)} \times 100 \%$
Climate change >0.0000 <0.0000 100% 0%
Human activities <0.0000 >0.0000 0% 100%
<0.0000 Climate change and human activities <0.0000 <0.0000 $\frac{\text { Slpoe }\left(\mathrm{kRSEI}_{\mathrm{CC}}\right)}{\text { Slpoe }\left(\mathrm{kRSEI}_{\text {obs }}\right)} \times 100 \%$ $\frac{\text { Slpoe }\left(\mathrm{kRSEI}_{\mathrm{HA}}\right)}{\text { Slpoe }\left(\mathrm{kRSEI}_{\text {obs }}\right)} \times 100 \%$
Climate change <0.0000 >0.0000 100% 0%
Human activities >0.0000 <0.0000 0% 100%
Table 3 Assessment criteria for identifying the drivers of kernel Remote Sensing Ecological Index (kRSEI) variation and calculation of contribution rate
Fig. 2 Scatterplot of kernal Remote Sensing Ecological Index (kRSEI) with Normalized Difference Vegetation Index (NDVI; a), Land Surface Temperature (LST; b), Wetness Index (WI; c), and Normalized Difference Built-up Soil Index (NDBSI; d)
Fig. 3 Spatial distribution of ecological environmental quality (EEQ) levels on the LP from 2000 to 2022. (a), 2000; (b), 2005; (c), 2010; (d), 2015; (e), 2020; (f), 2022.
Fig. 4 Proportion of area with different EEQ levels on the LP (a) and linear fit of the annual mean kRSEI values from 2000 to 2022 (b)
Fig. 5 Spatial distribution of slope values (a) and EEQ change categories (b) on the LP from 2000 to 2022
Change category Trend characteristic Area proportion (%)
4 Extremely significant increase 5.62
3 Significant increase 4.70
2 Slight increase 2.87
1 No significant increase 19.54
0 No change 1.04
-1 No significant decrease 30.28
-2 Slight decrease 6.46
-3 Significant decrease 12.03
-4 Extremely significant decrease 17.46
Table 4 Results of MK test showing the trends in EEQ on the LP from 2000 to 2022 and corresponding areas
Fig. 6 Spatial distribution of Hurst index (H) values (a) and future trends in EEQ (b) on the LP
Future trend category Trend characteristic Area proportion (%)
1 Strong sustained degradation 5.63
2 Weak sustained degradation 6.39
3 Reversed strong sustained improvement 1.95
4 Reversed weak sustained improvement 1.99
5 Reversed weak sustained degradation 6.90
6 Reversed strong sustained degradation 5.32
7 Weak sustained improvement 1.95
8 Strong sustained improvement 2.12
9 No change 0.01
Table 5 Future trends in EEQ and corresponding areas on the LP
Fig. 7 Correlation of kRSEI with precipitation (a) and temperature (b)
Fig. 8 Spatial distribution of significant correlation of kRSEI with precipitation (a) and temperature (b)
Fig. 9 Threshold effect of precipitation (a) and temperature (b) on kRSEI on the LP from 2000 to 2022, and three-dimensional response surface of kRSEI to temperature and precipitation on the LP in 2000 (c) and 2022 (d). The blue curve in Figure 9a1 and b1 represents the fitted response. Figure 9a2 and b2 shows the marginal effect of precipitation and temperature on kRSEI, respectively. The dkRSEI/dprecipitation and dkRSEI/dtemperature are first derivatives, reflecting the rate of variation.
Fig. 10 Impact of climate change (CC) and human activities (HA) on kRSEI variation across the LP from 2000 to 2022. (a), spatial distribution of the co-contribution of CC and HA to kRSEI variation; (b and c), spatial distribution of the contributions of CC and HA to kRSEI variation, respectively; (d), proportional contributions of CC and HA to kRSEI variation across different land use types. Slope>0.0000 represents positive effect on kRSEI, and slope<0.0000 represents negative effect on kRSEI.
Fig. 11 Spatial distribution of the six types of ecological zones on the LP determined based on the contributions of climate change and human activities to kRSEI variation. (a), synergistic improvement zone; (b), climate-advantage zone; (c), human-driven zone; (d), comprehensive degradation zone; (e), climate-sensitive zone; (f), human disturbance zone.
[1]   Alexander C. 2020. Normalised difference spectral indices and urban land cover as indicators of land surface temperature (LST). International Journal of Applied Earth Observation and Geoinformation, 86: 102013, doi: 10.1016/j.jag.2019.102013.
[2]   An M, Xie P, He W J, et al. 2022. Spatiotemporal change of ecologic environment quality and human interaction factors in three gorges ecologic economic corridor, based on RSEI. Ecological Indicators, 141: 109090, doi: 10.1016/j.ecolind.2022.109090.
[3]   Bai T T, Cheng J, Zheng Z H, et al. 2023a. Drivers of eco-environmental quality in China from 2000 to 2017. Journal of Cleaner Production, 396: 136408, doi: 10.1016/j.jclepro.2023.136408.
[4]   Bai X, Zhang Z W, Li Z, et al. 2023b. Spatial heterogeneity and formation mechanism of eco-environmental quality in the Yellow River Basin. Sustainability, 15(14): 10878, doi: 10.3390/su151410878.
[5]   Boori M S, Choudhary K, Paringer R, et al. 2021. Spatiotemporal ecological vulnerability analysis with statistical correlation based on satellite remote sensing in Samara, Russia. Journal of Environmental Management, 285: 112138, doi: 10.1016/j.jenvman.2021.112138.
[6]   Camps-Valls G, Campos-Taberner M, Moreno-Martínez Á, et al. 2021. A unified vegetation index for quantifying the terrestrial biosphere. Science Advances, 7(9): eabc7447, doi: 10.1126/sciadv.abc7447.
[7]   Cao J X, Wu E T, Wu S H, et al. 2022. Spatiotemporal dynamics of ecological condition in Qinghai-Tibet Plateau based on remotely sensed ecological index. Remote Sensing, 14(17): 4234, doi: 10.3390/rs14174234.
[8]   Chen S F, Zhang Q F, Chen Y N, et al. 2023. Vegetation change and eco-environmental quality evaluation in the Loess Plateau of China from 2000 to 2020. Remote Sensing, 15(2): 424, doi: 10.3390/rs15020424.
[9]   Chen W Y, Zhao R F, Lu H T. 2024. Response of ecological environment quality to land use transition based on dryland oasis ecological index (DOEI) in dryland: A case study of oasis concentration area in middle Heihe River, China. Ecological Indicators, 165: 112214, doi: 10.1016/j.ecolind.2024.112214.
[10]   Cheng X Y, Wang R Y, Liu H L, et al. 2023. Assessment of ecological environment in Zhanjiang based on RSEI and PCA. International Journal of Environment, Agriculture and Biotechnology, 8: 5, doi: 10.22161/ijeab.85.4.
[11]   Du B Z, Zhen L, Yan H M, et al. 2016. Effects of government grassland conservation policy on household livelihoods and dependence on local grasslands: Evidence from Inner Mongolia, China. Sustainability, 8(12): 1314, doi: 10.3390/su8121314.
[12]   Feng X J, Tian J, Wang Y X, et al. 2023. Spatio-temporal variation and climatic driving factors of vegetation coverage in the Yellow River Basin from 2001 to 2020 based on kNDVI. Forests, 14(3): 620, doi: 10.3390/f14030620.
[13]   Gan X T, Du X C, Duan C J, et al. 2024. Evaluation of ecological environment quality and analysis of influencing factors in Wuhan City based on RSEI. Sustainability, 16(13): 5809, doi: 10.3390/su16135809.
[14]   Gou R K, Zhao J. 2020. Eco-environmental quality monitoring in Beijing, China, using an RSEI-based approach combined with random forest algorithms. IEEE Access, 8: 196657-196666.
[15]   Gong C, Lü F N, Wang Y L. 2023. Spatiotemporal change and drivers of ecosystem quality in the Loess Plateau based on RSEI: A case study of Shanxi, China. Ecological Indicators, 155: 111060, doi: 10.1016/j.ecolind.2023.111060.
[16]   Gong X G, Zhang Y Z, Ren J, et al. 2025. Ecological response of green spaces to land use change in the Mu Us Desert-Loess Plateau transition zone, China, since the twenty-first century. Environmental Monitoring and Assessment, 197: 435, doi: 10.1007/s10661-025-13906-w.
[17]   Hamel P, Bryant B P. 2017. Uncertainty assessment in ecosystem services analyses: Seven challenges and practical responses. Ecosystem Services, 24: 1-15.
[18]   IPCC Intergovernmental Panel on Climate Change. 2023. Climate Change 2021: The Physical Science Basis—Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press.
[19]   Kamran M, Yamamoto K. 2023. Evolution and use of remote sensing in ecological vulnerability assessment: A review. Ecological Indicators, 148: 110099, doi: 10.1016/j.ecolind.2023.110099.
[20]   Li J, Roy D P. 2017. A global analysis of Sentinel-2A, Sentinel-2B and Landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sensing, 9(9): 902, doi: 10.3390/rs9090902.
[21]   Li W J, Xie S Y, Wang Y, et al. 2021. Effects of urban expansion on ecosystem health in Southwest China from a multi-perspective analysis. Journal of Cleaner Production, 294: 126341, doi: 10.1016/j.jclepro.2021.126341.
[22]   Liang L F, Song Y X, Shao Z F, et al. 2024. Exploring the causal relationships and pathways between ecological environmental quality and influencing factors: A comprehensive analysis. Ecological Indicators, 165: 112192, doi: 10.1016/j.ecolind.2024.112192.
[23]   Luo H X, Xu Y M, Han Q, et al. 2024. Remote sensing assessment of ecological quality of Baiyangdian wetland in response to extreme rainfall. Remote Sensing Applications: Society and Environment, 36: 101284, doi: 10.1016/j.rsase.2024.101284.
[24]   Naseri N, Mostafazadeh R. 2023. Spatial relationship of remote sensing ecological indicator (RSEI) and landscape metrics under urban development intensification. Earth Science Informatics, 16: 3797-3810.
[25]   Peng X F, Zhang S Q, Peng P H, et al. 2023. Unraveling the ecological tapestry: A comprehensive assessment of Changtang Nature Reserve's ecological and environmental using RSEI and GEE. Land, 12(8): 1581, doi: 10.3390/land12081581.
[26]   Qi G, Cong N, Luo M, et al. 2024. Contribution of climatic change and human activities to vegetation dynamics over Southwest China during 2000-2020. Remote Sensing, 16(18): 3361, doi: 10.3390/rs16183361.
[27]   Qin G X, Wang N L, Wu Y W, et al. 2024. Spatiotemporal variations in eco-environmental quality and responses to drought and human activities in the middle reaches of the Yellow River Basin, China from 1990 to 2022. Ecological Informatics, 81: 102641, doi: 10.1016/j.ecoinf.2024.102641.
[28]   Shao Y J, Liu Y S, Wang X C, et al. 2024. Exploring the evolution of ecosystem health and sustainable zoning: A perspective based on the contributions of climate change and human activities. Science of the Total Environment, 951: 175674, doi: 10.1016/j.scitotenv.2024.175674.
[29]   Shi S Y, Yu J J, Wang F, et al. 2021. Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau. Science of the Total Environment, 755(Part 2): 142419, doi: 10.1016/j.scitotenv.2020.142419.
[30]   Strassburg B B, Iribarrem A, Beyer H L, et al. 2022. Author correction: Global priority areas for ecosystem restoration. Nature, 609: E7, doi: 10.1038/s41586-022-05178-y.
[31]   Sun C, Li J L, Liu Y C, et al. 2022. Ecological quality assessment and monitoring using a time-series remote sensing-based ecological index (ts-RSEI). GIScience & Remote Sensing, 59(1): 1793-1816.
[32]   Tian Y, Wang Z L, Ji C N, et al. 2025. The influence of human activities and climate change on the spatiotemporal variations of eco-environmental quality in Shendong Mining Area, China from 1990 to 2023. Applied Sciences, 15(5): 2296, doi: 10.3390/app15052296.
[33]   Wang F, Lai H X, Li Y B, et al. 2022a. Dynamic variation of meteorological drought and its relationships with agricultural drought across China. Agricultural Water Management, 261: 107301, doi: 10.1016/j.agwat.2021.107301.
[34]   Wang J, Ma J L, Xie F F, et al. 2020. Improvement of remote sensing ecological index in arid regions: Taking Ulan Buh Desert as an example. Chinese Journal of Applied Ecology, 31(11): 3795-3804. (in Chinese)
doi: 10.13287/j.1001-9332.202011.011
[35]   Wang J J, Ding J L, Ge X Y, et al. 2022b. Assessment of ecological quality in Northwest China (2000-2020) using the Google Earth Engine platform: Climate factors and land use/land cover contribute to ecological quality. Journal of Arid Land, 14(11): 1196-1211.
[36]   Wang L C, Jiao L, Lai F B, et al. 2019. Evaluation of ecological changes based on a remote sensing ecological index in a Manas Lake wetland, Xinjiang. Acta Ecologica Sinica, 39(8): 2963-2972. (in Chinese)
[37]   Wang X, Yao X J, Jiang C Z, et al. 2022c. Dynamic monitoring and analysis of factors influencing ecological environment quality in northern Anhui, China, based on the Google Earth Engine. Scientific Reports, 12: 20307, doi: 10.1038/s41598-022-24413-0.
[38]   Wang Z Z, Fu B J, Wu X T, et al. 2023. Vegetation resilience does not increase consistently with greening in China's Loess Plateau. Communications Earth & Environment, 4: 336, doi: 10.1038/s43247-023-01000-3.
[39]   Wen C H, Long T F, He G J, et al. 2025. Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China's inaugural national parks. Ecological Indicators, 170: 112981, doi: 10.1016/j.ecolind.2024.112981.
[40]   Wessels K J, van den Bergh F, Scholes R J. 2012. Limits to detectability of land degradation by trend analysis of vegetation index data. Remote Sensing of Environment, 125: 10-22.
[41]   Xiao Z L, Liu R, Gao Y H, et al. 2022. Spatiotemporal variation characteristics of ecosystem health and its driving mechanism in the mountains of Southwest China. Journal of Cleaner Production, 345: 131138, doi: 10.1016/j.jclepro.2022.131138.
[42]   Xiong Y, Xu W H, Lu N, et al. 2021. Assessment of spatial-temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan Province, China. Ecological Indicators, 125: 107518, doi: 10.1016/j.ecolind.2021.107518.
[43]   Xu H Q. 2005. A study on information extraction of water body with the modified normalized difference water index (MNDWI). National Remote Sensing Bulletin, 9(5): 589-595. (in Chinese)
[44]   Xu H Q. 2013a. A remote sensing index for assessment of regional ecological changes. China Environmental Science, 33(5): 889-897. (in Chinese)
[45]   Xu H Q. 2013b. A remote sensing urban ecological index and its application. Acta Ecologica Sinica, 33(24): 7853-7862. (in Chinese)
[46]   Xu H Q, Wang M Y, Shi T T, et al. 2018. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological Indicators, 93: 730-740.
[47]   Xu H Q, Wang Y F, Guan H D, et al. 2019. Detecting ecological changes with a remote sensing based ecological index (RSEI) produced time series and change vector analysis. Remote Sensing, 11(20): 2345, doi: 10.3390/rs11202345.
[48]   Yang G, Chen Y H, Ren Q, et al. 2024. Remote sensing ecological index (RSEI) affects microbial community diversity in ecosystems of different qualities. Science of the Total Environment, 954: 176489, doi: 10.1016/j.scitotenv.2024.176489.
[49]   Yang W L, Zhou Y, Li C Z. 2023. Assessment of ecological environment quality in rare earth mining areas based on improved RSEI. Sustainability, 15(4): 2964, doi: 10.3390/su15042964.
[50]   Yao Z Y, Xiao J H, Ma X X. 2021. The impact of large-scale afforestation on ecological environment in the Gobi region. Scientific Reports, 11: 14383, doi: 10.1038/s41598-021-93948-5.
pmid: 34257401
[51]   Yu Y, Wu Y, Wang P, et al. 2021. Grassland subsidies increase the number of livestock on the Tibetan Plateau: why does the Payment for Ecosystem Services Policy have the opposite outcome? Sustainability, 13(11): 6208, doi: 10.3390/su13116208.
[52]   Yuan B D, Fu L N, Zou Y A, et al. 2021. Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. Journal of Cleaner Production, 302: 126995, doi: 10.1016/j.jclepro.2021.126995.
[53]   Yuan D H, Du M R, Yan C L, et al. 2024. Coupling coordination degree analysis and spatiotemporal heterogeneity between water ecosystem service value and water system in Yellow River Basin cities. Ecological Informatics, 79: 102440, doi: 10.1016/j.ecoinf.2023.102440.
[54]   Zhang J J, Zhou Q, Cao M, et al. 2022a. Spatiotemporal change of eco-environmental quality in the oasis city and its correlation with urbanization based on RSEI: A case study of Urumqi, China. Sustainability, 14(15): 9227, doi: 10.3390/su14159227.
[55]   Zhang K L, Feng R R, Zhang Z C, et al. 2022b. Exploring the driving factors of remote sensing ecological index changes from the perspective of geospatial differentiation: a case study of the Weihe River Basin, China. International Journal of Environmental Research and Public Health, 19(17): 10930, doi: 10.3390/ijerph191710930.
[56]   Zhang L Y, Li X, Liu X H, et al. 2025. Dynamic monitoring and drivers of ecological environmental quality in the Three-North region, China: Insights based on remote sensing ecological index. Ecological Informatics, 85: 102936, doi: 10.1016/j.ecoinf.2024.102936.
[57]   Zhang P P, Chen X D, Ren Y, et al. 2023a. A novel mine-specific eco-environment index (MSEEI) for mine ecological environment monitoring using Landsat imagery. Remote Sensing, 15(4): 933, doi: 10.3390/rs15040933.
[58]   Zhang X, Gao Z L, Li Y H, et al. 2023b. Eco-environment quality response to climate change and human activities on the Loess Plateau, China. Land, 12(9): 1792, doi: 10.3390/land12091792.
[59]   Zhang Y, She J Y, Long X R, et al. 2022c. Spatio-temporal evolution and driving factors of eco-environmental quality based on RSEI in Chang-Zhu-Tan metropolitan circle, central China. Ecological Indicators, 144: 109436, doi: 10.1016/j.ecolind.2022.109436.
[60]   Zhang Y B, Song T L, Fan J H, et al. 2022d. Land use and climate change altered the ecological quality in the Luanhe River Basin. International Journal of Environmental Research and Public Health, 19(13), 7719, doi: 10.3390/ijerph19137719.
[61]   Zheng Z H, Wu Z F, Chen Y B, et al. 2022. Instability of remote sensing based ecological index (RSEI) and its improvement for time series analysis. Science of the Total Environment, 814: 152595, doi: 10.1016/j.scitotenv.2021.152595.
[62]   Zhou M L, Li Z H, Gao M L, et al. 2024. Revealing the eco-environmental quality of the Yellow River Basin: Trends and drivers. Remote Sensing, 16(11): 2018, doi: 10.3390/rs16112018.
[63]   Zhou S, Huang Y F, Yu B F, et al. 2015. Effects of human activities on the eco-environment in the middle Heihe River Basin based on an extended environmental Kuznets curve model. Ecological Engineering, 76: 14-26.
[64]   Zhu D Y, Chen T, Wang Z W, et al. 2021. Detecting ecological spatial-temporal changes by remote sensing ecological index with local adaptability. Journal of Environmental Management, 299: 113655, doi: 10.1016/j.jenvman.2021.113655.
[65]   Zou S B, Qian J K, Xu B R, et al. 2022. Spatiotemporal changes of ecosystem health and their driving mechanisms in alpine regions on the northeastern Tibetan Plateau. Ecological Indicators, 143: 109396, doi: 10.1016/j.ecolind.2022.109396.
[1] Ilan STAVI, Gal KAGAN, Sivan ISAACSON. Properties, challenges, and opportunities of the loess plains in the northern Negev Desert: A review[J]. Journal of Arid Land, 2025, 17(6): 715-734.
[2] LIU Huan, YAO Yuyan, AI Zemin, DANG Xiaohu, CAO Yong, LI Qingqing, HOU Mengjia, HU Haoli, ZHANG Yuanyuan, CAO Tian. Spatial and temporal evolution of forage-livestock balance in the agro-pastoral transition zone of northern China[J]. Journal of Arid Land, 2025, 17(6): 754-771.
[3] LI Wei, WANG Yixuan, DUAN Limin, TONG Xin, WU Yingjie, ZHAO Shuixia. Non-stationary characteristics and causes of extreme precipitation in a desert steppe in Inner Mongolia, China[J]. Journal of Arid Land, 2025, 17(5): 590-604.
[4] CHAO Yan, ZHU Yonghua, WANG Xiaohan, LI Jiamin, LIANG Li'e. Dynamic evolution of the NDVI and driving factors in the Mu Us Sandy Land of China from 2002 to 2021[J]. Journal of Arid Land, 2025, 17(5): 605-623.
[5] CHE Cunwei, ZHANG Mingjun, XIAO Shengchun, YANG Wanmin, WANG Shengjie, WANG Zhilan, SUN Meiling. Radial growth of Platycladus orientalis Linn. and its growth resilience after extreme droughts along a precipitation gradient[J]. Journal of Arid Land, 2025, 17(3): 381-393.
[6] LYU Leting, JIANG Ruifeng, ZHENG Defeng, LIANG Liheng. Impact of climate change and land use/cover change on water yield in the Liaohe River Basin, Northeast China[J]. Journal of Arid Land, 2025, 17(2): 182-199.
[7] ZHANG Jing, XU Changchun, WANG Hongyu, WANG Yazhen, LONG Junchen. Runoff simulation and hydropower resource prediction of the Kaidu River Basin in the Tianshan Mountains, China[J]. Journal of Arid Land, 2025, 17(1): 1-18.
[8] LIU Yuke, HUANG Chenlu, YANG Chun, CHEN Chen. Spatiotemporal variation and driving factors of vegetation net primary productivity in the Guanzhong Plain Urban Agglomeration, China from 2001 to 2020[J]. Journal of Arid Land, 2025, 17(1): 74-92.
[9] CHEN Zhuo, SHAO Minghao, HU Zihao, GAO Xin, LEI Jiaqiang. Potential distribution of Haloxylon ammodendron in Central Asia under climate change[J]. Journal of Arid Land, 2024, 16(9): 1255-1269.
[10] LI Mingqian, WANG He, DU Wei, GU Hongbiao, ZHOU Fanchao, CHI Baoming. Responses of runoff to changes in climate and human activities in the Liuhe River Basin, China[J]. Journal of Arid Land, 2024, 16(8): 1023-1043.
[11] SUN Chao, BAI Xuelian, WANG Xinping, ZHAO Wenzhi, WEI Lemin. Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022[J]. Journal of Arid Land, 2024, 16(8): 1044-1061.
[12] YAN Yujie, CHENG Yiben, XIN Zhiming, ZHOU Junyu, ZHOU Mengyao, WANG Xiaoyu. Impacts of climate change and human activities on vegetation dynamics on the Mongolian Plateau, East Asia from 2000 to 2023[J]. Journal of Arid Land, 2024, 16(8): 1062-1079.
[13] YANG Jianhua, LI Yaqian, ZHOU Lei, ZHANG Zhenqing, ZHOU Hongkui, WU Jianjun. Effects of temperature and precipitation on drought trends in Xinjiang, China[J]. Journal of Arid Land, 2024, 16(8): 1098-1117.
[14] HAN Qifei, XU Wei, LI Chaofan. Effects of nitrogen deposition on the carbon budget and water stress in Central Asia under climate change[J]. Journal of Arid Land, 2024, 16(8): 1118-1129.
[15] SUN Hui, ZHAO Yunge, GAO Liqian, XU Mingxiang. Reasonable grazing may balance the conflict between grassland utilization and soil conservation in the semi-arid hilly areas, China[J]. Journal of Arid Land, 2024, 16(8): 1130-1146.