Preamble

Journal of Arid Land (2025) 17(7): 958–978
doi: 10.1007/s40333-025-0104-9; CSTR: 32276.14.JAL.02501049
Science Press & Springer-Verlag

Spatiotemporal Dynamics and Drivers of Ecological Environmental Quality on the Chinese Loess Plateau: Insights from kRSEI Model and Climate-Human Interaction Analysis

Ruiyun Xi¹, Tingting Pei¹, Ying Chen¹*, Baopeng Xie¹, Li Hou¹, Wen Wang²,³

¹College of Forestry, 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

Abstract: The Loess Plateau (LP), one of China's most ecologically fragile regions, faces severe soil erosion and environmental degradation. Despite large-scale ecological restoration efforts by the Chinese government in recent years, the region continues to encounter significant ecological challenges due to the combined impacts of climate change and human activities. In this context, we developed a kernel Remote Sensing Ecological Index (kRSEI) using Moderate Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine (GEE) platform to analyze spatiotemporal patterns and trends in ecological environmental quality (EEQ) across the LP from 2000 to 2022 and project future trajectories. We then applied partial correlation analysis and multivariate regression residual analysis to quantify the relative contributions of climate change and human activities to EEQ. During the study period, kRSEI values exhibited significant spatial heterogeneity, with a stepwise degradation pattern from southeast to northwest across the LP. The maximum (0.51) and minimum (0.46) kRSEI values were observed in 2007 and 2021, respectively. Trend analyses revealed a decline in EEQ across the LP. Hurst exponent analysis predicted a weak anti-persistent development trend in most plateau areas for 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. Moderate temperature increases potentially benefited the ecological environment within a certain range; however, temperatures of –1.00°C–7.95°C often negatively impacted the ecosystem. Climate change and human activities jointly influenced 65.78% of the LP area on EEQ, primarily exerting negative impacts. In terms of contribution, human activities played a dominant role in driving EEQ changes across the plateau. These findings provide crucial insights for accurately assessing the ecological state of the LP and inform the design of future restoration strategies.

Keywords: ecological environmental quality; Remote Sensing Ecological Index (RSEI); kernel Normalized Difference Vegetation Index (kNDVI); climate change; human activities; ecological restoration; Loess Plateau

Citation: Xi RY, Pei TT, Chen Y, Xie BP, Hou L, Wang W. 2025. Spatiotemporal dynamics and drivers of ecological environmental quality on the Chinese Loess Plateau: Insights from kRSEI model and climate-human interaction analysis. Journal of Arid Land, 17(7): 958–978. https://doi.org/10.1007/s40333-025-0104-9; https://cstr.cn/32276.14.JAL.02501049

Corresponding author: Ying Chen (E-mail: cheny@gsau.edu)

Received: 2025-02-23; revised: 2025-06-08; accepted: 2025-06-20
© Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2025

1 Introduction

The concept of ecological environmental quality (EEQ) refers to the status and accessibility of elements critical for societal development in a given environment, such as water, soil, biodiversity, and climatic conditions \cite{Yao2021, Strassburg2022}. This concept reflects the dynamic equilibrium between anthropogenic systems and natural ecosystems \cite{Bai2023a, Yuan2024}. As documented by the Intergovernmental Panel on Climate Change (IPCC, 2023), anthropogenic climate warming has accelerated in recent decades, causing more frequent and intense extreme weather events. Such environmental transformations particularly threaten arid areas, which are globally recognized as ecologically vulnerable zones \cite{Zhang2022a}, where amplified hydrological cycles under warming conditions increase climate sensitivity \cite{Wang2022a}. The Chinese Loess Plateau (LP), which possesses the world's largest loess deposits, faces distinctive environmental challenges, including soil erosion and ecosystem fragility. The government has implemented large-scale projects such as the Grain-for-Green program (initiated in 1999) and the Three-North Shelterbelt project in the region to address these issues \cite{Du2016, Yu2021}. Systematic evaluation of EEQ enables evidence-based policy making for ecological restoration, which is a priority given increasing pressure from climate change and human-induced environmental stresses.

Evaluating EEQ is a complex and challenging task, and numerous mathematical models and methods have been recently developed for this purpose \cite{Hamel2017}. For example, EEQ is frequently assessed at county, provincial, and eco-regional levels using the Ecological Index (EI). However, EI application is constrained by its reliance on costly ground monitoring data and significant human intervention, and its inability to provide visualization, making it challenging to achieve high-precision, real-time, and rapid large-scale ecological monitoring and assessment \cite{Zhu2021}. Remote sensing technology enables ecological evaluations across large areas, long time series, and different spatial scales \cite{Kamran2023}. Various remote sensing-based ecological indices have been developed to assist in quantifying and mapping ecosystem functions and characteristics \cite{Xu2018}. For instance, the Normalized Difference Vegetation Index (NDVI) characterizes vegetated areas \cite{Li2021}, Permanent Vegetation Fraction (PVF) serves as an indicator of vegetation coverage \cite{Naseri2023}, and Land Surface Temperature (LST) describes surface temperature variations \cite{Alexander2020}. However, relying solely on a single index is insufficient for comprehensively assessing complex ecosystems \cite{Zheng2022}. The Remote Sensing Ecological Index (RSEI) enables integration of different ecosystem components into a composite indicator to assess ecological conditions \cite{Xu2013a}. By applying principal component analysis (PCA) to generate load values, this method objectively determines the weight of each component, thereby eliminating subjective human influence. This approach not only simplifies the evaluation process but also enhances the efficiency of ecological assessments \cite{Yang2024}. RSEI has been widely applied to EEQ studies at different scales, including urban areas \cite{Zhang2022a, Gan2024}, river basins \cite{Xiong2021, Yuan2021}, mining areas \cite{Yang2023}, and ecological conservation areas \cite{Peng2023, Wen2025}, demonstrating strong practical utility.

Currently, methods such as geographical detectors \cite{An2022}, multiple linear regression \cite{Zhang2022b}, and correlation analysis \cite{Boori2021} are commonly employed to assess the impacts of climate change and human activities on EEQ \cite{Cao2022, Bai2023a}. Among influencing factors, temperature variations directly affect vegetation growth \cite{Qin2024}, whereas extreme precipitation events may trigger natural disasters such as debris flows, floods, and landslides \cite{Luo2024}. Meanwhile, rapid urban expansion disrupts soil and topographical structures, indirectly exerting negative impacts on RSEI spatial distribution \cite{Zhang2022c}. Research has indicated that due to the combined effects of global climate change and human activities, EEQ trends are more complex. However, most EEQ studies primarily focus on regional condition assessment and single-factor analysis. Only a few have investigated the combined effects of environmental alterations and anthropogenic factors on EEQ. To avoid saturation problems when calculating RSEI, Feng et al. (2023) replaced traditional NDVI with kernel NDVI (kNDVI), revealing greater stability and robustness of this modified index in describing various environments such as dense forests, grasslands, and mixed woodlands, compared with both NDVI and the near-infrared reflectance of vegetation (NIRV) index. Compared with NDVI, kNDVI captures vegetation growth dynamics more accurately and more effectively addresses limitations related to atmospheric noise, soil background interference, and saturation errors \cite{Camps-Valls2021, Wang2023}.

In this study, we analyzed Moderate Resolution Imaging Spectroradiometer (MODIS) images of the LP from 2000 to 2022 derived from the Google Earth Engine (GEE) platform, introducing kNDVI instead of traditional NDVI as a greenness indicator to develop an adjusted ecological index for EEQ assessment, referred to as the kernel Remote Sensing Ecological Index (kRSEI). The aims are to: (1) quantify EEQ on the LP from 2000 to 2022 and analyze its spatiotemporal evolution; (2) investigate EEQ trends during the 23-year period and forecast future scenarios; (3) evaluate the respective impacts of climate change and human activities on EEQ on the LP; and (4) propose restoration strategies for various ecological areas within the plateau.

2.1 Study Area

The LP is one of China's most densely populated areas, where the conflict between population growth and natural resource preservation (namely environmental health) is particularly pronounced \cite{Chen2023}. Located in north-central China, the plateau spans approximately 33°43′ to 41°16′N and 100°54′ to 114°33′E, covering an area of about 6.40×10⁵ km². It extends westward to the Qilian Mountains, eastward to the Taihang Mountains, southward to the Qinling Mountains, and reaches the Great Wall in the north. The LP is characterized by highly undulating terrain (with elevation ranging from 83 to 5022 m) and predominance of loess hills, tablelands, and gully landscapes [FIGURE:1]. The area experiences a temperate continental monsoon climate, with an annual mean temperature of 4.00°C–14.00°C and annual precipitation ranging from 200.00 to 800.00 mm, increasing from northwest to southeast. Strong seasonality and spatial variability of precipitation make the regional ecosystem highly sensitive to climate change, as fluctuations in humidity and temperature directly impact vegetation growth and ecosystem stability. The main land cover types are grasslands, forests, and farmlands, making the plateau a key dryland agricultural zone in China. Administratively, the LP spans several provinces including Henan, Shanxi, Shaanxi, Gansu, Qinghai, Inner Mongolia Autonomous Region, and Ningxia Hui Autonomous Region, with a total population of approximately 1.08×10⁸. Over time, intensive human activities have exacerbated ecosystem fragility, and the region's environmental quality is increasingly shaped by the combined effects of human activities and climate change, posing significant challenges for sustainable development.

2.2 Data Sources and Processing

The GEE remote sensing platform offers a robust solution for geospatial data analysis and visualization, enabling processing of large-scale remote sensing data over extended temporal and spatial ranges without preliminary steps such as atmospheric and radiometric corrections \cite{Yuan2021}. In this study, the GEE platform was leveraged to extract annual MOD13A1 NDVI, MOD11A2 LST, and MOD09A1 surface reflectance data for the LP during the vegetation growing season (April–October) from 2000 to 2022. Data preprocessing involved removing cloud contamination and masking water bodies using the Cloud and Cloud Shadow Mask (CFMASK) algorithm and Modified Normalized Difference Water Index (MNDWI), respectively \cite{Xu2005}. These processed datasets were then employed for kRSEI calculation and subsequent analysis.

Precipitation data from 2000 to 2022 were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5)-Land dataset; annual temperature records for the same timeframe were retrieved from the National Xizang Plateau Data Center; and the land cover dataset for China (at 30 m resolution) was obtained from Earth System Science Data released in 2021.

2.3.1 Construction and Improvement of kRSEI Model

Functioning as an integrated geospatial indicator, RSEI enables systematic quantification of ecological conditions by synthesizing surface parameters critical for EEQ assessment—greenness, wetness, dryness, and heat—via PCA \cite{Xu2013a}. The equations describing these four parameters are reported below \cite{Xu2019, Qin2024}. In this study, kNDVI was adopted as the greenness metric instead of conventional NDVI to develop kRSEI. This modification was intended to provide a more accurate representation of EEQ on the LP. The formulas are as follows \cite{Camps-Valls2021}:

$$
kNDVI = \tanh(NDVI^2), \quad (1)
$$

$$
NDVI = \frac{\rho_{nir} - \rho_{red}}{\rho_{nir} + \rho_{red}}, \quad (2)
$$

where $\rho_{nir}$ and $\rho_{red}$ are the reflectance of near-infrared and red bands, respectively; NDVI is the Normalized Difference Vegetation Index; and kNDVI is the kernel Normalized Difference Vegetation Index, representing greenness.

Using a tasseled cap transformation on the MOD09A1 dataset, the humidity component closely associated with vegetation and soil moisture was extracted. The formula for calculating Wetness Index (WI) is as follows \cite{Yuan2021}:

$$
WI = 0.1147\rho_{blue} + 0.2489\rho_{green} + 0.2408\rho_{nir1} + 0.3132\rho_{nir2} - 0.3242\rho_{mir1} - 0.4557\rho_{mir2}, \quad (3)
$$

where $\rho_{blue}$, $\rho_{green}$, $\rho_{nir1}$, $\rho_{nir2}$, $\rho_{mir1}$, and $\rho_{mir2}$ are surface reflectance of blue, green, near-infrared 1 (841–876 nm), near-infrared 2 (1230–1250 nm), mid-infrared 1 (1628–1652 nm), and mid-infrared 2 (2105–2155 nm) bands, respectively.

The Normalized Difference Built-up Soil Index (NDBSI) was used to represent surface dryness, which includes built-up and bare soil components. The formulas are as follows \cite{Sun2022}:

$$
NDBSI = \frac{SI + IBI}{2}, \quad (4)
$$

$$
SI = \frac{\rho_{mir1} + \rho_{red} - \rho_{nir1} - \rho_{blue}}{\rho_{mir1} + \rho_{red} + \rho_{nir1} + \rho_{blue}}, \quad (5)
$$

$$
IBI = \frac{2\rho_{mir1}}{\rho_{mir1} + \rho_{nir1}} - \frac{\rho_{nir1}}{\rho_{nir1} + \rho_{red}} - \frac{\rho_{green}}{\rho_{green} + \rho_{mir1}}, \quad (6)
$$

where SI and IBI are the soil index and index-based built-up index, respectively.

In this study, the MOD11A2 LST product served as a heat parameter indicator \cite{Gong2023}:

$$
LST = 0.02 \times DN - 273.15, \quad (7)
$$

where $DN$ is the digital number for the MOD11A2 image.

After calculating the four indices, kRSEI was determined by PCA \cite{Gou2020}:

$$
kRSEI_0 = PCA(kNDVI, WI, NDBSI, LST), \quad (8)
$$

$$
kRSEI = \frac{1 - kRSEI_0}{1 + kRSEI_0}, \quad (9)
$$

where kRSEI is the kernel Remote Sensing Ecological Index and $kRSEI_0$ is the initial kRSEI value.

The kRSEI was further normalized between 0.00 and 1.00. Based on \cite{Boori2021} and \cite{Cheng2023}, this study classified kRSEI values into five distinct categories representative of EEQ: poor (0.00–0.20), fair (0.20–0.40), moderate (0.40–0.60), good (0.60–0.80), and excellent (0.80–1.00).

2.3.2 Sen's Slope Analysis and Mann-Kendall (MK) Test

Sen's slope is a nonparametric technique employed to determine linear trends in time series data. Here, it was applied to assess variations in kRSEI on the LP from 2000 to 2022. The following equation was used:

$$
\text{slope} = \text{median}\left(\frac{x_i - x_j}{i - j}\right), \quad (10)
$$

where slope is the key indicator quantifying kRSEI changes, and $x_i$ and $x_j$ are kRSEI values at time points $i$ and $j$, respectively. The median function allows precise slope calculation, whose value directly indicates dynamic kRSEI change over time. Specifically, a positive slope indicates an upward trend, suggesting gradual EEQ improvement; a slope equal to zero signifies stability with no EEQ change; and a negative slope indicates a downward trend, reflecting EEQ deterioration.

The MK test assessed the significance of observed kRSEI variation over time. The $Z$ value, a standardized test statistic, quantifies trend statistical significance. For a given significance level $\alpha$, if $|Z| \leq Z_{\alpha/2}$, the variation was not significant at that level. Conversely, if $|Z| > Z_{\alpha/2}$, the variation was significant. We identified statistically significant trends based on $Z$ values exceeding critical thresholds: 1.65 (90.00% confidence), 1.96 (95.00% confidence), and 2.58 (99.00% confidence). kRSEI variation intervals are summarized in Table 1 [TABLE:1].

2.3.3 Hurst Exponent Analysis

Hurst exponent analysis quantitatively characterized EEQ trend persistence on the LP. This index, derived from rescaled range analysis \cite{Xu2013b}, generally ranges between 0.00 and 1.00. A Hurst index ($H$) close to 0.50 signifies little correlation between past and future EEQ trends. At $H$ values between 0.00 and 0.50, the time series demonstrates anti-persistence, meaning future trends likely oppose past observations, whereas values between 0.50 and 1.00 indicate persistence, suggesting future trends maintain the same direction as past ones \cite{Bai2023a}. Projected kRSEI trends based on Sen's slope analysis are summarized in Table 2 [TABLE:2].

2.3.4 Partial Correlation Analysis

Partial correlation analysis examined relationships between climate variables and kRSEI variation. The following equation was used \cite{Shao2024}:

$$
r_{xy,z} = \frac{r_{xy} - r_{xz}r_{yz}}{\sqrt{(1 - r_{xz}^2)(1 - r_{yz}^2)}}, \quad (11)
$$

where $r_{xy,z}$ is the partial correlation coefficient between $x$ and $y$ controlling for $z$, and $r_{xy}$, $r_{xz}$, and $r_{yz}$ are simple correlation coefficients between $x$ and $y$, $x$ and $z$, and $y$ and $z$, respectively. A t-test was conducted at the pixel level with significance set at 0.05. Based on results, five partial correlation levels were identified: significant positive correlation ($r_{xy,z} > 0; P < 0.05$), significant negative correlation ($r_{xy,z} < 0; P < 0.05$), non-significant positive correlation ($r_{xy,z} > 0; P \geq 0.05$), non-significant negative correlation ($r_{xy,z} < 0; P \geq 0.05$), and no linear correlation ($r_{xy,z} = 0$).

2.3.5 Multivariate Regression Residual Analysis

Multivariate regression residual analysis assessed impacts and relative contributions of human activities and climate change to kRSEI variation. The following two equations were used \cite{Qi2024}:

$$
kRSEI_{CC} = aT + bP + c, \quad (12)
$$

$$
kRSEI_{HA} = kRSEI_{obs} - kRSEI_{CC}, \quad (13)
$$

where $kRSEI_{CC}$ and $kRSEI_{obs}$ are the predicted kRSEI value based on the regression model (reflecting climate change influence) and the observed kRSEI value derived from remote sensing images, respectively; $a$, $b$, and $c$ are model parameters; $T$ and $P$ are average temperature (°C) and cumulative precipitation (mm), respectively; and $kRSEI_{HA}$ is the kRSEI residual representing human activity influence.

2.3.6 Determination of the Drivers of kRSEI Variation

Linear trends observed for $kRSEI_{CC}$ and $kRSEI_{HA}$ from 2000 to 2022 revealed distinct impacts of environmental factors and human activities on kRSEI variation over time across the LP. Specifically, positive trends suggest a stimulating effect, while negative ones reflect a restraining influence on kRSEI. Analysis of these linear trends in $kRSEI_{obs}$ allowed identification of main contributors to kRSEI variation and quantification of their relative contributions (Table 3 [TABLE:3]).

3.1 Spatiotemporal Variation of EEQ on the LP

PCA of the four combined indices (kNDVI, WI, LST, and NDBSI) revealed that the first principal component (PC1) contribution to overall kRSEI variance consistently exceeded 80.00% from 2000 to 2022. Specifically, contribution rates were 88.69% in 2000, 90.14% in 2005, 88.78% in 2010, 91.05% in 2015, 87.55% in 2020, and 89.08% in 2022, suggesting PC1 accounted for most variance among the four indices. This finding demonstrated the validity of using PC1 to construct kRSEI for EEQ evaluation on the LP. In PC1, kNDVI and WI showed positive values, while NDBSI and LST had negative values (Fig. 2 [FIGURE:2]).

Average kRSEI values for the LP from 2000 to 2022 were calculated. As shown in Figure 3 [FIGURE:3], higher values indicating improved EEQ were distributed in the southeast, whereas lower values were detected in the northwest, forming a spatial gradient declining from southeast to northwest. Areas with good or excellent EEQ were mainly concentrated in southern Shanxi Province, southern Yan'an City in Shaanxi Province, Pingliang and Tianshui cities in Gansu Province, and Xining City in Qinghai Province. Additionally, EEQ in the Hetao Plain region was relatively high, reflecting developed river systems in this area. In contrast, areas with poor or fair EEQ were primarily located in central Inner Mongolia Autonomous Region, northern Yan'an and Yulin cities in Shaanxi Province, Ningxia Hui Autonomous Region, and parts of Gansu Province including Dingxi, Lanzhou, Baiyin, and Wuwei cities. This spatial distribution was attributed to the LP's inland location, where only southeastern areas benefit from favorable temperature and humidity conditions determined by the summer monsoon, whereas central and northwestern regions experience limited precipitation and are characterized by sandy soils and challenging conditions for vegetation growth. Starting from 2000, areas with moderate EEQ or higher gradually expanded, with boundaries shifting northwestward. However, after 2015, this trend reversed, with the boundary contracting and moving back southeastward.

Trends in EEQ on the LP from 2000 to 2022 were further analyzed by visualizing proportions of areas with different EEQ levels (Fig. 4a [FIGURE:4]). Overall, the fair level accounted for the largest area, covering 29.20% of the plateau on average, followed by moderate level (24.39%), good (22.76%), poor (12.17%), and excellent (11.45%) levels. These proportions exhibited minimal variation from 2000 to 2022, with levels lower than moderate consistently occupying larger areas than levels higher than moderate. By calculating annual mean kRSEI values for the study area and fitting a linear regression trend, regional EEQ trends were described (Fig. 4b). During the study period, overall EEQ on the LP showed a general declining trend at a rate of 0.0014/a from 2000 to 2022. The highest (0.51) and lowest (0.46) kRSEI values were recorded in 2007 and 2021, respectively. Notable low inflection points were observed in 2005 and 2012, followed by brief periods of rapid but temporary increases. These low values were likely associated with extreme climate events that occurred in parts of the LP during those years. Calculation of interannual EEQ variation based on mean kRSEI values across all pixels indicated that localized degradation due to such extreme events may have significantly impacted overall EEQ, even if disturbances were spatially limited. The overall EEQ on the LP remained consistently moderate throughout the study period.

3.2.1 Sen's Slope Analysis and MK Test

EEQ variation on the LP from 2000 to 2022 was further investigated by employing Sen's slope estimation and MK test to analyze kRSEI trends at the pixel level. Results are presented in Figure 5 [FIGURE:5].

From 2000 to 2022, EEQ on the LP exhibited an overall declining trend. Areas with improved EEQ accounted for 32.73% of the total area and were concentrated in northern Yan'an City and central Yulin City in Shaanxi Province, as well as Linfen and Lüliang cities in Shanxi Province. These are key regions included in the Grain-for-Green program, and significant improvements observed highlight this initiative's effectiveness. Conversely, areas with declining EEQ comprised 67.27% of the total area, dominating the overall trend. This decline was most prominent in Xi'an, Weinan, and Xianyang cities in Shaanxi Province; Jincheng and Changzhi cities in Shanxi Province; Yinchuan City in Ningxia Hui Autonomous Region; Baotou and Bayannur cities in Inner Mongolia Autonomous Region; Wuwei City in Gansu Province; and Qinghai Province. These areas are typically characterized by high population density, intense urbanization, industrial development, and long-term agricultural exploitation. Pressures from these factors—such as rapid land-use change, groundwater over-extraction, and soil salinization—have led to significant ecological stress. Additionally, increasing climate variability and extreme weather events have further exacerbated ecological degradation in these regions. The MK test was employed to more accurately assess EEQ increases or decreases on the LP at various significance levels, allowing trend classification and statistical analysis of area proportions. As shown in Table 4 [TABLE:4], the area showing no significant decrease in EEQ was largest (30.28%), followed by that showing no significant increase (19.54%). Remaining trends—extremely significant decrease, significant decrease, slight decrease, extremely significant increase, significant increase, and slight increase—were observed in areas covering 17.46%, 12.03%, 6.46%, 5.62%, 4.70%, and 2.87% of the LP, respectively. Over the 23 years, 1.04% of the area showed no EEQ change, with no specific concentration.

3.2.2 Hurst Exponent Analysis

The Hurst exponent ($H$) was calculated for kRSEI values for each pixel. This index, along with Sen's slope analysis at the 95.00% confidence level, allowed determination of both future and past EEQ trends on the LP (Fig. 6 [FIGURE:6]). The average $H$ value was 0.43, with 34.40% and 65.60% of the area showing $H > 0.50$ and $H < 0.50$, respectively. When $H$ approached 0.50, correlation between historical and subsequent variations progressively decreased. Based on $H$ values, we identified four distinct persistence patterns in terms of strength and direction: strong reverse sustainability (0.00 < $H$ < 0.35), weak reverse sustainability (0.35 < $H$ < 0.50), weak same-direction sustainability (0.50 < $H$ < 0.65), and strong same-direction sustainability (0.65 < $H$ < 1.00). These findings implied that most LP regions will experience relatively weak reverse sustainability trends in EEQ in the future. By integrating Hurst exponent analysis with kRSEI trends, it was possible to effectively predict evolutionary trajectories and magnitudes of kRSEI fluctuations (Table 5 [TABLE:5]).

As shown in Figure 6 and Table 5, LP regions where EEQ may decline in the future comprise 15.96% of the total area. More specifically, 12.02% and 3.94% will experience sustainable degradation (both strong and weak) and counter-sustainable improvement (both strong and weak), respectively. Conversely, 16.29% of the LP may improve in EEQ in the future, with 4.07% and 12.22% experiencing sustainable improvement (both strong and weak) and counter-sustainable degradation (both strong and weak), respectively. Areas exhibiting sustainable degradation on the LP are concentrated in southern Xi'an, Xianyang, Weinan cities, and southern Yan'an City in Shaanxi Province, as well as Qinghai Province, the Hetao Plain (subjected to agricultural irrigation), and eastern Shanxi Province. The distribution is uneven, with higher concentration in densely populated urban areas and water systems, reflecting significant negative effects of human activities on EEQ in these regions. Areas showing sustainable improvement are primarily located in northern Yan'an City in Shaanxi Province and Lüliang City in Shanxi Province, as well as a few other isolated locations. In these regions, where main land cover types are forests and grasslands, the Grain-for-Green program has led to substantial vegetation cover recovery and protection, with promising future prospects. Areas that showed EEQ improvement in the past but may decline in the future are predominantly located near zones subjected to continuous degradation. Conversely, areas that degraded in the past but may show improvement going forward are mainly situated in northern Ordos City, which is characterized by barren land with sandy soils and presents challenging natural conditions that hinder vegetation growth. However, owing to recent initiatives such as the Three-North Shelterbelt project, EEQ has gradually improved in this region, and promisingly positive trends have been predicted.

3.3.1 Relationship Between kRSEI and Environmental Parameters

Correlation coefficients describing the relationship between kRSEI and precipitation on the LP ranged from –0.75 to 0.84, with a mean value of 0.13 (Fig. 7 [FIGURE:7]). This correlation was significant for only 8.23% of the LP area. Spatially, 73.56% and 26.44% of pixels showed positive and negative correlation, respectively, indicating that in most of the LP, kRSEI and precipitation were positively correlated. This positive correlation suggested that increased precipitation contributed to enhancing EEQ, especially in central and western LP characterized by grasslands, croplands, and barren lands with low precipitation (Fig. 8 [FIGURE:8]). Higher precipitation improves soil moisture and water availability, fostering vegetation growth and restoration, which in turn improves EEQ. Correlation coefficients describing the relationship between kRSEI and temperature ranged from –0.83 to 0.82, with an average value of –0.01, and the correlation was significant for 4.55% of the LP area. Positive and negative correlations were observed for 49.28% and 50.72% of pixels, respectively, indicating a more complex relationship between temperature and EEQ. A moderate temperature rise may benefit the ecological environment, but excessive increases typically lead to negative effects such as higher soil moisture evaporation and inhibited vegetation growth.

Most LP areas are characterized by arid and semi-arid climate, making EEQ particularly sensitive to precipitation changes. Adequate precipitation provides necessary conditions for vegetation growth, thereby enhancing regional EEQ. However, in areas with sparse vegetation or where precipitation is already sufficient, increased precipitation may lead to soil erosion and landslides, negatively impacting EEQ. As shown in Figure 9a [FIGURE:9], kRSEI exhibited a "rise-fall" trend with increasing precipitation, with an optimal precipitation threshold detected at 837.66 mm/a. Furthermore, the rate of kRSEI variation gradually decreased as precipitation increased. Regarding the more complex relationship between temperature variation and EEQ, in regions with abundant precipitation, moderate warming promotes vegetation growth, thereby improving EEQ, but simultaneously higher temperatures accelerate plant transpiration and soil moisture evaporation, reducing surface moisture and negatively affecting EEQ. As shown in Figure 9b, kRSEI exhibited a nonlinear "rise-fall-rise" pattern with temperature changes, indicating a complex response requiring further investigation. The rate of kRSEI variation decreased initially and then increased with rising temperatures. Moreover, overall, precipitation exerted a more pronounced positive effect on EEQ compared with temperature.

3.3.2 Relative Contribution of Climate Change and Human Activities to kRSEI Variation

Collectively, climate change and human activities affected 65.78% of the LP area, with 48.86% accounting for areas where both have negative effects (Fig. 10a [FIGURE:10]). These areas were scattered throughout the region without showing a clear distribution pattern. In contrast, areas where both climate change and human activities had positive effects accounted for 16.92% of the LP and were mainly located in central (northern Yulin and Yan'an cities in Shaanxi Province and Lüliang City in Shanxi Province) and northeastern (Datong and Shuozhou cities in Shanxi Province) regions. Areas affected by climate change alone accounted for 5.78% of the LP, with 1.31% and 3.37% experiencing positive and negative effects, respectively. These areas were mainly located in southeastern Yulin City, Shaanxi Province and more sporadically in Qingyang and Baiyin cities, Gansu Province. Areas affected only by human activities accounted for 28.43% of the LP, with 14.61% showing promoting effects in central and southern regions and 13.82% being negatively impacted in southern and northern regions. In particular, northern and central-western LP regions were significantly positively affected by the combined effects of climate change and human activities. Here, moderate temperature increases due to climate change combined with initiatives such as the Grain-for-Green program have contributed to improving EEQ. Conversely, in eastern and southern regions, both climate change and human activities exerted marked negative effects, exacerbating ecological degradation.

Contributions of climate change and human activities to kRSEI variation from 2000 to 2022 differed for each grid cell (Fig. 10b and c). The average contribution of climate change was 22.15%, while that of human activities was 77.85%, suggesting the latter was the primary driver of EEQ changes on the LP. In specific areas such as central LP (Yulin City in Shaanxi Province), Wuzhong City in Ningxia Hui Autonomous Region, Wuwei and Dingxi cities in Gansu Province, and the junction of Shanxi and Shaanxi provinces, human activity influence was less pronounced and climate change was the main factor affecting EEQ. Human activities were identified as the main contributor to kRSEI variation across different land cover types on the LP, consistently and strongly affecting EEQ (Fig. 10d).

3.4 Targeted Ecological Restoration Strategies for the LP

Multivariate residual regression analysis was used to evaluate contributions of various factors to kRSEI variation on the LP. Our approach provided insights into effect magnitudes on EEQ and its future trends at the raster scale. Based on this, we identified six ecological zone types: synergistic improvement zone, climate-advantage zone, human-driven zone, comprehensive degradation zone, climate-sensitive zone, and human disturbance zone (Fig. 11 [FIGURE:11]). Spatial aggregation was applied to enhance connectivity within each type.

The synergistic improvement zone, mainly located in central LP (a key area for the Grain-for-Green program), is positively affected by both climate change and human activities. In the future, continued focus on ecological protection and restoration, improved compensation mechanisms, and promotion of synergy between ecological restoration and economic development through agro-ecological compensation policies are essential. The climate-advantage zone, sparsely distributed in central and northern LP, benefits from climate change. In these areas, protection of existing ecological resources, optimization of land use, and prevention of overdevelopment and ecological damage should be prioritized. Human-driven zones, mostly located in parts of eastern Gansu Province and the junction of Shaanxi and Shanxi provinces, have seen EEQ improvements mainly due to human activities. Here, crucial future intervention will consist of monitoring human impacts and implementing appropriate restoration actions. Comprehensive degradation zones, widely distributed across the LP, are negatively impacted by both climate change and human activities. The priority is establishing comprehensive management strategies focusing on restoring ecological functions, enhancing biodiversity, improving soil quality, and reducing human interference. Specifically, measures may include water and soil conservation, vegetation restoration, artificial intervention, establishment of cropland-forest networks, reduction of overgrazing, and improvement of ecosystem self-restoration capacity. In climate-sensitive zones, mainly located in eastern Gansu Province and east-central Shanxi Province and highly affected by climate change, monitoring should be strengthened by adopting adaptive management approaches, such as agricultural technologies introducing drought-resistant and cold-resistant plants, improving land use, and enhancing climate adaptability to increase resilience. Finally, human disturbance zones, concentrated in northern and southern LP, require reduction of human interference and promotion of ecosystem self-restoration.

4.1 Impacts of Climate Change and Human Activities on EEQ

Vulnerable ecosystems in arid and semi-arid areas are highly sensitive to climate change \cite{Shi2021, Chen2024}. Climate change impacts on EEQ are primarily manifested through direct effects on plant growth and indirect regulation of human activities \cite{Shao2024}. Previous studies have shown that climate change accounts for 71.52% of RSEI variation in the Shendong Mining Area \cite{Tian2025}. Furthermore, it contributes to improving EEQ in 72.67% of this area, particularly in southwestern and southeastern parts \cite{Tian2025}. In contrast, areas experiencing negative effects are mainly located in northeastern and southeastern parts, largely consistent with the spatial distribution of precipitation contribution \cite{Tian2025}. Other studies have identified precipitation and temperature as primary climatic variables influencing EEQ on the LP \cite{Bai2023b, Zhou2024}. Over time, ecosystem distribution and biophysical processes have become increasingly correlated with rising temperatures and shifting precipitation patterns. In this study, a positive correlation between kRSEI and precipitation was detected in 73.56% of the LP, suggesting that increased precipitation generally promoted EEQ, particularly in central and western parts. These regions are characterized by low precipitation, with main land cover types being grasslands, croplands, and bare soil. Increased precipitation can enhance soil moisture and improve water availability, thereby supporting vegetation growth and ecological recovery with significant positive effects on EEQ \cite{Gong2025}. However, these improvements are limited by a precipitation threshold of 837.66 mm/a. Beyond this optimal level, EEQ tends to decline, as excess precipitation in areas with sparse vegetation and loose soil (typical of the LP) leads to soil organic matter loss and severe water erosion, ultimately degrading ecological quality \cite{Zhang2023b}. Actually, precipitation on the LP hardly reaches this threshold (837.66 mm/a), thus precipitation primarily exerted positive effects on EEQ. Regarding temperature, a positive correlation with kRSEI was observed in 49.28% of the LP, whereas 50.72% exhibited negative correlation, indicating a complex relationship between temperature and EEQ. Compared with temperature, precipitation exerted a more pronounced positive effect on EEQ.

Human activities, including population growth, land use change, and implementation of ecological restoration measures, also play a dual role in affecting EEQ \cite{Li2021, Xiao2022, Zou2022}. While many studies have identified climate change as the dominant factor driving regional EEQ improvements \cite{Wang2019, Liang2024, Tian2025}, our results showed human activity influence was much stronger. In arid and semi-arid areas, advanced agricultural irrigation systems and intensive human activities can lead to overexploitation of water and land resources, negatively impacting EEQ \cite{Zhang2022d}. Conversely, implementation of ecological restoration projects and environmental protection policies improves EEQ in the short term \cite{Zhang2025}, and these effects often surpass those of long-term climate change \cite{Zhou2015}. Therefore, within the context of climate change, effectively managing human activities is crucial for maintaining and improving EEQ on the LP. Comparative analyses of factors affecting EEQ in this region provide valuable scientific insights for understanding ecosystem dynamics and optimizing environmental management policies.

4.2 Comparison Between kRSEI and Traditional RSEI

Several studies have attempted to refine RSEI by tailoring it to specific regional ecological features. For example, the Arid Remote Sensing Ecological Index (ARSEI) was developed for evaluating EEQ in desert environments \cite{Wang2020}, and the Mine-Specific Eco-Environment Index (MSEEI) was created for monitoring EEQ in mining areas \cite{Zhang2023a}. This study further improved traditional RSEI by replacing the greenness indicator NDVI with kNDVI to construct kRSEI. The integration of kNDVI was particularly advantageous for LP investigations, which are characterized by undulating topography, diverse vegetation types, and severe soil erosion. The adaptive stretching-based algorithm for kNDVI significantly improves differentiation between various land cover types such as forests and barren land \cite{Wang2022c}. Since 1999, large-scale ecological restoration projects including the Grain-for-Green program and grazing bans have substantially increased vegetation cover in certain areas. However, traditional NDVI often exhibits saturation in regions with high vegetation density, limiting its ability to capture dynamic vegetation changes \cite{Wang2022b}. By dynamically adjusting the relationship between near-infrared and red spectral bands, kNDVI offers more accurate detection of ecological changes in densely vegetated areas. Moreover, vegetation growth cycles on the LP are strongly influenced by seasonal precipitation and temperature. The kNDVI, with its superior phenological sensitivity, enables more precise tracking of vegetation characteristics at different growth stages, thereby improving identification of temporal trends in vegetation dynamics. These capabilities are particularly crucial for the LP, where ecological monitoring can be very challenging due to complex terrain and diverse vegetation types.

4.3 Limitations and Prospects

This study assessed EEQ on the LP using an RSEI-based model incorporating kNDVI instead of traditional NDVI to evaluate EEQ more accurately in vegetated areas. However, the proposed model still has limitations in applicability, as it could not fully account for regional differences and potential inconsistencies in data obtained from different remote sensing sources (MODIS, Landsat, and Sentinel-2) \cite{Li2017}. Additionally, limitations exist in the multiple regression residual analysis employed to identify complex or combined effects of climate change and human activities on EEQ and quantify their relative contributions. One issue is the lack of agreement on selection of relevant climatic variables (such as precipitation, humidity, temperature, or solar radiation) when constructing the regression model for kRSEI. It also remains difficult to separate the influence of climatic shifts from that of human activities \cite{Wessels2012}. In simple terms, when anthropogenic impacts are more pronounced, the role of climatic factors may be mistakenly attributed to human actions. Therefore, future research should consider incorporating a broader set of climatic variables to more accurately assess relative contributions of environmental factors to kRSEI. Finally, some limits must be mentioned regarding ecological zone classification based on analysis of climatic and anthropogenic factor contributions to EEQ and proposed restoration strategies for each zone. While this study provides valuable insights for future restoration projects, current ecological restoration strategies don't fully consider the balance between financial investment and benefit returns. Moreover, implementation of restoration measures must account for potential management and coordination challenges posed by administrative boundaries. Moving forward, it is essential to further optimize restoration project implementation to ensure such efforts achieve ecological benefits while maintaining economic feasibility and functional regional coordination.

5 Conclusions

MODIS remote sensing images of the LP from 2000 to 2022 were used to analyze spatiemporal variations in kRSEI, including trends and projected future trajectories, and to assess impacts of climate change and human activities on EEQ and their respective contributions. Spatially, EEQ exhibited a stepwise degradation pattern from southeast to northwest. Starting from 2000, areas rated as having moderate EEQ or higher gradually expanded, with boundaries shifting northwestward. However, after 2015, this trend showed signs of reversal, with the boundary moving back toward the southeast. On an interannual scale, EEQ demonstrated a general declining trend during the study period, indicating that in the future, EEQ across most LP regions is projected to follow a weak anti-persistent trend. It is noteworthy that kRSEI showed a positive correlation with precipitation, especially in central and western LP. However, improvements appeared limited by a precipitation threshold of 837.66 mm/a, beyond which additional precipitation did not enhance EEQ. The relationship between temperature and EEQ was more complex: moderate temperature increases could be beneficial, whereas excessive warming tended to exert negative impacts on the ecological environment. Climate change and human activities jointly affected 65.78% of the LP area, predominantly via negative impacts on EEQ. Moreover, compared with climate change, human activities had stronger effects across all land cover types. To minimize anthropogenic disturbances and foster natural ecosystem recovery, we divided the LP into six ecological restoration zones. The results of this study provide important insights into patterns and mechanisms of ecological change on the LP and will assist in identifying key management units, offering a scientific basis for implementing region-specific ecological protection and restoration strategies.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was funded by the National Natural Science Foundation of China (42361017), the Gansu Provincial Science and Technology Program–Special Program for Key Research and Development (R&D) on Ecological Civilization Construction in Gansu Province (24YFFA050), and the Gansu Agricultural University–Gansu Provincial Academy of Natural Resources Planning Joint Graduate Training Base Project (GAU2024-003).

Author Contributions

Conceptualization: Tingting Pei
Data curation: Ruiyun Xi, Ying Chen, Baopeng Xie, Wen Wang, Li Hou
Formal analysis: Ruiyun Xi, Li Hou
Funding acquisition: Tingting Pei, Wen Wang
Investigation: Li Hou
Methodology: Ruiyun Xi, Tingting Pei
Project administration: Tingting Pei, Ying Chen
Resources: Ying Chen
Software: Ruiyun Xi, Tingting Pei, Ying Chen
Writing – original draft preparation: Ruiyun Xi
Writing – review and editing: Tingting Pei, Ying Chen

All authors approved the manuscript.

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