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Article

Monitoring and Evaluation of Ecological Environment Quality in the Tianshan Mountains of China Using Remote Sensing from 2001 to 2020

by
Yuting Liu
1,2,
Chunmei Chai
1,
Qifei Zhang
3,*,
Xinyao Huang
1 and
Haotian He
1
1
School of Life and Geography, Kashi University, Kashi 844000, China
2
Key Laboratory of Biological Resources and Ecology of Pamirs Plateau in Xinjiang Uygur Autonomous Region, Kashi 844000, China
3
School of Geographical Sciences, Shanxi Normal University, Taiyuan 030031, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(4), 1673; https://doi.org/10.3390/su17041673
Submission received: 5 December 2024 / Revised: 12 February 2025 / Accepted: 13 February 2025 / Published: 17 February 2025
Browse Figures
Figure 1
<p>Overview of the study area.</p> "> Figure 2
<p>(<b>a</b>) Spatiotemporal characteristics of RSEI in 2001, (<b>b</b>) spatiotemporal characteristics of RSEI in 2010, (<b>c</b>) spatiotemporal characteristics of RSEI in 2020, and (<b>d</b>) spatiotemporal characteristics of the average RSEI values from 2001 to 2020.</p> "> Figure 3
<p>Proportion of different levels of RSEI in the Tianshan Mountains from 2001 to 2020.</p> "> Figure 4
<p>The sustainability and stability of the ecological environment in the Tianshan Mountains of China from 2001 to 2020. (<b>a</b>) Spatial distribution of the RSEI coefficient of variation; (<b>b</b>) spatial distribution of Hurst exponent of the RSEI.</p> "> Figure 5
<p>(<b>a</b>) Spatiotemporal characteristics of the average NDVI values from 2001 to 2020; (<b>b</b>) temporal changes in NDVI from 2001 to 2020.</p> "> Figure 6
<p>(<b>a</b>) Spatiotemporal characteristics of FVC in 2001, (<b>b</b>) spatiotemporal characteristics of FVC in 2010, (<b>c</b>) spatiotemporal characteristics of FVC in 2020, and (<b>d</b>) spatiotemporal characteristics of the average FVC values from 2001 to 2020.</p> "> Figure 7
<p>Area and proportion of vegetation coverage grades in the Tianshan Mountains from 2001 to 2020.</p> "> Figure 8
<p>(<b>a</b>) Spatiotemporal characteristics of NPP in 2001, (<b>b</b>) spatiotemporal characteristics of NPP in 2010, (<b>c</b>) spatiotemporal characteristics of NPP in 2020, and (<b>d</b>) temporal changes in NPP in China’s Tianshan Mountains from 2001 to 2020.</p> "> Figure 9
<p>(<b>a</b>) Spatiotemporal characteristics of NEP in 2001, (<b>b</b>) spatiotemporal characteristics of NEP in 2010, (<b>c</b>) spatiotemporal characteristics of NEP in 2020, and (<b>d</b>) temporal changes in NEP in China’s Tianshan Mountains from 2001 to 2020.</p> "> Figure 10
<p>Annual spatiotemporal characteristics of climate factors in Tianshan Mountains from 2001 to 2020. (<b>a</b>) Precipitation spatial patterns, (<b>b</b>) precipitation temporal trends, (<b>c</b>) temperature spatial patterns, and (<b>d</b>) temperature temporal trends.</p> "> Figure 11
<p>Correlation coefficients between the RSEI and precipitation, temperature in the Tianshan Mountains from 2001 to 2020.</p> "> Figure 12
<p>Dynamic changes in land types in the Chinese Tianshan Mountains from 2000 to 2020.</p> ">
Review Reports Versions Notes

Abstract

:
High-altitude mountainous regions are highly vulnerable to climate and environmental shifts, with the current global climate change exerting a profound influence on the ecological landscape of the Tianshan Mountains in China. This study assesses the ecological security quality in the Tianshan Mountains of China from 2001 to 2020 by employing various remote sensing techniques such as the Remote Sensing Ecological Index (RSEI) for evaluation, Normalized Difference Vegetation Index (NDVI) for fractional vegetation cover (FVC) analysis, the CASA model for estimating vegetation primary productivity (NPP), and a carbon source/sink model for calculating the net ecosystem productivity (NEP) of vegetation. The research also delves into the evolutionary trends and impact mechanisms on the ecological environment using land use and meteorological data. The findings reveal that the RSEI’s principal component (PC1) exhibits significant explanatory power, showing a notable increase of 5.90% from 2001 to 2020. Despite relatively stable changes in the RSEI over the past two decades covering 61.37% of the study area, there is a prevalent anti-persistence pattern at 72.39%. Notably, NDVI, FVC, and NPP display upward trends in vegetation characteristics. While most areas in the Tianshan Mountains continue to emit carbon, there is a marked increase in NEP, signifying an enhanced carbon absorption capacity. The partial correlation coefficients between the RSEI and temperature, as well as precipitation, demonstrate statistically significant relationships (p < 0.05), encompassing 6.36% and 1.55% of the study area, respectively. Temperature displays a predominantly negative correlation in 98.71% of the significantly correlated zones, while precipitation exhibits a prevalent positive correlation. An in-depth analysis of how climate change affects the quality of the ecological environment provides crucial insights for strategic interventions to enhance regional environmental protection and promote ecological sustainability.

1. Introduction

The Tianshan Mountains are not only important ecological barriers but also natural reservoirs [1]. The region has experienced significant temperature rise [2] and changes in precipitation [3] patterns over the past few decades. These changes have exacerbated changes within the local ecosystem, primarily manifested in the continuous retreat of glaciers [4,5]. The glacier retreat has not only altered the river runoff and water resource distribution within the region but has also had profound impacts on water supply and ecological security in downstream areas [6,7]. Additionally, glacier retreat may further intensify evaporation in oases and groundwater depletion, exacerbating the trend toward aridification in the region [8,9]. Therefore, long-term remote sensing monitoring, the in-depth analysis of the driving forces of ecological environmental changes, and the formulation of corresponding adaptive management strategies are essential to provide a scientific basis for climate change adaptation in this region and globally.
Ecological environment remote sensing monitoring includes the monitoring of the terrain and land cover types of the Tianshan Mountains, as well as the remote sensing monitoring of vegetation, glaciers, and atmospheric elements [10,11]. Early scholars mostly used a single index to monitor the ecological environment of the Tianshan Mountains from the perspectives of land cover types, NPP, and NDVI temporal changes [12,13]. Hou et al. quantified the impact of oasification and climate change on NPP on the northern slope of the Tianshan Mountains, but a single index is inadequate to explain the overall ecological environment quality of the Tianshan Mountains [14]. Xu et al. [15] proposed a Remote Sensing Ecological Index model in 2013, which couples four ecological environmental indicators closely related to human activities in a region—surface wetness (WET), dryness (NDBSI), heat (LST), and greenness (NDVI). This model can avoid setting artificial weights and visualize the results, enabling the rapid and intuitive evaluation of the regional ecological environment [16,17]. Currently, the Remote Sensing Ecological Index is widely used for evaluating ecological environment quality in urban areas [18], watersheds [19], wetlands [20], and arid areas [21]. Numerous studies have shown that the RSEI combines the important indicators of ecological environment quality and has a high evaluation applicability, enabling the real-time monitoring of ecological environment quality in the research area, which is of great significance for the protection and governance of ecological environment quality in the research area [22,23]. Aizizi et al. [24] used MODIS remote sensing data products in Xinjiang to calculate the Remote Sensing Ecological Index with the Google Earth Engine remote sensing cloud platform, analyzing the RSEI for the summer and spring seasons spanning from 2000 to 2020, in order to provide a reference basis for ecological environment protection and high-quality development in Xinjiang. Ariken and colleagues [25] incorporated various remote sensing data sources, such as Landsat data and data from the Tiangong-2 satellite, to develop the RSEI which was utilized to assess the coupling coordination between urbanization and the ecological environment in the Yanqin Basin, a representative arid region in China, and the findings suggest that the RSEI shows a level of effectiveness and superiority in evaluating the ecological environment. Yuan et al. [26] used the RSEI to analyze the spatiotemporal changes in the ecological quality of the Dongting Lake Basin from 2001 to 2019, and identified land use/cover, climate (annual average rainfall), and human disturbance (GDP) as potential factors influencing the ecological quality of the Dongting Lake Basin.
In the Tianshan Mountains, the application of the RSEI helps to quantitatively analyze the spatiotemporal variation patterns of the ecological environment, especially under the influence of climate change and human activities, as well as the interactions and trends of different ecological elements [27,28]. Through the study of the RSEI, a deeper understanding of the mechanisms of ecological environment changes in the Tianshan Mountains can be achieved, providing a scientific basis for regional ecological environment protection and sustainable development. Therefore, this study selects MODIS data from 2001 to 2020, couples the greenness index, wetness index, dryness index, and heat index to construct the Remote Sensing Ecological Index, analyzes the spatiotemporal pattern changes in ecological environment quality in the Tianshan mountainous region over the past 20 years, uses the coefficient of variation and Hurst index to analyze the patterns of ecological quality changes, and combines climate factors to analyze the response mechanisms of ecological environment quality. This research aims to provide a scientific basis for ecological environment protection and sustainable development.

2. Materials and Methods

2.1. Overview of the Study Area

The Tianshan Mountains are located in the heartland of the Eurasian continent, consisting of multiple mountain ranges [29]. The Tianshan range within China spans from approximately 39°36′ N to 45°30′ N and 73°30′ E to 96°06′ E, with a length of about 1700 km from east to west (Figure 1). The Tianshan Mountains exhibit distinct continental climate characteristics, with low precipitation, arid climate, long sunshine hours, large diurnal temperature differences, and an annual average temperature of 9–11 °C, with annual natural precipitation of less than 180 mm. The average elevation of the Tianshan Mountains is about 4000 m, with alternating mountains and basins, significant height differences, and pronounced vertical differentiation, featuring vertical natural zones such as the temperate desert zone, mountain grassland zone, and alpine meadow zone [30]. Due to the elevation of the Tianshan Mountains obstructing the north–south water vapor transport, the precipitation on the northern and southern slopes of the Tianshan Mountains differs [31]. The northern slope of the Tianshan Mountains is windward, where the water vapor from the Atlantic Ocean enters through gaps in the western part of the Junggar Basin, leading to orographic rainfall and significant vertical differentiation in vegetation space. The southern slope of the Tianshan Mountains receives less rainfall, has high evaporation rates, and is mostly desert, with sparse vegetation [32].

2.2. Data Sources

This study employs vegetation indices, including NDVI, and ecological indices, such as the Remote Sensing Ecological Index (RSEI), to analyze and monitor the health of vegetation and ecosystems. Vegetation indices provide insights into the growth status and coverage of vegetation, whereas ecological indices offer a thorough assessment of ecosystem quality. The NDVI data used are from MOD13Q1 data, with a temporal resolution of 16 days spanning from 2001 to 2020 and a spatial resolution of 250 m. The calculation of the RSEI in this study also utilizes MOD09A1 and MOD11A2 data. Land use data are obtained from the Resource and Environment Data Cloud Platform of the Chinese Academy of Sciences (http://www.resdc.cn/), providing land use data for China in 2000, 2010, and 2020 at a resolution of 30 m. Additionally, the gridded temperature and precipitation data from 2001 to 2020 used in this study are from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn/, accessed on 4 December 2024). Details of the data used in this study and their sources are shown in Table 1.

2.3. Research Methods

2.3.1. Construction of Relatively Stable RSEI

The RSEI integrates four remote sensing indices comprehensively—greenness component (NDVI), wetness component (Wet), dryness component (NDBSI), and heat component (LST) to construct it, with the formula as follows [33]:
RSEI = ƒ WET , NDBSI , NDVI , LST
To avoid dimensional imbalances, each index is normalized, and a principal component analysis is performed. The initial Remote Sensing Ecological Index RSEI0 is calculated through the first principal component (PC1). To facilitate index measurement, RSEI0 is also normalized to obtain the final RSEI. The calculation formula is as follows:
RSEI 0 = 1 − PC 1 [ ƒ NDVI , Wet , NDBSI , LST ]
RSEI = RSEI 0 − RSEI min RSEI max − RSEI min
where RSEImin is the minimum value of the Remote Sensing Ecological Index, and RSEImax is the maximum value of the Remote Sensing Ecological Index. The RSEI ranges from 0 to 1, with higher values indicating better ecological environment quality in the research area.

2.3.2. Analysis Method of RSEI Trend

Utilize the coefficient of variation (Cv) to indicate the stability of RSEI changes and employ the Hurst index to analyze the trend of changes in each pixel over the past 20 years.
(1)
Coefficient of Variation (Cv)
The coefficient of variation (Cv) reflects relative variability, i.e., the degree of dispersion of a random variable, calculated as follows [34]:
C V = δ μ
where δ represents the standard deviation, and μ denotes the mean, according to the literature, when Cv ≤ 0.1, 0.10 < Cv ≤ 0.20, 0.20 < Cv ≤ 0.30, and Cv > 0.3, they are classified as stable, relatively stable, relatively unstable, and unstable, respectively.
(2)
Hurst Index
The Hurst Index can assess the future trend of a time series and has wide applications in hydrology, geology, climatology, and other fields. The calculation formula is as follows [35]:
H = 1 τ ∑ t = 1 τ H ( t ) ( τ = 1 , 2 , … , n )
when 0 < H < 0.5, it indicates anti-persistence in the RSEI sequence, meaning the future trend is opposite to the past, with smaller H values indicating stronger anti-persistence; when 0.5 < H < 1, it signifies persistence in the RSEI sequence, with larger H values indicating stronger persistence.

2.3.3. Vegetation Model Calculation

(1)
Fractional Vegetation Cover Calculation
The calculation of fractional vegetation cover is derived based on the two-point model principle, with the calculation formula as follows [36]:
FVC = NDVI − NDVI s o i l NDVI v e g − NDVI s o i l
where NDVIsoil represents the NDVI value in areas completely covered by bare soil or without vegetation, while NDVIveg represents the NDVI value of pixels entirely covered by vegetation.
(2)
CASA Model
Net primary productivity (NPP) serves as a critical indicator of carbon cycling and energy dynamics within terrestrial ecosystems. It is widely utilized as a key ecological metric for assessing vegetation carbon sequestration capacity. However, existing NPP datasets, such as MODIS products, face significant limitations when applied to large-scale regions with complex topographies. Issues include coarse spatial resolution, gaps in continuous observation and missing data, particularly in the lower basins of the Northwestern China and Central Asia region, where some areas remain entirely unrepresented.
Compared to other products, the simulated NPP from the CASA model showed a close match (R = 0.82, p < 0.01) with the NPP from MOD17A3H, which proved better in this study. This study employs a modified CASA model to estimate NPP on the GEE platform, with input data including monthly average temperature, monthly precipitation, monthly solar radiation, vegetation type, and NDVI. The expression is as follows [37]:
NPP ( x , t ) = APAR ( x , t ) × ε ( x , t )
where NPP(x, t) denotes the net primary productivity of vegetation at pixel x in month t; APAR(x, t) represents the photosynthetically active radiation of vegetation at pixel x in month t; ε(x, t) signifies the actual light energy utilization efficiency of vegetation at pixel x in month t.
(3)
Carbon Source/Sink Estimation Model
Net ecosystem productivity is represented by subtracting vegetation net primary productivity from soil heterotrophic respiration carbon emissions, widely used to measure the size of carbon sinks [38]. The calculation formula is as follows:
NEP ( x , t ) = NPP ( x , t ) − R h ( x , t )
R h = 0.22 × E x p ( 0.0913 T ) + L n ( 0.3145 P + 1 ) × 30 × 46.5 %
where NEP(x, t) indicates the net ecosystem productivity at pixel x in month t (gC m−2), Rh represents soil heterotrophic respiration (gC m−2), T is the temperature (°C), and P is the precipitation (mm), and when NEP > 0, the ecosystem acts as a carbon sink; conversely, when NEP < 0, it acts as a carbon source.

2.3.4. Pearson Correlation Coefficient

The Pearson correlation coefficient is used to evaluate between the RSEI and driving factors. The correlation coefficient R ranges from −1 to 1, where a larger absolute value of R indicates a stronger correlation; as R approaches 0, the correlation weakens. Significance testing of the correlation coefficient is conducted using t-tests, and the corresponding p-value for the correlation coefficient is obtained through table lookup, with the calculation formula as follows [39]:
r x y = n ∑ x i y i − ∑ x i y i n ∑ x i 2 − ( ∑ x i ) 2 n ∑ y i 2 − ( ∑ y i ) 2
where rxy represents the Pearson correlation coefficient between variables x and y, n is the number of observed pixels, xi denotes the i-th pixel value of x, and yi represents the i-th pixel value of y.

3. Results

3.1. Remote Sensing Ecological Index Characteristics

3.1.1. Basic Statistics of Remote Sensing Ecological Index

The results of principal component analysis for the four components of the RSEI in the Tianshan Mountains in 2001, 2010, and 2020 are shown in Table 2. From the table, it can be observed that the first principal component (PC1) after transformation had a relatively high contribution rate in the three years studied, at 46.69%, 47.63%, and 44.27%, respectively, indicating that PC1 after transformation already encapsulates most of the original image’s attribute features, hence, it can be used to represent the four component indices. The aridity index had a relatively low contribution rate in the three years studied, possibly due to the high altitude of the Tianshan Mountains, where the surface cover mainly comprises glaciers and snow.
Between 2001 and 2020, the contribution values of temperature to PC1 were positive, indicating a positive impact of temperature on ecological quality. This positive correlation between temperature and ecological quality is related to the augmenting effect of temperature on plant growth, especially in colder regions where higher temperatures help in photosynthesis and prolong the growth cycle of plants [40]. Conversely, the contribution values of greenness and humidity to PC1 were negative, implying a detrimental effect of greenness and humidity indicators on ecological quality. However, the negative effects of greenness and humidity indices reveal the pressure that high vegetation coverage or inappropriate humidity levels may exert on the ecosystem. For example, the abundance of vegetation may lead to intensified resource competition [41], while abnormal changes in humidity may affect soil quality and the efficiency of plant water use [42]. In 2010, the contribution values of temperature to PC1 were negative, indicating a negative impact of temperature on ecological quality, while the contributions of greenness and wetness to PC1 were positive, implying a positive impact of greenness and wetness indicators on ecological quality. Analyzing the load values of the first principal component (Table 2), the absolute sum of the contributions of the aridity index (NDBSI) and temperature index (LST) was smaller than the sum of the contributions from the greenness index (NDVI) and wetness index (WET) in all three years, indicating a greater influence of greenness and wetness on ecological environmental monitoring.

3.1.2. Spatiotemporal Variations in Remote Sensing Ecological Quality Index

To explore the spatial changes in the ecological environment from 2001 to 2020, the ecological environments across different stages were spatially classified into five ecological levels—poor, relatively poor, moderate, good, and excellent—with intervals of 0.2, as shown in Figure 2. Over the past 20 years, the overall ecological environment in the Tianshan Mountains exhibited a moderate level, with environmentally harsh areas (poor and relatively poor levels) mainly concentrated on the southern edge of the Tianshan, while areas with good ecological conditions (good and excellent levels) were concentrated near the Tianshan glaciers. Compared to 2001, the regions with excellent and good RSEI levels significantly increased in 2010, mainly concentrated near the Tianshan glaciers, Bosten Lake, and Konqi River. As of 2020, the regions in the northern and southwestern parts of the Tianshan Mountains have shown an expanding trend in the levels of the Remote Sensing Ecological Index (RSEI), with relatively high and good levels. At the same time, the areas surrounding the Bosten Lake and Kunlun River Basin have also experienced an increase in regions with lower RSEI levels.
The ecological index of the Tianshan Mountains increased from 0.46 in 2001 to 0.49 in 2010, representing a 7.64% increase. However, from 2010 to 2020, there was a 1.62% decrease, and there was a 5.90% increase by 2020 compared with 2001. Overall, over the past 20 years, the year 2011 had the best ecological environmental quality, followed by 2002. As shown in Figure 3, the ecological environmental quality in the Tianshan Mountains was mainly at a moderate level, with the highest proportion occurring in 2010 at 84.00%, followed by 73.02% in 2018. The areas with good ecological quality in the Tianshan Mountains were highest in 2002 at 42.10%, followed by 34.80% in 2011. In 2005, the RSEI for ecological quality was 0.52, with the highest proportion of excellent quality areas at 1.82%.

3.1.3. Stability and Persistence

The stability and persistence of ecological environmental quality in the Tianshan Mountains from 2001 to 2020 were calculated, as illustrated in Figure 4. The Tianshan Mountains exhibit very clear spatial distribution differences, with the ecological quality near the Tianshan glaciers showing relatively stable changes, while the southern edge areas of the Tianshan demonstrate relatively unstable changes, and areas near Konqi River exhibit unstable ecological quality changes. Over the past 20 years, the changes in ecological quality in the Chinese Tianshan Mountains have been predominantly stable, covering 61.37% of the study area, followed by areas with relatively unstable and relatively stable conditions, accounting for 18.81% and 18.64%, respectively, with a small proportion of unstable areas at only 1.17%. Based on Figure 4b, the Hurst index average value for the RSEI in the Tianshan Mountains over the past 20 years was 0.45, primarily exhibiting anti-persistence, accounting for 72.39%.

3.2. Vegetation Characteristics

3.2.1. NDVI Changes

Figure 5a illustrates the spatial variation in the NDVI in the Tianshan Mountains from 2001 to 2020, represented as ΔNDVI, which is calculated by subtracting the NDVI value in 2001 from that in 2020. From 2001 to 2020, the improvement area of the NDVI in the Tianshan vegetation (0 < ΔNDVI ≤ 1) reached 80.40%, mainly distributed south of the Tianshan, while the area with a decreased vegetation NDVI (−1 < ΔNDVI < 0) was distributed north of the Tianshan. Over the past 20 years, the NDVI of Tianshan vegetation has fluctuated over time, showing a significant increasing trend overall (p < 0.01) at a rate of 0.027/10a, with the NDVI value increasing from 0.262 in 2001 to 0.314 in 2020, a 19.85% increase, as shown in Figure 5b.

3.2.2. Fractional Vegetation Cover Changes

Based on the spatial distribution of the fractional vegetation cover of the Tianshan Mountains at intervals of 10 years from 2001 to 2020, and the multi-year averages, it is observed that the high coverage areas are mainly located in the northern part of the Tianshan, while the southern part shows lower fractional vegetation coverage. FVC in Tianshan Mountains increased from 0.315 to 0.34, representing a 7.62% rise between 2001 and 2010. From 2010 to 2020, the coverage increased from 0.339 to 0.355, a 4.72% increase. The vegetation coverage in the Tianshan Mountains showed an upward trend from 2001 to 2020, with a 12.70% increase. Based on the relevant literature, the vegetation coverage is divided into five levels with an interval of 0.2, ranging from poor, fair, moderate, good, to excellent for the years 2001, 2010, and 2020, and their corresponding spatial distributions are shown in Figure 6.
Figure 6 illustrates the spatial distribution of vegetation coverage levels in the Tianshan Mountains for the years 2001, 2010, and 2020. The figure clearly indicates that the northern regions of the Tianshan Mountains have consistently maintained a higher level of vegetation coverage, predominantly in the “good” and “excellent” categories. In contrast, the southern regions have shown a more varied pattern, with a mix of “fair”, “moderate”, and “good” coverage levels. Notably, the transition from 2001 to 2010 saw a significant expansion of “good” coverage areas, particularly in the central and eastern parts of the mountain range. By 2020, this trend persisted, showcasing enhanced vegetation coverage, particularly in the western and central Tianshan regions.
Analyzing the vegetation situation in the Tianshan Mountains based on the 5-level classification, the proportion of vegetation coverage at different levels is calculated, as shown in Figure 7. In 2001, the lowest vegetation coverage was observed, with the proportions of poor and fair vegetation coverage areas being 51.70% and 19.58%, respectively. In 2010, the proportions of good and excellent vegetation coverage areas significantly increased, mainly reflected in the increase in moderate to high vegetation coverage areas in the southern part. In 2020, the vegetation coverage rate was the highest, with the proportions of good and excellent coverage areas being 16.48% and 7.12%, respectively. Compared to 2001, the poor coverage area decreased by 15.56%. Spatial variability can be seen in Figure 7, and between 2001 and 2010, the comparison of vegetation coverage levels showed a decrease of 10.68% in poor vegetation coverage areas, while good and excellent coverage areas increased by 23.38% and 17.58%, respectively. Comparing 2010 to 2020, the changes in 2020 were relatively slow, with a decrease of 5.47% in poor vegetation coverage areas and an increase of 12.10% and 2.60% in good and excellent coverage areas, respectively.

3.2.3. Spatiotemporal Development of Vegetation NPP

From 2001 to 2020, the vegetation net primary productivity (NPP) in the Chinese Tianshan region exhibited significant spatial variations. In particular, in the Ili River Valley in the northern part of the Tianshan Mountains, the value of vegetation NPP reached a very high level, as clearly shown in Figure 8. Observing the region south of the Tianshan Mountains, it can be noted that the spatial distribution characteristics of NPP in 2010 improved compared to 2001. From 2001 to 2020, NPP in the entire Chinese Tianshan region showed a clear increasing trend. Specifically, the NPP value increased from 8.01 g·m−2 in 2001 to 9.56 g·m−2 in 2020, with an average annual growth rate of approximately 0.091 g C·m−2·a−1. Over the past two decades, the total NPP has increased by nearly 19.35%. For the entire Tianshan vegetation cover area, the average annual vegetation NPP from 2001 to 2020 was 8.615 g·m−2. A further analysis of the changes in vegetation NPP in the Tianshan region during different time periods from 2001 to 2020 revealed that during the period from 2001 to 2010, NPP showed a fluctuating decreasing trend, although this trend was not significant (p = 0.131), with a decrease rate of −0.0728 g C·m−2·a−1. However, since 2010, the growth trend of vegetation NPP in the Chinese Tianshan region has become highly significant (p = 0.01), with growth rates of 0.1718 g C·m−2·a−1 annually in the vegetation area.

3.2.4. Temporal and Spatial Dynamics of Vegetation Carbon Source/Sink

Significant spatial differences are observed in the vegetation NEP in the Chinese Tianshan Mountains, with the highest NEP values found in the Ili Valley in the northern part of Tianshan (Figure 9). About 76.68% of the Chinese Tianshan Mountains is classified as a carbon source area (NEP < 0), mainly concentrated in the southern part of Tianshan. The vegetation NEP in the Chinese Tianshan Mountains has shown a significant increasing trend from −67.81 g C·m−2 in 2001 to −48.86 g C·m−2 in 2020, with a high growth rate of 0.94 g C·m−2·a−1. Looking at the trend characteristics of vegetation NEP in the Tianshan Mountains at different periods (around 2010), from 2001 to 2010, the NEP in the vegetation area showed a fluctuating decreasing trend at a rate of −0.90 g C·m−2·a−1 (p > 0.1). However, since 2010, the vegetation NEP has shown a significant increasing trend (p < 0.01), with growth rates reaching 2.10 g C·m−2·a−1.

3.3. Exploring the Impact of Climate Factors on RSEI Variation

From 2001 to 2020, both the temperature and precipitation of the Tianshan Mountains in China have shown an upward trend, as shown in Figure 10, with growth rates of 5.983 mm/10a and 0.024 °C/10a, respectively. Compared to the temperature increase rate of 0.071 °C/10a from 2000 to 2016 [43], the temperature rise rate from 2001 to 2020 is relatively slow. This aligns with research on the warming and moistening trends in Central Asia over the past few decades [44]. The northern slope of Tianshan is humid and rainy, with an annual precipitation of over 600 mm and rich vegetation, mainly grasslands; while the southern slope faces the Tarim Basin, characterized by arid and low rainfall climates with an annual precipitation of around 100 mm, resulting in sparse vegetation, primarily desert and desert grasslands, making the ecological environment relatively fragile.
Based on resampling the 1 km resolution temperature and precipitation data from the literature, the partial correlation coefficients of the RSEI with temperature and precipitation were calculated, as shown in Figure 11. The mean partial correlation coefficients of the RSEI with temperature and precipitation were −0.14 and 0.02, respectively. The areas where the RSEI has a significant negative correlation with temperature through a significance test (p < 0.05) cover 6.36% of the study area, with a predominance of significantly negative correlations at 98.71%. The areas where the RSEI has a significant correlation with precipitation through a significance test (p < 0.05) cover 1.55% of the study area, with predominantly significant positive correlations accounting for 58.31%.
The RSEI shows a spatial correlation with FVC, with high alignment in their spatial distribution. Climate change mainly affects ecological quality by altering FVC, particularly significantly impacting the RSEI changes in densely vegetated areas such as forests. In pixels where the RSEI shows a significant positive correlation with temperature and precipitation, the average change rates of the RSEI are 0.05%/a and 0.03%/a, respectively. In pixels where the RSEI shows a significant negative correlation with temperature and precipitation, the average change rates of the RSEI are −0.01%/a and −0.09%/a, respectively. Therefore, it can be observed that climate factors have a more significant positive impact on the improvement trend of ecological quality in regions with a positive relationship with the RSEI, with temperature showing a higher correlation compared to precipitation.

4. Discussion

Using key indicators such as the Remote Sensing Ecological Index (RSEI), Normalized Difference Vegetation Index (NDVI), Net Primary Productivity (NPP), and Net Ecosystem Productivity (NEP), the ecological environment changes in the Tianshan Mountains of China from 2001 to 2020 were comprehensively evaluated.

4.1. Land Use Change

Grassland and unused land are widely distributed in the Chinese Tianshan region, accounting for 41.39% and 41.76% of the regional area, respectively, with a certain distribution pattern and regularity. Grassland and forests are mainly concentrated in mountainous areas with significant undulations, while cultivated land is mainly concentrated in the low-altitude western parts of the Tianshan Mountains where water resources are abundant, and unused land is mainly concentrated in the eastern regions with lower precipitation. Over the past 20 years, drastic transformations have occurred among different land use/cover types in the Chinese Tianshan Mountains, with significant changes in water bodies and industrial land areas, as shown in Figure 12. Over the past two decades, the areas of forests, grasslands, and water bodies decreased by 3311.49 km2 (30.09%), 2711.46 km2 (2.07%), and 11,713.87 km2 (60.58%), respectively. Meanwhile, the areas of cultivated land, industrial land, and unused land showed an opposite trend, increasing by 10,913.60 km2 (46.86%), 1207.58 km2 (79.91%), and 5644.07 km2 (4.56%), respectively.
The transformation of land use will have profound impacts on the ecosystems of the Tianshan region and can also reflect the degree of regional development changes more intuitively [45]. For instance, the reduction in forest and grassland areas may lead to a decrease in biodiversity and increase the risks of soil erosion and desertification. The reduction in water bodies may threaten the local aquatic ecosystems and the sustainable management of water resources [46]. On the other hand, the increase in cultivated land and industrial land areas reflects agricultural expansion and urbanization processes, which may further pressure natural landscapes and habitats of wildlife [47]. Therefore, effective land management strategies need to be implemented to balance the needs of economic development and ecological conservation, ensuring the sustainable development of the Tianshan region.

4.2. Climate Factors

Climate observations in the Tianshan Mountains from 2001 to 2020 indicate a rise in temperature and a shift in precipitation patterns in the region [48]. Against the backdrop of global warming, the glaciers in the Tianshan Mountains have been rapidly retreating over the past half century, with approximately 97.52% of the glaciers in a state of retreat [49]. The melting of snow and glaciers in the mountainous areas has intensified, leading to increased runoff in downstream rivers, primarily influenced by rising temperatures [50]. This changing factor has resulted in a reduction in glacier volume by approximately 27 ± 15% [51], causing a decrease in humidity and ecosystem degradation, and subsequently impacting the overall performance of the RSEI. Furthermore, the lengthening of the plant growing season, enhanced vegetation productivity, and improved NDVI and NEP have made the climate and environmental changes in the region increasingly complex.
To address the challenges posed by these climate changes, the Tianshan region needs to strengthen the development of climate monitoring and prediction systems to better understand the impact of climate change on ecosystems. Additionally, adaptive management measures should be implemented, such as adjusting agricultural planting structures, optimizing water resource allocation, and protecting and restoring critical ecosystems, to mitigate the adverse effects of climate change.

4.3. Uncertainty

Through observations of the Tianshan Mountains in China from 2001 to 2020, it has been found that vegetation recovery indicators such as NDVI and NEP show an improving trend, but the Remote Sensing Ecological Index (RSEI) shows a declining trend. The improvement in vegetation characteristics may be closely related to a series of ecological conservation policies implemented by the government, such as the Natural Forest Conservation Project [52], the Grain for Green Program [53], and the establishment of the Tianshan National Nature Reserve, which have increased the region’s vegetation coverage and improved the local vegetation ecological environment.
However, the RSEI typically encompasses various ecological elements such as humidity, thermal environment, and vegetation and soil degradation, as well as factors like climate change, changes in land use patterns (such as urbanization and industrial activities), and so on [54,55]. In recent years, the expansion of construction land in the urbanization process near the Tianshan Mountains, the development of industrial areas, and the associated industrial pollution emissions have had a negative impact on the ecological environment [56]. This is reflected in changes in land cover types and the intensification of the urban heat island effect, to some extent offsetting the positive effects of vegetation improvement and causing the decline in the RSEI [57]. Additionally, agricultural activities in the Tianshan Mountains may also affect the quality of the ecological environment. For example, the excessive use of agricultural fertilizers and pesticides may lead to soil degradation and the eutrophication of water bodies, indirectly affecting the overall ecological condition [58].
In order to more comprehensively assess the ecological environment quality of the Tianshan Mountains, it is recommended to adopt a multi-index comprehensive evaluation method [59]. In addition to the RSEI, other indicators such as the biodiversity index, water pollution index, and soil erosion index should also be considered to obtain a more accurate understanding of the ecological environment status [60,61]. Furthermore, research on climate change in the Tianshan region should be strengthened, especially focusing on the impact of extreme weather events on ecosystems. In addition, the long-term monitoring of land use changes is recommended to evaluate the long-term effects of urbanization and industrial activities on ecosystems and to develop corresponding ecological compensation and restoration measures. Through these comprehensive measures, a better understanding of the complexity of the ecological environment of the Tianshan Mountains can be achieved, providing a scientific basis for the formulation of effective conservation policies.

5. Conclusions

The ecological environmental quality of the Tianshan Mountains was assessed using the RSEI to evaluate the changes between 2001 and 2020, as well as the underlying factors that influenced these alterations. The analysis revealed a close correlation between FVC and the RSEI, jointly reflecting the changes in ecological quality and spatial distribution in the study area. Despite a slight overall decline in ecological quality, the RSEI in 2020 increased by 5.90%, compared to 2001. There are spatial differences in the distribution of ecological quality, with most regions showing unsustainable changes, but the surrounding areas of glaciers being relatively stable. From 2001 to 2020, NPP and NEP in the Tianshan Mountains exhibited significant spatial differences, with the highest values in the Ili River Valley region. The annual average NPP increased by 19.35%, NEP showed a significant increase, but the southern Tianshan Mountains were mostly carbon sources. Vegetation ecological functions significantly strengthened after 2010. Significance test results (p < 0.05) indicate that 6.36% of the significant negative correlation areas with temperature are associated with the RSEI, accounting for 98.71% of the significantly correlated areas. Similarly, 1.55% of the significant positive correlation areas with precipitation are linked to the RSEI, representing 58.31% of the significantly correlated regions.

Author Contributions

Conceptualization, Y.L. and Q.Z.; methodology, Y.L., Q.Z. and H.H.; writing, Y.L., C.C., Q.Z. and X.H.; visualization, Y.L. and Q.Z, writing—original draft preparation, Y.L., C.C. and X.H.; writing—review and editing, Q.Z and H.H., visualization, Y.L. and Q.Z; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by The Third Xinjiang Comprehensive Scientific Expedition Project (2021xjkk0100); Kashi University Campus-level Research Project ((2024)2880); Xinjiang Autonomous Region Science and Technology Plan Project (2021D01B05) funding; On campus project of Kashi University (19(2643)).

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Overview of the study area.
Figure 1. Overview of the study area.
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Figure 2. (a) Spatiotemporal characteristics of RSEI in 2001, (b) spatiotemporal characteristics of RSEI in 2010, (c) spatiotemporal characteristics of RSEI in 2020, and (d) spatiotemporal characteristics of the average RSEI values from 2001 to 2020.
Figure 2. (a) Spatiotemporal characteristics of RSEI in 2001, (b) spatiotemporal characteristics of RSEI in 2010, (c) spatiotemporal characteristics of RSEI in 2020, and (d) spatiotemporal characteristics of the average RSEI values from 2001 to 2020.
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Figure 3. Proportion of different levels of RSEI in the Tianshan Mountains from 2001 to 2020.
Figure 3. Proportion of different levels of RSEI in the Tianshan Mountains from 2001 to 2020.
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Figure 4. The sustainability and stability of the ecological environment in the Tianshan Mountains of China from 2001 to 2020. (a) Spatial distribution of the RSEI coefficient of variation; (b) spatial distribution of Hurst exponent of the RSEI.
Figure 4. The sustainability and stability of the ecological environment in the Tianshan Mountains of China from 2001 to 2020. (a) Spatial distribution of the RSEI coefficient of variation; (b) spatial distribution of Hurst exponent of the RSEI.
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Figure 5. (a) Spatiotemporal characteristics of the average NDVI values from 2001 to 2020; (b) temporal changes in NDVI from 2001 to 2020.
Figure 5. (a) Spatiotemporal characteristics of the average NDVI values from 2001 to 2020; (b) temporal changes in NDVI from 2001 to 2020.
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Figure 6. (a) Spatiotemporal characteristics of FVC in 2001, (b) spatiotemporal characteristics of FVC in 2010, (c) spatiotemporal characteristics of FVC in 2020, and (d) spatiotemporal characteristics of the average FVC values from 2001 to 2020.
Figure 6. (a) Spatiotemporal characteristics of FVC in 2001, (b) spatiotemporal characteristics of FVC in 2010, (c) spatiotemporal characteristics of FVC in 2020, and (d) spatiotemporal characteristics of the average FVC values from 2001 to 2020.
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Figure 7. Area and proportion of vegetation coverage grades in the Tianshan Mountains from 2001 to 2020.
Figure 7. Area and proportion of vegetation coverage grades in the Tianshan Mountains from 2001 to 2020.
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Figure 8. (a) Spatiotemporal characteristics of NPP in 2001, (b) spatiotemporal characteristics of NPP in 2010, (c) spatiotemporal characteristics of NPP in 2020, and (d) temporal changes in NPP in China’s Tianshan Mountains from 2001 to 2020.
Figure 8. (a) Spatiotemporal characteristics of NPP in 2001, (b) spatiotemporal characteristics of NPP in 2010, (c) spatiotemporal characteristics of NPP in 2020, and (d) temporal changes in NPP in China’s Tianshan Mountains from 2001 to 2020.
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Figure 9. (a) Spatiotemporal characteristics of NEP in 2001, (b) spatiotemporal characteristics of NEP in 2010, (c) spatiotemporal characteristics of NEP in 2020, and (d) temporal changes in NEP in China’s Tianshan Mountains from 2001 to 2020.
Figure 9. (a) Spatiotemporal characteristics of NEP in 2001, (b) spatiotemporal characteristics of NEP in 2010, (c) spatiotemporal characteristics of NEP in 2020, and (d) temporal changes in NEP in China’s Tianshan Mountains from 2001 to 2020.
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Figure 10. Annual spatiotemporal characteristics of climate factors in Tianshan Mountains from 2001 to 2020. (a) Precipitation spatial patterns, (b) precipitation temporal trends, (c) temperature spatial patterns, and (d) temperature temporal trends.
Figure 10. Annual spatiotemporal characteristics of climate factors in Tianshan Mountains from 2001 to 2020. (a) Precipitation spatial patterns, (b) precipitation temporal trends, (c) temperature spatial patterns, and (d) temperature temporal trends.
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Figure 11. Correlation coefficients between the RSEI and precipitation, temperature in the Tianshan Mountains from 2001 to 2020.
Figure 11. Correlation coefficients between the RSEI and precipitation, temperature in the Tianshan Mountains from 2001 to 2020.
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Figure 12. Dynamic changes in land types in the Chinese Tianshan Mountains from 2000 to 2020.
Figure 12. Dynamic changes in land types in the Chinese Tianshan Mountains from 2000 to 2020.
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Table 1. Data product type and source (accessed on 4 December 2024).
Table 1. Data product type and source (accessed on 4 December 2024).
ProductsVariablesSpatial
Resolution		Temporal
Resolution		Sources
MOD13A1/Q1NDVI500/250 m16 dhttps://modis.gsfc.nasa.gov/
MOD09A1SR500 m8 dhttps://modis.gsfc.nasa.gov/
MOD11A2LST1 km8 dhttps://modis.gsfc.nasa.gov/
MOD15A3HFPAR500 m4 dhttps://modis.gsfc.nasa.gov/
MCD12Q1Land cover (IGBP)500 m96 dhttps://modis.gsfc.nasa.gov/
TerraClimateSOL/Pre4 kmmonthlyhttps://www.ecmwf.int
T3H(GLDAS)Tem0.25°3 hhttp://ldas.gsfc.nasa.gov/
CRU TSV4.06CRU TSV4.060.5°monthlyhttps://crudata.uea.ac.uk/cru/data/hrg/
Monthly Precipitation Dataset with 1 km Resolution in China from 1901 to 2022Pre1 kmmonthlyhttp://www.geodata.cn/
Monthly Average Temperature Dataset with 1 km Resolution in China from 1901 to 2022Tem1 kmmonthlyhttp://www.geodata.cn/
CNLUCCLand use data30 m http://www.resdc.cn/
Note: SR (surface reference); SOL (total solar radiation); Tem (temperature); Pre (precipitation).
Table 2. Principal component analysis results.
Table 2. Principal component analysis results.
YearIndexPC1PC2PC3PC4
2001LST0.61−0.020.40−0.68
NDBSI−0.03−0.99−0.11−0.07
NDVI−0.630.10−0.26−0.73
WET−0.48−0.090.870.09
Eigenvalue1.871.010.750.38
Contribution (%)46.6925.1318.669.52
2010LST−0.62−0.020.280.73
NDBSI−0.031.00−0.080.04
NDVI0.60−0.04−0.430.67
WET0.500.080.860.10
Eigenvalue1.911.000.710.39
Contribution (%)47.6325.0417.659.67
2020LST0.64−0.000.220.74
NDBSI0.020.99−0.150.03
NDVI−0.61−0.07−0.440.66
WET−0.470.130.860.16
Eigenvalue1.771.010.790.44
Contribution (%)44.2725.1219.7210.88
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Liu, Y.; Chai, C.; Zhang, Q.; Huang, X.; He, H. Monitoring and Evaluation of Ecological Environment Quality in the Tianshan Mountains of China Using Remote Sensing from 2001 to 2020. Sustainability 2025, 17, 1673. https://doi.org/10.3390/su17041673

AMA Style

Liu Y, Chai C, Zhang Q, Huang X, He H. Monitoring and Evaluation of Ecological Environment Quality in the Tianshan Mountains of China Using Remote Sensing from 2001 to 2020. Sustainability. 2025; 17(4):1673. https://doi.org/10.3390/su17041673

Chicago/Turabian Style

Liu, Yuting, Chunmei Chai, Qifei Zhang, Xinyao Huang, and Haotian He. 2025. "Monitoring and Evaluation of Ecological Environment Quality in the Tianshan Mountains of China Using Remote Sensing from 2001 to 2020" Sustainability 17, no. 4: 1673. https://doi.org/10.3390/su17041673

APA Style

Liu, Y., Chai, C., Zhang, Q., Huang, X., & He, H. (2025). Monitoring and Evaluation of Ecological Environment Quality in the Tianshan Mountains of China Using Remote Sensing from 2001 to 2020. Sustainability, 17(4), 1673. https://doi.org/10.3390/su17041673

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