1. Introduction
Ongoing rapid climate change and intensifying human activities have significant impacts on the global ecosystem, especially in vulnerable dryland ecosystems [1,2,3]. Therefore, the environment in arid and semi-arid areas has received significant attention [4,5,6,7]. Ecological environment quality (EEQ) evaluation is an important tool to quantitatively evaluate the environment and also serves as a criterion for formulating sustainable plans or measures for regional environmental management.
Numerous methods have been used to evaluate the EEQ in recent years [8,9,10,11,12,13,14]. However, methods that use only one indicator in the evaluation of the EEQ usually fail to include the complexity and diversity of the eco-environment and make the evaluation incomprehensive [15]. To achieve a comprehensive understanding of the EEQ, composite indicators have been widely used in the evaluation of the EEQ in recent years [16,17,18]. Among these composite indicators, the environmental index based on remote sensing information [17], named the remote sensing ecological index (RSEI), has been widely accepted and used for ecological monitoring and evaluation [19,20,21,22,23]. The RSEI reflects the regional environment by integrating the greenness, humidity, heat, and dryness of the eco-environment through principal component transformation, which overcomes the limitation of using a single index and can quickly realize the comprehensive evaluation of regional environments [17]. However, it is complex and time consuming when applying the RSEI to large-scale environments and long-term changes. Therefore, previous studies on the RSEI mostly focused on small areas within a short time range [19,20,21,22,23].
The Google Earth Engine (GEE), a free cloud platform that can quickly process massive images, has become a good choice in recent years for large-scale and long-time calculation of the RSEI, and it has been found to be effective in different regions and ecosystems [24,25,26]. For example, based on the GEE platform, Yang et al. analyzed the spatiotemporal change of the EEQ of the Yangtze River Basin (1.8 × 106 km2) in China in the past 20 years (2001–2019) [24]; Yang et al. analyzed the EEQ in the Yellow River Basin (0.8 × 106 km2) in China during 1990–2019 [25], and Ji et al. analyzed the EEQ of the whole of China (9.6 × 106 km2) during 2001–2020 [26]. Therefore, it is feasible to calculate the RSEI for large-scale and long-time EEQ evaluation based on the GEE platform.
Central Asia (CA) is one of the largest arid and semi-arid regions in the Northern Hemisphere [27] and also contains some of the most vulnerable and sensitive terrestrial ecosystems in the world [28,29]. In recent years, climate change and anthropogenic activities have caused unprecedented damage to the ecosystems there, threatening environmental sustainability [30,31,32,33]. As one of the world’s greatest ecological disasters, the Aral Sea crisis in CA, in which the Aral Sea shrunk and largely disappeared after water was diverted for irrigation, led to desertification, and the resulting reduction in biodiversity has caused serious damage to the surrounding ecosystem [34,35]. Therefore, maintaining ecological security is of great significance to the ecological environment construction of arid CA and even the world. On the above basis, the objectives of this study were: (1) to construct the RSEI efficiently by MODIS sensing data based on the GEE platform; (2) to monitor spatial–temporal changes of the EEQ in CA from 2000 to 2020; and (3) to explore the spatial autocorrelation and identify the driving factors of the EEQ in CA. Our study provides a practical and economical approach for assessing spatial–temporal changes of the ecological environment quality based on RSEI and GEE.
2. Study Area
The CA region (46.50°E–87.35°E and 35.10°N–55.45°N) consists of the Xinjiang Uygur Autonomous Region (XJ) in northwest China and the five post-Soviet Union republics, namely, Kazakhstan (KAZ), Kyrgyzstan (KGZ), Tajikistan (TJK), Turkmenistan (TKM), and Uzbekistan (UZB). These nations and regions have a total area of 5.63 × 106 km2 and share the same ecological systems (Figure 1). Far from oceans, the CA region is characterized by a typical continental arid and semi-arid climate [36]. The annual average precipitation varies from 155 mm to 270 mm, whereas the annual average evaporation is between 900 mm and 1500 mm [37].
3. Data and Methodology
3.1. Data Collection
The products of MOD09A1 V6, MOD11A2 V6, and MOD13A1 V6 covering CA from June to September of 2000, 2005, 2010, 2015, and 2020 were obtained to map the RSEI. The Global Reservoir and Dam Database (GRanD) v1.3 (
3.2. Methods
The study workflow is shown in Figure 2. The process used was as follows: (1) based on the GEE, four remote sensing indicators were calculated, namely, the land surface temperature (LST), the normalized difference impervious surface index (NDBSI), the normalized difference vegetation index (NDVI), and the wetness (WET); (2) spatial and temporal distributions of RSEIs for CA in 2000, 2005, 2010, 2015, and 2020 were generated using the principal component analysis (PCA) module in GEE; and (3) spatial auto-correlation analysis was applied to analyze the spatial correlation, and the geographical detector model (GDM) was used to quantitatively analyze the driving factors.
3.2.1. Methods of RSEI
As a recently developed comprehensive ecological index, the RSEI is specifically used to assess the EEQ with remote sensing data because it can reflect the pressures on the environment caused by human activities (i.e., urbanization), changes in the environmental state (i.e., vegetation coverage), and the climate change responses (i.e., temperatures and humidity) [11]. In this process, we calculated four component indicators (heat (LST), dryness (NDBSI), greenness (NDVI), and wetness (WET)) using MODIS data [38] and the GEE cloud platform.
The MODIS data were preprocessed to remove clouds and mask the water body before being processed. The NDVI was selected from the MOD13A1 product, the LST was selected from the MOD11A2 product, and the WET and NDBSI were calculated by the MOD09A1 image band [38].
Considering that the units and magnitudes of above indicators are not uniform, these indicators are normalized by Equation (1) before PCA, and their values are normalized to [0, 1]. After normalization, the four indicators were synthesized into new images, and the initial RSEI0 was calculated by Equation (2). To facilitate the comparison of the four indicators, RSEI0 can also be normalized with Equation (3) [17]. Equations (1) through (3) are as follows:
(1)
(2)
(3)
where NIi is the image standardization result of the indicator, Ii is the i pixel value of the indicator, Imax is the maximum value of the indicator in the target year, Imin is the minimum value of the indicator in the target year, PC1 is the first principal component, f is the forward normalization of the four indicators, and and are the maximum and minimum values of RSEI0 for the target year, respectively.3.2.2. Spatial Auto-Correlation Analysis
Spatial auto-correlation measures and tests the correlation between an element’s attribute value and that of its adjacent space [39,40]. This reveals an attribute eigenvalue correlation between the spatial reference unit and the adjacent spatial unit. In this paper, we analyzed the global and local spatial correlation of the RSEI using Global Moran’s I and Local Moran’s I separately [19].
3.2.3. GDM
The GDM integrates various statistical methods to detect the driving forces of factors, and it has been widely used in the detection of geographical environmental or human factors responsible for the changes of the EEQ [41,42,43,44]. In this study, the GDM was used to quantify the impacts of the LST, NDVI, NDBSI, and WET and their interactions on the RSEI of CA. A detailed description of the GDM was published by Wang and Xu [45].
4. Results
This paper used the GEE platform’s PCA module to quantitatively invert the RSEI for every 5 years during 2000–2020 of CA. The PCA results of CA and its six regions (Table 1) showed that (1) the sum of the first principal component (PC1) and the second principal component (PC2) eigen contribution rates for CA and its six regions exceeded 80. Therefore, the weighted superposition of the results of the first two principal components can be represented by most features of the LST, NDBSI, NDVI, and WET; (2) the PC contribution rate of CA was consistent with its six regions, indicating that the RSEI obtained from MODIS images was fit for large-scale EEQ evaluation; and (3) NDVI and WET promoted the ecological benefits; however, the NDBSI and LST do the opposite.
4.1. Dynamic Changes in the EEQ of CA
According to the classification labels reported by Xu [17], during the monitoring period (2000, 2005, 2010, 2015, and 2020), the EEQ levels of CA were mainly poor, fair, and moderate (Figure 3a–f, Table 2). The RSEI in CA presented obvious spatial differentiation (Figure 3f); fair EEQ levels were found in the largest area, accounting for 40.25% of the total area, while moderate and poor EEQ levels accounted for 24.94% and 23.45% of the total area, respectively. The proportion of good EEQ levels was relatively small at 10.63%, and excellent EEQ levels accounted for only 0.73% of the total area. As shown in Table 2, from 2000 to 2020, the proportion of areas of the poor and fair was gradually increasing, and the proportion of areas below fair was over 87%, indicating that the overall ecological level of CA was poor and exhibited a deterioration trend.
Among the six regions in CA (Figure 3a–f, Table 2), (1) KGZ and TJK had the best ecological status, with high proportions of excellent, good, and moderate ecological quality and over 68.40% of their combined total areas classified as moderate or better; (2) TKM and UZB, located in the southwest of CA, had the worst ecological conditions, with poor ecological quality predominating in the majority of areas and accounting for 81.35% and 56.34% of TKM and UZB, respectively; and (3) KAZ and XJ had a high proportion of fair and moderate ecological quality areas, lending them overall moderate ecological status.
The mean values of the RSEI in CA and its six regions were calculated (Figure 4), with the following findings: (1) the level of ecological status in CA from 2000 to 2020 was fair, and the average value of the RSEI was between 0.327 and 0.394, with a decrease rate of 0.027/year, suggesting that the environment in CA was deteriorating (Figure 4a); (2) the EEQ grades of KGZ (0.471) and TJK (0.551) were moderate, the EEQ grades of KAZ (0.341), UZB (0.243), and XJ (0.373) were fair, and the EEQ grade of TKM was poor (0.175) (Figure 4b–g); (3) in the past 20 years, the RSEI of UZB and XJ showed an upward trend with increase rates of 0.012/year and 0.003/year, respectively, indicating that the ecological status improved (Figure 4f–g). KAZ, KGZ, TJK, and TKM showed a decreasing trend with decrease rates of 0.011/year, 0.020/year, 0.004/year, and 0.014/year, respectively, indicating that the ecological status was deteriorating (Figure 4b–e).
4.2. Spatiotemporal Characteristics of RSEI Evolution in CA
The spatiotemporal differences of the EEQ in CA were analyzed over four periods at 5-year intervals (2000–2005, 2005–2010, 2010–2015, and 2015–2020). The proportion of areas in which the RSEI remained unchanged in CA from 2000 to 2020 at about 80% (Figure 5). The proportions of improved areas in 2000–2005, 2005–2010, 2010–2015, and 2015–2020 were 7.66%, 6.68%, 11.99%, and 2.16%, respectively, showing a trend of decline. The proportions of deteriorated areas showed an opposite trend to that of improved areas, at 9.13%, 16.04%, 8.98%, and 16.55%, respectively. Although the overall EEQ of CA showed an unchanged trend from 2000 to 2020, more areas exhibited deterioration than improved areas, indicating that the EEQ of CA showed a gradual deterioration trend in recent years.
From the spatial distribution change of EEQ, although the EEQ in CA mainly remained unchanged in the four periods (2000–2005, 2005–2010, 2010–2015, and 2015–2020), the spatial distribution showed different characteristics in various periods (Figure 6a–d, Table 3).
From 2000 to 2005, the areas of slight deterioration and slight improvement in CA showed sporadic distribution, and the area of slight improvement was larger than slight deterioration. The proportions of slightly deteriorated and slightly improved areas in KAZ, KGZ, TJK, TKM, UZB, and XJ were 11.62%/7.98%, 5.40%/10.27%, 2.84%/16.92%, 1.11%/5.57%, 10.57%/10.07%, and 7.76%/6.0, respectively (Figure 6a, Table 3).
Mild deterioration mainly occurred in northern KAZ from 2005 to 2010, and the areas of slight improvement were mainly located in KGZ, TJK, and XJ. The proportion of slightly deteriorated and slightly improved areas in KAZ, KGZ, TJK, TKM, UZB, and XJ was 27.78%/3.72%, 1.55%/15.36%, 0.75%/18.17%, 7.34%/0.71%, 7.27%/2.28%, and 4.05%/12.75, respectively (Figure 6b, Table 3).
From 2010 to 2015, slight improvement was mainly concentrated in northern KAZ, and the areas with mild deterioration were mainly located in KGZ, TJK, and XJ. In KAZ, KGZ, TJK, TKM, UZB, and XJ, the proportions of slightly deteriorated and slightly improved areas were 6.73%/19.70%, 27.04%/0.96%, 21.29%/0.36%, 1.83%/3.13%, 6.26%/2.21%, and 12.69%/6.41, respectively (Figure 6c, Table 3).
The spatial–temporal distribution of EEQ in CA during 2015–2020 showed similar sporadic distribution characteristics to that from 2000 to 2005, but the areas with slight deterioration were all larger than those with slight improvement. In KAZ, KGZ, TJK, TKM, UZB, and XJ, the proportions of slightly deteriorated and slightly improved areas were 18.37%/2.01%, 8.65%/5.34%, 19.40%/1.82%, 5.15%/1.25%, 7.25%/1.73%, and 20.04%/2.47%, respectively (Figure 6d, Table 3).
5. Discussion
5.1. Spatial Autocorrelation Analysis of EEQ
Spatial statistical analysis helps to understand ecological patterns and regional agglomerations [15,19]. In this study, the pixel size of the RSEI images from 2000, 2005, 2010, 2015, and 2020 were resampled to a 5 km × 5 km scale, and a total of 225,449 sample points were collected to determine whether the variables were spatially correlated and their extent. The spatial autocorrelation was analyzed using Moran’s I index and LISA diagram.
Figure 7 shows Moran’s I scatter diagram of the RSEI, which is mainly distributed in the first and third quadrants of each year, indicating a strong positive spatial correlation of environmental quality in the study area. Moran’s I index values for 2000, 2005, 2010, 2015, and 2020 were 0.905, 0.893, 0.901, 0.898, and 0.884, respectively, indicating that the spatial distribution of EEQ in the whole CA was clustered rather than random over the years. Moran’s I value decreased gradually from 2000 to 2020, which was consistent with the change of the EEQ grade (Figure 5).
The local spatial correlation patterns of the EEQ were analyzed by the LISA cluster and the LISA significance level. As shown in the LISA clustering diagram (Figure 8), insignificant areas were mainly concentrated in the central and southern areas of KAZ and in the edge region of XJ. The high–high (H-H) clustering regions were mainly concentrated in the mountainous areas of KAZ, KGZ, TGK, and XJ. In 2010, the H-H distribution area of KAZ decreased, and the EEQ was poor. From 2015 to 2020, the H-H agglomeration area increased, and the ecological environment was good. The low–low (L-L) areas were mainly distributed in desert areas, such as the Taklimakan Desert in XJ, the Kyzyl-Kum Desert in UZB, and the Karakum Desert in TKM. These areas are characterized by scarce precipitation, high evaporation, and poor EEQ. The LISA cluster map directly reflected the spatial distribution of “H-H,” “L-L,” “high–low (H-L),” and “low–high (L-H),” and the LISA significant level map reflected the spatial significance level of environmental quality. The significant regions for 2000–2020 were mainly located in the H-H and L-L regions, most of which reached a significance level of 0.01, indicating a strong correlation of EEQ.
5.2. Impacts of Driving Factors on RSEI
The RSEI has been widely used to evaluate the EEQ in a variety of landscapes. RSEI values vary from landscape to landscape. Reported RSEI values include 0.18 in areas of severe soil erosion [46], 0.24 in desert areas [47,48], 0.27–0.68 in cities [11], 0.43–0.54 in tableland regions [49], 0.45–0.61 in islands [50], 0.49–0.69 in floodplains or the basins of large rivers [51], over 0.63 in forested/dense vegetation areas, and over 0.83 in good farmland [52]. In the present study, the RSEI ranged from 0.327 to 0.394. This value was at or near the values found in desert areas or cities, which correspond to the actual situation. The mean RSEI of CA ranged from 0.327 to 0.394 during 2000–2020, which might be closely related to changes in environmental factors and human interference.
5.2.1. Impacts of Natural Factors on RSEI
The GDM was applied to further reveal the dominant natural factors for the changes in the EEQ. The specific operation steps were as follows: four indicator images in the monitoring years (2000, 2005, 2010, 2015, and 2020) were resampled to 5 km × 5 km, and 225,449 points were generated in each image; the RSEI was taken as the independent variable, and four indicators were selected as the dependent variables; factor detection, ecological detection, interactive detection, and risk detection were carried out by matching the RSEI points with four index factor (LST, NDBSI, NDVI, and WET) points.
Factor detection was used to calculate the p-value of each factor to explore whether each factor had an impact on RSEI and its contribution rate [45]. According to the factor detection results, the p-values of the four indicators in CA and the six regions were less than 0.01, passing the significance test. The contribution rate of the LST was over 90%, which was higher than the other three factors (about 50%). Therefore, while the NDVI, NDBSI, LST, and WET all have significant effects on the EEQ of CA and its six regions, the LST is the main factor.
Ecological detectors were used to determine if there was a significant difference between two factors of the RSEI [45]. The results showed that although the difference between the two factors was significant in different years, the spatial distributions of the RSEI in CA and the six regions were mostly affected by the LST.
By calculating the p-value of the interaction between two natural factors, the interactive detector analyzes whether the two natural factors interact or are independent [45]. The interaction results of each of the two main influencing factors in CA and its six regions exhibited two-factor enhancement, and there was no independent factor, indicating that there was an interaction between two factors and it was not a simple superposition.
The risk detector can determine the best range partition or feature through which different factors promote RSEI growth, with a confidence level of 95%. The risk detection results of each factor could be divided into two parts: the statistical difference between different regions and the mean value of the RSEI [45]. The results showed that the mean value of the RSEI was negatively correlated with the LST and NDBSI, that is, the smaller the range of the LST and NDBSI, the larger the RSEI value. In contrast, the RSEI was positively correlated with the NDVI and WET; in other words, the larger the range of the NDVI and WET, the greater the RSEI value. This was consistent with the PCA results. In addition, statistical tests showed that the optimal region of the RSEI for each factor was significantly different from other regions, which further demonstrated that each factor could better promote the growth of the RSEI in the optimal region.
5.2.2. Impacts of Human Activities on RSEI
CA is typically arid and semi-arid and consists of a very fragile ecological environment, making it particularly sensitive and vulnerable to human disturbance [53,54]. Previous studies have shown primarily shrinkage in the Aral Sea basin due to diversion of rivers for agricultural irrigation [55] and construction of reservoirs [56]. Wang et al. confirmed a greater impact of human activities than climate change [57].
In recent years, large-scale human activities in CA have had an important impact on the local environment. For example, the population of CA and its six regions has shown a continuous growth trend, especially TJK and UZB, with population growth rates of 5.67 persons/km2/y and 5.17 persons/km2/y, respectively (Figure 9a). According to the Global Reservoir and Dam Database (GranD) v1.3, more than 40 large dams and reservoirs have been built over the last 30 years in CA (Figure 9a), significantly affecting the surrounding ecosystem [58]. The spatial distribution of the utilization intensity index of the main grazing grasslands in CA also showed an increasing trend. Areas with high utilization intensity index were mainly distributed in the north and east of KAZ, and low-utilization areas were mainly distributed in the south of KAZ, the southeast of UZB, and central TKM, which was consistent with areas of degraded ecological quality (Figure 9b). Environmental protection measures have played a positive role in promoting the improvement of local EEQ. For example, the improvement of the EEQ in XJ in recent years was related to a series of local ecological protection measures [19].
5.3. Limitations and Future Work
In this paper, the rapid and efficient comparative analysis of the regional environment was achieved by integrating multi-source remote sensing data and using the GEE cloud platform. This method can provide support for regional development planning and the formulation of environmental protection measures. However, the environment in arid areas is fragile, and the environmental quality is sensitive to changes in natural environmental factors and human activities [57,59,60,61,62,63]. In addition to the LST, NDBSI, NDVI, and WET, global warming, soil erosion, desertification, a decrease in biodiversity, population surges, and the intensification of urbanization will all damage the environmental balance. In addition to these factors, the Tienshan Mountains in CA, are known as the “water tower of CA” [64]. However, the RSEI calculations did not take the effects of glacial change into account. In addition, to prevent the interference of large areas of water with actual surface humidity conditions, a water mask was adopted in the RSEI calculation process, as water areas play a crucial role in the environmental development in arid areas [65,66,67]. Therefore, the evaluation of the environment in CA should be conducted while considering local conditions to obtain more comprehensive and scientific evaluation results.
6. Conclusions
Based on the GEE cloud platform and MODIS data, the RSEI was used to study the spatiotemporal dynamics and changes of EEQ in CA and its six regions during 2000–2020 to explore the spatial correlation and the driving factors of the EEQ. The results were as follows.
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(1). The RSEI values in CA during 2000, 2005, 2010, 2015, and 2020 were 0.379, 0.376, 0.349, 0.360, and 0.327, respectively, revealing that the EEQ was at a poor level and showed a deteriorating trend. Among the six regions of CA, although UZB and XJ had medium EEQ grades of fair, both of these regions showed a trend of improvement. KGZ and TJK had the best EEQ grades of moderate, KAZ had a medium EEQ grade of fair, and TKM had the worst EEQ grade of poor; all of these regions showed a trend of deterioration.
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(2). During 2000–2005, 2005–2010, 2010–2015, and 2015–2020, the unchanged/improved/deteriorated areas in CA were about 83.21/7.66%/9.13%, 77.28/6.68%/16.04%, 79.03/11.99%/8.98%, and 81.29/2.16%/16.55%, respectively, indicating that the overall EEQ in CA gradually deteriorated in recent years. The six regions of CA exhibited similar characteristics.
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(3). Analysis from Moran’s I index indicated that the spatial distribution of the EEQ in CA was clustered rather than random. Areas of H-H were mainly concentrated in mountainous areas, and L-L areas were mainly distributed in desert areas; the significant regions were mainly located in H-H and L-L areas, and most of them reached the significance level of 0.01, indicating that environmental quality had a strong correlation.
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(4). The results of the GDM revealed the influence of natural factors on the EEQ in CA. The findings showed that, although all factors had an impact on the RSEI in CA and its six regions, LST was the main factor. The impacts of various factors on the RSEI were not a simple superposition process but rather a mutual enhancement. The LST and NDBSI had a negative correlation with the RSEI, whereas the NDVI and WET had a positive correlation with the RSEI. In addition, human activities, such as population growth, overgrazing, and hydropower development, had an important impact on the EEQ.
By writing programs in the GEE platform to directly access databases for data processing and analysis, the problem of low-efficiency steps such as local downloading, storage, and pre-processing can be overcome, and index calculation can be conducted quickly in the GEE cloud. Therefore, GEE can be used as a computing platform to evaluate the EEQ, and its application can be extended to evaluate the EEQ by the RSEI over a large range and long time series.
All authors made significant contributions to this study. Conceptualization, Y.-N.C.; methodology, Q.-Q.X.; software, Q.-Q.X.; writing—original draft preparation, Q.-Q.X.; validation, X.-Q.Z.; formal analysis, J.-L.D.; writing—review and editing, Q.-Q.X.; project administration, Y.-N.C.; funding acquisition, Y.-N.C. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data presented in this study are available on request from the corresponding author.
The authors declare no conflict of interest.
| CA | Central Asia |
| MODIS | Moderate Resolution Imaging Spectroradiometer |
| GEE | Google Earth Engine |
| EEQ | ecological environment quality |
| RSEI | remote sensing ecological index |
| H-H | high-high |
| L-L | low-low |
| H-L | high-low |
| L-H | low-high |
| XJ | Xinjiang Uygur Autonomous Region |
| KAZ | Kazakhstan |
| KGZ | Kyrgyzstan |
| TJK | Tajikistan |
| TKM | Turkmenistan |
| UZB | Uzbekistan |
| LST | land surface temperature |
| NDBSI | normalized difference impervious surface index |
| NDVI | normalized difference vegetation index |
| WET | wetness |
| PCA | principal component analysis |
| GDM | geographical detector model |
| PC1 | the first principal component |
| PC2 | the second principal component |
| EV1 | eigenvalue of principal component 1 |
| EV2 | eigenvalue of principal component 2 |
| ECR1 | contribution rate of principal component 1 |
| ECR2 | contribution rate of principal component 2 |
| SD | significant degeneration |
| MD | mild degeneration |
| IN | invariability |
| MI | mild improvement |
| SI | significant improvement |
| NIi | the image standardization result of indicator |
| Ii | the i pixel value of indicator |
| Imax | the maximum values of indicator in the target year |
| Imin | the minimum values of indicator in the target year |
| f | the forward normalization of the four indicators |
| RSEI0max | the maximum values of RSEI0 for the target year |
| RSEI0min | the minimum values of RSEI0 for the target year |
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Enlarge this image.Figure 1. Central Asian region. The world and country borders are from the National Platform for Common Geospatial Information Services (https://www.tianditu.gov.cn/, accessed on 4 May 2022; GS(2021)5443), the lake outlines are from the Natural Earth Data (http://www.naturalearthdata.com/, accessed on 4 May 2022).
Enlarge this image.Figure 3. RSEI variation in CA ((a–f) are 2000, 2005, 2010, 2015, 2020, and the mean of 2000–2020, respectively).
Enlarge this image.Figure 4. Interannual variation in the mean RSEI throughout CA and its six regions ((a–g) are CA, KAZ, KGZ, TJK, TKM, UZB, and XJ, respectively).
Enlarge this image.Figure 6. Spatial distribution of changes in the EEQ of CA and its six regions from 2000 to 2020 (SD, MD, IN, MI, and SI correspond to significant degeneration, mild degeneration, invariability, mild improvement, and significant improvement, respectively).
Enlarge this image.Figure 7. Moran scatter plots of the RSEI in CA ((a–e) are 2000, 2005, 2010, 2015, and 2020, respectively).
Enlarge this image.Figure 8. LISA map ((a–e) and (f–j) are the LISA cluster maps and LISA significance maps of 2000, 2005, 2010, 2015, and 2020, respectively).
Enlarge this image.Figure 9. Impact of human activities ((a). population density and dams and reservoirs in CA; (b). the utilization intensity index of the main grazing grasslands in CA).
Results of PCA for four indicators.
| Year | Indicator | CA | KAZ | KGZ | TJK | TKM | UZB | XJ |
|---|---|---|---|---|---|---|---|---|
| 2000 | LST | −0.87 | −0.71 | 0.97 | 0.94 | −0.84 | −0.75 | −0.95 |
| NDBSI | 0.00 | −0.15 | 0.00 | 0.00 | −0.04 | −0.05 | −0.01 | |
| NDVI | 0.45 | 0.66 | 0.00 | 0.13 | 0.27 | 0.44 | 0.27 | |
| WET | 0.19 | 0.21 | −0.23 | −0.33 | 0.48 | 0.50 | 0.16 | |
| EV1 | 0.04 | 0.05 | 0.05 | 0.01 | 0.03 | 0.06 | 0.05 | |
| EV2 | 0.00 | 0.02 | 0.02 | 0.00 | 0.01 | 0.01 | 0.01 | |
| ECR1 | 79.51 | 85.67 | 68.30 | 70.54 | 62.95 | 74.12 | 80.35 | |
| ECR2 | 17.05 | 8.92 | 26.96 | 22.22 | 21.51 | 16.30 | 15.66 | |
| 2005 | LST | −0.82 | −0.70 | −0.97 | 0.93 | −0.80 | −0.76 | −0.92 |
| NDBSI | 0.00 | −0.01 | 0.00 | 0.01 | −0.01 | −0.02 | −0.01 | |
| NDVI | 0.53 | 0.68 | −0.07 | 0.12 | 0.32 | 0.47 | 0.34 | |
| WET | 0.21 | 0.22 | 0.23 | −0.36 | 0.51 | 0.46 | 0.21 | |
| EV1 | 0.04 | 0.05 | 0.06 | 0.01 | 0.03 | 0.06 | 0.05 | |
| EV2 | 0.00 | 0.03 | 0.02 | 0.00 | 0.01 | 0.02 | 0.01 | |
| ECR1 | 76.97 | 87.52 | 60.84 | 69.79 | 65.31 | 78.67 | 76.00 | |
| ECR2 | 19.57 | 8.96 | 34.55 | 23.93 | 19.06 | 13.77 | 18.86 | |
| 2010 | LST | −0.87 | −0.70 | −0.95 | 0.90 | −0.72 | −0.70 | −0.92 |
| NDBSI | 0.00 | −0.12 | 0.00 | 0.01 | −0.06 | −0.02 | 0.00 | |
| NDVI | 0.44 | 0.65 | −0.15 | 0.23 | 0.32 | 0.53 | 0.33 | |
| WET | 0.24 | 0.27 | 0.29 | −0.37 | 0.61 | 0.48 | 0.21 | |
| EV1 | 0.03 | 0.05 | 0.07 | 0.02 | 0.04 | 0.06 | 0.04 | |
| EV2 | 0.00 | 0.03 | 0.03 | 0.00 | 0.01 | 0.02 | 0.01 | |
| ECR1 | 76.58 | 85.33 | 60.63 | 67.51 | 67.26 | 78.84 | 75.82 | |
| ECR2 | 19.61 | 10.04 | 33.76 | 26.80 | 20.57 | 14.08 | 19.30 | |
| 2015 | LST | −0.83 | −0.72 | −0.95 | 0.93 | −0.74 | −0.70 | −0.89 |
| NDBSI | 0.00 | −0.03 | 0.00 | 0.01 | −0.01 | 0.00 | −0.01 | |
| NDVI | 0.49 | 0.65 | −0.04 | 0.12 | 0.35 | 0.51 | 0.38 | |
| WET | 0.25 | 0.24 | 0.31 | −0.34 | 0.58 | 0.50 | 0.24 | |
| EV1 | 0.04 | 0.05 | 0.06 | 0.02 | 0.04 | 0.06 | 0.05 | |
| EV2 | 0.00 | 0.02 | 0.02 | 0.01 | 0.01 | 0.02 | 0.01 | |
| ECR1 | 77.97 | 89.10 | 64.07 | 65.64 | 66.14 | 78.87 | 74.23 | |
| ECR2 | 18.01 | 7.42 | 29.99 | 27.60 | 22.03 | 13.01 | 20.35 | |
| 2020 | LST | −0.78 | −0.64 | −0.87 | −0.84 | −0.55 | −0.58 | −0.83 |
| NDBSI | 0.00 | −0.19 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | |
| NDVI | 0.45 | 0.63 | −0.04 | −0.07 | 0.27 | 0.43 | 0.39 | |
| WET | 0.42 | 0.39 | 0.49 | 0.53 | 0.79 | 0.69 | 0.41 | |
| EV1 | 0.04 | 0.06 | 0.07 | 0.02 | 0.05 | 0.06 | 0.05 | |
| EV2 | 0.00 | 0.02 | 0.02 | 0.01 | 0.01 | 0.01 | 0.01 | |
| ECR1 | 76.11 | 82.65 | 63.50 | 70.12 | 70.55 | 79.99 | 72.00 | |
| ECR2 | 14.71 | 8.73 | 25.29 | 18.84 | 21.83 | 12.38 | 15.91 |
Note: EV1: eigenvalue of Principal Component 1; EV2: eigenvalue of Principal Component 2; ECR1: contribution rate of Principal Component 1; ECR2: contribution rate of Principal Component 2; The values correspond to indicators is the result of eigenvectors.
Area and percentage change of each RSEI level in CA and its six regions.
| RSEI Level | 2000 | 2005 | 2010 | 2015 | 2020 | |
|---|---|---|---|---|---|---|
| CA | poor | 20.90% | 21.75% | 22.16% | 23.94% | 28.50% |
| fair | 37.67% | 38.37% | 45.56% | 39.71% | 39.91% | |
| moderate | 29.11% | 27.00% | 21.02% | 23.99% | 23.59% | |
| good | 11.66% | 11.89% | 10.27% | 11.52% | 7.84% | |
| excellent | 0.66% | 0.99% | 0.98% | 0.85% | 0.17% | |
| KAZ | poor | 2.99% | 3.25% | 5.18% | 6.31% | 11.66% |
| fair | 47.22% | 51.74% | 66.79% | 55.85% | 56.11% | |
| moderate | 39.98% | 34.42% | 22.89% | 28.18% | 27.14% | |
| good | 9.25% | 9.43% | 4.32% | 8.54% | 4.85% | |
| excellent | 0.57% | 1.17% | 0.82% | 1.11% | 0.24% | |
| KGZ | poor | 0.00% | 0.00% | 0.00% | 0.02% | 0.09% |
| fair | 10.21% | 7.45% | 4.49% | 11.50% | 11.59% | |
| moderate | 31.17% | 32.88% | 27.90% | 34.97% | 36.11% | |
| good | 55.76% | 56.23% | 60.99% | 51.62% | 51.60% | |
| excellent | 2.86% | 3.44% | 6.61% | 1.88% | 0.61% | |
| TJK | poor | 4.61% | 3.02% | 2.78% | 4.59% | 6.06% |
| fair | 24.18% | 20.28% | 15.80% | 20.61% | 25.54% | |
| moderate | 49.93% | 48.63% | 42.35% | 46.68% | 49.84% | |
| good | 21.25% | 27.84% | 37.39% | 28.00% | 18.54% | |
| excellent | 0.03% | 0.23% | 1.68% | 0.13% | 0.02% | |
| TKM | poor | 79.86% | 77.01% | 82.95% | 82.09% | 84.83% |
| fair | 17.89% | 19.26% | 13.87% | 14.41% | 12.70% | |
| moderate | 2.24% | 3.72% | 3.16% | 3.46% | 2.36% | |
| good | 0.01% | 0.02% | 0.02% | 0.04% | 0.10% | |
| excellent | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | |
| UZB | poor | 48.22% | 53.36% | 57.85% | 59.00% | 63.25% |
| fair | 35.90% | 26.75% | 22.48% | 23.25% | 19.78% | |
| moderate | 15.34% | 18.36% | 17.84% | 16.69% | 16.22% | |
| good | 0.54% | 1.52% | 1.83% | 1.06% | 0.74% | |
| excellent | 0.00% | 0.00% | 0.01% | 0.00% | 0.00% | |
| XJ | poor | 29.74% | 31.72% | 26.85% | 30.89% | 35.46% |
| fair | 32.14% | 29.69% | 32.60% | 29.29% | 30.67% | |
| moderate | 20.71% | 21.42% | 21.59% | 22.00% | 22.45% | |
| good | 16.40% | 16.13% | 17.82% | 16.99% | 11.29% | |
| excellent | 1.01% | 1.03% | 1.13% | 0.83% | 0.13% |
Spatial distribution of changes in the EEQ of CA and its six regions in various periods.
| Period | Region | SD | MD | IN | MI | SI |
|---|---|---|---|---|---|---|
| 2000–2005 | CA | 0.00% | 6.55% | 83.97% | 9.48% | 0.00% |
| KAZ | 0.00% | 11.62% | 80.40% | 7.98% | 0.00% | |
| KGZ | 0.00% | 5.40% | 84.33% | 10.27% | 0.00% | |
| TJK | 0.00% | 2.84% | 80.24% | 16.92% | 0.00% | |
| TKM | 0.00% | 1.11% | 93.32% | 5.57% | 0.00% | |
| UZB | 0.00% | 10.57% | 79.36% | 10.07% | 0.00% | |
| XJ | 0.00% | 7.76% | 86.16% | 6.08% | 0.00% | |
| 2005–2010 | CA | 0.00% | 8.12% | 83.04% | 8.83% | 0.00% |
| KAZ | 0.00% | 27.78% | 68.50% | 3.72% | 0.00% | |
| KGZ | 0.00% | 1.55% | 83.09% | 15.36% | 0.00% | |
| TJK | 0.00% | 0.75% | 81.07% | 18.17% | 0.00% | |
| TKM | 0.00% | 7.34% | 91.95% | 0.71% | 0.00% | |
| UZB | 0.00% | 7.27% | 90.45% | 2.28% | 0.00% | |
| XJ | 0.00% | 4.05% | 83.20% | 12.75% | 0.00% | |
| 2010–2015 | CA | 0.00% | 12.64% | 81.90% | 5.46% | 0.00% |
| KAZ | 0.00% | 6.73% | 73.57% | 19.70% | 0.00% | |
| KGZ | 0.00% | 27.04% | 72.00% | 0.96% | 0.00% | |
| TJK | 0.00% | 21.29% | 78.35% | 0.36% | 0.00% | |
| TKM | 0.00% | 1.83% | 95.05% | 3.13% | 0.00% | |
| UZB | 0.01% | 6.26% | 91.52% | 2.21% | 0.00% | |
| XJ | 0.00% | 12.69% | 80.90% | 6.41% | 0.00% | |
| 2015–2020 | CA | 0.00% | 13.14% | 84.42% | 2.44% | 0.00% |
| KAZ | 0.00% | 18.37% | 79.61% | 2.01% | 0.00% | |
| KGZ | 0.00% | 8.65% | 86.01% | 5.34% | 0.00% | |
| TJK | 0.00% | 19.40% | 78.78% | 1.82% | 0.00% | |
| TKM | 0.00% | 5.15% | 93.60% | 1.25% | 0.00% | |
| UZB | 0.00% | 7.25% | 91.03% | 1.73% | 0.00% | |
| XJ | 0.00% | 20.04% | 77.49% | 2.47% | 0.00% |
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