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Article

Monitoring and Analysis of Eco-Environmental Quality in Daihai Lake Basin from 1985 to 2022 Based on the Remote Sensing Ecological Index

by
Bowen Ye
1,
Biao Sun
1,2,3,*,
Xiaohong Shi
1,2,3,
Yunliang Zhao
1,
Yuying Guo
1,
Jiaqi Pang
1,
Weize Yao
1,
Yaxin Hu
1 and
Yunxi Zhao
1
1
College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
2
Inner Mongolia Water Resource Protection and Utilization Key Laboratory, Hohhot 010018, China
3
State Gauge and Research Station of Wetland Ecosystem, Wuliangsuhai Lake, Bayannur 014404, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(16), 6854; https://doi.org/10.3390/su16166854 (registering DOI)
Submission received: 4 July 2024 / Revised: 2 August 2024 / Accepted: 8 August 2024 / Published: 9 August 2024
Browse Figures
Figure 1
<p>Location of the study area.</p> "> Figure 2
<p>Flowchart.</p> "> Figure 3
<p>Correlation between RSEI and each index.</p> "> Figure 4
<p>Change curves of NDVI, WET, NDBSI, LST, and RSEI in the Daihai Lake Basin from 1985 to 2022 and mutation point test of RSEI change. (<b>a</b>) NDVI, (<b>b</b>) WET, (<b>c</b>) NDBSI, (<b>d</b>) LST, (<b>e</b>) RSEI, and (<b>f</b>) mutation point test.</p> "> Figure 5
<p>Spatial distribution of the RSEI index in the Daihai Lake Basin during change node years.</p> "> Figure 6
<p>Area and proportion of RSEI grades in the year of the change node in the Daihai Lake Basin. (<b>a</b>) Area of RSEI grades, (<b>b</b>) proportion of RSEI grades.</p> "> Figure 7
<p>Analysis of the dynamic change trend in the Daihai Lake Basin from 1985 to 2022. (<b>a</b>) slope, (<b>b</b>) F test.</p> "> Figure 8
<p>LISA clustering diagram of the RSEI index in the Daihai Lake Basin.</p> "> Figure 9
<p>Detection results of driving factors of the RSEI in the Daihai Lake Basin.</p> "> Figure 10
<p>Interactive detection results of factors influencing the RSEI in the Daihai Lake Basin from 1990 to 2015: (<b>a</b>) 1990, (<b>b</b>) 1995, (<b>c</b>) 2000, (<b>d</b>) 2005, (<b>e</b>) 2010, and (<b>f</b>) 2015.</p> "> Figure 11
<p>Correlation diagram of RSEI and CHEQ in the Daihai Lake Basin.</p> ">
Versions Notes

Abstract

:
Exploring eco-environmental quality dynamics in the Daihai Lake Basin has significant implications for the conservation of ecological environments in the semi-arid and arid regions of northern China. Based on the Google Earth Engine (GEE) platform, the remote sensing ecological index (RSEI) was constructed by coupling Landsat SR remote sensing data from 1985 to 2022. The spatial significance of the RSEI was analyzed using linear regression equations and an F-test. The spatial correlation, distribution characteristics, and driving factors behind the RSEI were explored using Moran’s index and a geodetector. The results indicated that (1) the RSEI was appropriate for evaluating eco-environmental quality in the Daihai Lake Basin. (2) From 1985 to 2022, the eco-environmental quality of the Daihai Lake Basin exhibited a positive trend but remained subpar. (3) A positive spatial autocorrelation was demonstrated for eco-environmental quality with increasing spatial aggregation. (4) Significant eco-environmental quality degradation (slope < 0) occurred primarily in Sanyiquan Town in the northeastern region of the basin and in Tiancheng Township in the southeastern region. Conversely, a notable improvement (slope > 0) was predominantly observed in Yongxing and Liusumu in southwestern Daihai. (5) The improvement in the ecological environment of the Daihai Lake Basin was primarily attributed to an increase in NDVI and WET and a decrease in NDBSI and LST. The interaction between NDVI and LST had the greatest explanatory power for the ecological environment. Among the external driving factors, DEM (elevation) was the dominant factor in the RSEI and had the strongest explanatory power. The interaction between DEM and LST was the most significant, and the driving factors were enhanced. This study provided a theoretical basis for the sustainable development of the Daihai Lake Basin, which is crucial for the local ecological environment and economic development.

1. Introduction

The semi-arid and arid areas of Northwest China are experiencing severe desertification. These challenges have affected the ecological environment and significantly hindered regional economic development [1,2]. These regions are ecologically sensitive and fragile areas known for their biodiversity and diverse landscapes. Human activities in these areas often hasten the eco-environmental quality [3,4]. Therefore, it is imperative to enhance the sustainable surveillance and evaluation of these regions to ensure the precise acquisition of spatiotemporal distributions of dynamic ecological changes and provide a crucial scientific basis for formulating protection measures.
Compared with traditional methods, such as field investigation and automatic ground observations, remote sensing offers advantages such as large-scale, real-time, rapid, and periodic data acquisition, rendering it widely employed in ecological research [5,6]. Currently, the methods for assessing regional eco-environmental quality can be classified into two categories. One involves constructing a comprehensive evaluation system, including the landscape pattern index method [7], the grey correlation degree method [8,9], the pressure-state-response (PSR) model [10], and the analytic hierarchy process method [11]. However, these methods face certain challenges, such as data acquisition, spatial visualization, and subjective influence on indicator weights [12]. Another method involves extracting a single remote sensing index from images, such as the enhanced vegetation index (EVI) [13,14], the normalized difference vegetation index (NDVI) [15,16], the net primary productivity of vegetation (NPP) [17,18], impervious surfaces [19], and surface temperature [20]. However, the regional habitat system can be affected by numerous factors, rendering the comprehensive and accurate evaluation of eco-environmental quality challenging using a single index [12]. Xu et al. utilized remote sensing imagery to derive indices such as NDVI, tasseled cap wetness (WET), normalized difference bare soil index (NDBSI), and land surface temperature (LST), to establish the RSEI. This index facilitates the quantitative evaluation and visual expression of regional eco-environmental quality [5,21]. It has been widely applied across various geographical settings such as urban areas, counties, wetlands, and wastelands [22,23]. Zhao et al. used Landsat data from 2001 to 2020 to establish an arid and semi-arid remote sensing ecological index (AWRSEI) to assess the quality of habitats in the Daihai Lake Basin [24]. Their findings revealed an improvement in the eco-environmental quality. The aforementioned studies only selected remote sensing images from several periods, making it difficult to reflect the overall dynamic change in the eco-environmental quality in the basin over a long period. This study obtained more stable and representative data using the average synthesis of vegetation growth period images. Furthermore, the selection of images within these periods is not favorable. The internal ecological environment, which functions as a relatively independent unit, exhibits scale effects and spatial lags when it interacts with external factors. Numerous scholars have regarded this as the ideal domain for integrating ecological theory and practice [25]. Daihai Lake, the third-largest lake in Inner Mongolia, has long grappled with issues such as declining water levels, reduced surface area, and significant water pollution [26]. Several experts have investigated the ecological environment of the region from various perspectives [26,27,28]. Previous studies on the Daihai Lake Basin have primarily focused on the vegetation index (NDVI) and other indicators [29,30,31]. However, there are few studies on the quality of the ecological environment, which are mainly qualitative descriptions [31], while less attention has been paid to the impact of climate or land use change. However, there is a lack of research on long-term sequence changes in the ecological environment quality of the Daihai Lake Basin using remote sensing image inversion of ecological indices.
In this study, the Daihai Lake Basin was selected as the research subject and an RSEI model was developed using Landsat data to comprehensively assess the spatiotemporal evolution and patterns of ecological environmental quality in the basin from 1985 to 2022. Correlation analysis, trend analysis, and geographical detection were employed to explore the topographic, meteorological, and socio-economic factors affecting the RSEI changes in the basin to elucidate the main factors that can influence the ecological environmental quality in the basin, the trends in the ecological environmental quality across basin regions, and the relationship between influencing factors. These findings provide a basis for the sustainable improvement of eco-environmental quality in the Daihai Lake Basin and inform the formulation of relevant strategies in future basin planning.

2. Materials and Methods

2.1. Overview of the Study Area

The study area is located in the southern region of Wulanchabu City and within the territory of Liancheng County in the Inner Mongolia Autonomous Region (112°10′–12°59′ E, 40°48′–40°55′ N) (Figure 1). This basin, covering an area of 2312.75 km2, is a small inland closed saltwater lake basin situated between the Yongding River Basin and the Yellow River Basin. The basin, situated in the southern part of the Inner Mongolian Plateau, features minimal terrain fluctuations. The terrain features low elevations in the middle and high elevations in the surrounding mountains, forming a basin. Daihai is situated within this basin, with altitudes ranging from 1167 to 2121 m. The dominant landforms include plains, hills, basins, and mountains [26,32]. It is situated on the periphery of the East Asian monsoon region, serving as a transitional area from semi-arid to semi-humid climates, and is characterized by notable climate sensitivity. The basin experiences an average annual precipitation of 392 mm, an average temperature of 5 °C, a relative humidity of 51.28%, and an annual average evaporation of 1938 mm, indicating substantial evaporation rates [28,33]. The basin primarily features vegetation types, such as forests, grasslands, wastelands, and cultivated land, with an inherently delicate ecological environment [26]. In 2001, the regional government designated the Daihai Lake Basin as a regional lake and wetland nature reserve.

2.2. Data Sources

Landsat series data have several advantages, including multiphase capability, robust data stability, wide coverage, convenience, and rapid accessibility. These data have found extensive applications in monitoring dynamic changes in large-scale eco-environmental quality. To ensure the accuracy of the RSEI index for eco-environmental quality, each ecological indicator component was extracted from the Landsat SR remote sensing data from June to September. This period coincided with favorable vegetation growth in the Daihai Lake Basin, exhibiting notable variations in vegetation coverage across different regions and facilitating the RSEI calculations. By leveraging the GEE remote sensing processing cloud platform, this study utilized Landsat SR data to extract indices such as WET, NDVI, NDBSI, and LST to construct the RSEI. Preprocessing for the annual ecological indicator component involved cloud removal, water masking, clipping, mosaicking, and resampling to 500 m × 500 m. Subsequently, median synthesis processing was performed on each ecological indicator raster annually to mitigate the impacts of meteorological factors such as cloud cover, atmosphere, and sun altitude angle. The primary data sources are listed in Table 1.
This study examined the factors affecting dynamic changes in the eco-environmental quality within the Daihai Lake Basin. Based on the ecological environment characteristics of the study area and existing research findings [34], four remote sensing ecological indicators (NDVI (X1), WET (X2), NDBSI (X3), and LST (X4)) were adopted as the internal driving factors, and 10 natural factors (annual average temperature (X5), annual precipitation (X6), annual evaporation (X7), elevation (X8), slope (X9), and slope direction (X10)) and socio-economic factors (population density (X11), GDP (X12), night light index (X13), and land use type (X14)) were applied as the external driving factors to explore the impacts on the eco-environmental quality of the Daihai Lake Basin (Table 1). Using ArcGIS 10.8, the grid data for each factor was subjected to local resampling and mask extraction. The data for population density, annual precipitation, annual average temperature, annual evaporation, nighttime light index, and land use type cover 1990–2021.

2.3. Construction of Remote Sensing Ecological Index

In this study, Landsat SR remote sensing data served as the data source from which NDVI, WET, NDBSI, and LST indices were extracted using GEE. Principal component analysis (PCA) was then employed to couple and construct the RSEI [35]. Specifically, NDVI characterizes the greenness index and effectively reflects vegetation biomass, regional coverage, growth, and spatial distribution [36]. WET, derived from Landsat remote sensing images via cap transformation, visually represents surface and vegetation moisture content and is closely associated with regional eco-environmental quality [37]. The NDBSI is derived from the average value of two indices: the intelligent building index (IBI), which represents the surface building density, and the soil index (SI), which indicates regional surface bare soil information [36]. The thermal index, which is crucial for surface temperature assessment, correlates closely with regional human activities and climate change, as expressed by LST [38]. The equations used to calculate these ecological indices and their definitions are presented in Table 2. To facilitate PCA and standardize the ecological indices to mitigate the differences caused by varied dimensions, the initial remote sensing ecological index (RSEI0) acquired after coupling required normalization, ensuring that the RSEI value consistently ranges from 0 to 1. Higher values near 1 indicate superior regional eco-environmental quality. The normalization formula is as follows:
R S E I 0 = 1 − P C 1 ( N D V I , W E T , N D B S I , L S T ) ,
R S E I = R S E I 0 − R S E I M i n R S E I M a x − R S E I M i n ,
where RSEI is the grid value after standardization, RSEI0 is the initial value of the grid pixel, RSEIMin is the minimum value of the grid pixel, and RSEIMax is the maximum value of the grid pixel.

2.4. Time Series Analysis Methods

2.4.1. Mann–Kendall Mutation Test

The Mann–Kendall mutation test, a nonparametric statistical method, can be applied to validate monotonicity in time series data. Widely applied in testing hydrological and meteorological data, the results remain robust to outliers [23,39]. The regional mean RSEI value commonly indicates an overall ecological trend. Hence, in this study, we conducted nonparametric tests on the 38-year mean value series of the RSEI in the Daihai Lake Basin. Additionally, we performed a subsection analysis of the long-term RSEI data for the study area to identify the characteristic points of eco-environmental quality changes.
S k = ∑ i = 1 k   r i k = 2,3 , ⋯ , n , r i = 1 x i > x j 0 x i ≤ x j j = 1,2 , ⋯ , i , U F k = S k − E ( S k ) V a r ( S k ) , E S k = k k − 1 4 , V a r S k = n n − 1 2 n + 5 72 ,
where var(Sk) and E(Sk) are the variance and the expectation of Sk, respectively. By reconstructing the inverse time series, the calculated statistic is defined as UBk, which satisfies UBk = −UFk. When UFk > 0, the series exhibits an upward trend, whereas when UFk < 0, the series exhibits a downward trend. When UFk and UBk intersect at a certain confidence level, mutation occurs.

2.4.2. Spatial Dynamic Change Trend Analysis of RSEI

Variate linear regression equations were used to process the RSEI for pixels in the study area to investigate spatiotemporal dynamic eco-environmental quality changes within the Daihai Lake Basin from 1985 to 2022. The slope of the equation denotes the RSEI change trend for each pixel [40,41]. Generally, a slope > 0 indicates an upward trend in the RSEI, a slope = 0 implies no change, and a slope < 0 signifies a downward trend in the RSEI. The equation used is as follows:
s l o p e = n × ∑ i = 1 n   i × b i − ∑ i = 1 n   i ∑ i = 1 n   b i n × ∑ i = 1 n   i 2 − ( ∑ i = 1 n   i ) 2 ,
b = y ¯ − s l o p e × x , ¯
y = slope × x   +   b ,
where slope is the slope of the one-variable linear regression equation of RSEI, i denotes the year, bi is the RSEI value in year i, and n is the total number of years (n = 38 in this study).
The F-test significance analysis assessed the significance of the variate linear regression equation of the RSEI, and dynamic changes in spatial significance were analyzed using the superposition analysis tool in ArcGIS 10.8. The formula for the F-test is as follows [42]:
F = U Q / ( n − 2 ) , U = b 2 ∑ i = 1 n   ( x i − x ¯ ) 2 Q = ∑ i = 1 n   ( y i − y ′ ) 2 , ,
where F is the statistic, U is the square sum of the regression, Q is the square sum of the errors, and y′ is the fitting value of y obtained using the linear equation. Upon computation of the F-value, the critical value of F was 4.113 for α = 0.05, n = 38, and 7.396 for α = 0.01 and n = 38, according to the distribution table. If the calculated F-value exceeded 4.113, the regression equation was considered significant; conversely, if it fell below this threshold, it was considered insignificant (Table 3).

2.5. Spatial Correlation Analysis

Spatial autocorrelation reflects the degree of correlation between a phenomenon in a certain region and that in adjacent regional units [43]. To explore the spatial dynamic distribution characteristics of eco-environmental quality in the Daihai Lake Basin, we conducted an analysis of the spatial correlation by employing both Global and Local Moran’s I. Global Moran’s I provided insights into the spatial correlation of the overall RSEI value distribution across the study area. Local Moran’s I, the local indicator of spatial association (LISA), offers a means to assess the local spatial aggregation and heterogeneity between individual grid pixels. The calculation formula for the Local Moran’s I is as follows [43,44]:
G l o b a l   M o r a n ′ s   I = n ∑ i = 1 n   ∑ j = 1 n   W i j ( x i − x ¯ ) ( x j − x ¯ ) ∑ i = 1 n   ∑ j = 1 n   W i j ∑ i = 1 n   ( x i − x ¯ ) 2 ,
where xi is the RSEI value of pixel i ; xj is the RSEI value on pixel j ; x ¯ is the average value of the RSEI for all pixels; and Wij is the spatial weight matrix. The Global Moran’s I index was in the [−1, 1] interval. Positive and negative values represent positive and negative correlations, respectively, and a zero value indicates independence among grid pixels within the study area. The proximity of this value to 1 indicated a stronger aggregation effect of the RSEI on the spatial distribution of the Daihai Lake Basin. Conversely, proximity to −1 indicated a more pronounced anisotropy in the spatial distribution of the RSEI within the Daihai Lake Basin.
L o c a l   M o r a n ′ s   I = n ( x i − x ¯ ) ∑ j   w i j ( x j − x ¯ ) ∑ i   ( x i − x ¯ ) 2 ,
where xi, xj, x ¯ , and Wij possess the same meaning as in the equation for the Global Moran’s I. The LISA clustering map involved five types of spatial aggregation, including low–high, high–high, high–low, and low–low clusters, and no significant clustering [45,46].

2.6. Geographical Detector

Geographical Detector, a novel model for spatial analysis, offers quantitative insights into the factors influencing spatial differentiation [47,48]. In our investigation of the dynamic changes in eco-environmental quality within the Daihai Lake Basin, the primary factors driving variations in the RSEI and the interactions among them were quantitatively analyzed by employing factor detection and interactive detection within the Geographical Detector framework. The impact of various factors was analyzed by applying factor and interactive detection methods. The effects of NDVI (X1), WET (X2), NDBSI (X3), LST (X4), annual average temperature (X5), annual precipitation (X6), annual evaporation (X7), elevation (X8), slope (X9), aspect (X10), population density (X11), GDP (X12), night light index (X13), and land use type (X14) on the eco-environmental quality in the Daihai Lake Basin were analyzed.

2.6.1. Factor Detection

The impact of each driving factor on eco-environmental quality in the Daihai Lake Basin was determined through factor detection and quantified using the q-value. The q-value is defined as follows [47,49].
q = 1 − ∑ h = 1 L N h σ h 2 N σ 2 ,
where q is the explanatory power of the variable for the spatial attributes. The greater the q-value, the greater its influence on the RSEI. Parameter h represents the class or subdivision of the variable, L represents the number of subregions, N denotes the number of sample units in the entire region, Nh denotes the number of sample units in the subregion, σ2 represents the variance of a single variable in the entire region, and σ2 is the subregion variance.

2.6.2. Interactive Detection

We analyzed the intensity of the interaction between two distinct influencing factors on the eco-environmental quality RSEI to determine whether the interaction between factors X1 and X2 strengthened or reduced the explanatory power of RSEI [50,51]. This relationship can be categorized into five cases, as listed in Table 4.

2.7. Processing Flow

A flowchart of dynamic monitoring and driver analysis of the ecological environment is presented in Figure 2.

3. Results

3.1. Construction and Test of the RSEI Model

3.1.1. RSEI Model Construction

The RSEI was derived through the PCA of ecological indicators from 1985 to 2022. The results are presented in Table 5. During the 38-year period, the average contribution rate of the first principal component (PC1) was 77.17%, indicating its robust explanatory capacity across ecological indicators and its representation of the majority of information. Therefore, PC1 was appropriate for evaluating the eco-environmental quality of the Daihai Lake Basin. In PC1, the eigenvalues of the NDVI and WET indicators consistently displayed positive values, indicating their positive effects on RSEI. Conversely, the eigenvalues of the NDBSI and LST indicators consistently exhibited negative values, suggesting their negative effects on the RSEI, which align with the actual scenario. For each year, the mean eigenvalues of the ecological indicators were ranked in the following order by magnitude: LST > NDBSI > NDVI > WET, implying that the impact on eco-environmental quality followed the sequence of heat > dryness > greenness > humidity. Furthermore, the sum of the absolute mean values of dryness and heat consistently exceeded those of greenness and humidity, indicating that, in the Daihai Lake Basin, the inhibitory effects of dryness and heat outweighed the promoting effects of greenness and humidity.

3.1.2. RSEI Model Test

In this study, the NDVI, WET, NDBSI, LST, and RSEI of the Daihai Lake Basin were sampled evenly in ArcGIS at 250-m intervals, resulting in 35,246 samples. The correlation between ecological factors was analyzed to determine the correlation between the RSEI index and each factor, as well as to assess comprehensive representativeness and data quality. As shown in Figure 3, the RSEI passed the normality test. The Pearson correlation coefficients between the NDVI, WET, and RSEI were greater than 0.70, whereas the correlation coefficients between NDBSI, LST, and RSEI exceeded 0.74, all passing the p < 0.01 significance test. The RSEI index was significantly correlated with the four components, making it more representative than any single index component (NDVI, WET, NDBSI, and LST) and capable of comprehensively reflecting the ecological environment.

3.2. Spatiotemporal Changes in the RSEI in the Daihai Lake Basin

3.2.1. Temporal Variation Characteristics of Each Index and RSEI in the Daihai Lake Basin

As depicted in Figure 4, over the 38-year study period, the NDVI and WET exhibited upward trends, with growth rates of 0.0065/a (slope = 0.0047, p < 0.01) and 0.0132/a (slope = 0.0172, p < 0.01), respectively. Conversely, the NDBSI and LST showed downward trends, with growth rates of −0.0072/a (slope = −0.0089, p < 0.01) and −0.0007/a (slope = −0.0016, p < 0.01), respectively. The mean RSEI value increased notably from 0.301 in 1985 to 0.439 in 2022, with a growth rate of 0.0036/a (slope = 0.0035, p < 0.01), indicating a significant enhancement in overall eco-environmental quality. However, notable temporal disparities were observed. According to the UF curve, the RSEI of the Daihai Lake Basin decreased before 1987, followed by an increase from 1987 to 2009, a subsequent decline from 2009 to 2012, and a subsequent continuous rise after 2012, intersecting the UF and UB curves in 2012. The year 2012 marked the year of the RSEI mutation. Furthermore, the impact of each index on the RSEI of the Daihai Lake Basin increased, aligning with the changing trend of the RSEI in the region. The analysis of individual curves revealed that the trend of the RSEI change curve did not align with any single index, underscoring the collective influence of all indices in driving the eco-environmental quality change in the Daihai Lake Basin.

3.2.2. Dynamic Grading Analysis of Eco-Environmental Quality in the Daihai Lake Basin

To quantitatively characterize the evolving eco-environmental quality in the Daihai Lake Basin, we adhered to the Technical Evaluation Standards for Ecological Environment Quality and the relevant research findings [52]. Consequently, the RSEI grid pixel values were categorized into five grades with uniform intervals: excellent (0.8 < RSEI ≤ 1), good (0.6 < RSEI ≤ 0.8), general (0.4 < RSEI ≤ 0.6), poorer (0.2 < RSEI ≤ 0.4), and poor (0 < RSEI ≤ 0.2).
Based on the spatial distribution of the RSEI in the Daihai Lake Basin (Figure 5), in 1985, regions with excellent and good eco-environmental quality were primarily clustered around the Daihai River and Maihutu Town in the northern part of the basin. In 1995, there was a notable improvement in the eco-environmental quality, particularly in Daihai Town to the east, Maihutu Town to the north, and Changhanying Town to the southeast. Subsequently, in 2005, the areas of good and excellent grades were further expanded to Daihai Town, Dayushu Township, Maihutu Town, and Sanyiquan Town in the north, and Changhanying Township in the southeast. In 2015, the good and excellent regions were concentrated in the northern and southeastern parts of the Daihai Lake Basin, whereas the poor and poor areas were concentrated in the same regions. In 2022, the poor and poorer grades diminished primarily in the northern part of the basin, whereas the southern areas with good and excellent grades experienced further enhancement.
From the temporal variations in RSEI in the Daihai Lake Basin (Figure 6), the combined areas with good and excellent eco-environmental quality expanded from 78.03 km2 in 1985 to 227.40 km2 in 2022, marking a 6.41% increase. In 2022, these grades peaked, encompassing 9.5% of the basin’s total area. The general grade area expanded from 276.54 km2 (1985) to 1159.68 km2 (2022), while the poor and very poor grades decreased from 1862.01 km2 (1985) to 902.20 km2 (2022), representing 44.59% of the area. The latter was at its minimum in 2022, comprising 39.41% of the basin’s total area. Overall, from 1985 to 2022, the areas with general, good, and excellent eco-environmental quality in the Daihai Lake Basin expanded, whereas those with poor and very poor quality gradually diminished, indicating an improvement in the eco-environmental quality in the basin.

3.3. Analysis of the Change Trend of the Eco-Environmental Quality in the Daihai Lake Basin

To explore the changes in eco-environmental quality within the Daihai Lake Basin over the 38-year study period, we employed a slope trend analysis and the F significance test. This enabled us to identify seven distinct categories of changes in the annual average RSEI: extremely significant degradation, significant degradation, no change, no significant degradation, no significant improvement, significant improvement, and extremely significant improvement (Figure 7). The findings indicated that the predominant types of eco-environmental quality changes observed from 1985 to 2022 were significantly degraded and improved (Table 6). Areas exhibiting significant degradation (slope < 0) were mainly concentrated in Sanyiquan Town in the northeastern part and Tiancheng Town in the southeastern part of the basin, covering an area of 168.74 km2. Conversely, areas with significant improvement in eco-environmental quality (slope > 0) were predominantly distributed in Yongxing Town and Liusumu Town in the southwestern part of the Daihai Lake Basin and were scattered throughout the basin, totaling 2028.4 km2. Notably, the largest area demonstrating extremely significant improvement spanned 2026.49 km2, constituting 92.23% of the basin, while the area with extremely significant degradation covered 165.61 km2, representing 7.54% of the basin. In summary, the area exhibiting eco-environmental quality improvement over the 38-year study period exceeded that of degradation, indicating an overall trend towards improvement.

3.4. Spatial Autocorrelation Analysis of RSEI

To further investigate the spatial correlation and distribution characteristics of eco-environmental quality in the Daihai Lake Basin, both Global and Local Moran’s I were used to analyze the RSEI data. The results presented in Figure 8 indicated that the Global Moran’s I was consistently higher than 0 from 1985 to 2022, with statistically significant results. This suggested a positive spatial autocorrelation of eco-environmental quality throughout the study period, indicating significant spatial clustering. Moreover, the Global Moran’s I value for the RSEI in the Daihai Lake Basin increased from 0.629 in 1985 to 0.661 in 2022, reflecting an escalating trend in spatial aggregation characteristics. Global Moran’s I peaked at its highest value (0.741) in 2005, indicating the strongest spatial aggregation effect in that year. Figure 8 illustrates the notable distributions of high–high and low–low clusters. The areas with higher eco-environmental quality displayed significant spatial correlations, and the clustering effect intensified over time. The high–high cluster areas, primarily located in Changhanying Township, Daihai Town, Liusumu Town, and Maihutu Town, expanded from 306.71 km2 in 1985 to 469.95 km2 in 2022, signifying a continuous improvement in eco-environmental quality. Low–low clusters predominated in Daihai, Liusumu, Tiancheng, and Sanyiquan. The area decreased from 515.35 km2 in 1985 to 324.12 km2 in 1995, followed by an increase to 528.70 km2 in 2022. This fluctuation in the low–low cluster area illustrates a pattern wherein the eco-environmental quality initially increased and then declined within the basin.

3.5. Driving Factors of Eco-Environmental Quality Changes in the Daihai Lake Basin

3.5.1. Factor Detection Analysis

To further elucidate the driving factors influencing the dynamic changes in eco-environmental quality within the Daihai Lake Basin over the 38-year study period, we analyzed the explanatory power of each factor on the spatial differentiation characteristics of the RSEI by employing a factor detector. Owing to missing data for some influencing factors, we analyzed six periods of data between 1990 and 2015. The results depicted in Figure 9 indicate a p-value of 0 for all detection factors, which significantly affected the spatial differentiation characteristics of the eco-environmental quality in the Daihai Lake Basin. The average q-value of the internal driving factors consistently exceeded that of the external driving factors (comprising natural factors (X5–X10) and socio-economic factors (X11–X14)) due to changes in remote sensing ecological indices (X1–X4). Second, the q-value of the DEM increased annually, whereas for other influencing factors, it remained below 0.15. Consequently, changes in RSEI were predominantly influenced by the NDVI and LST. Between 1990 and 2015, the average q-value of the internal driving factors declined gradually, while that of the external driving factors remained relatively stable, and the q-value of DEM exhibited a continuous decrease. In summary, from the perspective of internal driving factors, the spatial differentiation of RSEI in the Daihai Lake Basin was mainly affected by NDVI and LST. From the perspective of external driving factors, the change in RSEI was primarily affected by DEM.

3.5.2. Interactive Detection

Factor detection can facilitate the analysis of all factors on the spatial heterogeneity of the RSEI, whereas interactive detection enables the exploration of the interaction’s impact on the spatial differentiation of eco-environmental quality in the Daihai Lake Basin. In this study, we utilized an interactive Geographical Detector to examine the explanatory power of all the factors from 1990 to 2015 (Figure 10). The results revealed that throughout the study period, the explanatory power of interactions between the influencing factors on the spatial differentiation of RSEI was stronger than that of individual factors, primarily showing nonlinear synergy and double synergy. This indicated that the spatial heterogeneity of the RSEI in the Daihai Lake Basin was affected not only by individual factors but also by the combined effects of various remote sensing ecological indicators (X1 (NDVI), X2 (WET), X3 (NDBSI), and X4 (LST)) and DEM. The analysis indicated that the spatial heterogeneity of RSEI in the Daihai Lake Basin was primarily influenced by the interaction between internal driving factors, specifically NDVI (X1) and LST (X4), which had the strongest explanatory power for RSEI after interaction. This means that significant changes in NDVI enhanced the explanatory power of LST as an independent variable for the ecological environment. Although each external driving factor had limited explanatory power on the spatial differentiation of RSEI individually, their influence was significantly enhanced when interacting with internal driving factors. Notably, DEM (X8) showed the most significant increase in explanatory power when interacting with LST. This demonstrated that under the influence of internal driving factors, the roles of the external driving factors (both natural and socio-economic) on the spatial differentiation characteristics of RSEI in the Daihai Lake Basin were fully reflected.

4. Discussion

4.1. Test of the RSEI Model

The effectiveness of RSEI has been demonstrated in various environments [53] and applied to assess the eco-environmental quality of the Daihai Lake Basin. The RSEI was effectively linked to NDVI, WET, NDBSI, and LST indices using PCA. Over the 38-year study period, PC1 consistently contributed an average of 77.17%, indicating its strong explanatory power for each ecological index and its ability to represent the majority of information. Compared with any individual habitat index component, the RSEI provided a more comprehensive reflection of the environmental and ecological quality of the Daihai Lake Basin. The eigenvalues of the NDVI and WET indices were consistently positive, whereas those of the NDBSI and LST indices were consistently negative, which is consistent with actual observations and previous research conclusions. RSEI followed a normal distribution and was significantly correlated with the four index components, making it more representative than any single index component. This indicates that RSEI is suitable for the Daihai Lake Basin and can comprehensively reflect the ecological environment. In addition, the RSEI extraction results in this study were compared with the AWRSEI extraction results obtained by Zhao et al. [24], which showed a difference of less than 2.5% (Table 7). This is consistent with the findings of a study on the eco-environmental quality change trends in the Daihai Lake Basin [24]. On a global scale, the correlation between the RSEI data from 2005 and 2015 was significant, with correlation coefficients R2 of 0.7129 and 0.6755, respectively (p = 0.001) (Figure 11). The significance test confirmed a strong correlation between the RSEI and CHEQ calculated in this study [54]. Therefore, RSEI can effectively reflect the temporal and spatial variation characteristics of ecological environment quality at the regional scale of the Daihai Lake Basin.

4.2. Temporal and Spatial Variation Characteristics of Ecological Environment Quality in the Daihai Lake Basin

As a critical indicator of eco-environmental quality, a higher RSEI value after positive standardization indicates better eco-environmental quality in a region [55]. The average RSEI increased from 0.301 to 0.439 between 1985 and 2022. The average growth rate of the RSEI from 1985 to 1995 was 0.0062/a, whereas it accelerated to 0.0005/a from 1995 to 2005. From 2005 to 2015, the average annual growth rate of the RSEI was 0.0013/a. The growth rate of RSEI was 0.0062/a between 2015 and 2022. Significantly, the growth rate of the RSEI during 1995–2005 was comparatively lower than that in other periods, suggesting a pattern of fast–slow–fast eco-environmental quality improvement within the study area over the research period. This trend indicated an initial improvement, followed by the degradation of eco-environmental quality during the study period. Despite some improvements, the overall eco-environmental quality of the basin remained poor. Since 2000, Wulanchabu City has initiated several ecological greening projects, including the Three Mountains and Two Rivers Plan, farmland-to-forest conversion, Beijing–Tianjin sandstorm source mitigation, and the protection of natural forests, along with other national and local greening initiatives [56]. However, from 2000 to 2006, the RSEI in the study area demonstrated a decreasing trend, possibly because of the delayed recognition of the early ecological benefits of the farmland-to-forest (grass) project and its subsequent lag in impact. Since 2007, there has been significant improvement in the RSEI, indicating the influential role of government decisions in shaping the eco-environmental quality within the Daihai Lake Basin.
Notably, the RSEI exhibited higher values in the peripheral, northeastern, and southwestern regions than in the other areas. Specifically, the areas with elevated RSEI values were predominantly located in Daihai Town in the eastern region, Maihutu Town in the north, and Changhanying Town in the southeast. This aligns with the findings of previous studies on the spatial heterogeneity of eco-environmental quality in the Daihai Lake Basin [24]. The central basin, characterized by its flat terrain and abundant resources, experiences a high population density, elevated ecological pressure, and relatively fragile eco-environmental quality [57]. This is basically consistent with land use changes in the basin, where urbanization has caused some ecological deterioration [24]. Since 2000, the burgeoning tourism and fishery industries in Daihai, coupled with the development of tourism zones and fish farms, have resulted in an expansion of constructed land within the basin. Concurrently, there has been an increase in the annual average temperature and a decrease in summer precipitation. Moreover, agricultural irrigation demands [58] have escalated, and vegetation coverage has dwindled [27], exacerbating surface dryness and consequently lowering the ecological quality of the area. Therefore, the eco-environmental quality of the study area was generally deemed poor. With the initiation of farmland-to-forest and grassland restoration projects, there has been an increase in forest and grassland areas [58]. This enhanced soil water retention, elevated soil moisture content, and promoted a positive trajectory in eco-environmental quality improvement. These findings are consistent with those of previous studies, indicating an enhancement in the eco-environmental quality within the study area from 2015 to 2022.

4.3. Factors Influencing Changes in Ecological Environment Quality in the Daihai Lake Basin

Factor analysis highlighted the NDVI and LST as the main factors affecting spatial changes in eco-environmental quality in the Daihai Lake Basin, consistent with previous studies [33]. The synergistic influence of natural and anthropogenic factors, along with favorable climatic conditions and government policies promoting farmland conversion to forest, grassland restoration, and afforestation, fosters vegetation growth in the basin, thereby promoting regional eco-environmental quality improvement [56,59]. The interactive detection results indicated that spatial differentiation in eco-environmental quality within the Daihai Lake Basin can be affected by various factors, primarily demonstrating nonlinear and double synergy effects, which is consistent with the research of Zhou et al. [60]. The significant change in NDVI increased the explanatory power of LST as an independent variable in the ecological environment, aligning with the findings of Bao et al. [61] on LST in Daqing Mountain, Inner Mongolia. The elevational factor significantly enhanced its explanatory power following interactions with internal driving factors (NDVI, WET, NDBSI, and LST). Temperature and precipitation present vertical zonation patterns, with slopes directly impacting soil water retention capacity. NDVI serves as an effective indicator of the vegetation growth status. Hence, the elevation and slope factors exerted a considerable influence on the spatial differentiation of eco-environmental quality in the basin [62]. Although precipitation and evaporation are closely linked to vegetation growth and soil water conservation [62], the Daihai Lake Basin experiences relatively low and stable annual precipitation, which may explain its limited impact on eco-environmental quality [63].

4.4. Limitations and Future Perspectives

The innovation of this study lies in its multi-dimensional analysis of the dynamic changes in eco-environmental quality in the Daihai Lake Basin over the past 38 years conducted on the GEE cloud platform. This analysis included the temporal and spatial variation characteristics of RSEI, mutation analysis, spatial autocorrelation analysis, and driving factor analysis. However, relying solely on one indicator cannot provide a comprehensive understanding of eco-environmental quality [64]. Therefore, we used four different indicators to complement each other in the analysis. The advantage of this approach is that the use of RSEI for eco-environmental quality calculations and spatial autocorrelation can mutually confirm the research results, providing a more comprehensive understanding of the ecological environment in the Daihai Lake Basin. However, this study had certain limitations. Owing to data accessibility challenges, we analyzed the dynamics of the explanatory power of these factors in the Daihai Lake Basin from 1990 to 2015. Future research should consider a broader array of natural and socio-economic factors to address data gaps and comprehensively explore the changes in the explanatory power of the driving factors within the study area with greater scientific rigor and precision.

5. Conclusions

From 1985 to 2022, the average contribution rate of PC1 was 77.17% for the RSEI, which was composed of various ecological indicators. The NDVI and WET had positive impacts, whereas LST and NDBSI had negative impacts on RSEI, suggesting their suitability for assessing eco-environmental quality in the Daihai Lake Basin, accurately reflecting pertinent information. During this period, the average RSEI value increased from 0.301 to 0.430 at a rate of 0.0036/a (slope = 0.0035, p < 0.01). The area of poor and poor-grade regions declined from 1862.01 km2 to 902.20 km2, while that of good and excellent-grade areas rose from 78.03 km2 to 227.40 km2, demonstrating a significant overall improvement in eco-environmental quality. The regions experiencing notable degradation (slope < 0) were mainly located in Sanyiquan Town in the northeast and Tiancheng Town in the southeast, whereas those experiencing significant improvement (slope > 0) were primarily located in Yongxing Town and Liusumu Town in the southwest of the Daihai Lake Basin.
Throughout the study period, the Global Moran’s I consistently exceeded 0, escalating from 0.62 to 0.661. This indicated a positive spatial autocorrelation in eco-environmental quality within the Daihai Lake Basin, with steadily rising spatial aggregation. The high–high cluster areas expanded from 306.71 km2 to 469.95 km2, while that of low–low cluster areas initially shrank from 515.35 km2 to 324.12 km2, before increasing again to 528.70 km2. These trends further presented a pattern in which the eco-environmental quality initially improved before declining in the basin.
From 1990 to 2015, the improvement in the ecological environment of the Daihai Lake Basin was primarily due to an increase in the NDVI and WET and a decrease in the NDBSI and LST. The interaction between NDVI and LST had the strongest explanatory power for the ecological environment. An analysis of the influence of external driving factors on eco-environmental quality revealed that DEM was the dominant factor in RSEI with the strongest explanatory power. The interaction between DEM and LST was notably robust, enhancing all driving factors. This highlights that the ecological environment of the Daihai Lake Basin is shaped by numerous factors rather than by simple superposition or independent effects. Furthermore, it is necessary to establish sound and sustainable ecological protection policies for mountains, rivers, forests, fields, lakes, grasses, and sands.

Author Contributions

Conceptualization, methodology, formal analysis, writing—original draft and visualization, B.Y.; writing—review and editing, B.S.; writing—review and editing, X.S.; supervision, Y.Z. (Yunliang Zhao), Y.G., J.P., W.Y., Y.H. and Y.Z. (Yunxi Zhao). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key R&D Program of China, grant number 2023YFC3206504, the National Natural Science Foundation of China, grant number 52369014, 52260028, and 52060022, and the Natural Science Foundation of Inner Mongolia, grant number 2023YFDZ0022.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

We thank all supervisors for their efforts in reviewing and editing this study.

Conflicts of Interest

All of the authors state that there are no conflicts of interest.

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Figure 1. Location of the study area.
Figure 1. Location of the study area.
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Figure 2. Flowchart.
Figure 2. Flowchart.
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Figure 3. Correlation between RSEI and each index.
Figure 3. Correlation between RSEI and each index.
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Figure 4. Change curves of NDVI, WET, NDBSI, LST, and RSEI in the Daihai Lake Basin from 1985 to 2022 and mutation point test of RSEI change. (a) NDVI, (b) WET, (c) NDBSI, (d) LST, (e) RSEI, and (f) mutation point test.
Figure 4. Change curves of NDVI, WET, NDBSI, LST, and RSEI in the Daihai Lake Basin from 1985 to 2022 and mutation point test of RSEI change. (a) NDVI, (b) WET, (c) NDBSI, (d) LST, (e) RSEI, and (f) mutation point test.
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Figure 5. Spatial distribution of the RSEI index in the Daihai Lake Basin during change node years.
Figure 5. Spatial distribution of the RSEI index in the Daihai Lake Basin during change node years.
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Figure 6. Area and proportion of RSEI grades in the year of the change node in the Daihai Lake Basin. (a) Area of RSEI grades, (b) proportion of RSEI grades.
Figure 6. Area and proportion of RSEI grades in the year of the change node in the Daihai Lake Basin. (a) Area of RSEI grades, (b) proportion of RSEI grades.
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Figure 7. Analysis of the dynamic change trend in the Daihai Lake Basin from 1985 to 2022. (a) slope, (b) F test.
Figure 7. Analysis of the dynamic change trend in the Daihai Lake Basin from 1985 to 2022. (a) slope, (b) F test.
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Figure 8. LISA clustering diagram of the RSEI index in the Daihai Lake Basin.
Figure 8. LISA clustering diagram of the RSEI index in the Daihai Lake Basin.
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Figure 9. Detection results of driving factors of the RSEI in the Daihai Lake Basin.
Figure 9. Detection results of driving factors of the RSEI in the Daihai Lake Basin.
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Figure 10. Interactive detection results of factors influencing the RSEI in the Daihai Lake Basin from 1990 to 2015: (a) 1990, (b) 1995, (c) 2000, (d) 2005, (e) 2010, and (f) 2015.
Figure 10. Interactive detection results of factors influencing the RSEI in the Daihai Lake Basin from 1990 to 2015: (a) 1990, (b) 1995, (c) 2000, (d) 2005, (e) 2010, and (f) 2015.
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Figure 11. Correlation diagram of RSEI and CHEQ in the Daihai Lake Basin.
Figure 11. Correlation diagram of RSEI and CHEQ in the Daihai Lake Basin.
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Table 1. Description of the main data sources.
Table 1. Description of the main data sources.
Data TypeData NameResolutionUnitData Sources
Remote sensing image dataLandsat SR1 km—GEE platform Landsat SR series remote sensing products
(https://earthengine.google.com/, accessed on 3 July 2024)
Natural factorsMonthly precipitation1 kmmmNational Earth System Science Data Center (http://www.geodata.cn/, accessed on 3 July 2024)
Monthly average temperature1 km°C
Monthly evaporation1 kmmm
Elevation30 mmGeospatial Data Cloud
(https://www.gscloud.cn/, accessed on 3 July 2024)
Slope30 m°
Aspect30 m—
Socio-economic factorsLand use types30 m—Resource and Environmental Science Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 3 July 2024)
Nighttime light intensity1 km—National Qinghai-Tibet Plateau Scientific Data Center (https://data.tpdc.ac.cn/, accessed on 3 July 2024)
Population density1 kmPeople/km2World Population Data Website (https://hub.worldpop.org/, accessed on 3 July 2024)
Assisting dataVector boundary of the Daihai Lake Basin National Earth System Science Data Center (http://www.geodata.cn/, accessed on 3 July 2024)
Table 2. Landsat remote sensing ecological index calculation formulas.
Table 2. Landsat remote sensing ecological index calculation formulas.
IndicesCalculation FormulaParameter Meanings
NDVI N D V I = ( ρ nir 1   − ρ red   ) / ( ρ nir 1   + ρ red ) ρred, ρblue, ρgreen, ρmir1, ρmir2, ρnir1, and ρnir2 are the reflectance of each band of the Landsat data.
WET W E T = 0.2408 ρ blue + 0.3132 ρ green + 0.1147 ρ red +
0.2489 ρ nir 1 − 0.3122 ρ nir 2 − 0.6416 ρ mir 1 − 0.5087 ρ mir 2
NDBSI S I = ρ mir 1 + ρ red − ρ nir 1 + ρ blue ρ mir 1 + ρ red + ρ nir 1 + ρ blue
I B I = 2 ρ mir 1 / ρ mir 1 + ρ nir 1 − ρ nir 1 / ρ nir 1 + ρ red + ρ green / ρ green + ρ mir 1 2 ρ mir 1 / ρ mir 1 + ρ nir 1 + ρ nir 1 / ρ nir 1 + ρ red + ρ green / ρ green + ρ mir 1
N D B S I = I B I + S I / 2 		SI and IBI are the soil index and building index, respectively.
LST L S T = 0.02 D N − 273.15 DN is the gray value of the surface temperature.
Table 3. Eco-environmental quality trend test category in the Daihai Lake Basin.
Table 3. Eco-environmental quality trend test category in the Daihai Lake Basin.
βZTrend CategoryCharacteristic Trend
β > 0Z > 7.3963Significantly improved
4.113 < Z ≤ 7.3962Significantly improved
Z ≤ 4.1131Not significantly improved
β = 000No change
β < 0Z ≤ 4.113−1Not significantly degraded
4.113 < Z ≤ 7.396−2Significantly degraded
Z > 7.396−3Extremely significantly degraded
Table 4. Interaction types of probe factors.
Table 4. Interaction types of probe factors.
Interaction Typeq-Value Relationship
Nonlinear weakeningq(X1ÇX2) < Min[q(X1), q(X2)]
Single-factor nonlinear weakeningMin[q(X1), q(X2)] < q(X1ÇX2) < Max[q(X1), q(X2)]
Two-factor enhancementq(X1ÇX2) > Max[q(X1), q(X2)]
Independent of each otherq(X1ÇX2) = q(X1) + q(X2)
Nonlinear enhancementq(X1ÇX2) > q(X1) + q(X2)
Table 5. Results of different indicators for PC1.
Table 5. Results of different indicators for PC1.
YearNDVIWETNDBSILSTContribution Rate (%)YearNDVIWETNDBSILSTContribution Rate (%)
19850.380.57−0.41−0.6077.1620040.540.14−0.53−0.6477.77
19860.360.52−0.42−0.6576.3820050.380.15−0.34−0.8577.95
19870.290.53−0.43−0.6775.5920060.510.17−0.39−0.7578.46
19880.360.47−0.41−0.6973.3720070.440.15−0.37−0.8076.47
19890.320.50−0.43−0.6872.9820080.610.09−0.41−0.6783.80
19900.360.50−0.47−0.6377.4520090.590.09−0.37−0.7183.14
19910.370.50−0.43−0.6573.5120100.580.10−0.38−0.7183.78
19920.380.51−0.45−0.6275.4020110.540.10−0.40−0.7380.82
19930.400.50−0.55−0.5472.3920120.050.09−0.06−0.9979.31
19940.370.55−0.46−0.5973.1420130.560.14−0.53−0.6177.55
19950.360.57−0.49−0.5575.1620140.520.15−0.50−0.6777.83
19960.250.58−0.48−0.6173.8620150.460.14−0.46−0.7574.62
19970.330.51−0.43−0.6774.5720160.500.14−0.49−0.7079.28
19980.430.47−0.46−0.6275.4720170.520.15−0.55−0.6477.80
19990.540.16−0.38−0.7477.0020180.500.16−0.54−0.6680.62
20000.480.15−0.36−0.7977.0620190.550.16−0.61−0.5583.21
20010.530.15−0.44−0.7171.8720200.570.16−0.61−0.5380.56
20020.580.14−0.51−0.6373.1520210.510.18−0.65−0.5378.39
20030.570.14−0.57−0.5776.8520220.360.15−0.35−0.8578.76
Table 6. Areas and proportions of RSEI change trend categories in the Daihai Lake Basin from 1985 to 2022.
Table 6. Areas and proportions of RSEI change trend categories in the Daihai Lake Basin from 1985 to 2022.
TypeNot Significantly DegradedSignificantly DegradedExtremely Significantly DegradedNot Significantly ImprovedSignificantly ImprovedExtremely Significantly Improved
Area (km2)2.131.00165.611.430.482026.49
Proportion (%)0.100.057.540.060.0292.23
Table 7. Comparison of RSEI and AWRSEI results in Daihai Lake Basin.
Table 7. Comparison of RSEI and AWRSEI results in Daihai Lake Basin.
Year2001200920142020
RSEI0.3870.4050.4120.502
AWRSEI0.3770.4250.4050.500
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Ye, B.; Sun, B.; Shi, X.; Zhao, Y.; Guo, Y.; Pang, J.; Yao, W.; Hu, Y.; Zhao, Y. Monitoring and Analysis of Eco-Environmental Quality in Daihai Lake Basin from 1985 to 2022 Based on the Remote Sensing Ecological Index. Sustainability 2024, 16, 6854. https://doi.org/10.3390/su16166854

AMA Style

Ye B, Sun B, Shi X, Zhao Y, Guo Y, Pang J, Yao W, Hu Y, Zhao Y. Monitoring and Analysis of Eco-Environmental Quality in Daihai Lake Basin from 1985 to 2022 Based on the Remote Sensing Ecological Index. Sustainability. 2024; 16(16):6854. https://doi.org/10.3390/su16166854

Chicago/Turabian Style

Ye, Bowen, Biao Sun, Xiaohong Shi, Yunliang Zhao, Yuying Guo, Jiaqi Pang, Weize Yao, Yaxin Hu, and Yunxi Zhao. 2024. "Monitoring and Analysis of Eco-Environmental Quality in Daihai Lake Basin from 1985 to 2022 Based on the Remote Sensing Ecological Index" Sustainability 16, no. 16: 6854. https://doi.org/10.3390/su16166854

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