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Article

Assessing Environmental Sustainability in the Transnational Basin of the Tumen River Based on Remote Sensing Data and a Geographical Detector

by
Lin Jin
* and
Zhijie Zhang
School of Economics and Management, Yanbian University, Yanji 133002, China
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(18), 8121; https://doi.org/10.3390/su16188121
Submission received: 21 July 2024 / Revised: 31 August 2024 / Accepted: 5 September 2024 / Published: 18 September 2024
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Abstract

Evaluating environmental sustainability in the transnational basin of the Tumen River (TBTR) is of great significance for promoting sustainable development in Northeast Asia. However, past research has mostly concentrated on a particular environmental element, making it impossible to thoroughly and effectively show the environmental sustainability dynamics in this transnational area. In this study, we attempted to reveal environmental sustainability trends in the TBTR from 2000 to 2021 using the Environmental Degradation Index (EDI) and analyze the driving forces using a geographical detector. It was found that the TBTR’s environmental sustainability decreased significantly, with a degraded region (13,174.75 km2) accounting for 31.01% of the whole area from 2000 to 2021. The dynamics of environmental sustainability on the three sides of China, the Democratic People’s Republic of Korea (DPRK), and Russia have shown significant differences, with the most significantly improved in environmental sustainability being the subregion of China. On the Chinese side, the area that significantly improved in environmental sustainability accounted for 26.19% of the area on the Chinese side, which was 1.17 times higher than that of the DPRK’s side and 1.24 times higher than that of the Russian side. Land use intensity (LUI), land use and land cover (LULC), and population density (PD) were the most dominant driving forces for environmental sustainability dynamics on the three sides of China, the DPRK, and Russia. China, the DPRK, and Russia can improve international environmental cooperation to promote sustainable development in the TBTR and Northeast Asia.

1. Introduction

Environmental sustainability refers to the maintenance of natural capital (i.e., the natural environment) and is the basis for achieving sustainable development. Based on Daly [1] and Wu [2,3], environmental sustainability builds the basis for social and economic sustainability, which serves as a strong foundation for regional sustainable development. The encouragement of regional sustainable development is only possible through a complete awareness of environmental sustainability. During the last several decades, the change in global climate and intensifying human activities have triggered a variety of environmental problems that have led to severe environmental degradation, which consequently caused a serious deterioration of environmental sustainability. This has caused serious negative impacts on social and economic sustainability at different scales and impeded the realization of sustainable development goals. Therefore, it is important to comprehensively reveal environmental sustainability, analyze the driving forces behind environmental sustainability, and identify the impacts of climate change and human activities on environmental sustainability in order to achieve the sustainable development goals.
The transnational basin of the Tumen River (TBTR) is located along the border of China, the Democratic People’s Republic of Korea (DPRK), and Russia, and is an important outlet to the Japan Sea. In the past few decades, in the light of globalization, the TBTR has developed into a vital confluence of the Land Silk Road and the Silk Road on Ice under the Belt and Road Initiative, as well as a key location for synergistic growth in Northeast Asia in the future [4,5]. While international economic exchanges and cooperation have increased, the rapid urbanization and international trade in the TBTR has had significant negative effects on the natural environment [6,7,8]. The urban heat island in the TBTR has shown a significant upward trend from 2003 to 2016 [9]. Meanwhile, due to the continuous discharge of wastewater from the DPRK’s Mosan Iron Mine and China’s Kaishantun Chemical Fiber Pulping Plant, the water quality of the TBTR has significantly decreased, with an annual average of good quality waters of only 47.22% [10,11]. The ecological and environmental issues in the TBTR have led to a continuous degradation of environmental sustainability in this transnational basin, which has already had a serious negative impact on human well-being and sustainable development. Therefore, assessing environmental sustainability in a timely and efficient manner is important for achieving sustainable development goals in Northeast Asia [2,3].
In recent years, a number of research have previously been carried out to assess the environmental sustainability in the TBTR. For example, Zhu et al. [12], through field sampling surveys, found that the increase in urban land and cropland in the TBTR caused the decrease in water purification services. Tao et al. [13] measured the dynamics of the forest landscape and discovered that the loss of forests resulted in a decrease in habitat quality and a fall in carbon stock, both of which severely damaged the natural ecosystems in the northern region of the DPRK. Liu et al. [14] evaluated the ecological quality changes in Northeast China over the past 20 years using the Remote Sensing Ecological Index (RSEI) and discovered that numerous anthropogenic influences and climate change were the primary causes of the environmental changes in Northeast China. However, due to variations in institutions, environmental protection laws, and economic development, environmental sustainability differs greatly across all nations. Meanwhile, it is exceedingly challenging to obtain firsthand knowledge or maintain effective fieldwork in the DPRK because of its political isolation, making it difficult to quantify the environmental changes in the DPRK [15,16]. In addition, existing studies mostly considered single environment factors, such as vegetation cover and water resource, and lack comprehensive quantification of environmental sustainability dynamics as well as comparisons and analyses of environmental sustainability driving forces among different countries [17,18]. Remote sensing data have become effective tools to assess environmental sustainability, with their benefits of a lengthy time series and a broad spatial reach. Sun et al. [6] developed an Environmental Degradation Index (EDI) from a variety of remote sensing data in the cross-border region of China, the DPRK, and Russia. The EDI effectively integrated elements including water, soil, surface temperature, as well as vegetation cover, and can effectively assess environmental sustainability of different countries in the cross-border region.
Therefore, in this paper, we took the TBTR as the study area, evaluated the environmental sustainability dynamics using the EDI from 2000 to 2021, and analyzed the driving forces of environmental sustainability. Specifically, based on multi-source remote sensing data, we first computed the EDI using principal component analysis (PCA) and the geometric mean approach. We then combined this index with statistical data to confirm its validity. Then, we evaluated the TBTR’s environmental sustainability dynamics from 2000 to 2021, and analyzed the differences among China, the DPRK, and Russia. Finally, the driving forces for environmental sustainability were explored by using a geographical detector. This study can provide a scientific basis and recommendations to promote the sustainable development of the TBTR, as well as Northeast Asia.

2. Study Area and Data

2.1. Study Area

The TBTR (128°1′ E–132°28′ E, 41°12′ N–44°1′ N) spans the three countries of China, the DPRK, and Russia, with the Japan Sea to the east, and covers an entire region of approximately 42,200 km2 (Figure 1). The area of the Chinese side is 22,600 km2, accounting for 53.55% of the TBTR’s area; the area of the DPRK’s side is 12,500 km2, accounting for 29.62% of the TBTR’s area; and the area of the Russian side is only 0.71 million km2, accounting for 16.83% of the TBTR’s area. With an elevation of roughly 0–2745 m, annual ranges of temperature (−23 to 34 °C), and annual ranges of precipitation (0–650 mm), the TBTR boasts a moderate continental monsoon climate [19,20]. The Tumen River is the border river between China and the DPRK, and the main stream of the Tumen River marks the border between China and the DPRK. In the context of globalization and the Belt and Road Initiative, this transnational area has undergone urbanization, economic growth, and an obvious population increase. Specifically, from 1985 to 2020, the total population of the region increased from 3,646,600 to 4,193,900, with a growth of 15.36%. Among them, on the Chinese side, the total population increased from 1,337,000 to 1,596,000, with an increase of 19.37%; on the DPRK’s side, the total population increased from 1,353,600 to 1,800,000, with an increase of 32.97%; and the total population on the Russian side showed a decrease by 16.53% from 955,900 to 798,000 [21]. From 2000 to 2020, the region’s entire new urban land area was approximately 239.50 km2, of which 52.61%, 16.08%, and 31.32% were located on the Chinese, the DPRK, and Russian sides, respectively. Urbanization, socioeconomic development, and rapid population expansion have obviously placed pressure on the environmental quality of the region, and the natural environment has been significantly degraded [5,9]. Meanwhile, this trend will continue in the future [10,22]. Therefore, it is of great significance to carry out environmental sustainability monitoring and evaluation research in the TBTR.

2.2. Data

Four main types of data were used in this study: remote sensing data, land use and land cover (LULC) data, Digital Elevation Model (DEM), and geographical ancillary data. Moderate-resolution Imaging Spectroradiometer (MODIS) data products (https://search.earthdata.nasa.gov/ accessed on 1 August 2023) were the main source of the remote sensing data, including the Enhanced Vegetation Index (EVI), Vegetation Index (EVI), Gross Primary Productivity (GPP), Land Surface Temperature (LST), and reflectance data. For the period of 2000–2021, data with abundant vegetation growth, low cloudiness, and high image quality were chosen for this research, all of which were processed with radiometric and atmospheric corrections. The LULC data were obtained from the ESA Climate Change Initiative-Land Cover (CCI-LC) project (https://www.esa-landcover-cci.org accessed on 1 August 2023). The time scale was 2000–2021 with a resolution of 300 m. The population data were obtained from the History Database of the Global Environment published by the Netherlands Environmental Assessment Agency (NEAA). The PM2.5 concentration data were from “Global Estimates of Fine Particulate Matter-V4.GL.03” published by NASA Langley Research Center, with a spatial resolution of 0.01°. The DEM was obtained from the Geospatial Data Cloud Platform of the Chinese Academy of Sciences Computer Network Information Center (http://www.gscloud.cn accessed on 1 August 2023). The soil data were obtained from the Global Soil Database (Harmonized World Soil Database, HWSD). The geographical auxiliary data were obtained from the National Center for Basic Geographic Information (http://www.ngcc.cn accessed on 1 August 2023). All data were resampled to 500 m and standardized to transverse Mercator projection.

3. Methods

3.1. Developing the EDI

In this study, we first selected soil moisture (Land Surface Moisture, LSM), surface aridity (Normalized Difference Build-up and Soil Index, NDBSI), LST, and Vegetative State Index (VSI) to construct the EDI by referring to the United Nations Sustainability Indicator System (https://sdgs.un.org/ accessed on 1 August 2023) and Sun et al. [6]. Then, to confirm the accuracy of the EDI, the Remote Sensing Ecological Index (RSEI) was employed. Based on the results of the EDI, the dynamics of environmental sustainability in the TBTR from 2000 to 2021 was evaluated (Figure 2). Finally, the drivers of environmental sustainability changes on the three sides of China, the DPRK, and Russia were explored by using a geographical detector.
First, referring to the studies of He et al. [23] and Yao et al. [16], LSM, NDBSI, LST, and VSI were chosen to serve as elements of the environment, and the geometric mean method was utilized to construct the EDI, which was calculated by the following formula:
E D I = ( Δ L S M + 1 ) × ( Δ N D B S I + 1 ) × ( Δ L S T + 1 ) × ( Δ V S I + 1 ) 4
where ΔLSM, ΔNDBSI, ΔLST, and ΔVSI are the normalized changes in LSM, NDBSI, LST, and VSI from 2000 to 2021, respectively. LSM can be expressed as the humidity component of the K-T transform by referring to Lobser and Cohe [24], with the following formula:
L S M = 0.1147 b 1 + 0.2489 b 2 + 0.2408 b 3 + 0.3132 b 4 − 0.3122 b 5 − 0.6416 b 6 − 0.5087 b 7
where bi (i = 1, 2, …, 7) is the reflectance of the MOD09A1 product in each band.
NDBSI is composed of both bare land and impermeable surfaces [25]. Therefore, the normalized difference build-up index (NDBI) is expressed as a composite with the mean value of the bare soil index (SI) according to Xu et al. [26]. The formula is
N D B S I = ( N D B I + S I ) / 2
N D B I = ( b 6 − b 2 ) / ( b 6 + b 2 )
S I = [ ( b 6 + b 1 ) − ( b 2 + b 3 ) ] / [ ( b 6 + b 1 ) + ( b 2 + b 3 ) ]
where bi (i = 1, 2, …, 7) is the reflectance of the MOD09A1 product in each band. LST was derived from the MOD11A2 product.
Using PCA to remove the correlation between the various variables, VSI is calculated from partial vegetation cover (FVC), normalized difference vegetation senescence index (NDVSI), nitrogen reflection index (NRI), and GPP [16,27,28]. The formula can be expressed as follows:
V S I = P C A [ f ( F V C , G P P , N D V S I , N R I ) ]
F V C = ( E V I − E V I s ) / ( E V I v − E V I s )
N D S V I = ( b 6 − b 1 ) / ( b 6 + b 1 )
N R I = b 2 / b 4
where GPP is the total primary productivity of vegetation from the MOD15A2H product. EVIs and EVIv are the pixel values of pure bare soil and pure vegetation cover areas, respectively. b1, b2, b4, and b6 are the reflectance of MOD09A1 product in bands 1, 2, 4, and 6, respectively.
In this study, all indicators were standardized to the range [0, 1] to maintain comparability, and the formula can be expressed as follows:
Δ I i = I max − ( I i 2021 − I i 2000 ) I max − I min × 100
where I2000 and I2021 are each indicator’s pixel values for 2000 and 2021, respectively. Imax and Imin are the maximum and minimum of each indicator’s values, respectively.
The average correlation is a good response to the extent to which the four indicators, VSI, LST, LSM, and NDBSI are correlated with each other and the EDI’s ability to represent each of the elemental indicators in an integrated manner [29]. The expression is
P ¯ = ∑ i = 1 j | P i | j
where j is the number of indicators. Pi is the correlation coefficient between each indicator and EDI. When compared to a single indicator, the higher the average correlation between EDI and each indicator, the more appropriate EDI is for comprehensively assessing regional environmental sustainability [6].

3.2. Assessing Environmental Sustainability in the TBTR from 2000 to 2021 Using EDI

We calculated the EDI from 2000 to 2021 in the TBTR. To further evaluate the environmental sustainability dynamics, the EDI was classified into five levels of significantly improved, generally improved, unchanged, generally degraded, and seriously degraded with reference to the studies of He et al. [23] and Wang et al. [30] (Table S1). Finally, the TBTR’s environmental sustainability changes from 2000 to 2021 were assessed at multiple scales [31,32,33].

3.3. Analyzing Driving Forces of Environmental Sustainability Using Geographical Detector

3.3.1. Factors Selection

Environmental sustainability is affected by multiple environmental variables and human activities [14,34]. Referring to Zhu et al. [35] and Yao et al. [16], EDI was used as the dependent variable. By considering the availability, representativeness, and holistic nature of the data, 12 indicators from topography, soil, climate, and socio-economy were finally selected as independent variables (Table 1).
Specifically, land use intensity (LUI), LULC, population density (PD), distance from highway (Road), distance from railroad (Railway), and PM2.5 concentration are capable of effectively describing the scope of the impacts caused by human activities [36]. Temperature (Tem) and precipitation (Pre) are two key natural elements that affect the changes in environmental sustainability [37]. While some environmental parameters, such as soil type (ST), elevation (DEM), slope (Sp), and distance from a river (River), do not change a lot in time, they do serve as a crucial environmental foundation for improvements in environmental sustainability [38,39].
Referring to the studies of Lu et al. [34] and Liu et al. [14], LUI is a measure of how intensively land is used and developed by humans. The grading index of LUI for waterbody, wetland, woodland, paddy land, dry land, unused land, and urban land is 1–7, respectively. The formula of LUI is as follows:
L U I = ∑ j = 1 m A j × S j S
where LUI denotes the value of LUI in the sample area; Aj represents the sample area’s land use type’s intensity grading index; Sj denotes the jth land use type area; S denotes the sample area’s total land use area; and m denotes the number of LUI grading.

3.3.2. Geographical Detector

The geographic detector is a statistical technique that uses spatial analysis of variance (ANOVA) to examine the connection between a particular geographical occurrence and its possible causes. It has been extensively employed in estimating the impact of driving factors on geographical phenomena [40]. Referring to the study of Song et al. [41], we mainly adopted the factor detector and used the parametric optimization module in the optimal parameters-based geographical detector (OPGD) model to comprehensively analyze the relationship between EDI and driving forces.
The factor detector, which is the central part of the geographic detector, determines how much of the spatial divergence of dependent variable is explained by the potential driver, and is generally expressed as a q-statistic, which is determined by applying this formula:
q = 1 − ∑ h = 1 m N I , j σ E D I I , j 2 N σ E D I 2
where h = 1, …, m represents the stratification of EDI and the driving factor; NI,j and N represent the number of grid points in stratum h and the entire region, respectively; and denote the variance of the EDI in stratum h and the entire region, respectively. q takes a value in the range of [0–1]; the nearer the q value is to 1 indicates that the spatial differentiation of EDI is more pronounced, and the explanatory power of this driving force is greater.

4. Results

4.1. Performance of the EDI

The efficiency of EDI was tested using multiple methods (i.e., RSEI and average correlation), with reference to the studies of Xu et al. [42] and Sun et al. [6]. The results demonstrated that the TBTR’s environmental sustainability changes can be well described by the EDI. In the TBTR, there was a strong positive correlation between the EDI and the RSEI between 2000 and 2021 (Figure 3). The correlation coefficient was 0.650 (p < 0.01, two-tailed) at the image element scale. In addition, the average correlation between the four indicators (i.e., LSM, NDBSI, LST, and VSI) was considerably lower than the average correlation of 0.648 between the EDI and the indicators (Table 2). This suggests that the EDI is more suitable for an integrated assessment of changes in environmental sustainability than a single environmental indicator.

4.2. Environmental Sustainability Dynamics in the TBTR from 2000 to 2021

From 2000 to 2021, the TBTR’s environmental sustainability showed a distinct process of reduction (Figure 4), with generally degraded as the main form. Specifically, the area in which the decrease in environmental sustainability occurred was about 13,174.75 km2, about 31.01% of the TBTR’s total land area, which is primarily located in the cities of Helong, Yanji, and Hunchun in China, Chongjin and the eastern part of Sanchiyeon County in the DPRK, and the Hassan District in Russia. Among them, 81.29% of the entire area of degraded environmental sustainability was made up of about 10,710.25 km2 of generally degraded region. For the entire degraded area of environmental sustainability, the seriously degraded region accounted for approximately 2464.50 km2 (i.e., about 18.71%). In addition, the area of improved environmental sustainability in the TBTR was about 12,400.00 km2, about 29.18% of the TBTR’s total land area. Among them, 24.12% of the entire area of improved environmental sustainability was made up of about 2990.50 km2 of significantly improved region. For the entire improved area of environmental sustainability, the generally improved region accounted for approximately 9409.50 km2 (i.e., about 75.88%).
At the national scale, the Chinese, the DPRK, and the Russian subregions displayed a decreasing trend in environmental sustainability from 2000 to 2021, all in the form of generally degraded (Table 3). Specifically, the degraded environmental sustainability area on the Chinese side was 7017.00 km2, about 31.16% of the total land area on this side. In this area, the area of generally degraded was about 5831.50 km2, representing 83.11% of the environmental sustainability degraded area on the Chinese side. Meanwhile, approximately 1185.50 km2 of the area’s environmental sustainability was seriously degraded, representing 16.89% of the decrease in environmental sustainability area on the Chinese side, which was mainly concentrated in the southeastern part of Helong City and the eastern part of Hunchun City. On the other hand, in the Chinese subregion, about 6377.00 km2 of the area’s environmental sustainability was improved, accounting for 28.32% of the whole land area on this side. In this area, the significantly improved area was about 1670.25 km2, representing 26.19% of the total area of environmental improvement, while of the entire improved area of environmental sustainability, the generally improved region accounted for approximately 4706.75 km2, about 73.81% of the environmental sustainability improved area on the Chinese side (Table 3).
The area of degraded environmental sustainability on the DPRK’s side was about 4057.50 km2, accounting for 30.72% of the total land area on this side, and was mainly concentrated in the eastern part of Cheongjin City and the eastern part of Miikeyeon County. In this area, the generally degraded area was about 3226.00 km2, representing 79.51% of the environmental sustainability degraded area on the DPRK’s side. The seriously degraded area was about 831.50 km2, representing 20.49%. On the other hand, the area of improved environmental sustainability was about 3973.50 km2, accounting for 30.08% of the total land area on the DPRK’s side. In this area, the environmental sustainability significantly improved area was about 887.50 km2, which was only 22.34% of the environmental improvement area in the DPRK’s environmental sustainability improvement area, while the area of generally improved was 3086.00 km2, representing 77.66% of the environmental sustainability improved area on the DPRK’s side (Table 3).
The area of degraded environmental sustainability on the Russian side represented 31.06% of this subregion’s land area (i.e., 2100.25 km2), mainly in the eastern part of Hassan district and the southern part of Vladivostok. In this area, the generally degraded area was about 1652.75 km2, representing 78.69% of the Russian side’s environmental sustainability degraded land area. The seriously degraded area was about 447.50 km2, representing 21.31% of the area that decreased in environmental sustainability. On the other hand, the area of improved environmental sustainability was about 2049.50 km2, representing 30.31% of the Russian side’s land area. In this area, the significantly improved area was about 887.50 km2, representing only 21.11% of the total area that was improved in terms of environmental sustainability, while the generally improved area was represented 78.89% (i.e., 1616.75 km2) (Table 3).

4.3. Driving Forces of Environmental Sustainability in the TBTR from 2000 to 2021

The findings demonstrated that all the twelve factors in the TBTR significantly affected EDI changes (p < 0.05). From 2000 to 2021, human activities such as land use intensity and its changes were major influences on changes in environmental sustainability in TBTR. Specifically, LUI and LULC were the most dominant influencing factors in the TBTR, as well as the three subregions (i.e., China, the DPRK, and Russia), with an explanatory power of more than 30%, followed by PD, with an explanatory power of over 20% (Table 4).
For the three countries, on the Chinese side, human activities were major influences on changes in environmental sustainability, while natural environmental factors had a less impact. Specifically, LUI and LULC were the most important drivers of the differences in the overall spatial pattern of EDI, respectively, with an explanatory power of more than 30%. Meanwhile, the q-value of PD was 0.2853, with an explanatory power of over 20%. Compared with human activities, natural driving forces including Tem, Road, Railway, DEM, and PM2.5 were minor factors, with an explanatory power of less than 20% (Table 4).
On the DPRK’s side, human activities and topographical factors (especially DEM) were the two major factors affecting environmental sustainability. Specifically, LUI and LULC have q-values of 0.3774 and 0.3771, respectively, and an explanatory power higher than 30%. Meanwhile, the PD (0.2606) and the DEM (0.2016) had an explanatory power of over 20% (Table 4). On the other hand, several other factors (e.g., Railway, Pre, and ST) had an explanatory power of less than 10%.
Comparing with the other two countries, the Russian side exhibited a different result. Natural driving forces, especially topographical factors including the DEM and slope, caused important influences on changes in environmental sustainability. Specifically, the DEM had an explanatory power of more than 30%, and Sp had an explanatory power of over 20% (Table 4). Meanwhile, Pre, ST, PM2.5, and Tem were minor factors with an explanatory power of less than 10%.

5. Discussion

5.1. Differences in Environmental Sustainability Dynamics among the Subregions of China, the DPRK, and Russia

Over the past 20 years, the changes in environmental sustainability on the three sides of China, the DPRK, and Russia have shown significant differences. The Chinese side experienced the most significant improvement in environmental sustainability. At the same time, the least seriously degraded area in terms of environmental sustainability was also found in this subregion. Specifically, on the Chinese side, the area with significant improvement in environmental sustainability accounted for 26.19% of the area with improvement in environmental sustainability on this side, which was the highest among the three countries, higher than that of the DPRK’s side by 1.17 times, and higher than that of the Russian side by 1.24 times. Meanwhile, on the Chinese side, the area with a serious degradation in environmental sustainability was 1185.50 km2, representing 16.89% of the area with decline in environmental sustainability, which was the lowest among the three countries, 17.56% lower than the DPRK’s side, and 20.71% lower than the Russian side (Figure 5).

5.2. Differences in Driving Forces of Environmental Sustainability among China, the DPRK, and Russia

From 2000 to 2021, LUI and LULC were the most dominant influencing factors in the TBTR, as well as the area of the three subregions (i.e., China, the DPRK, and Russia), followed by PD, which dominated the influences on environmental sustainability. The other drivers showed significant differences in the influence on environmental sustainability among the three countries (Figure 6, Table 4). Specifically, in the subregion of China, the explanatory power of the drivers (i.e., Road, Railway, DEM, and PM2.5), which represented socio-economic development and human activities, was more than 10% and became important influencing factors on the change in environmental sustainability on the Chinese side. However, on the DPRK’s side, DEM, Tem, Road, River, Sp, and PM2.5 were the important influencing factors. On the Russian side, DEM, Sp, Railway, Road, and Pre were the important influencing factors (Figure 6). Both human activities and natural environmental factors affected regional environmental sustainability.
Environmental sustainability is strongly influenced by human activities and socio-economic factors such as land use change [14]. Ecological quality is directly impacted by land use change, meanwhile population increase, railroads and highways construction, and PM2.5 concentration are influential factors that lead to land use change [35,43]. In this study, we analyzed the LULC in the TBTR from 2000 to 2021. We found that urban expansion and woodland growth were the main causes of environmental sustainability changes in the Chinese side. From 2000 to 2021, the urban expansion in the subregion of China was the most significant, with an expansion area of about 126.00 km2. Within this area, cropland represented 56.15% of new urban land, with an area of about 70.75 km2, making it the primary source of new urban land. Meanwhile, the area of cropland converted to woodland on the Chinese side was about 77.50 km2, representing 99.68% of the new woodland area. From 2000 to 2021, China initiated the Belt and Road Initiative and Hunchun Marine Economy Cooperation Zone to stimulate economic development. At the same time, the Chinese government has implemented various ecological programs in Northeast China, such as encouraging ecological restoration by converting cropland back to forests [44]. China’s national forest protection program and the establishment of nature reserves have slowed down deforestation and promoted regeneration. On the DPRK’s side, the primary drivers of the decrease in environmental sustainability were the extensive expansion of cropland and forest loss. The cropland expansion was the most significant on the DPRK’s side, with an expansion area of about 367.50 km2. Within this area, the most important source of new cropland was forest, with an area of about 357.00 km2, accounting for 97.14% of the new cropland in the DPRK’s subregion. Meanwhile, forest loss was also significant, with an area of about 394.25 km2. Our results match those of previous research. Referring to the data released by the Statistics Division of the United Nations Department of Economic and Social Affairs (DESA) (https://unstats.un.org/ accessed on 1 January 2024), the DPRK’s forest land has drastically decreased over the previous 20 years, which has been a direct factor in this country for declining sustainability and environmental pollution.
In addition, climate change, elevation, and slope are elements of natural factors that have an effect on environmental sustainability. Topography distributes surface radiation and hydrothermal conditions have an impact on biological circumstances [45]. At the same time, topography limits the extent of human activities [46]. Climate change affects the spatial distribution of vegetation and the plant growth cycle, which in turn affects ecological quality [14]. In this study, the DEM and Sp in the subregions of the DPRK and Russia showed a greater influence on environmental sustainability. Higher elevation and slope regions on the DPRK’s side have steep topography, poor soil conditions, and less landscape restoration [47]. Meanwhile, the gently sloping coastal areas at lower elevations are the main areas contributing to urban land and cropland on the DPRK’s and Russian sides, where human activities and LULC changes lead to changes in environmental sustainability [14]. On the Russian side, the Pre shows a larger contribution. Since the Russian side is a coastal area dominated by forests, the vegetation growth will be inhibited with the increase in rainfall, the increase in cloudiness, and the decrease in incoming solar radiation [48]. In addition, forest fires alter the structure and cover of the plants, raise the temperature near the surface, and reduce air humidity, all of which reduce environmental sustainability [49,50].

5.3. Implications for Improving Environmental Sustainability for the Transnational Area of China, North Korea, and Russia

A series of actions have been made by China, the DPRK, and the Russian Federation to improve environmental sustainability. On the Chinese side, the State Council established the Changbai Mountain Forest Ecological Function Area in 2011. In 2017, the Chinese government approved the implementation of an ecological restoration project to protect and improve water quality in the TBTR [6]. On the DPRK’s side, in 1999, the Forest Act was enacted to restore forests damaged by floods, drought fires, and illegal logging [51]. In addition, to achieve the sustainable use of forest resources, the DPRK is pushing for the creation of an information base on forest resources and energy-saving technologies for residential fuelwood [15]. On the Russian side, with an emphasis on the preservation of forest and water resources, the Russian Federation’s Forest Code and Water Code was passed and revised in 2006, meanwhile the Leopar National Park was designated in the far east in 2012. However, the results of this study indicated that between 2000 and 2021, the TBTR’s environmental sustainability was decreasing, and the problem of environmental degradation was still serious. In particular, the demand for fuelwood, forest fires, landslides, pests, and diseases have accelerated the reduction in forest on the DPRK’s side. At the same time, in order to address the issue of food shortages, the DPRK has historically recovered a sizable portion of its forest for cultivation, and it is difficult to effectively restore damaged forest; meanwhile the capacity of ecosystem service supply has continued to decline. On the other hand, a significant area of the forest has been converted to grassland as a result of the growth of the Russian forest business sector, and environmental sustainability has continued to deteriorate [50,52].
Therefore, we suggest that environmental improvement in the TBTR should be carried out in the following ways. First, the protection of natural habitats should be paid special consideration during the process of urbanization and cropland expansion, and red lines for ecological protection should be drawn. Urban land, cropland, and natural habitats should be viewed as a whole. Second, the protection of natural habitats should be actively carried out, and artificial interventions should be implemented to restore damaged natural environments in areas with serious decline in environmental sustainability. Finally, China, North Korea, and Russia should step up their cooperation to create transnational ecological protection and environmental restoration agreements to collectively improve regional environmental sustainability.

5.4. Future Perspectives

In this study, we evaluated and analyzed the dynamics of environmental sustainability of the TBTR from 2000 to 2021 by using the EDI, which was important in directing sustainable growth and environmental conservation for the TBTR. This study still has several shortcomings. First, factors that are equally essential for environmental sustainability include air pollution and water quality. However, due to the limitation of data acquisition, these elements were not considered in this study. Second, it was challenging to characterize the internal characteristics of the shifting environmental sustainability areas due to the coarse resolution of the MODIS products utilized in this study. Finally, the scaling effects of the relationship between environmental sustainability and driving forces need to be further explored. However, the conclusions of this study were not significantly impacted by these flaws. In this research, we evaluated the environmental sustainability dynamics obtained from remote sensing data in the TBTR during 2000–2021 by considering multiple elements, and explored the drivers of environmental sustainability dynamics, which had theoretical and practical applications for this area’s environmental improvement.
In the future, our focus will be utilizing integrated evaluation methods to assess multiple ecological elements including water pollution and air quality, improve the environmental sustainability evaluation index, and more comprehensively and accurately evaluate regional environmental sustainability and its driving forces. Second, the utilization of cloud processing platforms, machine learning, and higher resolution remote sensing data are going to be taken into consideration to improve the computational capacity of modeling techniques and the dependability of assessment outcomes [6]. Finally, the influences of environmental sustainability changes on socio-economic sustainability can be further explored to improve the regional sustainability across the three countries [14].

6. Conclusions

In this study, we evaluated the environmental sustainability dynamics in the TBTR over the last two decades using the EDI. From 2000 to 2021, the results showed that the TBTR’s environmental sustainability showed a degraded trend, with generally degraded as the main form. The area of degraded environmental sustainability was about 13,174.75 km2, accounting for 31.01% of the total land area of the TBTR. In the area of significant changes in environmental sustainability in the previous 20 years, the Chinese side has shown the most significant improvement of environmental sustainability. Specifically, in the subregion of China, the significantly environmental sustainability improved area accounted for 26.19% of the entire environmental sustainability improved area on the subregion of China, which was 1.17 times and 1.24 times higher than that in the subregions of the DPRK and Russia, respectively.
The three factors (i.e., LUI, LULC and PD) were the most important driving forces on environmental sustainability changes for the whole TBTR, as well as the subregions of China, the DPRK, and Russia. The influences of other drivers on environmental sustainability showed significant differences among the three countries. Specifically, on the Chinese side, Road, Railway, the DEM, and PM2.5 became important influencing factors on the change in environmental sustainability. In the subregion of the DPRK, the DEM, Tem, Road, River, Sp, and PM2.5 were important influencing factors. Meanwhile, the DEM, Sp, Railway, Road, and Pre were important influencing factors for the area of the Russian side. Therefore, we suggest that cooperation between China, North Korea, and Russia should be strengthened to establish transnational ecological protection and environmental restoration policies to jointly promote regional sustainable development.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su16188121/s1, Table S1: EDI classification standard and description.

Author Contributions

All authors made substantial contributions to conception and design of the study. Conceptualization, L.J.; data curation, Z.Z.; formal analysis, Z.Z.; funding acquisition, L.J.; methodology, L.J.; project administration, L.J.; validation, Z.Z.; visualization, Z.Z.; writing—original draft, Z.Z.; writing—review and editing, L.J. and Z.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Department of Education of Jilin Province (Grant No. JJKH20230644SK) and the National Social Science Fund of China (Grant No. 23XMZ067).

Data Availability Statement

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

Acknowledgments

We want to express our respects and gratitude to the anonymous reviewers and editors for their professional comments and suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The study area.
Figure 1. The study area.
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Figure 2. Flowchart.
Figure 2. Flowchart.
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Figure 3. Correlation relationship between EDI and RSEI.
Figure 3. Correlation relationship between EDI and RSEI.
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Figure 4. Environmental changes in the TBTR from 2000 to 2021. Note: (a–c) are typical regions distributed in the three subregions, respectively.
Figure 4. Environmental changes in the TBTR from 2000 to 2021. Note: (a–c) are typical regions distributed in the three subregions, respectively.
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Figure 5. Environmental sustainability area and percentage of the environmental sustainability change area.
Figure 5. Environmental sustainability area and percentage of the environmental sustainability change area.
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Figure 6. The q statistics of influencing factors in the subregions of China, the DPRK, and Russia.
Figure 6. The q statistics of influencing factors in the subregions of China, the DPRK, and Russia.
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Table 1. The detailed descriptions of driving forces.
Table 1. The detailed descriptions of driving forces.
RespectsData Name (Abbreviation)Unit
Human activity Land use intensity (LUI)-
Land use and Land cover (LULC)categorical
Population Density (PD)Persons/km2
Distance to the railway (Railway)km
Distance to the road (Road)km
PM2.5μg/m3
Natural environmentClimateTemperature (Tem)°C
Precipitation (Pre)mm
TopographyElevation (DEM)m
Slope (Sp)°
RiverDistance to the rivers (River)km
SoilSoil type (ST)categorical
Table 2. Correlation matrix among EDI and LSM, NDBSI, LST, VSI.
Table 2. Correlation matrix among EDI and LSM, NDBSI, LST, VSI.
Factors Δ LSM Δ NDBSI Δ LST Δ VSIEDI
Δ LSM10.533 **0.247 **−0.034 **0.522 **
Δ NDBSI 10.170 **0.665 **0.903 **
Δ LST 1−0.071 **0.452 **
Δ VSI 10.713 **
Mean correlation0.2490.4560.1150.1870.648
Note: ** Significant at the 0.01 probability level.
Table 3. Environmental changes in the subregions of China, the DPRK, and Russia from 2000 to 2021.
Table 3. Environmental changes in the subregions of China, the DPRK, and Russia from 2000 to 2021.
Seriously DegradedGenerally DegradedUnchangedGenerally ImprovedSignificantly Improved
Area
(km2)		Percentage *Area
(km2)		Percentage *Area
(km2)		Percentage *Area
(km2)		Percentage *Area
(km2)		Percentage *
China1185.50 5.26 5831.50 25.90 9125.50 40.52 4706.75 20.901670.257.42
DPRK831.50 6.30 3226.00 24.42 5177.75 39.20 3086.00 23.36887.506.72
Russia447.50 6.62 1652.75 24.44 2612.50 38.63 1616.75 23.91432.756.40
TBTR2464.50 5.80 10,710.25 25.21 16,915.75 39.81 9409.50 22.142990.507.04
Note: * This percentage represents the proportion of the area of each environmental degradation level to the total area of the TBTR.
Table 4. The importances of driving factors on the EDI in the subregions of China, the DPRK, and Russia.
Table 4. The importances of driving factors on the EDI in the subregions of China, the DPRK, and Russia.
LUILULCPDRailwayRoadPM2.5TemPreDEMSpRiverST
Chinaq0.3505 0.3387 0.2853 0.1279 0.1326 0.1022 0.1874 0.0357 0.1135 0.0788 0.0860 0.0963
DPRKq0.3774 0.3771 0.2606 0.0970 0.1744 0.1004 0.1798 0.0652 0.2016 0.1210 0.1215 0.0497
Russiaq0.3139 0.3089 0.3400 0.2391 0.2051 0.0952 0.0946 0.1441 0.3680 0.2464 0.0375 0.0969
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Jin, L.; Zhang, Z. Assessing Environmental Sustainability in the Transnational Basin of the Tumen River Based on Remote Sensing Data and a Geographical Detector. Sustainability 2024, 16, 8121. https://doi.org/10.3390/su16188121

AMA Style

Jin L, Zhang Z. Assessing Environmental Sustainability in the Transnational Basin of the Tumen River Based on Remote Sensing Data and a Geographical Detector. Sustainability. 2024; 16(18):8121. https://doi.org/10.3390/su16188121

Chicago/Turabian Style

Jin, Lin, and Zhijie Zhang. 2024. "Assessing Environmental Sustainability in the Transnational Basin of the Tumen River Based on Remote Sensing Data and a Geographical Detector" Sustainability 16, no. 18: 8121. https://doi.org/10.3390/su16188121

APA Style

Jin, L., & Zhang, Z. (2024). Assessing Environmental Sustainability in the Transnational Basin of the Tumen River Based on Remote Sensing Data and a Geographical Detector. Sustainability, 16(18), 8121. https://doi.org/10.3390/su16188121

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