Satellite Reveals a Coupling between Forest Displacement and Landscape Fragmentation across the Economic Corridor of the Eurasia Continent

Abstract

Jointly building the Economic Corridor of the Eurasia Continent (ECEC), which is one of the most important parts of the Silk Road Economic Belt, is a pivotal initiative for fostering regional development. Forests, which serve as a green foundation of economic resilience, underpin this effort. However, there is an imbalance in ecological status due to differences in natural resources and the social economy along the economic corridor. This imbalance has led to alterations in landscapes, yet the specific changes and their underlying relationships are still much less understood. Here, we quantitatively detected changes in the forest landscape and its ecological efforts over the ECEC via widespread, satellite-based and long-term land cover maps released by the European Space Agency (ESA) Climate Change Initiative (CCI). Specifically, the coupling between changes in forest coverage and landscape patterns, e.g., diversity, was further examined. The results revealed that forest coverage fluctuated and declined over the ECEC from 1992 to 2018, with an overall reduction of approximately 9784.8 km² (i.e., 0.25%). Conversions between forests and other land cover types were widely observed. The main displacements occurred between forests and grasslands/croplands (approximately 48%/21%). Moreover, the landscape diversity in the study area increased, as measured by the effective diversity index (EDI), during the study period, despite obvious spatial heterogeneity. Notably, this pattern of landscape diversity was strongly associated with forest displacement and local urban development through coupling analysis, consequently indicating increasing fragmentation rather than biological diversity. This study highlights the coupled relationship between quantitative and qualitative changes in landscapes, facilitating our understanding of environmental protection and policy management.

1. Introduction

The construction of the “Silk Road Economic Belt” is one of the two wings of China’s “Belt and Road Initiative”. It is also a major initiative to open China more widely to the outside world and is the top-level design of China’s economic diplomacy. The initiative of cobuilding “the Belt and Road” provides China’s input to promote global peace cooperation and common development [1]. As an important part of the Silk Road Economic Belt, the Economic Corridor of the Eurasia Continent (ECEC), which is composed of the China–Mongolia–Russia Economic Corridor, the New Eurasian Continental Bridge Economic Corridor and the China–Central Asia–West Asia Economic Corridor, is one of the key routes for the steady development of China’s society and economy. The protection and development of forest resources are not only an important part of the environmental aspects of the ECEC initiative but also the basic support for the cobuilding of the “Silk Road Economic Belt” initiative. There are differences in natural resource endowment and the ecological environment as well as the status, level and mode of economic development along the ECEC, which causes spatial heterogeneity in resource development and utilization and history and status [2,3,4]. Failure to clarify the development history and status of natural resources along the route will affect the planning and use of forestry and limit the sustainable development of the ECEC. Therefore, it is necessary to gain a comprehensive understanding of the trends in forest change, the exchange relationships between forests and other land cover types and the ecological impacts of changes in the spatial and temporal patterns of forests to better improve the policy of the green “Silk Road Economic Belt” and scientifically promote the overall process of building a green corridor.

Forest change is one of the main topics of global change [5]. The spatial distribution of forest cover has an important effect on physicochemical characteristics, such as the water–heat distribution and radiation balance of the land surface, as well as interactions between the flow of water and the flow of energy [6]. Forest resources are also indispensable basic information and key parameters in the monitoring of geographic conditions and sustainable development planning, and the study of forest dynamics helps to further understand the anthropogenic activities of a region [7,8,9,10,11,12]. Variations in forest resources not only manifest as quantitative changes in each cover type but also affect the quality of the ecological environment, which can be reflected through landscape patterns. Landscape patterns are the spatial arrangement and combination of landscape elements of different sizes and shapes, and landscape patterns and their dynamic changes reflect the ecological process of environmental change [13,14]. The study of land cover change and landscape pattern evolution can help explore the impacts of human activities on forest resources under climate change.

Exploring forest resources and their distribution in the ECEC is crucial for environmental protection. However, in recent years, the forest resources in the ECEC may have been threatened. Siberia has the largest forest coverage in the world, accounting for about 20% of global forest resources, and plays a vital role in the global carbon cycle [15]. However, deforestation and forest fires have caused significant ecological degradation [16]. In Central Asia, where the forest coverage rate is less than 5%, desertification poses a severe problem [17]. In West Asia, forest resources are also extremely scarce, with most of the land being deserts and arid areas. Protecting forest resources in these regions can help reduce soil degradation and conserve water and soil. As a chief means of monitoring large-scale land cover and its dynamic changes, satellite-based remote sensing technology plays an irreplaceable role in global land cover mapping, which is characterized by rapidity, accuracy and short cycle times [18,19,20]. With remote sensing technology, many achievements have been made in the study of land cover and its corresponding ecological impacts, e.g., ecological services and diversity, via landscape pattern indices. Yue et al. used remote sensing images to study the effects of the ecological environment on urban land use types and spatial structure and identified differences in ecological effects caused by different urban land types [21]. Ji et al. used remote sensing images to analyze the dynamic changes in forest landscape patterns across the coastal urban groups of eastern China and confirmed that studying landscape spatial patterns and dynamic changes through remote sensing images is an effective option for understanding landscape ecology [22]. With the support of GIS technology and the transition matrix algorithm, Chen studied spatial changes in a forest resource area, which revealed that transition matrix technology not only analyzes the changes in resources quantitatively but can also portray areas with variations and improve the depth and level of research on the dynamic changes in forest resources [23]. Wang et al. used land cover data to study the variation in land cover and landscape patterns of the Poyang Lake area and noted that studying land cover change and landscape patterns can reflect the ecological impacts of regional land cover type change and generation more effectively [24].

However, the coupled relationship between land cover and landscape changes is still poorly understood. That is, the quantity (i.e., coverage) and quality (i.e., landscape pattern) of landscapes coordinately vary at a regional scale. Specifically, whether the landscape pattern index can objectively denote ecological or biological effects, e.g., diversity, in the context of remote sensing-based land cover change is a valuable consideration for exploring regional planning and sustainable development.

With the support of remote sensing land cover data, this study aimed to quantitatively detect the temporal and spatial dynamics of forest cover and its displacement along the Economic Corridor of the Eurasia Continent from 1992 to 2018. We clarified the characteristics of changes in forest landscape patterns and analyzed the current status of forest resources along the three economic corridors via a transition matrix and landscape pattern analysis. These achievements can help us to further understand the development of forest resources, respond to the policy of the green Silk Road Economic Belt and provide references to strengthen the ecological governance of regions and the synergistic development of countries.

2. Materials and Methods

2.1. Study Area

The study area of this study is the Economic Corridor of the Eurasia Continent (ECEC) (30–120° E, 35–60° N), which includes three subroutes, i.e., the China–Mongolia–Russia Economic Corridor (CMREC), the New Asia–Europe Continental Bridge (NAECB) and the China–Central Asia–West Asia Economic Corridor (CCAWAEC) (Figure 1). The CMREC is a transport and energy corridor connecting Beijing–Tianjin–Hebei and Northeast China with Russia, Mongolia and other countries and also connecting with Europe to the west through Russia. The NAECB starts from Lanzhou and connects the West to Kazakhstan and Eastern Europe through Xinjiang. The CCAWAEC starts from Xinjiang and connects to the Persian Gulf, the Mediterranean coast and the Arabian Peninsula, which mainly involves five Central Asian countries (i.e., Kazakhstan, Kyrgyzstan, Tajikistan, Uzbekistan, and Turkmenistan), Iran, Turkey and others. The terrain of the study area is high in the east and low in the west, with many mountains and plateaus. The ECEC is dominated by a temperate continental climate and a temperate monsoon climate. The main vegetation types in the study area are temperate forests, grasslands and croplands. Although forests are not the main vegetation type in the Economic Corridor of the Eurasia Continent, the key urban areas are relatively endowed with vast forest resources. Moreover, forests are not only important resources for urban and economic development but also support the health of the ecological environment. Therefore, it is important to consider the forest dynamics of the Economic Corridor of the Eurasia Continent in the context of sustainable regional development.

2.2. Datasets

The remote sensing land surface coverage data used in the study are derived from annual global land cover time series products from 1992 to 2018 at a 300 m spatial resolution published on the ESA CCI LC map website (http://maps.elie.ucl.ac.be/CCI/viewer/download.php (accessed on 22 December 2019)). The long-term sequence product is obtained by integrating the global daily surface reflectance from five different observing systems. After being preprocessed, the global daily multispectral radiometric measurements reported from 1992 to 2018 generated 27 yearly land element maps through a series of fused machine learning and unsupervised algorithms. The product maintains good consistency over time, and its overall classification accuracy reaches 75.4%, with a forest identification accuracy of 80.4% [25,26]. According to the United Nations Land Cover Classification System (UNLCS), CCI LC maps describe the Earth’s land surface with 37 primitive classes [22,27], which include all major land covers (LCs), such as forests, grasslands, crops and urban areas. Moreover, the primitive classes can potentially be transformed into plant functional types (PFTs), facilitating the analysis of changes in major feature types from LC classes to PFTs [28,29].

2.3. Methods

2.3.1. Reclassification of Land Cover Products

Figure 2 shows the overall technique flow chart for this study, with a detailed explanation of the analysis process. As mentioned above, PFT was used in the present study because of the limitations of qualitative land cover types for quantifying LUCC. PFT refers to plant species with similar response mechanisms to environmental conditions, representing the combination of dominant plant species in major terrestrial ecosystems. Studying this topic from the perspective of PFTs helps to describe the vegetation dynamics mechanistically.

On the basis of the cross-walking table in the ESA land cover type product, the LC types can be categorized into 14 different PFTs, such as forests, shrubs, grasslands and farmlands. These classes were further altered, especially for vegetation mosaics and sparsely vegetated areas. According to the proportion of each PFT in each LC type, the land cover change process could be better described quantitatively [30].

In accordance with the methods of Ji et al. [22], the spatial and temporal dynamics of forests were analyzed as follows: (1) Proportion of each forest type and total forest. In terms of cells, the forests were classified into evergreen broad-leaved forest (EBF), evergreen needle-leaved forest (ENF), deciduous broad-leaved forest (DBF), and deciduous needle-leaved forest (DNF) according to PFTs to obtain the yearly proportion of each forest type over 27 years. Through the accumulation of the proportion of each forest PFT, the proportions of total forest each year were calculated. (2) Spatial and temporal trends of the forest proportion in a sliding window. Within a 17 × 17 sliding window (approximately 5 × 5 km), the proportion of forest content in the window was calculated annually, and linear regression was derived from the proportions of forests. The significant slope of the regression was reflected in the pixels in the window. A sliding window was applied over the entire region to investigate the spatial and temporal trends of forest coverage.

2.3.2. PFT-Based Transition Matrix Method

After clarifying the changes in the total forest resources over time, we further analyzed how the forest resources have transformed over the past 27 years. A transition matrix based on PFT was used in this study to determine the transformation relationships between forests and other land cover types [22,31]. First, the land cover data for every two years were analyzed via a transition matrix of LC classes. The matrix was subsequently converted to transform PFTs that were classified into forest, shrub, grassland, farmland and other types. The transition matrix of the five PFTs could be obtained. In the transformation process, this study hypothesized that the same PFT was directly converted, and the extra PFT contents after being transformed were allocated according to the percentage of the PFT content required for transformation. The allocation model (Equations (1)–(6)) is as follows:

• The sequence of plant functional type proportions across different years in one pixel was determined. Five PFTs were considered in the study.

• PFT sequence for the first year:

  $$X_{t1}=\left\{X_{t1}\left(i\right)\right|i=\mathrm{1,2},\mathrm{3,4},5\};$$

  (1)
• PFT sequence for the second year:

  $$X_{t2}=\left\{X_{t2}\left(i\right)\right|i=\mathrm{1,2},\mathrm{3,4},5\};$$

  (2)

2.

The sequences of PFT proportions that were consistent between the two land cover types were calculated.

$${\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{t}}_{t1}^{t2}\left(i\right)=\min \left(X_{t1}\left(i\right),X_{t2}\left(i\right)\right)i=1,2,3,4,5$$

(3)

3.

The sequence of PFT proportions that changed in the first year was calculated.

$${rem}_{t1}\left(i\right)=X_{t1}\left(i\right)-\min \left(X_{t1}\left(i\right),X_{t2}\left(i\right)\right)i=1,2,3,4,5$$

(4)

4.

The sequence of PFT proportions that changed in the second year was calculated.

$${need}_{t2}\left(i\right)=X_{t2}\left(i\right)-\min \left(X_{t1}\left(i\right),X_{t2}\left(i\right)\right)i=1,2,3,4,5$$

(5)

5.

The proportions of PFT transitions that changed were calculated.

$${\mathrm{T}\mathrm{r}\mathrm{a}\mathrm{n}\mathrm{s}\mathrm{f}}_{t1}^{t2}\left(i,j\right)={\displaystyle \frac{X_{t1}\left(i\right)\times X_{t2}\left(i\right)}{\sum _{i=1}^5{X}_{t2}\left(i\right)}}i,j=1,2,3,4,5$$

(6)

Notably, there might be differences between the assumption and the actual transformation between the land covers. Nevertheless, the PFT assigns each type according to the proportion based on the LC class, which is a semiquantitative way of describing the types that can better reflect the transitions of land cover types [26].

The following analyses were carried out based on the PFT transfer matrix method: (1) Transition matrix of forest change. According to the PFT transition matrix, the contents of forests transferred to shrubs, grasses and other areas, as well as the contents of shrubs, grasses and other areas transferred to forests, increased. (2) Spatial–temporal pattern of forest displacement. With a sliding window of 17 × 17 pixels (approximately 5 × 5 km), the amount of forest transferred from and to the other types within the window was calculated annually during the 27 years and assigned to the pixels. A linear regression analysis was subsequently performed. A sliding window was applied over the entire study area to derive the spatial and temporal patterns of forest displacement.

2.3.3. Landscape Diversity Index

Landscape patterns are the spatial arrangement and combination of landscape elements of different sizes and shapes. The landscape diversity index, a key measurement used to quantify landscape patterns and dynamics [22,26,32], can effectively reflect the feedback process between land cover and landscape structure, function and dynamics. The effective diversity index (EDI) was used in this study to comprehensively reflect the richness and complexity of important land cover types in regional landscapes, depending on the amount and distribution uniformity of land cover types. Therefore, a larger EDI value indicates greater landscape diversity and complexity [21,33]. The effective diversity index is expressed in Equations (7) and (8):

$$EDI=\mathrm{e}\mathrm{x}\mathrm{p}\left(SHDI\right)$$

(7)

$$SHDI=-\sum _{i=1}^{m}pi\times \ln pi$$

(8)

where SHDI refers to Shannon’s diversity index, pi refers to the area proportion occupied by land cover type i, and m refers to the number of land cover types. The present work used a sliding window of 17 × 17 pixels (approximately 5 × 5 km) for the EDI calculation and trend analysis across the study area.

3. Results

3.1. Spatial and Temporal Patterns of Forests

The forests of the ECEC were located mainly in the Beijing–Tianjin–Hebei region of China and the plains and mountainous regions of Russia at the beginning of the CMREC and in Turkey at the end of the CCAWAEC (Figure 3). Deciduous broad-leaved forests and evergreen needle-leaved forests constitute a large proportion of the four forest types. Therefore, they dominated the study area and significantly influenced the total amount of forest (Figure 4). For the spatial distribution of forest coverage, 57.89% of the entire area showed a decreasing trend. The forest coverage declined at a rate of more than 0.012 km²/year, accounting for 7.7% of the area, mainly at the beginning of the CMREC, which includes the northern part of China, the Russian–Mongolia border and its northern part. A decreasing trend (<0.012 km²/year) was also observed in Turkmenistan, Uzbekistan and the area along the border between China and Mongolia. Moreover, 19.25% of the study area presented an increase in forest area. The region with the largest increase (>0.012 km²/year) was located mainly in the middle part of the CMREC and in the central and northeastern parts of Kazakhstan, presenting an east–west-oriented belt-like distribution. In addition, a large increase was also observed in the end sections of the three economic corridors, such as the Russian–Ukrainian border, Moscow and Turkey. Statistically, the total amount of forests in the study area has decreased by 9784.8 km² at a rate of 0.25% over the past thirty years. For the temporal change in forest coverage, a decreasing followed by increasing trend was observed. The total area of forests decreased by an average of 1965.6 km² each year from 1992 to 2002 and had a slow average increase of 617 km² each year from 2002 to 2018. The interannual changes in different types of forests are shown in Figure 4a–d. The total amount of evergreen needle-leaved forests decreased by 33,942.6 km², with an average decrease of 1305.54 km² each year. The decline in the total amount of forests from 1992 to 2002 was affected mainly by this decrease. The total amount of deciduous broad-leaved forests showed an increasing trend, with an overall increase of 18,429 km², which resulted in an increase in the total amount of forests from 2002 to 2018. The total amount of deciduous needle-leaved forests increased slowly from 1992 to 2008, with an average increase of 316.1 km² each year, but then slowly declined from 2008 to 2018, with an average decrease of 1069.2 km² each year. The total amount of evergreen broad-leaved forests maintained a steady upward trend during the study period, with an increase of 11,359 km² (approximately 436.9 km²/year).

3.2. Changes in Landscape Diversity

Changes in land cover have led to changes in landscape diversity and certain ecological impacts [34,35]. Figure 5 shows that the landscape of the study area was unevenly distributed, with significant landscape diversity and spatial heterogeneity. The landscape diversity was the lowest (EDI < 1) in the Xinjiang Basin; the border area between China and Mongolia; the tri-border area between Kazakhstan, Uzbekistan and Turkmenistan; and the area along the Caspian Sea, which indicates strong surface uniformity in these areas. With EDIs ranging from 1 to 3, the landscape diversity was the second lowest at the beginning and middle of the New Eurasian Continental Bridge Economic Corridor and the start of the CMREC, which included the Xinjiang region of China; the eastern part of the study area in the tri-border region of China, Mongolia and Russia; and the region of Kazakhstan. The landscape diversity was high (EDI > 7) in areas along the CMREC and the middle and end of the CCAWAEC, including the Beijing–Tianjin–Hebei region of China, the Russian–Mongolian border region and the Turkish region, which indicates a complex land surface.

Figure 5 shows the changes in landscape diversity in 1992 and 2018. The landscape diversity increased in general. In terms of the spatial distribution of landscape diversity, there was a significant increase in landscape diversity in the Almaty region, the capital of Kazakhstan (ΔEDI > 1). However, landscape diversity significantly decreased in the Kazakhstan–Russia border region, the Urumqi region in Xinjiang, China, and the Lanzhou region in Gansu, China (ΔEDI > 1). In terms of the landscape diversity trend, there was a small increase in landscape diversity (0%–3%) at the end of the three economic corridors and in Kazakhstan, especially at the beginning of the CMREC (e.g., North China) and at the beginning of the CMREC. In particular, there was a significant increase in landscape diversity (>3%) in the border areas between Beijing–Tianjin–Hebei and North China at the beginning of the CCAWAEC and in the border areas between Kazakhstan and Kyrgyzstan at the beginning of the “CCAWAEC”. In addition, there was a small decrease in landscape diversity (0%–0.15%), mainly in the Xinjiang Basin region of China, the Chinese–Mongolian border region, Uzbekistan and Turkmenistan, and a large decrease in the central and northeastern parts of Kazakhstan, with a rate of decrease of more than 3%.

3.3. Status of Forest Conversion

Figure 6 shows the forest transfer in the study area from 1992 to 2018, in which the positive part of the vertical coordinate axis indicates forest gain and the negative part indicates forest loss. The different colors indicate the conversion of different vegetation types, and the length of the bar represents the amount of conversion. The conversion between forests and other land cover types in the study area fluctuated periodically and mainly occurred between forests and farmland or between forests and cropland. The total amount of forest loss (3352.5 km²) was greater than the total amount of forest gain (2401.7 km²) from 1992 to 1999. Hence, the forested areas exhibited a net decrease. Most of the conversions came from grasslands, and the proportions of grassland transferred in and out were 48.3% and 47.1%, respectively. The proportions of cropland transferred in and out were 21.7% and 20.8%, respectively. From 2000 to 2007, the total amount of forest gain (3673.35 km²) exceeded the total amount of forest loss (2685.5 km²). Most of the displacement came from grassland and cropland. The proportions of grassland transfer and cropland transfer were 36.5% and 31.1%, respectively. Forests were mainly converted to grasslands, and the total amount of forest loss reached 1227.87 km². The total amount of forest gain (1224.5 km²) was smaller than the total amount of forest loss (1892.4 km²) from 2008 to 2018. The conversion mainly came from grasslands, and the proportions of grasslands transferred in and out were 31.6% and 42.7%, respectively. The proportion of cropland transferred in and out was 31.9% and 29%, respectively.

Throughout the entire CCAWAEC, many forest conversions occurred. For the NAECB and the CMREC, there were few forest conversions, except for the beginning, where the transformation from forests to other vegetation was relatively wild. Specifically, the areas where the amount of forest converted to other vegetation was large (>100 km²/year) were located mainly along the CCAWAEC, including the tri-border areas of China, Kyrgyzstan and Tajikistan, as well as the central region of Turkey. In contrast, the areas where the amount of other vegetation types converted to forest was large (>50 km²/year) were located mainly in the NAECB Economic Corridor, the beginning of the CMREC, and the CCAWAEC, including the Beijing–Tianjin–Hebei region of China; the tri-border region of China, Kyrgyzstan and Tajikistan; and the southern part of Turkey (Figure 7).

3.4. Coupling between Land Cover and Landscape Diversity Changes

The dynamics of forest changes and landscape fragmentation were further explored. Figure 7 indicates that forest alterations typically involve concurrent losses and gains, resulting in minimal net changes in total forest area. Despite the relative stability in forest quantity, Figure 6 highlights a marked deterioration in forest quality, primarily due to increased fragmentation. Figure 8 shows the coupled relationship between land cover and landscape diversity. These results revealed a pronounced correlation between forest depletion and fragmentation. The coupled relationship between forest displacement and landscape diversity can be categorized into two types: forest loss with increased fragmentation and forest loss with decreased fragmentation. Predominantly, the first pattern prevailed, representing approximately 70% of the observed changes. This notable correlation was especially evident in urban areas, such as the Beijing–Tianjin–Hebei metropolitan area.

One possible explanation for the coupled relationship between forest changes and landscape fragmentation is rapid urbanization. Urbanization has led to a decrease in forest area. However, due to the implementation of the Belt and Road Initiative and the Green Silk Road, reforestation has also occurred. This interplay of urbanization and reforestation causes the total forest area to fluctuate, with periodic increases and decreases, resulting in overall stability. Nevertheless, there was a notable increase in landscape fragmentation. Over the 27 years, forest coverage has experienced both expansions and contractions, culminating in heightened fragmentation.

4. Discussion

In this study, we provided a comprehensive assessment of the spatial and temporal changes in forest coverage and landscape fragmentation along the ECEC from 1992 to 2018. We further revealed a heterogeneous coupling interaction relationship between forest displacement and landscape patterns. Overall, the results highlighted a general decline in forest coverage, particularly in regions with high urbanization or agricultural conversion. Several uncertainties should be considered. The selection of diversity indices, such as the effective diversity index (EDI), plays a significant role in interpreting landscape dynamics. While the EDI measures the richness and evenness of land cover types, it may not fully capture ecological or biological diversity. Many other indices can also describe fragmentation [36]. In the future, combining various indices could potentially provide more robust results. Another source of uncertainty lies in the land cover products used. While the ESA CCI dataset has proven highly valuable for large-scale analyses, with its 300 m spatial resolution reducing uncertainties compared to coarser products like MODIS, challenges remain in heterogeneous areas such as mountainous and hilly regions [37]. In these areas, land cover types often overlap and mix, which introduces potential errors and limitations. This issue is particularly relevant for studying mixed landscapes like those in the ECEC, where forest boundaries with grasslands or croplands are frequently unclear. Enhancing the accuracy of these remote sensing land cover products would provide a clearer picture of actual landscape changes and help reduce potential uncertainties. Additionally, using grid-based remote sensing image classification products may lead to uncertainties, as noted by Yu et al. [38]. Remote sensing data can be less accurate when dealing with large pixels. Investigating forests and landscapes in specific countries would also be valuable. Overall, these considerations could improve the present work.

5. Conclusions

This study quantitatively investigated the spatial and temporal changes in forest coverage and landscape patterns based on remote sensing land cover data and landscape ecology methods within the ECEC in the Silk Road Economic Belt from 1992 to 2018. Forest coverage in the study area first increased but then decreased, indicating a net decreasing trend. Spatially, there was a decrease in the total forest area in the initial section of the ECEC, whereas there was an increase in the final section. The alterations between forests and other land cover types occurred mainly between forests and grasslands/croplands. Among the three subeconomic corridors of the ECEC, there were many forest gains along the entire CCAWAEC, whereas a small number of forest losses were observed in the NAECB and the CMREC, except in the initial sections where the forest displacements were relatively substantial. For the landscape patterns, the landscape diversity was low at the beginning parts but high at the middle and end parts. Moreover, there was an overall increasing trend, whereas reduced diversity was observed in the middle parts of the three subeconomic corridors. Notably, this pattern of landscape diversity was strongly associated with forest displacement and local urban development, consequently indicating increasing fragmentation rather than biological diversity. Both quantitative and qualitative inquiries are essential to comprehensively understand the potential achievements and challenges of the Belt and Road Initiative. The results of this study may provide useful information for clarifying the current situation of forest resources in the Silk Road Economic Belt and promoting ecologically sustainable development.