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Article

Land Use Change in the Russian Far East and Its Driving Factors

by
Cong Wang
1,2,
Xiaohan Zhang
1,2,* and
Liwei Liu
1,2
1
Joint International Research Laboratory of Catastrophe Simulation and Systemic Risk Governance, Beijing Normal University, Zhuhai 519087, China
2
School of National Safety and Emergency Management, Beijing Normal University, Zhuhai 519087, China
*
Author to whom correspondence should be addressed.
Land 2025, 14(4), 804; https://doi.org/10.3390/land14040804
Submission received: 24 February 2025 / Revised: 2 April 2025 / Accepted: 6 April 2025 / Published: 8 April 2025
(This article belongs to the Section Land Use, Impact Assessment and Sustainability)
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Abstract

:
This study systematically analyzes land use changes in the Russian Far East from 2000 to 2020, identifying key transformations and their driving factors. Using multi-temporal remote sensing images combined with land use dynamics analysis, transition matrices, and gray relational analysis, this research comprehensively evaluates land use evolution and its influencing factors. The purpose of this study is to elucidate how land use patterns shift under the influence of natural conditions, demographic trends, and cross-border cooperation with a particular emphasis on the border areas adjacent to northeast China. The findings reveal that during the observed period, the Far East underwent substantial expanses in arable land and built-up areas, while forest areas underwent a decline. Grassland areas demonstrated relative stability, water bodies continued to decrease, and unused land exhibited fluctuating trends, initially increasing and then decreasing. In the three border regions (Amur Oblast, the Jewish Autonomous Region, and Primorsky Krai), these transformations were more pronounced compared to the Far East overall, reflecting intensified agricultural development and urban growth in these strategic zones. Gray relational analysis shows that climate change and local population growth are the principal drivers of land use change, while regional trade—particularly China–Russia trade in industrial raw materials, agriculture, and food exports—plays a moderate role. The evolving land use patterns in the Far East carry significant implications for resource acquisition, ecological security, and regional cooperation. The study underscores the necessity of formulating scientifically sound land management policies to balance economic development with ecological protection, thus fostering sustainable development and regional stability.

1. Introduction

In the contemporary world, the global community is confronted with formidable challenges such as climate change, resource scarcity, and sustainable development issues. According to the report by the Intergovernmental Panel on Climate Change (IPCC), the global average temperature has risen by approximately 1.5 °C since pre-industrial times [1], and the frequency of extreme weather events has increased, posing significant threats to both ecological systems and economic stability [2]. Moreover, nearly one third of the world’s population now faces water scarcity [3], and the rapid depletion of non-renewable resources further intensifies this predicament [4,5]. Against this specific backdrop, regional cooperation—through resource integration, information sharing, and technology transfer—offers practical solutions to these pressing issues [6]. The promotion of regional cooperation not only fosters the effective utilization of resources and the harmonized progression of economic activities but also provides substantial support for ecological preservation and the adaptation to global shifts [7]. The Russian Far East, geographically proximate to northeast China and of comparable geopolitical importance and urgent development needs, serves as a robust foundation for regional collaboration [8,9]. The region’s abundance of natural resources, coupled with shared interests in ecological protection, resource development, and infrastructure construction between the two countries, positions it as a pivotal nexus for cooperative endeavors. Such shared interests manifest in land use changes that facilitate cross-border infrastructure projects, resource extraction sites, and protected ecological corridors. On one hand, the expansion of built-up areas to accommodate transportation networks and energy facilities supports economic and industrial growth; on the other hand, conservation-driven policies and collaborative frameworks encourage the establishment of transboundary nature reserves, reflecting both countries’ commitment to ecological sustainability. By deepening their cooperative endeavors, China and Russia can leverage each other’s strengths in the Far East, thereby promoting sustainable regional development and offering valuable insights for global regional cooperation.
As a vital part of the Eurasian continent, land use changes in the Far East have a profound impact on regional ecological environments, resource distribution, and sustainable development [10,11,12]. Land, as a crucial resource for human survival and development, directly influences regional development and the quality of life of residents [13,14]. Existing studies have demonstrated that the traditional pattern of dispersed, long-term settlements across the Far East is gradually shifting toward a more centralized model focused on the southern regions, which benefit from milder climates and enhanced transportation infrastructure [15]. This evolving settlement pattern is concomitantly driving changes in land use types. Moreover, under the influence of environmental disturbances, deforestation and degradation in the Far East are expected to intensify [16]. The scientific and reasonable utilization of land resources, as well as the optimization of land use structures, has emerged as a pivotal subject in the fields of geography and resource-environment research.
Research on land use change has revealed the interaction mechanisms between human activities and the natural environment, thereby providing a scientific basis for regional sustainable development. Research indicates that land use changes are driven by both natural and socio-economic factors [17]. Among the natural factors, climate change [18] and topographic features [19] have been identified as significant contributors. Climate change not only directly affects vegetation distribution, ecosystem productivity, and land suitability through altered precipitation and temperature patterns but also indirectly influences land use decisions by interacting with urban thermal environments and exacerbating urban heat island effects, particularly under rapid urbanization. Recent studies have increasingly recognized the complex coupling mechanisms among climate change, land cover change, urban heat islands, and anthropogenic activities, demonstrating how their synergistic effects significantly shape land use dynamics and urban microclimates [20,21,22,23]. Moreover, changes in land use can alter ecosystem processes such as carbon storage, soil fertility, and hydrological balance, thereby affecting the stability and resilience of local habitats [24,25]. On the other hand, socio-economic factors, which include population growth and migration [26,27,28], urbanization [29,30], policy factors [31,32], economic development levels [26,31], and market globalization [27], play a crucial role in shaping land use patterns. Specifically, population growth and urbanization lead to intensified urban expansion and infrastructure construction, altering land cover types and contributing significantly to urban thermal environment changes. The Russian Far East, with its rich forest, wetland, and grassland ecosystems [33], is a region of particular interest for studying the impacts of human activity on natural environments due to the rapid and often unpredictable changes in its natural environment. In-depth analysis of the land use changes in the Far East is therefore crucial for formulating effective land management policies and promoting sustainable development. As global changes intensify, there is an urgent need to understand the dynamic changes in land resources, analyze driving factors, and propose sustainable development strategies. Remote sensing-based studies on land use change provide high temporal and spatial resolution land information and unveil the complex mechanisms of land use evolution [34].
Recent advancements in remote sensing technology have rendered studies on land use changes based on multi-temporal remote sensing images crucial tools for regional land resource management [35]. The availability of high-resolution remote sensing data facilitates the accurate capture of spatial–temporal features of land use changes and the subsequent analysis of their driving mechanisms. The present study focuses on the Russian Far East, using multi-temporal remote sensing images from 2000, 2005, 2010, 2015, and 2020 to systematically analyze the spatial–temporal evolution patterns of land use in the region. Moreover, the findings can function as a reference point for cross-border cooperation and ecological security research, offering both academic value and practical significance.

2. Overview of the Study Area and Data Sources

2.1. Overview of the Study Area

The Far East region constitutes one third of Russia’s total area and occupies a pivotal position in the northeastern part of Asia. Its strategic geographical location, bordering the Pacific Ocean to the east, the Arctic Ocean to the north, the Ural Mountains to the west, and China, North Korea, and Mongolia to the south (see Figure 1), positions it as a nexus of regional connectivity and economic opportunity [36]. The region’s abundance of natural resources is significant with approximately one third of Russia’s mineral reserves located there, and a substantial portion of the land area dedicated to forests, accounting for 48% of the total area [37]. The Far East is a prolific source of oil, natural gas, and metal ores, which contribute substantially to Russia’s economy [38,39]. Vladivostok, the largest city in the Far East and an important international port, serves as a conduit between the Asia–Pacific region and Russia’s interior, facilitating trade and economic exchanges [40]. Despite comprising one third of Russia’s total area, the region has a relatively low population of about 8 million, which poses a significant challenge to its development [41]. In response, the Russian government has initiated a series of measures, including investment promotion and tax incentives, to attract population inflows and corporate investments, with the aim of stimulating economic growth in the region [42,43,44]. The Russian Far East, with its abundant resources, strategic location, and government development policies, plays a vital role in the global context.

2.2. Data Sources and Preprocessing

The socio-economic data for the study area were obtained from the Federal State Statistics Service of Russia, the World Bank, and the Statistical Bureaus of the three northeastern provinces of China. The land use classification data were obtained from the University of Maryland GLAD Global Land Cover and Land Use Change dataset, which was downloaded from the Google Earth Engine (GEE) platform (https://glad.earthengine.app/view/glcluc-2000-2020, accessed on 10 November 2024) [45]. This dataset encompasses the global distribution of forest cover, arable, built-up areas, water bodies, perennial snow, and glaciers, with a resolution of 30 m, for the period 2000 to 2020. The data were generated using the GLAD Landsat analysis-ready data and machine learning tools with each thematic product independently derived and validated through statistical sampling methods. The enhanced analytical accuracy of the dataset is attributable to the utilization of remote sensing images from the same season to the greatest extent possible. In this study, the GEE remote sensing image processing platform was employed to vector clip, reclassify, and statistically analyze five images. The reclassified results are presented in Figure 2.
Given the concentration of the population and economy in the border regions of Russia and China in the Far East, as well as the area’s status as a significant agricultural production zone characterized by complex population dynamics influenced by regional cooperation, the study places particular emphasis on three border areas of the Far East: Amur Oblast, the Jewish Autonomous Region, and Primorsky Krai. These three regions will hereafter be referred to as “the three regions of the Far East” (see Figure 3 for a visual representation of land use changes). It is important to note that due to changes in the map of the Far East, the Republic of Buryatia and Transbaikal Krai are not classified under the Far Eastern Federal District in this study.

3. Research Methods

3.1. Land Use Classification

The Land Use/Land Cover Change (LUCC) system is a classification framework for land use types, categorizing land into seven groups: arable, built-up land, forest, grassland, water bodies, unused land, and marine areas. Since marine areas are not present in the study area, the data were reclassified into six land use types: arable, built-up land, forest, grassland, water bodies, and unused land.

3.2. Land Use Transition Matrix

The land use transition matrix illustrates the direction and magnitude of land use type conversions over a specific period [46]. It represents the areas of land use transitions, including outflows, inflows, and unchanged portions, across different time periods. The calculation formula is as follows:
S i j = S 11 ⋯ S 1 n ⋮ ⋱ ⋮ S n 1 ⋯ S n n
where S i j represents the areas of each land use type at the beginning and end of the study period, and n denotes the number of land use types.

3.3. Land Use Intensity Analysis

The land use intensity index is a metric that quantifies the degree to which human activities and natural conditions influence land use in a specific area over a defined period. Different land use types are assigned grade values to quantitatively assess land use intensity across different periods [47]. The calculation formula is as follows:
L a = 100 × ∑ i = 1 n A i × C i  
where L a represents the land use intensity index for different periods; and A i is the land use grading index. According to Zhuang et al. [47], the assigned values are as follows: built-up land (4), arable land (3), forest (2), grassland (2), water bodies (2), and unused land (1). C i denotes the ratio of each land use type’s area to the total area of the study region.

3.4. Gray Relational Analysis

Gray relational analysis is a multi-factor statistical method used to examine the degree of interdependence between variables by comparing their developmental trends [48]. Initially, all data undergo normalization through dimensionless processing. Subsequently, the reference series X 0 and comparison series X j are established, and their differences are computed to determine the relational degree. These values are substituted into Formulas (3) and (4) to calculate the correlation coefficient and the degree of correlation. The specific formulas are
ξ j ( K ) = m i n j , k | X 0 ( K ) − X j ( K ) | + ρ m a x j , k | X 0 ( K ) − X j ( K ) | | X 0 ( K ) − X j ( K ) | + ρ m a x j , k | X 0 ( K ) − X j ( K ) |  
ξ j = 1 n ∑ K = 1 n ξ j ( K )
In these formulas, ξ j ( K ) represents the correlation coefficient, while ξ j denotes the degree of correlation. The parameter ρ is the distinguishing coefficient, ranging from 0 to 1, where a smaller value indicates greater significance in the differences between correlation coefficients. X 0 is the reference sequence, and X j is the comparison sequence. Based on references [49,50,51,52], the value of ρ is set to 0.5.

4. Results and Analysis

4.1. Land Use Change Characteristics

4.1.1. Analysis of Land Use Changes

An examination of the data presented in Table 1 reveals that the land use categories within the Russian Far East underwent substantial alterations from 2000 to 2020. First, the area of arable land demonstrated an overall increasing trend, rising from 15,025.04 km2 in 2000 to 20,738.43 km2 in 2020, with a significant acceleration in the growth rate after 2010. This phenomenon is concomitant with the expansion of agriculture, rising food demands, and supportive agricultural policies, suggesting that a concerted effort has been made to augment agricultural production and to actively develop and utilize land resources. Concurrently, the area of built-up land exhibited a persistent upward trajectory, expanding from 9340.99 km2 in 2000 to 14,619.59 km2 in 2020, which is a development that can be attributed to the acceleration of urbanization and infrastructure expansion. This growth not only mirrors the intensification of economic activities but also reflects the demand driven by population concentration and industrial development.
The forest area exhibited a slight increase between 2000 and 2010, expanding from 2,725,460.73 km2 to 2,740,931.28 km2. However, subsequent to 2010, a gradual decline was observed, reaching 2,674,318.46 km2 in 2020. This trend may be indicative of an intensification of forest development and utilization as well as an insufficient natural restoration capacity. Such a scenario could potentially have negative ramifications on ecosystem services, biodiversity conservation, and carbon storage. Conversely, grassland areas exhibited relative stability during the study period with a marginal increase from 3,099,461.29 km2 in 2000 to 3,121,230.47 km2 in 2020, suggesting the resilience of grassland ecosystems, which may be ascribed to the development of livestock farming and the implementation of ecological conservation measures. Conversely, water bodies exhibited a persistent decline from 122,978.06 km2 in 2000 to 105,789.53 km2 in 2020. Conversely, the area of unused land exhibited an increase from 170,115.6 km2 in 2000 to 174,480.05 km2 in 2010, which was followed by a substantial surge to 209,968.30 km2 in 2015 before a slight decrease to 205,685.19 km2 in 2020.
The Far East has undergone significant changes in land use, which mirror the broader trends observed in the region as a whole while also exhibiting distinct regional characteristics. The area of arable land in these regions has shown continuous growth, increasing from 13,879.05 km2 in 2000 to 19,973.22 km2 in 2020. The increase in built-up land was more substantial, rising from 4448.1 km2 to 6454.9 km2, suggesting a gradual deepening of industrial and urban development along the border. Conversely, the forest area decreased from 404,525.5 km2 in 2000 to 396,280.67 km2 in 2020.
Overall, between 2000 and 2020, land use in the Russian Far East demonstrated a consistent expansion of arable land and built-up areas, a general decrease in forest areas, a stable increase in grassland, a continuous reduction in water bodies, and fluctuating changes in unused land. It is noteworthy that the aforementioned changes in land use within the three border regions of the Far East exhibited a concurrence with the prevailing trend of the entire region (see Table 2). However, these changes were more pronounced in the domains of agricultural development and urbanization. This phenomenon can be attributed to the practical needs and geopolitical dynamics of cross-border cooperation.

4.1.2. Analysis of Land Use Transition

The land use transition matrix for the Russian Far East from 2000 to 2020 (Table 3) illustrates considerable shifts in land allocation, which were driven by both natural and socio-economic factors. One of the most salient findings is the notable two-way exchange between arable land and grassland. While approximately 2823.89 km2 of arable land converted to grassland, a substantially larger area of grassland—over 8700 km2—shifted to arable land. This indicates an overall net expansion of agricultural activities, which was likely in response to rising market demand and evolving land use policies.
In parallel, forest remains a crucial yet dynamic category. Although over 113,600 km2 of grassland turned into forest, suggesting localized afforestation or forest regeneration efforts, a considerably larger area of forest (161,401.24 km2) transitioned to grassland. When coupled with the conversion of forest to built-up land (2822.99 km2), this trend underscores ongoing deforestation processes, which have been partly attributed to infrastructure expansion and timber resource exploitation. Such a pattern signals mounting ecological challenges, including reduced carbon storage and potential impacts on regional biodiversity.
Built-up land demonstrates persistent growth, primarily fed by forest (2822.99 km2) and grassland (2239.90 km2), with smaller inflows from arable, water bodies, and unused land. Importantly, built-up land exhibits a high level of permanence: once an area is developed, it rarely transitions back to other uses. This underscores the lasting implications of urbanization and industrial growth for the region’s land use patterns and ecological sustainability.
Unused land also shows notable conversions, moving into grassland (10,583.73 km2) and forest (2129.32 km2), which are indicative of ongoing land development, ecological succession, or reforestation initiatives. Conversely, some unused parcels emerge from forest, grassland, and water bodies, potentially reflecting areas where resources have been depleted or where economic incentives for active land use are low.
From the perspective of water bodies, although some inflow is observed—particularly from grassland (3230.90 km2) and unused land (6247.13 km2)—outflow to other categories, especially unused land (22,142.60 km2), outstrips these gains, hinting at the overall contraction of water bodies over this period. Such transformations have implications for aquatic ecosystems and water resource management, especially in regions sensitive to climate variability and cross-border river systems.
Turning to the three border regions of the Far East (Table 4), a similar pattern emerges with strong interactions among arable land, grassland, and forest. Notably, grassland contributes a large fraction (8411.35 km2) to arable land, far surpassing the opposite flow (2174.23 km2), indicating a marked net increase in arable along these strategic frontier zones. Forest-to-grassland conversion (12,474.73 km2) and forest-to-built-up transitions (978.64 km2) highlight intensified resource extraction and urbanization pressures near border corridors. Nevertheless, the reverse flow from grassland to forest (5263.91 km2) also illustrates that certain areas are undergoing reforestation or are subject to land management measures aiming to restore forest cover. As in the broader Far East, built-up land in these border regions remains effectively irreversible.
Overall, the 2000–2020 land use changes in the Russian Far East—both at the regional scale and in the three border areas—reveal the interplay of agricultural expansion, urban growth, and resource exploitation. These processes not only reshape local ecosystems but also generate far-reaching consequences for cross-border environmental governance and bilateral trade in raw materials and agricultural products. As the Russian Far East continues to undergo rapid development, striking a balance between economic objectives (e.g., arable expansion, infrastructure projects) and ecological stability (e.g., forest conservation, water resource protection) remains a central challenge for policymakers and stakeholders on both sides of the border.

4.1.3. Analysis of Land Use Intensity

By calculating the land use intensity index for the study area over a period of three years, the overall land use intensity index for the Russian Far East in 2000, 2005, 2010, 2015, and 2020 was 197.78, 197.76, 197.78, 197.28, and 197.47, respectively. According to the theoretical value range of the land use intensity index [100, 400], the land use intensity in the study area remained relatively high, exhibiting a moderate concentration of land resources. However, it did not reach the level of rational development. The moderate fluctuations in the land use intensity index from 2000 to 2020 indicate the relative stability of land use patterns despite certain land type conversions during this period, such as the shift from arable land to built-up land or grassland. This stability may be attributed to the implementation of effective land use planning and control measures that prevent large-scale, unplanned land conversions, thus maintaining the sustainability of land use.
For the three regions of the Far East, the land use intensity index during the same period was 203.53, 203.56, 203.91, 206.36, and 205.19, respectively. The overall intensity was slightly higher than the Far East as a whole, indicating that land development in areas such as agriculture and built-up land was more concentrated. Furthermore, the fluctuations in the index during the monitoring period were found to be minimal, thereby suggesting a notable degree of stability in land use patterns.

4.2. Analysis of Driving Factors

In order to analyze the driving factors and mechanisms behind changes in land use intensity in the study area from 2000 to 2020, this study conducted a systematic assessment from three perspectives: population change, climate change, and Russia’s export trade to China. A total of 13 factors were normalized, and the gray relational model was applied to calculate the relational degrees of each factor and perform a comparative analysis. Due to constraints in data availability, this study employs both regional- and national-level indicators for population and agricultural GDP. The population in the Russian Far East reflects more proximal demographic forces within the study area, whereas the population in Russia captures broader national-level population dynamics that can exert indirect pressures on regional land use patterns via resource distribution, migration policies, and economic strategies. Russia’s agricultural GDP serves as a proxy measure of the overall agricultural sector’s growth trajectory when consistent Far East-specific data are unavailable. This multi-level approach to indicator selection acknowledges the complexity of land use drivers and leverages all feasible data sources to maintain temporal consistency.
The reference series X 0 was designated as the land use intensity index over five-year intervals in the study area, while the comparison series X j represented the influencing factors. The relational degree is denoted as R j . To facilitate quantitative analysis, the gray relational degree was classified into three levels: weak correlation [0, 0.35], moderate correlation (0.35, 0.70], and strong correlation (0.70, 1.0]. The specific driving factors and their relational degrees are presented in Table 5 and Table 6, as well as in Figure 4 and Figure 5.
The results of the gray relational degree analysis for the driving factors of land use intensity in the Russian Far East from 2000 to 2020 (see Table 5) indicate that climate change and local population growth are the primary drivers of land use changes in the region. Specifically, precipitation (X4, relational degree 0.964) and temperature (X5, relational degree 0.925) exhibit remarkably high relational degrees, suggesting that climatic conditions exert a pivotal influence on shaping land use patterns. Sufficient precipitation and suitable temperatures foster agricultural production and vegetation growth, thereby facilitating the expansion of arable land and forests.
Furthermore, population changes in the Russian Far East (X1, relational degree 0.916) demonstrate a strong correlation, underscoring the direct impact of population growth on land demand. This growth may be propelled by regional economic development, infrastructure enhancements, and the promotion of China–Russia cooperative initiatives, resulting in the sustained expansion of arable and built-up land to accommodate agricultural production and urbanization. Consequently, climate conditions and population dynamics collectively function as the fundamental driving forces that shape land use patterns in the Russian Far East.
Regarding the three border regions specifically, Table 6 shows a similar trend: precipitation (X4, 0.932) and temperature (X5, 0.722) strongly influence land use changes, while population growth in the Russian Far East (X1, 0.972) emerges as an even more dominant factor than in the region as a whole. This indicates that in border areas, demographic pressures, driven by cross-border trade opportunities and industrial development, can further amplify land conversion processes linked to agriculture and built-up expansion.
The correlation between economic factors, particularly Russia’s export trade to China, and land use intensity is moderate. Specifically, the export of industrial raw materials (X6, relational degree 0.637) and agricultural and food exports (X8, relational degree 0.667) significantly impact land use changes. This finding indicates that as China–Russia cooperation intensifies in the domains of energy development, mineral resource extraction, and agriculture, the expansion of related industries has led to an increased demand for land resources, thereby driving the growth of arable and built-up areas. For instance, the rising export of industrial raw materials may stimulate mining and infrastructure development, resulting in the requirement for additional built-up land, while the growth in agricultural and food exports may drive the expansion of arable land to meet export market demands.

5. Discussion

5.1. Spatial Patterns of Land Use Evolution in the Russian Far East

This study explores the intricate spatial evolution patterns of land use change in the Russian Far East, revealing a multifaceted interplay of natural factors, socio-economic activities, and a complex, intertwined driving mechanism. Through a comprehensive analysis of land use data from 2000 to 2020, this study unveils the transformation of land use patterns in the Far East and identifies the underlying deep-seated driving forces behind these changes.
The spatial pattern of land use change is not evenly distributed; rather, it is influenced by factors such as resource endowment, geographical conditions, and economic development. For example, the increase in arable and built-up land is concentrated mainly in border regions, and it is closely linked to population inflow, accelerated infrastructure construction, and agricultural expansion. The progression of urbanization and the augmentation of regional economic activities have precipitated substantial alterations in the land use structure of these regions. Nevertheless, these transformations have concomitantly engendered certain ecological pressures, particularly in the context of diminishing forest areas, incessant reduction in water bodies, and challenges to the sustainability of the ecological environment.
In this regard, the spatial heterogeneity of land use changes further accentuates how distinct development trajectories emerge under varying local conditions. Regions endowed with fertile soils and better transport access, such as the three border areas adjacent to northeast China, tend to attract larger-scale investments in agriculture and industries. Meanwhile, remote or ecologically sensitive zones face heightened conservation dilemmas due to their critical habitat functions. This spatial divergence underscores the importance of differentiated land management strategies that weigh local ecological vulnerabilities alongside economic imperatives. Moreover, the complex interactions among policy directives, private sector interests, and shifting climate patterns have resulted in uneven land transformations, urging regional planners to integrate multiple scales of analysis—from localized land parcels to transboundary ecosystems—into decision-making processes.
Changes in land use patterns serve as a clear barometer of how the ecological carrying capacity is being tested by human activities, reflecting both the inherent potential of natural resources and the upper limits of what the environment can sustain. While the expansion of arable and built-up land has undoubtedly promoted economic growth—stimulating investments and enhancing market connectivity —the over-exploitation of critical resources, especially forest areas, highlights a growing tension between development imperatives and ecological stewardship. Deforestation, for instance, not only disrupts habitat connectivity and threatens biodiversity but also reduces carbon sequestration capacity, thereby exacerbating climate change impacts at both local and global scales. Equally concerning is the depletion of water resources through intensified agricultural irrigation, industrial usage, and urban consumption. Such water stresses can impair hydrological cycles, diminish freshwater availability for downstream ecosystems, and magnify the risks of droughts and flooding. As a result, the degradation of ecosystem services—ranging from pollination and soil fertility to carbon storage and water purification—poses significant challenges to long-term regional sustainability. Therefore, striking a balance between resource extraction and ecological conservation remains a central task that necessitates integrated policy frameworks, robust environmental governance, and the adoption of sustainable land management practices to safeguard the region’s natural capital for future generations.
Moreover, the three regions of the Far East exhibit more pronounced changes compared to the region as a whole. Their proximity to northeast China not only facilitates cross-border economic exchanges but also intensifies land conversion pressures. For instance, rising market demand for agricultural products—both domestically and from cross-border trade—has led to more aggressive arable expansion. Meanwhile, urbanized zones near key logistics corridors, such as the Trans-Siberian Railway and new cross-border transport routes, continue to grow. These factors collectively result in accelerated deforestation, especially in areas suitable for timber extraction or agricultural frontier expansion. Consequently, the ecological constraints in these border zones are more acute, as rapid land use change puts pressure on ecosystems already vulnerable to climate fluctuations and resource overexploitation.
In the context of the three regions, this challenge is further compounded by the interplay of multiple stakeholders—local communities, transnational enterprises, and governmental agencies—making it imperative to design region-specific conservation and land management strategies. Attention must also be given to the socio-economic disparities within and between these regions, as livelihood demands often compete with longer-term conservation goals. In many instances, localized economic opportunities stemming from cross-border trade and resource extraction can overshadow broader ecological concerns, necessitating policy interventions that incentivize more sustainable land uses.

5.2. Driving Mechanisms of Land Use Change in the Russian Far East and Their Implications for Regional Cooperation

This study employed gray relational analysis to identify the driving factors of land use change. The analysis revealed that climate change, population growth, and cross-border trade between China and Russia are the primary influencing factors. Specifically, changes in precipitation and temperature are significant natural factors driving land use change in the Far East. In particular, alterations in precipitation distribution and increasing temperatures can shift agricultural suitability zones, extend growing seasons, and modify soil moisture regimes, thereby incentivizing farmers to expand or relocate arable lands. For forest ecosystems, rising temperatures may heighten risks of pest outbreaks and wildfires, while changing precipitation patterns can affect regeneration rates and species composition, ultimately prompting land managers to re-evaluate forestry practices and potentially convert forested areas for alternative uses. Increased precipitation and rising temperatures have facilitated the expansion of agricultural production, particularly in the transformation of arable land and grassland.
Population growth is another key factor influencing land use. The Far East has a relatively sparse population, with growth primarily concentrated in border regions between China and Russia, particularly in the three regions of the Far East. This pattern reflects the role of economic development and infrastructure expansion in driving population concentration. As infrastructure development and urbanization accelerate, population aggregation and mobility have intensified the demand for arable and built-up land. Furthermore, as China and Russia deepen their cooperative endeavors in resource development, agriculture, and other sectors, cross-border trade has emerged as a significant driving force behind land use changes. The rise in agricultural and food exports has contributed to the expansion of arable land, while increasing China–Russia energy cooperation has driven demand for built-up land, particularly for energy extraction and transportation infrastructure.
The broader implications of regional cooperation are evident in the direct impact of cross-border economic activities on the spatial patterns of land use. Cooperation between China and Russia in the Far East has not only promoted the efficient utilization of resources but has also intensified the dual pressures of land development and ecological degradation. While regional cooperation has driven infrastructure expansion, industrial growth, and resource development in border areas, it has also had negative environmental consequences, particularly in terms of deforestation and water resource exploitation. Therefore, as regional cooperation advances, balancing economic development with ecological protection and resource utilization with sustainability will be a critical challenge in the future. Policymakers must implement effective land protection measures while fostering economic growth to ensure the sustainable use of land resources and the long-term stability of ecosystems.
In light of this, the question of how to maintain ecosystem stability amid rapid land use expansion has become a key issue in land management in the region. What is needed is not only to raise the issue but also to propose feasible governmental actions to address it. Potential measures include reinforcing forest protection policies, strengthening cross-border ecological monitoring programs, and promoting integrated land use planning with ecological compensation mechanisms. In doing so, future governments can more effectively reconcile rapid economic development with the imperative of safeguarding ecosystems in the Far East.
Moreover, cross-border collaborations could be broadened beyond immediate economic interests to include joint research and shared governance frameworks that address long-term environmental risks. For instance, establishing transboundary conservation corridors or coordinated watershed management programs could help mitigate some of the adverse impacts of land conversion and resource extraction. Such integrated efforts would not only enhance local ecological resilience but also fortify bilateral relations through a shared commitment to sustainability.
In order to achieve effective future land use management, it is essential to integrate climate change, population dynamics, and the driving mechanisms of regional cooperation. A comprehensive approach to land use management should prioritize the coordination of economic development and ecological protection, thereby promoting greener and more sustainable regional development models. This may entail the adoption of adaptive management practices, where ongoing policy decisions are revised in response to emerging environmental feedback, thus reducing the risk of lock-in effects that can arise from the single-minded pursuit of short-term gains.

5.3. Research Limitations and Future Prospects

Despite the in-depth analysis of land use change in the Russian Far East provided by this study, certain limitations remain. First, the study primarily relies on remote sensing images and gray relational analysis. While these methods are effective in revealing the spatiotemporal characteristics of land use change, they may not fully capture changes in land use in certain areas due to the limitations of spatial and temporal resolution in remote sensing data. Consequently, future research endeavors should consider incorporating a more diverse array of high-resolution remote sensing data, in conjunction with field survey data, to enhance the precision of the research outcomes.
Secondly, while this paper analyzes driving factors such as climate change, population growth, and China–Russia trade, it lacks an in-depth exploration of the dynamic changes in regional policies and international cooperation and their impact on land use. As China and Russia adjust their policies on environmental protection, climate change responses, and resource development, land use trends may undergo significant changes. Consequently, future research should prioritize the examination of policy changes and their repercussions on land use patterns, particularly within the context of global climate change and shifts in international trade policies. Moreover, there is a need to explore effective strategies for addressing the novel challenges confronting regional land use. In addition, it is necessary to investigate how local governments can implement these policy adjustments more efficiently—such as through targeted land use zoning, stricter environmental oversight, and stronger stakeholder engagement—to effectively address emerging challenges and sustainably manage land resources.

6. Conclusions

This study systematically analyzed land use changes and driving factors in the Russian Far East from 2000 to 2020, identifying significant expansions in arable and built-up areas, reductions in forest cover, stable grasslands, shrinking water bodies, and fluctuating unused land. At the regional scale, land use transitions were mainly driven by climate change and local population growth, reflecting the critical influence of natural conditions and demographic dynamics. Additionally, regional cooperation, particularly cross-border economic exchanges between China and Russia, played a moderate but important role, significantly shaping land use patterns through increased agricultural production, infrastructure development, and industrial expansion.
The three border regions demonstrated even more pronounced land use transformations compared to the Far East overall, emphasizing intensified agricultural development and urbanization linked directly to cross-border cooperation. Population inflows, infrastructure investment, and growing trade exchanges intensified land demand, causing notable shifts from grasslands and forests to arable and built-up lands in these strategic areas. The analysis highlights regional cooperation as a vital influencing factor, underscoring how bilateral economic activities have substantially reshaped land use trajectories in these border regions, with significant implications for regional resource allocation, ecological conditions, and long-term development trends.

Author Contributions

C.W.: Writing—review and editing, Writing—original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation. X.Z.: Writing—review and editing, Supervision, Funding acquisition, Conceptualization. L.L.: Writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Beijing Normal University Special Fund for Enhancing the Quality of Talent Cultivation, the Fundamental Research Funds for the Central Universities (124330008) and the Beijing Normal University Research Start-up Funding for Talent (No. 310432104). The financial support is gratefully acknowledged.

Data Availability Statement

The data sources are listed in the text.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Land 14 00804 g001
Figure 1. Geographical location and overview of the Russian Far East.
Figure 1. Geographical location and overview of the Russian Far East.
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Figure 2. Land use change in the Russian Far East from 2000 to 2020.
Figure 2. Land use change in the Russian Far East from 2000 to 2020.
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Figure 3. Land use change in the three regions of the Far East from 2000 to 2020.
Figure 3. Land use change in the three regions of the Far East from 2000 to 2020.
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Figure 4. Gray relational degree analysis of the driving factors for land use intensity change in the Russian Far East.
Figure 4. Gray relational degree analysis of the driving factors for land use intensity change in the Russian Far East.
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Figure 5. Gray relational degree analysis of the driving factors for land use intensity change in the three regions of the Far East.
Figure 5. Gray relational degree analysis of the driving factors for land use intensity change in the three regions of the Far East.
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Table 1. Changes in land use area in the Russian Far East from 2000 to 2020.
Table 1. Changes in land use area in the Russian Far East from 2000 to 2020.
Land Use Type2000 (km2)2005 (km2)2010 (km2)2015 (km2)2020 (km2)
Arable15,025.0414,084.6515,129.5317,369.5120,738.43
Built-up9340.9910,624.8311,551.0112,664.2314,619.59
Forest2,725,460.732,734,941.932,740,931.282,714,982.262,674,318.46
Grassland3,099,461.293,087,004.053,081,245.533,081,470.623,121,230.47
Water Bodies122,978.06122,497.82119,044.31105,926.75105,789.53
Unused170,115.6173,228.37174,480.05209,968.30205,685.19
Table 2. Changes in land use area in the three regions of the Far East from 2000 to 2020.
Table 2. Changes in land use area in the three regions of the Far East from 2000 to 2020.
Land Use Type2000 (km2)2005 (km2)2010 (km2)2015 (km2)2020 (km2)
Unused2947.943170.983006.823465.353701.24
Grassland128,924128,415.10128,759.50130,916.50127,772.50
Forest404,525.50404,887.10402,855.00398,002.80396,280.67
Water Bodies7169.287163.107556.647080.837711.31
Arable13,879.0513,313.7814,476.8516,763.2119,973.22
Built-up4448.104943.745238.995665.116454.90
Table 3. Land use transition matrix for the entire Russian Far East from 2000 to 2020 (km2).
Table 3. Land use transition matrix for the entire Russian Far East from 2000 to 2020 (km2).
2000
Land Use Type		2020 Land Use Type
ArableBuilt-UpForestGrasslandWater BodiesUnused
Arable12,013.61136.229.732823.897.713.91
Built-up09340.990000
Forest7.612822.992,558,355.32161,401.24202.082671.49
Grassland8709.532239.9113,642.162,941,861.583230.929,777.22
Water Bodies1.0510.68161.934560.0596,101.7522,142.6
Unused6.6468.812129.3210,583.736247.13151,079.97
Table 4. Land use transition matrix for the three regions of the Far East from 2000 to 2020 (km2).
Table 4. Land use transition matrix for the three regions of the Far East from 2000 to 2020 (km2).
2000
Land Use Type		2020 Land Use Type
ArableBuilt-UpForestGrasslandWater BodiesUnused
Arable11,549.30118.7317.72174.236.9512.14
Built-up04448.10000
Forest5.62978.64390,734.2712,474.7365.74266.50
Grassland8411.35881.955263.91112,791.01484.981090.8
Water Bodies0.833.748.6133.876739.16383.07
Unused6.123.73256.22298.67414.511948.71
Table 5. Gray relational degree analysis of driving factors for land use intensity in the Russian Far East from 2000 to 2020.
Table 5. Gray relational degree analysis of driving factors for land use intensity in the Russian Far East from 2000 to 2020.
DimensionIndicatorCodeRelational DegreeRankLevel
PopulationPopulation in the Russian Far EastX10.9163Strong
Population in RussiaX20.5849Moderate
Population in northeast ChinaX30.53511Moderate
ClimatePrecipitation in the Russian Far EastX40.9641Strong
Temperature in the Russian Far EastX50.9252Strong
Russia’s export trade to ChinaRussia’s export of industrial raw materials to ChinaX60.6377Moderate
Russia’s export of manufactured goods and equipment to ChinaX70.50713Moderate
Russia’s export of agricultural and food products to ChinaX80.6676Moderate
China’s export trade to RussiaChina’s export of agricultural and food products to RussiaX90.58210Moderate
China’s export of industrial raw materials to RussiaX100.53412Moderate
China’s export of manufactured goods and equipment to RussiaX110.5868Moderate
Agricultural productionRussia’s agricultural GDPX120.7214Strong
China’s agricultural GDPX130.6715Moderate
Table 6. Gray relational degree analysis of driving factors for land use intensity in the three regions of the Far East from 2000 to 2020.
Table 6. Gray relational degree analysis of driving factors for land use intensity in the three regions of the Far East from 2000 to 2020.
DimensionIndicatorCodeRelational DegreeRankLevel
PopulationPopulation in the Russian Far EastX10.9721Strong
Population in RussiaX20.5919Moderate
Population in northeast ChinaX30.54111Moderate
ClimatePrecipitation in the Russian Far EastX40.9322Strong
Temperature in the Russian Far EastX50.7224Strong
Russia’s export trade to ChinaRussia’s export of industrial raw materials to ChinaX60.6337Moderate
Russia’s export of manufactured goods and equipment to ChinaX70.51313Moderate
Russia’s export of agricultural and food products to ChinaX80.6755Moderate
China’s export trade to RussiaChina’s export of agricultural and food products to RussiaX90.58910Moderate
China’s export of industrial raw materials to RussiaX100.54012Moderate
China’s export of manufactured goods and equipment to RussiaX110.5928Moderate
Agricultural productionRussia’s agricultural GDPX120.7303Strong
China’s agricultural GDPX130.6386Moderate
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Wang, C.; Zhang, X.; Liu, L. Land Use Change in the Russian Far East and Its Driving Factors. Land 2025, 14, 804. https://doi.org/10.3390/land14040804

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Wang C, Zhang X, Liu L. Land Use Change in the Russian Far East and Its Driving Factors. Land. 2025; 14(4):804. https://doi.org/10.3390/land14040804

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Wang, Cong, Xiaohan Zhang, and Liwei Liu. 2025. "Land Use Change in the Russian Far East and Its Driving Factors" Land 14, no. 4: 804. https://doi.org/10.3390/land14040804

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Wang, C., Zhang, X., & Liu, L. (2025). Land Use Change in the Russian Far East and Its Driving Factors. Land, 14(4), 804. https://doi.org/10.3390/land14040804

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