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Article

Dynamics in Land Cover and Landscape Patterns of Myanmar: A Three-Decade Perspective (1990–2020)

by
Ruonan Li
1,
Cansong Li
1,*,
Dongyang Hou
2,
Huaqiao Xing
3 and
A-Xing Zhu
4
1
The Faculty of Geography, Yunnan Normal University, Kunming 650500, China
2
The School of Geosciences and Info Physics, Central South University, Changsha 410083, China
3
The School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan 250101, China
4
Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Land 2024, 13(12), 2212; https://doi.org/10.3390/land13122212
Submission received: 16 September 2024 / Revised: 11 December 2024 / Accepted: 12 December 2024 / Published: 18 December 2024
(This article belongs to the Special Issue Land Use Change (LUC) and Resources and Environmental Carrying Capacity (RECC) in Tropics and Sub-tropics)
Browse Figures
Figure 1
<p>Location of Myanmar.</p> "> Figure 2
<p>Land cover distribution of Myanmar: (<b>a</b>) 1990; (<b>b</b>) 2000; (<b>c</b>) 2010; (<b>d</b>) 2020.</p> "> Figure 3
<p>Spatial distribution changes in core land cover types in Myanmar, spanning from 1990 to 2020: (<b>a</b>) cultivated land, (<b>b</b>) forests, (<b>c</b>) wetlands, (<b>d</b>) water bodies, and (<b>e</b>) artificial surfaces.</p> "> Figure 4
<p>Distribution map of comprehensive land use dynamic degree in Myanmar’s states and provinces: (<b>a</b>) 1990–2000; (<b>b</b>) 2000–2010; (<b>c</b>) 2010–2020.</p> "> Figure 5
<p>Transition of land cover types across Myanmar from 1990 to 2020.</p> "> Figure 6
<p>Spatial distribution map of land cover type transitions in Myanmar: (<b>a</b>) 1990–2000; (<b>b</b>) 2000–2010; (<b>c</b>) 2010–2020.</p> "> Figure 7
<p>Spatial distribution map of key land cover type transitions in Myanmar: (<b>a</b>) cultivated land/forest to artificial surface; (<b>b</b>) forest to wetland/water bodies.</p> "> Figure 8
<p>Statistics on changes in landscape pattern metrics at the landscape level from 1990 to 2020.</p> ">
Versions Notes

Abstract

:
A comprehensive scientific assessment of the dynamic changes in land cover and landscape patterns in Myanmar, considering both human activities and natural factors such as climate change, is essential for a thorough understanding of the transformations in the country’s ecological environment. This assessment also provides data-driven insights into the complex interactions between humans, climate, and the environment. This study aims to examine the dynamic changes in land cover in Myanmar over a thirty-year period from a comprehensive perspective. This paper, based on the MLC30 land cover dataset for Myanmar from 1990 to 2020, employs land use dynamic degree and land use transition matrix to analyze the extent and process of land cover changes in Myanmar. Furthermore, using landscape pattern indicators, the paper explores the changes in the spatial structural characteristics of land cover in Myanmar at both the patch scale and the landscape scale. The results indicate the following: (a) Areas with significant land cover changes are primarily located in the eastern, southeastern, and southwestern regions bordering China, Laos, and Thailand, as well as the coastal areas, with the change intensity from 2000 to 2020 being notably higher than before 2000. (b) Myanmar’s cultivated land, artificial surfaces, and water bodies show an expanding trend, with cultivated land expansion mainly at the expense of forests, while the increase in artificial surfaces and water bodies is through the conversion of the existing cultivated land. (c) Myanmar’s landscape patterns remained stable from 1990 to 2000. However, after 2000, the land cover has shown a clear trend towards fragmentation and spatial distribution dispersion, especially for the dominant forest and cultivated land types. Despite Myanmar’s rapid economic development, the trend toward the fragmentation and irregularization of cultivated land patches indicates a lack of attention to cultivated land use and planning. The reduction and fragmentation of forest areas have led to a decline in ecological connectivity, posing risks of ecological environment deterioration. Consequently, Myanmar must prioritize scientific land use planning and the rational allocation of land resources to foster the sustainable development of agriculture and the protection of natural ecosystems.

1. Introduction

Land cover change is a key factor driving global environmental change as land use affects land surface properties and the provision of ecosystem services [1]. The dynamics of land cover—acting as a critical interface between economic development, social activities, and environmental health—have gained considerable attention [2,3]. Myanmar, a vital resource center and habitat in Southeast Asia, has biodiversity hotspots that rank among the world’s most significant [4,5,6], making the conservation of its rich natural resources crucial for maintaining global ecological balance [4,5,7]. Myanmar’s natural ecosystems, however, are increasingly threatened by the accelerating intensity of land use and the overexploitation of natural resources, with changes in land cover emerging as a major driver of this degradation [5]. Therefore, gaining a comprehensive understanding of the spatiotemporal patterns and dynamic characteristics of land cover change in Myanmar provides invaluable spatial data and a robust scientific foundation for research on global change and sustainable development.
Current research on land cover change in Myanmar is generally divided into three categories. The first category examines the dynamic changes in individual land cover types, such as urban expansion [8], mangrove forests [9,10], and the spatial shifts in rubber plantations under alternative planting policies [11]. This perspective, however, does not fully capture the processes of conversion between different land cover types. The second category focuses on the integrated analyses of land cover changes in specific local areas, particularly near the border [12], urban regions, and areas along major infrastructure projects, such as the former and current capitals of Myanmar [13,14] and the China–Myanmar oil and gas pipeline [15,16]. These studies, while informative, do not encompass the spatiotemporal changes in land cover across the entire country, limiting the understanding of their interaction with and impact on China’s southwestern border regions. The third category includes the comprehensive analyses of land cover dynamics across all of Myanmar. For instance, Wang [17] employed land use dynamic degree and transition matrix methods to assess the extent and processes of land cover changes from 2000 to 2020. Although this approach overcomes some limitations of the previous two categories, it still does not fully capture the spatial distribution of land cover types and neglects variations in landscape patterns.
Land cover changes are evident not only in the extent and processes of these changes but also in the spatial distribution and configuration of land cover patches, that is, landscape patterns [18,19,20]. For instance, infrastructure development such as roads and railways often fragment continuous forest areas into smaller patches, increasing habitat fragmentation and isolating habitats, which can hinder species movement [21]. Additionally, urban expansion typically creates a mosaic of farmland and built-up areas, leading to significant habitat differences that can pose threats to agricultural ecosystem health and functionality. The shifts in these patterns provide a critical insight into how human activities reshape the Earth’s surface. The analysis of landscape patterns is useful in the case of land cover where imbalances between structure and function can have serious consequences [22]. Thus, the change in landscape patterns is also a vital aspect of land cover dynamics.
However, very little research has been conducted on Myanmar that comprehensively analyzes the extent, processes, and landscape patterns of land cover change from an integrated perspective. In particular, the remarkable land use changes in Myanmar during recent decades have been extensively documented. The landscape patterns also find changes. And, this study aims to investigate the spatiotemporal dynamics of land cover and landscape patterns in Myanmar from 1990 to 2020, which not only aids in the scientific planning of land use in Myanmar but also provides essential information for ecological environment assessment.

2. Materials and Methods

2.1. Study Area

Myanmar occupies a strategically pivotal position within Southeast Asia, situated within tropical latitudes (92°10′–101°10′ E, 9°32′–28°31′ N). It is contiguous with Bangladesh, China, India, Laos, and Thailand, and is bounded by the Bay of Bengal and the Andaman Sea (shown in Figure 1). Spanning a land area of 676,578 km2, the nation harbors an array of biomes, natural endowments, cultural diversity, and historical customs [23]. Most of Myanmar’s territory lies below the Tropic of Cancer and is subject to a tropical monsoon climate regime, characterized by minimal diurnal temperature variation and a concentration of precipitation. Based on geographical features and climatic conditions, Myanmar can be broadly categorized into five distinct regions: the mountainous domain, the Shan Highlands, the central arid expanse, the deltaic area, and the maritime zone, encompassing a spectrum from alpine to tropical rainforest environments and a coastline extending 6300 km that supports diverse marine ecological systems [4,23]. Myanmar’s forested landscapes rank second in extent within Southeast Asia [24], with approximately 44% of its territory under natural forest cover [25].
Myanmar is encircled by mountain ranges, resulting in varied topography with higher elevations in the north and lower elevations in the south. The Naga Hills and Arakan Mountains define the western landscape, while the Shan Plateau dominates the east. In the northeast, the Gaoligong Mountains, located along the China–Myanmar border, house Yunnan’s largest national nature reserve [26]. Between these western mountains and the eastern plateau lies the low-lying Irrawaddy River alluvial plain, a key region for agricultural production.
Additionally, Myanmar is a polyethnic state comprising 135 distinct ethnic groups, many of which have transboundary distributions. The administrative structure of the country is organized into seven states, seven provinces, and one federal territory, with a total population of approximately 54.18 million individuals. Major urban centers within Myanmar include Yangon, Mandalay, and Naypyidaw [27]. Myanmar’s socio-economic development remains comparatively underdeveloped, with agriculture constituting the bedrock of its national economy, accounting for approximately 40% of the gross domestic product (GDP). Rice is the predominant cultivation in Myanmar and constitutes a primary source of income for a substantial portion of the agricultural population [28]. Furthermore, the forestry sector is of significant economic importance, with Myanmar being the preeminent global producer of teak, a species recognized for its high value and superior quality. The export of timber is a critical component of the country’s foreign exchange revenue [23].

2.2. Land Use and Land Cover Data

To analyze the spatiotemporal dynamics of land cover change in Myanmar, the land cover dataset for Myanmar (MLC30) [29] was employed. The dataset, which covers the period from 1990 to 2020, was produced at biennial intervals, resulting in a total of 16 distinct products. Landsat satellite imagery was utilized to construct the MLC30 dataset, with data being sourced via the Google Earth Engine (GEE) cloud platform. Imagery from Landsat 5 (1990–2010), Landsat 7 (2012), and Landsat 8 (2014–2020) was specifically used. During the image preprocessing phase, a masking technique was applied to remove clouds and shadows. Additionally, annual composite images were generated using GEE’s multi-temporal image compositing methods, and these composites were employed for land cover classification. The classification process was carried out using a transfer learning framework with training samples, and the Random Forest machine learning algorithm was applied to the spectral, index, and texture features extracted from the remote sensing images within the GEE platform. The dataset includes nine commonly recognized land use and land cover (LULC) categories: cultivated land, forests, grasslands, shrublands, wetlands, water bodies, artificial surfaces, bare land, and permanent snow and ice, with definitions aligned with the Globeland30 product specifications [30].
The accuracy of the MLC30 product was evaluated by Xing et al. [29] using both validation samples and comparisons with other publicly available datasets, yielding positive results. The accuracy assessment, based on the validation samples, indicated that the average overall accuracy of the MLC30 dataset from 1990 to 2020 was 0.83, with a Kappa coefficient of 0.79. The highest levels of accuracy were recorded for the years 2000, 2010, and 2020, with both overall accuracy and Kappa coefficients surpassing 0.85. When compared with other 2020 land cover datasets, such as GlobeLand30 [31], FROM GLC [32], and Dynamic World [33], the MLC30-2020 product exhibited notable improvements in overall accuracy. In conclusion, the MLC30 product is distinguished by its extensive temporal coverage, consistent spatial and temporal resolution, and satisfactory accuracy, making it a reliable choice. Therefore, the MLC30 product was selected for this study as the land cover dataset, with analyses conducted on the data from the years 1990, 2000, 2010, and 2020.

2.3. Methods

2.3.1. Temporal Dynamic Analysis Method

The study begins by analyzing the trends in the area changes in various land cover types across Myanmar. It examines whether these areas have increased or decreased over time. Subsequently, the study employs the land use dynamic degree (LUDD) [34] method to describe the intensity of changes in land cover types throughout different periods. The single land use dynamic degree refers to the degree of change in a specific land cover type within the study area over a defined time frame, with the calculation method outlined in Formula (1). In contrast, the comprehensive land use dynamic degree describes the overall intensity of changes in land cover types across the entire study area, with the calculation method detailed in Formula (2).
K = U b − U a U a ∗ 1 T ∗ 100 %
L = ∑ i = 1 n ∣ U b − U a ∣ U ∗ 1 T ∗ 100 %
In this context, K is the dynamic degree of a particular land use type during the period of this study, which is the rate of change. L represents the dynamic degree of the overall land use types in the study area. Ua and Ub refer to the area of a specific land type at the beginning and end of the study period, respectively. U denotes the total area of the study region. T is the length of the study period in years, which is T = 10 for this study. n refers to the number of land cover types, which is n = 9 in this study.

2.3.2. Spatial Dynamic Analysis Method

The purpose of the spatial change analysis is to identify key areas of land cover change, highlight the characteristics of land cover transitions in Myanmar, and summarize the overall spatial change patterns [35]. Additionally, the analysis examines the spatial structural changes in land cover at the landscape scale. To understand the spatial transfers of land types, land cover data with raster format from adjacent years were compared. This comparison helps analyze spatial changes in land types and creates land use transition matrices for different periods to illustrate the relationships between land type conversions [36].
On the landscape scale, changes in land cover were characterized using landscape pattern indices. These indices were used to provide information on the spatial distribution, shape, size, and quantity of various land cover units, thereby describing the spatial structure of land cover [18,37]. Landscape patterns were evaluated using three sets of metrics: fragmentation, heterogeneity, and connectivity. The selection of these metrics was guided by their effectiveness in addressing issues related to landscape fragmentation. The fragmentation metrics encompassed those related to composition, such as the diversity and richness of patch types within the landscape, and configuration, including the spatial characteristics, arrangement, position, or orientation of patches within or across the landscape [38,39,40].
Based on the MLC30 land cover data, the metrics (Table 1) were calculated at both the landscape level (which assesses the overall structural characteristics of the landscape) and the class level (which evaluates the structural characteristics of each land cover type) using Python programming. As detailed in Table 1, eight metrics were selected at the class level: patch number (NP), percentage of landscape (PLAND), largest patch index (LPI), patch density (PD), edge density (ED), landscape shape index (LSI), mean shape index (SHAPE_MN), and area-weighted mean fractal dimension (FRAC_AM). At the landscape level, six metrics were chosen: largest patch index (LPI), contagion index (CONTAG), mean shape index (SHAPE_MN), area-weighted mean fractal dimension (FRAC_AM), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI).

3. Results

3.1. Land Cover Changes

3.1.1. Spatial Dynamics

Over the past three decades, significant spatial variations have been observed in the distribution of various land cover types, notably cultivated land, forests, wetlands, water bodies, and artificial surfaces.
Cultivated land in Myanmar is predominantly located in the central plains and delta regions (Figure 2). Despite a significant reduction in cultivated areas within the central regions of the Mandalay, Magway, and Ayeyarwady provinces, there has been notable spatial expansion into other regions, including northern Sagaing, western Magway and Bago provinces, and the eastern areas of Shan state (Figure 3a). Additionally, an increase in cultivated land has been observed in the southern coastal regions, which is associated with the low-intensity rice cultivation practices characteristic of these areas [42].
Forests are extensively distributed across the northern regions, including Kachin state, Shan state, Sagaing province, Bago province, and Chin state (Figure 2). A notable expansion in forest areas was observed in the northern parts of Kachin state, Shan state, and the northwestern of Naypyidaw, as depicted in Figure 3b.
Wetlands in Myanmar are predominantly distributed across Tanintharyi province, Rakhine state, and Ayeyarwady province. However, a marked decline in the expanse of these wetland areas has been documented, particularly along the coastal stretches of Rakhine state, Ayeyarwady province, and Tanintharyi province (Figure 3c). Conversely, significant wetland expansion has occurred in the northeastern part of Ayeyarwady province, which, due to its fertile soils and abundant precipitation, represents one of Myanmar’s major rice cultivation areas.
The extent of shrubland and grassland has shown considerable variability. Over the past 30 years, water bodies have experienced substantial expansion, particularly along the coastlines of Rakhine state, Ayeyarwady province, and southern Tanintharyi province (Figure 3d). This expansion is likely driven by increased coastal traffic from engine-powered maritime vessels and rising sea levels, which have contributed to coastal erosion [43]. Additionally, marked increases in water bodies have been recorded in the non-coastal areas of central Sagaing province, central Bago province, and northeastern Ayeyarwady province.
There was a significant spatial expansion of artificial surfaces between 1990 and 2020. Initially, these surfaces were primarily concentrated in Yangon, located in the southern part of the country. However, by 2020, they had expanded to the central regions, encompassing Mandalay and Naypyidaw (Figure 3e). Simultaneously, some regions have experienced a decline in artificial surfaces, primarily due to armed conflicts, which have significantly contributed to the reduction in residential areas [44].

3.1.2. Temporal Dynamics

The lowest intensity was observed during 1990–2000, indicating relative stability in land use patterns, while the period from 2000 to 2010 experienced the most significant transformations, likely driven by major socio-economic or policy shifts. In contrast, the intensity of changes during 2010–2020 was moderate.
From the perspective of land use dynamics (Table 2), the most significant change occurred in bare land between 1990 and 2000, with a dynamic degree of −7.88%. Shrubland experienced the second-largest change, decreasing by approximately 5744 km2. During this period, water bodies increased by about 1972.6 km2, resulting in a dynamic degree of 2.21%. Other land use types such as cultivated land, forest, grassland, and artificial surfaces remained relatively stable. Overall, the dynamic degree for the entire study area was 0.48%.
Between 2000 and 2010, the dynamic degree for the study area reached its peak at 0.82%. The most substantial increase was observed in shrubland, which expanded by approximately 4330.5 km2. Artificial surfaces also saw a significant rise, with an increase of about 1550.1 km2, reflecting a notable change compared to the previous period. Water bodies continued to expand, though at a reduced rate compared to the earlier decade. Bare land remained relatively stable during this period.
From 2010 to 2020, the area of artificial surfaces continued to increase, with a dynamic degree that surpassed that of the 2000–2010 period, totaling an increase of 2383.7 km2. In contrast, bare land exhibited the most dramatic change, with a dynamic degree of 11.61%, marking the highest variability among land cover types. Water bodies maintained their upward trend, with a dynamic degree of 2.41%. Forests and wetlands, however, remained relatively stable throughout this period.
Temporal variations in land use dynamics were identified across Myanmar’s administrative districts (Figure 4), with significant changes in land cover largely occurring in Myanmar’s eastern, southeastern, southwestern, and coastal regions, especially those bordering China, Laos, Thailand, and the coastline during 1990–2020, with an increasing trend observed in the eastern provinces adjacent to China and Laos.
Kayin state in the southeast has consistently experienced substantial changes. In the southwestern coastal regions, such as Rakhine state and Magway province, land use dynamics have exceeded 1.2% since the 2000–2010 period. Notably, during 1990–2000, Bago province, Karen state, and Kayin state exhibited particularly high land use dynamics, exceeding 1.2%. During 2000–2010, five provinces had dynamic degrees greater than 1.2%, including the Magway and Rakhine states, while Mon state and Bago province experienced a decrease in dynamic degrees, ranging between 1.0% and 1.2%. In the 2010–2020 period, significant changes continued to be observed in Magway province, Rakhine state, Kayin state, and Mon state.
An analysis of land use dynamics across various land cover types revealed that in 1990, the forest cover in Myanmar comprised 62.15% of the total land area. This proportion experienced an increase to 64.29% by the year 2000. Subsequently, it underwent a slight decline to 61.09% in 2010, before rising again to 62.80% by 2020 (Table 3).
Cultivated land has demonstrated a trend that contrasts with that of forest cover. From 1990 to 2000, cultivated land decreased by approximately 3985 km2 (Table 3). This trend reversed from 2000 to 2010, during which cultivated land increased by about 22,052 km2, but subsequently decreased again by approximately 20,490 km2 from 2010 to 2020. The area of artificial surfaces has consistently expanded, with its proportion rising from 0.48% in 1990 to 1.01% in 2020, representing a nearly twofold increase in area.
Wetlands have been subject to a continuous decline, with their extent reducing from 8299.26 km2 in 1990 to 6470.66 km2 in 2020 (Table 3), reflecting a long-term reduction trend. Water bodies have shown increases of approximately 1973 km2, 1152 km2, and 2904 km2 over the three respective periods. This increase is attributed to both the growth of aquaculture activities and climatic changes. Notably, alterations in rainfall patterns and an increase in the total annual precipitation have contributed to heightened flooding.

3.1.3. Transition Patterns

Myanmar has experienced significant land cover transitions, with notable changes occurring between different vegetation types, the conversion of cultivated land into built-up areas, and, in some regions, the transformation of forests into wetlands and water bodies. These transitions are illustrated in Figure 5, while the land use transition matrices in Table A1, Table A2 and Table A3 provide a detailed account of the area changes among various land cover types.
The analysis of land cover transitions in Myanmar highlights significant shifts among cultivated land, grasslands, shrublands, and forests. For instance, during the period from 1990 to 2000, while forests were predominantly converted to cultivated land, grassland, and shrubland, localized transitions from cultivated land, grasslands, and shrublands to forests were observed in Magway province, Mandalay province, and Shan state (Figure 6a).
Compared to the period from 1990 to 2000, the years between 2000 and 2010 were characterized primarily by the conversion of forests into other vegetation types, particularly the transformation of forests into cultivated land (Figure 6b). As illustrated in Figure 6b, the conversion of forests to cultivated land primarily occurred in the central plains of Myanmar, while the transition of forests to grasslands and shrubs was more pronounced in Shan state.
From 2010 to 2020, like the 1990–2000 period, significant transitions were observed from cultivated land, shrublands, and grasslands to forests too. These transitions occurred in areas such as the border between Magway province and Chin state; the junction of Magway province, Mandalay province, and Bago province; and the boundary between Magway province and Rakhine state, with additional occurrences in central Shan state (see Figure 6c).
Additionally, the expansion of artificial surfaces primarily originates from cultivated land, with a minor contribution from deforested areas (Figure 7a). Urban expansion and development have consumed significant cultivated land to meet infrastructure demands such as roads, hydropower stations, and dams, often necessitating further deforestation [8,45]. There is also evidence of forests transitioning to wetlands and water bodies, especially in the southern coastal and central provinces of Myanmar (Figure 7b). This phenomenon is largely driven by mangrove deforestation. Myanmar ranks seventh globally in mangrove area [46], and mangrove deforestation typically results in a conversion to water bodies or wetlands due to activities such as rice paddy expansion and aquaculture (e.g., shrimp farms and fish ponds) [10,47,48]. Moreover, the construction of hydropower dams also contributes to the conversion of forest resources into water bodies [49].

3.2. Landscape Pattern Changes

Using the computed results of landscape pattern indices, this study examines the evolution of landscape patterns in Myanmar between 1990 and 2020 from two perspectives: the class level (focusing on land cover types) and the landscape level (focusing on the overall landscape characteristics of the study area).

3.2.1. Class Level

Between 1990 and 2000, the shape characteristics of various landscape patches remained relatively stable, showing no significant changes. However, after 2000, there was a marked shift, with patches becoming increasingly complex and irregular in shape. This transformation is reflected in the increasing values of edge density, landscape shape index, and mean shape index. Additionally, the patches have become more fragmented and dispersed, with previously large, continuous patches subdivided into smaller, isolated units. This fragmentation is especially noticeable in Myanmar’s major landscape types, such as cultivated land and forests. Despite a reduction in the total area of these patches, both their number and density have increased. Table 4 presents the changes in landscape pattern indices for different land cover types in Myanmar from 1990 to 2020.
Cultivated land, as a secondary dominant patch type, has undergone substantial changes over time. By 2020, the number of cultivated lands (NP) had increased by 18,972 compared to 1990, accompanied by a notable rise in PD. However, this growth was offset by a reduction in the percentage of cultivated land area (PLAND) by approximately 0.36%, and a decline in LPI by 1.03%. This indicates a transition from large, contiguous patches of cultivated land to numerous smaller, fragmented units. The fragmentation is primarily due to the subdivision of cultivated areas into smaller plots, with roads, rivers, and other linear features acting as fragmentation factors, which have intensified the spatial discontinuity of cultivated land.
Despite a spatial expansion of cultivated land, the overall area of cultivated land has decreased. This suggests that the existing cultivated patches have become smaller while new areas are being created through the conversion of forests and grasslands. The observed increase in the number and density of patches highlights a trend toward fragmentation and dispersion within the cultivated land landscape. This fragmentation is further evidenced by the continuous rise in ED and increases in both SHAPE_MN and FRAC_AM. These metrics indicate that cultivated land patches are becoming increasingly irregular and complex in shape.
Over nearly three decades, the shape of cultivated land patches has increasingly deviated from regular rectangular forms, becoming more irregular. This trend may indicate underlying issues or shortcomings in Myanmar’s land use planning and distribution strategies. To address these challenges, future efforts should prioritize scientific planning and rational layout in the protection and utilization of cultivated land. Such measures are essential for supporting sustainable agricultural development and improving land use efficiency.
Forests, as the predominant patch type in Myanmar’s landscape, exhibit a significantly larger contiguous area compared to other land types, as indicated by a notably higher LPI. Despite this, the number of forest NP is substantially lower than that of cultivated land. The evolution of forest landscape patterns over time can be divided into two distinct phases. In the first phase (1990–2000), there was a substantial reduction in the number of forest patches, alongside an increase in the LPI and the PLAND, signifying a trend towards larger and more contiguous forest patches. During this period, the LSI, SHAPE_MN, and FRAC_AM decreased, reflecting a consolidation of forest areas into more continuous patches. In contrast, the second phase (2000–2020) saw a reversal of these trends. The number of forest patches increased while the total forest area decreased. This period was characterized by rises in the LSI, SHAPE_MN, and FRAC_AM, indicating a shift towards greater fragmentation and reduction in forest areas, with especially marked fragmentation observed between 2010 and 2020.
Grasslands exhibit a higher NP and PD compared to the other land cover types, though their LPI is significantly lower than that of forests and cultivated land. With a PLAND of approximately 5%, grasslands are relatively sparse, featuring small continuous areas. Over the period from 1990 to 2020, both the PLAND and the number of grassland patches have decreased. ED for grasslands initially declined from 1990 to 2010 but saw an increase in 2020, reflecting changes in grassland areas. The reduction in grassland areas led to a decrease in boundary length; however, despite a decrease in PLAND and PD from 1990 to 2020, the increase in ED indicates a growing complexity of grassland edges. This suggests that although the total area of grasslands has diminished, the fragmentation of these areas has increased, resulting in more pronounced edge effects.
From 1990 to 2020, the LPI of artificial surfaces increased by approximately 0.03%, reflecting ongoing urban expansion and consolidation. This growth indicates that urban areas have been expanding and becoming more connected, particularly with the development of new roads that enhance connectivity between the existing roads. During this period, SHAPE_MN for artificial surfaces was notably low, which is typical as urban features such as buildings and roads are often planned and designed to have more regular shapes compared to natural land cover types. However, despite these regular shapes, LSI, SHAPE_MN, and FRAC_AM have all increased, signaling that the shapes of artificial patches have become more complex. This complexity arises from the expansion of urban areas beyond their original, more regularly shaped road boundaries, leading to increasingly irregular patch shapes.
Shrubland patches, characterized by an LPI of less than 0.5%, are notably small and have experienced minimal changes in PD and ED over the study period. Conversely, water bodies have shown a significant increase in the number of patches, landscape percentage, patch density, and LPI, with the latter rising by approximately 0.2%. The SHAPE_MN for water bodies has also increased substantially, reflecting the more dispersed and irregular shapes that are typical of water networks. Wetlands consistently exhibit a SHAPE_MN above 1.5 across all years, with human activities such as the construction of artificial lakes and dams leading to alterations in their shapes and boundaries. Bareland, similarly, maintains an LPI of less than 0.5%, with minimal trend changes observed. In contrast, glaciers and permanent snow, primarily located in the high-altitude regions of Kachin state, have shown minimal change due to limited human impact.

3.2.2. Landscape Level

In terms of landscape shape, as illustrated in Figure 8, SHAPE_MN and FRAC_AM exhibited relative stability from 1990 to 2010, showing minimal variation. However, between 2000 and 2020, both metrics increased significantly to 1.7399 and 1.3268, respectively. This increase signifies a shift towards more irregular patch shapes and greater self-similarity within the landscape. This transformation is largely attributable to the expansion of infrastructure, including roads, bridges, and communication networks, which has contributed to the fragmentation of landscape patches. For example, the Myawaddy-Sinphyukyun Road in Kayin state has divided several protected forest areas, illustrating the impact of infrastructure development on landscape fragmentation [50].
Regarding landscape connectivity, the CONTAG index showed a notable increase from 1990 to 2000, indicating enhanced landscape connectivity, particularly in terms of the continuity of forest patches. This improvement is closely linked to the expansion of forested areas. Given that forests are the predominant landscape type, the rise in the largest patch index (LPI) underscores the extensive and interconnected nature of forest cover, further supported by the conversion of grasslands, shrublands, and cultivated areas into forested land. However, from 2000 to 2020, the CONTAG index exhibited a gradual decline (Figure 8), which may reflect emerging challenges to landscape connectivity due to development pressures or increased ecological fragmentation.
With respect to landscape diversity, the overall trend exhibited an initial decline followed by a gradual increase. The SHDI experienced a significant decrease from 1990 to 2000, largely due to forest expansion and the substantial reduction in shrublands, grasslands, and barren lands. This shift led to greater disparity among the landscape types and a decrease in the overall diversity. Between 2000 and 2020, SHDI increased slowly, indicating a reduction in the dominance of the major landscape types. Fluctuations in the LPI reflect changes in the prominence of the key landscape types in Myanmar, with a notable rise in the first decade of the study followed by a decrease in the subsequent two decades. These changes highlight the evolving nature of the essential landscape types, including agricultural land, forests, and grasslands. Additionally, water bodies, a critical landscape component in Myanmar, exhibited a continuous increase in both patch density and edge density over the 30-year period. The variations in landscape diversity and evenness underscore the instability of Myanmar’s aquatic ecosystems.

4. Discussion

4.1. Integrating Land Cover Changes and Landscape Pattern Dynamics

During 1990–2020, urbanization was a vital landscape transformation process. The spatial expansion of artificial surfaces in Myanmar has been characterized by a shift from the southern regions to the central areas, a finding that aligns with the research of Wang [8]. This expansion is consistent with the spatial–temporal evolution of economic centers, reflecting the broader trends in urbanization and development across the country. Yangon, the former capital, continues to serve as the primary economic center of the country, characterized by a high concentration of industries and rapid port trade development [51]. Mandalay is also a region marked by significant economic growth [52]. The relocation of the capital to Naypyidaw has shifted the nation’s political center to central Myanmar, positioning Naypyidaw as a new focal point for economic development, where population and industry have rapidly concentrated [52].
In Myanmar, cultivated land and forests are the two dominant land cover types, both of which experienced significant fluctuations over the past three decades, with notable transitions occurring in each decade (Figure 5). This transformation pattern has also been demonstrated by the research conducted by Phyu Thaw Tun [43], which supports the observed trends.
Contrary to conventional research conclusions, the forest changes in Myanmar have not been characterized by a continuous decline. Our research shows that from 1990 to 2000, the area of forests in Myanmar increased. Nevertheless, the forest areas declined between 2000 and 2010, a period marked by significant reports of inadequate forest management, overexploitation, and deforestation [53,54]. Moreover, the decrease in forest areas by 2020 is relative to the 2000 levels. This trend is the same as the research result of Wang [17]. However, in contrast to 1990, there was a rising trend, which can be attributed to the inclusion of both natural and planted forests in the MLC30 data. The new government’s promotion of hardwood exports and repurposing of previously fallow lands for rubber cultivation resulted in these areas being recorded as forests in remote sensing imagery [55,56].
While changes in land cover can be described using metrics such as area fluctuations and land use transition matrices, these metrics alone fail to capture the spatial structural changes underlying land use dynamics. Landscape pattern analysis provides a complementary perspective. Metrics such as PD, LSI, SHAPE_MN, and FRAC_AM reveal key trends in cultivated land, including increased fragmentation, dispersion, and morphological complexity (Table 4). Consequently, Myanmar must prioritize scientific land use planning and the rational allocation of land resources to foster the sustainable development of agriculture and the protection of natural ecosystems.
Despite forests having a much larger total area compared to cultivated land, the number of forest patches is significantly lower (Table 4), indicating a higher degree of continuity in forest cover. Furthermore, the similarity in SHAPE_MN and FRAC_AM values between forests and cultivated land suggests that despite differences in area and patch number, the shapes of their patches share certain characteristics.
Landscape patterns are crucial determinants of the sustainability of many ecological processes, which underpin essential ecosystem goods and services. Thus, changes in landscape patterns often signal significant shifts in ecological functions [22]. For example, the connectivity of forest landscapes directly affects species migration and habitat integrity, while the size and shape of cultivated landscapes influence nutrient cycling and energy flows within agricultural systems. If land cover trend monitoring is to be established as a long-term and continuous activity, incorporating landscape pattern analyses is essential for monitoring and assessing ecosystem health, as landscape patterns are widely recognized as integrative indicators of ecosystem conditions [22].

4.2. Governance and Policy-Driven Changes in Land Cover and Landscape Transformation

4.2.1. Regime Reforms

In recent years, Myanmar has shifted from being an economically isolated military dictatorship to a more democratic nation increasingly receptive to foreign investment [57]. This transformation has led to a surge in international partnerships and resource development initiatives [3,58,59,60], particularly in sectors such as transportation infrastructure, the development of the China–Myanmar oil and gas pipelines [15], large-scale hydropower projects, mineral resource extraction, and agroforestry activities, including alternative cropping practices [12]. Furthermore, changes in land cover driven by the construction of international corridors in China–Myanmar cooperation zones have provided potential pathways for invasive species [61], threatening native species and diminishing their habitats in China. Driven by the expansion of the existing villages, the establishment of new towns, and the intensive construction of infrastructure—including roads, bridges, buildings, and water facilities—urban development has significantly contributed to the increase in the artificial land surface [8]. This process of urbanization leads to a noticeable growth in human-modified landscapes.
The broad expansion of these economic endeavors has caused substantial alterations in land cover, presenting significant challenges to Myanmar’s ecological systems [8] and indirectly impacting the environmental conditions along China’s southwestern frontier. For example, parts of the China–Myanmar border region are experiencing the degradation of cross-border forest ecosystems, with forested areas facing the most extensive reduction among all the land types [61]. This includes the conversion of primary and secondary forests into extensive monocultures, exacerbating habitat fragmentation and biodiversity loss [12], thereby affecting the migration and survival of species across the border into southwestern China [62].

4.2.2. Forestry Policy

The Protection of Wild Animals and Wild Plants and the Protection of Natural Areas Act, enacted in 1994, facilitated the establishment of numerous national parks and wildlife reserves, contributing to the preservation of forest resources [63]. Additionally, the implementation of the Myanmar Forest Policy in 1995 aimed to balance conservation with development by promoting community-based forestry and local involvement in forest management to protect environmental and biodiversity values [23,64]. The establishment of these policies contributed to the increase in forest areas between 1990 and 2000.
Nevertheless, forest area declined between 2000 and 2010, a period marked by significant reports of inadequate forest management, overexploitation, and deforestation [53,54]. During the military dictatorship (1962–2011), Myanmar’s political and economic isolation contributed to the preservation of much of its primary forest by 2010 [65,66]. Since the political reforms initiated in 2010, Myanmar’s forestry sector has gradually introduced a series of forward-looking reforms. In 2017, the Ministry of Forestry launched a significant afforestation and restoration program, accompanied by the continuous expansion of community forestry management areas across the country [55]. However, the implementation of these policies has been largely confined to regions under the control of the military government. In ethnic minority-dominated states, the remaining natural forests remain inadequately managed, facing severe threats from large-scale deforestation and conversion to other forest types [67].
The reduction in Myanmar’s natural forests is closely linked to the government’s allocation of private agricultural concessions. A significant portion of these concession areas overlaps with designated forest reserves, while a series of newly introduced land and investment laws continue to facilitate the conversion of forests into private agricultural land. Private enterprises and foreign investors often exploit these concessions to clear high-conservation-value primary forests, subsequently replacing them in some areas with economically valuable tree species [10]. For instance, between 2010 and 2013, the Myanmar government granted numerous large-scale private agricultural concessions in the southeastern Tanintharyi region, primarily for oil palm and rubber plantations, and in northern Kachin state, focusing on crops such as rubber, sugarcane, and cassava. These regions not only represent some of Myanmar’s most remote areas, characterized by the highest carbon stocks and rich biodiversity within the remaining forests, but are also ethnic minority-dominated areas historically plagued by prolonged civil conflict and targeted violence [68].

4.2.3. Alternative Development

Myanmar remains the world’s second largest producer of illicit opium [69,70], with the majority of cultivation concentrated in northern Shan state [71,72,73]. To combat opium production, the Myanmar government introduced policies and alternative development programs aimed at replacing poppy cultivation with economically viable crops. These efforts have brought about significant changes in land cover [72], particularly in agricultural and forested regions.
Collaborative initiatives involving China, Laos, and Myanmar have promoted the cultivation of crops such as rubber tree (Hevea brasiliensis), tea, and rice, along with the development of livestock farming, as substitutes for opium poppies [72,73,74]. Rubber cultivation, in particular, was introduced to Shan state—especially its eastern and northern regions—during the poppy substitution efforts of the 1990s [74]. These initiatives have led to notable changes in the China–Laos–Myanmar border region, characterized by a continuous decline in natural forest cover and a rapid expansion of rubber plantations [72]. The northeastern areas of Myanmar, particularly near the Chinese border, have experienced the most significant growth in rubber cultivation [75].
This expansion of rubber plantations has extended beyond Myanmar’s traditional rubber-growing regions, such as Mon state and Tanintharyi province in the south, to include northern Kachin state and northeastern Shan state [74]. These changes have led to extensive transitions between vegetation types, as detailed in Section 3.1.3, particularly in the region of Southeast Asia’s Golden Triangle. These transitions include a significant reduction in natural forest cover and the expansion of rubber agroforestry systems, which have, in turn, contributed to localized increases in forested areas.

4.3. Limitations

Although this study aims to reveal the dynamic changes in land cover types and landscape patterns over an extended period in Myanmar, it still faces certain limitations. Specifically, we conducted a phase-based characterization of land cover using data from four independent years, each separated by a decade. While this time span is sufficient to capture the overall trends of long-term, sustained changes—such as the far-reaching impacts of national policies and broader developmental trajectories—it is inadequate for depicting the more detailed dynamics of changes within each specific time interval. The impacts of long-term human activities, once established, tend to be persistent, with their changing trajectories becoming apparent only over extended time scales (depicted in Section 4.2). However, at finer temporal resolutions—such as within the decade intervals used in this study—the specific patterns of land cover and landscape evolution remain unclear, which represents a significant limitation of this research.
As one of the most climate-vulnerable countries in the world [76,77], Myanmar frequently experiences rapid, significant natural disasters, such as cyclones [78], floods [79], landslides, and earthquakes. Such events are often characterized by distinct seasonal and cyclical patterns, capable of causing significant changes in land cover within a short period [10,80,81], followed by rapid recovery. However, due to the time span defined in this study, these rapid changes and their recovery processes are not adequately captured in the results. This pattern has also been extensively documented in land cover change studies in other countries [36,82,83]. Consequently, addressing this limitation will be essential in future research.
Given the current insufficient understanding of the relationship between land use and land cover change intensity and the evolution of landscape patterns, particularly the lack of in-depth exploration into the specific mechanisms through which human activities influence this process, it is recommended that future research focus on elucidating the interactions between the intensity of land use and land cover change and the evolution of landscape patterns. Additionally, considering the profound impact of human activities on land cover change, it is also crucial to further investigate the relationship between the intensity of human activities, the intensity of LULC change, and the dynamics of landscape patterns.

5. Conclusions

This study investigates the spatial–temporal dynamics of land cover in Myanmar from 1990 to 2020 utilizing the MLC30 land cover product. The analysis encompasses three key aspects: the extent of land use changes, the transition processes, and the alterations in spatial structure. The findings reveal substantial land cover changes over the past thirty years, with significant alterations occurring primarily in the eastern, southeastern, and southwestern regions, particularly near the borders with China, Laos, and Thailand, as well as in the coastal areas. The magnitude of these changes from 2000 to 2020 exceeds that observed before 2000.
In terms of land cover transitions, significant expansions have been observed in cultivated land, artificial surfaces, and water bodies. The increase in cultivated land is primarily attributed to the conversion of forested areas, driven largely by the growth of commercial agriculture rather than traditional subsistence farming. Artificial surfaces have expanded notably, particularly southward from Yangon to Mandalay and Naypyidaw, facilitated by the conversion of cultivated land. Despite their relatively small proportion, the doubling of artificial surfaces over the past thirty years highlights Myanmar’s rapid urbanization. The area covered by water bodies has also significantly increased, reflecting the government’s emphasis on aquaculture and fisheries development. Contrary to the common belief that forest cover has continuously declined, the forest area in Myanmar increased from 1990 to 2000 before beginning to decrease post-2000. This increase is attributed to Myanmar’s previous political and economic isolation, which limited large-scale industrial and agricultural activities and helped preserve much of the original forest until 2000. However, the surge in investment and development in the 21st century resulted in a notable reduction in primary forests and an increase in plantation forests.
In terms of landscape patterns, Myanmar’s landscape patterns have evolved from simple, homogeneous, and continuous patches to more complex and heterogeneous mosaics, particularly from 2000 to 2020. During the 1990–2000 period, landscape connectivity improved; however, since 2000, major patch types, such as forests, have become increasingly fragmented into smaller, isolated units, leading to greater landscape fragmentation. This fragmentation poses threats to ecosystem integrity and stability, affects species survival and reproduction, and heightens the risk of environmental degradation. Additionally, the shapes of various patch types have become more irregular and complex since 2000, especially agricultural patches, which now exhibit more irregular boundaries. Although agricultural patches have expanded outward, their overall area has decreased, and their spatial distribution has become more fragmented. This trend undermines the efficiency of land use and agricultural modernization, contrasting with the goals of land consolidation emphasized in land management policies.
In the face of the study’s revelations, it is crucial for the government of Myanmar to adopt proactive strategies to address the challenges of landscape fragmentation. To safeguard the integrity and stability of ecosystems, policymakers must take decisive preventive and remedial measures, including enhancing environmental monitoring and enforcing ecological protection policies, such as creating corridors and preserving key ecosystems to boost ecological connectivity and stability. Furthermore, to counter the fragmentation observed in the spatial distribution of agricultural landscapes, there is a need for a strategic realignment of land management policies to ensure the efficient and rational use of land resources. By doing so, Myanmar can mitigate the adverse effects of landscape fragmentation on ecosystem services and agricultural productivity, thereby promoting sustainable land use that aligns with the nation’s environmental and developmental objectives.

Author Contributions

Conceptualization, R.L. and C.L.; formal analysis, H.X. and A.-X.Z.; methodology, R.L. and D.H.; supervision, H.X.; writing—original draft, R.L.; writing—review and editing, C.L., D.H. and A.-X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Yunnan Provincial Young and Middle-aged Academic and Technical Leaders Reserve Talent Project (Grant No. 202105AC160059); Yunnan Provincial Philosophy and Social Science Innovation Team Construction Project (No. 2023CX02); Yunnan Key Laboratory of International Rivers and Transboundary Eco-Security Program; Yunnan Provincial Department of Education Science Research Fund Project (No. 2024Y170); the Philosophy and Social Sciences Planning Project of Yunnan Province (No. YB202444).

Data Availability Statement

The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy restrictions.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Area (km2) transition matrix of land cover of land use classes between 1990 and 2000 in Myanmar.
Table A1. Area (km2) transition matrix of land cover of land use classes between 1990 and 2000 in Myanmar.
2000
Cultivated
Land		ForestGrasslandShrublandWetlandWater BodiesArtificial SurfacesBarelandPermanent Snow and Ice
1990Cultivated Land152,6879063124421395719191315251724
Forest8994391,7059957240311004552764910
Grassland831816,45712930395146634342809
Shrubland1653704196568764500
Wetland12331318466645027441882
Water Bodies11942143402115662791125124
Artificial Surfaces177628920916466380692
Bareland2003082275686128122436341
Permanent Snow and Ice9373440321354796
Table A2. Area (km2) transition matrix of land cover of land use classes between 2000 and 2010 in Myanmar.
Table A2. Area (km2) transition matrix of land cover of land use classes between 2000 and 2010 in Myanmar.
2010
Cultivated
Land		ForestGrasslandShrublandWetlandWater BodiesArtificial SurfacesBarelandPermanent Snow and Ice
2000Cultivated Land153,0976168890018271022225327077613
Forest23,219387,48610,55252691098758514190120
Grassland14,49310,17910,47011813133631916426
Shrubland115820877497683723041411
Wetland1134136834335915371500
Water Bodies1841240155270271273710415
Artificial Surfaces1601255158158419805415
Bareland191571902481180420
Permanent Snow and Ice753290140255957
Table A3. Area (km2) transition matrix of land cover of land use classes between 2010 and 2020 in Myanmar.
Table A3. Area (km2) transition matrix of land cover of land use classes between 2010 and 2020 in Myanmar.
2020
Cultivated
Land		ForestGrasslandShrublandWetlandWater BodiesArtificial SurfacesBarelandPermanent Snow and Ice
2010Cultivated Land153,11721,4131184318169723143383241419
Forest10,634381,6489879167689510389231109191
Grassland7466886012,196193550254155168153
Shrubland143653841277908474832
Wetland931123426133998811183
Water Bodies1528372220842842572249
Artificial Surfaces1959453106277617532201
Bareland2368771011273218296
Permanent Snow and Ice65635409700951220

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Figure 1. Location of Myanmar.
Figure 1. Location of Myanmar.
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Figure 2. Land cover distribution of Myanmar: (a) 1990; (b) 2000; (c) 2010; (d) 2020.
Figure 2. Land cover distribution of Myanmar: (a) 1990; (b) 2000; (c) 2010; (d) 2020.
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Figure 3. Spatial distribution changes in core land cover types in Myanmar, spanning from 1990 to 2020: (a) cultivated land, (b) forests, (c) wetlands, (d) water bodies, and (e) artificial surfaces.
Figure 3. Spatial distribution changes in core land cover types in Myanmar, spanning from 1990 to 2020: (a) cultivated land, (b) forests, (c) wetlands, (d) water bodies, and (e) artificial surfaces.
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Figure 4. Distribution map of comprehensive land use dynamic degree in Myanmar’s states and provinces: (a) 1990–2000; (b) 2000–2010; (c) 2010–2020.
Figure 4. Distribution map of comprehensive land use dynamic degree in Myanmar’s states and provinces: (a) 1990–2000; (b) 2000–2010; (c) 2010–2020.
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Figure 5. Transition of land cover types across Myanmar from 1990 to 2020.
Figure 5. Transition of land cover types across Myanmar from 1990 to 2020.
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Figure 6. Spatial distribution map of land cover type transitions in Myanmar: (a) 1990–2000; (b) 2000–2010; (c) 2010–2020.
Figure 6. Spatial distribution map of land cover type transitions in Myanmar: (a) 1990–2000; (b) 2000–2010; (c) 2010–2020.
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Figure 7. Spatial distribution map of key land cover type transitions in Myanmar: (a) cultivated land/forest to artificial surface; (b) forest to wetland/water bodies.
Figure 7. Spatial distribution map of key land cover type transitions in Myanmar: (a) cultivated land/forest to artificial surface; (b) forest to wetland/water bodies.
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Figure 8. Statistics on changes in landscape pattern metrics at the landscape level from 1990 to 2020.
Figure 8. Statistics on changes in landscape pattern metrics at the landscape level from 1990 to 2020.
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Table 1. Landscape pattern metrics used in the study [41].
Table 1. Landscape pattern metrics used in the study [41].
MetricFormulaDescriptionApplication Level
NP N P i = n i NP ≥ 1Class Level
PLAND P L A N D i = 1 A ∑ j n a i , j 0 < PLAND ≤ 100Class Level
LPI L P I i = 1 A max ⁡ a i , j j = 1 n i 0 < LPI ≤ 100Class Level
Landscape Level
PD P D i = n i A PD > 0Class Level
ED E D i = 1 A ∑ k = 1 m e i , k ED ≥ 0Class Level
LSI L S I i = 0.25 ∑ k = 1 m e i , k A LSI ≥ 1Class Level
CONTAG C O N T A G = 1 + ∑ i = 1 m ∑ k = 1 m [ P i g i , k ∑ k = 1 m g i , k ] [ l n ( P i g i , k ∑ k = 1 m g i , k ) ] 2 ln ⁡ m 0 < CONTAG ≤ 100Landscape Level
SHAPE_MN S H A P E _ M N = ∑ i = 1 m ∑ j = 1 n ( 0.25 p i j a i j ) N SHAPE_MN ≥ 1Class Level
Landscape Level
FRAC_AM F R A C _ A M = ∑ i = 1 m   ∑ j = 1 n   2 l n ⁡ ( 0.25 p i j ) ln ⁡ a i j a i j ∑ i = 1 m   ∑ j = 1 n   a i j 1 ≤ FRAC_AM ≤ 2Class Level
Landscape Level
SHDI S H D I = − ∑ i = 1 m ( P i l n P i ) SHDI ≥ 0Landscape Level
SHEI S H E I = S H D I ln ⁡ m 0 ≤ SHEI ≤ 1Landscape Level
Table 2. Statistics on single- and comprehensive land use dynamic degree in Myanmar during 1990–2020 (km2, %).
Table 2. Statistics on single- and comprehensive land use dynamic degree in Myanmar during 1990–2020 (km2, %).
Year1990–20002000–20102010–2020
Land Cover TypeArea Change
(km2)		LUDD
(%)		Area Change
(km2)		LUDD
(%)		Area Change
(km2)		LUDD
(%)
Cultivated Land3984.9−0.2122,052.41.1720,489.8−0.97
Forest15,392.90.3423,007.6−0.5012,303.30.28
Grassland2139.1−0.516242.7−1.574495.71.34
Shrubland5744.6−5.124330.57.902996.6−3.05
Wetland1333.8−1.61193.6−0.28301.2−0.44
Water Bodies1972.62.211152.01.062903.62.41
Artificial Surfaces91.2−0.261550.14.622383.74.86
Bareland4125.8−7.8852.90.481347.711.61
Permanent Snow and Ice53.90.38306.02.07353.71.98
Entire Study Area 0.48 0.82 0.66
Table 3. Area changes in various land cover types in Myanmar from 1990 to 2020.
Table 3. Area changes in various land cover types in Myanmar from 1990 to 2020.
Year1990200020102020
Land Cover TypeArea
(km2)		Percentage
(%)		Area
(km2)		Percentage
(%)		Area
(km2)		Percentage
(%)		Area
(km2)		Percentage
(%)
Cultivated Land192,300.0526.68188,315.1726.13210,367.5929.19189,877.7526.35
Forest447,917.1762.15463,310.0464.29440,302.4061.09452,605.7462.80
Grassland41,934.685.8239,795.575.5233,552.914.6638,048.605.28
Shrubland11,224.540.005479.960.769810.461.366813.830.95
Wetland8299.261.156965.440.976771.840.946470.660.90
Water Bodies8912.961.2410,885.541.5112,037.561.6714,941.172.07
Artificial Surfaces3449.200.483357.960.474908.020.687291.721.01
Bareland5233.650.731107.890.151160.780.162508.430.35
Permanent Snow and Ice1427.280.201481.220.211787.230.252140.890.30
Total720,698.79100.00720,698.79100.00720,698.79100.00720,698.79100.00
Table 4. Landscape index statistics in patch type level for Myanmar from 1990 to 2010.
Table 4. Landscape index statistics in patch type level for Myanmar from 1990 to 2010.
TypeYearNPLPI/%PD
(Units·hm−2)		ED
(m·hm−2)		PLAND
%		LSISHAPE_MNFRAC_AM
Cultivated Land199058,0428.8806 8.6716 5.5306 26.8400 221.5062 1.4338 1.2634
200054,0188.5100 8.0718 5.4396 26.2844 220.1409 1.4274 1.2659
201086,7429.3109 12.9647 7.6334 29.3456 291.4652 1.3969 1.2880
202077,0147.8482 11.9536 8.8962 26.4796 356.4336 1.6780 1.2954
Forest199040,59546.5452 6.0741 6.7566 61.9474178.3909 1.42261.342
200038,39652.3745 5.7452 6.4927 64.0756168.7698 1.4061.3405
201042,69145.9881 6.3828 6.8103 60.9087181.4162 1.41631.338
202039,20750.5942 6.3195 8.8081 62.6287230.6817 1.7541.3626
Grassland1990131,1990.4163 19.6113 5.3038 5.8685 448.9625 1.4049 1.1685
2000127,9920.3314 19.1255 5.2346 5.5532 455.2823 1.3998 1.1483
201082,2920.1604 12.2839 3.8298 4.6631 363.4586 1.4058 1.1638
202098,4670.1244 15.2718 6.1310 5.2958 545.6819 1.7549 1.1906
Shrubland199031,8550.0659 4.7547 1.3369 1.5470 220.1145 1.3991 1.1413
200034,6210.0037 5.1738 1.0709 0.7549 252.9306 1.4041 1.0955
201036,9820.0571 5.5307 1.4743 1.3535 259.3883 1.4171 1.1391
202025,5150.0138 4.0048 1.4145 0.9462 297.5475 1.8131 1.1699
Wetland199014,0250.0767 2.0974 0.7066 1.1848 143.5153 1.5237 1.1652
200012,4000.0412 1.8560 0.6538 0.9975 143.7521 1.5318 1.1550
201014,1560.0505 2.1162 0.6995 0.9695 155.0778 1.5040 1.1501
202091810.0387 1.4384 0.7496 0.9223 167.6260 1.9566 1.1841
Water
Bodies		199098420.4100 1.4738 0.7379 1.2485 151.8918 1.6989 1.2221
200012,7760.4870 1.9088 0.9938 1.5262 181.5694 1.7230 1.2252
201014,4050.4550 2.1539 1.1002 1.6861 189.4379 1.6827 1.2201
202016,8360.6320 2.5755 1.6674 2.0931 252.4358 1.9386 1.2511
Artificial surfaces199023,8120.0121 3.5573 0.6698 0.4803 198.4059 1.3448 1.0942
200021,2870.0233 3.1797 0.5938 0.4666 178.7473 1.3290 1.0952
201027,3530.0248 4.0826 0.8372 0.6832 207.8623 1.3371 1.1117
202032,4400.0473 4.9770 1.3707 1.0148 279.7020 1.6115 1.1453
Bareland199055470.1068 0.8281 0.3391 0.6955 85.2595 1.4409 1.1811
200020900.0066 0.3113 0.1221 0.1463 66.5412 1.5129 1.1526
201029480.0323 0.4389 0.1298 0.1549 69.8631 1.4483 1.1534
202062330.0235 0.9653 0.3520 0.3371 125.7311 1.6702 1.1766
Permanent Snow and Ice19906650.03920.09970.08070.188039.98901.53591.2350
20005860.10820.08660.06630.195233.32931.49531.2311
20106440.16600.09640.07370.235332.87801.46121.2669
20208900.1583 0.1515 0.1382 0.2825 55.2690 1.8307 1.2846
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Li, R.; Li, C.; Hou, D.; Xing, H.; Zhu, A.-X. Dynamics in Land Cover and Landscape Patterns of Myanmar: A Three-Decade Perspective (1990–2020). Land 2024, 13, 2212. https://doi.org/10.3390/land13122212

AMA Style

Li R, Li C, Hou D, Xing H, Zhu A-X. Dynamics in Land Cover and Landscape Patterns of Myanmar: A Three-Decade Perspective (1990–2020). Land. 2024; 13(12):2212. https://doi.org/10.3390/land13122212

Chicago/Turabian Style

Li, Ruonan, Cansong Li, Dongyang Hou, Huaqiao Xing, and A-Xing Zhu. 2024. "Dynamics in Land Cover and Landscape Patterns of Myanmar: A Three-Decade Perspective (1990–2020)" Land 13, no. 12: 2212. https://doi.org/10.3390/land13122212

APA Style

Li, R., Li, C., Hou, D., Xing, H., & Zhu, A. -X. (2024). Dynamics in Land Cover and Landscape Patterns of Myanmar: A Three-Decade Perspective (1990–2020). Land, 13(12), 2212. https://doi.org/10.3390/land13122212

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