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Article

Time-Series Satellite-Based Monitoring of Land-Use Change and Forest Loss in Bhutan: Implications for Forest Carbon Measurement, Reporting, and Verification

by
Mina Hong
1,
Hangnan Yu
2,
Yongho Song
3,
Minkyung Song
1,
Kyoungmin Kim
1 and
Woo-Kyun Lee
4,*
1
Forest Carbon Center on Climate Change, National Institute of Forest Science, Seoul 02841, Republic of Korea
2
College of Geography and Ocean Sciences, Yanbian University, Yanji 133000, China
3
BK21 FOUR R&E Center for Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
4
Department of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Republic of Korea
*
Author to whom correspondence should be addressed.
Land 2026, 15(3), 432; https://doi.org/10.3390/land15030432
Submission received: 23 January 2026 / Revised: 3 March 2026 / Accepted: 4 March 2026 / Published: 7 March 2026
(This article belongs to the Special Issue Monitoring Forest Dynamics Using Remote Sensing and Spatial Data)
Browse Figures
Versions Notes

Abstract

Human-driven land-use change has significantly altered forest ecosystems and carbon dynamics in mountainous regions. This study aims to quantify land cover transitions and associated forest carbon stocks changes in Bhutan. It also seeks to support the development of a national measurement, reporting, and verification system. Using Landsat-based satellite imagery and object-based image classification techniques, we assessed forest cover transitions, stand structure variations, and forest type changes across temporal intervals. The analysis revealed a consistent increase in agricultural and built-up areas. It also showed a concomitant decline in coniferous forest cover. In particular, agricultural land increased by approximately 0.77 million ha, while coniferous forest decreased by approximately 0.19 million ha over the study period. These changes were driven by both climatic shifts and socio-economic factors. Approximately 57% of Bhutan’s population depends on agriculture. Correspondingly, forest carbon stocks declined from approximately 570 million tC in 1995 to 405 million tC in 2017. This decline was largely attributed to coniferous forest loss and climate-induced mortality. Bhutan has made significant preparations for the implementation of the Warsaw REDD+ framework under the United Nations Framework Convention on Climate Change. These preparations include the establishment of a forest reference emission level for submission. However, challenges remain in detecting small-scale land use changes. Additional challenges include addressing spectral misclassification in mountainous regions. Our study provides a scientific baseline to support national forest monitoring and carbon accounting systems. It also offers policy-relevant insights for achieving Bhutan’s nationally determined contributions and enhancing its carbon sink potential.

1. Introduction

Over the past millennium, approximately three-quarters of the earth’s terrestrial surface has been altered by human activities [1]. Such widespread land-use change is closely associated with global environmental challenges, including accelerated climate change, biodiversity loss, and threats to food security [2,3,4]. Monitoring land-use status and changes has therefore become increasingly important for establishing management priorities in national land-use planning [5]. As land-use change directly affects carbon sources and sinks and induces habitat loss, the mitigation potential of the land use, land-use change, and forestry (LULUCF) sector has been officially recognized as a key element in achieving the goals of the Paris Agreement [6]. As a result, land-use change has emerged as a central agenda in contemporary international climate policy discussions [7]. According to the Intergovernmental Panel on Climate Change (IPCC) AR6 report, forests sequester approximately 30% of global anthropogenic carbon emissions. They therefore play a pivotal role in global climate change mitigation [8]. Accordingly, the prevention of forest degradation and the promotion of sustainable forest management have emerged as critical strategies for effective climate change mitigation and adaptation [9].
Bhutan officially graduated from the least developed country (LDC) category in 2023 and transitioned to developing country status. This transition has been accompanied by increasing greenhouse gas emissions associated with land-use conversion [10]. According to Global Forest Watch, approximately 790 ha of primary forest loss was reported in Bhutan in 2017 [11]. To address forest loss and land degradation, Bhutan joined the Glasgow Leaders’ Declaration on Forests and Land Use at the 26th Conference of the Parties (COP26). Through this commitment, the country pledged to halt and reverse forest loss and land degradation by 2030 [12]. Bhutan is also experiencing the impacts of climate change. It ranks 38th globally in climate vulnerability and 62nd in climate change preparedness. These rankings indicate a relatively low level of readiness to respond effectively [13]. Notably, climate change impacts in the Himalayan region are amplified. The mean temperature rise exceeds the global average trend, and the environmental threats are significant [14]. These trends can primarily be attributed to rapid population growth driven by accelerated socio-economic development following the democratic transition in 2008. Urban expansion has been particularly pronounced around the capital city, Thimphu. In addition, migration from rural areas to urban centers has intensified land-use pressure [15]. The expansion of land use, along with insufficient infrastructure, has limited Bhutan’s capacity to effectively manage climate change-induced forest hazards, such as wildfires and landslides. These challenges are accelerating the degradation of forest ecosystems. Consequently, they are weakening the country’s overall climate resilience [16].
In response, the Royal Government of Bhutan submitted its national adaptation plan (NAP) to the United Nations Framework Convention on Climate Change (UNFCCC) in 2023. The plan outlines strategies for climate change adaptation and mitigation, including improved land-use practices and climate crisis response measures. More recently, the government submitted its second nationally determined contribution (NDC). This submission emphasizes forest conservation through a domestic strategy adopting the Reducing Emissions from Deforestation and Forest Degradation in developing countries (REDD+) framework and the establishment of Measurement, Reporting, and Verification (MRV) mechanisms [17,18]. The implementation of Bhutan’s national REDD+ strategy is estimated to require approximately USD 54.5 million. The strategy consists of five strategic action areas: sustainable forest management and conservation, designation of protected areas, climate-smart restoration, promotion of agroforestry, and wetland conservation [19].
This study analyzed land cover changes and forest type patterns based on object-based image classification using satellite imagery from 1995 to 2017. Forest carbon stocks were estimated with reference to forest reference level (FRL) and forest reference emission level (FREL) baselines. Land cover classification was also evaluated from the perspective of MRV. Overall, this study provides foundational data to support the development of a forest carbon MRV system in Bhutan.

2. Materials and Methods

2.1. Study Area

Bhutan is a landlocked Himalayan country in South Asia, situated between China, to the north, and India, to the south. It is located at approximately 27.45° N and 90.45° E (Figure 1) [20]. Bhutan has a total land area of 3,839,400 ha, of which 2,717,160 ha is under forest cover [21]. The country is predominantly a high-altitude region, with most of its terrain lying above 2000 m and elevations ranging from 76 to 7500 m. While the southern areas are situated at lower altitudes, elevation increases progressively toward the north, where the landscape merges with the Himalayan mountain range [22].
The northern region has a cold alpine climate with year-round snow cover, the central region falls under a temperate climate, and the southern region is classified as part of the subtropical climatic zone [23,24]. The northern region is predominantly composed of glacial lakes, while the central region is rich in forest resources and has a well-developed forestry sector. Agriculture is prominent in the western region. The southern region is characterized by a subtropical climate and covered with deciduous forests and consists mainly of mountainous terrain at an elevation of approximately 1500 m [25,26].
Although temperatures in Bhutan vary with elevation, most of the central region experiences cool and temperate weather. The southern region is generally hot and humid, with average temperatures around 15 °C and summer highs reaching up to 35 °C. Annual precipitation patterns vary significantly across regions, ranging from approximately 40 mm in the north, 1000 mm in the central region, and up to 7800 mm in the south [27,28].
Bhutan, along with Suriname, is one of the few countries in the world where carbon sequestration exceeds emissions, as most of its territory consists of carbon sinks, such as forests [29]. However, it is also facing a reduction in carbon sinks driven by climate change, urbanization, and agricultural expansion [30]. Therefore, Bhutan was selected as the subject of analysis to examine temporal changes in land cover and to assess trends in forest carbon.

2.2. Data Acquisition and Preparation

To analyze long-term land cover changes in Bhutan, this study used satellite imagery from Landsat 5 and Landsat 8 for five target years: 1995, 1999, 2004, 2011, and 2017 (Table 1). Since Landsat 5 was officially decommissioned in 2013, the 2017 imagery was obtained from Landsat 8, which was launched in 2013 and has been operational since then. To address differences in sensor characteristics and acquisition timing between the two datasets, preprocessing was conducted using the Environment for Visualizing Images (ENVI) 5.2 software. The procedures included geometric correction, cloud masking, image co-registration, and spatial alignment to ensure consistency across time steps [31]. Subsequently, the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric correction model was applied to remove atmospheric noise. Topographic correction was also performed to minimize the influence of slope-induced variations in radiance values due to terrain relief. In addition, mosaic and subset images were generated to define the study area and prepare the images for classification [32].

2.3. Pre-Processing and Classification

Object-based classification was performed to monitor area changes in land cover type. This classification method was based on the spatial relationship between a reference pixel and its surrounding pixels, using information from sets of similar pixels (objects). The classification results are expressed in vector format. This approach was adopted because object-based classification facilitates accurate area extraction and provides high accuracy in monitoring land cover area changes [33,34]. The satellite imagery selected for each target year was segmented using the image segmentation tool in eCognition Developer software (v10.3). This procedure groups pixels with homogeneous spectral characteristics and represents a fundamental step in the object-based classification workflow [35,36].
During the image segmentation process, all spectral bands of Landsat 5 and 8 imagery were used except the thermal band. To emphasize changes in vegetation distribution, the near-infrared (NIR) band was assigned a weighting factor of 2. Following the segmentation, training data were assigned based on land cover types, and classification was performed using the nearest neighbor algorithm to group spectrally similar objects [37,38].
Image classification involved a first step based on the spectral characteristics and indexes of each land cover type and a second step based on sample extraction and training. In the first classification step, threshold values were set using the normalized difference vegetation index (NDVI), Modified normalized difference water index (MNDWI), and normalized difference built-up index (NDBI) to identify vegetation, water bodies, and built-up areas, respectively [39,40,41]. The classification was further refined using a fuzzy logic approach [42]. In the second classification step, training data samples were collected by forest type based on the Food and Agriculture Organization of the United Nations (FAO) land cover map, and the nearest neighbor classification algorithm was applied to classify spectrally similar values. The forest types were categorized as mixed conifer, broadleaf, and conifer forests (including blue pine, chir pine, etc.) [43]. Finally, misclassified objects were visually interpreted using Google Earth Pro and corrected accordingly (Figure 2) [44].

2.4. Assessment of Classification Accuracy

To evaluate the classification accuracy of the land cover analysis, a statistical sampling method was adopted based on previous research [45,46,47]. Approximately 500 random points were generated across the entire territory of Bhutan. Class values from the classified map were extracted by spatially overlaying the random points onto the classified layer, ensuring one-to-one correspondence between the sample points and classification units. Validation points were established by assigning reference labels based on visual interpretation of land cover types using Google Earth imagery (Figure 1d) [48]. These validation points were then used to evaluate the land cover classification map produced in this study. Differences between the classified and reference data were used to construct an error matrix, and user’s accuracy, producer’s accuracy, and overall accuracy were calculated for each land cover class.

2.5. Land Cover and Forest Carbon Sink Change Analysis

Forest carbon stock changes were estimated using the gain-loss method in accordance with the IPCC 2006 guidelines (Equation (1)) [49]. Carbon stock trends were further analyzed by forest type, and changes were assessed by applying per hectare forest type carbon values from Bhutan’s 2016–2018 national inventory data [50].
∆ C t 1 − t 2 = ∆ A t 2 − A t 1 × C d e n s i t y
  • ∆ C = Change in forest carbon stock (tonnes of C);
  • ∆ A = Change in forest area between two time periods (ha);
  • ∆ C d e n s i t y = Forest type-specific carbon stock density (tonnes C ha−1).

3. Results

3.1. Land Cover Change

The analysis of land cover change in Bhutan from 1995 to 2017 revealed an overall trend of expansion of built-up and agricultural areas, along with changes in forest stand density and forest type patterns. These changes are presumably influenced by shifts in forest types due to climate change, as well as by Bhutan’s socioeconomic context. Approximately 57% of the population depends on agriculture (Figure 3) [51,52].
An analysis of land-use changes between 1995 and 1999 using a land cover change matrix revealed that the largest area of broadleaf forest, which is the dominant forest type in Bhutan, was converted from mixed conifer forest. This was followed by conversions from conifer forest. In the case of mixed conifer forest, the most significant transitions were due to regeneration from both broadleaf forest and conifer forest. For conifer forests, the largest area of conversion originated from mixed conifer forests and broadleaf forests. Additionally, conversions from grasslands to forested areas were estimated at approximately 38,808 ha for conifer forest, 101,534 ha for broadleaf forest, and 24,125 ha for mixed conifer forest.
The land cover change matrix for the period between 1999 and 2004 revealed that, for conifer forest, the largest conversion occurred to broadleaf forest, covering approximately 328,905 ha. For broadleaf forest, excluding transitions within forest categories, the most significant change was conversion to grassland. This trend partly reflects the classification of shrublands as grassland. Mixed conifer forests also showed a similar pattern. Most of the conversion was to broadleaf forest.
Between 2004 and 2011, coniferous forest showed the most significant land cover transitions to mixed coniferous forest and broadleaf forests among forest-type changes. These were the main transitions within forest categories. This was followed by conversions to agricultural land and grassland. For broadleaf forest, the largest transition outside of forest categories was to agricultural land. For mixed conifer forests, approximately 154,003 ha were converted to agricultural land.
From 2011 to 2017, the most significant conversion to broadleaf forest came from conifer forest. Outside forest areas, major conversions to broadleaf forest occurred from grassland and agricultural land. Mixed conifer forest showed a notable conversion to grassland. The converted area was approximately 31,593 ha. Since 1998, there has been a marked decline in conifer forest and mixed conifer forest areas, whereas grassland, agricultural land, and urban areas have shown increasing trends.

3.2. Validation Results and Accuracy Metrics

In this study, random points were distributed across the entire territory of Bhutan. Verification points were established by assigning attribute values through visual interpretation of land cover using Google Earth imagery. These points were then compared with the land cover map produced in this study. The classification accuracy ranged from 81 to 88%, while the Kappa coefficient ranged from 0.77 to 0.83. These results confirm the reliability and overall accuracy of the land cover map.

3.3. Forest Cover Change

As a basis for forest monitoring in Bhutan, changes in land cover within forest areas and forest types between 1995 and 2017 were examined. These analyses were used to produce a forest cover change map (Figure 4). A land cover change matrix was used to identify both forest loss and gain. Forest loss was defined as transitions from forest to other land-cover classes. Forest gains through planting were defined as transitions from non-forest classes to forest. Forest loss included conversion of forest to built-up areas, grassland, water, and agricultural land.
The results indicate that total forest loss amounted to approximately 1,286,673 ha, while planting accounted for approximately 233,837 ha. In addition, transitions among forest types within existing forest areas covered approximately 1,127,091 ha.
Consistent with previous studies [53,54], the results indicate a reduction in stand density associated with conifer mortality, alongside an expansion of broadleaf forest areas. This expansion was likely driven by climate change. Planted forest areas accounted for approximately 20% of total forest loss. Transitions from existing forest types to broadleaf forest represented a substantial proportion of the observed forest type changes. Among coniferous forests, changes were more pronounced in major Bhutanese species such as blue pine and chir pine. These changes were greater than those observed in mixed conifer forests (Figure A1).

3.4. Forest Carbon Stocks

The forest carbon stocks in the forest sector were estimated based on the preceding land cover change (Figure A2). Using Bhutan’s national inventory reports, the carbon stock was analyzed in detail by forest type. Overall, forest area decreased over time. As a result, the total carbon stock, including both aboveground and belowground biomass, showed a declining trend. The values were 570 million tC in 1995, 577 million tC in 1999, 552 million tC in 2004, 503 million tC in 2011, and 405 million tC in 2017. In addition, climate change led to a sharp decline in carbon sequestration. This decline was due to mortality and conversion of coniferous species. However, carbon sequestration increased in broadleaf forests. This increase occurred because these adaptive species expanded in response to climate change.

4. Discussion

This study was conducted as a pilot project to establish a foundation for developing an MRV system in Bhutan. By utilizing satellite imagery, we assessed land cover change using a transition matrix approach. We also evaluated forest carbon stocks. Between 1995 and 2017, an overall decline in forest cover was observed. Forest types also showed notable changes over time. Specifically, broadleaf forest showed growth and expanded in area by approximately 8%. In contrast, coniferous forests and mixed conifer forests experienced reductions in both growth and spatial extent. Cropland, built-up areas, grassland, and water bodies generally increased in area.
These results may have been influenced by rapid socio-economic development following Bhutan’s democratic transition in 2008. This transition led to increased migration from rural to urban areas. It also contributed to urban expansion through the conversion of forest land for infrastructure development, particularly for hydropower energy production [55]. The proportion of the urban population increased from 30.9% in 2005 to 37.8% in 2017 and is projected to reach 56.8% by 2047. This trend suggests that forest areas may continue to decline [56,57]. Moreover, the annual population growth rate has increased in recent years. It has reached an average of approximately 1.3%, which has further accelerated this trend [58]. Forest loss has also been accelerated by agricultural expansion through shifting cultivation. In addition, illegal timber extraction for fuel has contributed to deforestation [59]. This phenomenon has been particularly prominent in coniferous forests, which are considered important sources of highly efficient fuelwood [60].
In addition, rapid climate change and the increasing frequency of natural disasters have affected forest areas. Migration of population toward urban centers has also influenced land use patterns [61]. Since the 1990s, temperature and precipitation have increased rapidly. Consequently, glaciers near the Himalayas have melted at an accelerated rate, resulting in the expansion of glacial lakes [26].
However, this study is based on satellite imagery with a 30 m spatial resolution. This resolution limits the accurate classification of small-scale land areas. Previous research has shown that due to the spectral reflectance characteristics of the bands, some parts of the snow-covered northern Himalayan region in Bhutan are misclassified as water instead of forest, and shrublands are often misclassified as grasslands [62]. In addition, classification accuracy may vary slightly due to differences in sensor specifications among the satellite images used in the time-series analysis. Therefore, future research should incorporate high-resolution satellite imagery, such as Sentinel-2 (10 m spatial resolution), RapidEye, or CAS500-4 (5 m resolution). Advanced deep learning-based classification algorithms and model-based carbon estimation approaches should also be applied. The integration of national forest inventory (NFI) data through regression-based modeling could support the development of spatial biomass and carbon stock distribution layers. This approach would strengthen MRV systems and support carbon credit mechanisms [63,64]. In this context, consideration of soil erosion and broader ecological dynamics related to land use change would provide a more comprehensive understanding of ecosystem transformation [65].
Currently, Bhutan is exploring various approaches to reduce greenhouse gas emissions and achieve carbon neutrality through the submission of its second NDC [17]. In addition, Bhutan has completed the readiness phase for implementing REDD+ initiatives aimed at preventing deforestation and forest degradation [19]. It has also submitted its proposed FREL and FRL to the UNFCCC. These submissions are currently under technical review. Therefore, this study contributes to the establishment of MRV baselines through time-series land cover change monitoring and carbon stock estimation. Furthermore, it provides a foundation for building a national monitoring system. It also supports the development of national inventory data and emission and removal factors.
Previous studies were mostly limited to site-specific or regional pilot projects. They primarily focused on monitoring protected forest areas that support forest conservation policies [66]. Rather than addressing nationwide dynamics, these studies emphasized regional-level assessments. They also examined social factors contributing to land degradation and general research trends in Bhutan’s forest sector [67,68,69]. Given that this study provides foundational research to support the development of a national MRV system for tropical forests in Bhutan, it is expected to contribute to the country’s future carbon neutrality strategy and advance the implementation of the REDD+ framework.

5. Conclusions

This study aimed to contribute to the establishment of an MRV system by assessing changes in forest carbon stocks through periodic time-series monitoring of land cover changes in Bhutan from 1995 to 2017. Using satellite imagery and carbon estimation equations, we analyzed forest type changes and quantitatively estimated carbon stocks and evaluated their role as a forest carbon sink. Forest areas in Bhutan are gradually declining due to climate change and socio-economic factors. This decline is particularly pronounced in coniferous forests. These findings provide an important scientific basis for assessing the feasibility and foundation of Bhutan’s second NDC and for supporting the potential implementation of the REDD+ framework.
Bhutan has developed its NAP and sustainable management plans. The country is actively implementing strategies to achieve its second NDC targets. When establishing an MRV system for both the NDC and REDD+ initiatives, the government should ensure systematic and quantitative evaluations aligned with national strategic objectives. In addition, comprehensive and project-specific deliberations should be conducted. Successful implementation of these plans will require robust scientific baseline analyses. It will also require careful assessments of socio-environmental factors related to carbon sink enhancement and project implementation.

Author Contributions

Conceptualization: M.H., H.Y. and W.-K.L.; Data curation: M.H. and Y.S.; Formal analysis: M.H.; Methodology: M.H., H.Y. and W.-K.L.; Supervision: W.-K.L.; Validation: M.H. and Y.S.; Visualization: M.H., H.Y. and W.-K.L.; Writing—original draft: M.H.; Writing—review and editing: M.S., K.K. and W.-K.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Institute of Forest Science (NIFoS) under the general project “Study on Expanding Foreign Carbon Sinks Using REDD+” (FM0800-2022-01-2026).

Data Availability Statement

Satellite imagery is available online through the USGS Global Visualization Viewer (GloVis) (https://glovis.usgs.gov/ (accessed on 7 November 2022)). The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors express their gratitude to the Korea Forest Service for their financial support.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
COPConference of the Parties
DEMDigital Elevation Model
ENVIEnvironment for Visualizing Images
FAOFood and Agriculture Organization of the United Nations
FLAASHFast Line-of-sight Atmospheric Analysis of Hypercubes
FRLForest Reference Level
FRELForest Reference Emission Level
IPCCIntergovernmental Panel on Climate Change
LDCLeast Developed Country
LULUCFLand Use, Land-Use Change and Forestry
MNDWIModified Normalized Difference Water Index
MRVMeasurement, Reporting, and Verification
NAPNational Adaptation Plan
NDBINormalized Difference Built-up Index
NDCNationally Determined Contribution
NIRNear-Infrared
NDVINormalized Difference Vegetation Index
REDD+Reducing Emissions from Deforestation and Forest Degradation in developing countries
UNFCCCUnited Nations Framework Convention on Climate Change

Appendix A

Land 15 00432 g0a1
Figure A1. Temporal variation in forest area maintained within the same forest type.
Figure A1. Temporal variation in forest area maintained within the same forest type.
Land 15 00432 g0a1
Land 15 00432 g0a2
Figure A2. Carbon stock by forest type classification (Million tC).
Figure A2. Carbon stock by forest type classification (Million tC).
Land 15 00432 g0a2

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Figure 1. (a) Location of the study area; (b) elevation derived from the digital elevation model (DEM); (c) land cover map; and (d) spatial distribution of validation points for accuracy assessment.
Figure 1. (a) Location of the study area; (b) elevation derived from the digital elevation model (DEM); (c) land cover map; and (d) spatial distribution of validation points for accuracy assessment.
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Figure 2. Study flow.
Figure 2. Study flow.
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Figure 3. Land cover classification and change analysis in Bhutan.
Figure 3. Land cover classification and change analysis in Bhutan.
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Figure 4. Forest cover map of Bhutan from 1995 to 2017 (C: Conifer forest, B: Broadleaf forest, MC: Mixed conifer forest).
Figure 4. Forest cover map of Bhutan from 1995 to 2017 (C: Conifer forest, B: Broadleaf forest, MC: Mixed conifer forest).
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Table 1. Information on Landsat TM and OLI/TIRS imagery used in this study.
Table 1. Information on Landsat TM and OLI/TIRS imagery used in this study.
Landsat Scene IDDatePath/RowCloud (%)
LT51370411995309BKT015 November 1995137/412
LT51380411999327BKT0023 November 1999138/415
LT51390411999334BKT0130 November 1999139/416
LT51370412004334BKT0129 November 2004137/418
LT51390412011239KHC0027 August 2011139/4116
LT51380412011296KHC0023 October 2011138/417
LC81380412017312LGN008 November 2017138/414.09
LC81390412017335LGN001 December 2017139/416.71
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Hong, M.; Yu, H.; Song, Y.; Song, M.; Kim, K.; Lee, W.-K. Time-Series Satellite-Based Monitoring of Land-Use Change and Forest Loss in Bhutan: Implications for Forest Carbon Measurement, Reporting, and Verification. Land 2026, 15, 432. https://doi.org/10.3390/land15030432

AMA Style

Hong M, Yu H, Song Y, Song M, Kim K, Lee W-K. Time-Series Satellite-Based Monitoring of Land-Use Change and Forest Loss in Bhutan: Implications for Forest Carbon Measurement, Reporting, and Verification. Land. 2026; 15(3):432. https://doi.org/10.3390/land15030432

Chicago/Turabian Style

Hong, Mina, Hangnan Yu, Yongho Song, Minkyung Song, Kyoungmin Kim, and Woo-Kyun Lee. 2026. "Time-Series Satellite-Based Monitoring of Land-Use Change and Forest Loss in Bhutan: Implications for Forest Carbon Measurement, Reporting, and Verification" Land 15, no. 3: 432. https://doi.org/10.3390/land15030432

APA Style

Hong, M., Yu, H., Song, Y., Song, M., Kim, K., & Lee, W.-K. (2026). Time-Series Satellite-Based Monitoring of Land-Use Change and Forest Loss in Bhutan: Implications for Forest Carbon Measurement, Reporting, and Verification. Land, 15(3), 432. https://doi.org/10.3390/land15030432

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