Assessment of the three factors affecting Myanmar's forest cover change using Landsat and MODIS vegetation continuous fields data

Liu, Fujiang; Huang, Chengquan; Pang, Yong; Li, Mengxue; Song, Dan-Xia; Song, Xiao-Peng; Channan, Saurabh; Sexton, Joseph O.; Jiang, Die; Zhang, Ping; Guo, Yan; Li, Yao‐Feng; Townshend, J. R. G.

doi:10.1080/17538947.2015.1111451

Cited by 12 publications

(13 citation statements)

References 15 publications

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“…Eight-day MODIS time-series have been extensively used to map paddy rice fields, forests, and plantations [17,28,29]. The key phenology phase can be identified through analysis of differences on annual time-series of MODIS NDVI, EVI, and LSWI of different land cover types.…”

Section: Identifying the Key Phenology Phasementioning

confidence: 99%

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Kou

Liang

Wei

et al. 2017

Forests

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Updated extent, area, and spatial distribution of tropical evergreen forests from inventory data provides valuable knowledge for research of the carbon cycle, biodiversity, and ecosystem services in tropical regions. However, acquiring these data in mountainous regions requires labor-intensive, often cost-prohibitive field protocols. Here, we report about validated methods to rapidly identify the spatial distribution of tropical forests, and obtain accurate extent estimates using phenology-based procedures that integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat imagery. Firstly, an analysis of temporal profiles of annual time-series MODIS Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Land Surface Water Index (LSWI) was developed to identify the key phenology phase for extraction of tropical evergreen forests in five typical lands cover types. Secondly, identification signatures of tropical evergreen forests were selected and their related thresholds were calculated based on Landsat NDVI, EVI, and LSWI extracted from ground true samples of different land cover types during the key phenology phase. Finally, a map of tropical evergreen forests was created by a pixel-based thresholding. The developed methods were tested in Xishuangbanna, China, and the results show: (1) Integration of Landsat and MODIS images performs well in extracting evergreen forests in tropical complex mountainous regions. The overall accuracy of the resulting map of the case study was 92%; (2) Annual time series of high-temporal-resolution remote sensing images (MODIS) can effectively be used for identification of the key phenology phase (between Julian Date 20 and 120) to extract tropical evergreen forested areas through analysis of NDVI, EVI, and LSWI of different land cover types; (3) NDVI and LSWI are two effective metrics (NDVI ≥ 0.670 and 0.447 ≥ LSWI ≥ 0.222) to depict evergreen forests from other land cover types during the key phenology phase in tropical complex mountainous regions. This method can make full use of the Landsat and MODIS archives as well as their advantages for tropical evergreen forests geospatial inventories, and is simple and easy to use. This method is suggested for use with other similar regions.

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Section: Identifying the Key Phenology Phasementioning

confidence: 99%

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Kou

Liang

Wei

et al. 2017

Forests

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“…These include studies at global [53], national [6,12,24,54,55], and sub-national scales [14,23,[55][56][57]. Countrywide studies have frequently focused on estimating overall rates of forest loss, without quantifying the unique threats faced by particular forest types (e.g., [6,12,24,58]). However, Wang and Myint [54] estimated nationwide deforestation rates (2001-2010) for each of five unique forest types and found that mangrove and deciduous forest had experienced deforestation rates that were 2-5 times the national average.…”

Section: Mapping Of Forest Types and Degradation Extentmentioning

confidence: 99%

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

Connette

Oswald²,

Songer

et al. 2016

Remote Sensing

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Abstract:We investigated the use of multi-spectral Landsat OLI imagery for delineating mangrove, lowland evergreen, upland evergreen and mixed deciduous forest types in Myanmar's Tanintharyi Region and estimated the extent of degraded forest for each unique forest type. We mapped a total of 16 natural and human land use classes using both a Random Forest algorithm and a multivariate Gaussian model while considering scenarios with all natural forest classes grouped into a single intact or degraded category. Overall, classification accuracy increased for the multivariate Gaussian model with the partitioning of intact and degraded forest into separate forest cover classes but slightly decreased based on the Random Forest classifier. Natural forest cover was estimated to be 80.7% of total area in Tanintharyi. The most prevalent forest types are upland evergreen forest (42.3% of area) and lowland evergreen forest (21.6%). However, while just 27.1% of upland evergreen forest was classified as degraded (on the basis of canopy cover <80%), 66.0% of mangrove forest and 47.5% of the region's biologically-rich lowland evergreen forest were classified as degraded. This information on the current status of Tanintharyi's unique forest ecosystems and patterns of human land use is critical to effective conservation strategies and land-use planning.

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“…A previous study has provided an estimate of forest cover (75.45%) in 2000 and an annual estimate of 0.094% forest cover loss between 2000 and 2005 for Hukaung Valley Wildlife Sanctuary based on MODIS VCF and Landsat images 55 . One challenge to the robustness of the conclusions about tree cover change in our analysis is the lack of groundtruthing for tree cover loss.…”

Section: Discussionmentioning

confidence: 99%

The impact of gold mining and agricultural concessions on the tree cover and local communities in northern Myanmar

Papworth

Rao

et al. 2017

Sci Rep

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Myanmar offers unique opportunities for both biodiversity conservation and foreign direct investment due to projected economic growth linked to natural resource exploitation. Industrial-scale development introduces new land uses into the landscape, with unknown repercussions for local communities and biodiversity conservation. We use participatory mapping of 31 communities, focus groups in 28 communities, and analyses of forest cover change during 2000–2010 using MODIS vegetation continuous fields images, to understand the social and environmental impacts of gold mining and agricultural concessions in Myanmar’s Hukaung Valley (~21,800 km2). Local communities, particularly the poorest households, benefit from work and trade opportunities offered by gold mining and agricultural companies but continue to depend on forests for house construction materials, food, and income from the sale of forest resources. However, gold mining and agricultural concessions reduce tree cover, potentially reducing access to forest resources and further marginalizing these households. Our analyses do not provide evidence that long-term resident communities contributed to forest cover loss between 2000 and 2010. We argue that landscape management, which recognizes local community rights to customary community use areas, and appropriate zoning for commercial land uses and protected areas could contribute to both local livelihoods and protect biodiversity throughout Myanmar during economic growth.

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Assessment of the three factors affecting Myanmar's forest cover change using Landsat and MODIS vegetation continuous fields data

Cited by 12 publications

References 15 publications

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery

Mapping Distinct Forest Types Improves Overall Forest Identification Based on Multi-Spectral Landsat Imagery for Myanmar’s Tanintharyi Region

The impact of gold mining and agricultural concessions on the tree cover and local communities in northern Myanmar

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