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

ABSTRACT Long-term observation of the earth is essential for studying the factors affecting global environmental changes. Digital earth technology can facilitate the monitoring of global environmental change with its ability to process vast amounts of information. In this study, we map the forest cover change of Myanmar from 2000 to 2005 using a training data automation procedure and support vector machines algorithm. Our results show that Myanmar's forests have declined 0.68% annually over this six-year period. We validated our derived change results and found the overall accuracy to be greater than 88%. We also assessed forest loss from protected areas, areas close to roads, and areas subject to fire, which were most likely to lose forested area. The results revealed the main reasons for forest losses in some hotspots to be increased agricultural conversion, fire, and the construction of highways. This information is useful for identifying the driving forces behind forest changes and to support environmental policy development in Myanmar.

[1]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[2]  William F. Laurance,et al.  Land use: A global map for road building , 2013, Nature.

[3]  D. Roberts,et al.  A comparison of methods for monitoring multitemporal vegetation change using Thematic Mapper imagery , 2002 .

[4]  J. Townshend,et al.  Assessment of Paraguay's forest cover change using Landsat observations , 2009 .

[5]  W. Laurance,et al.  Impacts of roads and linear clearings on tropical forests. , 2009, Trends in ecology & evolution.

[6]  Takeda Shinya,et al.  Forest Cover Changes Under Selective Logging in the Kabaung Reserved Forest, Bago Mountains, Myanmar , 2009 .

[7]  Chengquan Huang,et al.  Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error , 2013, Int. J. Digit. Earth.

[8]  Chris Brown,et al.  ASIA-PACIFIC FORESTRY SECTOR OUTLOOK STUDY , 1997 .

[9]  Chengquan Huang,et al.  Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges , 2012, Int. J. Digit. Earth.

[10]  Peter Leimgruber,et al.  Forest cover change patterns in Myanmar (Burma) 1990–2000 , 2005, Environmental Conservation.

[11]  Chengquan Huang,et al.  Use of a dark object concept and support vector machines to automate forest cover change analysis , 2008 .

[12]  J. Townshend,et al.  MODIS Vegetative Cover Conversion and Vegetation Continuous Fields , 2010 .

[13]  G. Applegate,et al.  Forest fire and biological diversity , 2002 .

[14]  Tsuyoshi Kajisa,et al.  Deforestation and forest degradation as measures of Popa Mountain Park (Myanmar) effectiveness , 2009, Environmental Conservation.

[15]  M. Hansen,et al.  Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia , 2011 .

[16]  J. Townshend,et al.  Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data , 2002 .

[17]  Valerie Kapos,et al.  ASSESSING FOREST INTEGRITY AND NATURALNESS IN RELATION TO BIODIVERSITY , 2002 .

[18]  United Kingdom,et al.  GLOBAL FOREST RESOURCES ASSESSMENT 2005 , 2005 .

[19]  Chengquan Huang,et al.  Use of remote sensing coupled with a vegetation change tracker model to assess rates of forest change and fragmentation in Mississippi, USA , 2009 .