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Partnerships for enhanced engagement in research (PEER) SCIENCE
Cycle 2 (2012 Deadline)

Analysis of historical forest carbon changes in Myanmar and Thailand and the contribution of climate variability and extreme weather events

PI: Amnat Chidthaisong (The Joint Graduate School of Energy and Environment), with co-PI Khin Lay Swe, Yezin Agriculture University
U.S. Partners: Merryl Alber and Monique Y. LeClerc (University of Georgoa)
Project Dates: December 2013 to January 2016
This project will focus on forest locations in two tropical countries, Thailand and Myanmar, to investigate forest responses to climate variability and extreme climate events. The project aims to increase scientific knowledge by merging advanced remote sensing techniques and geographic information systems technology with eddy flux tower measurements to track changes in tropical forest carbon stock and exchanges that respond to historical extreme climate conditions. This knowledge will help improve our understanding of the unknown key driving forces affecting forest health changes and associated processes occurring in Southeast Asia, including carbon gain and loss, water stress, and response mechanisms to stresses.
This project will lead to at least three pillars of development impacts. First, in order to institute appropriate forestry management to maintain a sustainable forest and its ecosystem, we need greater scientific knowledge of historical change in forests responding to extreme events. The results of this project will help us differentiate between changes due to natural extreme events and those due to anthropogenic effects. Second, in supporting the implementation of agreements under international climate negotiation schemes, forest and carbon maps from this project can serve as the basis for a greenhouse gas inventory and national baselines in both Thailand and Myanmar. Finally, building capacity through training, workshops, and a summer school conducted in this project will serve as a platform to disseminate knowledge and know-how among participants from these two countries and other ASEAN nations. This is expected to create linkages among a network of scientists and researchers for further collaboration.
2-473 North Thailand Field Survey2-473 North Thailand Field Survey 2
The project team surveys various forests during a field trip to northern Thailand (Photo courtesy of Dr. Chidthaisong)

Summary of Recent Activities
In this quarter, the project team utilized literature review and experts to analyze and identify individual forest types in the project survey area. The resulting forest classification identified four major forest types which include bamboo, dry deciduous, dry evergreen, and mixed deciduous, for 2005. Accurately identifying the correct forest type and geolocated points is critical for the development and assessment of satellite image classification and to verify their points, the project team conducted their first field survey in the first week of March in the northeastern part of Thailand. A random sampling approach was used in order to collect 198 field samples of different forest and non-forest classes. However, due to limited time and resources, the forest classification maps have not been validated at this point and further data collection is required.

The team carried out additional work to correct the topographic data from satellite images during this reporting period. Different methods of topographic correction of Landsat TM images were tested on selected sites and the success of these methods was tested using virtual observation, reflectance and reduction in standard deviation of each vegetation type after the correction. Furthermore, in order to evaluate the forest decline phenomenon over the last decade caused by extreme climate events, the team focused on exploring the potential of MODIS images for studying forest health. Preliminary analysis by the team discovered that many cloud pixels remain in the images and removal of these pixels is crucial for the forest health studies. The team chose four different pixels to study the cloud effects in order to develop feasible cloud removal methodologies to overcome the cloud cover.

In the coming months, the team will validate the forest classification maps in Northeast Thailand. The entire Thailand forest database will be updated using recent satellite data from 1990- 2015 to subdivide the areas into individual forest types. The team will also validate the accuracy of their topographic correction and continue development of a feasible cloud removal methodology to improve the quality of their analysis. The initial forest and non-forest map will be prepared for a field survey to Myanmar which will be conducted at the end of May. Lastly, the project’s third capacity building workshop entitled “Geospatial-temporal analysis of climate data and remote sensing for vegetation monitoring with R” will take place at JGSEE - KMUTT from October 12-16, 2015.

Project Website

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