Application of Geo-Informatics in Predicting Carbon Sequestration and Greenhouse Gases Emission in Khao Soi Dao Wildlife Sanctuary, Chanthaburi Province

Abstract

Land uses changes were investigated using geographic information systems, the Land Change Modeler and the Ecosystem Services Modeler together with driving factor data and forest inventory data to predict greenhouse gas emissions in the Khao Soi Dao Wildlife Sanctuary. Land use types in 2005, 2010 and 2015 were classified from LANDSAT imageries using visual interpretation techniques.

The results revealed that between 2005 and 2010, there were increases in the agriculture, urban and built-up, and water body landuse categories, while there was a decrease in dry evergreen forest, miscellaneous and mixed deciduous forest. Between 2010 and 2015, there was an increase in dry evergreen forest, agriculture, urban and built-up, and water body, while there was a decrease in miscellaneous and mixed deciduous forest. The accuracy assessment of the land use classification in 2015 revealed an overall accuracy of 89.47% and a kappa statistic of 92.57%. Land use predictions for the 2025, using the Land Change Modeler, indicated that between 2015 and 2025, the urban and built-up, agriculture, and water body categories increased by 171.56, 107.77, and 7.75 ha, respectively. These increments represented 29.88%, 18.77%, and 1.35% of the total changes. Conversely, miscellaneous, mixed deciduous forests, and dry evergreen forests decreased by 130.56, 79.65, and 76.86 ha, respectively. These reductions accounted for 22.74%, 13.87%, and 13.39% of the total changes. The predicted greenhouse gas emissions for the year 2025 were 6,320 tons. Measures to reduce greenhouse gas emissions in forest areas could include reducing deforestation and planting trees in deforested areas.