Application of Geoinformatics in Predicting Carbon Sequestration Trends in Thung Salaeng Luang National Park

Abstract

Currently, Thailand has the capacity to sequester 90 million t of CO₂ equivalent with the goal of increasing this to 120 million t of CO₂ equivalent in 2037. Therefore, the objectives of this study were to analyze land use changes and to predict land use and carbon sequestration in 2030 in the Thung Salaeng Luang National Park, Thailand to contribute to the national goal. Geoinformatics and visual interpretation techniques were applied to classify land use based on the LANDSAT 5 and LANDSAT 8 satellite images from 2004, 2009 and 2014. Additionally, the Land Change Modeler was used to integrate environmental factors, such as distance from roads and villages, as well as elevation and slope direction, to analyze and predict land use patterns in 2030, with the Ecosystem Services Modeler being used to predict carbon sequestration in 2030.

The findings revealed 11 distinct land use types. Analysis of land use changes between 2009 and 2014 indicated increases in the areas of dry evergreen forest (36.35%), mixed deciduous forest (12.30%), dry dipterocarp forest (1.23%), water body (0.07%), and urban and built-up (0.05%). Conversely, miscellaneous and agricultural decreased by 40.46% and 9.54%, respectively, while hill evergreen forest, moist evergreen forest, pine forest, and forest plantation remained unchanged. Predictions for land use in 2030 suggested important increases in agricultural (47.70%) and urban and built-up (2.30%). Conversely, mixed deciduous forest, miscellaneous, dry evergreen forest, and dry dipterocarp forest were projected to decrease by 32.72%, 12.27%, 4.40%, and 0.61%, respectively. Furthermore, the prediction of the carbon sequestered in forest in 2014 was 10,033,789 tC, while in 2030, it was 9,766,154 tC. These finding indicated a negative trend in carbon sequestration during 2014–2030, with a potential 267,635 tC net emission.