Warasan Witthayasat Lae Theknoloyi Mahawitthayalai Mahasarakham (Dec 2020)
Evaluation of Above-Ground Carbon Sequestration of Forest in Mahasarakham University Using Remote Sensing Data
Abstract
At present, the increase of greenhouse gas has led to anincrease in global temperature. This problem can be solved by extending green areas to reduce the amount of gas. The purposes of this study was to classify a forest area in Mahasarakham University by using Unmanned Aerial Vehicle (UAV) and Sentinel-2 images and to assess the above-ground carbon stock in 2018 using Object-Based Image Analysis (OBIA). In order to do this, a Nearest Neighbor (NN) method was applied to identify forest and validate the classification accuracy. Data for all 44samplingplots were collected from field surveying including height, diameter, and number of tree. These were measured and biomass was calculated the, and the excess green index (ExG) with ground data generated using correlation coefficient (r) for carbon stock monitored by the allometry equation. The finding demonstrated the overall accuracy of UAV and sentinel-2 images as 89% and 68%, respectively. UAV imageshadhigher accuracythan othersbecause of very high spatial resolution, clear image object segmentation, and less effect from atmosphere. The biomass was high related with EXG index (r: 0.80). The EXG index was used to measure biomass covering the forest area as 16,166,339 kilograms and the amount of carbon stock of7,598,179 kilograms. The related agencies can apply this method to evaluate carbon stock for increasing the green areain the University.
