Techniques for Estimating Carbon Sequestration in Rubber Plantations using an Unmanned Aerial Vehicle
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Abstract
The objective of this research is to estimate the above-ground biomass and carbon sequestration of rubber trees using an Unmanned Aerial Vehicle (UAV) and machine learning. The research methodology is divided into two parts: field data collection and analysis through anatomical measurements of the trees, and image analysis from a UAV. A sample plot of 2 rai, sized 40x40 meters, from a total of 345 rai of rubber plantation, was designated for testing and demonstration in this study. The study results from the sample plot revealed that there were 181 rubber trees. When analyzing and calculating the biomass quantity in the sample plot using the Allometric equation, it was found that the average above-ground biomass for leaves was 0.0060 ton/tree, for trunks and branches was 0.4050 ton/tree, and for stumps and roots was 0.0696 ton/tree, with a total average above-ground biomass of 0.4806 tons/tree. The average carbon sequestration was estimated to be 0.2259 tCO2e /tree. Subsequently, the results from calculating carbon sequestration from field surveys and UAV image analysis were compared. The sample plot was divided into two test plots. In the first plot, 82 rubber trees were identified, with the total carbon sequestration from field data and UAV data using the NDVI vegetation index averaging 0.2058 and 0.1889 tCO2e /tree, respectively. In the second plot, 99 rubber trees were identified, with averages of 0.2425 and 0.2398 tCO2e /tree, respectively.
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