Year 2025, Volume 71 Issue 4
| Year : | 2025 |
| Volume : | 71 |
| Issue : | 4 |
| Authors : | Teerawong LAOSUWAN, Jumpol ITSARAWISUT,Yannawut UTTARUK, Satith SANGPRADID,Tanutdech ROTJANAKUSOL, Thanakrizt PEEBKHUNTHOD |
| Title : | URBAN TREE CARBON STOCK ASSESSMENT BASED ON REMOTE SENSING AND FIELD INVENTORY DATA |
| Abstract : | The increasing concentration of CO2 in the atmosphere is one of the causes that has lead to what we define as global warming with dramatic events on ecosystems, biodiversity and human life. An accurate prediction of aboveground biomass (AGB) and carbon stock is crucial for the assessment of forest and urban trees' contributions to climate game. The objective of this study was to establish a reliable way to estimate aboveground biomass and potential carbon stock by integrating remote sensing methods with ground-based measurements. The procedure consisted of the following three major steps: (1) calculation of NDVI, GNDVI and SAVI; (2) systematic sampling of field plots using species distribution by biomass and carbon quantification through allometric equations according to the species; and (3) exponential regression between biomass vs vegetation indices. Results indicated that GNDVI has the maximum correlation with biomass in comparison with NDVI, and SAVI. When extrapolated, GNDVI model predicted 2798.23 and 1315.17 tons of above ground biomass and carbon respectively. In the field, biomass and carbon was measured as 4,650.75 tons and 2,185.85 tons respectively. These results also highlight the significance of integrating remote sensing and field data for precise estimation of biomass and carbon, providing useful information even for resource management, urban green planning and global climate change countermeasures such as a carbon credit program. |
| For citation : | Laosuwan, T., Itsarawisuti, J., Uttaruk, Y., Sangpradid, S., Rotjanakusol, T., Peebkhunthod, T. (2025). Urban tree carbon stock assessment based on remote sensing and field inventory data. Agriculture and Forestry, 71 (4): 17-35. https://doi:10.17707/AgricultForest.71.4.02 |
| Keywords : | remote sensing, urban forest, aboveground biomass, carbon sequestration, allometric equations, carbon credit, net zero |
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