Improving the accuracy of aboveground biomass and carbon estimation using LiDAR metrics : a case study of Mo Singto in Khao Yai National Park, Thailand | |
| Author | Netnapa Udom |
| Call Number | AIT Thesis no.RS-18-02 |
| Subject(s) | Biomass energy--Thailand--Khao Yai National Park--Case studies Optical radar--Thailand--Khao Yai National Park Carbon sequestration |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Remote Sensing and Geographic Information Systems, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Abstract | Light Detection and Ranging (LiDAR) is a new remote sensing technology in terms of forest. Currently, LiDAR is one of the major methods used for estimating aboveground biomass and carbon in large-scale, and it can provide high accuracy and resolution about forest structure. The main objective of this research is to improve the accuracy of estimation techniques for aboveground biomass and carbon using LiDAR metrics in Mo Singto plot, Khao Yai National Park, Thailand. The result illustrated that canopy height model was the best biomass estimation model, followed by all returns and first returns, respectively. The best LiDAR point density for aboveground biomass estimating of canopy height model was 100 percent, followed by 40 percent (R2 = 0.987 and 0.725), and all return was 100 percent, followed by 40 percent (R2 = 0.986 and 0.980). Moreover, the best LiDAR point density of first returns for aboveground biomass estimating was 100 percent, followed by 80 percent (R2 = 0.967 and 0.933). In addition, the stem biomass that estimated from canopy height model was highest, followed by branch and leaf component which was 258.8 ± 38.6, 89.6 ± 13.7 and 5.3 ± 0.6 ton per hectare, respectively. Moreover, aboveground biomass was 349.5 ± 52.8 ton per hectare, and carbon was nearly half of aboveground biomass 164.3 ± 26.4 ton carbon per hectare. Therefore, the results presented that the coefficient of determination of stem, branch, leaf, aboveground biomass and carbon was 0.987, 0.982, 0.965, 0.987 and 0.987, respectively. Consequently, R2 of aboveground biomass was 0.987 indicated that very strong correlation between field measurement and LiDAR structural variables. Furthermore, this research has improved accuracy based on R2 value when compared to previous researches (R2 = 0.820, 0.815, 0.736 and 0.600). However, the accuracy often varies spatially depending on the complexity of landscape, density of sample plot, LiDAR point density, LiDAR returns, variable derived from LiDAR metrics and variable in biomass estimation model. |
| Year | 2018 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Remote Sensing (RS) |
| Chairperson(s) | Tripathi, Nitin Kumar |
| Examination Committee(s) | Nakamura, Tai ; Sasaki, Nophea |
| Scholarship Donor(s) | Royal Thai Government |
| Degree | Thesis (M. Sc.) - Asian Institute of Technology, 2018 |