Allometric characterization using airborne LiDAR and determination of biomass in Mo Singto Plot at Khao Yai National Park, Thailand | |
| Author | Nitchanan Sithiprom |
| Call Number | AIT Thesis no.RS-17-27 |
| Subject(s) | Airborne radar--Thailand--Khao Yai National Park Biomass--Thailand--Khao Yai National Park |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Remote Sensing and Geographic Information System |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. RS-17-27 |
| Abstract | KhaoYai National Park is the first national park of Thailand which has 4 boundary area cover includes Saraburi province, Nakhon Ratchasima province, Prachinburi province, and Nakorn Nayok province. Thus, KhaoYai National park get name that heritage parks of ASEAN which has many biodiversity includes mixed deciduous forest, tropical rain forest, dry evergreen forest, hill evergreen forest, and grassland. Estimation of Above Ground Biomass (AGB) is very important for knowing the carbon stock in a forest type. AGB can be estimated ways by field survey and LiDAR. Field survey provides information about tree measurement including DBH, species, and type of trees while LiDAR data provides information in three dimensions. In this study, LiDAR data required more analysis because original information from the sensor which is required in a cloud of points. Then, a Digital Surface Model (DSM) and a Digital Terrain Model (DTM) were derived from the cloud points. Moreover, Canopy height Model (CHM) which is height of tree was calculated as the different between DSM and DTM. The height of the tree derived from LiDAR data were compared to the height of tree measured in the fieldwork by using Rstudio software. The main objective of this research study was to comparison allometric equation between LiDAR data and field survey. Moreover, there are specific objectives of this study include to calculated individual tree, estimated tree height and crown width using Remote sensing, and used DBH algorithm for estimating diameter at breast height (DBH) by using LiDAR data. However, the LiDAR data cannot directly to estimate DBH. Thus, multi-linear regression can used to predict DBH derived from LiDAR data. Then matching data is importance also because the field survey has many points (157,389) more than LiDAR data (4,523). Hence, in this thesis, PHP code is used to matching data between LiDAR and Field survey. For Mo Singto plot, Individual trees are detected from canopy height of LiDAR data which has height more than 4.5 meters and The LiDAR data detected many individual trees between 20 to 25 meters of 1,297 that is 28.68% of Mo Singto plot because the canopy height model of Mo Singto plot has 20.134 meters. There are 2 equations for estimating biomass in Mo Singto plot which are Ogawa et al., (1965) and Chave et al. (2014). The result of Ogawa et al., (1965) method is 4,038.86 of field survey include stems value (3,299.221), branches value (689.787), leaves (49.851) and 3,729.076 of LiDAR data include stems value (3,041.419), branches value (638.517), leaves (49.139). The result of Chave et al. (2014) is 3,209.887 of field survey and 2,631.354 of LiDAR data. |
| Year | 2017 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. RS-17-27 |
| 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) | Sarawut Ninsawat;Sasaki, Nophea |
| Scholarship Donor(s) | Royal Thai Government;AIT Fellowship |
| Degree | Thesis (M. Eng.) - Asian Institute of Technology, 2017 |