Airborne LiDAR is fast becoming an innovation for forest inventory. It aids in obtaining forest characteristics in areas or cases where actual field inventory would be very tedious. This study aims to estimate diameter at breast height (DBH) using airborne LiDAR point-cloud parameters with Worldview-2 satellite images, and to validate these with actual measurements done in the field. The study site is a field plot with forest inventory at Mt. Makiling, Laguna, Philippines that was surveyed into 20m, 10m and 5m subplots or grids. The estimation of DBH was carried out by extracting the said parameters from the LiDAR point-cloud, and extracting different bands from the Worldview image and performing linear and log-linear regression of these values. The regressions were done in four different cases, namely: LiDAR parameters without intensity (case1), LiDAR parameters without intensity with Worldview bands (case 2), intensity of LiDAR points (case 3), and LiDAR parameters with intensity and Worldview bands (case 4). From these it was found that the best case for estimating DBH is with the use of LiDAR parameters with intensity and Worldview bands in a 10x10 grid, in Log-Linear regression with a root mean squared error of 1.96 cm and an adjusted R2 value of 0.65. This was further improved through stepwise regression, and adjusted R2 value was 0.71.
The extent at which mangrove forest characterization can be done through utilization of Light Detection and Ranging (LiDAR) data is investigated in this paper. Particularly, the ability of LiDAR parameters, such as its point density to provide height and structural information was explored to supplement manual field surveys which are time-consuming and requires great effort. Point cloud information was used to produce separability measure within a mangrove forest. The study aims to validate the point density distribution curves (PDDC) that were established to characterize the structural attributes between Rhizophoraceae and Avicenniaceae. The applicability of the PDDC was applied to fifteen (15) 5x5 sample plots of pure Rhizophoraceae and fifteen (15) 5x5 sample plots of pure Avicenniaceae in a one hectare (1ha) natural riverine mangrove forest. 15 out of 15 plots were correctly discriminated as Rhizophoraceae; however, Avicenniaceae plots were not correctly discriminated using the established separability measure. This study had determined that the two mangrove families are difficult to separate in terms of point density distribution alone. Enhancement of the PDDC as a separability measure should be improved to pave way for a more sensitive and robust way to separate the two families.
Gio Zaragosa, Enrico Paringit, Carlyn Ann Ibañez, Regine Anne Faelga, Reginald Jay Argamosa, Mark Anthony Posilero, Fe Andrea Tandoc, Matthew Malabanan
KEYWORDS: LIDAR, Breast, Data acquisition, Clouds, Data processing, Global Positioning System, Vegetation, Calibration, Image processing, Data modeling
LiDAR Overlap is the area that is common to two or more flight lines. This is essential to ensure the continuity of data as the acquisition moves from one flight line to another. Looking into overlaps is important when doing DBH Estimation using point cloud data because it doubles the density of points in the overlap region. To remove this effect when determining the DBH of a forest area, the LiDAR data was processed using a point-cloud processing software. The processes include separating flight lines using the GPS time when the points were acquired. After separating, the number of points in the overlap region were decreased by removing excess points within the area of twice the point spacing. The parameters needed for DBH estimation were then obtained. The absolute number of points in the whole overlap area was originally 4,960,726 after decreasing the number of points, it was reduced to 1,479,884. The number of points would have an effect on DBH estimation because the values obtained were significantly different at 95% level of confidence.
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