Abstract
Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem volume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters, except DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was done on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy cover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to get LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions. To know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and automated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The combination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike’s Information Criterion) and BIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid size and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for further studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser- Meyer-Olkin (KMO) Coefficient and the Barlett’s Test for Spherecity (BTS).
Highlights
Proper forest resources assessment and management highly depends on accurate data gathering of tree inventories in an authentic ground survey to get detailed and up-to-date forest characteristics and attributes
It has the capacity for direct calculation and estimation of important forest characteristics such as canopy height, canopy cover, stand volume, basal area and above ground biomass
Airborne LiDAR Scanning (ALS) is capable of directly measuring forest characteristics based on height but not DBH
Summary
Proper forest resources assessment and management highly depends on accurate data gathering of tree inventories in an authentic ground survey to get detailed and up-to-date forest characteristics and attributes. The needed forest information are often based on species name, diameter-at-breast-height (DBH), basal area, merchantable height, total height and, in some cases, age. DBH is obtained by measuring the diameter of the trunk at 1.3 meters above the ground. This method is too intensive and costly to conduct when applied in large areas. LiDAR is a breakthrough in forestry application. It has the capacity for direct calculation and estimation of important forest characteristics such as canopy height, canopy cover, stand volume, basal area and above ground biomass. Airborne LiDAR Scanning (ALS) is capable of directly measuring forest characteristics based on height but not DBH
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