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Diameter-at-Breast-Height Estimation is a prerequisite in various allometric equations estimating important forestry indices like stem\udvolume, basal area, biomass and carbon stock. LiDAR Technology has a means of directly obtaining different forest parameters,\udexcept DBH, from the behavior and characteristics of point cloud unique in different forest classes. Extensive tree inventory was\uddone on a two-hectare established sample plot in Mt. Makiling, Laguna for a natural growth forest. Coordinates, height, and canopy\udcover were measured and types of species were identified to compare to LiDAR derivatives. Multiple linear regression was used to\udget LiDAR-derived DBH by integrating field-derived DBH and 27 LiDAR-derived parameters at 20m, 10m, and 5m grid resolutions.\udTo know the best combination of parameters in DBH Estimation, all possible combinations of parameters were generated and\udautomated using python scripts and additional regression related libraries such as Numpy, Scipy, and Scikit learn were used. The\udcombination that yields the highest r-squared or coefficient of determination and lowest AIC (Akaike’s Information Criterion) and\udBIC (Bayesian Information Criterion) was determined to be the best equation. The equation is at its best using 11 parameters at 10mgrid\udsize and at of 0.604 r-squared, 154.04 AIC and 175.08 BIC. Combination of parameters may differ among forest classes for\udfurther studies. Additional statistical tests can be supplemented to help determine the correlation among parameters such as Kaiser-\udMeyer-Olkin (KMO) Coefficient and the Barlett’s Test for Spherecity (BTS)

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

ESTIMATING DBH OF TREES EMPLOYING MULTIPLE LINEAR REGRESSION OF THE BEST LIDAR-DERIVED PARAMETER COMBINATION AUTOMATED IN PYTHON IN A NATURAL BROADLEAF FOREST IN THE PHILIPPINES