FULLY AUTOMATED GIS-BASED INDIVIDUAL TREE CROWN DELINEATION BASED ON CURVATURE VALUES FROM A LIDAR DERIVED CANOPY HEIGHT MODEL IN A CONIFEROUS PLANTATION

The generation of high resolution canopy height model (CHM) from LiDAR makes it possible to delineate individual tree crown by means of a fully-automated method using the CHM’s curvature through its slope. The local maxima are obtained by taking the maximum raster value in a 3 m x 3 m cell. These values are assumed as tree tops and therefore considered as individual trees. Based on the assumptions, thiessen polygons were generated to serve as buffers for the canopy extent. The negative profile curvature is then measured from the slope of the CHM. The results show that the aggregated points from a negative profile curvature raster provide the most realistic crown shape. The absence of field data regarding tree crown dimensions require accurate visual assessment after the appended delineated tree crown polygon was superimposed to the hill shaded CHM

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

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)

MANGROVE PLANTATION FOREST ASSESSMENT USING STRUCTURAL ATTRIBUTES DERIVED FROM LIGHT DETECTION AND RANGING (LiDAR) DATA

Estimating the structural and functional attributes of forests is integral in performing management strategies and for understanding forest ecosystem functions. Field sampling methods through plot level is one of the known strategies in forest studies; however, these methods have its limitations and are prone to subjected biases. Remote Sensing data, particularly that of Light Detection and Ranging (LiDAR) can be utilized to alleviate the limitations of extracting forest structure parameters. The study aims to characterize a Rhizophoraceae-dominated mangrove forest plantation. Point cloud distribution within a 1-hectare plot was processed by utilizing thirty (30) samples of 5x5 meter plots, which were analysed for the characterization and forest structure assessment. Point densities were grouped at intervals of 10% of the plot’s maximum height (Height at Bincentile or HBn) to determine where the clustering of points occur per plot. The result shows that most of the points are clustered at HBn with height values ranging from 2.98 to 4.15 meters for plots located at the middle part of the forest, with a standard deviation of 1.78 to 3.69, respectively. On the other hand, sample plots that are located at the periphery part of the forest shows that the point clustering occurs at different heights ranging from 1.71 meters to 4.43 meters, with standard deviation values ranging from 1.69 to 3.81.Plots that are located along the fringes of the forest reflect a stunted clustering of points, while plots that explicitly show mangrove trimmings and cuts reflect even distribution in terms of point density within each HBn. Both species present in the area (R. mucronata and R. apiculata) exhibits similar clustering, which could represent detection of Rhizophoraceae mangroves