ABOVE GROUND BIOMASS (AGB) ESTIMATION OF COCONUT (Cocos nucifera) and MANGO (Mangifera indica) TREES FROM LIDAR DERIVATIVES USING REMOTE SENSING TECHNOLGY

摘要

This paper demonstrates the utilization of LiDAR derivatives for above ground biomass (AGB) estimation of coconut (Cocos nucifera) and mango {Mangifera indica) crops in Butuan City, Agusan del Norte, Philippines. The estimation of potential biomass of coconut and mango crops is important for various applications such as yield prediction, nutrient management and analysis of carbon sequestration. With the aid of LiDAR Technology, feature extraction is more precise and accurate. Prior to estimation, classification of image-objects was done by developing rule sets in eCognition software. LiDAR point cloud data was used to generate LiDAR derivatives such as Normalized Digital Surface Model (nDSM), Digital Surface Model (DSM) intensity and Canopy Height Model (CHM). The certain class vegetation objects were pre-classified into classes such as High Elevation Group (HE) of which coconut and mango trees belong, Medium Elevation Group (ME) and Low Elevation Group (LE) according to heights in the LiDAR nDSM that also contained sub-classes. Tree height and crown area as the parameters used for AGB estimation could be determined in the eCognition environment. Actual measurement of coconut and mango diameter at breast were conducted at the field using stratified sampling method. Based on the results, this study shows 94.36% overall accuracy of classification of maps resulted to a significant estimation of above ground biomass.