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.

Date Published: 5 May 2016
PDF: 9 pages
Proc. SPIE 9879, Lidar Remote Sensing for Environmental Monitoring XV, 98791F (5 May 2016); doi: 10.1117/12.2224367

Published in SPIE Proceedings Vol. 9879:
Lidar Remote Sensing for Environmental Monitoring XV
Upendra N. Singh; Nobuo Sugimoto; Achuthan Jayaraman; Mullapudi V. R. Seshasai, Editor(s)