Performance Analysis of Forest Canopy Height Model Generated from UAV and InSAR

Abstract

Forest canopy height is a crucial parameter in ecosystem process modelling, yet research on generating canopy height models (CHMs) using photogrammetry method from UAV data remains limited compared to methods such as light detection and ranging (LiDAR). This study investigates the performance of accuracy and effectiveness of CHM generated from low-cost Unmanned Aerial Vehicle (UAV) via the photogrammetry method together with the well-known long-established Interferometric Synthetic Aperture Radar (InSAR). Leveraging advancements in UAV technology for three-dimensional land surface modelling, this research focuses on the Pasoh Research Station (nearby the Arboretum area) of Pasoh Forest Reserve in Negeri Sembilan, Malaysia. A series of images were captured at ten different UAV flying altitudes ranging from 120m to 500m above the mean sea level. Subsequently, CHMs were derived from both UAV and InSAR data, and compared against field measurements of tree height. The results indicate that UAV with a flying height below 200m can generate much more accurate results (with average height difference less than 10m) than InSAR (average height difference of 10.532m). It is noticeable that when comparing the InSAR-generated CHM to field measurements at lower altitudes (below 150m), the canopy height obtained from InSAR data is less accurate (more than 10m difference with field measurement) than the CHM obtained via UAV photogrammetry processing (only 5.353 m difference with field measurement). However, when UAV flying height is above 200m, InSAR data is performed more reliably.  A 120m UAV flying height has a good potential to generate canopy height that is closer to the field measurement.