Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest

Abstract

Airborne laser scanning (ALS) is increasingly being used to enhance the accuracy of biomass estimates in tropical forests. Although the technological development of ALS instruments has resulted in ever-greater pulse densities, studies in boreal and sub-boreal forests have shown consistent results even at relatively small pulse densities. The objective of the present study was to assess the effects of reduced pulse density on (1) the digital terrain model (DTM), and (2) canopy metrics derived from ALS data collected in a tropical rainforest in Tanzania. We used a total of 612 coordinates measured with a differential dual frequency Global Navigation Satellite System receiver to analyze the effects on DTMs at pulse densities of 8, 4, 2, 1, 0.5, and 0.025 pulses·m . Furthermore, canopy metrics derived for each pulse density and from four different field plot sizes (0.07, 0.14, 0.21, and 0.28 ha) were analyzed. Random variation in DTMs and canopy metrics increased with reduced pulse density. Similarly, increased plot size reduced variation in canopy metrics. A reliability ratio, quantifying replication effects in the canopy metrics, indicated that most of the common metrics assessed were reliable at pulse densities >0.5 pulses·m −2 at a plot size of 0.07 ha.

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

“…Herein, we simulated low pulse density lidar datasets by removing pulses randomly, however, other approaches of removing lidar pulses have also been found in the literature [16][17][18][19]32,44] and may lead to different outcomes when considering the covarying effect of pulse density when survey parameters are changed. Nevertheless, our results on HMEAN variation patterns agree with a previous study [31], and independent of the approach used, a realistic thinning approach on real lidar data is always extremely challenging [45].…”

“…Previous studies have evaluated the impact of lidar pulse density on forest attribute estimation from lidar data (e.g., [31][32][33]), yet few studies have evaluated the impacts of lidar pulse density on forest AGB stock estimates in tropical forest [19,20,34]. To our knowledge, this is the first study to assess the impact of airborne lidar pulse density on AGB stocks and AGB change estimations in tropical forest, and in the context of using an airborne lidar system in selective logging for monitoring forest AGB change for REDD+ and emission reduction programs.…”

“…Hansen et al [31] evaluated the effects of lidar pulse density on DTM and canopy structure metrics in a tropical forest, and showed also that HMEAN was one of the most stable predictor variables for modelling forest attributes using airborne lidar data. In our study, under both DTM scenarios, reduced pulse density did not significantly affect the variability of HMEAN among plots.…”

“…In this study, we mapped AGB change at the landscape level at a spatial resolution of 50 m. Therefore, unlike most previous studies (e.g., [31][32][33]), we not only evaluated the impact of pulse density on AGB stocks estimation at plot the level, but also at the landscape scale. Andersen et al [8] used repeated lidar flights to monitor selective logging in western Amazonia, and AGB stock and changes maps were accurately derived from lidar data acquired in 2010 and 2011 with pulse densities of 25 pulses·m −2 and 14 pulses·m −2 , respectively.…”

“…While the pulse density was appropriate for the study, they are not economically feasible for large-area acquisitions and for monitoring selective logging over time. Hansen et al [31] suggest that canopy metrics derived from sparse pulse density ALS data can be used for AGB estimation in a tropical forest; however, the authors either estimated AGB or expanded the analysis to landscape level in their studies. Wilkes et al [47] found that structural metrics (canopy height, canopy cover and vertical canopy structure) derived from pulse densities < 0.5 pulses·m −2 returned larger differences, particularly for tropical forest.…”