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

Abstract

Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Pará, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m −2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m −2 . For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha −1 when pulse density decreased from 12 to 0.2 pulses·m −2 . The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha −1 . The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

“…In addition, we combined the ground returns from both surveys to generate a single DTM at 1-m horizontal resolution, in order to generate a more accurate LiDAR-derived DTM [49,50]. Accurate terrain characterization is required for accurate lidar-derived forest metrics [47,51], and the combination of two surveys improves the quality of the DTM and reduces uncertainty from subsequent multitemporal analysis. The terrain height derived from the combined DTM was subtracted from each return to remove the topographic influence on the forest height.…”

Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest

“…, Silva et al . ) and are increasingly used to overcome the potential sampling error in plot‐based studies of heterogeneous landscapes (Getzin et al . , Asner et al .…”

“…Lidar data provide direct measurements of height, which can be used to generate estimates of forest biomass (Lefsky et al 2002, Asner & Mascaro 2014. These height measurements and biomass estimates are used to study forest structure and dynamics (Longo et al 2016, Silva et al 2017 and are increasingly used to overcome the potential sampling error in plot-based studies of heterogeneous landscapes (Getzin et al 2017, Asner et al 2018. Similar to small plot-based chronosequences, lidar can be used to study forest recovery by combining the data with forest age maps derived from historical aerial photography or satellite imagery.…”

Dynamics of a human‐modified tropical peat swamp forest revealed by repeat lidar surveys

“…More work is needed to standardize methods. For example, Silva et al (2017) demonstrated that pulse density influenced AGB predictions in a tropical forest in Brazil, particularly on steep slopes, but found that AGB change predictions were relatively uninfluenced by changing point densities, and that even low point densities provided good results. Spatial scale of analyses is also likely to be important.…”