In recent years, LiDAR technology has provided accurate forest aboveground biomass (AGB) maps in several forest ecosystems, including tropical forests. However, its ability to accurately map forest AGB changes in high-biomass tropical forests has seldom been investigated. Here, we assess the ability of repeated LiDAR acquisitions to map AGB stocks and changes in an old-growth Neotropical forest of French Guiana. Using two similar aerial small-footprint LiDAR campaigns over a four year interval, spanning ca. 20 km2, and concomitant ground sampling, we constructed a model relating median canopy height and AGB at a 0.25-ha and 1-ha resolution. This model had an error of 14% at a 1-ha resolution (RSE = 54.7 Mg ha− 1) and of 23% at a 0.25-ha resolution (RSE = 86.5 Mg ha− 1). This uncertainty is comparable with values previously reported in other tropical forests and confirms that aerial LiDAR is an efficient technology for AGB mapping in high-biomass tropical forests. Our map predicts a mean AGB of 340 Mg ha− 1 within the landscape. We also created an AGB change map, and compared it with ground-based AGB change estimates. The correlation was weak but significant only at the 0.25-ha resolution. One interpretation is that large natural tree-fall gaps that drive AGB changes in a naturally regenerating forest can be picked up at fine spatial scale but are veiled at coarser spatial resolution. Overall, both field-based and LiDAR-based estimates did not reveal a detectable increase in AGB stock over the study period, a trend observed in almost all forest types of our study area. Small footprint LiDAR is a powerful tool to dissect the fine-scale variability of AGB and to detect the main ecological controls underpinning forest biomass variability both in space and time.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)