Silvilaser 2019 - Poster Presentations »
The challenge to assess the Amazon forest structure
The identification of forest structure is critical for several lines of investigation, including ecology, ecosystem functioning, micrometeorology, forest dynamic, forest management, among others. Assessing forest structure at a regional level is challenging and, often, expensive. The ALS (airborne laser scanning) technology and associated remote sensing are used to directly retrieve vegetation structure variables such as canopy height, number of individuals, volume, crown diameter, and indirect biophysical measurements over a much larger geographical extent than plot-based forest inventories. The diversity of information that can be extracted from ALS data provide exciting opportunities for improving forest monitoring, especially related to degradation, precision forestry, and carbon-stock estimation across large areas. Besides that, the surveyed extent allows reducing the levels of uncertainty in forest biomass estimation. We launched a large survey, randomly distributing 375 hectares ALS transects within the 3.5 million km2 of the Brasilian Amazon forest, the largest continuous tropical humid forest in the globe. The point cloud obtained from the ALS surveys allowed us to spatially describe the distribution of the emergent trees, to assess the forest structure across the Amazon basin, to produce a forest biomass map (assessing the uncertainty) and to correlate the variation of the environmental factor to the actual field sampling plots. At field plot level (first level), the data are used to validate the forest biomass estimated by LiDAR scanning (second level), based on allometric equations and the third level by extrapolating the forest biomass to the Brazilian Amazon Biome, by the use a suite of satellite information and metrics. Despite the incredible results already published, the ALS data is still offering the opportunity for new investigations.