Use of airborne laser scanning to improve selective logging and to assess size-frequency distribution of forest gaps in the Brazilian Amazon - Reis, Cristiano Rodrigues (2022)
Airborne laser scanning (ALS) can support the management of complex natural forests. The thesis presents two studies, one focuses on a small scale ALS assessment that was used to qualify emergent trees and to assess how many commercial trees can be found in order to improve forest management operational plans. The second study demonstrates the usefulness of a large-scale ALS sampling assessment to evaluate the distribution of canopy gaps across the Amazonian region in Brazil. In the first study (chapter 1) by using the emergent canopy model from ALS-Lidar we classified 66 individuals as having potential for commerce, from which 58 individuals presented the best stem quality for logging, which represented more than seven high quality commercial trees per hectare. We propose that ALS operational planning can be used to more efficiently direct field surveys without the need for a full census that would reduce field work in the initial stages of management. In the chapter 2, we mapped the variation of the power-law scale coefficient (α) function, widely used to describe the gap size-frequency distribution in different forests around the world. We observed a large-scale Northwest to Southeast pattern in (α higher proportion of large gaps in the Southeast), which aligns with recent work on tree mortality rates. We also explained the relationship of these canopy gaps with forest structure and environmental variables. The proportion of large gaps in the forest canopy varied substantially over the Brazilian Amazon as a result of canopy structure and disturbance rates. Scenarios of climate change is a point of concern since increasing extreme weather events may therefore increase the proportion of large gaps in currently intact forests, causing them to resemble human modified forests. Keywords: Emergent crowns, Forest dynamic, Gap size distribution, LiDAR, Selective logging, Sustainable forest management, Tropical rainforest