The estimation of selective logging impact in Amazon forest using LIDAR data

Abstract

Forest management activities are crucial for the sustainable development of Brazil. Those activities require, however, a strict monitoring that are often difficult to operationalize. The mapping of impacted areas by selective logging and the measurement of forest impacts because of logging operations are mostly based on extensive and costly field surveys. In this study, the Light Detection and Ranging (LiDAR) airborne technology was used to assess the impacts caused by selective logging within 21 units of forest annual production in the Amazon. The study sites are in the states of Rondônia and Pará, within National Forests under federal forestry concession. We used two metrics derived from the point cloud LiDAR for mapping forest impacts: The Canopy Height Model (CHM) and the Relative Density Model (RDM) as forest understory metric. The results of detection of forest impacts derived from the LiDAR dataset showed similar performance of field-based surveys. We estimated that selective logging activities had impacted an average of 6.8% (± 1.3%, standard deviation) of the forest understory of the Annual Production Units (APU) studied and caused an increase of 4.9% (± 0.9%) in areas of forest canopy opening. The LiDAR technology showed to be effective for assessing and monitoring forest impacts of selective logging in the federal forest concessions in the Amazon.

Keywords:
Airborne LiDAR; Sustainable forest management; Amazon forest; Impact