Indicators for monitoring reduced impact logging in the Brazilian amazon derived from airborne laser scanning technology

Abstract

Monitoring reduced impact logging (RIL) activities in sustainably managed forest areas in the Amazon is a costly and complex, yet crucial endeavor. One viable monitoring option is the use of airborne laser scanning (LiDAR), which enables estimating forest structure parameters over large areas in a reduced timeframe with high accuracy. In this study, we propose and assess the applicability of five monitoring indicators for RIL based on Light Detection and Ranging (LiDAR) data acquisition in areas under forest concession. Five Annual Production Units (APUs) were investigated within the Forest Management Unit (FMU) III of the Jamari National Forest, located in the Southwest of the Brazilian Amazon. These sites were surveyed by LiDAR in 2014 and 2015 (one year after the exploration). Digital Terrain Models (DTMs), Surface Models (DSMs), and Canopy Height Models (CHMs) were generated for each APU. The proposed indicators were: i. Detection and identification of crown removal in dominant and co-dominant trees above 30 m; ii. Gap detection in the forest canopy; iii. Impacts of Reduced Impact Logging on the Understory; iv. Changes in the vertical canopy profile; and v. Affected areas within Permanent Preservation Areas (PPAs) and restricted areas. There was a 3.95% reduction in the occurrence of taller canopies after RIL, and a higher occurrence of small gaps (λ > 1), with λ values (2.34) being higher in the area with the oldest logging history (APU 1). Gini coefficient values in all APUs were below 0.5, indicating a low intensity of disturbances in the forest canopy. The shape (γ) and scale (β) parameters of the understory and canopy were not significantly correlated with variables related to selective logging. Restricted areas were considered for the allocation of roads, trails, log landings, and places with slopes equal to or >15%, and the indices of areas affected by RIL in PPAs and restricted areas were <2%. The proposed indicators using LiDAR data show great potential for monitoring managed areas in the Amazon and can be utilized by concession companies and government oversight.