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Assessing the Impacts of Selective Logging on the Forest Understory in the Amazon Using Airborne LiDAR.

Authors :
Ferreira, Leilson
Bias, Edilson de Souza
Barros, Quétila Souza
Pádua, Luís
Matricardi, Eraldo Aparecido Trondoli
Sousa, Joaquim J.
Source :
Forests (19994907); Jan2025, Vol. 16 Issue 1, p130, 26p
Publication Year :
2025

Abstract

Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory—a critical area for assessing logging impacts—remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rondônia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19994907
Volume :
16
Issue :
1
Database :
Complementary Index
Journal :
Forests (19994907)
Publication Type :
Academic Journal
Accession number :
182435163
Full Text :
https://doi.org/10.3390/f16010130