Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

Abstract

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of −4.14 ± 0.76 MgC ha−1 y−1. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

Bamboo, climate change and forest use: A critical combination for southwestern Amazonian forests?

“…There is an active discussion as to whether global climate change and regional deforestation make Amazonia so much drier that large areas of lowland rain forest can reach a tipping point after which the ecosystem suddenly turns to savanna (Boulton et al 2017;da Silva et al 2018;Marengo et al 2018;Ambrizzi et al 2019;Goodman et al 2019;Rangel Pinagé et al 2019). We share this scenario but argue that, in the large bamboo-dominated forest area in southwestern Amazonia, the tipping point is going to be reached with a smaller increase in dryness.…”

Urban Forest Growth and Gap Dynamics Detected by Yearly Repeated Airborne Light Detection and Ranging (LiDAR): A Case Study of Cheonan, South Korea

“…These days, with its acceptable accuracy and advantages in sensing vertical canopy structures, multitemporal LiDAR surveys have also shown great potential for detecting changes in forest structure [23]. For example, the use of adequate multitemporal airborne LiDAR datasets represents a reliable and efficient method for detecting canopy changes and estimating canopy growth and forest biomass dynamics at a fine temporal resolution [24][25][26][27][28][29][30][31][32][33][34]. Recently, Song et al [24], Zhao et al [29], Dalponte et al [30], and Cao et al [31] successfully estimated forest biomass dynamics and tree growth in forests using repeated airborne LiDAR data.…”

“…Recently, Song et al [24], Zhao et al [29], Dalponte et al [30], and Cao et al [31] successfully estimated forest biomass dynamics and tree growth in forests using repeated airborne LiDAR data. Moreover, Dalagnol et al [32] and Rangel et al [33] reported quantification of canopy dynamics focusing on human-induced disturbances (e.g., logging) by using multitemporal airborne LiDAR data in Amazon forests. Rangel et al [33] reported that setting a height differences threshold was an efficient way to map logged trees and small gaps usually closed within short periods (within two years).…”

“…Moreover, Dalagnol et al [32] and Rangel et al [33] reported quantification of canopy dynamics focusing on human-induced disturbances (e.g., logging) by using multitemporal airborne LiDAR data in Amazon forests. Rangel et al [33] reported that setting a height differences threshold was an efficient way to map logged trees and small gaps usually closed within short periods (within two years). Dalagnol et al [32] found that gap formation occurred more frequently in logged areas than in intact forests.…”

How Amazon trees growth are affected and react to forest fragmentation and climate changes? A dendrochronology approach

“…Recent studies realized in Amazonia using remote sensing techniques, revealed that after selecting logging, the forest understory structure resembled its original state within four to five years while differences in canopy structure were still detected eight years following logging (Rangel-Pinagé et al, 2019). Also using remote sensing techniques, de Paula et al…”