Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest. (7th March 2018)

• Record Type: Journal Article
• Authors: Becknell, Justin M.; Keller, Michael; Piotto, Daniel; Longo, Marcos; Nara dos‐Santos, Maiza; Scaranello, Marcos A.; Bruno de Oliveira Cavalcante, Rodrigo; Porder, Stephen
• Is Part Of: Biotropica. Volume 50:Number 3(2018)
• Page Start: 520
• Page End: 530
• Publication Date: 2018-03-07
• Subjects: airborne laser scanning -- second growth -- Southern Bahia -- topography -- tropical moist forest; Biotic communities -- Tropics -- Periodicals; Applied ecology -- Tropics -- Periodicals; Biology -- Tropics -- Periodicals; 577.80913
• DOI: 10.1111/btp.12538
• Languages: English
• ISSNs: 0006-3606

Abstract

Abstract: Secondary forests account for more than half of tropical forests and represent a growing carbon sink, but rates of biomass accumulation vary by a factor of two or more even among plots in the same landscape. To better understand the drivers of this variability, we used airborne lidar to measure forest canopy height and estimate biomass over 4529 ha at Serra do Conduru Park in Southern Bahia, Brazil. We measured trees in 30 georeferenced field plots (0.25‐ha each) to estimate biomass using allometry. Then we estimated aboveground biomass density (ABD) across the lidar study area using a statistical model developed from our field plots. This model related the 95th percentile of the distribution of lidar return heights to ABD. We overlaid this map of ABD on a Landsat‐derived forest age map to determine rates of biomass accumulation. We found rapid initial biomass regeneration (~6 Mg/ha yr), which slowed as forests aged. We also observed high variability in both height and biomass across the landscape within forests of similar age. Nevertheless, a regression model that accounted for spatial autocorrelation and included forest age, slope, and distance to roads or open areas explained 62 and 77 percent of the landscape variation in ABD and canopy height, respectively. Thus, while there is high spatial heterogeneity in forest recovery, and the drivers of this heterogeneity warrant further investigation, we suggest that a relatively simple set of predictor variables is sufficient to explain the majority of variance in both height and ABD in this landscape.