Quantifying tree mortality with lasers: using a state-of-the-art model-data fusion approach to estimate biomass loss in tropical forests

Abstract

The Amazon Forest is an essential component of the global climate system. It consists in ca. 40% of tropical forests, store ca. 120 ± 30 Pg of carbon in biomass, and constantly exchange carbon with the atmosphere by productivity, mortality and decomposition, and also due to deforestation and fires. Our limited knowledge of tree mortality processes constrains our ability to accurately quantify the tropical forest carbon budget, and affects our predictions of climate and environmental change effects on the future states of forest ecosystems. In this context, remote sensing is the only way to obtain spatially-consistent observations of tree mortality over large regions. Moreover, airborne LIght Detection And Ranging (LiDAR) data can precisely retrieve forest structure and canopy gap information, which has the potential to be used for parametrizing and constraining tree mortality processes in ecosystem models, such as the LPJ-GUESS. The spatial variability of forest structure and gaps across contrasting environmental and climate conditions in Amazon Forest is largely unknown; and we do not fully understand the influence of local-scale drivers (topography) and/or regional-scale drivers such as climate (precipitation, seasonality), and edaphic properties on forest structure and gap dynamics, nor have these gap dynamics been converted into tree mortality rates. In this project, my goal is to provide a systematic and spatially-unbiased assessment of tree mortality rates and related carbon cycling in tropical forests using a novel model-data fusion approach, leveraging the recent availability of a massive single-pass LiDAR dataset. I aim to answer the following questions: (Q1) How does tropical forest structure and gap dynamics derived from airborne LiDAR data vary across the Brazilian Amazon Forest? (Q2) What local- and/or regional-scale factors drive forest dynamics in Brazilian Amazon? (Q3) Can forest structure and canopy gap information derived from single-pass airborne LiDAR data be used to characterize tree mortality rates and improve their representation in large-scale ecosystem models? (Q4) How does this constraint on forest dynamics improves our understanding of the Amazon Forest carbon cycle and what is the contribution of small-scale mortality to carbon turnover? The outcome of this research will consist in novel data of forest gap size-frequency distribution, height and crown sizes, and their spatial distribution across the Brazilian Amazon; a new model-data fusion method to derive tree mortality rates that has potential to be applied worldwide; and more accurate estimates of carbon cycle for the Amazon Forest. (AU)