Importance of the forest state in estimating biomass losses from tropical forests: combining dynamic forest models and remote sensing

Abstract

Abstract. Disturbances, such as extreme weather events, fires, floods, and biotic agents, can have strong impacts on the dynamics and structures of tropical forests. In the future, the intensity of disturbances will likely further increase, which may have more serious consequences for tropical forests than those we have already observed. Thus, quantifying aboveground biomass loss of forest stands due to stem mortality (hereafter biomass loss rate) is important for the estimation of the role of tropical forests in the global carbon cycle. So far, the long-term impacts of altered stem mortality on rates of biomass loss have not been adequately described. This study aims to analyse the consequences of long-term elevated stem mortality rates on forest dynamics and biomass loss rate. We applied an individual-based forest model and investigated the impacts of permanently increased stem mortality rates on the growth dynamics of humid, terra firme forests in French Guiana. Here, we focused on biomass, leaf area index (LAI), forest height, productivity, forest age, quadratic mean stem diameter, and biomass loss rate. Based on the simulation data, we developed a multiple linear regression model to estimate biomass loss rates of forests in different successional states from the various forest attributes. The findings of our simulation study indicated that increased stem mortality altered the succession patterns of forests in favour of fast-growing species, which increased the old-growth forests' gross primary production, though net primary production remained stable. The stem mortality rate had a strong influence on the functional species composition and tree size distribution, which led to lower values in LAI, biomass, and forest height at the ecosystem level. We observed a strong influence of a change in stem mortality on biomass loss rate. Assuming a doubling of stem mortality rate, the biomass loss rate increased from 3.2 % yr−1 to 4.5 % yr−1 at equilibrium. We also obtained a multidimensional relationship that allowed for the estimation of biomass loss rates from forest height and LAI. Via an example, we applied this relationship to remote sensing data on LAI and forest height to map biomass loss rates for French Guiana. We estimated a countrywide mean biomass loss rate of 3.0 % yr−1. The approach described here provides a novel methodology for quantifying biomass loss rates, taking the successional state of tropical forests into account. Quantifying biomass loss rates may help to reduce uncertainties in the analysis of the global carbon cycle.