Characterizing forest carbon dynamics using multi-temporal LiDAR data

Dalponte et al. 2019 RSE.jpg

Recent years have seen a rapid surge in the use of remote sensing technologies for characterizing the structure, composition and function of forested landscapes. Yet with few exceptions these studies have only provided snapshots of the ecosystem at one point in time, thereby limiting our ability to understand how forests are responding to global change. New approaches for characterizing forest dynamics using multi-temporal remotely sensed data are therefore urgently needed if we are to integrate these new technologies into conservation and management decision making processes. By combining data from a network of forest plots with repeat airborne lidar, here we develop an approach to (i) map fine-scale variation in aboveground carbon density (ACD) and its change over time across the landscape, and (ii) link these changes in ACD to forest structural attributes, species composition, disturbance regimes and local topography. We tested this framework on a temperate forest in the Alps characterized by the presence of three dominant species: spruce (Picea abies), silver fir (Abies alba) and beech (Fagus sylvatica). We found that between 2007 and 2011 the majority of the landscape (61.0%) increased in ACD, with a much smaller fraction of the study area exhibiting evidence of small-scale natural disturbances (3.7%) or ACD loss as a result of logging activities (13.7%). On average, areas of the landscape that actively sequestered carbon did so at a rate of 3.6% per year. However, rates of ACD accumulation varied considerably across the landscape, being greatest in forest stands characterized by multi-layered heterogeneous canopies, in ones dominated by spruce and at lower elevations. We demonstrated the potential of repeat lidar for characterizing not only the structure, but also the composition and aboveground carbon dynamics of forests. In doing so we open the door to monitor forests across large and inaccessible landscapes in order to better understand how they are responding to rapid global change and refine how we manage and conserve these critical ecosystems.

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