Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data

Abstract

Abstract Background Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is "invisible" to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data. Results ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on 1) approximating the initial soil carbon stocks using simulations; 2) approximating the annual litter input based on the predicted growing stocks in each cell; 3) estimating the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated mean total carbon change at the stand level (for 3324 stands) was 10.6 Mg ha-1. The biomass carbon change was 5.5 Mg ha-1, the litter carbon change (e.g., deadwood and leaves) was 5.2 Mg ha-1, and the change in SO carbon was -0.151 Mg ha-1. Conclusions Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, reliable inference can be made under a model-based inferential approach.