As climate change becomes more of a pressing issue, the importance of monitoring regional and local changes across ecosystems increases. Biomass provide information on the condition of that ecosystem, the carbon balance, and can be used as indicators of any underlying processes. The boreal continental peatlands are one ecoregion where biomass information is needed. Peatlands play an important role in the global carbon cycle because of their ability to store carbon; however, the threat of global warming could potentially turn peatlands into becoming a major carbon source. Mapping peatland biomass can expand our knowledge on the condition of these ecosystems or be predictive of changes underway. The use of remote sensing techniques for biomass monitoring is increasing because remote sensing offers wider spatial and temporal coverage while being less labor intensive than traditional field-based methods. In this study, we used orthophotography and photogrammetric point clouds acquired from Unmanned Aerial Vehicles (UAV). Our research assesses the accuracy of biomass estimates generated from the UAV across three peatland sites in Northern Alberta. A canopy height model is derived from the photogrammetric point clouds which captures the unique shape of each shrub/tree. From the model, the volume of each individual tree is calculated.
Results/Conclusions
Volume is found to be a significant predictor (P< 0.001) of aboveground biomass (r2 = 0.89) when compared against the harvested samples. The harvested samples represent three genus groups: Alnus spp., Salix spp., and Betula Pumila. The results also indicate that genus information does not influence the relationship of volume and aboveground biomass. This research demonstrates how UAV imagery was able to capture the low-stature trees/shrubs (< 1m) and how UAV-generated volume estimates was a strong predictor of aboveground biomass. The successful application of UAV imagery in biomass estimation studies has the potential for wider and more frequent monitoring of ecosystems while requiring less fieldwork.