Despite storing most of the aboveground terrestrial carbon across global ecosystems, forests are currently acting as greenhouse gas emission sources due to land use change caused by human activities. Forest conservation schemes, like REDD+ (Reducing emissions from deforestation and forest degradation), disincentivize deforestation and forest degradation by paying for projects that reduce forest emissions in developing countries. This mechanism requires the development of emission baselines and monitoring of forest biomass, which both rely on models using field measurements and remote sensing data. Nevertheless, the development of forest biomass models is still complex due to the challenging collection of field samples and the still understudied use of remote sensing data for this purpose. In Chile, progress developing accurate forest biomass models has been very limited and has not taken advantage of the potential in current remote sensing techniques to produce accurate biomass models. As a consequence, uncertainty in the outcome of REDD+ projects has been a barrier for landowners to engage and potential funding for forest conservation and restoration initiatives has been lost.
Considering the latter, my research will address the following questions: Is it possible to develop forest biomass models in Chile? Which remote sensing methods produce the most accurate models? To answer these questions, I will use a combination of optical sensor, radar, and LiDAR datasets to identify forest biomass models with the highest accuracy, appropriate for developing REDD+ emission baselines, in a pilot area in southern Chile covered by evergreen forests.