Abstract
Abstract
Natural climate solutions (NCSs) are conservation, restoration, and improved land management actions that have potential to provide climate mitigation across different land cover types. NCS related to forests offer a significant portion of cost-effective NCS mitigation required to limit warming to below 2 °C. Afforestation—planting trees in areas where forests can occur but does not currently exist has been proposed as a viable NCS. Here, we examine how long-term, medium resolution satellite datasets and physiological growth models can be used to inform potential carbon accumulation from forest afforestation. We leverage free and open Landsat-derived datasets to examine potential increases in aboveground biomass (AGB) and tons of CO2 equivalent (CO2e) that afforestation may provide by 2050 in the Canadian boreal. We utilized contemporary Landsat-scale definitions of land cover, forest age, and species datasets to identify opportunities for new forest growth in areas previously unforested across study sites. These datasets, along with terrain and climate, were used as inputs for the 3-PG physiological growth model, which converts solar radiation into net primary productivity on a monthly time-step, and was parameterized for key natural species to simulate forest growth and carbon accumulation under three different future climate scenarios. We compared these new fine-scale, climate-adapted estimates with previous findings. The amount of CO2e sequestered per hectare per year increased with increasing CO2 emissions (4.0%–12.4% more carbon). Using a reference area, the fertilized simulation sequestered 24.38 Tg CO2e yr−1 in 2050 compared to 24.9 Tg CO2e yr−1 proposed in other research under the warmest scenario. The use of physiological models linked to satellite data to support NCS calculations, particularly for unforested areas, is a new application. The results highlight the potential for 3-PG to be used to estimate AGB and provide valuable information for the performance of NCS under a changing climate.