Uncertainty Analysis for Southwest Forest Biomass and Carbon Stocks Using Active and Passive Remote Sensing
Thursday, 18 December 2014
On the San Carlos Apache Reservation in east-central Arizona, fire suppression and other factors have led to overstocked forests and woodlands, and woodland encroachment into grasslands. In an effort to retain traditional relationships with the land, the San Carlos Apache Tribe is working to restore the land to approximate pre-European settlement conditions using a combination of mechanical thinning and fire. Restoring the historic fire return interval in forests and woodlands with the current unnaturally high fuel loads in times of prolonged drought is a challenge in the Southwest US, and therefore creating an accurate forest biomass baseline is very important for future carbon balance projections considering ongoing climate change. Forest aboveground biomass was estimated from active and passive remote sensing sources including high point density airborne lidar, high and moderate spatial resolution WorldView-2 and Landsat 8 satellites. General (all species inclusive) and species-specific biomass models were created using active and passive remote sensing data sources. Across all species, lidar derived height and intensity metrics in combination provided the most robust estimate for aboveground biomass, producing models with R-square values above 0.8 and RMSE less than 14 Mg ha-1. Landsat 8 based species-specific aboveground biomass models yielded errors ranging from 9 to 28 Mg ha-1, whereas WorldView-2 based model yielded errors of 17 to 44 Mg ha-1. Structural information extracted from lidar provided accurate estimates for fine-scale aboveground biomass mapping, while spectral information derived from Landsat 8 can be used for large scale biomass mapping beyond lidar spatial coverage. Long term forest carbon records could be used to direct forest resource management efforts (e.g., thinning and burning) to avoid catastrophic fires, retain stored carbon and maintain long-term carbon sink strength of forest ecosystems.