Tens of millions of trees reportedly died in California during the 2012–2016 drought, resulting in marked increases in heavy fuel loads on the landscape. In conjunction with warming temperatures, drier conditions, and over a century of fire suppression, the accumulation of fuel can result in catastrophic fires that are beyond the predictive capacity of traditional fire behavior models. These extreme fires can cause an enormous amount of damage; one example is the 2020 Castle Fire, which burned into numerous giant sequoia groves at unprecedented severity. Preliminary estimates suggest the fire may have killed over 10% of the largest giant sequoias in the species range.
Developing treatments to reduce the likelihood of these fires requires landscape-scale assessments of catastrophic fire risk. USGS scientists and partners have recently launched a collaboration with a statewide consortium to produce fire risk maps at Sequoia and Kings Canyon National Parks (SEKI) using in-development next-generation fire behavior models. Production of these risk projections will require robust empirical data that quantify existing fuels, including an accurate map of tree mortality from the drought. USGS scientists are currently working with the USFS Region 5 Remote Sensing Lab (RSL) to develop high-quality dead-tree maps using remote sensing data from both the Global Airborne Observatory (GAO) and Landsat.
Ground-based data collection is focused on two GAO study areas that cover large portions of the conifer forests at SEKI. Researchers are collecting ground-based data that include precise tree locations, tree species, and tree status (live or dead). The RSL is combining these data with remote sensing measures to develop a species map with dead trees being a category of “species”. USGS will then collect additional data to validate the map. This adaptive process will continue until acceptable levels of accuracy are reached. An important incidental product from this project will be a canopy species map for the parks, which will be useful for a variety of applications, including quantifying and locating pine species in decline.
Preliminary species map for mixed conifer forest in the Sierra Nevada, with dead trees shown in black.