LAUSR

Abstract Greenhouse gas emissions have altered global climate significantly, increasing the frequency of drought, fire, and insect- and pathogen-related mortality in forests across the western United States. The accuracy of satellite-based estimates of canopy change has been limited by difficulties associated with discriminating overstory canopy from understory vegetation. To overcome this issue, we developed a method to quantify forest canopy cover using winter-season fractional snow covered area (FSCA) data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow covered area and grain size (MODSCAG) algorithm. The method utilizes time series of FSCA data to identify images with continuous ground snow coverage and a snow-free overstory, effectively masking out the influence of understory vegetation. Using this method, we determined that MODSCAG-retrieved viewable gap fraction (VGF; i.e. fraction of pixel sub-canopy viewable area) was significantly correlated with an independent product of yearly crown mortality caused by mountain pine beetles derived from Landsat imagery at 25 high-mortality sites in northern Colorado ( r − = 0.96 ± 0.03 , p 0.03 ). Additionally, we determined the temporal lag between tree mortality and needlefall, showing that needlefall occurred an average of 2.6 ± 1.2 years after year of attack. The canopy change detection method described herein is the first to utilize snow cover to mask understory impacts on overstory detection. The method can be applied anywhere in the seasonal snow zone and therefore has wide applicability given that 30% of the global land surface is seasonally snow covered. In this regard, the approach addresses significant limitations of previously published methods of canopy change detection and has broad implications with regard to understanding forest mortality and the representation of disturbance within hydrologic, land surface, and climate models.