Landsat Time Series and Lidar as Predictors of Live and Dead Basal Area Across Five Bark Beetle-Affected Forests

Bark beetle-caused tree mortality affects important forest ecosystem processes. Remote sensing methodologies that quantify live and dead basal area (BA) in bark beetle-affected forests can provide valuable information to forest managers and researchers. We compared the utility of light detection and ranging (lidar) and the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm to predict total, live, dead, and percent dead BA in five bark beetle-affected forests in Alaska, Arizona, Colorado, Idaho, and Oregon, USA. The BA response variables were predicted from lidar and LandTrendr predictor variables using the random forest (RF) algorithm. RF models explained 28%-61% of the variation in BA responses. Lidar variables were better predictors of total and live BA, whereas LandTrendr variables were better predictors of dead and percent dead BA. RF models predicting percent dead BA were applied to lidar and LandTrendr grids to produce maps, which were then compared to a gridded dataset of tree mortality area derived from aerial detection survey (ADS) data. Spearman correlations of beetle-caused tree mortality metrics between lidar, LandTrendr, and ADS were low to moderate; low correlations may be due to plot sampling characteristics, RF model error, ADS data subjectivity, and confusion caused by the detection of other types of forest disturbance by LandTrendr. Provided these sources of error are not too large, our results show that lidar and LandTrendr can be used to predict and map live and dead BA in bark beetle-affected forests with moderate levels of accuracy.