Using Landsat-derived disturbance and recovery history and lidar to map forest biomass dynamics

To the best of our knowledge, one or more authors of this paper were federal employees when contributing to this work. This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/remote-sensing-of-environment.Improved monitoring of forest biomass and biomass change is needed to quantify natural and anthropogenic effects\ud on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, moderate spatial resolution, and\ud long history of earth observations provide a unique opportunity for characterizing vegetation changes across\ud large areas and long time scales. However, like with other multi-spectral passive optical sensors, Landsat's relationship\ud of single-date reflectance with forest biomass diminishes under high leaf area and complex canopy conditions.\ud Because the condition of a forest stand at any point in time is largely determined by its disturbance and\ud recovery history, we conceived a method that enhances Landsat's spectral relationships with biomass by including\ud information on vegetation trends prior to the date for which estimates are desired. With recently developed\ud algorithms that characterize trends in disturbance (e.g. year of onset, duration, and magnitude) and post-disturbance\ud regrowth, it should now be possible to realize improved Landsat-based mapping of current biomass\ud across large regions. Moreover, given that we now have 40 years of Landsat data, it should also be possible to use\ud this approach to map historic biomass densities.\ud In this study, we developed regression tree models to predict current forest aboveground biomass (AGB) for a\ud mixed-conifer region in eastern Oregon (USA) using Landsat-based disturbance and recovery (DR) metrics.\ud We employed the trajectory-fitting algorithm LandTrendr to characterize DR trends from yearly Landsat time\ud series between 1972 and 2010. The most important DR predictors of AGB were associated with magnitude of\ud disturbance, post-disturbance condition and post-disturbance recovery, whereas time since disturbance and\ud pre-disturbance trends showed only weak correlations with AGB. Including DR metrics substantially improved\ud predictions of AGB (RMSE = 30.3 Mg ha⁻¹, 27%) compared to models based on only single-date reflectance\ud (RMSE = 39.6 Mg ha⁻¹, 35%). To determine the number of years required to adequately capture the effect of\ud DR on AGB, we explored the relationship between time-series length and model prediction accuracy. Prediction\ud accuracy increased exponentially with increasing number of years across the entire observation period, suggesting\ud that in this forest region the longer the historic record of disturbance and recovery metrics the more accurate\ud the mapping of AGB. However, time series lengths of between 10 and 20 years were adequate to significantly\ud improve model predictions, and lengths of as little as 5 years still had a meaningful impact. To test the concept\ud of historic biomass prediction, we applied our model to Landsat time series from 1972–1993 and estimated\ud AGB biomass change between 1993 and 2007. Our estimates compared well with historic inventory data, demonstrating\ud that long-term Landsat observations of DR processes can aid in monitoring AGB and AGB change.\ud Instead of directly linking Landsat data with the limited amount of available field-based AGB data, in this study\ud we used the field data to map AGB with airborne lidar and then sampled the lidar data for model training and\ud error assessment. By using lidar data to build and test our prediction model, this study illustrates that lidar\ud data have great value for scaling between field measurements and Landsat data