Time series of remote sensing data offers the opportunity to predict changes in vegetation extent and to estimate forest parameter change such as biomass. However, as sensors and technology advance, it is important to ensure that estimates obtained from different time periods or using different, but related, instruments are consistent in order to have confidence in detected change. This study compares estimates of biomass from small-footprint discrete-return LiDAR data and medium-footprint full-waveform LiDAR for Howland Experimental Forest, Maine, USA. Data were collected from both sensors during Summer 2009.
Similar results were found using the same height metric with R2 = 0.67, SE = 58.5 Mg ha-1 and R2 = 0.52, SE = 58.1 Mg ha-1, respectively. The predicted model of the relationship between LiDAR metrics and biomass was applied to data captured in 2003. Identified areas of change corresponded well with a map of forest management operations of varying intensities. Where sensitivity to change allows, vegetation age estimated using time series of Landsat observations, combined with biomass estimates, allows growth curves to be produced to monitor the effect of pests or disease, recovery rates following disturbance, or carbon sequestration.