Digital aerial photogrammetry (DAP) is emerging as an alternate data source to airborne laser scanning (ALS) data for three-dimensional characterization of forest structure. In this study we compare point cloud metrics and plot-level model estimates derived from ALS data and an image-based point cloud generated using semi-global matching (SGM) for a complex, coastal forest in western Canada. Plot-level estimates of Lorey’s mean height (H), basal area (G), and gross volume (V) were modelled using an area-based approach. Metrics and model outcomes were evaluated across a series of strata defined by slope and canopy cover, as well as by image acquisition date. We found statistically significant differences between ALS and SGM metrics for all strata for five of the eight metrics we used for model development. We also found that the similarity between metrics from the two data sources generally increased with increasing canopy cover, particularly for upper canopy metrics, whereas trends across slope classes were less consistent. Model outcomes from ALS and SGM were comparable. We found the greatest difference in model outcomes was for H (ΔRMSE% = 5.04%). By comparison, ΔRMSE% was 2.33% for G and 3.63% for V. We did not discern any corresponding trends in model outcomes across slope and canopy cover strata, or associated with different image acquisition dates.

In this paper, we demonstrate the effectiveness of digital stereo images and canopy height models (CHMs) derived from them for forest height change assessment. Top heights were derived for 199 terrestrial inventory plots from forest inventories conducted in 2008 and 2013 in a forest near Traunstein, Germany. Semi-Global Matching was applied to two sets of aerial stereo images, acquired in 2009 and 2012, respectively, to compute CHMs. Subsequently, several height percentiles were calculated from the areas in the CHMs that lay within the inventory plot locations. The maximum CHM value (h_max) had the highest correlation with the field-based canopy top heights and was selected for use in all further analysis. Periodic annual increments (PAIs) of forest height were calculated from both the remote sensing and the field data at the inventory plot locations. Scatterplots of the PAIs over top height revealed similar patterns in the results derived from the two data sets. The inventory plots were assigned to three height classes representing various forest successional stages – youth, full vigour and old age. The PAI distributions within the three height classes were significantly different from one another. Our findings suggest that CHMs derived from repeat aerial image surveys can be a viable tool to measure canopy heights and to assess forest height changes over time, even for a highly structured, mixed forest in central Europe.