Evaluations of forest inventories usually end when accuracy and precision have been quantified by researchers in the forest inventory and monitoring research group and published in Annals of forest Science.
Earlier this year a study investigating the use of multispectral imagery in addition to measurements from airborne laser scanning (ALS) for tree species identification was published in Canadian Journal of Remote Sensing. Multispectral imagery from a medium-format digital frame camera acquired simultaneously with ALS data were utilized and compared with imagery from a large-format digital frame camera acquired on a separate flight mission from a higher altitude. The two acquisitions represent cost efficient methods for data collection of both three-dimensional and spectral information. The classification accuracy was assessed using 1520 segmented spruce, pine, and deciduous trees. Furthermore, ALS intensity was normalized using the range from sensor to the target (range normalization). In addition, a source of variation in intensity known as banding, is described together with a normalization procedure for diminishing this effect. The normalized intensity was better than using the raw intensity, but it did not improve the classification compared with using only ALS structural information, which provided overall classification accuracies of 74%–77%. The combined use of ALS and multispectral imagery from the medium-format imagery acquired simultaneously and the separate acquisition of large-format imagery provided overall accuracies of 87%–89% and 83%–85%, respectively. Simultaneous acquisition of ALS and medium-format digital imagery provides an efficient data acquisition strategy for tree species identification in forest inventory and will likely reduce data acquisition costs by 10%–20%.