Combining high spatial resolution multi-temporal satellite data with leaf-on LiDAR to enhance tree species discrimination at the crown level

The long-standing goal of discriminating tree species at the crown-level from high spatial resolution imagery remains challenging. The aim of this study is to evaluate whether combining (a) high spatial resolution multi-temporal images from different phenological periods (spring, summer and autumn), and (b) leaf-on LiDAR height and intensity data can enhance the ability to discriminate the species of individual tree crowns of red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina) in the Fernow Experimental Forest, West Virginia, USA. We used RandomForest models to measure a loss of classification accuracy caused by iteratively removing from the classification one or more groups from six groups of variables: spectral reflectance from all multispectral bands in the (1) spring, (2) summer, and (3) autumn images, (4) vegetation indices derived from the three multispectral datasets, (5) canopy height and intensity from the LiDAR imagery, and (6) texture related variables from the panchromatic and LiDAR datasets. We also used ANOVA and decision tree analyses to elucidate how the multispectral and LiDAR datasets combine to help discriminate tree species based on their unique phenological, spectral, textural, and crown architectural traits. From these results, we conclude that combing high spatial resolution multi-temporal satellite data with LiDAR datasets can enhance the ability to discriminate tree species at the crown level.