Abstract
Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58°27′18.35″N, 13°39′8.03″E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user’s and producer’s accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification.
Highlights
Multiple studies have examined how three-dimensional data from Airborne Laser Scanning (ALS) might benefit forestry [1]
The goal of this study was to investigate what type of features from multispectral ALS data that are best suited for tree species classification
The best model, which had a cross-validated accuracy of 76.5%, used one structural and seven spectral features
Summary
Multiple studies have examined how three-dimensional data from Airborne Laser Scanning (ALS) might benefit forestry [1]. ALS has been used to produce nationwide estimations of forest variables, such as height, volume, stem diameter and basal area, with high accuracy [1,2]. Estimations are usually made by using regression analysis of height distribution and density features of the ALS-derived point cloud. An efficient, automated and objective forest inventory of large areas can be performed with a relatively small set of field sample plots and remote sensing data, as an alternative to the common subjective, manual inventory used when making forest management plans. The height distribution of the ALS data has not shown great potential for tree species classification at stand level. Several tree species have crowns with similar height distributions and cannot be separated based on the height distribution features from ALS data. Multi-layered forest stands have smaller trees below the top-most canopy layer, which makes it difficult to differentiate trees with tall crowns from those with short crowns and an understory beneath
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
