Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity

• Published in: Remote sensing (Basel, Switzerland) Vol. 10; no. 5; p. 714
• Main Authors: Tuominen, Sakari, Näsi, Roope, Honkavaara, Eija, Balazs, Andras, Hakala, Teemu, Viljanen, Niko, Pölönen, Ilkka, Saari, Heikki, Ojanen, Harri
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 05.05.2018
• Subjects: Algorithms; Arboreta; Biodiversity; Calibration; Cameras; Classifiers; Color imagery; Digital imaging; Forest management; Genera; Genetic algorithms; I.R. radiation; Image acquisition; Image processing; Infrared cameras; Infrared detectors; Mosaics; Photogrammetry; Plant species; Rainforests; Reflectance; Remote sensing; Sensors; Short wave radiation; Spatial resolution; Species composition; Species diversity; Three dimensional models; Trees; Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs10050714

Recognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated reflectance mosaics and was tested along with the mosaics based on original image digital number values (DN). Two alternative classifiers, a k nearest neighbor method (k-nn), combined with a genetic algorithm and a random forest method, were tested for predicting the tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. The combination of VNIR, SWIR, and 3D features performed better than any of the data sets individually. Furthermore, the calibrated reflectance values performed better compared to uncorrected DN values. These trends were similar with both tested classifiers. Of the classifiers, the k-nn combined with the genetic algorithm provided consistently better results than the random forest algorithm. The best result was thus achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features; the proportion of correctly-classified trees was 0.823 for tree species and 0.869 for tree genus.