Northern Conifer Forest Species Classification Using Multispectral Data Acquired from an Unmanned Aerial Vehicle

Abstract

Object-based image analysis and machine learning classification procedures, after field calibration and photogrammetric processing of consumer-grade unmanned aerial vehicle (<small>UAV</small>) digital camera data, were implemented to classify tree species in a conifer forest in the Great Lakes/St Lawrence Lowlands Ecoregion, Ontario, Canada. A red-green-blue (<small>RGB</small>) digital camera yielded approximately 72 percent classification accuracy for three commercial tree species and one conifer shrub. Accuracy improved approximately 15 percent, to 87 percent overall, with higher radiometric quality data acquired separately using a digital camera that included near infrared observations (at a lower spatial resolution). Interpretation of the point cloud, spectral, texture and object (tree crown) classification Variable Importance (<small>VI</small>) selected by a machine learning algorithm suggested a good correspondence with the traditional aerial photointerpretation cues used in the development of well-established large-scale photography northern conifer elimination keys, which use three-dimensional crown shape, spectral response (tone), texture derivatives to quantify branching characteristics, and crown size, development and outline features. These results suggest that commonly available consumer-grade <small>UAV</small>-based digital cameras can be used with object-based image analysis to obtain acceptable conifer species classification accuracy to support operational forest inventory applications.