A Two-Level Approach for Species Identification of Coniferous Trees in Central Ontario Forests Based on Multispectral Images

Abstract

This study aims to provide detailed spatial information of valuable tree species to support improved management of winter habitat of white-tailed deer. To achieve this, we proposed a novel approach using information from two spatial scales and a suite of methods for analysis and classification of remotely sensed data. High-spatial resolution, multispectral images were employed to test the proposed method. A new structure-based remote sensing feature [local binary pattern (LBP) index] was developed and proved to be effective for species classification. A simple but effective fusion approach based on information entropy theory was proposed to incorporate features derived from different methods and their uncertainties. Based on tenfold cross validation, an overall accuracy (OA) of 77% was obtained for the classification of three tree species groups. The proposed approach has high potential to improve species mapping for operational ecological modeling.