YorkSpace has migrated to a new version of its software. Access our Help Resources to learn how to use the refreshed site. Contact diginit@yorku.ca if you have any questions about the migration.
 

Individual tree delineation and species identification in deciduous and mixed Canadian forests using high spatial resolution airborne LiDAR and image data

Loading...
Thumbnail Image

Date

Authors

Li, Jili

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Analysis of individual trees in forests is of great value for the monitoring and sustainable management of forests. For the past decade, remote sensing has been a useful tool for individual tree analysis. However, accuracies of individual tree analysis remain insufficient because of the inadequate spatial resolution of most remote sensing data and unsophisticated methods. The improvement of individual tree analysis becomes feasible because of recent advances in LiDAR (Light Detection And Ranging) and airborne image sensing technologies. However, it is challenging to fully exploit and utilize small-footprint LiDAR data and high spatial resolution imagery for detailed tree analysis. This dissertation presents a number of effective methods on individual tree crown delineation and species classification to improve individual tree analysis with advanced remote sensing data.

The individual tree crown delineation is composed of a five-step framework, which is unique in its automated determination of dominant crown sizes in a given forest scene and its determination of the number of trees in a segment based on LiDAR profiles. This framework correctly delineated 74% and 72% of the tree crowns in two plots with mixed-wood and deciduous trees, respectively.

The study on individual tree species classification is focused on developing novel LiDAR and image features to characterize tree structures. First of all, coniferous and deciduous trees are classified. Features are extracted from LiDAR data to characterize crown shapes and vertical profiles of individual trees, followed by the C4.5 decision tree classification algorithm. Furthermore, groups of new LiDAR features are developed to characterize the internal structures of a tree. Important features are selected via a genetic algorithm and utilized in the multi-species classification based on linear discriminant analysis. An overall accuracy of 77 .5% is obtained for an investigation on 1, 122 sample trees in natural forests. In addition, statistical features based on gray-level co-occurrence matrix (GLCM) and structural texture-features derived from the local binary pattern (LBP) method are proved to be useful to improve the species classification using high spatial resolution aerial image.

The research demonstrates that LiDAR data and high spatial resolution images can be used to effectively characterize tree structures and improve the accuracy and efficiency of individual tree species identification.

Description

Keywords

Citation