Vegetation and Tree Species Classification Using Multidate and High-resolution Satellite Imagery and Lidar Data
Abstract
Remote sensing can play a key role in understanding the makeup of urban forests. This thesis analyzes how high-resolution multispectral imagery, lidar point clouds, and multidate multispectral imagery allow for improved classification of London, Ontario’s urban forest. Chapter 2 uses object-based support vector machine classification (SVM) to classify five types of trees using features derived from Geoeye-1 imagery and lidar data. This results in an overall accuracy of 85.08% when features from both data sources are combined, compared with 77.73% when using only lidar features, and 71.85% when using only imagery features. Chapter 3 makes use of Planetscope and VENuS images from different seasons to classify deciduous trees, conifers, non-tree vegetation, and non-vegetation using SVM. Using multidate Planetscope images increases overall accuracy to 83.11% (8.19 percentage points more than single-date Planetscope classification), while using multidate VENuS images increases accuracy to 72.18% (2.22 percentage points higher than single-date VENuS classification).