Abstract
Abstract. The objective of this study was to explore the use of multi-source remotely sensed data for individual tree species. To achieve this, a neutrosophic logic-based method was developed for tree species classification using the combined spectral, textural and structural information derived from WorldView-2 (WV-2) multispectral bands, WV-2 panchromatic band, and LiDAR (Light Detection And Ranging)-derived canopy height model (CHM), respectively. The developed method was tested on the data obtained over the Keele campus, York University, Toronto Canada and the KNN (K Nearest Neighbour) classification method. Twenty-one spectral, three textural and three structural features were used to classify five species (Norway maple, honey locust, Austrian pine, blue spruce, and white spruce). For this study, 522 trees were used for training and 223 for testing. The overall classification accuracy obtained by the proposed method was 0.82. It was significantly improved compared with the KNN (0.73), weighted KNN (0.76), and fuzzy KNN (0.75) methods. In addition, Dempster-Shafer (DS) theory was explored to perform information fusion at the decision level in comparison to that at the feature level. The accuracies obtained by the fusion at the decision level were generally lower than those at the feature level. Even though promising results based on the neutrosophic logic were obtained during this proof-concept stage, studies are underway to perform more tests with a large number of tree crowns and more species and exploit other classification methods, such as support vector machine.
Highlights
Trees are essential components of urban ecosystems and provide a wide range of environmental, ecological, social, cultural and economic benefits
Compared with the results obtained by the fusion at the feature level (Table 2), one can see that for both fuzzy K Nearest Neighbor (KNN) and neutrosophic KNN, the feature level fusion outperformed the decision level fusion
The decision level fusion based on weighted KNN performed better than the fusion at the feature level
Summary
Trees are essential components of urban ecosystems and provide a wide range of environmental, ecological, social, cultural and economic benefits. Accurate tree species classification is important to city planning, ecological management, and other urban studies (Iovan et al, 2008). The recent advance in remote sensing technologies makes a huge amount of data from different sensors, such as high spatial resolution imagery and high point density LiDAR (Light Detection And Ranging), readily available. Considering individual tree crowns as the basic units provides a flexible platform to integrate information from different sources of data. Multi-source remotely sensed data challenge researchers to develop effective methods to utilize fully all available information of individual tree crowns in species classification
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