Abstract
Our objective is to identify and map individuals of nine tree species in a Hawaiian lowland tropical forest by comparing the performance of a variety of semi-supervised classifiers. A method was adapted to process hyperspectral imagery, LiDAR intensity variables, and LiDAR-derived canopy height and use them to assess the identification accuracy. We found that semi-supervised Support Vector Machine classification using tensor summation kernel was superior to supervised classification, with demonstrable accuracy for at least eight out of nine species, and for all combinations of data types tested. We also found that the combination of hyperspectral imagery and LiDAR data usually improved species classification. Both LiDAR intensity and LiDAR canopy height proved useful for classification of certain species, but the improvements varied depending upon the species in question. Our results pave the way for target-species identification in tropical forests and other ecosystems.
Highlights
Large-scale mapping of tree species composition is of growing interest in ecology, conservation, and ecosystem management
Our study provides a comparison of several methods and data types for detecting and mapping target species in tropical forests
The results indicate that imaging spectroscopy, combined with the full suite of Light Detection and Ranging (LiDAR) variables, can provide highly accurate species identification
Summary
Large-scale mapping of tree species composition is of growing interest in ecology, conservation, and ecosystem management. 2012, 4 to be among the most useful technologies for species mapping [1,2,3,4,5,6,7,8,9,10]. Active remote sensing instruments, such as Light Detection and Ranging (LiDAR) scanners, have played a relatively small role in efforts to detect and map tree species [11,12]. Studies using LiDAR in conjunction with spectral data [13,14,15,16] have yielded promising results, indicating the potential of combining remote sensing technologies for species mapping. Despite the demonstrable value of combined spectral and LiDAR data for species mapping, the application of these approaches to high-diversity tropical forests presents a unique challenge. All studies agree that the risk of spectral confusion will rise with increased species richness
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