| Summary: | 碩士 === 國立中央大學 === 土木工程研究所 === 93 === Invasive species usually cause enormous ecological and environmental impacts and alter the ecological balance. In Heng-Chun area of southern Taiwan, Leucaena from South America spreads rapidly because of past human decisions and its flourishing adaptability. In order to make this problem under control and to develop strategies to maintain or restore local bio-diversity, it is necessary to understand the current status of Leucaena spreading.
Traditionally, detecting invasive plants relies heavily on field investigations or human interpretations of aerial photos or satellite images. It is time-consuming and the result might not be reliable. On the other hand, the results of using automatic classification are often limited by multispectral data because there is not enough information for detecting specific plant in the limited spectral bands. To overcome this limitation, hyperspectral remote sensing data may be of more help.
Hyperspectral remote sensing images provide more complete and detailed spectral information about ground coverage and have a great potential to the identification of specific plant species in vegetation covered areas. However the high data dimensionality of hyperspectral data can cause substantial impact to its applications. Principal component analysis is a common technique used for feature reduction in remote sensing image analysis, but it may overlook subtle but useful information. This research developed a segmented principal component analysis scheme that can be used not only to reduce the dimensionality of a hyperspectral image but also to extract critical spectral features helpful in discriminating different vegetation types.
The analysis results of this research demonstrated that segmented principal component analysis performed better than regular PCA in providing critical information for distinguishing the target plant species from other vegetation types.
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