Using CASI Hyperspectral Imagery to Detect Mortality and Vegetation Stress Associated with a New Hardwood Forest Disease
Document Type
Article
Publication Date
1-2008
Digital Object Identifier (DOI)
https://doi.org/10.14358/PERS.74.1.65
Abstract
A Compact Airborne Spectrographic Imager-2 (CASI) dataset was used for detecting mortality and vegetation stress associated with a new forest disease. We first developed a multilevel classification scheme to improve classification accuracy. Then, the CASI raw data were transformed to reflectance and corrected for topography, and a principal component (PC) transformation of all 48 bands and the visible bands and NIR bands were separately conducted to extract features from the CASI data. Finally, we classified the calibrated and corrected CASI imagery using a maximum likelihood classifier and tested the relative accuracies of classification across the scheme. The multilevel scheme consists of four levels (Levels 0 to 3). Level 0 covered the entire study area, classifying eight classes (oak trees, California bay trees, shrub areas, grasses, dead trees, dry areas, wet areas, and water). At Level 1, the vegetated and non-vegetated areas were separated. The vegetated and non-vegetated areas were further subdivided into four vegetated (oak trees, California bay trees, shrub areas, grasses) and four non-vegetated (dead trees, dry areas, wet areas, and water) classes at Level 2. Level 3 identified stressed and non-stressed oak trees (two classes). The ten classes classified at different levels are defined as final classes in this study. The experimental results indicated that classification accuracy generally increased as the detailed classification level increased. When the CASI topographically corrected reflectance data were processed into ten PCs (five PCs from the visible region and five PCs from NIR bands), the classification accuracy for Level 2 vegetated classes (non-vegetated classes) increased to 80.15 percent (94.10 percent) from 78.07 percent (92.66 percent) at Level 0. The accuracy of separating stressed from non-stressed oak trees at Level 3 was 75.55 percent. When classified as a part of Level 0, the stressed and non-stressed were almost inseparable. Furthermore, we found that PCs derived from visible and NIR bands separately yielded more accurate results than the PCs from all 48 CASI bands.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Photogrammetric Engineering & Remote Sensing, v. 74, issue 1, p. 65-75
Scholar Commons Citation
Pu, Ruiliang; Kelly, Maggi; Anderson, Gerald L.; and Gong, Peng, "Using CASI Hyperspectral Imagery to Detect Mortality and Vegetation Stress Associated with a New Hardwood Forest Disease" (2008). School of Geosciences Faculty and Staff Publications. 376.
https://digitalcommons.usf.edu/geo_facpub/376
