An angular vegetation index for imaging spectroscopy data¡ªPreliminary results on forest damage detection in the Bavarian National Park, Germany
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- 作者：Fabian Ewald Fassnacht ; Hooman Latifi ; Barbara Koch
- 关键词：Vegetation indices ; Imaging spectroscopy data ; Genetic algorithm ; Forest damage mapping ; TAI ; Angular index ; Hyperspectral remote sensing
- 刊名：International Journal of Applied Earth Observation and Geoinformation
- 出版年：2012
- 出版时间：October, 2012
- 年：2012
- 卷：19
- 期：Complete
- 页码：308-321
- 全文大小：4987 K
文摘
A vegetation index (VI) is presented which is calculated as the inner angles of a triangle. The triangle is spanned between three distinct points (on a spectral curve of imaging spectroscopy data) which are defined for each individual pixel by the wavelength (x-axis) and the reflection value (y-axis). The ideal wavelengths of the three points are dependent on the response variable investigated. The case-study within this paper in which this angular VI is applied, aims at the development of an index to automatically detect spruce bark beetle infections in the Bavarian National Park, Germany. In order to determine the optimum wavelengths to separate damaged from non-damaged trees the three-angle-indices (TAIs) for all possible combinations of available wavelengths of HyMap imaging spectroscopy data in the vis-NIR-region (0.455-0.986 ¦Ìm) were calculated. The resulting 27,417 images served as input predictor variables to a genetic algorithm (GA) which used nearest centroid classifier as fitness functions to detect the most stable predictors and separate the six classes (three damage classes) defined within the study area.
The fitness functions integrated in the GA reached classification accuracies of up to 94.8 % when using forward selection models of the most stable genes featuring maximum 50 predictors. Based on those results, three from the original 27,417 predictors were extracted to map forest damages over the full image extent based on a support vector machines (SVMs) classification. We conducted the same experiment with 82 vegetation indices described in literature and achieved slightly lower GA overall accuracies of 86.8 % using the nearest centroid classifier. The SVM maps produced with the three best VI predictors were comparable to the TAI results.