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• W2963385711 endingPage "111308" @default.
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• W2963385711 abstract "Accurate knowledge of seasonal and inter-annual distributions of plant species is required for many research and management agendas that track ecosystem health. Airborne imaging spectroscopy data have been used successfully to map plant species, but often only in a single season or over a limited spatial extent due to data availability. NASA's Hyperspectral Infrared Imager (HyspIRI) preparatory airborne campaign flew an imaging spectrometer from 2013 to 2015. This dataset captured a severe drought and thus provided the opportunity to evaluate species discrimination over an extreme range in environmental conditions. Here we evaluate the portability of image-based training data and accuracy of species discrimination. The imagery was acquired in the spring, summer, and fall seasons of 2013–2015 with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Reference spectral libraries were developed with three sets of spectra: spectra from a single image date, combining spectra from multiple dates (by season, by year, and from all dates), and creating leave-one-out (LOO) libraries that pooled spectra from all dates but one. Canonical discriminant analysis (CDA) was applied to reduce data dimensionality, and classification was performed using linear discriminant analysis (LDA). When only spectra from the same image date were used, plant species were classified with a mean kappa accuracy ranging between 0.80 and 0.86 for the nine dates. Seasonal and annual spectral libraries had comparable accuracies with mean kappa 0.79–0.83 and 0.78–0.83, respectively. Seasonal libraries performed slightly better than annual libraries for species because they better-incorporated changes in spectra due to phenology. Spectral libraries were not transferable across dates, with mean kappa accuracies dropping to 0.31–0.73 for LOO spectral libraries. These results emphasize that spectral libraries built from previously collected imagery may not be able to map plant species over new images accurately. Specifically, our results highlight the need to use reference spectra that adequately represent the phenological and biophysical status of the plant species within an image for accurate mapping. Our research provides relevant insight for advanced species-mapping techniques across broad spatial and temporal scales using imagery from sensors like HyspIRI." @default.
• W2963385711 created "2019-07-30" @default.
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• W2963385711 date "2019-10-01" @default.
• W2963385711 modified "2023-10-12" @default.
• W2963385711 title "Classifying California plant species temporally using airborne hyperspectral imagery" @default.
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• W2963385711 doi "https://doi.org/10.1016/j.rse.2019.111308" @default.
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