Session Information
| Session | Poster Session | | Date | Monday (2008-04-07) | | Time | 5:00 PM - 7:00 PM | | Room | Grand Terrace |
Presentation Information
| Presenter | John Maingi | | Title | Mapping forest attributes in southwestern Ohio using multispectral and hyperspectral satellite data | | Affiliation | Miami University | | Authors | John Maingi, Thomas Crist | | Keywords | ALI, Biodiversity, Hyperion, Landsat, SPOT | | Presentation Type | Poster | Abstract:
In this study, data from EO-1 hyperspectral imager (Hyperion), Advanced Land Imager (ALI), SPOT, and Landsat TM were evaluated for their ability to discriminate among five forest types in southwestern Ohio. Forest types studied ranged from remnants of old-growth beech-maple forests to early successional juniper forests. Training data were obtained from 0.1 ha plots located in representative patches of the five forest types encountered. Within each plot, forest stand data including composition by species, stem density, basal area, and crown cover were obtained. Training data and spectral data from each sensor were then used to produce maps of the five forest types using decision tree classifiers. Field data not used in the training of the classifiers were used to perform an accuracy assessment of the maps obtained from the classifications. Further, the suitability of Hyperion, ALI, SPOT, and Landsat TM data to predict various forest stand attributes was explored by correlating stand attribute data to spectral bands from each sensor after these were corrected for atmospheric effects and converted to top-of-the atmosphere reflectances. Results of these analyses are presented and discussed here. |
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