Author(s)

Samuel G. Jenkins¹, Peter Oduor^2*, Larry Kotchman³, Michael Kangas⁴

¹Moorhead Public Service, Moorhead, MN, USA.
²Department of Geosciences, North Dakota State University, Fargo, ND, USA.
³North Dakota Forest Service, Bottineau, ND, USA.
⁴North Dakota State University, Fargo, ND, USA.

Orbital platforms spectral sensitivity can be a major limitation in ascertaining detailed identification and mapping of arboreal ecosystems. Field-derived spectral signatures using a narrow-band sensor, for example, ASD (Analytical Spectral Devices, Boulder, CO, USA) FieldSpec^® Pro cover a spectral FR (Full Range) of 350 - 2500 nm exceeding spectral sensitivities of commonly used orbital platforms. The plausibility of deriving a spectral library of trees or forests within a training set is venerable. On the other hand, diagnostic spectral features between tree species or types are inherently difficult to ascertain from orbital platforms. This is so especially when the spectral library is applied to a demarcated region beyond the extents of training set. Basic suborbital limitations in detailed identification of trees and forests are presented in this study. We draw attention to spectral or temporal deficiencies and offer probable solutions depending on preferred or optimal spectral sensitivities. For example, Hyperion with 220 bands (400 - 2500 nm), one of the three primary instruments on the EO-1 spacecraft, has narrow bandwidths and covers the entire range of the spectral profiles collected for North Dakota tree species. With a 30 m spatial resolution, it is still useful in species identification in moderate stands of forest. Hyperion is a tasking satellite with limited passes over North Dakota (≈7% of total area) limiting its use as a platform of choice for statewide forest resource mapping.

Spectral Angle, Spectral Correlation Matrix, Leaf Reflectance

Jenkins, S. , Oduor, P. , Kotchman, L. and Kangas, M. (2016) Analyzing Field-Derived Arboreal Spectral Signatures. Journal of Geographic Information System, 8, 193-204. doi: 10.4236/jgis.2016.82018.