The planning of amphibious operations requires knowledge of the bearing strength and trafficability of the landing area as well as the bathymetry in the vicinity of the landing zone. In addition to these parameters, the identification and location of shallow submerged obstacles and areas such as tidal flats, beaches and wetlands is also required. Hyperspectral remote sensing data analyzed in conjunction with a field collected spectral library provides the means to characterize a wide range of soil properties, such as soil type, grain sizes, and moisture content, that are key to estimating the bearing capacity of littoral and near shore surfaces. Hyperspectral imagery is also able to map the locations of obstacles and provides information on near shore bathymetry.
In one study, Bachmann et al. (2009) used a combined dataset that included hyperspectral imagery, field spectral measurements collected using an ASD FieldSpec spectroradiometer, and the measurement of soil properties to develop a relationship between the field measured spectral signatures and soil bearing strength. They were then able to accurately map the spatial distribution of the observed field spectral signatures and their associated soil bearing strength values.
This use of field collected spectral signatures and associated material characteristics for the mapping of those characteristics is applicable to a wide range of applications. The success of the technique relies on the existence on an underlying relationship between the measured spectral signatures and the associated material characteristics. In the above case, this requirement is met as the desired soil bearing strength is related to soil properties such as soil type, grain sizes, and moisture content all of which have been clearly shown to be predictive of the soil’s overall bearing strength (Bachmann et al. 2009).
Shallow water bathymetry is also possible using hyperspectral remote sensing techniques. Bachmann et al. (2009) found that in depths of less than 2 meters water depth could be estimated by the usage of a field spectral database of different bottom type collected at a range of water depths. By matching the change in reflectance observed in the hyperspectral imagery to the sequences of reflectance spectra of a given bottom type, it is possible to produce maps of near shore bathometry. This worked well since bottom type and depth were the dominant factors in determining the observed reflectance and, in the study area, optical properties of the water column were of secondary importance.
C. M. Bachmann, C. R. Nichols, M.J. Montes, R.-R. Li, P.K. Woodward, R.A. Fusina, W. Chen, V. Mishra, W. Kim, J. Monty, K. McIlhany, K Kessler, D. Korwan, D. Miller, E. Bennert, G. Smith, D. Gillis, J Sellars, C. Parrish, A. Weidemann, W. Goode (2009) Airborne Remote Sensing of Trafficability in the Coastal Zone. 2009 NRL Review, pp. 223-228.
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