A first assessment of the use of high spatial resolution hyperspectral imagery in discriminating among animal species, and between animals and their surroundings

Abstract

This paper presents the results of a first evaluation of the feasibility of using data from a high spatial resolution hyperspectral sensor to discriminate among animal species, and between animals and their surroundings. The evaluation was performed by simulating 1000 pixels produced by a shortwave infrared (1051–1789 nm) hyperspectral sensor with a pixel size of either 2 or 4 m, and a spectral resolution of 11.5 nm. The simulated pixels consisted of five classes: pure vegetation, and mixtures of four animal species (cattle, horses, sheep, and pigs) with vegetation. The data were processed by converting from reflectance to absorption, taking the first difference of the data, and then using stepwise discriminant analysis to create discriminant functions based on 250 of the 1000 pixels. These discriminant functions were then used to assign the remaining 750 pixels to classes. The results indicate that the spectra of the four animal species are quite different from each other, and that these differences include variations in the presence and strength of absorptions in the shortwave infrared portion of the spectrum. Overall accuracies were 90% based on the 2 m data and 61% based on the 4 m data. User's and producer's accuracies for individual classes based on the 2 m data ranged from 72 to 100%, but were lower for the 4 m data (40–90%). These results suggest that further study of the use of high spatial resolution hyperspectral imagery in locating and identifying animals is merited.