Submitted to: Environmental Monitoring and Assessment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/18/2008
Publication Date: 6/28/2008
Publication URL: http://hdl.handle.net/10113/14142
Citation: Pu, R., Gong, P., Tians, Y., Miao, X., Carruthers, R.I., Anderson, G.L. 2008. Invasive species change detection using artificial neural networks and CASI hyperspectral imagery. Environmental Monitoring and Assessment.140:15-32. Interpretive Summary: Hyperspectral remote sensed data can be to assess plant species and densities across wide areas. This paper used such data to assess an invasive species, saltcedar, using multiple bands of reflected light of different wavelengths. These data were assessed across two different sample dates to conduct a change analysis using a complex neural network that allowed changes in plant density and distribution to be concluded following the implementation of a biological control program aimed at controlling this invasive weed species.
Technical Abstract: For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (saltcedar) change detection in the study area of Lovelock, Nevada. With multi-date Compact Airborne Spectrographic Imager (CASI) hyperspectral data sets, two types of hyperspectral CASI input data and two classifiers have been examined and compared for mapping and monitoring the saltcedar change. Then two types of input data area all two-date original CASI bands and 12 principal component images (PCs) derived from the two-date CASI images. The two classifiers are an artificial neural network (ANN) and linear discriminant analysis (LDA). The experimental results indicate that (1) the direct multi-temporal image classification method applied in land cover change detection is feasible either with original CASI bands or PCs, but a better accuracy was obtained from the CASI PCA transformed data; (2) with the same inputs of 12 PCs, the ANN outperforms the LDA due to the ANN's non-linear property and ability of handling data without a prerequisite of a certain distribution of the analysis data.