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Invasive species change detection using artificial neural networks and CASI hyperspectral imagery

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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 (salt cedar) change detection in the study area of Lovelock, Nevada. With multidate 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 salt cedar change. The two types of input data are 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 multitemporal 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.

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Authors and Affiliations

  1. Department of Geography, University of South Florida, 4202 E. Fowler Ave., NES 107, Tampa, FL, 33620, USA

    Ruiliang Pu

  2. Center for Assessment & Monitoring of Forest & Environmental Resources (CAMFER), University of California, 137 Mulford Hall, Berkeley, CA, 94720-3114, USA

    Peng Gong

  3. Environmental, Earth and Ocean Sciences Department, University of Massachusetts-Boston, 100 Morrissey Blvd, Boston, MA, 02125, USA

    Yong Tian

  4. Department of Geography, Geology & Planning, Missouri State University, 901 South National Avenue, Springfield, MO, 65897, USA

    Xin Miao

  5. USDA, Agricultural Research Service, Western Regional Research Center, 800 Buchanan Street, Albany, CA, 94710, USA

    Raymond I. Carruthers

  6. USDA, Agricultural Research Service, 1500 N. Central Ave., Sidney, MT, 59270, USA

    Gerald L. Anderson

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  1. Ruiliang Pu
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  2. Peng Gong
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  3. Yong Tian
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  4. Xin Miao
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  5. Raymond I. Carruthers
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  6. Gerald L. Anderson
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Correspondence to Ruiliang Pu.

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Pu, R., Gong, P., Tian, Y. et al. Invasive species change detection using artificial neural networks and CASI hyperspectral imagery. Environ Monit Assess 140, 15–32 (2008). https://doi.org/10.1007/s10661-007-9843-7

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  • DOI: https://doi.org/10.1007/s10661-007-9843-7

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