Detecting Musk Thistle (Carduus nutans) Infestation Using a Target Recognition Algorithm

Mustafa Mirik, Yves Emendack, Ahmed Attia, Sriroop Chaudhuri, Mimi Roy, Georges F. Backoulou, Song Cui

Department of Crop, Soil and Environmental Sciences, Auburn University, Auburn, USA.
Middle Tennessee State University, Murfreesboro, USA.
Oklahoma State University, 127 Noble Research Center, Stillwater, USA.
Texas A & M AgriLife Research and Extension Center, Vernon, USA.

DOI: 10.4236/ars.2014.33008                

Abstract

The outbreaks of invasive plant species can cause great ecological and agronomic problems through aggressively competing for environmental resources that could be otherwise utilized by other desirable species. Thus, it is crucial for detecting small infestations before they reach a significant extent that can cause ecological and economic damages over a large geological area. Remote sensing is a proven method for mapping invasion extent and pattern based on geospatial imagery and indicated great repeatability, large coverage area, and lower cost compared with traditional ground-based methods before. We investigated the feasibility and performances of adopting multispectral satellite imagery analyses for mapping infestation of musk thistle (Carduus nutans) on native grassland, crop field, and residential areas in early June using spectral angle mapper classifier. Our results showed an overall classification accuracy of 94.5%, indicating great potential of using moderate resolution multispectral satellite-based remote sensing techniques for musk thistle detection over a large spatial scale.

Keywords

Accuracy Assessment, Invasive Plant, Weed Management, Weed Infestation, Remote Sensing, Geospatial Data, Nodding Thistle

Conflicts of Interest

The authors declare no conflicts of interest.

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