TITLE:
Detecting Musk Thistle (Carduus nutans) Infestation Using a Target Recognition Algorithm
AUTHORS:
Mustafa Mirik, Yves Emendack, Ahmed Attia, Sriroop Chaudhuri, Mimi Roy, Georges F. Backoulou, Song Cui
KEYWORDS:
Accuracy Assessment, Invasive Plant, Weed Management, Weed Infestation, Remote Sensing, Geospatial Data, Nodding Thistle
JOURNAL NAME:
Advances in Remote Sensing,
Vol.3 No.3,
September
3,
2014
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.