Mapping giant salvinia with satellite imagery and image analysis

Abstract

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer's and user's accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.

Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images

“…Most remote sensing studies of biodiversity concentrate on trees and larger plants which can be more readily discriminated using remote sensors. In areas which have a few dominant species, such as temperate forests or mangroves, hyperspatial data have been used to delineate tree canopies and directly identify individual trees at the species level [4][5][6]. This task, challenging enough when there are a few species, becomes close to impossible when the number of species approaches the level of tens or hundreds.…”

Using High-Resolution, Regional-Scale Data to Characterize Floating Aquatic Nuisance Vegetation in Coastal Louisiana Navigation Channels

“…Previous studies have shown the ability to classify a variety of aquatic vegetation species using satellite imagery (Jakubauskas et al 2002, Everitt et al 2007, Silva et al 2008. Preliminary inspection of Landsat imagery indicated that it should be possible to establish not only the presence and absence information for AV (as was done in this study), but also to identify detailed spectral signatures for various species or species groups of AV.…”

Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies

“…Remote sensing techniques offer rapid acquisition of data with generally short turn-around time at lower costs than ground surveys (Tueller 1982). Multispectral airborne and satellite imagery have been used extensively to distinguish and map aquatic vegetation (Carter 1982, Martyn et al 1986, Tiner 1997, Jakubauskas et al 2002, Everitt et al 2008, John 2010. Multispectral ground reflectance measurements have also been used to characterize and differentiate among wetland and aquatic plant species.…”

“…Marshall and Lee (1994) found that the process of selecting training classes and the subsequent signature evaluation needed in a supervised classification was a time consuming process. Everitt et al (2005Everitt et al ( , 2008 showed that a supervised classification does not produce significantly better results than an unsupervised classification when mapping macrophyte species. For these reasons, an unsupervised classification using the Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm was run on the study area in the WorldView-2 image in four different spectral band subsets which was decided according to band dissimilarity derived from the PCA.…”