Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques

Mundt, Jacob T.; Glenn, Nancy F.; Weber, Keith T.; Prather, Timothy S.; Lass, Lawrence W.; Pettingill, Jeffrey

doi:10.1016/j.rse.2005.04.004

Cited by 81 publications

(50 citation statements)

References 22 publications

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“…Our classification results were consistent with the findings of other studies. For example, hoary cress (Cardaria draba) with 30% cover [76], leafy spurge with cover as low as 10% [51] and spotted knapweed (Centaurea maculosa) with only 1% cover [53] were successfully detected using hyperspectral images with pixel sizes of 3 by 3-m, 3.5 by 3.5-m and 5 by 5-m, respectively.…”

Section: Discussionmentioning

confidence: 99%

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

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Remote detection of non-native invasive plant species using geospatial imagery may significantly improve monitoring, planning and management practices by eliminating shortfalls, such as observer bias and accessibility involved in ground-based surveys. The use of remote sensing for accurate mapping invasion extent and pattern offers several advantages, including repeatability, large area coverage, complete instead of sub-sampled assessments and greater cost-effectiveness over ground-based methods. It is critical for locating, early mapping and controlling small infestations before they reach economically prohibitive or ecologically significant levels over larger land areas. This study was designed to explore the ability of hyperspectral imagery for mapping infestation of musk thistle (Carduus nutans) on a native grassland during the preflowering stage in mid-April and during the peak flowering stage in mid-June using the support vector machine classifier and to assess and compare the resulting mapping accuracy for these two OPEN ACCESSRemote Sens. 2013, 5 613 distinctive phenological stages. Accuracy assessment revealed that the overall accuracies were 79% and 91% for the classified images at preflowering and peak flowering stages, respectively. These results indicate that repeated detection of the infestation extent, as well as infestation severity or intensity, of this noxious weed in a spatial and temporal context is possible using hyperspectral remote sensing imagery.

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Section: Discussionmentioning

confidence: 99%

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Mırık¹,

Ansley²,

Steddom³

et al. 2013

Remote Sensing

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“…MTMF and BSVM enabled us to estimate the proportion of canopy vertical layers occupied by Psidium cattleianum (relative canopy density), which helped us to determine areas with low to high dominance of this species. Previous studies have also mapped invasive species distribution using MTMF (e.g., [18,28]), among others spectral unmixing methods [17,19,21]. However, here, we first use this methodological approach to estimate invader fractional abundance within different canopy vertical layers.…”

Section: Discussionmentioning

confidence: 99%

“…We therefore used the arithmetic mean of the reflectance of the 50 P. cattleianum pixels from the training dataset. Defining the endmember from the average spectra of the training dataset (as in [18,29,45]) partially accounted for the spectral variability of P. cattleianum trees growing with varying canopy structure, ambient illumination, and at different elevation or substrate characteristics [18].…”

Section: Mixture Tuned Matched Filteringmentioning

confidence: 99%

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Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy

et al. 2016

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High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest managers there are two main technical challenges that must be addressed: (1) mapping a single focal species amongst a diverse array of other tree species; and (2) detecting early outbreaks of invasive plant species that are often hidden beneath the forest canopy. To address these challenges, we investigated the performance of two single-class classification frameworks-Biased Support Vector Machine (BSVM) and Mixture Tuned Matched Filtering (MTMF)-to estimate the degree of Psidium cattleianum incidence over a range of forest vertical strata (relative canopy density). We demonstrate that both BSVM and MTMF have the ability to detect relative canopy density of a single focal plant species in a vertically stratified forest, but they differ in the degree of user input required. Our results suggest BSVM as a promising method to disentangle spectrally-mixed classifications, as this approach generates decision values from a similarity function (kernel), which optimizes complex comparisons between classes using a dynamic machine learning process.

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“…More recently, airborne hyperspectral surveys have been tested in a variety of environments in an attempt to discriminate individual vegetation species (e.g., Miao et al, 2007;Lawrence et al, 2006;Clark et al, 2005;Mundt et al, 2005;Schmidt and Skidmore 2003). The creation of such species maps with hyperspectral remote sensing data has the potential to provide a more automated approach for creating botanical inventories on federal lands.…”

Section: Discrimination Of Vegetation At the Species Levelmentioning

confidence: 99%

Final Report for the MANNRRSS II Program Management of Nevada's Natural Resources with Remote Sensing Systems, Beatty, NV

Miller¹,

Horowitz²,

Kratt³

et al. 2009

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Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques

Cited by 81 publications

References 22 publications

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Remote Distinction of A Noxious Weed (Musk Thistle: CarduusNutans) Using Airborne Hyperspectral Imagery and the Support Vector Machine Classifier

Determining Subcanopy Psidium cattleianum Invasion in Hawaiian Forests Using Imaging Spectroscopy

Final Report for the MANNRRSS II Program Management of Nevada's Natural Resources with Remote Sensing Systems, Beatty, NV

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