Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques
Introduction
Hoary cress (Cardaria draba) is an invasive plant that is listed as a noxious weed in the State of Idaho (Prather et al., 2002). It is a 1–2 ft tall rhizomatous perennial that thrives in disturbed semiarid landscapes. During bloom, hoary cress exhibits dense white flowers that give the plant a flat and mat-like appearance. Management of hoary cress is moderately difficult, often requiring the use of herbicides. The weed spreads rapidly via anthropogenic and wildlife vectors, and as a result land managers have the need to understand its distribution to aid in management decisions.
The objectives of this study are: (1) to map the distribution of hoary cress using hyperspectral imagery acquired over a study area in southwestern Idaho, and (2) to evaluate and compare different hyperspectral analysis parameters for hoary cress discrimination. This study was completed under a NASA sponsored initiative to bring science and technology applications into an operational context. The methods and discussion presented herein are intended to serve as guide for hyperspectral image processing techniques for vegetative species discrimination.
The study area (− 111°20′W, 43°30′N) is located in relatively flat terrain that is heavily utilized for agriculture in Ada County, Idaho (Fig. 1). Ten-mile Creek, several ditches and canals, and a well-developed network of roads provide transport vectors for hoary cress, which forms infestations diverse in both size (contiguous hectares to smaller than 10 m2) and percent canopy cover (herein referred to as ‘percent cover’ or ‘cover’). Vegetation in the study area is locally variable, including mesic (e.g. bluegrass (Poa spp.) and orchardgrass (Dactylis glomerata L.)), and xeric (e.g. cheatgrass (Bromus tectorum L.), blue mustard (Chorispora tenella (Pall.) D.C.) and wild rose (Rosa multiflora Thunb. ex Murr)) vegetative regimes. Typically, infestations of hoary cress with high cover (∼ 100%) are correlated with mesic regimes, while xeric regimes host smaller infestations with lower cover (∼ 60% to 80%).
Section snippets
Previous work and technical background
Because different vegetation types have different spectral characteristics (Everitt et al., 2001, Gates et al., 1965, Kondrat'yev & Fedchenko, 1981), many studies have utilized remote sensing techniques to generate distribution maps for invasive species rather than relying solely on the use of a field crew (e.g. Everitt et al., 1995, Glenn et al., 2005, Parker-Williams & Hunt, 2002). In general, studies using multispectral data for species distribution mapping have found limited success, while
Data collection
Hyperspectral data was acquired by the HyMap sensor (operated by HyVista, Inc.) over the study area (1.75 by 22 km; 3 m spatial resolution) on May 21, 2003, while hoary cress was blooming (Fig. 1). The hyperspectral sensor collected 126 contiguous spectral bands representing the intensity of reflected solar radiation between the wavelengths of 450 nm and 2500 nm, with radiometric resolutions of ∼ 15 nm (Kruse et al., 2000). Concurrent with image acquisition in 2003, Global Positioning System
Processing
Data reduction transformations demonstrated large accuracy differences when classifying with SAM using the mesic endmember. The PCA classification resulted in producer's, user's, and overall accuracies of 31%, 96%, and 46%, respectively (Table 2; SAM/PCA/M). The same classification applied to the MNF transformed data generated producer's, user's, and overall accuracies of 54%, 96%, and 63%, respectively (Table 2; SAM/MNF/M). While significance testing between the respective Kappa values
Conclusions
This study successfully discriminated hoary cress in Ada County, Idaho, with producer's, user's, and overall accuracies of 82%, 79%, and 86%, respectively, for infestations with at least 30% cover. The most successful classification approach utilized MNF transformed reflectance data using two endmembers for hoary cress. In this study, the SAM (threshold of 1 radian) and MTMF (MF values greater than 0 and infeasibility values less than 15) comparison performed nearly identically (maximum 3%
Acknowledgements
This project was funded by the NASA BAA program (BAA-01-0ES-01; NAG13-02029). We thank Mr. William Graham of NASA Stennis for his continued support. Additional funding was contributed by the Idaho National Laboratory (INL) through the Idaho State University–INL Partnership for Integrated Environmental Analysis Education Outreach Program. The authors would like to acknowledge the Weed Control crews from Ada County and Bonneville County, Idaho, as well as Charlla Adams, Mark Strom and Ellen
References (31)
Use of logistic regression for validation of maps of the spatial distribution of vegetation species derived from high spatial resolution hyperspectral remotely sensed data
Ecological Modeling
(2002)A review of assessing the accuracy of classifications of remotely sensed data
Remote Sensing of Environment
(1991)Status of land cover classification accuracy assessment
Remote Sensing of Environment
(2002)- et al.
Hyperspectral data processing for repeat detection of small infestations of leafy spurge
Remote Sensing of Environment
(2005) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation
Remote Sensing of Environment
(1970)- et al.
The spectral image processing system (SIPS)—Interactive visualization and analysis of imaging spectrometer data
Remote Sensing of Environment
(1993) - et al.
Estimation of leafy spurge cover from hyperspectral imagery using Mixture Tuned Matched Filtering
Remote Sensing of Environment
(2002) - et al.
Mapping chaparral in the Santa Monica Mountains using multiple endmember spectral mixture models
Remote Sensing of Environment
(1998) - et al.
Design and analysis for thematic map accuracy assessment: Fundamental principles
Remote Sensing of Environment
(1998) - et al.
Mapping nonnative plants using hyperspectral imagery
Remote Sensing of Environment
(2003)
Considerations in collecting, processing, and analyzing high spatial resolution hyperspectral data for environmental investigations
Journal of Geographic Systems
Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: Mixture tuned matched filtering
The effect of training strategies on supervised classification at different spatial resolutions
Photogrammetric Engineering and Remote Sensing
Assessing the accuracy of remotely sensed data: Principles and practices
Statistics and data analysis in geology
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