Classification of wetland habitat and vegetation communities using multi-temporal Ikonos imagery in southern Saskatchewan

Dechka, J.A.; Franklin, Steven E.; Watmough, Michael D.; Bennett, Rebecca; Ingstrup, D W

doi:10.5589/m02-064

Cited by 84 publications

(34 citation statements)

References 10 publications

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“…The spatial resolution of these systems offers new opportunities for distinguishing noxious weeds from space platforms. Multispectral IKONOS data have been used successfully to classify wetland habitat types (Dechka, Franklin, Watmough, Bennet, & Inhstrup, 2002) and to distinguish invasive aquatic weeds (Jakubauskas et al, 2002). QuickBird imagery has proven useful for mapping rangeland cover types and weeds in riparian areas (Everitt, Yang, & Deloach, 2005).…”

Section: Introductionmentioning

confidence: 99%

Canopy Spectra and Remote Sensing of Ashe Juniper and Associated Vegetation

Everitt¹,

Yang²,

Johnson

2006

Environ Monit Assess

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A study was conducted in central Texas to determine the potential of using remote sensing technology to distinguish Ashe juniper (Juniperus ashei Buchholz) infestations on rangelands. Plant canopy reflectance measurements showed that Ashe juniper had lower near-infrared reflectance than other associated woody plant species and lower visible reflectance than mixed herbaceous species in spring and summer. Ashe juniper could be distinguished on color-infrared aerial photographs acquired in March, April, June, and August and on QuickBird false color satellite imagery obtained in June, where it had a distinct dark reddish-brown tonal response. Unsupervised classification techniques were used to classify aerial photographic and satellite imagery of study sites. An accuracy assessment performed on a computer classified map of a photographic image showed that Ashe juniper had producer's and user's accuracies of 100% and 92.9%, respectively, whereas an accuracy assessment performed on a classified map of a satellite image of the same site showed that Ashe juniper had producer's and user's accuracies of 94.1% and 88.1%, respectively. Accuracy assessments performed on classified maps of satellite images of two additional study sites showed that Ashe juniper had producer's and user's accuracies that ranged from 87.1% to 96.4%. These results indicate that both color-infrared photography and false color satellite imagery can be used successfully for distinguishing Ashe juniper infestations.

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Section: Introductionmentioning

confidence: 99%

Canopy Spectra and Remote Sensing of Ashe Juniper and Associated Vegetation

Everitt¹,

Yang²,

Johnson

2006

Environ Monit Assess

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“…In comparison to the literature, as above, many studies have been conducted at coarser scales than the current study, and all for within wetland water and vegetation types; no studies have been found that detect and map deadwood within wetlands as a significant habitat feature. For example, Dillabaugh et al [34] and Dechka et al [71] used brightness and texture of 4 m pixel IKONOS imagery to classify within wetland vegetation communities with high accuracy. Gilmore et al [72] utilized LiDAR and multi-temporal Quickbird imagery with an object-oriented classification approach to classify three marsh vegetation species.…”

Section: Discussionmentioning

confidence: 99%

Blanding’s Turtle (Emydoidea blandingii) Potential Habitat Mapping Using Aerial Orthophotographic Imagery and Object Based Classification

Barker

King

2012

Remote Sensing

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Blanding's turtle (Emydoidea blandingii) is a threatened species under Canada's Species at Risk Act. In southern Québec, field based inventories are ongoing to determine its abundance and potential habitat. The goal of this research was to develop means for mapping of potential habitat based on primary habitat attributes that can be detected with high-resolution remotely sensed imagery. Using existing spring leaf-off 20 cm resolution aerial orthophotos of a portion of Gatineau Park where some Blanding's turtle observations had been made, habitat attributes were mapped at two scales: (1) whole wetlands; (2) within wetland habitat features of open water, vegetation (used for camouflage and thermoregulation), and logs (used for spring sun-basking). The processing steps involved initial pixel-based classification to eliminate most areas of non-wetland, followed by object-based segmentations and classifications using a customized rule sequence to refine the wetland map and to map the within wetland habitat features. Variables used as inputs to the classifications were derived from the orthophotos and included image brightness, texture, and segmented object shape and area. Independent validation using field data and visual interpretation showed classification accuracy for all habitat attributes to be generally over 90% with a minimum of 81.5% for the producer's accuracy of logs. The maps for each attribute were combined to produce a habitat suitability map for Blanding's turtle. Of the 115 existing turtle observations, 92.3% were closest to a wetland of the two highest suitability classes. High-resolution imagery combined with object-based classification and habitat suitability mapping methods such as those presented provide a much more spatially OPEN ACCESSRemote Sens. 2012, 4 195 explicit representation of detailed habitat attributes than can be obtained through field work alone. They can complement field efforts to document and track turtle activities and can contribute to species inventory planning, conservation, and management.

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“…The high resolution of these satellites has considerable potential for assessing natural resources. Multispectral IKONOS data have been used successfully to classify wetland habitat types (Dechka et al 2002) and to distinguish invasive aquatic weeds (Jakubauskas et al 2002). Wang et al (2004) used IKONOS and QuickBird multispectral imagery for mapping mangrove vegetation in coastal areas.…”

Section: Introductionmentioning

confidence: 99%

Mapping giant salvinia with satellite imagery and image analysis

Everitt¹,

Fletcher²,

Elder

et al. 2007

Environ Monit Assess

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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.

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Classification of wetland habitat and vegetation communities using multi-temporal Ikonos imagery in southern Saskatchewan

Cited by 84 publications

References 10 publications

Canopy Spectra and Remote Sensing of Ashe Juniper and Associated Vegetation

Canopy Spectra and Remote Sensing of Ashe Juniper and Associated Vegetation

Blanding’s Turtle (Emydoidea blandingii) Potential Habitat Mapping Using Aerial Orthophotographic Imagery and Object Based Classification

Mapping giant salvinia with satellite imagery and image analysis

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