A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery

Becker, Brian L.; Lusch, David P.; Qi, Jiaguo

doi:10.1016/j.rse.2006.11.005

Cited by 70 publications

(47 citation statements)

References 6 publications

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“…Remote sensing methods for determining macrophyte cover in littoral zones of lakes are effective tools for high-resolution mapping of large areas in space and time, and thus useful for monitoring and ecological assessment (Becker et al, 2005(Becker et al, , 2007Schmieder et al, 2010). Although most applications in lake littoral zones are based on airborne hyper-spectral scanner data (Woithon & Schmieder 2004;Becker et al, 2005Becker et al, , 2007Schmieder et al, 2010), less cost and effort are required to test commercially available, operational satellite data with lower spectral resolution.…”

Section: Discussionmentioning

confidence: 99%

“…Govender et al (2007) reviewed applications of hyper-and multi-spectral remote sensing in vegetation and water resources studies. Becker et al (2005Becker et al ( , 2007 derived optimal band combinations from hyper-spectral scanner data and optimal spatial resolutions for wetland vegetation classification. They recommended an optimal spatial resolution of 2 m and a minimum of 7 m, with strategically located bands in the VIS-NIR wavelength region.…”

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confidence: 99%

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High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

Heblinski¹,

Schmieder

Heege

et al. 2010

Hydrobiologia

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Physics-based remote sensing in littoral environments for ecological monitoring and assessment is a challenging task that depends on adequate atmospheric conditions during data acquisition, sensor capabilities and correction of signal disturbances associated with water surface and water column. Airborne hyper-spectral scanners offer higher potential than satellite sensors for wetland monitoring and assessment. However, application in remote areas is often limited by national restrictions, time and high costs compared to satellite data. In this study, we tested the potential of the commercial, high-resolution multi-spectral satellite QuickBird for monitoring littoral zones of Lake Sevan (Armenia). We present a classification procedure that uses a physics-based image processing system (MIP) and GIS tools for calculating spatial metrics. We focused on classification of littoral sediment coverage over three consecutive years (2006)(2007)(2008) to document changes in vegetation structure associated with a rise in water levels. We describe a spectral unmixing algorithm for basic classification and a supervised algorithm for mapping vegetation types. Atmospheric aerosol retrieval, lake-specific parameterisation and validation of classifications were supported by underwater spectral measurements in the respective seasons. Results revealed accurate classification of submersed aquatic vegetation and sediment structures in the littoral zone, documenting spatial vegetation dynamics induced by water level fluctuations and interannual variations in phytoplankton blooms. The data prove the cost-effective applicability of satellite remote-sensing approaches for high-resolution mapping in space and time of lake littoral zones playing a Guest editor: Martin A. Stapanian / QuickBird satellite imagery as a tool for restoration and rehabilitation of Lake Sevan, Armenia

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

Heblinski¹,

Schmieder

Heege

et al. 2010

Hydrobiologia

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“…Both types of distance measurements had been proved to be with high efficiency in reflecting the spectral discrepancy (South et al, 2004;Goovaerts et al, 2005;Becker et al, 2007). The Mah distance can be written as:…”

Section: Spectral Matching Algorithmsmentioning

confidence: 99%

Crop Disease and Pest Monitoring by Remote Sensing

Huang¹,

Luo²,

Zhang³

et al. 2012

Remote Sensing - Applications

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“…In case of multispectral image analysis with only few wave lengths, good classification results can be achieved by Maximum Likelihood [77,78]. In contrast, Spectral Angle Mapper obtained good classifications with highly complex hyperspectral images [79,80].…”

Section: Detection Accuracy and Time Frame Of The Application Of Exismentioning

confidence: 99%

Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat

2014

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Abstract:In recent years, market pressures have reinforced the demand to solve the problem of an increased occurrence of Fusarium head blight (FHB) in cereal production, especially in wheat. The symptoms of this disease are clearly detectable by means of image analysis. This technique can therefore be used to map occurrence and extent of Fusarium infections. From this perspective, a separate harvest in the field can be taken into consideration. Based on the application of chlorophyll fluorescence and hyperspectral imaging, characteristics, requirements and limitations of Fusarium detection on wheat, both in the field and in the laboratory, are discussed. While the modification of spectral signatures due to fungal infection allows its detection by hyperspectral imaging, the decreased physiological activity of tissues resulting from Fusarium impacts provides the base for CFI analyses. In addition, the two methods are compared in view of their usability for the detection of Fusarium, and different approaches for data analysis are presented.

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A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery

Cited by 70 publications

References 6 publications

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

High-resolution satellite remote sensing of littoral vegetation of Lake Sevan (Armenia) as a basis for monitoring and assessment

Crop Disease and Pest Monitoring by Remote Sensing

Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat

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