Hyperspectral Imagery in Fluvial Environments

Fonstad, Mark A.

doi:10.1002/9781119940791.ch4

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…The use of image classification techniques for river mapping is well documented and has been applied successfully on hyperspectral imagery for the identification of hydraulic and habitat patterns [25], woody debris [14], channel substrate [26] and riparian vegetation [26]. Although hyper and multispectral bands are the preferred wavelength bands to classify hydromorphological features, they require exhaustive data processing algorithms and post-interpretation [26].…”

Section: Introductionmentioning

confidence: 99%

“…Although hyper and multispectral bands are the preferred wavelength bands to classify hydromorphological features, they require exhaustive data processing algorithms and post-interpretation [26]. Spaceborne hyperspectral imagery (30 to 50 m ground resolution) does not offer the required resolution for detail river feature identification and fails to provide global spatio-temporal coverage.…”

Section: Introductionmentioning

confidence: 99%

“…Airborne hyperspectral imagery can address this limitation but data capture can be expensive as it requires hired hyperspectral flights tailored to the objectives of the research. The spatial (ground) resolution of airborne hyperspectral imagery (typically above meter resolution) is significantly larger than typical high-resolution RGB photographs as a result of a lower number of photons per channel than imaging spectrometers [26]. Hyperspectral imagery may also be highly sensitive to imaging geometry (e.g., differences from the middle to the edge of the flight path) and environmental conditions (e.g., water vapour).…”

Section: Introductionmentioning

confidence: 99%

“…, there is a lag between acquisitions of consecutive pixels. For airborne imagery, this translates into unequal pixel geometries and major issues in image rectification [26]. …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

Casado

Ballesteros²,

Kriechbaumer

et al. 2015

Sensors

108

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European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…, there is a lag between acquisitions of consecutive pixels. For airborne imagery, this translates into unequal pixel geometries and major issues in image rectification [26]. …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

Casado

Ballesteros²,

Kriechbaumer

et al. 2015

Sensors

108

View full text Add to dashboard Cite

show abstract

“…Surian et al [2009] used aerial photographs to assess morphological effects of different discharges in a gravel-bed river. Some other authors employed passive optical sensors [Bertoldi et al, 2010;Lane et al, 2010], hyperspectral [Fonstad, 2012], or multispectral images [Bertoldi et al, 2011a] to detect channel morphological changes and analyze the dynamic of riparian vegetation. LiDAR (Light Detection And Ranging) was also used in combination with aerial photographs to observe the impact of riparian vegetation on channel forms [Bertoldi et al, 2011b], and in combination with high resolution multispectral images [Demarchi et al, 2016] to map the riverscape units.…”

Section: Introductionmentioning

confidence: 99%

River morphology monitoring using multitemporal SAR data: preliminary results

Mitidieri

Papa

Amitrano³

et al. 2016

European Journal of Remote Sensing

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In this paper, we test the capability of satellite synthetic aperture radar (SAR) images to enhance the monitoring of river geomorphological processes. The proposed approach exploits the recently introduced Level-1α products. These products are bi-temporal RGB composites in which the association color-object, being physical-based, is stable whatever the scene is considered. This favors the detection of temporary rivers' characteristics for classification purposes in a change-detection environment. The case study was implemented on the Orco river (northwest Italy), where a set of 39 COSMO-SkyMed SAR stripmap images acquired from October 2008 to November 2014 was used to monitor channel planform changes. This preliminary study is devoted to assess the suitability of Level-1α images for geomorphologist, with particular reference to the detection of phenomena of interest in river monitoring. This is prior for semi-automatic or automatic classification activities.

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Remote Sensing as a Tool for Analysing Channel Dynamics and Geomorphic Effects of Floods

Righini

Surian

2017

Springer Remote Sensing/Photogrammetry

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Hyperspectral Imagery in Fluvial Environments

Cited by 5 publications

References 35 publications

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery

River morphology monitoring using multitemporal SAR data: preliminary results

Remote Sensing as a Tool for Analysing Channel Dynamics and Geomorphic Effects of Floods

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