High-Resolution Maps of Bathymetry and Benthic Habitats in Shallow-Water Environments Using Multispectral Remote Sensing Imagery

Eugênio, Francisco; Marcello, Javier; Martín, Josune

doi:10.1109/tgrs.2014.2377300

Cited by 212 publications

(143 citation statements)

References 28 publications

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“…These bands are often highly correlated, thus offering limited information per band. Additional bands in the visible spectrum enhances the ability to retrieve bathymetry, benthic, and water quality characteristics [19,[22][23][24][25][26][27].…”

Section: Factors Affecting Image Qualitymentioning

confidence: 99%

“…Previous studies reported bathymetric retrievals from WV2 data (e.g., [5,25,26,35]). However, with the exception of [35], all of these studies report results from single images, selected for the most optimal conditions available.…”

Section: Objectivesmentioning

confidence: 99%

“…Glint correction of the WV2 images is complicated by the sensor design where two sensor arrays collect four spectral bands each (creating the total of eight spectral bands) with offsets in both the location of the sensor array and in the time of collection [26,36,44]. Due to the reliance of the glint-correction method on near infrared (NIR) reflectance, the glint correction algorithm has to be applied separately on the image bands that were collected with each individual sensor array [45].…”

Section: Pre-processingmentioning

confidence: 99%

“…Water-leaving radiance is often small, compared to the reflected radiance from other sources, such as atmospheric and water surface scattering [26,30]. It is, thus, important to be aware of all of the environmental noise factors that can impact the precision and accuracy with which desired environmental variables can be retrieved from a satellite image.…”

mentioning

confidence: 99%

“…As accurate as possible atmospheric correction during image pre-processing is important for reliable water column composition, benthic, and bathymetric information retrieval [18,26].…”

mentioning

confidence: 99%

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Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

2016

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Increased sophistication of high spatial resolution multispectral satellite sensors provides enhanced bathymetric mapping capability. However, the enhancements are counter-acted by per-pixel variability in sunglint, atmospheric path length and directional effects. This case-study highlights retrieval errors from images acquired at non-optimal geometrical combinations. The effects of variations in the environmental noise on water surface reflectance and the accuracy of environmental variable retrievals were quantified. Two WorldView-2 satellite images were acquired, within one minute of each other, with Image 1 placed in a near-optimal sun-sensor geometric configuration and Image 2 placed close to the specular point of the Bidirectional Reflectance Distribution Function (BRDF). Image 2 had higher total environmental noise due to increased surface glint and higher atmospheric path-scattering. Generally, depths were under-estimated from Image 2, compared to Image 1. A partial improvement in retrieval error after glint correction of Image 2 resulted in an increase of the maximum depth to which accurate depth estimations were returned. This case-study indicates that critical analysis of individual images, accounting for the entire sun elevation and azimuth and satellite sensor pointing and geometry as well as anticipated wave height and direction, is required to ensure an image is fit for purpose for aquatic data analysis.

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Section: Factors Affecting Image Qualitymentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

mentioning

confidence: 99%

“…As accurate as possible atmospheric correction during image pre-processing is important for reliable water column composition, benthic, and bathymetric information retrieval [18,26].…”

mentioning

confidence: 99%

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Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

2016

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Removing sun glint from optical remote sensing images of shallow rivers

Overstreet

Legleiter

2016

Earth Surf Processes Landf

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Sun glint is the specular reflection of light from the water surface, which often causes unusually bright pixel values that can dominate fluvial remote sensing imagery and obscure the water‐leaving radiance signal of interest for mapping bathymetry, bottom type, or water column optical characteristics. Although sun glint is ubiquitous in fluvial remote sensing imagery, river‐specific methods for removing sun glint are not yet available. We show that existing sun glint‐removal methods developed for multispectral images of marine shallow water environments over‐correct shallow portions of fluvial remote sensing imagery resulting in regions of unreliable data along channel margins. We build on existing marine glint‐removal methods to develop a river‐specific technique that removes sun glint from shallow areas of the channel without over‐correction by accounting for non‐negligible water‐leaving near‐infrared radiance. This new sun glint‐removal method can improve the accuracy of spectrally‐based depth retrieval in cases where sun glint dominates the at‐sensor radiance. For an example image of the gravel‐bed Snake River, Wyoming, USA, observed‐versus‐predicted R2 values for depth retrieval improved from 0.66 to 0.76 following sun glint removal. The methodology presented here is straightforward to implement and could be incorporated into image processing workflows for multispectral images that include a near‐infrared band. Copyright © 2016 John Wiley & Sons, Ltd.

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Cloud‐native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel‐2

Poursanidis

Traganos

Teixeira

et al. 2020

Remote Sens Ecol Conserv

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The lack of detailed spatial information on coastal resources, notably shallow water coral reefs and associated benthic habitats, impedes our ability to protect and manage them in the face of global climate change and anthropogenic impacts. Here, we develop a semi-automated workflow in the cloud that uses freely available Sentinel-2 data from the European Space Agency (ESA) Copernicus programme to derive information on near-shore coral reef habitats in the Quirimbas National Park (QNP), a recently declared biosphere reserve in northern Mozambique. We use an end-to-end cloud-based framework within the Google Earth Engine cloud geospatial platform to process imagery from raw pixels to cloud-free composites which are corrected for glint and surface artefacts, water column and derived estimated depth and then classified into four benthic habitats. Using independent training and validation data, we apply three supervised classification algorithms: random forests (RF), support vector machine (SVM) and classification and regression trees (CART). Our results show that random forests are the most accurate supervised algorithm with over 82% overall accuracy. We mapped over 105 000 ha of shallow water habitat inside the protected area, of which 18% are dominated by coral and hardbottom; 27.5% are seagrass and submerged aquatic vegetation and another 23.4% are soft and sandy substrates, and the remaining area is optically deep water. We employ satellite-derived bathymetry to assess slope, bathymetric position, rugosity and underwater topography of these habitats. Finally, a spectral unmixing model provides further sub-pixel-level information of habitats with the potential to monitor changes over time. This effort provides the first, consistent and repeatable and also scalable coastal information system for an east African tropical marine protected area, which hosts shallow-water ecosystems which are of great significance to local communities and building resilience towards climate change.

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High-Resolution Maps of Bathymetry and Benthic Habitats in Shallow-Water Environments Using Multispectral Remote Sensing Imagery

Cited by 212 publications

References 28 publications

Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

Effects of Per-Pixel Variability on Uncertainties in Bathymetric Retrievals from High-Resolution Satellite Images

Removing sun glint from optical remote sensing images of shallow rivers

Cloud‐native seascape mapping of Mozambique’s Quirimbas National Park with Sentinel‐2

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