Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows

Valle, Mireia; Palà, Vicenç; Lafon, Virginie; Dehouck, Aurélie; Garmendia, Joxe Mikel; Borja, Ángel; Chust, Guillem

doi:10.1016/j.ecss.2015.07.034

Cited by 34 publications

(19 citation statements)

References 40 publications

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“…Unmixing algorithms separate the pixel spectra into a collection of constituent pure spectral signatures, named endmembers, and the corresponding set of fractional abundances, representing the percentage of each endmember that is present in the pixel [27]. Recent research to create seabed maps using remote sensing imagery has been mainly devoted to map coral reefs [28][29][30][31][32][33][34] or seagrass meadows [3,18,[35][36][37][38][39][40]. Commonly, these studies address very shallow, clear and calm waters, and very dense vegetal species (i.e., Posidonia oceanica).…”

Section: Study Areamentioning

confidence: 99%

Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

et al. 2018

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Coastal ecosystems experience multiple anthropogenic and climate change pressures. To monitor the variability of the benthic habitats in shallow waters, the implementation of effective strategies is required to support coastal planning. In this context, high-resolution remote sensing data can be of fundamental importance to generate precise seabed maps in coastal shallow water areas. In this work, satellite and airborne multispectral and hyperspectral imagery were used to map benthic habitats in a complex ecosystem. In it, submerged green aquatic vegetation meadows have low density, are located at depths up to 20 m, and the sea surface is regularly affected by persistent local winds. A robust mapping methodology has been identified after a comprehensive analysis of different corrections, feature extraction, and classification approaches. In particular, atmospheric, sunglint, and water column corrections were tested. In addition, to increase the mapping accuracy, we assessed the use of derived information from rotation transforms, texture parameters, and abundance maps produced by linear unmixing algorithms. Finally, maximum likelihood (ML), spectral angle mapper (SAM), and support vector machine (SVM) classification algorithms were considered at the pixel and object levels. In summary, a complete processing methodology was implemented, and results demonstrate the better performance of SVM but the higher robustness of ML to the nature of information and the number of bands considered. Hyperspectral data increases the overall accuracy with respect to the multispectral bands (4.7% for ML and 9.5% for SVM) but the inclusion of additional features, in general, did not significantly improve the seabed map quality.

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Section: Study Areamentioning

confidence: 99%

Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

et al. 2018

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“…The data from the CASI imagery increased the OA to 95% for all habitats, whilst the fusion of CASI and LiDAR data only improved the accuracy of seagrass classification. Using the same hyper-spectral imagery for seagrass mapping, Valle et al [139] applied MLC to six different band combinations of CASI imagery and yielded the highest producer accuracy using 10 bands’ combination (92%). A comparison of the performance was also found for Hyperion and a group of Landsat-5 TM, EO–1, and IKONOS [121,129], which exhibited a higher accuracy of seagrass mapping than hyper-spectral images.…”

Section: Background and Methodsmentioning

confidence: 99%

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Pham

Xia

et al. 2019

Sensors

114

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Blue carbon (BC) ecosystems are an important coastal resource, as they provide a range of goods and services to the environment. They play a vital role in the global carbon cycle by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, there has been a large reduction in the global BC ecosystems due to their conversion to agriculture and aquaculture, overexploitation, and removal for human settlements. Effectively monitoring BC ecosystems at large scales remains a challenge owing to practical difficulties in monitoring and the time-consuming field measurement approaches used. As a result, sensible policies and actions for the sustainability and conservation of BC ecosystems can be hard to implement. In this context, remote sensing provides a useful tool for mapping and monitoring BC ecosystems faster and at larger scales. Numerous studies have been carried out on various sensors based on optical imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), aerial photographs (APs), and multispectral data. Remote sensing-based approaches have been proven effective for mapping and monitoring BC ecosystems by a large number of studies. However, to the best of our knowledge, this is the first comprehensive review on the applications of remote sensing techniques for mapping and monitoring BC ecosystems. The main goal of this review is to provide an overview and summary of the key studies undertaken from 2010 onwards on remote sensing applications for mapping and monitoring BC ecosystems. Our review showed that optical imagery, such as multispectral and hyper-spectral data, is the most common for mapping BC ecosystems, while the Landsat time-series are the most widely-used data for monitoring their changes on larger scales. We investigate the limitations of current studies and suggest several key aspects for future applications of remote sensing combined with state-of-the-art machine learning techniques for mapping coastal vegetation and monitoring their extents and changes.

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“…The atmosphere corrected image still contains information about water depth, water column constituents and the reflectance properties of substrate cover types [12,15,37,40,[44][45][46][47][48][49][50]. Some studies did not conduct water column correction in their seagrass mapping for various data sources such as RGB photos by lightweight drone Duffy et al [17], airborne color infrared imagery [13], airborne hyperspectral data [11], and high spatial resolution satellite imagery [14]. Actually Duffy et al [17], Green and Lopez [13] did not conduct atmospheric correction neither.…”

Section: Water Column Correctionmentioning

confidence: 99%

“…Both satellite and airborne images have been used to investigate the seagrass meadows distributed across the shallow waters. Some instances are medium spatial resolution data [6][7][8], aerial hyperspectral scanners [9][10][11], high spatial resolution imagery [12][13][14][15][16], and photos acquired by a lightweight drone [17]. Various techniques and input features were used in seagrass mapping.…”

Section: Introductionmentioning

confidence: 99%

Seagrass Resource Assessment Using WorldView-2 Imagery in the Redfish Bay, Texas

Huang

2019

JMSE

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Seagrass meadows play important roles as habitats for many marine organisms, traps for sediment, and buffers against wave actions. The objective of this paper is to map seagrass meadows in the Redfish Bay, Texas from WorldView-2 imagery. Seagrass meadows grow in shallow and clear water areas in the Redfish Bay. The WorldView-2 satellite can acquire multispectral imagery from the bay bottom with 2 m spatial resolution 8 multispectral bands and 0.46 m panchromatic imagery. The top of atmosphere radiance was transformed to the bottom reflectance through the atmospheric correction and the water column correction. The object based image analysis was used to identify seagrass meadows distributions in the Redfish Bay. This investigation demonstrated that seagrass can be identified with 94% accuracy, although seagrass species cannot be satisfactorily recognized. The results implied that the WorldView-2 satellite imagery is a suitable data source for seagrass distribution mapping.

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Mapping estuarine habitats using airborne hyperspectral imagery, with special focus on seagrass meadows

Cited by 34 publications

References 40 publications

Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

Seabed Mapping in Coastal Shallow Waters Using High Resolution Multispectral and Hyperspectral Imagery

A Review of Remote Sensing Approaches for Monitoring Blue Carbon Ecosystems: Mangroves, Seagrassesand Salt Marshes during 2010–2018

Seagrass Resource Assessment Using WorldView-2 Imagery in the Redfish Bay, Texas

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