Mapping Baltic Sea shallow water environments with airborne remote sensing

Vahtmäe, Ele; Kutser, Tiit; Kotta, Jonne; Pärnoja, Merli; Möller, Tiia; Lennuk, Lennart

doi:10.1134/s0001437012060148

Cited by 13 publications

(6 citation statements)

References 8 publications

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“…Five different benthic habitat classes were defined for the imaged area based on the video data and expert knowledge: "hard bottom with ephemeral algae," "dense higher-order plant habitats," "dense charophyte community," "sparse higher order plants and/or Charophytes on soft bright bottom" and "optically deep water (>2 m)." As indicated previously [12,43], the spectral appearance of the same habitat type varies depending on the water depth and water quality. Therefore, training regions were selected from different water depths and water quality conditions for each benthic habitat class.…”

Section: Image-based Classificationmentioning

confidence: 58%

“…For example, the benthic vegetation signal rapidly decreases as water depth increases and almost completely disappears within a depth of 2.0 m in the Haapsalu Bay area. The results from more open sea areas on the western side of the Estonian biggest island of Saaremaa showed that the benthic vegetation could be detected down to depths of 5-6 m [43]. Therefore, the areas below 2.0 m were classified as deep-turbid in the Haapsalu Bay area, where no information about benthic types could be retrieved with remote sensing.…”

Section: Discussionmentioning

confidence: 98%

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Classifying the Baltic Sea Shallow Water Habitats Using Image-Based and Spectral Library Methods

Vahtmäe

Kutser

2013

Remote Sensing

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Abstract:The structure of benthic macrophyte habitats is known to indicate the quality of coastal water. Thus, a large-scale analysis of the spatial patterns of coastal marine habitats enables us to adequately estimate the status of valuable coastal marine habitats, provide better evidence for environmental changes and describe processes that are behind the changes. Knowing the spatial distribution of benthic habitats is also important from the coastal management point of view.A big challenge in remote sensing mapping of benthic habitats is to define appropriate mapping classes that are also meaningful from the ecological point of view. In this study, the benthic habitat classification scheme was defined for the study areas in the relatively turbid north-eastern Baltic Sea coastal environment. Two different classification methods-image-based and the spectral library-method were used for image classification. The image-based classification method can provide benthic habitat maps from coastal areas, but requires extensive field studies. An alternative approach in image classification is to use measured and/or modelled spectral libraries. This method does not require fieldwork at the time of image collection if preliminary information about the potential benthic habitats and their spectral properties, as well as variability in optical water properties exists from earlier studies. A spectral library was generated through radiative transfer model HydroLight computations using measured reflectance spectra from representative benthic substrates and water quality measurements.Our previous results have shown that benthic habitat mapping should be done at high spatial resolution, owing to the small-scale heterogeneity of such habitats in the Estonian OPEN ACCESSRemote Sens. 2013, 5 2452 coastal waters. In this study, the capability of high spatial resolution hyperspectral airborne a Compact Airborne Spectrographic Imager (CASI) sensor and a high spatial resolution multispectral WorldView-2 satellite sensor were tested for mapping benthic habitats. Initial evaluations of habitat maps indicate that image-based classification provides higher quality benthic maps compared to the spectral library method.

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Section: Image-based Classificationmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 98%

Classifying the Baltic Sea Shallow Water Habitats Using Image-Based and Spectral Library Methods

Vahtmäe

Kutser

2013

Remote Sensing

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“…In addition to the community level %SAV assessment, we also addressed the issue of species and/or class level %SAV mapping. Previous studies from the Baltic Sea showed that species level habitat maps cannot be retrieved from the Baltic Sea by remote sensing methods [8,53]. The reason for it is the high spatial heterogeneity, as well as the high spectral similarity between different species belonging to the same vegetation class (brown macroalgae, red macroalgae, green macroalgae, higher plants, etc.).…”

Section: Sav Percent Cover Assessmentmentioning

confidence: 99%

Performance and Applicability of Water Column Correction Models in Optically Complex Coastal Waters

2020

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Maps of submerged aquatic vegetation (SAV) are of primary importance for the sustainable management of coastal areas and serve as a basis for fundamental ecological studies. Various water column correction (WCC) models are successfully applied in clear Case-1 waters to compensate for the variable water depth effect. The performance of the WCC in less clear Case-2 waters is rarely assessed. In this study, the performance and applicability of model-based WCC algorithms in the complex Baltic Sea were analyzed. The bottom reflectance was retrieved from the Compact Airborne Spectrographic Imager (CASI) water surface reflectance by applying the Maritorena and Lee WCC algorithms. The Maritorena model retrieved bottom spectra that showed large variations in reflectance magnitudes. The Lee model was more successful in retrieving reasonable spectral magnitudes, although only in a rather narrow wavelength region (550–600 nm). Shorter and longer spectral regions were significantly overcorrected, resulting in unrealistic spectral shapes. Sensitivity analysis indicated that slight under- or overestimation of water depth and water column constituents affect retrieval of correct bottom spectra in Case-2 waters. To assess the performance of WCC models in improving the SAV quantification, the surface reflectance, as well as the retrieved bottom reflectance, were correlated with the corresponding in situ estimated SAV percent cover (%SAV). Although the quality of the Lee WCC model was not considered high, the spectral region least affected by the input parameters variations (550–600 nm) can be used for the SAV quantification. Application of the Lee model provided better results in %SAV assessment than not performing the WCC correction.

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“…Maximum Likelihood Classifier (MLC) was chosen to carry out the classification since it is the most widely used supervised classification method (Yang et al, 2015). MLC automatically categorizes pixels in an image into a trained (i.e., target) class (Vahtmäe et al, 2012). MLC evaluates the brightness of one band compared to the other (variance and covariance) in the training class and then it categorizes pixels based on its maximum probability of belonging in a class (McCarthy and Halls, 2014).…”

Section: Supervised Classification and Accuracy Assessmentmentioning

confidence: 99%

Developing High Resolution Baseline Coast Resource Maps Using World View 2 Imagery for a Coastal Village in Fiji

et al. 2019

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In Fiji, like most Pacific Island countries, there have been numerous reports of degradation of coastal resources, including adverse changes in abundance and stock distribution of numerous aquatic species associated with the coastal habitat. To develop effective management plans, assessment of existing coastal resources is pertinent. High spatial resolution satellite imagery, combined with geographic information systems allow for efficient and synoptic mapping of coastal resources to provide a baseline for developing effective and improved management plans. The purpose of this study was to develop a baseline habitat map of the intertidal benthic cover in Komave Village, Coral Coast, Sigatoka, Fiji. Resource mapping was based on high resolution (2 m) WorldView-2 imagery. Ground-truthing was attained by means of on-site data logging of the intertidal resources, image capturing and GPS recording. Based on these records, the benthic cover was classified into seven classes: 'coral,' 'algae,' 'brown algae,' 'volcanic rocks,' 'sand and gravel,' 'sea grass,' and 'bare.' Ground referencing points were randomly assigned for either supervised classification training or accuracy assessment. A community participatory research approach was used to conduct interviews to assimilate information on fishing sites and coastal land use activities. This exercise explored the social-ecological approach in natural resource management and how it can become an important tool in coastal conservation practices. The coastal resource map generated through this study serves as a baseline for monitoring the status and spatial distribution of the coastal resources in Komave. Annual mapping of the resources and enrichment of maps along with iterative village consultation will enable managers to develop and gauge the effectiveness of coastal management plans. This high resolution map is particularly relevant to Fiji as it is the first of its kind for the country. This work also serves to reduce the global information gap of coastal resource status for Fiji.

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Mapping Baltic Sea shallow water environments with airborne remote sensing

Cited by 13 publications

References 8 publications

Classifying the Baltic Sea Shallow Water Habitats Using Image-Based and Spectral Library Methods

Classifying the Baltic Sea Shallow Water Habitats Using Image-Based and Spectral Library Methods

Performance and Applicability of Water Column Correction Models in Optically Complex Coastal Waters

Developing High Resolution Baseline Coast Resource Maps Using World View 2 Imagery for a Coastal Village in Fiji

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