Comparison of methods for integrating biological and physical data for marine habitat mapping and classification

Shumchenia, Emily; King, John W.

doi:10.1016/j.csr.2010.07.007

Cited by 75 publications

(39 citation statements)

References 41 publications

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“…Shumchenia and King [29] showed that a bottom-up approach worked better for classifying habitats than a top-down approach. Top-down generally uses broad-scale remote sensing to map an area and then minimal samples are collected to characterize each class determined from the maps, but this inherently implies that habitats are tied to certain geologic features of the seafloor [29].…”

Section: Combining Biotic and Abioticmentioning

confidence: 99%

“…CMECS should also provide more instruction on how to apply their classification system because the results can vary depending on the approach taken [29,30]. If the classification is to be standardized, then a common methodology for assigning hierarchical classes and at what scale should be developed [9].…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Top-down generally uses broad-scale remote sensing to map an area and then minimal samples are collected to characterize each class determined from the maps, but this inherently implies that habitats are tied to certain geologic features of the seafloor [29]. Other environmental and ecological characteristics are not taken into consideration and are often important in determining the distribution of biotic communities [29]. Shumchenia and King [29] also did not observe a simple relationship between the biotic groups and the broad scale acoustic classes (silt and sand); linkages to abiotic features were at fine scales.…”

Section: Combining Biotic and Abioticmentioning

confidence: 99%

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Applying the Coastal and Marine Ecological Classification Standard (CMECS) to Nearshore Habitats in the Northeastern Gulf of Mexico

Kingon

2018

Geosciences

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Many countries have classification standards for their environmental resources including criteria for classifying coastal and marine ecosystems. Until 2012, the United States just had a nationwide protocol for classifying terrestrial and aquatic habitats with no national standard for marine and most coastal habitats. In 2012 the Coastal and Marine Ecological Classification Standard (CMECS) was implemented to address this need. In the past, coastal and marine classifications were developed at the regional or local level. Since its inception, the CMECS has not been applied in many geographic areas. My study was one of the first to apply the CMECS to the benthic habitats in the nearshore Northeastern Gulf of Mexico. Sidescan sonar mapping and dive surveys were completed at 33 sites at depths 10-23 m. Hardbottom and sand habitats characterized the study area, and the underwater surveys revealed hard corals, sponges, and macroalgae as the dominant taxa on the hardbottom. The CMECS was applied to the overall study area rather than each individual site or groups of similar sites because habitat and environmental characteristics, primarily outside the context of the CMECS, appeared to influence the distribution of taxa across sites more than the CMECS geoform, substrate, and water column components. The CMECS worked well for classifying the entire study area, but was not adequate for classifying the complex fine-scale habitats and temporal variations observed; modifications to the CMECS could help resolve these issues.

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Section: Combining Biotic and Abioticmentioning

confidence: 99%

Section: Data Acquisitionmentioning

confidence: 99%

Section: Combining Biotic and Abioticmentioning

confidence: 99%

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Applying the Coastal and Marine Ecological Classification Standard (CMECS) to Nearshore Habitats in the Northeastern Gulf of Mexico

Kingon

2018

Geosciences

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“…Focusing on the interactions between the abiotic (terrain features) and biotic (communities) environments result in a habitat map representing biotopes, that are ecologically relevant [31,33,34]. Since ecosystems are structured hierarchically, using a hierarchical approach when classifying thematic habitat maps is a logical path.…”

Section: Introductionmentioning

confidence: 99%

Multiscale and Hierarchical Classification for Benthic Habitat Mapping

et al. 2018

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Developing quantitative and objective approaches to integrate multibeam echosounder (MBES) data with ground observations for predictive modelling is essential for ensuring repeatability and providing confidence measures for benthic habitat mapping. The scale of predictors within predictive models directly influences habitat distribution maps, therefore matching the scale of predictors to the scale of environmental drivers is key to improving model accuracy. This study uses a multi-scalar and hierarchical classification approach to improve the accuracy of benthic habitat maps. We used a 700-km 2 region surrounding Cape Otway in Southeast Australia with full MBES data coverage to conduct this study. Additionally, over 180 linear kilometers of towed video data collected in this area were classified using a hierarchical classification approach. Using a machine learning approach, Random Forests, we combined MBES bathymetry, backscatter, towed video and wave exposure to model the distribution of biotic classes at three hierarchical levels. Confusion matrix results indicated that greater numbers of classes within the hierarchy led to lower model accuracy. Broader scale predictors were generally favored across all three hierarchical levels. This study demonstrates the benefits of testing predictor scales across multiple hierarchies for benthic habitat characterization.

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“…remotely sensed reflectance or bathymetric swath data sets and their derivatives), but only point samples of benthic communities (e.g. phototransects and associated detailed community inventories) (Lehmann et al 2003, Shumchenia & King 2010, McClanahan & Karnauskas 2011.…”

Section: Introductionmentioning

confidence: 99%

Spatial modelling of benthic cover using remote sensing data in the Aldabra lagoon, western Indian Ocean

Hamylton¹,

Spencer²,

Hagan³

2012

Mar. Ecol. Prog. Ser.

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Spatially explicit ecological modelling was used to predict the distribution of 4 benthic components (live coral, carbonate sand, macroalgae and dead coral) inside the Aldabra lagoon, southern Seychelles, western Indian Ocean. Both classic ordinary least-squares and spatial autoregression techniques were carried out on a field data set of 774 spatially referenced records and 3 satellite remote sensing images to define an empirical relationship between local environmental conditions (water depth and water level variation) and benthic cover. This relationship was then used to generate a synoptic model of the spatial cover and distribution of each benthic component at the landscape (i.e. whole lagoon) scale. Environmental conditions were estimated from satellite remote sensing data (water depth) and using GIS techniques (water level variation). By drawing on species−environment relationships applicable to many lagoons, continuous records of percentage benthic cover were derived for the extensive lagoon (174 km 2 ) at a high measurement level (ratio) for use in conservation and resource management applications. The transition from the ordinary least-squares model to the spatially lagged model was accompanied by a marked growth in predictive power (R 2 = 0.25 to 0.79), indicating that neighbourhood context interactions play an important role in determining benthic cover of the Aldabra lagoon.

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Comparison of methods for integrating biological and physical data for marine habitat mapping and classification

Cited by 75 publications

References 41 publications

Applying the Coastal and Marine Ecological Classification Standard (CMECS) to Nearshore Habitats in the Northeastern Gulf of Mexico

Applying the Coastal and Marine Ecological Classification Standard (CMECS) to Nearshore Habitats in the Northeastern Gulf of Mexico

Multiscale and Hierarchical Classification for Benthic Habitat Mapping

Spatial modelling of benthic cover using remote sensing data in the Aldabra lagoon, western Indian Ocean

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