Self-adaptive analysis scale determination for terrain features in seafloor substrate classification

Shang, Xiaodong; Robert, Katleen; Misiuk, Benjamin; Mackin-McLaughlin, Julia; Zhao, Jianhu

doi:10.1016/j.ecss.2021.107359

Cited by 10 publications

(10 citation statements)

References 61 publications

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“…In contrast, RF has only two parameters that generally require manipulation, and even appears quite robust to these in practice, making it a more user-friendly algorithm (Brieman, 2001;Liaw and Weiner, 2002). RF has demonstrated repeated success in benthic habitat mapping studies comparing techniques (Lucieer et al, 2013;Robert et al, 2015;Rooper et al, 2017;Misiuk et al, 2019;Pillay et al, 2020;Shang et al, 2021). However, RF may be biased towards computational efficiency in favor of accuracy (Fernandez-Delgado et al, 2011;Wainberg et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Three features were calculated using a grey-level co-occurrence matrix via the R package 'GLCM' (Zvoleff, 2020): contrast (local variation), homogeneity (closeness of distribution), and entropy (randomness) (Haralick et al, 1973). These features were selected for their common application in previous studies of similar systems (Blondel and Sichi, 2009;Samsudin and Hasan, 2017;Shang et al, 2021).…”

Section: Environmental Surveymentioning

confidence: 99%

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Spatial distribution of benthic flora and fauna of coastal placentia bay, an ecologically and biologically significant area of the island of newfoundland, atlantic Canada

Mackin-McLaughlin

Nemani²,

Misiuk³

et al. 2022

Front. Environ. Sci.

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Coastal habitats have the potential to be biodiversity hotspots that provide important ecosystem services, but also hotspots for human development and exploitation. Continued use of coastal ecosystem services requires establishing baselines that capture the present state of the benthos. This study employs habitat mapping to establish a baseline describing the spatial distribution of benthic organisms along the western coast of Placentia Bay, an Ecologically and Biologically Significant Area (EBSA) in Newfoundland, Canada. The influence of seafloor characteristics on the distribution of four dominant epifaunal assemblages and two macrophyte species were modelled using two machine learning techniques: the well-established Random Forest and the newer Light Gradient Boosting Machine. When investigating model performance, the inclusion of fine-scale (<1 m) substrate information from the benthic videos was found to consistently improve model accuracy. Predictive maps developed here suggest that the majority of the surveyed areas consisted of a species-rich epifaunal assemblage dominated by ophiuroids, porifera, and hydrozoans, as well as prominent coverage by Agarum clathratum and non-geniculate crustose coralline algae. These maps establish a baseline that enables future monitoring of Placentia Bay’s coastal ecosystem, helping to conserve the biodiversity and ecosystem services this area provides.

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Section: Discussionmentioning

confidence: 99%

Section: Environmental Surveymentioning

confidence: 99%

Spatial distribution of benthic flora and fauna of coastal placentia bay, an ecologically and biologically significant area of the island of newfoundland, atlantic Canada

Mackin-McLaughlin

Nemani²,

Misiuk³

et al. 2022

Front. Environ. Sci.

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“…We also note other alternatives to multi-scale terrain attribute calculation methods that differ fundamentally from those presented here, such as quadratic surface estimation (Evans 1972;Wood 1996). The analysis distance is still attainable in such casesthough it may vary with location (Shang et al 2021).…”

Section: Discussion and Recommendationsmentioning

confidence: 99%

“…Multibeam echosounder survey of D'Argent Bay, Newfoundland and Labrador (NL), Canada, was conducted on December 4-7, 2018 andFebruary 19-23, 2019 to provide data for benthic habitat mapping in Placentia Bayan area designated as an Ecologically and Biologically Significant Area by Fisheries and Oceans Canada (Templeman 2007; Figure 1A). Drop camera observations at this site suggested a heterogeneous seabed primarily comprising gravel and boulder, with patches of soft sediment near the coast (Shang et al 2021). Approximately 43 km 2 were mapped using a Kongsberg EM2040p (400 kHz) with differential GPS (Fugro 3610 DGNSS) aboard the research vessel D. Cartwright.…”

Section: D'argent Baymentioning

confidence: 99%

“…Options for 'k Â k-window' calculations are not supported for many attributes in commercial software packages. Finally, several approaches have been proposed for calculating multi-scale attributes that simultaneously integrate information from multiple spatial scales in one or several layers (Method 5 in Table 1; e.g., Gallant and Dowling 2003;Schmidt and Andrew 2005;Dr aguţ, Eisank, et al 2009;Dr aguţ, Schauppenlehner, et al 2009;Wood 2009;Gorini and Mota 2011;Lindsay and Newman 2018;Shang et al 2021). Many of these integrated methods differ fundamentally from the first four in Table 1 and are outside of the scope of this article.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Suitability of Multi-Scale Terrain Attribute Calculation Approaches for Seabed Mapping Applications

et al. 2021

Self Cite

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The scale dependence of benthic terrain attributes is wellaccepted, and multi-scale methods are increasingly applied for benthic habitat mapping. There are, however, multiple ways to calculate terrain attributes at multiple scales, and the suitability of these approaches depends on the purpose of the analysis and data characteristics. There are currently few guidelines establishing the appropriateness of multi-scale raster calculation approaches for specific benthic habitat mapping applications. First, we identify three common purposes for calculating terrain attributes at multiple scales for benthic habitat mapping: (i) characterizing scale-specific terrain features, (ii) reducing data artefacts and errors, and (iii) reducing the mischaracterization of ground-truth data due to inaccurate sample positioning. We then define criteria that calculation approaches should fulfill to address these purposes. At two study sites, five raster terrain attributes, including measures of orientation, relative position, terrain variability, slope, and rugosity were calculated at multiple scales using four approaches to compare the suitability of the approaches for these three purposes. Results suggested that specific calculation approaches were better suited to certain tasks. A transferable parameter, termed the 'analysis distance', was necessary to compare attributes calculated using different approaches, and we emphasize the utility of such a parameter for facilitating the generalized comparison of terrain attributes across methods, sites, and scales.

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Segment-level modeling of wildfire susceptibility in Iranian semi-arid oak forests: Unveiling the pivotal impact of human activities

Sadeghi,

Ahmadi Nadoushan,

Ahmadi Sani

2024

Trees, Forests and People

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Self-adaptive analysis scale determination for terrain features in seafloor substrate classification

Cited by 10 publications

References 61 publications

Spatial distribution of benthic flora and fauna of coastal placentia bay, an ecologically and biologically significant area of the island of newfoundland, atlantic Canada

Spatial distribution of benthic flora and fauna of coastal placentia bay, an ecologically and biologically significant area of the island of newfoundland, atlantic Canada

Evaluating the Suitability of Multi-Scale Terrain Attribute Calculation Approaches for Seabed Mapping Applications

Segment-level modeling of wildfire susceptibility in Iranian semi-arid oak forests: Unveiling the pivotal impact of human activities

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