TY - JOUR

T1 - Evaluation of Boulder Characteristics for Improved Boulder Detection Based on Machine Learning Techniques

AU - Hansen, Signe Schilling

AU - Ernstsen, Verner Brandbyge

AU - Andersen, Mikkel Skovgaard

AU - Al-Hamdani, Zyad

AU - Baran, Ramona

AU - Niederwieser, Manfred

AU - Steinbacher, Frank

AU - Kroon, Aart

PY - 2022

Y1 - 2022

N2 - Detailed maps of the seabed and knowledge of its habitats are critical for a wide range of tasks, such as sustainable development, and environmental protection. Boulders on the seabed form an important environment for ecosystems, but the detection of them is challenging. In this study, we aim to improve the understanding of boulder predictors and to determine connections between predictors and boulder environments on different spatial scales. The Relief-F filter feature selection algorithm was used on four 30 m x 30 m areas in Rodsand lagoon, containing one boulder each, to determine the most relevant predictors. The predictors could be divided into four groups detecting different boulder characteristics: colour contrast, height, boulder boundaries, and spherical geometry. Twelve different types of boulder environments were evaluated. Bare, spherical boulders on sandy seabeds can be predicted from all four predictor groups. It is not possible to detect non-spherical boulders on seabed covered by vegetation. The best predictors for boulder detection depend on the shape and size of the boulder and the surrounding sediment and vegetation. The predictors were evaluated on a larger 400 x 2500 m area. When up-scaling the boulder detection area, larger seabed structures may affect the results. Therefore, knowledge about these structures can be used to remove errors and uncertainties from machine learning input data.

AB - Detailed maps of the seabed and knowledge of its habitats are critical for a wide range of tasks, such as sustainable development, and environmental protection. Boulders on the seabed form an important environment for ecosystems, but the detection of them is challenging. In this study, we aim to improve the understanding of boulder predictors and to determine connections between predictors and boulder environments on different spatial scales. The Relief-F filter feature selection algorithm was used on four 30 m x 30 m areas in Rodsand lagoon, containing one boulder each, to determine the most relevant predictors. The predictors could be divided into four groups detecting different boulder characteristics: colour contrast, height, boulder boundaries, and spherical geometry. Twelve different types of boulder environments were evaluated. Bare, spherical boulders on sandy seabeds can be predicted from all four predictor groups. It is not possible to detect non-spherical boulders on seabed covered by vegetation. The best predictors for boulder detection depend on the shape and size of the boulder and the surrounding sediment and vegetation. The predictors were evaluated on a larger 400 x 2500 m area. When up-scaling the boulder detection area, larger seabed structures may affect the results. Therefore, knowledge about these structures can be used to remove errors and uncertainties from machine learning input data.

KW - boulder characteristics

KW - boulder detection

KW - boulder predictors

KW - habitat mapping

KW - machine learning

KW - seabed environments

U2 - 10.3390/geosciences12110421

DO - 10.3390/geosciences12110421

M3 - Journal article

VL - 12

JO - Geosciences

JF - Geosciences

SN - 2076-3263

IS - 11

M1 - 421

ER -