Optimizing bathymetric position index (BPI) calculation: An analysis of parameters and recommendations for the selection of their optimal values

Abstract

The present research paper addresses a critical gap in existing literature concerning the absence of a standardized methodology for parameter selection in the computation of the Bathymetric Position Index (BPI) values. The BPI is a measure of where a georeferenced location, with a defined depth, is relative to the neighbouring seascape, and it plays a significant role in characterizing benthic terrain for modelling and classification. Arguably, the two most important parameters when calculating the BPI are the size and the shape of the neighbourhood of analysis. With regards to the radius parameter, which defines the size of the neighbourhood, the optimal radius value for calculating the BPI must be carefully chosen, considering both the size of the target morphology and the scale factor, which is equal to the radius in map units multiplied by the cell size. It is recommended that the optimal radius value should closely match the size of the target morphology. Tests were performed using an annular neighbourhood shape and they have revealed that the outer radius is the most influential factor in the BPI calculation. Further experimentations and comparisons between circular and annular shapes have indicated that the use of different shapes has no significant impact on the results. The study has found no substantial correlation between the BPI values and other examined terrain variables, such as depth, slope, and curvature. This lack of correlation may be attributed to the BPI values accounting for the specific neighbourhood size, while for the studied variables the default window size was used, which is a considerably smaller scale than the ones used in most BPI calculations. In conclusion, this research highlights the importance of parameter selection in BPI calculations and provides valuable insights into the optimal radius choice and the negligible impact of neighbourhood shape. The findings also shed light on the unique nature of BPI values and their relationship with other geospatial variables.