The Black Top Hat function applied to a DEM: A tool to estimate recent incision in a mountainous watershed (Estibère Watershed, Central Pyrenees)

Abstract

[1] The Top Hat Transform function is a grey-level image analysis tool that allows extracting peaks and valleys in a nonuniform background. This function can be applied onto a greylevel Digital Elevation Model (DEM). It is herein applied to quantify the volume of recent incised material in a mountainous Pyrenean watershed. Grey-level Closing operation applied to the Present-Day DEM gives a new image called ''paleo'' DEM. The Black Top Hat function consists in the subtraction of the ''paleo'' DEM with the Present-Day DEM. It gives a new DEM representing all valleys whose sizes range between the size of the structuring element and the null value as no threshold is used. The calculation of the incised volume is directly derived from the subtraction between the two DEM's. The geological significance of the quantitative results is discussed.

Estimating hydraulic conductivity for the Martian subsurface based on drainage patterns — A case study in the Mare Tyrrhenum Quadrangle

“…The morphology-based algorithm was used because it extracts valleys that reflect spatial variation of dissection patterns and the algorithm eliminates the influence of large impact craters through a number of image processing techniques (Molloy and Stepinski, 2007). d was estimated by subtracting the present day DEM (Smith et al, 2003) from a pre-incision surface constructed using the Black Top Hat (BTH) function (Rodriguez et al, 2002;Luo et al, 2010) and averaged by watershed. The BTH function involves a dilation operation followed by a closing operation.…”

A progressive black top hat transformation algorithm for estimating valley volumes on Mars

“…The volume of excavation by fluvial processes is a critical piece of information for better understanding these research questions. With the ready availability of digital elevation model (DEM) data for Mars (Smith et al, 2003), we are now in a better position to update the estimated volume of excavation by VNs. This paper presents a new Progressive Black Top Hat Transformation algorithm that is inspired by the Simple Morphological Filter (Pingel et al, 2013) used in LiDAR data processing to separate bare-Earth surface and above-ground features and by the Black Top Hat Transformation algorithm (Soille, 2003) used in image processing to extract dark features on a varied background (Rodriguez et al, 2002).…”

“…The Black Top Hat (BTH) transformation, a morphological image processing technique, has been successfully applied to DEM data to extract valley depth at pixel level and thus offers more accurate estimates of volumes (Rodriguez et al, 2002). It works by constructing a pre-incision surface from the current DEM and calculating the difference between the pre-incision surface and the current DEM to estimate the valley depth and derive valley volume.…”

“…The dilation operation calculates the maximum elevation within a moving circular window centered on a target cell in the original DEM. The closing operation calculates the minimum elevation within a moving circular window centered on a target cell from the dilation result (Rodriguez et al, 2002;Luo et al, 2010Luo et al, , 2011. A threshold value t is applied to remove noise in the data to obtain valley depth at each pixel within the valley.…”

Application of Bayesian networks to large-scale predictive ecosystem mapping

“…I generated each of the four types of curvature, plus slope gradient, using Wood's (1996) multi-scale geomorphic characterization ofDEMs. I created terrain context, which describes whether a raster cell was a valley or hill, using the TOPHA T function of Rodriguez et al (2002).…”