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Identifying the mechanical properties of the skin has been the subject of much study in recent years, as such knowledge can provide insight into wound healing, wrinkling and minimization of scarring through surgical planning.

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Capturing sediment topography is one component in understanding how large animals in soft sediment ecosystems affect ecosystem processing. Traditional approaches for sampling soft‐sediment systems are too invasive and destructive for the fragile sediment structures. In contrast, non‐invasive approaches, such as LIDAR and monocular structure‐from‐motion (SfM) systems, can be expensive and cumbersome for data processing. We developed a low cost and practical framework for measuring morphological characteristics of soft sediment topography at the millimetre scale. Using an RGB‐D (red, green, blue and depth) device and a semi‐opaque container, the sediment surface can be imaged rapidly while operating in outdoor environments. The RGB‐D device imaged 0.3 m2 intertidal sediment surfaces, creating depth imagery over seven sites and 82 surfaces. First, using simulated surface data, we formulated and tested four variations of a geometrical detrending model to extract and measure the undulating surface microtopography. We then computed nine morphological characteristics, including arithmetic mean roughness, mean sediment peaks and rugosity, for the detrending models. We then used the two best performing models, linear and quadratic detrending models, to compare extracted morphological characteristics from the field data. We found a strong correlation between the models for extracted surface measures and similar extracted sediment trends. The results showed that RGB‐D real‐time imaging is a promising rapid scanning tool for collecting field data in intertidal regions and can be expanded to other fragile sediment surfaces outside the marine environment. The low costs and real‐time feedback makes it an attractive data collection tool for environments where data collection is challenging.