HSC3D: A Python package to quantify three‐dimensional habitat structural complexity

Abstract

Abstract Habitat structural complexity (HSC) is a key variable to help interpret ecological patterns and processes among different ecosystems. Existing metrics used to quantify HSC often, however, result in insufficient or biased representation of structural complexity. As such, our understanding of how HSC affects biodiversity and related ecological patterns is often limited by these measures. Recent advances in photogrammetry and computer vision have enabled the ability to reconstruct 3D habitats with high efficiency and accuracy. Point clouds, for example, better represent the structural properties of target objects with reduced sampling bias as compared to traditional formats like 2.5D raster (i.e. DEM, DSM, DTM). The analysis of point clouds is, however, limited by the lack of readily available packages with mathematically well‐defined metrics for ecologists to help interpret relevant HSC properties. To address this gap, novel metrics are provided in the present Python package HSC3D version 0.2.0, which allows quantification of structural complexity of targeted habitats based on photogrammetry point clouds. This package is designed to help ecologists better describe the structural characteristics of a specific habitat and is bundled with visualisation functions to help interpret the computational processes used. To demonstrate functions implemented in HSC3D we use a case study that compares the structural complexity differences formed by two intertidal ecosystem engineers, mussels and oysters. Results indicate that HSC3D is a versatile and easily adapted package, which can provide both ecological and analytical insights on photogrammetry point clouds which should prove a useful tool for ecologists wanting to quantify HSC.

Keywords

• computer vision
• differential geometry
• Gaussian mixture model
• habitat structural complexity
• photogrammetry point cloud
• singular value decomposition