Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems.

Ian Paynter,Francesco Peri,Daniel Genest,Crystal Schaaf

doi:10.1098/rsfs.2017.0043

Abstract

Volumetric models with known biases are shown to provide bounds for the uncertainty in estimations of volume for ecologically interesting objects, observed with a terrestrial laser scanner (TLS) instrument. Bounding cuboids, three-dimensional convex hull polygons, voxels, the Outer Hull Model and Square Based Columns (SBCs) are considered for their ability to estimate the volume of temperate and tropical trees, as well as geomorphological features such as bluffs and saltmarsh creeks. For temperate trees, supplementary geometric models are evaluated for their ability to bound the uncertainty in cylinder-based reconstructions, finding that coarser volumetric methods do not currently constrain volume meaningfully, but may be helpful with further refinement, or in hybridized models. Three-dimensional convex hull polygons consistently overestimate object volume, and SBCs consistently underestimate volume. Voxel estimations vary in their bias, due to the point density of the TLS data, and occlusion, particularly in trees. The response of the models to parametrization is analysed, observing unexpected trends in the SBC estimates for the drumlin dataset. Establishing that this result is due to the resolution of the TLS observations being insufficient to support the resolution of the geometric model, it is suggested that geometric models with predictable outcomes can also highlight data quality issues when they produce illogical results.

Highlights

This study investigates whether methods with guaranteed directions of bias can constrain the uncertainty in volume estimates of ecosystem objects observed by terrestrial laser scanner (TLS) data
Volume estimates were highest for each object using the Bounding Cuboid method, followed in descending order by the 3D CHP method, the Outer Hull Model (OHM), with Square Based Columns (SBCs) producing the lowest volume estimates
Voxel estimates sometimes resided between OHM and SBC estimates, as in Ceiba pentandra Tree 1

Summary

Introduction

This study investigates whether methods with guaranteed directions of bias can constrain the uncertainty in volume estimates of ecosystem objects observed by terrestrial laser scanner (TLS) data. Retrievals of ecosystem structural properties from lidar data, from TLS instruments, are often achieved through modelling approaches, referred to as geometric models, that reconstruct approximations of the geometry of objects in the ecosystem from the discrete and discontinuous observations of structure made by the TLS lidar. The detailed reconstruction of tree structure from TLS data uses quantitative structure models to form a hierarchical, connected network of cylinders [25]. These cylinder models permit estimates of woody volume, linked to biomass estimates with allometric equations for wood density [18,26,27]

Methods

Results

Discussion

Conclusion