New Structural Complexity Metrics for Forests from Single Terrestrial Lidar Scans

نویسندگان

چکیده

We developed new measures of structural complexity using single point terrestrial laser scanning (TLS) clouds. These metrics are depth, openness, and isovist. Depth is a three-dimensional, radial measure the visible distance in all directions from plot center. Openness percent scan pulses near-omnidirectional view without return. Isovists measurement area location, quantified viewshed within forest canopy. 243 scans were acquired 27 forested stands Pacific Northwest region United States, different ecoregions representing broad gradient complexity. All designated natural areas with little to no human perturbations. created “structural signatures” depth openness that can be used qualitatively visualize differences structures quantitively distinguish composition at differing height strata. In most cases, signatures effective providing statistically significant differentiating forests various growth patterns. less between across multiple ecoregions, but they still quantify ecological important metric occlusion. appear capture high level precision low observer bias have great potential for quantifying change ecosystems, effects management activities, describing habitat organisms. Our structure ground truth data obtained aerial lidar develop models estimating structure.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Automatic extraction of tree stem models from single terrestrial lidar scans in structurally heterogeneous forest environments

An important application of terrestrial laser scanning is the extraction of tree stem models for diameter at breast height (DBH) assessment and forest inventory. Much work has been done to automate this process using adjacent co-registered lidar scans. Existing studies, however, have focused on pre-registered point clouds obtained from commercial lidar systems. We envision an affordable and eff...

متن کامل

Voxel Space Analysis of Terrestrial Laser Scans in Forests for Wind Field Modeling

Meteorological simulation tools to model gas exchange phenomena within forests require well defined information of forest structure (e.g., 3D forest models) as a basis for the computation of the turbulent flow shaped by the drag of the vegetation. The paper describes techniques to obtain 3D data describing forest stands from dense terrestrial laser scanner point clouds. In a first step, stems a...

متن کامل

Introducing Structural Considerations into Complexity Metrics

Field observations and focused interviews of Air Traffic Controllers have been used to generate a list of key complexity factors in Air Traffic Control. The underlying structure of the airspace was identified as relevant in many of the factors. A preliminary investigation has revealed that the structure appears to form the basis for abstractions that reduce the difficulty of maintaining Situati...

متن کامل

A novel method for locating the local terrestrial laser scans in a global aerial point cloud

In addition to the heterogeneity of aerial and terrestrial views, the small scale terrestrial point clouds are hardly comparable with large scale and overhead aerial point clouds. A hierarchical method is proposed for automatic locating of terrestrial scans in aerial point cloud. The proposed method begins with detecting the candidate positions for the deployment of the terrestrial laser scanne...

متن کامل

Simulated full-waveform LiDAR compared to Riegl VZ-400 terrestrial laser scans

A 3-D Monte Carlo ray-tracing simulation of LiDAR propagation models the reflection, transmission and absorption interactions of laser energy with materials in a simulated scene. In this presentation, a model scene consisting of a single Victorian Boxwood (Pittosporum undulatum) tree is generated by the high-fidelity tree voxel model VoxLAD using high-spatial resolution point cloud data from a ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Remote Sensing

سال: 2022

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs15010145