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Abstract
Visibility analyses, used in many disciplines, rely on viewshed algorithms that map locations visible to an observer based on a given surface model. Mapping continuous visibility over broad extents is uncommon due to extreme computational expense. This study introduces a novel method for spatially-exhaustive visibility mapping using airborne lidar and random forests that requires only a sparse sample of viewsheds. In 24 topographically and vegetatively diverse landscapes across the contiguous US, 1000 random point viewsheds were generated at four different observation radii (125 m, 250 m, 500 m, 1000 m), using a 1 m resolution lidar-derived digital surface model. Visibility index – the proportion of visible area to total area – was used as the target variable for site-scale and national-scale modeling, which used a diverse set of 146 terrain- and vegetation-based 10 m resolution metrics as predictors. Variables based on vegetation, especially those based on local neighborhoods, were more important than those based on terrain. Visibility at shorter distances was more accurately estimated. National-scale models trained on a wider range of vegetation and terrain conditions resulted in improved R2, although at some sites error increased compared to site-scale models. Results from an independent test site demonstrate potential for application of this methodology to diverse landscapes.
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Disclosure statement
No potential conflict of interest was reported by the author(s).
Data and codes availability statement
The data, codes and instructions that support the findings of this study are available in OSF at https://doi.org/10.17605/OSF.IO/5UGRE. Lidar data on which this analysis is based is available from the USGS 3D Elevation Program at https://www.usgs.gov/3d-elevation-program.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
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Funding
Notes on contributors
Katherine A. Mistick
Katherine A. Mistick is a Research Associate at the Utah Remote Sensing Applications Lab at the University of Utah whose research interests include geographic information science, lidar remote sensing and geospatial modeling of wildland firefighter safety. She contributed to the conceptualization, software, data curation, formal analysis, visualization and writing.
Michael J. Campbell
Michael J. Campbell is a Research Assistant Professor at the University of Utah whose research interests include remote sensing of vegetation structure and function, forest and woodland ecology, change detection and time series analysis, lidar remote sensing and geospatial modeling of wildland firefighter safety. He contributed to the conceptualization, methodology, writing (review & editing), visualization and funding acquisition.
Matthew P. Thompson
Matthew P. Thompson is a Research Forester at the Rocky Mountain Research Station, Human Dimensions Program, whose research interests include the application of principles from systems engineering, industrial engineering, risk analysis, operations research, economics and decision-making under uncertainty to complex resource management with economic and environmental objectives. He contributed to the writing (review & editing) and funding acquisition.
Philip E. Dennison
Philip E. Dennison is Professor and Chair in the Department of Geography at the University of Utah. He is also Director of the Utah Remote Sensing Applications Lab. His research interests include wildfire and firefighter safety, remote sensing of vegetation, imaging spectroscopy and mapping greenhouse gas emissions using remote sensing. He contributed to the conceptualization, methodology, writing (review & editing), resources and funding acquisition.