Using airborne lidar and machine learning to predict visibility across diverse vegetation and terrain conditions

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-e...

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Published in:	International journal of geographical information science : IJGIS Vol. 37; no. 8; pp. 1728 - 1764
Main Authors:	Mistick, Katherine A., Campbell, Michael J., Thompson, Matthew P., Dennison, Philip E.
Format:	Journal Article
Language:	English
Published:	Abingdon Taylor & Francis 03.08.2023 Taylor & Francis LLC
Subjects:	Algorithms Landscape Lidar Machine learning Mapping random forest Scale models Terrain Vegetation Viewshed Visibility Visibility maps
ISSN:	1365-8816, 1362-3087, 1365-8824
Online Access:	Get full text
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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 R 2 , 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.
AbstractList	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. 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 R 2 , 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.
Author	Mistick, Katherine A. Thompson, Matthew P. Campbell, Michael J. Dennison, Philip E.
Author_xml	– sequence: 1 givenname: Katherine A. surname: Mistick fullname: Mistick, Katherine A. organization: Department of Geography, University of Utah – sequence: 2 givenname: Michael J. surname: Campbell fullname: Campbell, Michael J. organization: Department of Geography, University of Utah – sequence: 3 givenname: Matthew P. surname: Thompson fullname: Thompson, Matthew P. organization: Rocky Mountain Research Station, USDA Forest Service – sequence: 4 givenname: Philip E. surname: Dennison fullname: Dennison, Philip E. organization: Department of Geography, University of Utah
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SubjectTerms	Algorithms Landscape Lidar Machine learning Mapping random forest Scale models Terrain Vegetation Viewshed Visibility Visibility maps
Title	Using airborne lidar and machine learning to predict visibility across diverse vegetation and terrain conditions
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