Mapping tree genera using discrete LiDAR and geometric tree metrics

Maps of tree genera are useful in applications including forest inventory, urban planning, and the maintenance of utility transmission line infrastructure. We present a case study of using high density airborne LiDAR data for tree genera mapping along the right of way (ROW) of a utility transmission...

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Autores principales:	Ko,Connie, Remmel,Tarmo K, Sohn,Gunho
Lenguaje:	English
Publicado:	Universidad Austral de Chile, Facultad de Ciencias Forestales 2012
Materias:	Airborne LiDAR tree genera mapping tree geometry Random Forest Classification
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-92002012000300015
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id	oai:scielo:S0717-92002012000300015
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spelling	oai:scielo:S0717-920020120003000152014-11-05Mapping tree genera using discrete LiDAR and geometric tree metricsKo,ConnieRemmel,Tarmo KSohn,Gunho Airborne LiDAR tree genera mapping tree geometry Random Forest Classification Maps of tree genera are useful in applications including forest inventory, urban planning, and the maintenance of utility transmission line infrastructure. We present a case study of using high density airborne LiDAR data for tree genera mapping along the right of way (ROW) of a utility transmission line corridor. Our goal was to identify single trees that showed or posed potential threats to transmission line infrastructure. Using the three dimensional mapping capability of LiDAR, we derived tree metrics that are related to the geometry of the trees (tree forms). For example, the dominant growth direction of trees is useful in identifying trees that are leaning towards transmission lines. We also derived other geometric indices that are useful in determining tree genera; these metrics included their height, crown shape, size, and branching structures. Our pilot study was situated north of Thessalon, Ontario, Canada along a major utility corridor ROW and surrounding woodlots. The geometric features used for general classification could be categorized into five broad categories related to: 1) lines, 2) clusters, 3) volumes, 4) 3D buffers of points, and 5) overall tree shape that provide parameters as an input for the Random Forest classifier.info:eu-repo/semantics/openAccessUniversidad Austral de Chile, Facultad de Ciencias ForestalesBosque (Valdivia) v.33 n.3 20122012-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-92002012000300015en10.4067/S0717-92002012000300015
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Airborne LiDAR tree genera mapping tree geometry Random Forest Classification
spellingShingle	Airborne LiDAR tree genera mapping tree geometry Random Forest Classification Ko,Connie Remmel,Tarmo K Sohn,Gunho Mapping tree genera using discrete LiDAR and geometric tree metrics
description	Maps of tree genera are useful in applications including forest inventory, urban planning, and the maintenance of utility transmission line infrastructure. We present a case study of using high density airborne LiDAR data for tree genera mapping along the right of way (ROW) of a utility transmission line corridor. Our goal was to identify single trees that showed or posed potential threats to transmission line infrastructure. Using the three dimensional mapping capability of LiDAR, we derived tree metrics that are related to the geometry of the trees (tree forms). For example, the dominant growth direction of trees is useful in identifying trees that are leaning towards transmission lines. We also derived other geometric indices that are useful in determining tree genera; these metrics included their height, crown shape, size, and branching structures. Our pilot study was situated north of Thessalon, Ontario, Canada along a major utility corridor ROW and surrounding woodlots. The geometric features used for general classification could be categorized into five broad categories related to: 1) lines, 2) clusters, 3) volumes, 4) 3D buffers of points, and 5) overall tree shape that provide parameters as an input for the Random Forest classifier.
author	Ko,Connie Remmel,Tarmo K Sohn,Gunho
author_facet	Ko,Connie Remmel,Tarmo K Sohn,Gunho
author_sort	Ko,Connie
title	Mapping tree genera using discrete LiDAR and geometric tree metrics
title_short	Mapping tree genera using discrete LiDAR and geometric tree metrics
title_full	Mapping tree genera using discrete LiDAR and geometric tree metrics
title_fullStr	Mapping tree genera using discrete LiDAR and geometric tree metrics
title_full_unstemmed	Mapping tree genera using discrete LiDAR and geometric tree metrics
title_sort	mapping tree genera using discrete lidar and geometric tree metrics
publisher	Universidad Austral de Chile, Facultad de Ciencias Forestales
publishDate	2012
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-92002012000300015
work_keys_str_mv	AT koconnie mappingtreegenerausingdiscretelidarandgeometrictreemetrics AT remmeltarmok mappingtreegenerausingdiscretelidarandgeometrictreemetrics AT sohngunho mappingtreegenerausingdiscretelidarandgeometrictreemetrics
_version_	1718444186972192768

Mapping tree genera using discrete LiDAR and geometric tree metrics

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