Mapping woodland species composition and structure using airborne spectral and LiDAR data
Authors: Hill, R.A. and Thomson, A.G.
Journal: International Journal of Remote Sensing
Volume: 26
Issue: 17
Pages: 3763-3779
eISSN: 1366-5901
ISSN: 0143-1161
DOI: 10.1080/01431160500114706
Abstract:Tree and shrub species composition and vegetation structure are key components influencing the quality of woodland or forest habitat for a wide range of organisms. This paper investigates the unique thematic classes that can be derived using integrated airborne LiDAR and spectral data. The study area consists of a heterogeneous, semi-natural broadleaf woodland on an ancient site and homogeneous broadleaf and conifer woodland on an adjoining plantation. A parcel-based unsupervised classification approach was employed, using the first two Principal Components from 12 selected wavebands of HyMap data and a Digital Canopy Height Model extracted from LiDAR data. The resultant 52 data clusters were amalgamated into 10 distinct thematic classes that contain information on species composition and vegetation structure. The thematic classes are relevant to the National Vegetation Classification (NVC) scheme for woodlands and scrub of Great Britain. Furthermore, in distinguishing structural subdivisions within the species-based NVC classes, the thematic classification provides greater information for quantifying woodland habitat. The classes show degeneration from and regeneration to mature woodland communities and thus reflect the underlying processes of vegetation succession and woodland management. This thematic classification is ecologically relevant and is a forward development in woodland maps created from remote sensing data. © 2005 Taylor & Francis.
Source: Scopus
Mapping woodland species composition and structure using airborne spectral and LiDAR data
Authors: Hill, R.A. and Thomson, A.G.
Journal: INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume: 26
Issue: 17
Pages: 3763-3779
eISSN: 1366-5901
ISSN: 0143-1161
DOI: 10.1080/01431160500114706
Source: Web of Science (Lite)
Mapping Woodland Species Composition and Structure Using Airborne Spectral and LiDAR Data
Authors: Hill, R.A. and Thomson, A.G.
Journal: International Journal of Remote Sensing
Volume: 26
Pages: 3763-3779
ISSN: 0143-1161
DOI: 10.1080/01431160500114706
Abstract:Tree and shrub species composition and vegetation structure are key components influencing the quality of woodland or forest habitat for a wide range of organisms. This paper investigates the unique thematic classes that can be derived using integrated airborne LiDAR and spectral data. The study area consists of a heterogeneous, semi-natural broadleaf woodland on an ancient site and homogeneous broadleaf and conifer woodland on an adjoining plantation. A parcel-based unsupervised classification approach was employed, using the first two Principal Components from 12 selected wavebands of HyMap data and a Digital Canopy Height Model extracted from LiDAR data. The resultant 52 data clusters were amalgamated into 10 distinct thematic classes that contain information on species composition and vegetation structure. The thematic classes are relevant to the National Vegetation Classification (NVC) scheme for woodlands and scrub of Great Britain. Furthermore, in distinguishing structural subdivisions within the species-based NVC classes, the thematic classification provides greater information for quantifying woodland habitat. The classes show degeneration from and regeneration to mature woodland communities and thus reflect the underlying processes of vegetation succession and woodland management. This thematic classification is ecologically relevant and is a forward development in woodland maps created from remote sensing data.
http://www.informaworld.com/smpp/content~db=all?content=10.1080/01431160500114706
Source: Manual
Preferred by: Ross Hill