Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models

Authors: Alexander, C., Korstjens, A.H. and Hill, R.

http://eprints.bournemouth.ac.uk/29942/

Journal: International Journal of Applied Earth Observation and Geoinformation

Volume: 65

Pages: 105-113

Publisher: Elsevier BV

ISSN: 1569-8432

DOI: 10.1016/j.jag.2017.10.009

Tree or canopy height is an important attribute for carbon stock estimation, forest management and habitat quality assessment. Airborne Laser Scanning (ALS) based on Light Detection and Ranging (LiDAR) has advantages over other remote sensing techniques for describing the structure of forests. However, sloped terrain can be challenging for accurate estimation of tree locations and heights based on a Canopy Height Model (CHM) generated from ALS data; a CHM is a height-normalised Digital Surface Model (DSM) obtained by subtracting a Digital Terrain Model (DTM) from a DSM. On sloped terrain, points at the same elevation on a tree crown appear to increase in height in the downhill direction, based on the ground elevations at these points. A point will be incorrectly identified as the treetop by individual tree crown (ITC) recognition algorithms if its height is greater than that of the actual treetop in the CHM, which will be recorded as the tree height. In this study, the influence of terrain slope and crown characteristics on the detection of treetops and estimation of tree heights is assessed using ALS data in a tropical forest with complex terrain (i.e. micro-topography) and tree crown characteristics. Locations and heights of 11,442 trees based on a DSM are compared with those based on a CHM. The horizontal (DH) and vertical displacements (DV) increase with terrain slope (r: 0.47 and r: 0.54 respectively, p < 0.001). The overestimations in tree height are up to 16.6 m on slopes greater than 50° in our study area in Sumatra. The errors in locations (DH) and tree heights (DV) are modelled for trees with conical and spherical tree crowns. For a spherical tree crown, DH can be modelled as R sin θ, and DV as R (sec θ – 1). In this study, a model is developed for an idealised conical tree crown, DV = R (tan θ – tan ψ), where R is the crown radius, and θ and ψ are terrain and crown angles respectively. It is shown that errors occur only when terrain angle exceeds the crown angle, with the horizontal displacement equal to the crown radius. Errors in location are seen to be greater for spherical than conical trees on slopes where crown angles of conical trees are less than the terrain angle. The results are especially relevant for biomass and carbon stock estimations in tropical forests where there are trees with large crown radii on slopes.

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Authors: Alexander, C., Korstjens, A.H. and Hill, R.A.

http://eprints.bournemouth.ac.uk/29942/

Journal: Int. J. Applied Earth Observation and Geoinformation

Volume: 65

Pages: 105-113

This data was imported from Scopus:

Authors: Alexander, C., Korstjens, A.H. and Hill, R.A.

http://eprints.bournemouth.ac.uk/29942/

Journal: International Journal of Applied Earth Observation and Geoinformation

Volume: 65

Pages: 105-113

eISSN: 1872-826X

ISSN: 1569-8432

DOI: 10.1016/j.jag.2017.10.009

© 2017 The Author(s) Tree or canopy height is an important attribute for carbon stock estimation, forest management and habitat quality assessment. Airborne Laser Scanning (ALS) based on Light Detection and Ranging (LiDAR) has advantages over other remote sensing techniques for describing the structure of forests. However, sloped terrain can be challenging for accurate estimation of tree locations and heights based on a Canopy Height Model (CHM) generated from ALS data; a CHM is a height-normalised Digital Surface Model (DSM) obtained by subtracting a Digital Terrain Model (DTM) from a DSM. On sloped terrain, points at the same elevation on a tree crown appear to increase in height in the downhill direction, based on the ground elevations at these points. A point will be incorrectly identified as the treetop by individual tree crown (ITC) recognition algorithms if its height is greater than that of the actual treetop in the CHM, which will be recorded as the tree height. In this study, the influence of terrain slope and crown characteristics on the detection of treetops and estimation of tree heights is assessed using ALS data in a tropical forest with complex terrain (i.e. micro-topography) and tree crown characteristics. Locations and heights of 11,442 trees based on a DSM are compared with those based on a CHM. The horizontal (D H ) and vertical displacements (D V ) increase with terrain slope (r = 0.47 and r = 0.54 respectively, p < 0.001). The overestimations in tree height are up to 16.6 m on slopes greater than 50° in our study area in Sumatra. The errors in locations (D H ) and tree heights (D V ) are modelled for trees with conical and spherical tree crowns. For a spherical tree crown, D H can be modelled as R sin θ and D V as R (sec θ – 1). In this study, a model is developed for an idealised conical tree crown, D V = R (tan θ – tan ψ), where R is the crown radius, and θ and ψ are terrain and crown angles respectively. It is shown that errors occur only when terrain angle exceeds the crown angle, with the horizontal displacement equal to the crown radius. Errors in location are seen to be greater for spherical than conical trees on slopes where crown angles of conical trees are less than the terrain angle. The results are especially relevant for biomass and carbon stock estimations in tropical forests where there are trees with large crown radii on slopes.

This data was imported from Web of Science (Lite):

Authors: Alexander, C., Korstjens, A.H. and Hill, R.A.

http://eprints.bournemouth.ac.uk/29942/

Journal: INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

Volume: 65

Pages: 105-113

ISSN: 0303-2434

DOI: 10.1016/j.jag.2017.10.009

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