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

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Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models. / Alexander, Cici; Korstjens, Amanda H.; Hill, Ross A.

I: International Journal of Applied Earth Observation and Geoinformation, Bind 65, Nr. March, 2018, s. 105-113.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Alexander, C, Korstjens, AH & Hill, RA 2018, 'Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models', International Journal of Applied Earth Observation and Geoinformation, bind 65, nr. March, s. 105-113. https://doi.org/10.1016/j.jag.2017.10.009

APA

Alexander, C., Korstjens, A. H., & Hill, R. A. (2018). Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models. International Journal of Applied Earth Observation and Geoinformation, 65(March), 105-113. https://doi.org/10.1016/j.jag.2017.10.009

CBE

MLA

Alexander, Cici, Amanda H. Korstjens og Ross A. Hill. "Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models". International Journal of Applied Earth Observation and Geoinformation. 2018, 65(March). 105-113. https://doi.org/10.1016/j.jag.2017.10.009

Vancouver

Author

Alexander, Cici ; Korstjens, Amanda H. ; Hill, Ross A. / Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models. I: International Journal of Applied Earth Observation and Geoinformation. 2018 ; Bind 65, Nr. March. s. 105-113.

Bibtex

@article{7c35e3b0488b4aeb85dbea98f4c11619,
title = "Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models",
abstract = "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.",
keywords = "Airborne laser scanning, Forestry, ITC, REDD, Tree delineation, CARBON, LASER-SCANNING DATA, RAIN-FOREST, VARIABLE WINDOW SIZE, INDIVIDUAL TREES, EXTRACTION, POINT CLOUDS, TERRAIN MODELS, AIRBORNE LIDAR, VEGETATION",
author = "Cici Alexander and Korstjens, {Amanda H.} and Hill, {Ross A.}",
year = "2018",
doi = "10.1016/j.jag.2017.10.009",
language = "English",
volume = "65",
pages = "105--113",
journal = "International Journal of Applied Earth Observation and Geoinformation",
issn = "0303-2434",
publisher = "Elsevier BV",
number = "March",

}

RIS

TY - JOUR

T1 - Influence of micro-topography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models

AU - Alexander, Cici

AU - Korstjens, Amanda H.

AU - Hill, Ross A.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Airborne laser scanning

KW - Forestry

KW - ITC

KW - REDD

KW - Tree delineation

KW - CARBON

KW - LASER-SCANNING DATA

KW - RAIN-FOREST

KW - VARIABLE WINDOW SIZE

KW - INDIVIDUAL TREES

KW - EXTRACTION

KW - POINT CLOUDS

KW - TERRAIN MODELS

KW - AIRBORNE LIDAR

KW - VEGETATION

UR - http://www.scopus.com/inward/record.url?scp=85036539960&partnerID=8YFLogxK

U2 - 10.1016/j.jag.2017.10.009

DO - 10.1016/j.jag.2017.10.009

M3 - Journal article

AN - SCOPUS:85036539960

VL - 65

SP - 105

EP - 113

JO - International Journal of Applied Earth Observation and Geoinformation

JF - International Journal of Applied Earth Observation and Geoinformation

SN - 0303-2434

IS - March

ER -