TY - JOUR
T1 - A comparative study for obtaining effective Leaf Area Index from single Terrestrial Laser Scans by removal of wood material
AU - Arslan, Adil Enis
AU - Erten, Esra
AU - Inan, Muhittin
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/6
Y1 - 2021/6
N2 - Leaf Area Index (LAI) is a dimensionless parameter that has a significant impact on forestry applications. With conventional methods, LAI can be calculated with destructive sample collection or with a relatively new non-destructive method called hemispherical photography. With the engagement of surveying instruments in forestry, obtaining LAI value for large areas in a short time has recently become more prominent and possible with the use of Terrestrial Laser Scanners (TLS). Although promising, TLS data evaluation techniques for LAI calculation are still subject to development. This paper aims to make a comparative evaluation of existing novel techniques with newly proposed methods and incorporates the use of neural networks and connected component analysis for segmentation purposes. The in-situ measurements, as a case study, were conducted in Istanbul- University-Cerrahpasa research forest – a part of Belgrad forest – Istanbul, Turkey. The Results obtained from the study show that segmentation and removal of wood materials from forest point cloud data, by using neural network algorithms and connected component analysis methods, albeit time and resource consuming, have a promising future on the calculation of effective LAI values of large areas.
AB - Leaf Area Index (LAI) is a dimensionless parameter that has a significant impact on forestry applications. With conventional methods, LAI can be calculated with destructive sample collection or with a relatively new non-destructive method called hemispherical photography. With the engagement of surveying instruments in forestry, obtaining LAI value for large areas in a short time has recently become more prominent and possible with the use of Terrestrial Laser Scanners (TLS). Although promising, TLS data evaluation techniques for LAI calculation are still subject to development. This paper aims to make a comparative evaluation of existing novel techniques with newly proposed methods and incorporates the use of neural networks and connected component analysis for segmentation purposes. The in-situ measurements, as a case study, were conducted in Istanbul- University-Cerrahpasa research forest – a part of Belgrad forest – Istanbul, Turkey. The Results obtained from the study show that segmentation and removal of wood materials from forest point cloud data, by using neural network algorithms and connected component analysis methods, albeit time and resource consuming, have a promising future on the calculation of effective LAI values of large areas.
KW - Biomass
KW - LeaF Area Index
KW - Neural networks
KW - TerreStrial Laser Scanning
UR - http://www.scopus.com/inward/record.url?scp=85103696256&partnerID=8YFLogxK
U2 - 10.1016/j.measurement.2021.109262
DO - 10.1016/j.measurement.2021.109262
M3 - Article
AN - SCOPUS:85103696256
SN - 0263-2241
VL - 178
JO - Measurement: Journal of the International Measurement Confederation
JF - Measurement: Journal of the International Measurement Confederation
M1 - 109262
ER -