TY - JOUR
T1 - Determining woody-to-total area ratio using terrestrial laser scanning (TLS)
AU - Ma, Lixia
AU - Zheng, Guang
AU - Eitel, Jan U.H.
AU - Magney, Troy S.
AU - Moskal, L. Monika
N1 - Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2016/11/15
Y1 - 2016/11/15
N2 - Accurately determining woody-to-total area ratio (WTA) is a key step to indirectly retrieve leaf area index (LAI) from terrestrial laser scanning (TLS) data. In this work, we first collected both individual tree and forest plot point cloud data (PCD) from broadleaf and coniferous tree species and leaf characteristics using both side-lateral and full field-of-view TLS field setups with scan distances between 2.5 to 28 m. Using a local geometrical feature-based algorithm, the generated PCD were automatically classified into three different categories including photosynthetic canopy components, non-photosynthetic canopy components, and bare earth. To convert each classified point into a surface area, we then developed and validated a novel approach that considers sampling space, laser incidence angle, and leaf orientation information. The estimated surface areas from this approach showed strong agreements with validation datasets for single leaf (91.44%), photosynthetic (95.64%), and non-photosynthetic canopy components (89.60%) of an artificial tree and stems of an old-growth coniferous tree (93.53%), two individual broadleaf trees (98.31% and 97.46%) and a broadleaf forest plot (90.26%). By doing this, we computed the parameter WTA for an individual artificial tree (10.90%), an old-growth coniferous tree (29.97%), two individual broadleaf tree (14.83% and 4.27%) and four natural forest stands ranging from 7.74%–15.57%, respectively. The proposed method can effectively improve the accuracy of retrieving true LAI by removing the effects of woody components and converting each point into a surface area.
AB - Accurately determining woody-to-total area ratio (WTA) is a key step to indirectly retrieve leaf area index (LAI) from terrestrial laser scanning (TLS) data. In this work, we first collected both individual tree and forest plot point cloud data (PCD) from broadleaf and coniferous tree species and leaf characteristics using both side-lateral and full field-of-view TLS field setups with scan distances between 2.5 to 28 m. Using a local geometrical feature-based algorithm, the generated PCD were automatically classified into three different categories including photosynthetic canopy components, non-photosynthetic canopy components, and bare earth. To convert each classified point into a surface area, we then developed and validated a novel approach that considers sampling space, laser incidence angle, and leaf orientation information. The estimated surface areas from this approach showed strong agreements with validation datasets for single leaf (91.44%), photosynthetic (95.64%), and non-photosynthetic canopy components (89.60%) of an artificial tree and stems of an old-growth coniferous tree (93.53%), two individual broadleaf trees (98.31% and 97.46%) and a broadleaf forest plot (90.26%). By doing this, we computed the parameter WTA for an individual artificial tree (10.90%), an old-growth coniferous tree (29.97%), two individual broadleaf tree (14.83% and 4.27%) and four natural forest stands ranging from 7.74%–15.57%, respectively. The proposed method can effectively improve the accuracy of retrieving true LAI by removing the effects of woody components and converting each point into a surface area.
KW - Leaf area index (LAI)
KW - Leaf orientation
KW - Sampling space
KW - Terrestrial lidar
KW - Woody-to-total area ratio
UR - https://www.scopus.com/pages/publications/84978909202
U2 - 10.1016/j.agrformet.2016.06.021
DO - 10.1016/j.agrformet.2016.06.021
M3 - Article
AN - SCOPUS:84978909202
SN - 0168-1923
VL - 228-229
SP - 217
EP - 228
JO - Agricultural and Forest Meteorology
JF - Agricultural and Forest Meteorology
ER -