Retrieving the gap fraction, element clumping index, and leaf area index of individual trees using single-scan data from a terrestrial laser scanner

2017 ◽  
Vol 130 ◽  
pp. 308-316 ◽  
Author(s):  
Yumei Li ◽  
Qinghua Guo ◽  
Yanjun Su ◽  
Shengli Tao ◽  
Kaiguang Zhao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Area Index ◽  
Gap Fraction ◽  
Clumping Index ◽  
Scan Data ◽  
Individual Trees

Terrestrial laser scanner based 3D reconstruction of trees and retrieval of leaf area index in a forest environment

2019 ◽  
Vol 53 ◽  
pp. 100986 ◽  
Author(s):  
Indu Indirabai ◽  
M.V. Harindranathan Nair ◽  
R. Nair Jaishanker ◽  
Rama Rao Nidamanuri
Keyword(s):  
3D Reconstruction ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Forest Environment ◽  
Area Index

Modified gap fraction model of individual trees for estimating leaf area using terrestrial laser scanner

2017 ◽  
Vol 11 (03) ◽  
pp. 1 ◽  
Author(s):  
Donghui Xie ◽  
Yan Wang ◽  
Ronghai Hu ◽  
Yiming Chen ◽  
Guangjian Yan ◽  
...  
Keyword(s):  
Leaf Area ◽  
Laser Scanner ◽  
Terrestrial Laser Scanner ◽  
Gap Fraction ◽  
Individual Trees

A new method to estimate clumping index integrating gap fraction averaging with the analysis of gap size distribution

2019 ◽  
Vol 49 (5) ◽  
pp. 471-479 ◽  
Author(s):  
Francesco Chianucci ◽  
Jie Zou ◽  
Peng Leng ◽  
Yinguo Zhuang ◽  
Carlotta Ferrara
Keyword(s):  
Leaf Area Index ◽  
Size Distribution ◽  
Leaf Area ◽  
New Method ◽  
Gap Size ◽  
Area Index ◽  
Canopy Density ◽  
Gap Fraction ◽  
Direct Measurements ◽  
Clumping Index

Estimates of clumping index (Ω) are required to improve the indirect estimation of leaf area index (L) from optical field-based instruments such as digital hemispherical photography (DHP). A widely used method allows estimation of Ω from DHP using simple gap fraction averaging formulas (LX). This method is simple and effective but has the disadvantage of being sensitive to the spatial scale (i.e., the azimuth segment size in DHP) used for averaging and canopy density. In this study, we propose a new method to estimate Ω (LXG) based on ordered weighted gap fraction averaging (OWA) formulas, which addresses the disadvantages of LX and also accounts for gap size distribution. The new method was tested in 11 broadleaved forest stands in Italy; Ω estimated from LXG was compared with other commonly used clumping correction methods (LX, CC, and CLX). Results showed that LXG yielded more accurate Ω estimates, which were also more correlated with the values obtained from the gap size distribution methods (CC and CLX) than Ω obtained from LX. Leaf area index estimates, adjusted by LXG, are only 5%–6% lower than direct measurements obtained from litter traps, while other commonly used clumping correction methods yielded more underestimation.

scholarly journals Extracting Leaf Area Index Using Viewing Geometry Effects - A New Perspective on High-Resolution Unmanned Aerial System Photography.

2020 ◽  
Author(s):  
Lukas Roth ◽  
Helge Aasen ◽  
Achim Walter ◽  
Frank Liebisch
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Ecology ◽  
Growth And Yield ◽  
Unmanned Aerial System ◽  
Area Index ◽  
Handheld Device ◽  
Gap Fraction ◽  
Rgb Images ◽  
New Perspective

Abstract Extraction of leaf area index (LAI) is an important prerequisite in numerous studies related to plant ecology, physiology and breeding. LAI is indicative for the performance of a plant canopy and of its potential for growth and yield. In this study, a novel method to estimate LAI based on RGB images taken by an unmanned aerial system (UAS) is introduced. Soybean was taken as the model crop of investigation. The method integrates viewing geometry information in an approach related to gap fraction theory. A 3-D simulation of virtual canopies helped developing and verifying the underlying model. In addition, the method includes techniques to extract plot based data from individual oblique images using image projection, as well as image segmentation applying an active learning approach. Data from a soybean field experiment were used to validate the method. The thereby measured LAI 14 prediction accuracy was comparable with the one of a gap fraction-based handheld device (R2 of 0.92, RMSE of 0.42 m2 m2) and correlated well with destructive LAI measurements (R2 of 0.89, RMSE of 0.41 m2 m2). These results indicate that, if respecting the range (LAI ≤3) the method was tested for, extracting LAI from UAS derived RGB images using viewing geometry information represents a valid alternative to destructive and optical handheld device LAI measurements in soybean. Thereby, we open the door for automated, high-throughput assessment of LAI in plant and crop science.

scholarly journals Data synergy between leaf area index and clumping index Earth Observation products using photon recollision probability theory

2018 ◽  
Vol 215 ◽  
pp. 1-6 ◽  
Author(s):  
Jan Pisek ◽  
Henning Buddenbaum ◽  
Fernando Camacho ◽  
Joachim Hill ◽  
Jennifer L.R. Jensen ◽  
...  
Keyword(s):  
Probability Theory ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Earth Observation ◽  
Area Index ◽  
Clumping Index ◽  
Data Synergy

Use of airborne lidar for estimating canopy gap fraction and leaf area index of tropical montane forests

2015 ◽  
Vol 36 (10) ◽  
pp. 2569-2583 ◽  
Author(s):  
Janne Heiskanen ◽  
Lauri Korhonen ◽  
Jesse Hietanen ◽  
Petri K.E. Pellikka
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Gap ◽  
Airborne Lidar ◽  
Montane Forests ◽  
Area Index ◽  
Tropical Montane Forests ◽  
Gap Fraction
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