Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems

2015 ◽  
Vol 205 ◽  
pp. 83-95 ◽  
Author(s):  
William Woodgate ◽  
Simon D. Jones ◽  
Lola Suarez ◽  
Michael J. Hill ◽  
John D. Armston ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Openness ◽  
Area Index ◽  
Gap Fraction

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.

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

GRAPEVINE LEAF AREA INDEX EVALUATION BY GAP FRACTION INVERSION

2000 ◽  
pp. 87-94 ◽  
Author(s):  
S. Cohen ◽  
M.J. Striem ◽  
M. Bruner ◽  
I. Klein
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Gap Fraction ◽  
Index Evaluation

Retrieving leaf area index in discontinuous forest using ICESat/GLAS full-waveform data based on gap fraction model

2019 ◽  
Vol 148 ◽  
pp. 54-62 ◽  
Author(s):  
Xuebo Yang ◽  
Cheng Wang ◽  
Feifei Pan ◽  
Sheng Nie ◽  
Xiaohuan Xi ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Waveform Data ◽  
Full Waveform ◽  
Gap Fraction

scholarly journals Leaf area index mapping with optical methods and allometric models in SMEAR flux tower footprint at Järvselja, Estonia

2015 ◽  
Vol 63 (1) ◽  
pp. 85-99
Author(s):  
Marta Mõistus ◽  
Mait Lang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Growth ◽  
Height Distribution ◽  
Area Index ◽  
Flux Tower ◽  
Gap Fraction ◽  
Plant Area Index ◽  
The Relationship

AbstractLeaf area index (LAI) characterizes the amount of photosynthetically active tissue in plant canopies. LAI is one of the key factors determining ecosystem net primary production and gas and energy exchange between the canopy and the atmosphere. The aim of the present study was to test different methods for LAI and effective plant area index (PAIe) estimation in mixed hemiboreal forests in Järvselja SMEAR Estonia (Station for Measuring Ecosystem-Atmosphere Relations) flux tower footprint. We used digital hemispherical images from sample plots, forest management inventory data, allometric foliage mass models, airborne discrete-return recording laser scanner (ALS) data and multispectral satellite images. The free ware program HemiSpherical Project Manager (HSP) was used to calculate canopy gap fraction from digital hemispherical photographs taken in 25 sample plots. PAIewas calculated from the gap fraction for up-scaling based on ALS point cloud metrics. The all ALS pulse returns-based canopy transmission was found to be the most suitable lidar metric to estimate PAIein Järvselja forests. The 95-percentile (H95) of lidar point cloud height distribution correlates very well with allometric regression models based LAI and in birch stands the relationship was fitted with 0.7 m2m−2residual error. However, the relationship was specific to each allometric foliage mass model and systematic discrepancies were detected at large LAI values between the models. Relationships between the spectral reflectance and allometric LAI were not good enough to be used for LAI mapping. Therefore, airborne laser scanning data-based PAIemap was created for areas near SMEAR tower. We recommend to establish a network of permanent sample plots for forest growth and gap fraction measurements into the flux footprint of SMEAR Estonia flux tower in Järvselja to provide consistent up to date data for interpretation of the flux measurements.

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