Estimation of Canopy Cover, Gap Fraction and Leaf Area Index with Airborne Laser Scanning

Reliable estimates of canopy light transmission are critical to understanding the structure and function of vegetation communities but are difficult and costly to attain by traditional field inventory methods. Airborne laser scanning (ALS) data uniquely provide multi-angular vertically resolved representation of canopy geometry across large geographic areas. While previous studies have proposed ALS indices of canopy light transmission, new algorithms based on theoretical advancements may improve existing models. Herein, we propose two new models of canopy light transmission (i.e., gap fraction, or Po, the inverse of angular canopy closure). We demonstrate the models against a suite of existing models and ancillary metrics, validated against convex spherical densiometer measurements for 950 field plots in the foothills of Alberta, Canada. We also tested the effects of synthetic hemispherical lens models on the performance of the proposed hemispherical Voronoi gap fraction (Phv) index. While vertical canopy cover metrics showed the best overall fit to field measurements, one new metric, point-density-normalized gap fraction (Ppdn), outperformed all other gap fraction metrics by two-fold. We provide suggestions for further algorithm enhancements based on validation data improvements. We argue that traditional field measurements are no longer appropriate for ‘ground-truthing’ modern LiDAR or SfM point cloud models, as the latter provide orders of magnitude greater sampling and coverage. We discuss the implications of this finding for LiDAR applications in forestry.

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Assessing the Contribution of Woody Materials to Forest Angular Gap Fraction and Effective Leaf Area Index Using Terrestrial Laser Scanning Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2015.2481492 ◽

2016 ◽

Vol 54 (3) ◽

pp. 1475-1487 ◽

Cited By ~ 21

Author(s):

Guang Zheng ◽

Lixia Ma ◽

Wei He ◽

Jan U. H. Eitel ◽

L. Monika Moskal ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Area Index ◽

Gap Fraction ◽

Effective Leaf Area Index

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Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photograpies

Computers and Electronics in Agriculture ◽

10.1016/j.compag.2016.02.011 ◽

2016 ◽

Vol 123 ◽

pp. 195-202 ◽

Cited By ~ 24

Author(s):

Marco Mora ◽

Felipe Avila ◽

Marcos Carrasco-Benavides ◽

Gonzalo Maldonado ◽

Jeissy Olguín-Cáceres ◽

...

Keyword(s):

Image Processing ◽

Leaf Area Index ◽

Leaf Area ◽

Canopy Cover ◽

Fruit Trees ◽

Area Index ◽

Image Processing Algorithms ◽

Processing Algorithms

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Derivation, Validation, and Sensitivity Analysis of Terrestrial Laser Scanning-Based Leaf Area Index

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2016.1220829 ◽

2016 ◽

Vol 42 (6) ◽

pp. 719-729 ◽

Cited By ~ 12

Author(s):

Yumei Li ◽

Qinghua Guo ◽

Shengli Tao ◽

Guang Zheng ◽

Kaiguang Zhao ◽

...

Keyword(s):

Sensitivity Analysis ◽

Leaf Area Index ◽

Leaf Area ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Area Index

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

10.31220/osf.io/jcxa9 ◽

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.

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