Retrieving Directional Gap Fraction, Extinction Coefficient, and Effective Leaf Area Index by Incorporating Scan Angle Information From Discrete Aerial Lidar Data

The leaf area index (LAI) is a crucial structural parameter of forest canopies. Light Detection and Ranging (LiDAR) provides an alternative to passive optical sensors in the estimation of LAI from remotely sensed data. However, LiDAR-based LAI estimation typically relies on empirical models, and such methods can only be applied when the field-based LAI data are available. Compared with an empirical model, a physically-based model—e.g., the Beer–Lambert law based light extinction model—is more attractive due to its independent dataset with training. However, two challenges are encountered when applying the physically-based model to estimate LAI from discrete LiDAR data: i.e., deriving the gap fraction and the extinction coefficient from the LiDAR data. We solved the first problem by integrating LiDAR and hyperspectral data to transfer the LiDAR penetration ratio to the forest gap fraction. For the second problem, the extinction coefficient was estimated from tiled (1 km × 1 km) LiDAR data by nonlinearly optimizing the cost function of the angular LiDAR gap fraction and simulated gap fraction from the Beer–Lambert law model. A validation against LAI-2000 measurements showed that the estimates were significantly correlated to the reference LAI with an R2 of 0.66, a root mean square error (RMSE) of 0.60 and a relative RMSE of 0.15. We conclude that forest LAI can be directly estimated by the nonlinear optimization method utilizing the Beer–Lambert model and a spectrally corrected LiDAR penetration ratio. The significance of the proposed method is that it can produce reliable remotely sensed forest LAI from discrete LiDAR and spectral data when field-measured LAI are unavailable.

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An intensity, image-based method to estimate gap fraction, canopy openness and effective leaf area index from phase-shift terrestrial laser scanning

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2019.107766 ◽

2020 ◽

Vol 280 ◽

pp. 107766 ◽

Cited By ~ 2

Author(s):

Mirko Grotti ◽

Kim Calders ◽

Niall Origo ◽

Nicola Puletti ◽

Alessandro Alivernini ◽

...

Keyword(s):

Phase Shift ◽

Leaf Area Index ◽

Leaf Area ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Canopy Openness ◽

Area Index ◽

Gap Fraction ◽

Intensity Image ◽

Effective Leaf Area Index

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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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The method of extracting forest leaf area index based on Lidar data

Information Technology and Computer Application Engineering ◽

10.1201/b15936-79 ◽

2013 ◽

pp. 349-352

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Lidar Data ◽

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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