Analysis of the Influence of Leaf Inclination Angle Distribution on the Leaf Area Inversion of Isolated Tree Based on Terrestrial Laser Scanning

2021 ◽  
Author(s):  
Shiyu Cheng ◽  
Guangjian Yan ◽  
Ronghai Hu ◽  
Hailan Jiang
Keyword(s):  
Leaf Area ◽  
Inclination Angle ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Angle Distribution ◽  
Leaf Inclination

scholarly journals Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Guangjian Yan ◽  
Hailan Jiang ◽  
Jinghui Luo ◽  
Xihan Mu ◽  
Fan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Inclination Angle ◽  
Three Dimensional ◽  
Good Choice ◽  
Optical Remote Sensing ◽  
Angle Distribution ◽  
Area Index ◽  
Leaf Inclination

Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

scholarly journals Dynamic Simulation on Canopy Light Distribution and Dry Matter Accumulation of Natural Colored Cotton in Alar Reclamation Area of Xinjiang

2020 ◽  
Vol 10 (3) ◽  
pp. 123-134
Author(s):  
Zhenqi Fan ◽  
◽  
Lixin Zhang
Keyword(s):  
Leaf Area ◽  
Inclination Angle ◽  
Dry Matter ◽  
Light Distribution ◽  
Physiological Age ◽  
Dry Matter Accumulation ◽  
Angle Distribution ◽  
Area Index ◽  
Colored Cotton ◽  
Leaf Inclination

Based on Ross’s theory of optical radiation transmission and full consideration of influences of vertical distribution of canopy leaf area and leaf inclination angle distribution of colored cotton on the light distribution, the Gaussian 5-point distance was used to divide the canopy into 5 layers on basis of the leaf area index. The leaf inclination angle on each layer was divided into 6 equal parts by 15°. The types of radiation in canopy, spatial distribution of light radiation, as well as diurnal variation with solar hour angles were quantified in detail. After comprehensively considering influences of temperature, physiological age and other factors on photosynthesis and respiration, the canopy light distribution, photosynthetic production and dry matter accumulation of colored cotton were simulated with strong mechanistic and physiological & ecological significance. The colored cotton samples sown on April 16, 2019 were used to verify the model. The RMSEs of simulated and measured canopy PAR values at Beijing time 10:00, 12:00, 14:00 and 16:00 on July 30 were 58.2, 64.1, 43.4 and 39.7 µmol•m-2•s-1, respectively. The RMSE of simulated and observed values of the dry matter accumulation above ground was 412.6 kgDM•hm-2, reflecting the good predictability of the model.

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

2016 ◽  
Vol 42 (6) ◽  
pp. 719-729 ◽  
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

scholarly journals Determining leaf area index and leafy tree roughness using terrestrial laser scanning

2010 ◽  
Vol 46 (6) ◽  
Author(s):  
A. S. Antonarakis ◽  
K. S. Richards ◽  
J. Brasington ◽  
E. Muller
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Area Index

scholarly journals Using terrestrial laser scanning to constrain forest ecosystem structure and functions in the Ecosystem Demography model (ED2.2)

2021 ◽  
Author(s):  
Félicien Meunier ◽  
Sruthi M. Krishna Moorthy ◽  
Marc Peaucelle ◽  
Kim Calders ◽  
Louise Terryn ◽  
...  
Keyword(s):  
Leaf Area ◽  
Forest Structure ◽  
Laser Scanning ◽  
Initial Conditions ◽  
Structural Parameters ◽  
Terrestrial Laser Scanning ◽  
Dimensional Structure ◽  
Forest Site ◽  
Ecosystem Structure ◽  
Area Index

Abstract. Terrestrial Biosphere Modeling (TBM) is an invaluable approach for studying plant-atmosphere interactions at multiple spatial and temporal scales, as well as the global change impacts on ecosystems. Yet, TBM projections suffer from large uncertainties that limit their usefulness. A large part of this uncertainty arises from the empirical allometric (size-tomass) relationships that are used to represent forest structure in TBMs. Forest structure actually drives a large part of TBM uncertainty as it regulates key processes such as the transfer of carbon, energy, and water between the land and atmosphere, but remains challenging to measure and reliably represent. The poor representation of forest structure in TBMs results in models that are able to reproduce observed land fluxes, but which fail to realistically represent carbon pools, forest composition, and demography. Recent advances in Terrestrial Laser Scanning (TLS) techniques offer a huge opportunity to capture the three-dimensional structure of the ecosystem and transfer this information to TBMs in order to increase their accuracy. In this study, we quantified the impacts of integrating structural observations of individual trees (namely tree height, leaf area, woody biomass, and crown area) derived from TLS into the state-of-the-art Ecosystem Demography model (ED2.2) at a temperate forest site. We assessed the relative model sensitivity to initial conditions, allometric parameters, and canopy representation by changing them in turn from default configurations to site-specific, TLS-derived values. We show that forest demography and productivity as modelled by ED2.2 are sensitive to the imposed initial state, the model structural parameters, and the way canopy is represented. In particular, we show that: 1) the imposed openness of the canopy dramatically influenced the potential vegetation, the optimal ecosystem leaf area, and the vertical distribution of light in the forest, as simulated by ED2.2; 2) TLS-derived allometric parameters increased simulated leaf area index and aboveground biomass by 57 and 75 %, respectively; 3) the choice of model structure and allometric coefficient both significantly impacted the optimal set of parameters necessary to reproduce eddy covariance flux data.

scholarly journals Suitability of Airborne and Terrestrial Laser Scanning for Mapping Tree Crop Structural Metrics for Improved Orchard Management

2020 ◽  
Vol 12 (10) ◽  
pp. 1647 ◽  
Author(s):  
Dan Wu ◽  
Kasper Johansen ◽  
Stuart Phinn ◽  
Andrew Robson
Keyword(s):  
Leaf Area ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Tree Structure ◽  
Crown Height ◽  
Crown Area ◽  
Vertical Leaf ◽  
Fractional Cover ◽  
Crown Volume ◽  
Crown Structure

Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for individual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops.

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