scholarly journals Evaluation of the FluorWPS Model and Study of the Parameter Sensitivity for Simulating Solar-Induced Chlorophyll Fluorescence

2021 ◽  
Vol 13 (6) ◽  
pp. 1091
Author(s):  
Chiming Tong ◽  
Yunfei Bao ◽  
Feng Zhao ◽  
Chongrui Fan ◽  
Zhenjiang Li ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Radiative Transfer ◽  
High Performance ◽  
Forest Canopy ◽  
Structural Parameters ◽  
Coefficient Of Determination ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Structural Factors ◽  
Area Index

Solar-induced chlorophyll fluorescence (SIF) has been used as an indicator for the photosynthetic activity of vegetation at regional and global scales. Canopy structure affects the radiative transfer process of SIF within canopy and causes the angular-dependencies of SIF. A common solution for interpreting these effects is the use of physically-based radiative transfer models. As a first step, a comprehensive evaluation of the three-dimensional (3D) radiative transfers is needed using ground truth biological and hyperspectral remote sensing measurements. Due to the complexity of forest modeling, few studies have systematically investigated the effect of canopy structural factors and sun-target-viewing geometry on SIF. In this study, we evaluated the capability of the Fluorescence model with the Weighted Photon Spread method (FluorWPS) to simulate at-sensor radiance and SIF at the top of canopy, and identified the influence of the canopy structural factors and sun-target-viewing geometry on the magnitude and directional response of SIF in deciduous forests. To evaluate the model, a 3D forest scene was first constructed from Goddard’s LiDAR Hyperspectral and Thermal (G-LiHT) LiDAR data. The reliability of the reconstructed scene was confirmed by comparing the calculated leaf area index with the measured ones from the scene, which resulted in a relative error of 3.5%. Then, the performance of FluorWPS was evaluated by comparing the simulated at-sensor radiance spectra with the spectra measured from the DUAL and FLUO spectrometer of HyPlant. The radiance spectra simulated by FluorWPS agreed well with the measured spectra by the two high-performance imaging spectrometers, with a coefficient of determination (R2) of 0.998 and 0.926, respectively. SIF simulated by the FluorWPS model agreed well with the values of the DART model. Furthermore, a sensitivity analysis was conducted to assess the effect of the canopy structural parameters and sun-target-viewing geometry on SIF. The maximum difference of the total SIF can be as large as 45% and 47% at the wavelengths of 685 nm and 740 nm for different foliage area volume densities (FAVDs), and 48% and 46% for fractional vegetation covers (FVCs), respectively. Leaf angle distribution has a markedly influence on the magnitude of SIF, with a ratio of emission part to SIF range from 0.48 to 0.72. SIF from the grass layer under the tree contributed 10%+ more to the top of canopy SIF even for a dense forest canopy (FAVD = 3.5 m−1, FVC = 76%). The red SIF at the wavelength of 685 nm had a similar shape to the far-red SIF at a wavelength of 740 nm but with higher variability in varying illumination conditions. The integration of the FluorWPS model and LiDAR modeling can greatly improve the interpretation of SIF at different scales and angular configurations.

scholarly journals Simulations of Leaf BSDF Effects on Lidar Waveforms

2020 ◽  
Vol 12 (18) ◽  
pp. 2909
Author(s):  
Benjamin D. Roth ◽  
Adam A. Goodenough ◽  
Scott D. Brown ◽  
Jan A. van Aardt ◽  
M. Grady Saunders ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radiative Transfer ◽  
Forest Canopy ◽  
Near Infrared ◽  
Difficult Problem ◽  
Leaf Angle ◽  
Lidar Data ◽  
Angle Distribution ◽  
Visible Wavelengths ◽  
Leaf Angle Distribution

Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data.

scholarly journals EXTRACTION OF LEAF ANGLE DISTRIBUTION FROM AN INDIVIDUAL BROADLEAF TREE USING TERRESTRIAL LASER SCANNING DATA

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 957-962
Author(s):  
Y. Chen ◽  
Z. Liu ◽  
W. Zhang ◽  
C. Qiao ◽  
H. Gu
Keyword(s):  
Field Measurement ◽  
Point Cloud ◽  
Structural Parameters ◽  
Leaf Angle ◽  
Structural Parameter ◽  
Angle Distribution ◽  
Area Index ◽  
Broadleaf Tree ◽  
Leaf Angle Distribution ◽  
Leaf Segmentation

<p><strong>Abstract.</strong> The angular distribution of leaves is a key vegetation structural parameter for evaluating the reflection and transmission of solar radiation through vegetation canopies. Accurate extraction of Leaf Angle Distribution (LAD) is of great importance in estimating other vegetation structural parameters such as the canopy clumping and leaf area index. However, field measurement of LAD is time-consuming, labour-intensive and subjective. In most studies, LAD is assumed to follow the spherical distribution assumption within canopy which may lead to considerable errors. To address this issue, we proposed a new approach for leaf segmentation and LAD measurement of individual broadleaf tree based on the TLS point cloud data. Based on the point density, point continuity and the distribution of intensity in the point cloud, this approach provides a fast and accurate leaf segmentation and LAD extraction strategy. Results of this TLS-based LAD method compared well with that extracted by the field measurement and the MDI-based method. This strategy shows its potential and applicability in accurate LAD measurement and LAI estimation.</p>

scholarly journals Optimizing smartphone-based canopy hemispherical photography

2021 ◽  
Author(s):  
Gastón Mauro Díaz
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Accuracy Assessment ◽  
Low Cost ◽  
R Package ◽  
Coefficient Of Determination ◽  
Native Forest ◽  
Hemispherical Photography ◽  
Area Index ◽  
Plant Area Index

1) Hemispherical photography (HP) is a long-standing tool for forest canopy characterization. Currently, there are low-cost fisheye lenses to convert smartphones into high-portable HP equipment; however, they cannot be used whenever since HP is sensitive to illumination conditions. To obtain sound results outside diffuse light conditions, a deep-learning-based system needs to be developed. A ready-to-use alternative is the multiscale color-based binarization algorithm, but it can provide moderate-quality results only for open forests. To overcome this limitation, I propose coupling it with the model-based local thresholding algorithm. I call this coupling the MBCB approach. 2) Methods presented here are part of the R package CAnopy IMage ANalysis (caiman), which I am developing. The accuracy assessment of the new MBCB approach was done with data from a pine plantation and a broadleaf native forest. 3) The coefficient of determination (R^2) was greater than 0.7, and the root mean square error (RMSE) lower than 20 %, both for plant area index calculation. 4) Results suggest that the new MBCB approach allows the calculation of unbiased canopy metrics from smartphone-based HP acquired in sunlight conditions, even for closed canopies. This facilitates large-scale and opportunistic sampling with hemispherical photography.

scholarly journals Comparison of Modeling Algorithms for Forest Canopy Structures Based on UAV-LiDAR: A Case Study in Tropical China

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1324
Author(s):  
Xi Peng ◽  
Anjiu Zhao ◽  
Yongfu Chen ◽  
Qiao Chen ◽  
Haodong Liu ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Sustainable Forest Management ◽  
Mean Squared Error ◽  
Structural Parameters ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Lidar Data ◽  
Area Index

Knowledge of forest structure is vital for sustainable forest management decisions. Terrestrial laser scanning cannot describe the canopy trees in a large area, and it is unclear whether unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the ability to capture the forest canopy structural parameters in tropical forests. In this study, we estimated five forest canopy structures (stand density (N), basic area (G), above-ground biomass (AGB), Lorey’s mean height (HL), and under-crown height (hT)) with four modeling algorithms (linear regression (LR), bagged tree (BT), support vector regression (SVR), and random forest (RF)) based on UAV-LiDAR data and 60 sample plot data from tropical forests in Hainan and determined the optimal algorithms for the five canopy structures by comparing the performance of the four algorithms. First, we defined the canopy tree as a tree with a height ≥70% HL. Then, UAV-LiDAR metrics were calculated, and the LiDAR metrics were screened by recursive feature elimination (RFE). Finally, a prediction model of the five forest canopy structural parameters was established by the four algorithms, and the results were compared. The metrics’ screening results show that the most important LiDAR indexes for estimating HL, AGB, and hT are the leaf area index and some height metrics, while the most important indexes for estimating N and G are the kurtosis of heights and the coefficient of variation of height. The relative root mean squared error (rRMSE) of five structure parameters showed the following: when modeling HL, the rRMSEs (10.60%–12.05%) obtained by the four algorithms showed little difference; when N was modeled, BT, RF, and SVR had lower rRMSEs (26.76%–27.44%); when G was modeled, the rRMSEs of RF and SVR (15.37%–15.87%) were lower; when hT was modeled, BT, RF, and SVR had lower rRMSEs (10.24%–11.07%); when AGB was modeled, RF had the lowest rRMSE (26.75%). Our results will help facilitate choosing LiDAR indexes and modeling algorithms for tropical forest resource inventories.

Modelling understory light availability in a heterogeneous landscape using drone-derived structural parameters and a 3D radiative transfer model

2020 ◽  
Author(s):  
Dominic Fawcett ◽  
Jonathan Bennie ◽  
Karen Anderson
Keyword(s):  
Radiative Transfer ◽  
Vegetation Index ◽  
Structural Parameters ◽  
Point Clouds ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Area Index ◽  
Vegetation Height ◽  
Plant Area Index ◽  
The Impact

&lt;p&gt;The light environment within vegetated landscapes is a key driver of microclimate, creating varied habitats over small spatial extents and controls the distribution of understory plant species. Modelling spatial variations of light at these scales requires finely resolved (&lt; 1 m) information on topography and canopy properties. We demonstrate an approach to modelling spatial distributions and temporal progression of understory photosynthetically active radiation (PAR) utilising a three dimensional radiative transfer model (discrete anisotropic radiative transfer model: DART) where the scene is parameterised by drone-based data.&lt;/p&gt;&lt;p&gt;The study site, located in west Cornwall, UK, includes a small mixed woodland as well as isolated free-standing trees. Data were acquired from March to August 2019. Vegetation height and distribution were derived from point clouds generated from drone image data using structure-from-motion (SfM) photogrammetry. These data were supplemented by multi-temporal multispectral imagery (Parrot Sequoia camera) which were used to generate an empirical model by relating a vegetation index to plant area index derived from hemispherical photography taken over the same time period. Simulations of the 3D radiative budget were performed for the PAR wavelength interval (400 &amp;#8211; 700 nm) using DART.&lt;/p&gt;&lt;p&gt;Besides maps of instantaneous above and below canopy irradiance, we provide models of daily light integrals (DLI) which are assessed against field validation measurements with PAR quantum sensors. We find relatively good agreement for simulated PAR in the woodland. The impact of simplifying assumptions regarding leaf angular distributions and optical properties are discussed. Finally, further opportunities which fine-grained drone data can provide in a radiative transfer context are highlighted.&lt;/p&gt;

scholarly journals Sensitivity Analysis of Canopy Structural and Radiative Transfer Parameters to Reconstructed Maize Structures Based on Terrestrial LiDAR Data

2021 ◽  
Vol 13 (18) ◽  
pp. 3751
Author(s):  
Bitam Ali ◽  
Feng Zhao ◽  
Zhenjiang Li ◽  
Qichao Zhao ◽  
Jiabei Gong ◽  
...  
Keyword(s):  
Hausdorff Distance ◽  
Leaf Angle ◽  
Phenotypic Traits ◽  
Lidar Data ◽  
Angle Distribution ◽  
Area Index ◽  
Terrestrial Lidar ◽  
Scale Parameters ◽  
Gap Fraction ◽  
Macro Scale

The maturity and affordability of light detection and ranging (LiDAR) sensors have made possible the quick acquisition of 3D point cloud data to monitor phenotypic traits of vegetation canopies. However, while the majority of studies focused on the retrieval of macro scale parameters of vegetation, there are few studies addressing the reconstruction of explicit 3D structures from terrestrial LiDAR data and the retrieval of fine scale parameters from such structures. A challenging problem that arises from the latter studies is the need for a large amount of data to represent the various components in the actual canopy, which can be time consuming and resource intensive for processing and for further applications. In this study, we present a pipeline to reconstruct the 3D maize structures composed of triangle primitives based on multi-view terrestrial LiDAR measurements. We then study the sensitivity of the details with which the canopy architecture was represented for the computation of leaf angle distribution (LAD), leaf area index (LAI), gap fraction, and directional reflectance factors (DRF). Based on point clouds of a maize field in three stages of growth, we reconstructed the reference structures, which have the maximum number of triangles. To get a compromise between the details of the structure and accuracy reserved for later applications, we carried out a simplified process to have multiple configurations of details based on the decimation rate and the Hausdorff distance. Results show that LAD is not highly sensitive to the details of the structure (or the number of triangles). However, LAI, gap fraction, and DRF are more sensitive, and require a relatively high number of triangles. A choice of 100−500 triangles per leaf while maintaining the overall shapes of the leaves and a low Hausdorff distance is suggested as a good compromise to represent the canopy and give an overall accuracy of 98% for the computation of the various parameters.

ORIENTATION OF SORBUS AUCUPARIA LEAVES BY THE RESULTS OF FIELD SURVEYS IN THE TERRITORY OF THE LENINGRAD REGION

2021 ◽  
Author(s):  
I. V. Matelenok ◽  
◽  
F. A. Alekseev ◽  
E. A. Evdokimova ◽  
◽  
...  
Keyword(s):  
Forest Canopy ◽  
Integral Function ◽  
Leaf Angle ◽  
Distribution Data ◽  
Leningrad Region ◽  
Angle Distribution ◽  
Office Work ◽  
Sorbus Aucuparia ◽  
Inclination Angles ◽  
Leaf Angle Distribution

Methods for retrieving leaf inclination angles in a forest canopy are considered. To acquire data on the orientation of Sorbus aucuparia leaves, a technique based on leveled camera digital photography well suited for conducting surveys in a boreal forest was used. In the course of field and office work, leaf angle distribution data for the specified species in the Priozersky district of the Leningrad region was obtained and analyzed. Values of the Ross-Nielson integral function were estimated.

Forest canopy structural parameters and Leaf Area Index retrieval using multi-sensors synergy observations

2009 ◽  
Author(s):  
Zhuo Fu ◽  
Jindi Wang ◽  
Jinling Song ◽  
Hongmin Zhou ◽  
Yong Pang ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Canopy ◽  
Structural Parameters ◽  
Area Index

scholarly journals Simulation-Based Evaluation of the Estimation Methods of Far-Red Solar-Induced Chlorophyll Fluorescence Escape Probability in Discontinuous Forest Canopies

2020 ◽  
Vol 12 (23) ◽  
pp. 3962
Author(s):  
Weiwei Liu ◽  
Shezhou Luo ◽  
Xiaoliang Lu ◽  
Jon Atherton ◽  
Jean-Philippe Gastellu-Etchegorry
Keyword(s):  
Chlorophyll Fluorescence ◽  
Zenith Angle ◽  
Solar Zenith Angle ◽  
Forest Canopy ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Escape Probability ◽  
Forest Canopies ◽  
Area Index ◽  
Spectral Invariants

The escape probability of Solar-induced chlorophyll fluorescence (SIF) can be remotely estimated using reflectance measurements based on spectral invariants theory. This can then be used to correct the effects of canopy structure on canopy-leaving SIF. However, the feasibility of these estimation methods is untested in heterogeneous vegetation such as the discontinuous forest canopy layer under evaluation here. In this study, the Discrete Anisotropic Radiative Transfer (DART) model is used to simulate canopy-leaving SIF, canopy total emitted SIF, canopy interceptance, and the fraction of absorbed photosynthetically active radiation (fAPAR) in order to evaluate the estimation methods of SIF escape probability in discontinuous forest canopies. Our simulation results show that the normalized difference vegetation index (NDVI) can be used to partly eliminate the effects of background reflectance on the estimation of SIF escape probability in most cases, but fails to produce accurate estimations if the background is partly or totally covered by vegetation. We also found that SIF escape probabilities estimated at a high solar zenith angle have better estimation accuracy than those estimated at a lower solar zenith angle. Our results show that additional errors will be introduced to the estimation of SIF escape probability with the use of satellite products, especially when the product of leaf area index (LAI) and clumping index (CI) was underestimated. In other results, fAPAR has comparable estimation accuracy of SIF escape probability when compared to canopy interceptance. Additionally, fAPAR for the entire canopy has better estimation accuracy of SIF escape probability than fPAR for leaf only in sparse forest canopies. These results help us to better understand the current estimation results of SIF escape probability based on spectral invariants theory, and to improve its estimation accuracy in discontinuous forest canopies.

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