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.

Estimating Rice LAI Based on Digital Camera and Image Processing

2012 ◽  
Vol 500 ◽  
pp. 586-591 ◽  
Author(s):  
Gui Ying Pan ◽  
Lian Qing Zhou ◽  
Zhou Shi
Keyword(s):  
Image Processing ◽  
Low Cost ◽  
Digital Camera ◽  
Canopy Gap ◽  
Transmission Model ◽  
Distribution Model ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Area Index ◽  
Gap Fraction

A fast, low-cost method for rice canopy leaf area index (LAI) estimation is proposed. Take photos of rice canopy with a 57° view angle from above using a common digital camera. Extract canopy gap fraction by digital image processing technology. Then LAI can be estimated using canopy gap fraction based on optical transmission model and Leaf angle distribution model. AccuPAR-LP80 and direct measurement were employed to provide Comparative data. Comparison of the three methods, we obtained high correlation coefficients (R²≥0.6). The result shows that the method is especially suitable for estimating LAI in early growth stage of rice.

Variation of leaf angle distribution quantified by terrestrial LiDAR in natural European beech forest

2019 ◽  
Vol 148 ◽  
pp. 208-220 ◽  
Author(s):  
Jing Liu ◽  
Andrew K. Skidmore ◽  
Tiejun Wang ◽  
Xi Zhu ◽  
Joe Premier ◽  
...  
Keyword(s):  
European Beech ◽  
Beech Forest ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Terrestrial Lidar ◽  
Leaf Angle Distribution

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 Leaf Segmentation Based on k-Means Algorithm to Obtain Leaf Angle Distribution Using Terrestrial LiDAR

2019 ◽  
Vol 11 (21) ◽  
pp. 2536 ◽  
Author(s):  
Kuangting Kuo ◽  
Kenta Itakura ◽  
Fumiki Hosoi
Keyword(s):  
Canopy Structure ◽  
Field Measurements ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Angle Estimation ◽  
Terrestrial Lidar ◽  
Common Solution ◽  
Segmentation Approach ◽  
Leaf Angle Distribution ◽  
Leaf Segmentation

It is critical to take the variability of leaf angle distribution into account in a remote sensing analysis of a canopy system. Due to the physical limitations of field measurements, it is difficult to obtain leaf angles quickly and accurately, especially with a complicated canopy structure. An application of terrestrial LiDAR (Light Detection and Ranging) is a common solution for the purposes of leaf angle estimation, and it allows for the measurement and reconstruction of 3D canopy models with an arbitrary volume of leaves. However, in most cases, the leaf angle is estimated incorrectly due to inaccurate leaf segmentation. Therefore, the objective of this study was an emphasis on the development of efficient segmentation algorithms for accurate leaf angle estimation. Our study demonstrates a leaf segmentation approach based on a k-means algorithm coupled with an octree structure and the subsequent application of plane-fitting to estimate the leaf angle. Furthermore, the accuracy of the segmentation and leaf angle estimation was verified. The results showed average segmentation accuracies of 95% and 90% and absolute angular errors of 3° and 6° in the leaves sampled from mochi and Japanese camellia trees, respectively. It is our conclusion that our method of leaf angle estimation has high potential and is expected to make a significant contribution to future plant and forest research.

scholarly journals Retrieving Corn Canopy Leaf Area Index from Multitemporal Landsat Imagery and Terrestrial LiDAR Data

2019 ◽  
Vol 11 (5) ◽  
pp. 572 ◽  
Author(s):  
Wei Su ◽  
Jianxi Huang ◽  
Desheng Liu ◽  
Mingzheng Zhang
Keyword(s):  
Laser Scanning ◽  
Landsat Imagery ◽  
Terrestrial Laser Scanning ◽  
Distribution Functions ◽  
Leaf Angle ◽  
Angle Distribution ◽  
Area Index ◽  
Leaf Angle Distribution ◽  
Modis Lai

Leaf angle is a critical structural parameter for retrieving canopy leaf area index (LAI) using the PROSAIL model. However, the traditional method using default leaf angle distribution in the PROSAIL model does not capture the phenological dynamics of canopy growth. This study presents a LAI retrieval method for corn canopies using PROSAIL model with leaf angle distribution functions referred from terrestrial laser scanning points at four phenological stages during the growing season. Specifically, four inferred maximum-probability leaf angles were used in the Campbell ellipsoid leaf angle distribution function of PROSAIL. A Lookup table (LUT) is generated by running the PROSAIL model with inferred leaf angles, and the cost function is minimized to retrieve LAI. The results show that the leaf angle distribution functions are different for the corn plants at different phenological growing stages, and the incorporation of derived specific corn leaf angle distribution functions distribute the improvement of LAI retrieval using the PROSAIL model. This validation is done using in-situ LAI measurements and MODIS LAI in Baoding City, Hebei Province, China, and compared with the LAI retrieved using default leaf angle distribution function at the same time. The root-mean-square error (RMSE) between the retrieved LAI on 4 September 2014, using the modified PROSAIL model and the in-situ measured LAI was 0.31 m2/m2, with a strong and significant correlation (R2 = 0.82, residual range = 0 to 0.6 m2/m2, p < 0.001). Comparatively, the accuracy of LAI retrieved results using default leaf angle distribution is lower, the RMSE of which is 0.56 with R2 = 0.76 and residual range = 0 to 1.0 m2/m2, p < 0.001. This validation reveals that the introduction of inferred leaf angle distributions from TLS data points can improve the LAI retrieval accuracy using the PROSAIL model. Moreover, the comparations of LAI retrieval results on 10 July, 26 July, 19 August and 4 September with default and inferred corn leaf angle distribution functions are all compared with MODIS LAI products in the whole study area. This validation reveals that improvement exists in a wide spatial range and temporal range. All the comparisons demonstrate the potential of the modified PROSAIL model for retrieving corn canopy LAI from Landsat imagery by inferring leaf orientation from terrestrial laser scanning data.

scholarly journals Bi-Hemispherical Canopy Reflectance Model with Surface Heterogeneity Effects for the Estimation of LAI and fAPAR from MODIS White-Sky Spectral Albedo Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1976
Author(s):  
Wouter Verhoef
Keyword(s):  
Optical Properties ◽  
Material Properties ◽  
Land Surface ◽  
Spatial Coherence ◽  
Surface Heterogeneity ◽  
Leaf Angle ◽  
Canopy Reflectance ◽  
Angle Distribution ◽  
Area Index ◽  
Leaf Canopy

Bi-hemispherical reflectance (BHR), in the land surface research community also known as “white-sky albedo”, is independent of the directions of incidence and viewing. For vegetation canopies, it is also nearly independent of the leaf angle distribution, and therefore it can be considered an optical quantity that is only dependent on material properties. For the combination leaf canopy and soil background, the most influential material properties are the canopy LAI (leaf area index), optical properties of the leaves, and soil brightness. When the leaf and soil optical properties are known or assumed, one may estimate the canopy LAI from its white-sky spectral albedo. This is also because a simple two-stream radiative transfer (RT) model is available for the BHR of the leaf canopy and soil combination. In this contribution, crown clumping and lateral linear mixing effects are incorporated in this model. A new procedure to estimate soil brightness is introduced here, even under a moderate layer of green vegetation. The procedure uses the red and NIR spectral bands. A MODIS white-sky albedo product at a spatial resolution of 0.05° is used as a sample input to derive global maps of LAI, soil brightness, and fAPAR at the local moments of minimum and maximum NDVI over a 20-year period. These maps show a high degree of spatial coherence and demonstrate the possible utility of products that can be generated with little effort by using a direct LUT technique.

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