Modeling Small-Footprint Airborne Lidar-Derived Estimates of Gap Probability and Leaf Area Index

Airborne lidar point clouds of vegetation capture the 3-D distribution of its scattering elements, including leaves, branches, and ground features. Assessing the contribution from vegetation to the lidar point clouds requires an understanding of the physical interactions between the emitted laser pulses and their targets. Most of the current methods to estimate the gap probability ( P gap ) or leaf area index (LAI) from small-footprint airborne laser scan (ALS) point clouds rely on either point-number-based (PNB) or intensity-based (IB) approaches, with additional empirical correlations with field measurements. However, site-specific parameterizations can limit the application of certain methods to other landscapes. The universality evaluation of these methods requires a physically based radiative transfer model that accounts for various lidar instrument specifications and environmental conditions. We conducted an extensive study to compare these approaches for various 3-D forest scenes using a point-cloud simulator developed for the latest version of the discrete anisotropic radiative transfer (DART) model. We investigated a range of variables for possible lidar point intensity, including radiometric quantities derived from Gaussian Decomposition (GD), such as the peak amplitude, standard deviation, integral of Gaussian profiles, and reflectance. The results disclosed that the PNB methods fail to capture the exact P gap as footprint size increases. By contrast, we verified that physical methods using lidar point intensity defined by either the distance-weighted integral of Gaussian profiles or reflectance can estimate P gap and LAI with higher accuracy and reliability. Additionally, the removal of certain additional empirical correlation coefficients is feasible. Routine use of small-footprint point-cloud radiometric measures to estimate P gap and the LAI potentially confirms a departure from previous empirical studies, but this depends on additional parameters from lidar instrument vendors.

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Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model

Remote Sensing of Environment ◽

10.1016/s0034-4257(03)00005-1 ◽

2003 ◽

Vol 85 (3) ◽

pp. 257-270 ◽

Cited By ~ 144

Author(s):

H Fang

Keyword(s):

Genetic Algorithm ◽

Radiative Transfer ◽

Leaf Area Index ◽

Leaf Area ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index

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Leaf Area Index Retrieval From SPARC Data: Assessment Of Radiative Transfer Model Inversion

10.1063/1.2349347 ◽

2006 ◽

Cited By ~ 1

Author(s):

L. Dini

Keyword(s):

Radiative Transfer ◽

Leaf Area Index ◽

Leaf Area ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index ◽

Model Inversion ◽

Data Assessment

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A Density-Based Approach for Leaf Area Index Assessment in a Complex Forest Environment Using a Terrestrial Laser Scanner

Remote Sensing ◽

10.3390/rs11151791 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1791 ◽

Cited By ~ 6

Author(s):

Ali Rouzbeh Kargar ◽

Richard MacKenzie ◽

Gregory P. Asner ◽

Jan van Aardt

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Point Cloud ◽

Forest Canopy ◽

Low Cost ◽

Point Clouds ◽

Grid Size ◽

Density Estimator ◽

Forest Environment ◽

Area Index

Forests are an important part natural ecosystems, by for example providing food, fiber, habitat, and biodiversity, all of which contribute to stable natural systems. Assessing and modeling the structure and characteristics of forests, e.g., Leaf Area Index (LAI), volume, biomass, etc., can lead to a better understanding and management of these resources. In recent years, Terrestrial Laser Scanning (TLS) has been recognized as a tool that addresses many of the limitations of manual and traditional forest data collection methods. In this study, we propose a density-based approach for estimating the LAI in a structurally-complex forest environment, which contains variable and diverse structural attributes, e.g., non-circular stem forms, dense canopy and below-canopy vegetation cover, and a diverse species composition. In addition, 242 TLS scans were collected using a portable low-cost scanner, the Compact Biomass Lidar (CBL), in the Hawaii Volcanoes National Park (HAVO), Hawaii Island, USA. LAI also was measured for 242 plots in the site, using an AccuPAR LP-80 ceptometer. The first step after cleaning the point cloud involved detecting the higher forest canopy in the light detection and ranging (lidar) point clouds, using normal change rate assessment. We then estimated Leaf Area Density (LAD), using a voxel-based approach, and divided the canopy point cloud into five layers in the Z (vertical) direction. These five layers subsequently were divided into voxels in the X direction, where the size of these voxels were obtained based on inter-quartile analysis and the number of points in each voxel. We hypothesized that the intensity returned to the lidar system from woody materials, like branches, would be higher than from leaves, due to the liquid water absorption feature of the leaves and higher reflectance for woody material at the 905 nm laser wavelength. We also differentiated between foliar and woody materials using edge detection in the images from projected point clouds and evaluated the density of these regions to support our hypothesis. Density of points, or the number of points divided by the volume of a grid, in a 3D grid size of 0.1 m, was calculated for each of the voxels. The grid size was determined by investigating the size of the branches in the lower portion of the canopy. Subsequently, we fitted a Kernel Density Estimator (KDE) to these values, with the threshold set based on half of the area under the curve in each of the density distributions. All the grids with a density below the threshold were labeled as leaves, while those grids above the threshold were identified as non-leaves. Finally, we modeled LAI using the point densities derived from the TLS point clouds and the listed analysis steps. This model resulted in an R 2 value of 0.88. We also estimated the LAI directly from lidar data using the point densities and calculating LAD, which is defined as the total one-sided leaf area per unit volume. LAI can be obtained as the sum of the LAD values in all the voxels. The accuracy of LAI estimation was 90%, with an RMSE value of 0.31, and an average overestimation of 9 % in TLS-derived LAI, when compared to field-measured LAI. Algorithm performance mainly was affected by the vegetation density and complexity of the canopy structures. It is worth noting that, since the LAI values cannot be considered spatially independent throughout all the plots in this site, we performed semivariogram analysis on the field-measured LAI data. This analysis showed that the LAI values can be assumed to be independent in plots that are at least 30 m apart. As a result, we divided the data into six subsets in which the plots were 30 m spaced. The R 2 values for these subsets, based on modeling of the field-measured LAI using leaf point density values, ranged between 0.84–0.96. The results bode well for using this method for efficient, automatic, and accurate/precise estimation of LAI values in complex forest environments, using a low-cost, rapid-scan TLS.

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Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Remote Sensing ◽

10.3390/rs70404604 ◽

2015 ◽

Vol 7 (4) ◽

pp. 4604-4625 ◽

Cited By ~ 25

Author(s):

Gaofei Yin ◽

Jing Li ◽

Qinhuo Liu ◽

Weiliang Fan ◽

Baodong Xu ◽

...

Keyword(s):

Remote Sensing ◽

Model Selection ◽

Radiative Transfer ◽

Leaf Area Index ◽

Leaf Area ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index

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Estimation of leaf area index (LAI) of South Africa from MODIS imager by inversion of PROSAIL radiative transfer model

2014 IEEE Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2014.6947003 ◽

2014 ◽

Cited By ~ 2

Author(s):

Moses Azong Cho ◽

Abel Ramoelo ◽

Renaud Math

Keyword(s):

South Africa ◽

Radiative Transfer ◽

Leaf Area Index ◽

Leaf Area ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index

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The Model Inversion of Leaf Area Index of Vegetation by Means of Electromagenetic Wave Radiative Transfer Model

Earth Sciences ◽

10.11648/j.earth.20170606.15 ◽

2017 ◽

Vol 6 (6) ◽

pp. 131

Author(s):

Wei Fu

Keyword(s):

Radiative Transfer ◽

Leaf Area Index ◽

Leaf Area ◽

Radiative Transfer Model ◽

Transfer Model ◽

Area Index ◽

Model Inversion

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Retrieval of Vertical Foliage Profile and Leaf Area Index Using Transmitted Energy Information Derived from ICESat GLAS Data

Remote Sensing ◽

10.3390/rs12152457 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2457

Author(s):

Lei Cui ◽

Ziti Jiao ◽

Kaiguang Zhao ◽

Mei Sun ◽

Yadong Dong ◽

...

Keyword(s):

Radiative Transfer ◽

Leaf Area Index ◽

Leaf Area ◽

Transfer Model ◽

Simulation Data ◽

Individual Tree ◽

Area Index ◽

Transmitted Energy ◽

Modis Lai ◽

Vertical Gap

The vertical foliage profile (VFP) and leaf area index (LAI) are critical descriptors in terrestrial ecosystem modeling. Although light detection and ranging (lidar) observations have been proven to have potential for deriving the VFP and LAI, existing methods depend only on the received waveform information and are sensitive to additional input parameters, such as the ratio of canopy to ground reflectance. In this study, we proposed a new method for retrieving forest VFP and LAI from Ice, Cloud and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) data over two sites similar in their biophysical parameters. Our method utilized the information from not only the interaction between the laser and the forest but also the sensor configuration, which brought the benefit that our method was free from an empirical input parameter. Specifically, we first derived the transmitted energy profile (TEP) through the lidar 1-D radiative transfer model. Then, the obtained TEP was utilized to calculate the vertical gap distribution. Finally, the vertical gap distribution was taken as input to derive the VFP based on the Beer–Lambert law, and the LAI was calculated by integrating the VFP. Extensive validations of our method were carried out based on the discrete anisotropic radiative transfer (DART) simulation data, ground-based measurements, and the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product. The validation based on the DART simulation data showed that our method could effectively characterize the VFP and LAI under various canopy architecture scenarios, including homogeneous turbid and discrete individual-tree scenes. The ground-based validation also proved the feasibility of our method: the VFP retrieved from the GLAS data showed a similar trend with the foliage distribution density in the GLAS footprints; the GLAS LAI had a high correlation with the field measurements, with a determination coefficient (R2) of 0.79, root mean square error (RMSE) of 0.49, and bias of 0.17. Once the outliers caused by low data quality and large slope were identified and removed, the accuracy was further improved, with R2 = 0.85, RMSE = 0.35, and bias = 0.10. However, the MODIS LAI product did not present a good relationship with the GLAS LAI. Relative to the GLAS LAI, the MODIS LAI showed an overestimation in the low and middle ranges of the LAI and a saturation at high values of approximately LAI = 5.5. Overall, this method has the potential to produce continental- and global-scale VFP and LAI datasets from the spaceborne lidar system.

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