Large-scale retrieval of leaf area index and vertical foliage profile from the spaceborne waveform lidar (GLAS/ICESat)

2014 ◽  
Vol 154 ◽  
pp. 8-18 ◽  
Author(s):  
Hao Tang ◽  
Ralph Dubayah ◽  
Matthew Brolly ◽  
Sangram Ganguly ◽  
Gong Zhang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Large Scale ◽  
Area Index ◽  
Waveform Lidar

scholarly journals Examining the link between vegetation leaf area and land-atmosphere exchange of water, energy, and carbon fluxes using FLUXNET data

2020 ◽  
Author(s):  
Anne J. Hoek van Dijke ◽  
Kaniska Mallick ◽  
Martin Schlerf ◽  
Miriam Machwitz ◽  
Martin Herold ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Large Scale ◽  
Vegetation Type ◽  
Carbon Fluxes ◽  
Surface Fluxes ◽  
Energy Fluxes ◽  
Area Index ◽  
Vegetation Control

Abstract. Vegetation regulates the exchange of water, energy, and carbon fluxes between the land and the atmosphere. This regulation of surface fluxes differs with vegetation type and climate, but the effect of vegetation on surface fluxes is not well understood. A better knowledge of how and when vegetation influences surface fluxes could improve climate models and the extrapolation of ground-based water, energy, and carbon fluxes. We aim to study the large-scale link between vegetation and surface fluxes by combining MODIS leaf area index with flux tower measurements of water (latent heat), energy (sensible heat), and carbon (gross primary productivity and net ecosystem exchange). We show that the correlation between leaf area index and water and energy fluxes depends on vegetation and aridity. In water-limited conditions, the link between vegetation and water and energy fluxes is strong, which is in line with a strong stomatal or vegetation control found in earlier studies. In energy-limited forest we found no vegetation control on water and energy fluxes. In contrast to water and energy fluxes, we found a strong correlation between leaf area index and gross primary productivity that was independent of vegetation type and aridity index. This study provides insight in the large-scale link between vegetation and surface fluxes. The study indicates that for modelling or extrapolating large-scale surface fluxes, LAI can be useful in savanna and grassland, but only of limited use in deciduous broadleaf forest and evergreen needleleaf forest.

scholarly journals Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography

2020 ◽  
Vol 126 (4) ◽  
pp. 765-773 ◽  
Author(s):  
Yingpu Che ◽  
Qing Wang ◽  
Ziwen Xie ◽  
Long Zhou ◽  
Shuangwei Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Plant Height ◽  
Large Scale ◽  
High Efficiency ◽  
Point Clouds ◽  
Phenotypic Traits ◽  
Area Index ◽  
Area Density ◽  
Leaf Area Density

Abstract Background and Aims High-throughput phenotyping is a limitation in plant genetics and breeding due to large-scale experiments in the field. Unmanned aerial vehicles (UAVs) can help to extract plant phenotypic traits rapidly and non-destructively with high efficiency. The general aim of this study is to estimate the dynamic plant height and leaf area index (LAI) by nadir and oblique photography with a UAV, and to compare the integrity of the established three-dimensional (3-D) canopy by these two methods. Methods Images were captured by a high-resolution digital RGB camera mounted on a UAV at five stages with nadir and oblique photography, and processed by Agisoft Metashape to generate point clouds, orthomosaic maps and digital surface models. Individual plots were segmented according to their positions in the experimental design layout. The plant height of each inbred line was calculated automatically by a reference ground method. The LAI was calculated by the 3-D voxel method. The reconstructed canopy was sliced into different layers to compare leaf area density obtained from oblique and nadir photography. Key Results Good agreements were found for plant height between nadir photography, oblique photography and manual measurement during the whole growing season. The estimated LAI by oblique photography correlated better with measured LAI (slope = 0.87, R2 = 0.67), compared with that of nadir photography (slope = 0.74, R2 = 0.56). The total number of point clouds obtained by oblique photography was about 2.7–3.1 times than those by nadir photography. Leaf area density calculated by nadir photography was much less than that obtained by oblique photography, especially near the plant base. Conclusions Plant height and LAI can be extracted automatically and efficiently by both photography methods. Oblique photography can provide intensive point clouds and relatively complete canopy information at low cost. The reconstructed 3-D profile of the plant canopy can be easily recognized by oblique photography.

Estimating leaf area index of maize using airborne full-waveform lidar data

2015 ◽  
Vol 7 (2) ◽  
pp. 111-120 ◽  
Author(s):  
Sheng Nie ◽  
Cheng Wang ◽  
Pinliang Dong ◽  
Xiaohuan Xi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Lidar Data ◽  
Area Index ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Full Waveform Lidar

Clumping Effects in Leaf Area Index Retrieval From Large-Footprint Full-Waveform LiDAR

2021 ◽  
pp. 1-20
Author(s):  
Hailan Jiang ◽  
Shiyu Cheng ◽  
Guangjian Yan ◽  
Andres Kuusk ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Full Waveform ◽  
Waveform Lidar ◽  
Full Waveform Lidar

scholarly journals Characterizing leaf area index (LAI) and vertical foliage profile (VFP) over the United States

2016 ◽  
Vol 13 (1) ◽  
pp. 239-252 ◽  
Author(s):  
H. Tang ◽  
S. Ganguly ◽  
G. Zhang ◽  
M. A. Hofton ◽  
R. F. Nelson ◽  
...  
Keyword(s):  
United States ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Structure ◽  
The United States ◽  
Data Set ◽  
Area Index ◽  
Continental Scale ◽  
Comparison Results ◽  
Waveform Lidar

Abstract. Leaf area index (LAI) and vertical foliage profile (VFP) are among the important canopy structural variables. Recent advances in lidar remote sensing technology have demonstrated the capability of accurately mapping LAI and VFP over large areas. The primary objective of this study was to derive and validate a LAI and VFP product over the contiguous United States (CONUS) using spaceborne waveform lidar data. This product was derived at the footprint level from the Geoscience Laser Altimeter System (GLAS) using a biophysical model. We validated GLAS-derived LAI and VFP across major forest biomes using airborne waveform lidar. The comparison results showed that GLAS retrievals of total LAI were generally accurate with little bias (r2 =  0.67, bias  =  −0.13, RMSE  =  0.75). The derivations of GLAS retrievals of VFP within layers were not as accurate overall (r2 =  0.36, bias  =  −0.04, RMSE  =  0.26), and these varied as a function of height, increasing from understory to overstory – 0 to 5 m layer: r2 =  0.04, bias  =  0.09, RMSE  =  0.31; 10 to 15 m layer: r2 =  0.53, bias  =  −0.08, RMSE  =  0.22; and 15 to 20 m layer: r2 =  0.66, bias  =  −0.05, RMSE  =  0.20. Significant relationships were also found between GLAS LAI products and different environmental factors, in particular elevation and annual precipitation. In summary, our results provide a unique insight into vertical canopy structure distribution across North American ecosystems. This data set is a first step towards a baseline of canopy structure needed for evaluating climate and land use induced forest changes at the continental scale in the future, and should help deepen our understanding of the role of vertical canopy structure in terrestrial ecosystem processes across varying scales.

scholarly journals Estimating Leaf Area Index with Dynamic Leaf Optical Properties

2021 ◽  
Vol 13 (23) ◽  
pp. 4898
Author(s):  
Hu Zhang ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Yadong Dong ◽  
Songze Li ◽  
...  
Keyword(s):  
Optical Properties ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Large Scale ◽  
Radiative Transfer Model ◽  
Inversion Method ◽  
Transfer Model ◽  
Area Index ◽  
Model Based ◽  
Leaf Optical Properties

Leaf area index (LAI) plays an important role in models of climate, hydrology, and ecosystem productivity. The physical model-based inversion method is a practical approach for large-scale LAI inversion. However, the ill-posed inversion problem, due to the limited constraint of inaccurate input parameters, is the dominant source of inversion errors. For instance, variables related to leaf optical properties are always set as constants or have large ranges, instead of the actual leaf reflectance of pixel vegetation in the current model-based inversions. This paper proposes to estimate LAI with the actual leaf optical property of pixels, calculated from the leaf chlorophyll content (Chlleaf) product, using a three-dimensional stochastic radiative transfer model (3D-RTM)-based, look-up table method. The parameter characterizing leaf optical properties in the 3D-RTM-based LAI inversion algorithm, single scattering albedo (SSA), is calculated with the Chlleaf product, instead of setting fixed values across a growing season. An algorithm to invert LAI with the dynamic SSA of the red band (SSAred) is proposed. The retrieval index (RI) increases from less than 42% to 100%, and the RMSE decreases to less than 0.28 in the simulations. The validation results show that the RMSE of the dynamic SSA decreases from 1.338 to 0.511, compared with the existing 3D-RTM-based LUT algorithm. The overestimation problem under high LAI conditions is reduced.

scholarly journals Retrieval of Leaf Area Index by Linking the PROSAIL and Ross-Li BRDF Models Using MODIS BRDF Data

2021 ◽  
Vol 13 (23) ◽  
pp. 4911
Author(s):  
Xiaoning Zhang ◽  
Ziti Jiao ◽  
Changsen Zhao ◽  
Siyang Yin ◽  
Lei Cui ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Large Scale ◽  
Near Infrared ◽  
Canopy Structure ◽  
Leaf Angle ◽  
Infrared Band ◽  
Area Index ◽  
Input Parameters ◽  
Scattering Kernel

Canopy structure parameters (e.g., leaf area index (LAI)) are key variables of most climate and ecology models. Currently, satellite-observed reflectances at a few viewing angles are often directly used for vegetation structure parameter retrieval; therefore, the information content of multi-angular observations that are sensitive to canopy structure in theory cannot be sufficiently considered. In this study, we proposed a novel method to retrieve LAI based on modelled multi-angular reflectances at sufficient sun-viewing geometries, by linking the PROSAIL model with a kernel-driven Ross-Li bi-directional reflectance function (BRDF) model using the MODIS BRDF parameter product. First, BRDF sensitivity to the PROSAIL input parameters was investigated to reduce the insensitive parameters. Then, MODIS BRDF parameters were used to model sufficient multi-angular reflectances. By comparing these reference MODIS reflectances with simulated PROSAIL reflectances within the range of the sensitive input parameters in the same geometries, the optimal vegetation parameters were determined by searching the minimum discrepancies between them. In addition, a significantly linear relationship between the average leaf angle (ALA) and the coefficient of the volumetric scattering kernel of the Ross-Li model in the near-infrared band was built, which can narrow the search scope of the ALA and accelerate the retrieval. In the validation, the proposed method attains a higher consistency (root mean square error (RMSE) = 1.13, bias = −0.19, and relative RMSE (RRMSE) = 36.8%) with field-measured LAIs and 30-m LAI maps for crops than that obtained with the MODIS LAI product. The results indicate the vegetation inversion potential of sufficient multi-angular data and the ALA relationship, and this method presents promise for large-scale LAI estimation.

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