Abstract
Leaf area index (LAI) is an important vegetation parameter. Active light detection and ranging (LiDAR) technology has been widely used to estimate vegetation LAI. In this study, LiDAR technology, LAI retrieval and validation methods, and impact factors are reviewed. First, the paper introduces types of LiDAR systems and LiDAR data preprocessing methods. After introducing the application of different LiDAR systems, LAI retrieval methods are described. Subsequently, the review discusses various LiDAR LAI validation schemes and limitations in LiDAR LAI validation. Finally, factors affecting LAI estimation are analyzed. The review presents that LAI is mainly estimated from LiDAR data by means of the correlation with the gap fraction and contact frequency, and also from the regression of forest biophysical parameters derived from LiDAR. Terrestrial laser scanning (TLS) can be used to effectively estimate the LAI and vertical foliage profile (VFP) within plots, but this method is affected by clumping, occlusion, voxel size, and woody material. Airborne laser scanning (ALS) covers relatively large areas in a spatially contiguous manner. However, the capability of describing the within-canopy structure is limited, and the accuracy of LAI estimation with ALS is affected by the height threshold and sampling size, and types of return. Spaceborne laser scanning (SLS) provides the global LAI and VFP, and the accuracy of estimation is affected by the footprint size and topography. The use of LiDAR instruments for the retrieval of the LAI and VFP has increased; however, current LiDAR LAI validation studies are mostly performed at local scales. Future research should explore new methods to invert LAI and VFP from LiDAR and enhance the quantitative analysis and large-scale validation of the parameters.
Highlights
Leaf area index (LAI) is defined as one half the total green leaf area per unit ground surface area [1]
Figurre 9 shows the ffllowchart of LAI estimation based on return number or intensity: (1) the ground and canopy returns are separated according to the height threshold; (2) the gap fraction is calculated as the ratio of the number of ground returns to the number of total returnsr; nasn)d; a(n3)dt(h3e) LthAeILisAdIeitsedrmetienremdinuesdinugstihneggtahpe gfraapctfiroanctbiaosnedbaosnedthoenBteheer–BLeaemr–bLearmt lbaewrt. lTahwe
LAI is mainly estimated from light detection and ranging (LiDAR) data by means of the correlation with the gap fraction and contact frequency, and LAI is estimated from the regression of forest biophysical parameters derived from LiDAR, such as LiDAR height and foliage density metrics
Summary
Leaf area index (LAI) is defined as one half the total green leaf area per unit ground surface area [1]. The LAI values obtained from ground measurement are often used as references for remote sensing validation. These methods are labor-intensive and time-consuming, and the deployment over large areas is difficult. Passive optical remote sensing has been widely used to estimate the LAI [9,10,11,12]. LiDAR is an active remote sensing technology for indirect LAI measurements, which alleviates the saturation problem because of the direct detection of the vertical structure [16].
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