Abstract
Forest canopy leaf area index (LAI) is a critical variable for the modeling of climates and ecosystems over both regional and global scales. This paper proposes a physically based method to retrieve LAI and foliage area volume density (FAVD) profile directly from full-waveform Light Detection And Ranging (LiDAR) data using a radiative transfer (RT) model. First, a physical interaction model between LiDAR and a forest scene was built on the basis of radiative transfer theories. Next, FAVD profile of each laser shot of full-waveform LiDAR was inverted using the physical model. In addition, the missing LiDAR data, caused by high-density forest and LiDAR system limitations, were filled in based on the inverted FAVD and the ancillary CHM data. Finally, LAI of the study area was retrieved from the inverted FAVD at a 10-m resolution. CHM derived LAI based on the Beer-Lambert law was compared with the LAI derived from full-waveform data. Also, we compared the results with the field measured LAI. The values of correlation coefficient r and RMSE of the estimated LAI were 0.73 and 0.67, respectively. The results indicate that full-waveform LiDAR data is a reliable data source and represent a useful tool for retrieving forest LAI.
Highlights
Forest canopy leaf area index (LAI) is a critical variable in modeling climates and ecosystems and is required by many terrestrial ecosystem models [1]
The first is based on developing the empirical or semi-empirical relationship between the vegetation indices and the LAI, and the other is based on radiation transfer models using a look-up table (LUT) or a neural network (NN) [5], or an optimation method such as Levenberg–Marquardt algorithm [6]
The red waves were modeled by the Light Detection And Ranging (LiDAR)-forest radiative transfer model
Summary
Forest canopy leaf area index (LAI) is a critical variable in modeling climates and ecosystems and is required by many terrestrial ecosystem models [1]. LAI of high accuracy will promote accurate modeling of energy, carbon, water, and climate [2,3]. Estimating forest structure potentially provides an ecologically significant advance over laborious ground-based estimation methods and has become widely used to characterize forest structure [4]. LAI is often retrieved from passive optical remote sensing, and there are two primary types of LAI inversion methods. The first is based on developing the empirical or semi-empirical relationship between the vegetation indices and the LAI, and the other is based on radiation transfer models using a look-up table (LUT) or a neural network (NN) [5], or an optimation method such as Levenberg–Marquardt algorithm [6].
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