Estimating FPAR of maize canopy using airborne discrete-return LiDAR data

Abstract

The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize.

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

“…Findings of previous studies indicated that the accuracy of LiDAR-derived DTM is largely determined by: (1) vegetation canopy structure characteristics [33]; (2) terrain, mainly including slope and terrain irregularities; and (3) sensor characteristics, such as laser point density. Thus, the accuracy of LiDAR-derived DTM generally showed significant difference in different environments [31,37].…”

“…First, dense vegetation limits laser penetration to the ground, which may be lower the estimation accuracy of vegetation biophysical variables. Second, the discrete-return LiDAR data provides limited information about the vertical vegetation structure because it only records one return for each emitted pulse in areas with short vegetation [31][32][33]. Finally, airborne LiDAR data cannot provide spectral characteristics of vegetation canopy because they work with a single-wavelength.…”

Airborne LiDAR technique for estimating biomass components of maize: A case study in Zhangye City, Northwest China

“…Raw LiDAR data were converted to LAS binary format files by recording the geographical coordinates, intensity, and number of returns. The raw LiDAR data were first filtered by removing outliers that were extremely higher or lower than other points and were easily found in the air or below ground (Luo et al, 2014). Point clouds were then classified into ground and off-ground returns in TerraScan software (TerraSolid, Ltd., Finland).…”

“…The extinction coefficient k was set to 0.5 by assuming that the foliage angle distribution was spherical, and LiDAR conducted approximately vertical scanning (Luo et al, 2014;Richardson et al, 2009;Solberg et al, 2009). According to Richardson et al (2009), the theoretical k value (equal to 0.5) is adequate for estimating LAI of vegetation using LiDAR data after investigating the relationship between the field-measured LAI and intensity ratio.…”

Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

“…LiDAR intensity values are the amount of energy backscattered from features to the LiDAR sensor; these values are increasingly used [54][55][56][57]. The LiDAR intensity is closely related to laser power, incidence angle, object reflectivity and range of the LiDAR sensor to the object [43].…”