A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

Abstract

Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu’er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., >750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.

Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data

“…The convergent pattern displayed in our case after BRDF correction to reflectance values with various sensor viewing angles is consistent with previous studies [48], [102], indicating the BRDF correction is able to produce reflectance data that are comparable at various viewing angles. It is worth noting that, compared to the diurnal data samples, after BRDF correction, the change of STD of reflectance values derived from pixels at different viewing angles is less apparent (Figs.…”

“…A variety of kernel functions have been developed in previous studies. For geometric-optical scattering kernel (K geo ) [45], Li Sparse-Reciprocal kernel (LSRK) that assumes sparse canopy, Li-Dense Reciprocal kernel (LDRK) for a dense canopy [51], which often provides superior performance in the case of high solar and/or view zenith angles [48], [52], as well as sparse and dense transition Li Transit-Reciprocal kernel (LTRK) [53] were developed in previous studies. The kernels can be derived as follows [47], [51], [54]: The values for the parameters h/b and b/r (object shape and height) were determined after iterative testing [49].…”

Bidirectional Reflectance Distribution Function (Brdf) of Mixed Pixels

“…The notations vol k , geo k , vol f , and geo f indicate the volumetric and geometric kernels and their weight coefficients, respectively, and iso f is a scalar represents the isotropic reflectance. To create a semi-empirical model that fits for cases with high solar and(/or) view angles (Jia et al, 2020), we use the hotspot-revised Ross-Thick-Maignan (RTM) (Maignan et al, 2004) volumetric kernel and Li-Transit-Reciprocal (LTR) geometric kernel (Li et al, 1999;. The computation of the RTM and LTR kernels is given by:…”

“…A least-squares solution of this equation system provides an estimate of the coefficients , , iso vol geo f f f . We use these coefficients to calculate the anisotropy factor (Jia et al, 2020) for each measurement. The anisotropic factor describes the ratio between the directional reflectance and a reference reflectance.…”

About the Transferability of Topographic Correction Methods From Spaceborne to Airborne Optical Data

“…Such developments have been published in the past to (i) map forest heterogeneity [35]- [37], (ii) characterise various surface scattering types (i.e., land cover types) [38], [39], and (iii) correct BRDF effects in forest canopies [40]. For alpine landscapes, a respective BRDF model has to be adapted to the predominant land cover types.…”