Remote sensing provides high accuracy/precision for quantifying forest biophysical parameters needed for ecological management. Although the significant impact of bidirectional scattering distribution functions (BSDFs) on remote sensing of vegetation is well known, current forest metrics derived from sensor data seldom take leaf BSDF into account, and despite the importance of BSDF effects, leaf directional scattering measurements are almost nonexistent. Previous studies have been limited in the spectral coverage and resolution of observed electromagnetic radiation and lacked models to interpolate all source-sensor angles beyond measurements. This study captured deciduous broadleaf bidirectional reflectance distribution functions (BRDFs) from the visible through shortwave infrared spectral regions (350-2500 nm) and accurately modeled the BRDF for extension to any illumination angle, viewing zenith, or azimuthal angle. We measured biconical directional reflectance factor of leaves from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra). We then fit the data through nonlinear regression to physical, microfacet BRDF models, resulting in normalized root-mean-square errors of less than 8%, averaged across all wavelengths (excluding low signalto-noise spectral regions). We extracted leaf physical parameters, including the index of refraction and a relative physical roughness from the microfacet models delineating the three species. The implications for forestry remote sensing are important, as rigorous models to represent leaves allow for the creation of more accurate forest scenes for radiative transfer modeling. Such accuracy enables higher fidelity sensor evaluations and data processing algorithms.

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Establishing linkages between light detection and ranging (lidar) data, produced from interrogating forest canopies, to the highly complex forest structures, composition, and traits that such forests contain, remains an extremely difficult problem. Radiative transfer models have been developed to help solve this problem and test new sensor platforms in a virtual environment. Many forest canopy studies include the major assumption of isotropic (Lambertian) reflecting and transmitting leaves or non-transmitting leaves. Here, we study when these assumptions may be valid and evaluate their associated impacts/effects on the lidar waveform, as well as its dependence on wavelength, lidar footprint, view angle, and leaf angle distribution (LAD), by using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) remote sensing radiative transfer simulation model. The largest effects of Lambertian assumptions on the waveform are observed at visible wavelengths, small footprints, and oblique interrogation angles relative to the mean leaf angle. For example, a 77% increase in return signal was observed with a configuration of a 550 nm wavelength, 10 cm footprint, and 45° interrogation angle to planophile leaves. These effects are attributed to (i) the bidirectional scattering distribution function (BSDF) becoming almost purely specular in the visible, (ii) small footprints having fewer leaf angles to integrate over, and (iii) oblique angles causing diminished backscatter due to forward scattering. Non-transmitting leaf assumptions have the greatest error for large footprints at near-infrared (NIR) wavelengths. Regardless of leaf angle distribution, all simulations with non-transmitting leaves with a 5 m footprint and 1064 nm wavelength saw around a 15% reduction in return signal. We attribute the signal reduction to the increased multiscatter contribution for larger fields of view, and increased transmission at NIR wavelengths. Armed with the knowledge from this study, researchers will be able to select appropriate sensor configurations to account for or limit BSDF effects in forest lidar data.

The United States Air Force Academy (USAFA) operates the Falcon Telescope Network (FTN) to support its research program in the utility of satellite optical signatures in Space Situational Awareness. In addition to collecting photometric, spectroscopic, and polarimetric data, the FTN sensors which are equipped with diffraction grating elements also operate as slitless spectrographs. FTN spectroscopic data has been used to demonstrate that it can effectively distinguish different stable geosynchronous satellites (GEO). Because the attitude of the GEO's unarticulated parts (e.g. bus) and the axis of rotation of the articulated parts (e.g., solar panel) are predominantly fixed, the light curves and the time-resolved spectra are expected to be nearly repeatable from night to night. Furthermore, the spectra of GEOs may be effective identifying signatures. To demonstrate the ability to distinguish GEOs using spectroscopic data, we reduce the spectra to vectors of features with smaller dimensionality. That can be accomplished by applying a linear dimensionality-reduction technique, e.g., Principal Component Analysis (PCA) or using a physics-based transformation that consists of smoothing and under-sampling the spectra. The PCA features consists of up to the five most prominent principal components. The physics-based feature vector is the smoothed GEO spectral reflectance sampled at 37 fixed and equally spaced wavelengths. The first approach also generates a visualizable 2-dimensional representation using the first two PCA components, while the second approach preserves as much information as allowed by the effective spectrograph's resolution. Using satellite names or numbers as labels of the classes, we trained a number of classifiers with the GEO's feature vectors. Our analyses showed that multi-GEO classification can achieve accuracy as high as 98%. We also demonstrated that instead of collecting many spectra in the range of solar phase angles as training data, we can synthesize training spectra with a limited number of reference spectra and still achieve satisfactory classification accuracies.