Consistent and scalable estimation of vegetation structural parameters from imaging spectroscopy is essential to remote sensing for ecosystem studies, with applications to a wide range of biophysical assessments. NASA has proposed the Hyperspectral Infrared Imager (HyspIRI) imaging spectrometer, which measures the radiance between 380-2500 nm in 10 nm contiguous bands with 60 m ground sample distance (GSD), in support of global vegetation assessment. However, because of the large pixel size on the ground, there is uncertainty as to the effects of sub-pixel vegetation structure on observed radiance. The purpose of this research was to evaluate the link between vegetation structure and imaging spectroscopy spectra. Specifically, the goal was to assess the impact of sub-pixel vegetation density and position, i.e., structural variability, on large-footprint spectral radiances. To achieve this objective, three virtual forest scenes were constructed, corresponding to the actual vegetation structure of the National Ecological Observatory Network (NEON) Pacific Southwest domain (PSW; D17; Fresno, CA). These scenes were used to simulate anticipated HyspIRI data (60 m GSD) using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, a physics-driven synthetic image generation model developed by the Rochester Institute of Technology (RIT). Airborne Visible / Infrared Imaging Spectrometer (AVIRIS) and NEON's high-resolution imaging spectrometer (NIS) data were used to verify the geometric parameters and physical models. Multiple simulated HyspIRI data sets were generated by varying within-pixel structural variables, such as forest density, tree position, and distribution of trees, in order to assess the impact of sub-pixel structural variation on the observed HyspIRI data. As part of the effort, a partial least squares (PLS) regression model, along with narrow-band vegetation indices (VIs), were used to characterize the sub-pixel vegetation structure from simulated HyspIRI-like spectroscopy data-like. These simulations were extended to quantitative assessments of within-pixel impact on pixel-level spectral response.
The correlation coefficients (R^2) of leaf area index-to-normalized difference vegetation index (LAI-NDVI), canopy cover-to-vegetation index (VI), and PLS models were 0.92, 0.98, and 0.99, respectively. Results of the research have shown that HyspIRI is sensitive to sub-pixel vegetation density variation in the visible to short-wavelength infrared spectrum, due to vegetation structural changes, and associated pigment and water content variation. These findings have implications for improving the system's suitability for consistent global vegetation structural assessments by adapting calibration strategies to account for this sub-pixel variation.
Library of Congress Subject Headings
Hyperspectral imaging--Evaluation; Vegetation mapping--Remote sensing
Imaging Science (Ph.D.)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Jan van Aardt
Yao, Wei, "Investigating the impact of spatially-explicit sub-pixel structural variation on the assessment of vegetation structure from imaging spectroscopy data" (2017). Thesis. Rochester Institute of Technology. Accessed from
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