Share Email Print
cover

Proceedings Paper

Towards an improved understanding of the influence of subpixel vegetation structure on pixel-level spectra: a simulation approach
Author(s): Wei Yao; Martin van Leeuwen; Paul Romanczyk; Dave Kelbe; Mingming Wang; Scott D. Brown; Adam A. Goodenough; Jan van Aardt
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The planned NASA Hyperspectral Infrared Imager (HyspIRI) mission, equipped with an imaging spectrometer that has the capability of monitoring ecosystems globally, will provide an unprecedented opportunity to address scientific challenges related to ecosystem function and change. However, uncertainty remains around the impact of subpixel vegetation structure, in combination with the point spread function, on pixel-level imaging spectroscopy data. We estimated structural parameters, e.g., leaf area index (LAI), canopy cover, and tree location, from HyspIRI spectral data, with the goal of assessing how subpixel variation in these parameters impact pixel-level imaging spectroscopy data. The fine-scale variability of real vegetation structure makes this a challenging endeavor. Therefore, we utilized a simulation-based approach to counter the time-consuming and often destructive sampling needs of vegetation structural analysis and to simultaneously generate synthetic HyspIRI data pre-launch. Three virtual scenes were constructed, corresponding to the actual vegetation structure in the National Ecological Observatory Network’s (NEON) Pacific Southwest Domain (Fresno, CA). These included an oak savanna, a dense coniferous forest, and a conifer-manzanita-mixed forest. Simulated spectroscopy data for these scenes were then generated using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model. Simulations first were used to verify the physical model, virtual scene geometrical information, and simulation parameters. This was followed by simulations of HyspIRI data, where within-pixel structural variability was introduced, e.g., by iteratively changing per-pixel canopy cover and tree placement, tree clustering, leaf area index (LAI), etc., between simulation runs for the virtual scenes. Finally, narrow-band vegetation indices (VIs) were extracted from the data in an attempt to describe the variability of the subpixel structural parameters; this was done in order to assess VI robustness to changes in structural “levels”, as well as placement of trees/canopies within the instrument’s instantaneous field-of-view (IFOV). Our ultimate goal is not only to better understand how such subpixel variability influence imaging spectroscopy outputs, but also to better estimate vegetation structural parameters using spectra. We constructed regression models for LAI (R2 = 0.92) and canopy cover (R2 = 0.97) with narrow-band VIs via this simulation approach. Our models ultimately are intended to improve the HyspIRI mission’s ability to monitor global vegetation structure.

Paper Details

Date Published: 17 May 2016
PDF: 9 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401H (17 May 2016); doi: 10.1117/12.2224141
Show Author Affiliations
Wei Yao, Rochester Institute of Technology (United States)
Martin van Leeuwen, Univ. College London (United Kingdom)
Paul Romanczyk, Rochester Institute of Technology (United States)
Dave Kelbe, Rochester Institute of Technology (United States)
Mingming Wang, Rochester Institute of Technology (United States)
Scott D. Brown, Rochester Institute of Technology (United States)
Adam A. Goodenough, Rochester Institute of Technology (United States)
Jan van Aardt, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

© SPIE. Terms of Use
Back to Top