Brightness gradient-corrected hyperspectral image mosaics for fractional vegetation cover mapping in northern California

Sebastian Van Der Linden,Matthew Clark,Akpona Okujeni,Patrick Hostert,Sam Cooper,Clemens Jänicke

doi:10.1080/2150704x.2019.1670518

Sebastian Van Der Linden, Matthew Clark + Show 4 more

Open Access

https://doi.org/10.1080/2150704x.2019.1670518

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Abstract

ABSTRACTWe evaluated the effectiveness of different approaches to compensate for across-track brightness gradients within a hyperspectral image mosaic comprised of multiple flight lines in the San Francisco Bay Area. We calculated the spectral consistency of adjacent flight lines and conducted regression-based unmixing of woody- and non-woody vegetation fractions to assess the comparative benefits of the methods. Results showed that a class-wise empirical approach produced the most spectrally consistent, nearly seamless image mosaics and led to accurate vegetation fraction maps (mean absolute error = 12.6%). Overall, a class-wise empirical approach is recommended as a simple, flexible and transferable technique to compensate for brightness gradients over a global empirical approach, brightness normalization or continuum removal.

Highlights

Imaging spectroscopy, or hyperspectral remote sensing, is becoming increasingly viable for quantitative vegetation assessments
continuum removal (CONREM) shows the greatest increase in spectral consistency, followed by brightness normalization (BNORM), EMPCLASS, and EMPGLOB
We showed that a pre-classification into green vegetation (GV) and non-photosynthetically active vegetation (NPV) effectively supports brightness gradient correction and improves subsequent mapping

Summary

Introduction

Hyperspectral remote sensing, is becoming increasingly viable for quantitative vegetation assessments. Large area ecosystem analysis with hyperspectral data relies on mosaics from multiple airborne acquisitions acquired over the course of one or multiple days, due to a lack of functional spaceborne hyperspectral sensors. Changing sun-sensor geometries during this period often lead to distinct across-track brightness gradients within and between flight lines. These variations hinder pixel spectral comparisons within and among images, affect spectral ratios, complicate image mosaicking, and hamper the integration of lab- or field-based spectral libraries into image analysis (Beisl and Woodhouse 2004; Ben-Dor, Levin, and Saaroni 2001). We refer to Nicodemus et al (1977) for

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