alexa Fusion of Hyperspectral and L-Band SAR Data to Estimate Fractional Vegetation Cover in a Coastal California Scrub Community
ISSN: 2469-4134

Journal of Remote Sensing & GIS
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Research Article

Fusion of Hyperspectral and L-Band SAR Data to Estimate Fractional Vegetation Cover in a Coastal California Scrub Community

Shuang Li1, 2, Christopher Potter1*, Cyrus Hiatt3 and John Shupe3
1NASA Ames Research Centre Mail Stop 242-4, Moffett Field, CA 94035, USA
2Henan University, College of Environment and Planning, Kaifeng, Henan 475004, China
3California State University Monterey Bay, Seaside, CA, USA
Corresponding Author : Christopher Potter
NASA Ames Research Centre Mail Stop 242-4
Moffett Field, CA 94035, USA
Tel: 650-604-6164
Fax: 650-604-4680
E-mail: [email protected]
Received May 05, 2012; Accepted May 25, 2012; Published May 30, 2012
Citation: Li S, Potter C, Hiatt C, Shupe J (2012) Fusion of Hyperspectral and L-Band SAR Data to Estimate Fractional Vegetation Cover in a Coastal California Scrub Community. J Geophys Remote Sensing 1:104. doi:10.4172/2169-0049.1000104
Copyright: © 2012 Li S, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
 

Abstract

A study was carried out to investigate the utility of airborne hyperspectral and satellite L-band Synthetic Aperture Radar (SAR) data for estimating fractional coverages of herbaceous, coastal scrub, and bare ground cover types on the central California coast. Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery collected in September of 2008 and Phased Array L-band SAR (PALSAR) (HH- and HV-polarizations) captured in April and July of 2008 were combined for vegetation cover mapping. Hyperspectral features, computed as AVIRIS indices (NDVI, TCARI/OSAVI, and PRI), and textural information (energy, contrast, homogeneity, and fractal dimension) produced by L-band SAR were fused together to generate a new feature space. We used global Ordinary Least Squares (OLS) linear regression to integrate and decompose the new feature space for fractional vegetation mapping. Ground measurements of fractional cover were collected from plots located within the U.S. Forest Service’s Brazil Ranch study site for validation of the OLS model predictions. Significant linear relationships were found between fractional cover mapping from remote sensing and the ground-truth data. The estimation accuracy of fractional coverage mapping from remote sensing in terms of Root Mean Square Error (RMSE) was 17%, 12%, and 10%, for the herbaceous, coastal scrub, and bare ground covers, respectively. Decomposition results showed that textural information from L-band SAR strongly supported herbaceous and coastal scrub fractional mapping, while indices features from AVIRIS significantly improved mapping of herbaceous cover and bare ground.

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