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Proceedings Paper

Joint processing of Landsat ETM+ and ALOS-PALSAR data for species richness and forest biodiversity monitoring
Author(s): Sara Attarchi; Richard Gloaguen
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Paper Abstract

Optical remote sensing data is commonly used for estimating biophysical characteristics of forest like tree biodiversity and species richness. Recent advances in radar remote sensing technology raise significant interest to take advantage of the complementary nature of optical and radar data. This paper proposes an approach combining Landsat ETM+ and Advanced Land-Observing Satellite- Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) data for forest biodiversity and species richness monitoring. Inventory data from one part of the Hyrcanian forest in north of Iran is used as field data. Visible and infrared ETM+ bands, indices and textures information as well as HH, HV backscattering, polarimetric features (alpha angle, entropy and anisotropy) and SAR texture are extracted. We use the multiple linear regression model to find the best components to describe the biodiversity indices in the study area. We show how tree biodiversity is related to information derived from ETM+ and ALOS/PALSAR data at 95% confidence level (R!=0.63, root mean square error (RMSE) =1.70). Also, the effects of each component on the variation of biodiversity are shown. ETM+ reflectance, polarimetric features and texture from ALOS/PALSAR can describe approximately 63% of biodiversity. The results of multi source monitoring of tree biodiversity and species richness are promising and worth further investigation.

Paper Details

Date Published: 17 October 2013
PDF: 6 pages
Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920Y (17 October 2013); doi: 10.1117/12.2029328
Show Author Affiliations
Sara Attarchi, Technische Univ. Bergakademie Freiberg (Germany)
Richard Gloaguen, Technische Univ. Bergakademie Freiberg (Germany)
Helmholtz-Institut Freiberg für Ressourcentechnologie (Germany)


Published in SPIE Proceedings Vol. 8892:
Image and Signal Processing for Remote Sensing XIX
Lorenzo Bruzzone, Editor(s)

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