Remote characterization of fuel types using multi- and hyper-spectral data

Rosa Lasaponara; Antonio Lanorte

doi:10.1117/12.683088

17 October 2006 Remote characterization of fuel types using multi- and hyper-spectral data

Rosa Lasaponara, Antonio Lanorte

Proceedings Volume 6359, Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII; 635914 (2006) https://doi.org/10.1117/12.683088
Event: SPIE Remote Sensing, 2006, Stockholm, Sweden

Abstract

This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as, hyperspectral (Multispectral Infrared and Visible Imaging Spectrometer) MIVIS and Landsat- Temathic Mapper (TM) acquired in 1998 were analysed for a test area of Southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, were used to assess the results obtained for the considered test areas.. The method comprised the following three steps: (I) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (II) model construction for the spectral characterization and mapping of fuel types; (III) accuracy assessment for the performance evaluation based on the comparison of satellite-based results with ground-truth. Two different approaches have been adopted for fuel type mapping: the well-established classification techniques and spectral mixture analysis. Results from preliminary analysis have showed that the use of unmixing techniques allows an increase in accuracy at around 7% compared to the accuracy level obtained by applying a widely used classification algorithm.

Citation Download Citation

Rosa Lasaponara and Antonio Lanorte "Remote characterization of fuel types using multi- and hyper-spectral data", Proc. SPIE 6359, Remote Sensing for Agriculture, Ecosystems, and Hydrology VIII, 635914 (17 October 2006); https://doi.org/10.1117/12.683088

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