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Proceedings Paper

Exploring the possibilities of a vegetation index (GESAVI) from remotely sensed data
Author(s): Beatriz Martinez; F. Camacho-de Coca; Joan Garcia-Haro; M. A. Gilabert
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Paper Abstract

In the field of remote sensing applications, more than 40 vegetation indices have been developed in recent years with the aim of minimizing the influence of internal and external factors (such as soil properties and atmosphere) which can affect the radiometric response of vegetation canopies. However, although those indices have showed good performances from laboratory and simulated data, most of them are difficult to be implemented from satellite data because of their complex definition that frequently requires the knowledge of different parameters besides the reflectance itself. That is the case of the generalized soil-adjusted vegetation index (GESAVI). The GESAVI was developed on the basis of a simple vegetation canopy model. It is defined in terms of the near-infrared NIR and red R reflectances and the soil line parameters (A and B) as: GESAVI = (NIR-BR-A)/(R+Z), where Z is related to red reflectance at the cross point between the soil line and the vegetation isolines in the NIRJR plane. This new index showed a better normalization of soil background effects when compared to the traditional NDVI using different reflectance data sets (acquired under laboratory conditions as well as by means of a simulation procedure). At present, a methodology is proposed to implement the GESAVI from satellite data. We will focus our attention mainly on semiarid landscapes, where the perturbance introduced by soil optical properties is very important. It would be desirable that the application of this new vegetation index to satellite images would require only information contained in the image itself. This is the main goal of the present research. Results show that GESAVI can be easily obtained from NDVI.

Paper Details

Date Published: 23 January 2001
PDF: 9 pages
Proc. SPIE 4171, Remote Sensing for Agriculture, Ecosystems, and Hydrology II, (23 January 2001); doi: 10.1117/12.413935
Show Author Affiliations
Beatriz Martinez, Univ. de Valencia (Spain)
F. Camacho-de Coca, Univ. de Valencia (Spain)
Joan Garcia-Haro, Univ. de Valencia (Spain)
M. A. Gilabert, Univ. de Valencia (Spain)


Published in SPIE Proceedings Vol. 4171:
Remote Sensing for Agriculture, Ecosystems, and Hydrology II
Manfred Owe; Guido D'Urso; Eugenio Zilioli, Editor(s)

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