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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

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Nonlin. Processes Geophys., 24, 141-155, 2017
https://doi.org/10.5194/npg-24-141-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
16 Mar 2017
Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis
Carmelo Alonso1,2, Ana M. Tarquis2,3, Ignacio Zúñiga4, and Rosa M. Benito2 1Earth Observation Systems, Indra Sistemas S.A., Madrid, Spain
2Grupo de Sistemas Complejos, U.P.M, Madrid, Spain
3CEIGRAM, E.T.S.I.A.A.B., U.P.M, Madrid, Spain
4Dpt. Física Fundamental, Facultad de Ciencias, Universidad Nacional de Educación a Distancia (UNED), Madrid, Spain
Abstract. Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery.

In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).

In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution.

From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.


Citation: Alonso, C., Tarquis, A. M., Zúñiga, I., and Benito, R. M.: Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis, Nonlin. Processes Geophys., 24, 141-155, https://doi.org/10.5194/npg-24-141-2017, 2017.
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Short summary
NDVI and EVI vegetation indexes, estimated from satellite images, can been used to estimate root zone soil moisture. However, depending on the spatial and radiometric resolution of the sensors used, estimations could change. In this work, images taken by satellites IKONOS-2 and LANDSAT-7 of the same location are compared on the four bands involved in these vegetation indexes. The results show that spatial resolution has a similar scaling effect in the four bands, but not radiometric resolution.
NDVI and EVI vegetation indexes, estimated from satellite images, can been used to estimate root...
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