Dynamic characterization of vegetation using remote sensing for hydrological modelling at basin scale

E. Carpintero; M. P. González-Dugo; M. J. Polo

doi:10.1117/12.2029168

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16 October 2013 Dynamic characterization of vegetation using remote sensing for hydrological modelling at basin scale

E. Carpintero, M. P. González-Dugo, M. J. Polo

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Proceedings Volume 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV; 88870V (2013) https://doi.org/10.1117/12.2029168
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

Spatio-temporal changes in vegetation at the basin scale are difficult to characterize, and remote sensing is a major source of data for this purpose. These sensors may provide distributed series of spectral properties of the vegetation with different spatial and temporal resolutions, but they do not always satisfy the requirements of some of the applications. These limitations can be overcome with the use of image integration techniques, which allow for the combination of sensors with different characteristics. This work presents the monitoring of the vegetation cover in the Guadalfeo River Basin (Spain), with a view to its hydrological modeling, by using Landsat-TM and MODIS data, analyzing the implications of the scale differences in an heterogeneous area. A preliminary study is carried out into the deviations of NDVI and ground cover fraction (fv) between the concurrent data of both sensors. Thereafter, the STARFM integration algorithm is applied and evaluated to obtain synthetic NDVI images at the spatial resolution of Landsat-TM data with MODIS time steps. The comparison between Landsat-TM and MODIS parameters revealed deviations on average between 2-5% for NDVI and 3-5% for fv. No direct relationship was found between these deviations and basin topography. However, higher deviations corresponded with the vegetation types with higher ground cover fractions and heterogeneous landuses (fv relative deviations of 10% and 6% for conifers and quercus-scrub, respectively) The STARFM algorithm improved the NDVI estimations when compared to the previous Landsat-TM date, with reductions in the average NDVI differences of around 0.02 on average for the six simulated dates, with the accuracy of the predictions depending on data input for the model and vegetation cover types.

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E. Carpintero, M. P. González-Dugo, and M. J. Polo "Dynamic characterization of vegetation using remote sensing for hydrological modelling at basin scale", Proc. SPIE 8887, Remote Sensing for Agriculture, Ecosystems, and Hydrology XV, 88870V (16 October 2013); https://doi.org/10.1117/12.2029168

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