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J Environ Manage. 2009 May;90(7):2219-25. doi: 10.1016/j.jenvman.2007.08.021. Epub 2008 Apr 18.

Predictive models of turbidity and water depth in the Doñana marshes using Landsat TM and ETM+ images.

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1
Laboratorio de Sig y Teledetección (LAST-EBD), Estación Biológica de Doñana, Consejo Superior de Investigaciones Cientificas, Avda. María Luisa s/n, Seville, Spain. jbustamante@ebd.csic.es

Abstract

We have used Landsat-5 TM and Landsat-7 ETM+ images together with simultaneous ground-truth data at sample points in the Doñana marshes to predict water turbidity and depth from band reflectance using Generalized Additive Models. We have point samples for 12 different dates simultaneous with 7 Landsat-5 and 5 Landsat-7 overpasses. The best model for water turbidity in the marsh explained 38% of variance in ground-truth data and included as predictors band 3 (630-690 nm), band 5 (1550-1750 nm) and the ratio between bands 1 (450-520 nm) and 4 (760-900 nm). Water turbidity is easier to predict for water bodies like the Guadalquivir River and artificial ponds that are deep and not affected by bottom soil reflectance and aquatic vegetation. For the latter, a simple model using band 3 reflectance explains 78.6% of the variance. Water depth is easier to predict than turbidity. The best model for water depth in the marsh explains 78% of the variance and includes as predictors band 1, band 5, the ratio between band 2 (520-600 nm) and band 4, and bottom soil reflectance in band 4 in September, when the marsh is dry. The water turbidity and water depth models have been developed in order to reconstruct historical changes in Doñana wetlands during the last 30 years using the Landsat satellite images time series.

PMID:
18395320
DOI:
10.1016/j.jenvman.2007.08.021
[Indexed for MEDLINE]
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