Modeling Photosynthesis of \u3ci\u3eSpartina alterniflora\u3c/i\u3e (Smooth Cordgrass) Impacted by the Deepwater Horizon Oil Spill Using Bayesian Inference

To study the impact of the Deepwater Horizon oil spill on photosynthesis of coastal salt marsh plants in Mississippi, we developed a hierarchical Bayesian (HB) model based on field measurements collected from July 2010 to November 2011. We sampled three locations in Davis Bayou, Mississippi (30.375 degrees N, 88.790 degrees W) representative of a range of oil spill impacts. Measured photosynthesis was negative (respiration only) at the heavily oiled location in July 2010 only, and rates started to increase by August 2010. Photosynthesis at the medium oiling location was lower than at the control location in July 2010 and it continued to decrease in September 2010. During winter 2010-2011, the contrast between the control and the two impacted locations was not as obvious as in the growing season of 2010. Photosynthesis increased through spring 2011 at the three locations and decreased starting with October at the control location and a month earlier (September) at the impacted locations. Using the field data, we developed an HB model. The model simulations agreed well with the measured photosynthesis, capturing most of the variability of the measured data. On the basis of the posteriors of the parameters, we found that air temperature and photosynthetic active radiation positively influenced photosynthesis whereas the leaf stress level negatively affected photosynthesis. The photosynthesis rates at the heavily impacted location had recovered to the status of the control location about 140 days after the initial impact, while the impact at the medium impact location was never severe enough to make photosynthesis significantly lower than that at the control location over the study period. The uncertainty in modeling photosynthesis rates mainly came from the individual and micro-site scales, and to a lesser extent from the leaf scale

Comparisons of Regression Tree Models For Sub-Pixel Imperviousness Estimation In a Gulf Coast City of Mississippi, USA

We studied the impact of shaded impervious surface area (ISA), atmospheric correction, and seasonal sensitivity, which have been generally ignored in previous studies, on ISA estimation at the sub-pixel scale using regression tree modelling. The study area is Pascagoula City on the Mississippi Gulf Coast, USA. Results showed that inclusion of shaded ISA as the response variable improved the model performance by reducing average error (AE) from 10.17 to 9.36%. Modelling with model-based atmospherically corrected imagery as predictors further reduced AE to 9.27%. The regression tree model using summer imagery as predictors (summer model) finally improved AE to 8.56%, compared with 9.28%, 9.50%, and 8.80% when using early spring, late spring, and autumn images as predictors, respectively; therefore the summer model was considered the optimal model. It was further applied to other seasonal images (i.e. early spring, late spring, and autumn images, as predictors) and the AE was 9.93%, 10.09%, and 9.12%, respectively, showing low seasonal sensitivity within this region. The findings in our study improved the modelling accuracy and expanded the scope of its future application in ISA estimation. © 2014 Taylor & Francis

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Programska potpora za odlučivanje koja koristi metodu Electre pomoću koje će korisnik preko grafičkog korisničkog sučelja moći na temelju određenih kriterija doći do konačne odluke. Podaci za domenu problema se nalazi u bazi podataka