No abstract

Abstract:In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non-mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category OPEN ACCESSRemote Sens. 2011, 3 1569 obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.

We reviewed global P export and its controlling factors from 685 world rivers. We used available continuous (runoff, rainfall, catchment area, % land use, and population density) and discrete (runoff type, soil type, biome, dominant land use, dominant type of forest, occurrence of stagnant water bodies in catchment, and Gross Product per Capita [GPC]) variables to predict export of P fractions. P export (kg P km 22 y 21 ) spanned 6 orders of magnitude worldwide. The distribution of all fractions of P export (total P [TP], soluble reactive P [SRP], and nonSRP [dissolved organic and particle-bound P]) was right skewed. Export of nonSRP had the highest coefficient of variability, and nonSRP was the dominant part of export. The available environmental variables predicted global P export fairly well (R 2 = 0.73) if total N export was included in calculations. The unexplained variance in P export might be attributed to noise in the data set, inaccuracy of measurements of environmental variables at fine scales, lack of quantitative data on anthropogenic P sources, insufficient knowledge of P behavior in catchment soils, and nonlinearity of controlling processes. P exports were highly variable among catchment types, and runoff and population density were the predictors shared by most models. P export appeared to be controlled by different sets of environmental variables in different types of catchments. Quasi-empirical, mechanistic models of P export performed better than did empirical models. Our mechanistic understanding of P export could be improved by refining current analytical methods to obtain fast and reliable values of all P fractions in aquatic ecosystems and by incorporating better and more detailed data on catchment features, anthropogenic sources of P, and instream variables in a mechanistic modelling framework.