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Texas Rio Grande Valley Red–crowned Parrots (Psittaciformes: Amazona viridigenalis [Cassin, 1853]) primarily occupy vegetated urban rather than natural areas. We investigated the utility of raw vegetation indices and their derivatives as well as elevation in modelling the Red–crowned parrot's general use, nest site, and roost site habitat distributions. A feature selection algorithm was employed to create and select an ensemble of fine–scale, top–ranked MaxEnt models from optimally–sized, decorrelated subsets of four to seven of 199 potential variables. Variables were ranked post hoc by frequency of appearance and mean permutation importance in top–ranked models. Our ensemble models accurately predicted the three distributions of interest (x¯ Area Under the Curve [AUC] = 0.904–0.969). Top–ranked variables for different habitat distribution models included: (a) general use–percent cover of preferred ranges of entropy texture of Normalized Difference Vegetation Index (NDVI) values, entropy and contrast textures of NDVI, and elevation; (b) nest site–entropy textures of NDVI and Green–Blue NDVI, and percent cover of preferred range of entropy texture of NDVI values; (c) roost site–percent cover of preferred ranges of entropy texture of NDVI values, contrast texture of NDVI, and entropy texture of Green–Red Normalized Difference Index. Texas Rio Grande Valley Red–crowned Parrot presence was associated with urban areas with high heterogeneity and randomness in the distribution of vegetation and/or its characteristics (e.g., arrangement, type, structure). Maintaining existing preferred vegetation types and incorporating them into new developments should support the persistence of Red–crowned Parrots in southern Texas. [ABSTRACT FROM AUTHOR]

A simple approach to enable water-management agencies employing free data to create a single set of water quality predictive equations with satisfactory accuracy is proposed. Multiple regression-derived equations based on surface reflectance, band ratios, and environmental factors as predictor variables for concentrations of Total Suspended Solids (TSS) and Total Nitrogen (TN) were derived using a hybrid forward-selection method that considers both p-value and Variance Inflation Factor (VIF) in the forward-selection process. Landsat TM, ETM+, and OLI/TIRS images were jointly utilized with environmental factors, such as wind speed and water surface temperature, to derive the single set of equations. Through splitting data into calibration and validation groups, the coefficients of determination are 0.73 for TSS calibration and 0.70 for TSS validation, respectively. The coefficients of determination for TN calibration and validation are 0.64 and 0.37, respectively. Among all chosen predictor variables, ratio of reflectance of visible red (Band 3 for Landsat TM and ETM+, or Band 4 for Landsat OLI/TIRS) to visible blue (Band 1 for Landsat TM and ETM+, or Band 2 for Landsat OLI/TIRS) has a strong influence on the predictive power for TSS retrieval. Environmental factors including wind speed, remote sensing-derived water surface temperature, and time difference (in days) between the image acquisition and water sampling were found to be important in water-quality quantity estimation. The hybrid forward-selection method consistently yielded higher validation accuracy than that of the conventional forward-selection approach. [ABSTRACT FROM AUTHOR]