**4.4 PRC**

*Lagoon Environments around the World - A Scientific Perspective*

ous throughout the area therefore being "stationary" data.

optimal sampling distances between data points, adding a geospatial component to regression analysis. The OLS regression is designed as a "Global Model" with an assumption that the explanatory and dependent variables have global trends over a particular study area. In simplified context, it is assumed that the data are continu-

For the OLS analysis, the Jarque-Bera statistic tests for model bias that can arise from nonstationary data, misspecification of independent variables, and skewed residuals [32]. Due to the positively skewed data for LDI and runoff depth values, a logarithmic transformation was applied to data to ensure a normal distribution of the datasets while making the variance independent of the mean. The Koenker's Studentized Breusch-Pagan (Koenker BP) statistic tests for nonstationary with a null hypothesis that the dependent and independent variables have a consistent relationship in geographic space, thus being stationary [33]. A rejected null hypothesis of this test indicates that there are local trends between the variables within the study area. Presence of significant spatial autocorrelation using the Global Moran's Index (Moran's I) is based on the assumption of stationary data. In this case there will be clustering of standard residuals from heteroscedasticity, thus indicating a local model such as GWR is more appropriate. Therefore, the standard residuals produced from both regression analyses were tested for significant clustering using the Moran's I test. On the other hand, a GWR is a "nonstationary" model that accounts for the local trends in relationships between the variables. In OLS analysis, LDI data from the FDEP and 11-year mean precipitation were used as the independent variable, and the 11-year mean runoff depth as dependent variable. The GWR only included the LDI as independent, and runoff depth as dependent variable due to collinear relationships with rainfall within clustered locations within the study area.

There are six LC/LU classes delineated from the supervised classification. The land that mostly consists of agriculture occurs in the southern section of the watershed. The overall accuracy of the LC/LU classification image was 0.82, and the lowest accuracies were in the impervious (User accuracy of 0.65) and bare soil (0.53) classes. This may have been due to the spectral similarity between bare sand along the coast and impervious surfaces such as roof tops. The reference points that appeared to exist in unhealthy brown vegetation were misclassified as bare soil. The kappa coefficient for the LC/LU image was 0.77, with an overall average of 0.82.

The slope for the study area was assigned a quantile classification to exclude the effects from outliers in the digital elevation map. The elevation in the state of Florida is relatively flat with an average slope of ~0.47 m per pixel. The areas of high percent slope are manmade structure such as buildings, walls, or homes in developed areas. Some manmade structures with unusually high slopes were identified as the outliers. The other cities have slopes ranging from 0.50 to 1.79 average percent.

The soil texture classification for central east Florida consists of mostly fine with Myakka Fine Sand as a native soil, covering more than 1.5 million acres of land, and

**126**

**4.3 Soil**

**4. Results**

**4.2 Slope**

**4.1 LC/LU classification**

The PRC values range from 3 to 100% (**Figure 2**). The PRCs are higher in runoff values in developed areas that are in close proximity to the coastal waterbodies of the IRL and Halifax River. The spatial resolution (10 m) of the image shows a detailed delineation of the manmade infrastructure within urban coastal communities such as roads, buildings, homes, and airports.
