Dr. Johan Liebens

In response to increased awareness of the negative impacts of fluvial erosion on natural resources and infrastructure, efforts to understand channel sediment sources have increased so cost-effective mitigation may be achieved. Attempts to quantify the important contribution of streambank erosion to channel sediment loading have been made with modeling and in-situ measurement approaches. Many of these efforts involve time-consuming or technologically advanced techniques while practitioners often need a reliable and rapid assessment method to quantify local streambank erosion. One such method, the Bank Assessment of Non-Point Source Consequences of Sediment (BANCS) model, predicts average annual streambank erosion from field estimates of near-bank stress (NBS) and bank erodibility (BEHI) and observed erosion rates resulting from bankfull events. The model provides a means to contextually transpose observed erosion rates of streambanks to predict erosion rates of other local streambanks with similar characteristics. Given the empirical nature of BANCS, its BEHI category-based predictive models have to be established for every physiographic region to account for local environmental controls. Our study examined the applicability of the BANCS model to an area along the north coast of the Gulf of Mexico dominated by sandy soils and a warm and wet climate with frequent high-intensity precipitation events. Bank erosion incurred at 18 study sites located in 10 different stream reaches was monitored for one year and morphometric variables were recorded. Bankfull flow events, which are the critical erosion-causing events in the BANCS model, were identified through the use of constructed stage gages. Bank erosion associated with individual bankfull events was between 0.01 m and 0.12 m, but some sites experienced more than one bankfull event and some reached flood stage. The mean total annual erosion rate for all study sites was 0.054 m/yr. Based on the BANCS model, total annual erosion rates showed moderate to strong relationships with NBS for High and Very High BEHI categories. As the values for BEHI and NBS increased between sites, so did the measured erosion rates. During this study, BEHI appeared a stronger predictor of bank erosion than NBS and further evaluation of the application of the various NBS methods is needed for the study area.

This paper presents methodology using dasymetric mapping from remotely sensed imagery, geographic information system (GIS), spatial analysis and spatial statistics to explore relationship between asthma and air pollution in the Pensacola metropolitan region of Florida. Health outcome indicators thought to be sensitive to increased exposure of airborne environmental hazards are mortality and morbidity rates for total population asthma patients. Environmental data for the time around the year 1999 include point source pollution sites and emissions, traffic count with emission estimates, and a Landsat ETM+ image. Standardized mortality/morbility ratios (SMRs) were used as dependent variables for the analysis. A centroid map was created from the zip code map with each centroid assigned the corresponding SMR values. Then spatial interpolation using the Kriging method was used to generate continuous SMR surfaces. An emission or point count based kernel density raster map was created from each of the air pollution maps. A raster layer ‘greenness’ was extracted using tasseled cap transformation from the Landsat ETM+ image. The dasymetric mapping technique was employed to limit the analysis and modeling to the area where human activities occur. The ETM+ image was classified into a thematic land use/cover map and the developed area extracted. A road network was combined with the developed area to generate a buffer (buffer distance = 1.5 km). A random sample with enough number of points was generated across the study area and 505 points were found within the developed area and the buffer. Data values at these sample points were extracted and used for statistical modeling. Two spatial autoregressive models (spatial error and spatial lag) were fitted. Both models show relationship between the asthmas outcome indicators and air pollution (positive) and ‘greenness’ (negative).