Tracing the source of groundwater for three different coastal peatlands along Lake Superior

The goal of this project was to investigate the influence of a large inland lake on adjacent coastal freshwater peatlands. The specific aim was to determine the source of groundwater for three differently formed peatlands located on the southern shore of Lake Superior. The groundwater study was conducted at Bete Grise, a peatland complex in a dune-swale system; Pequaming, a peatland developed in the swale of a tombolo; and Lightfoot Bay, a peatland developed in a barrier beach wetland complex. To determine the source of groundwater in the peatlands, transects of six groundwater monitoring wells were established at each study site, covering distinctly different vegetation zones. At Pequaming and Lightfoot Bay the transects monitored two vegetation zones: transition zone from upland and open fen. At Bete Grise, the transects monitored dunes and swales. Additionally, at all three sites, upland groundwater was monitored using three wells that were installed into the adjacent upland forest. Biweekly measurements of well water pH and specific conductance were carried out from May to October of 2010. At each site, vegetation cover, peat depths and surface elevations were determined and compared to Lake Superior water levels. From June 14 – 17, July 20 – 21 and September 10 – 12, stable isotopes of oxygen (18O/16O) ratios were measured in all the wells and for Lake Superior water. A mixing model was used to estimate the percentage of lake water influencing each site based on the oxygen isotope ratios. During the sampling period, groundwater at all three sites was supported primarily by upland groundwater. Pequaming was approximately 80 % upland groundwater supported and up to 20 % Lake water supported in the uppermost 1 m layer of peat column of the transition zone and open fen. Bete Grise and Lightfoot Bay were 100 % upland groundwater supported throughout the season. The height of Lake Superior was near typical levels in 2010. In years when the lake level is higher, Lake water could intrude into the adjacent peatlands. However, under typical hydrologic conditions, these coastal peatlands are primarily supported by upland groundwater.

Evaluation of surface defect detection in reinforced concrete bridge decks using terrestrial LiDAR

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.