Vegetation and hydrologic influences on carbon and nitrogen in subsurface water of a forested riparian wetland

Vegetation communities affect carbon and nitrogen dynamics in the subsurface water of mineral wetlands through the quality of their litter, their uptake of nutrients, root exudation and their effects on redox potential. However, vegetation influence on subsurface nutrient dynamics is often overshadowed by the influences of hydrology, soils and geology on nutrient dynamics. The effects of vegetation communities on carbon and nitrogen dynamics are important to consider when managing land that may change vegetation type or quantity so that wetland ecosystem functions can be retained. This study was established to determine the magnitude of the influences and interaction of vegetation cover and hydrology, in the form of water table fluctuations, on carbon and nitrogen dynamics in a northern forested riparian wetland. Dissolved organic carbon (DOC), dissolved inorganic carbon (DIC), nitrate (NO3-) and ammonium (NH4+) concentrations were collected from a piezometer network in four different vegetation communities and were found to show complex responses to vegetation cover and water table fluctuations. Dissolved organic carbon, DIC, NO3- and NH4+ concentrations were influenced by forest vegetation cover. Both NO3- and NH4+ were also influenced by water table fluctuations. However, for DOC and NH4+ concentrations there appeared to be more complex interactions than were measured by this study. The results of canonical correspondence analysis (CCA) and analysis of variance (ANOVA) did not correspond in relationship to the significance of vegetation communities. Dissolved inorganic carbon was influenced by an interaction between vegetation cover and water table fluctuations. More hydrological information is needed to make stronger conclusions about the relationship between vegetation and hydrology in controlling carbon and nitrogen dynamics in a forested riparian wetland.

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.