This study implemented a GIS-based analysis of local climate zones (LCZ) in Denton, TX with data sets from 2009, 2011, 2015, and 2016. The LCZ scheme enables evaluation of distinct regions' thermal characteristics with greater granularity than conventional urban-rural dichotomies. Further, the GIS-based approach to LCZ mapping allows use of high-resolution lidar data, the availability of which for the study area enabled estimation of geometric and surface cover parameters: height of roughness elements, sky-view factor, and building surface fraction. Pervious surface fraction was estimated from National Landcover Database impervious imagery. A regular grid was used to estimate per-cell mean values for each parameter, and with a decision-making algorithm (if/then statements) two maps were produced (2011 and 2015) and six LCZ identified in each: LCZ 6 (open low-rise), LCZ 8 (large low-rise), LCZ 9 (sparsely built), LCZ A (dense trees), LCZ B (scattered trees), and LCZ C (bush/scrub). Post-processing was carried out to ensure identified zones met the spatial minimum for qualification as LCZ. Landsat Collection 2 Level 2 surface temperature products from various seasons of 2011 and 2015 were acquired to examine LCZ thermal differentiability, and preliminary surface urban heat island intensity values were estimated. Particular attention was afforded to …

This research project focuses on using a multiproxy approach to discriminate between overwash and non-hurricane marsh sediments within the bed of a coastal lake. 3 marsh cores were collected in an area of McFaddin National Wildlife Refuge just south of Clam Lake that are known to contain 4 hurricane overwash deposits, Ike, Rita, Carla, and Audrey. LOI and XRF analysis were used to determine the signature of the hurricane overwash layers. 3 more cores were collected from Clam Lake where there are no visible sand layers. The elemental signature of the overwash layers found in the marsh cores was used to run a hierarchical cluster analysis on the lake cores. This was able to determine the effectiveness of XRF's ability to distinguish between hurricane overwash and marsh sediments. The combination of cluster analysis, LOI, and XRF can tentatively identify hurricane overwash deposits in a coastal lake, however, it is more successful in the marsh cores. Results in the lake cores are somewhat inconsistent and uncertain, possibly because there may have not been enough overwash deposits to identity or that the XRF analysis needs more distinct sand layers to distinguish between overwash and marsh.