Date of Award
Doctor of Philosophy
Geological and Environmental Sciences
Dr. Mohamed Sultan
Dr. Alan E. Kehew
Dr. Matt Reeves
Dr. Richard Becker
early warning system, remote sensing, landslides, harmful algal blooms, intensity-duration, threshold
This study focused on developing early warning systems for two types of geohazards using methods that heavily rely on remote sensing data. The first investigation attempted to develop a prototype version of an early warning system for landslide development, whereas the second focused on harmful algal bloom prediction.
Construction of intensity-duration (ID) thresholds, and early warning and nowcasting systems for landslides (EWNSL) are hampered by the paucity of temporal and spatial archival data. This work represents significant steps towards the development of prototype EWNSL to forecast and nowcast landslides over Faifa Mountains in the Red Sea Hills. The developed methodologies rely on temporal, readily available, archival Google Earth and Sentinel-1A imagery, precipitation measurements, and limited field data to construct an ID threshold for Faifa. Adopted procedures entailed the generation of an ID threshold to identify the intensity and duration of precipitation events that cause landslides in the Faifa Mountains, and the generation of pixel-based ID curves to identify locations where movement is likely to occur. Spectral and morphologic variations in temporal Google Earth imagery following precipitation events were used to identify landslide-producing storms and to generate the Faifa ID threshold (I = 4.89*D−0.65). Backscatter coefficient variations in radar imagery were used to generate pixel-based ID curves and to identify locations where mass movements are likely to occur following landslide-producing storms. These methodologies accurately distinguished landslide-producing storms from non–landslide producing ones and identified the locations of these landslides with an accuracy of 60%.
Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. I developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. I constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the historical events. The models were designed to capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web map. Our findings include: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, sea surface temperature (SST), ocean chlorophyll three-band algorithm for MODIS [chlorophyll-a OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable at a local scale, dependent on readily available remote sensing data, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.
Karki, Sita, "Developing Early Warning Systems for Debris Flows and Harmful Algal Blooms" (2019). Dissertations. 3417.