Geographic information systems

This thesis presents the development of an innovative Geographic Information System (GIS)-based Interactive Online Watershed Dashboard aimed at flood risk assessment and mitigation in Charlotte County, Florida. The research leverages advanced GIS techniques, including flood inundation simulations using CASCADE 2001, integrating LiDAR DEM data and GIS layers such as impervious surfaces, waterbodies, and soil characteristics to model flood behavior in 61 inundation probability scenarios. Key results include detailed flood inundation probability maps categorizing risk levels based on Z-scores, providing actionable insights for flood risk management and emergency planning. Spatial analysis reveals demographic vulnerabilities, with population density and ethnic compositions intersecting flood vulnerability. The study assesses flood impacts on transportation infrastructure and prioritizes critical facilities for resilience strategies. The dashboard's design integrates diverse datasets and analytical results, allowing users to interactively explore flood risk scenarios, critical infrastructure vulnerabilities, and demographic impacts. This research contributes essential tools for informed decision-making, enhancing flood resilience and disaster preparedness in Charlotte County, Florida.

Accurate information about built-up land cover and population density is
essential for sustainable urban growth, especially in lesser developed countries.
Unfortunately, this data is often too expensive for planning agencies, prompting use
of outdated and unreliable information. As a proxy for estimating population density,
a linear regression model is proposed to test the relationship between the percentage
of built-up land cover and vegetation in Pucallpa, Peru. Expert knowledge, low-cost
moderate-resolution sate llite imagery, and high-resolution Google Earth images are
used to estimate the percentage of built-up land cover at randomly assigned reference
locations. Normalized Difference Vegetation Index (NDVI) data, acquired at each
reference point, is the independent variable in a linear regression model constructed
to predict the percentage of built-up land cover. The results were successful, with an
adjusted R2 = 0.774 at 95% confidence. Strength and accuracy are further evaluated
against zoning maps and population estimates provided by local authorities.

Peatlands act as carbon sinks while representing major sources of biogenic gases
such as methane (CH4) and carbon dioxide (CO2), two potent greenhouse gases. Gas
production and release in these peats soils are also influenced by overall warm
temperatures and water table fluctuations due to the naturally shallow water table in the
Florida Everglades. Releases of biogenic gases from Florida Everglades peat soils are not
well understood and the temporal distribution and dynamics are uncertain. The general
objective of this work was geared towards a methodological approach which aimed to
examine the feasibility of capacitance moisture probes to investigate biogenic gas
dynamics in various Florida Everglades peat soils at high temporal resolution. This work
has implications for establishing capacitance moisture probes as a method to monitor gas
dynamics in peat soils at high temporal resolution and better understanding patterns of
gas build-up and release from peat soils in the Everglades.

Human activities in the past century have caused a variety of environmental
problems in South Florida. In 2000, Congress authorized the Comprehensive Everglades
Restoration Plan (CERP), a $10.5-billion mission to restore the South Florida ecosystem.
Environmental projects in CERP require salinity monitoring in Florida Bay to provide
measures of the effects of restoration on the Everglades ecosystem. However current
salinity monitoring cannot cover large areas and is costly, time-consuming, and laborintensive.
The purpose of this dissertation is to model salinity, detect salinity changes, and
evaluate the impact of salinity in Florida Bay using remote sensing and geospatial
information sciences (GIS) techniques. The specific objectives are to: 1) examine the
capability of Landsat multispectral imagery for salinity modeling and monitoring; 2)
detect salinity changes by building a series of salinity maps using archived Landsat images; and 3) assess the capability of spectroscopy techniques in characterizing plant
stress / canopy water content (CWC) with varying salinity, sea level rise (SLR), and
nutrient levels.
Geographic weighted regression (GWR) models created using the first three
imagery components with atmospheric and sun glint corrections proved to be more
correlated (R^2 = 0.458) to salinity data versus ordinary least squares (OLS) regression
models (R^2 = 0.158) and therefore GWR was the ideal regression model for continued
Florida Bay salinity assessment. J. roemerianus was also examined to assess the coastal
Everglades where salinity modeling is important to the water-land interface. Multivariate
greenhouse studies determined the impact of nutrients to be inconsequential but increases
in salinity and sea level rise both negatively affected J. roemerianus. Field spectroscopic
data was then used to ascertain correlations between CWC and reflectance spectra using
spectral indices and derivative analysis. It was determined that established spectral
indices (max R^2 = 0.195) and continuum removal (max R^2= 0.331) were not significantly
correlated to CWC but derivative analysis showed a higher correlation (R^2 = 0.515 using
the first derivative at 948.5 nm). These models can be input into future imagery to
predict the salinity of the South Florida water ecosystem.

Planners and managers often rely on coarse population distribution data from the
census for addressing various social, economic, and environmental problems. In the
analysis of physical vulnerabilities to sea-level rise, census units such as blocks or block
groups are coarse relative to the required decision-making application. This study
explores the benefits offered from integrating image classification and dasymetric
mapping at the household level to provide detailed small area population estimates at the
scale of residential buildings. In a case study of Boca Raton, FL, a sea-level rise
inundation grid based on mapping methods by NOAA is overlaid on the highly detailed
population distribution data to identify vulnerable residences and estimate population
displacement. The enhanced spatial detail offered through this method has the potential to
better guide targeted strategies for future development, mitigation, and adaptation efforts.

Thermal remote sensing is a powerful tool for measuring the spatial variability of
evapotranspiration due to the cooling effect of vaporization. The residual method is a
popular technique which calculates evapotranspiration by subtracting sensible heat from
available energy. Estimating sensible heat requires aerodynamic surface temperature
which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for
this problem by calibrating the relationship between sensible heat and retrieved surface
temperature. Disadvantage of these calibrations are 1) user must manually identify
extremely dry and wet pixels in image 2) each calibration is only applicable over limited
spatial extent. Producing larger maps is operationally limited due to time required to
manually calibrate multiple spatial extents over multiple days. This dissertation develops
techniques which automatically detect dry and wet pixels. LANDSAT imagery is used
because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet
pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in
Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5)
Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available
energy, roughness length and wind speed is tested. A technique for temporally
interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and
tested.
The automated algorithm is successful at detecting wet and dry pixels (if they
exist). Including wet pixels in calibration and assuming constant atmospheric
conductance significantly improved results for all but Big Cypress and Gainesville.
Evaporative fraction is not very sensitive to instantaneous available energy but it is
sensitive to temperature when wet pixels are included because temperature is required for
estimating wet pixel evapotranspiration. Data fusion techniques only slightly
outperformed linear interpolation. Eddy covariance comparison and temporal
interpolation produced acceptable bias error for most cases suggesting automated
calibration and interpolation could be used to predict monthly or annual ET. Maps
demonstrating spatial patterns of evapotranspiration at field scale were successfully
produced, but only for limited spatial extents. A framework has been established for
producing larger maps by creating a mosaic of smaller individual maps.

The development of geographic information systems (GIS) has changed the way in which geographers are able to visualize and investigate spatial topics. Current research has now shown a need to incorporate the element of time into a GIS for the purpose of better understanding the processes that are related to change. This study investigates two methods of creating spatiotemporal databases, using the evolution of an airline route system as an example. Also discussed are the ways in which a user-friendly interface may be incorporated for easier data exploration.

As use of the Internet becomes pervasive, user interface design of web sites becomes increasingly important. Consumers must be able to easily and quickly perform the functions they desire. Travel industry applications have a large market potential on the Internet. Because of the geographical relationships of locations and functions in the travel industry, the use of cartography and GIS can be very beneficial to user interfaces of these applications. This paper examines functions, inputs, and user interface of current airline reservation web sites, and looks at some current examples of GIS use on the Internet. It then discusses ways to improve the user interface design of airline reservation web sites using GIS to create more powerful and easy-to-use applications that also incorporate other aspects of the travel industry.

Trip-based data can provide important information about travel patterns and travel characteristics of people. To handle this type of transportation movement data in GIS (Geographic Information Systems), a new data model is needed to incorporate different aspects of a transportation movement with its spatial properties. From the study presented in this thesis, an event-based data model for transportation movement data was developed with the concepts of object orientation, feature-based approach, and relative referencing methods. The implementation of this new data model with a prototype system shows the capability of this data model in handling transportation movement data. This data model has also improved the information retrieval ability provided by the current GIS data models.

The South Florida Water Management District in conjunction with Florida Atlantic University began an effort to record vegetation invading Lake Okeechobee in 1994. This effort included a mapping project that would include all detectable vegetation within the expanding littoral zone. There were several problems associated with remote sensing aspects of this project. These problems resulted in inaccurate classification of species and a redundancy of mapping for large areas. This thesis will review the remote sensing methods used for the mapping project, analyze the associated errors within the map product, and lastly offer an alternative approach, incorporating the use of iterative remote sensing, for mapping the vegetation of Lake Okeechobee and other areas of complex vegetation.