Geographic information systems.

Urbanization is a fundamental reality in the developed and developing countries
around the world creating large concentrations of the population centering on cities and
urban centers. Cities can offer many opportunities for those residing there, including
infrastructure, health services, rescue services and more. The living space density of
cities allows for the opportunity of more effective and environmentally friendly housing,
transportation and resources. Cities play a vital role in generating economic production
as entities by themselves and as a part of larger urban complex. The benefits can provide
for extraordinary amount of people, but only if proper planning and consideration is
undertaken.
Global urbanization is a progressive evolution, unique in spatial location while
consistent to an overall growth pattern and trend. Remotely sensing these patterns from
the last forty years of space borne satellites to understand how urbanization has
developed is important to understanding past growth as well as planning for the future. Imagery from the Landsat sensor program provides the temporal component, it
was the first satellite launched in 1972, providing appropriate spatial resolution needed to
cover a large metropolitan statistical area to monitor urban growth and change on a large
scale. This research maps the urban spatial and population growth over the Miami – Fort
Lauderdale – West Palm Beach Metropolitan Statistical Area (MSA) covering Miami-
Dade, Broward, and Palm Beach counties in Southeast Florida from 1974 to 2010 using
Landsat imagery. Supervised Maximum Likelihood classification was performed with a
combination of spectral and textural training fields employed in ERDAS Image 2014 to
classify the images into urban and non-urban areas. Dasymetric mapping of the
classification results were combined with census tract data then created a coherent
depiction of the Miami – Fort Lauderdale – West Palm Beach MSA. Static maps and
animated files were created from the final datasets for enhanced visualizations and
understanding of the MSA evolution from 60-meter resolution remotely sensed Landsat
images. The simplified methodology will create a database for urban planning and
population growth as well as future work in this area.

Everglades National Park is a hydro-ecologically significant wetland experiencing salinity ingress over the years. This motivated our study to map water salinity using a spatially weighted optimization model (SWOM); and soil salinity using land cover classes and EC thresholds. SWOM was calibrated and validated at 3-km grids with actual salinity for 1998–2001, and yielded acceptable R2 (0.89-0.92) and RMSE (1.73-1.92 ppt). Afterwards, seasonal water salinity mapping for 1996–97, 2004–05, and 2016 was carried out. For soil salinity mapping, supervised land cover classification was firstly carried out for 1996, 2000, 2006, 2010 and 2015; with the first four providing average accuracies of 82%-94% against existing NLCD classifications. The land cover classes and EC thresholds helped mapping four soil salinity classes namely, the non saline (EC = 0~2 dS/m), low saline (EC = 2~4 dS/m), moderate saline (EC = 4~8 dS/m) and high saline (EC >8 dS/m) areas.

Reliable geographic intelligence is essential for urban areas; land-cover classification creates the data for urban spatial decision making. This research tested a methodology to create a land-cover map for the main campus of Florida Atlantic University in Boca Raton, Florida. The accuracy of nine separate land-cover classification results were tested; the one with the highest accuracy was chosen for the final map. Object-based image segmentation was applied to fused and LiDAR point cloud (elevation and intensity) data and aerial imagery. These were classified by Random Forest, k-Nearest Neighbor and Support Vector Machines classifiers. Shadow features were reclassified hierarchically in order to create a complete map. The Random Forest classifier used with the fused data set gave the highest overall accuracy at 82.3%, and a Kappa value at 0.77. When combined with the results from the shadow reclassification, the overall accuracy increased to 86.3% and the Kappa value improved to 0.82.

This study is a spatial analysis conducted in the Main Chamber of Actun Tunichil Muknal, a Terminal Classic Maya ceremonial cave (A.D. 830--950), located in Western Belize. The research examines ancient Maya ritual cave use by analyzing artifact deposition patterns. Using a Geographical Information System (GIS), it provides a methodology for the development of comparative models of spatial organization. The system facilitated data visualization, exploration, and generation. The GIS was instrumental in the analysis of the proximity of artifacts to natural morphological features of the cave. Artifact deposition patterns were correlated with known ritual behavior patterns from the region. Using this method, boundary markers, artifact pathways, and a centrally located symbolic three-stone-hearth feature were identified. This study suggests that, within the cave, the ancient Maya employed a cognitive model of spatial organization similar to that witnessed by ethnographers in other venues, or reported in ethnohistorical texts in rites of foundation.