Roberts, Charles

The project of reviewing our campus ecosystem arose from recognition within the faculty leadership of the Environmental Initiative that much still needs 10 be done in the ways of multidisciplinaryteam buildingand the environmental education of ourselves. Although the indoor environment has not been forgotten, the emphasis in this first review is on the outdoor environment. The notion that these 880 acres have become isolated within a sea of urbanization, so that "FAU Island" can be spoken of, is fundamental to the present project.

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.

Normal color digital ortho-photos with six inch resolution were used to test the
feasibility of mapping the habitat of the Florida apple snail, the exclusive food
source of the Endangered Florida snail kite. Several remote sensing techniques
such as unsupervised and supervised classifications are evaluated and accuracy
assessed. While the overall ability to classify apple snail habitat is significant,
sparse areas of emergent vegetation which are preferred foraging areas for the kite
were excluded by the classification and needed to be re-introduced for further
analysis. The methodology can be repeated to measure change over time since the
NC imagery of Grassy Waters Preserve is acquired frequently. Additionally, three
spectral levels of density abundance of wetland graminoid species that denote apple
snail habitat were examined for mapping potential on the imagery. A site suitability
analysis using a weighed overlay tool in ArcGIS is suggested as a means to include
numerous other key factors influencing habitat selection by the snail kite in a
mapping project.

High-resolution imagery is becoming readily available to the public. Private firms and government organizations are using high-resolution images but are running into problems with storage space and processing time. High-resolution images are extremely large files, and have proven cumbersome to work with and control. By resampling fine resolution imagery to a lower resolution, storage and processing space can be dramatically reduced. Fine-resolution imagery is not needed to map most features and resampled high-resolution imagery can be used as a replacement for low-resolution satellite imagery in some cases. The effects of resampling on the spectral quality of a high-resolution image can be demonstrated by answering the following questions: (1) Is the quality of spectral information on a color infrared DOQQ comparable to SPOT and TM Landsat satellite imagery for the purpose of digital image classification? (2) What is the appropriate resolution for mapping surface features using high-resolution imagery for spectral categories of information? (3) What is the appropriate resolution for mapping surface features using high-resolution imagery for land-use land-cover information?

South Florida summers are characterized by small intense convective storms which lead to extreme variations in local cloud cover. Estimating insolation from high-resolution satellite data accounts for these variations. A statistical model is presented for estimating incident solar radiation (insolation) using 1km x 1km resolution visible images from the GOES8 satellite. The model relates insolation to top-of-the-atmosphere radiation, readily available climate data, and cloud cover (from the satellite). Regression coefficients were developed and verified based on one month of insolation measurements from eight ground-based measurement stations operated by the South Florida Water Management District. The model allows almost continuous areal estimation of daily or hourly insolation at resolutions suitable for small-scale hydrologic modeling. Model estimates are comparable to measured values at ground stations and superior to estimates derived from interpolating between ground stations.

This thesis develops a technique that combines the use of historical geography with photo interpretation and physical geography to reconstruct the predevelopment landcover of an urban area in southeastern Florida. This technique differs from traditional map making methods by utilizing Geographic Information System modeling procedures to create a conceptual map and test it against independent historical maps and accounts. Historical maps and accounts provided new information about the historic landscape that could not be deferred from present physiographic information. The resulting maps had more detail about the most highly altered areas than the conceptual map created from physiographic information.

Digital orthophoto quadrangle quarter (DOQQ) is a new type of imagery. The DOQQ is a product of the United States Geological Survey, which has many uses. The spectral and spatial qualities of a DOQQ are equal to and surpass the qualities of traditional satellite imagery. These qualities can be demonstrated by answering the following questions: (1) Is the quality of the spectral information on a color infrared DOQQ comparable to a SPOT and TM Landsat satellite imagery for the purpose of digital image classification? (2) What are the optimal number of classes for achieving an accuracy supervised classification of a one-meter resolution DOQQ? (3) What is the effect of changing the spatial resolution on image classification of a DOQQ?

Like so many natural areas in south Florida, the Dupuis Reserves' landscape has gone through extreme alteration due to numerous cultural activities. Activities including drainage, cattle ranching, and citrus farming have altered the reserves natural hydrology causing the formation of several watersheds. This hydrologic alteration has caused the once homogenous ecosystem to fragment into two separate landscapes: one dominated by drainage and human impact while the other remains in a relatively natural state. This study will attempt to determine, with the use of remote sensing, geographic information systems and digital image classifications if culturally derived watersheds cause significant change in the spectral signatures of vegetation. Results indicate that when the study site is sectioned along culturally derived watersheds, classification accuracy is significantly improved.

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.

Satellite image processing should not be dismissed as a tool to monitor vegetation cover because of cost concerns or classification problems due to topographic extremes. Low cost imagery and image processing software's are available and the image ratio technique can be used to minimize classification problems due to topography. The Forestry Division of St. Vincent island has minimum resources and the island's topography varies from sea level to 1,324 meters. The plight of the endemic, endangered St. Vincent Parrot (Amazona guildingii) was used as a case study. An appropriate habitat monitoring technique was formulated for the Forestry Division so that it may better conserve the species. This technique employs 57 meter resolution imagery and the IDRISI software package. The resulting classification maps areas of habitat versus non-habitat. This technique of low cost, vegetation cover mapping can be used for many natural resource planning and interpretation applications throughout the Caribbean.