Environmental Sciences

Encrusters have a proven history as indicators of environmental conditions in nearshore habitats and are useful in both ecological and paleoenvironmental research within benthic ecosystems. Off the coast of Pompano Beach, Florida, a Holocene storm deposit contains large accumulations of subfossil Acropora palmata fragments with these same encrusting organisms attached to their surfaces. The objective of this research was to create an inventory of encrusters found within the storm deposit and document their successional outgrowth to determine the post-depositional history of sampled coral fragments. Foraminifera and coralline algae were the most common species found, and various sequences of successional outgrowth were observed that indicated fragments were either deposited gradually, immediately buried, or reworked after initial burial. This information is vital for understanding modern biodiversity on the Pompano coast, and the development of nearshore benthic marine ecosystems during the mid-late Holocene.

The U.S. Environmental Protection Agency's program, MULTIMED, was evaluated using a parametric analysis and result comparisons the with programs MODFLOW and MT3D. The validity and accuracy of the MULTIMED model results were determined and independent parameter sensitivities identified. The dilution calculations in the model are sensitive to several parameters. A parameter determined critical is the seepage velocity which is used in the transport calculations of the model, as well as a "Near Mixing Factor". Under Florida's aquifer conditions, the Near Mixing Factor as calculated in the model is susceptible to overestimating the dilution of the initial concentration due to relatively high recharge rates and low hydraulic conductivities. Florida's aquifer conditions also typically result in Near Mixing Factor values greater than one, for which the model's analytical solutions are not valid.

This thesis examines the quantification of tropical deforestation, the use of remote sensing techniques for its scientific measurement, and the many controversies surrounding the problem. Aerial photographs and Landsat-based planimetric maps were used to determine the conversion of montane rain forest in a 1,000 km$\sp2$ sector of Peru's Huallaga River Valley. Between 1963 and 1976, 244 km$\sp2$ of forest (approximately a quarter of the study area) were converted to agricultural and other land uses, an apparent deforestation rate of 19 km$\sp2$/yr or approximately 1,872 ha/yr. The method entailed the cutting and weighing of strips of Mylar overlays. Despite the photogrammetric limitations, the results demonstrate an economical and practical technique that is readily applicable to developing countries. The potential of other remote sensing systems and the application of change detection techniques such as digital image subtraction to monitor deforestation is detailed.

Fine-scale urban land cover information is important for a number of applications, including urban tree canopy mapping, green space analysis, and urban hydrologic modeling. Land cover information has traditionally been extracted from satellite or aerial images using automated image classification techniques, which classify pixels into different categories of land cover based on their spectral characteristics. However, in fine spatial resolution images (4 meters or better), the high degree of within-class spectral variability and between-class spectral similarity of many types of land cover leads to low classification accuracy when pixel-based, purely spectral classification techniques are used. Object-based classification methods, which involve segmenting an image into relatively homogeneous regions (i.e. image segments) prior to classification, have been shown to increase classification accuracy by incorporating the spectral (e.g. mean, standard deviation) and non-spectral (e.g. te xture, size, shape) information of image segments for classification. One difficulty with the object-based method, however, is that a segmentation parameter (or set of parameters), which determines the average size of segments (i.e. the segmentation scale), is difficult to choose. Some studies use one segmentation scale to segment and classify all types of land cover, while others use multiple scales due to the fact that different types of land cover typically vary in size. In this dissertation, two multi-scale object-based classification methods were developed and tested for classifying high resolution images of Deerfield Beach, FL and Houston, TX. These multi-scale methods achieved higher overall classification accuracies and Kappa coefficients than single-scale object-based classification methods.