Aerial photogrammetry

The Florida Everglades ecosystem is experiencing increasing threats from anthropogenic modification of water flow, spread of invasive species, sea level rise (SLR), and more frequent and/or intense hurricanes. Restoration efforts aimed at rehabilitating these ongoing and future disturbances are currently underway through the implementation of the Comprehensive Everglades Restoration Plan (CERP). Efficacy of these restoration activities can be further improved with accurate and site-specific information on the current state of the coastal wetland habitats. In order to produce such assessments, digital datasets of the appropriate accuracy and scale are needed. These datasets include orthoimagery to delineate wetland areas and map vegetation cover as well as accurate 3-dimensional (3-D) models to characterize hydrology, physiochemistry, and habitat vulnerability.

Invasive exotic plant species cause a number of problems in native south
Florida ecosystems, and a great deal of effort is being put into controlling the
populations ofthese species. Control efforts require updated information on the
locations of exotic species. This information can be obtained from high resolution
remotely sensed data such as digital orthoimagery and LIDAR. Extraction of
information from these data sources is often problematic using traditional pixel-based
image processing techniques. An object oriented method of image analysis, however,
has been shown to be better suited to this task.
One invasive exotic species that has become widespread in south Florida is
Casuarina equisetifolia, also known as Australian pine. This study develops a semiautomated
procedure for detecting Australian pine over a large, diverse area with high
resolution remotely sensed data using the object oriented method of analysis.

The development of high resolution LIDAR DSM combined with digital infrared ortho-photography data enhances the ability to map canopy structures with a higher degree of accuracy and precision than with either data set alone. The purpose of this thesis is to map Australian Pine (Casuarina equisetifolia with a 85% or greater accuracy by creating a methodology that uses LIDAR and color infrared ortho-photography and to test it within three different landscape types within Broward County. LIDAR features below a determined height threshold (i.e. Deerpoint 25 ft) were eliminated and recoded to 0 to create Mask 1. NDVI technique separated non-vegetative features from vegetative features to create Mask 2. Mask 1 and Mask 2 were merged and overlaid on the raw LIDAR data set to perform isodata clustering, as well as density slicing to identify mature Australian Pines. Careful delineation of study areas is critical to obtain the highest possible accuracy. Density slicing proved to be a faster and less time consuming technique for achieving 85% level of accuracy than compared to isodata clustering.