Vegetation mapping--Remote sensing

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 derived vegetative data of urban areas is normally classified into several classes of trees, fields, grass and bare soil using unsupervised and supervised classification methods. Normalized Difference Vegetation Indexes (NDVI) have traditionally been applied to agricultural satellite images to assess the health and maturity of commercial crops. When a NDVI is used to examine urban vegetation, many discrete data values are generated which can be differentiated into meaningful vegetation classes.