The TNC Vegetation Classification and Vegetation Mapping in Florida - Simplifying the Complex
By John Stenberg, Leonard Pearlstine and Wiley Kitchens
|
The primary objective of this project is to simplify the process of utilizing The Nature Conservancy (TNC) Vegetation Classification in vegetation mapping of Florida. Our mapping process uses a team of image processors and botanists working with remotely sensed spectral data (LANDSAT) coupled with auxiliary spatial data and field and aerial photograph/videograph groundtruth data. Reduction in complexity or improvement in quality of any task in this process will improve the final map product.
This project is directed toward improving the interaction between the botanical staff and the TNC Vegetation Classification while interpreting low altitude remotely sensed landcover data (aerial photography/videography) or applying vegetation landcover codes to field data. The vegetation classification was designed as a hierarchical classification that allows for landcover classification at multiple spatial scales. As landcover mapping and classification becomes more concerned with biotic community dynamics the need to have a consistent ecologically driven classification has lead to the TNC Vegetation Classification. The resulting classification is HUGE and somewhat intimidating for many potential users. To reduce this "classification anxiety" we are developing a user interface that will eliminate a number of steps in the utilitzation of the classification. For more experienced users, comfortable with the TNC classification, the interface speeds decision making and data entry.
The interface will be presented to the user as a simple "Data Entry Form" in Excel 97. A second open window running ArcView allows the user to view and query the aerial photography/videography flight lines with a satellite image backdrop. The aerial photography is viewed with graphics software (to allow image manipulation and enhancement), and aerial videography is viewed on an adjacent monitor.
Objectives:
Reduce error
Speed image interpretation and increase the number of images interpreted
Improve quality of image interpretation with consistent labeling
Available to minimally trained user
Flexible interface for (1) Imagery database construction, (2) Accuracy assessment, and (3) Field data collection
Get the Excel/Visual Basic Code here
Go to Florida Gap Home Page......... 
Go to National Gap Home Page
July 24, 1997