Land Surface Analysis of the Florida Everglades

Data collected at key points or along important environmental gradients help us understand microscale processes and advance techniques for characterizing South Florida’s land surface using imagery collected from the air or space. This project is developing rapid, non-destructive protocols for vegetation assessment (Figure 1) to create models that estimate biophysical characteristics of vegetation from airborne and satellite imagery (Figure 2). Similarly, the project has provided experience in using instruments that collect high spectral resolution data about the reflectance of South Florida land cover types (Figure 3). This experience has been used to create a library of Everglades land cover spectra (Figure 4) that provide useful information about subtle variations in the reflectivity of different land surfaces or the same land surfaces over time. Data from this instrument can be used for biophysical modeling and can be manipulated to portray the types of measurements made by broader-band sensors. This allows us to experiment with algorithms to extract biophysical parameters from multi-spectral image data collected from aircraft and satellite platforms. It also yields information we can use to suggest future sensing system requirements. Thus, project field-based measurements allow more precise map-making, provide for complex experiments with remote sensing procedures, and suggest future remote sensing system specifications required to meet particular science needs. Collaborative efforts to improve the functioning and value of the Greater Florida Everglades Ecosystem require accurate information on the status and dynamics of South Florida land surfaces. The goal of this project is to produce and distribute scientific techniques, geographic information, and understanding of Everglades land surfaces to improve adaptive management of South Florida ecosystem resources. Field-based activities are providing data needed for the development and testing of image-based spatial models. The creation of multi-scale, multi-sensor remotely sensed databases is providing the foundation for innovative land cover mapping and change detection. Geospatial analysis of image-derived products is creating baselines for land surface monitoring, yielding insights regarding land surface processes, and improving restoration-relevant modeling efforts.