Figure 1. Non-destructive, field collection of vegetation biomass 

along transects for remote sensing algorithm development and 

evaluation.

U.S. Department of the Interior

U.S. Geological Survey

Fact Sheet 2004-3132

November 2004 Printed on recycled paper

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. 

Figure 2. Vegetation biomass estimated by combining field vegetation 

data and airborne multispectral imagery.

Field methods development and data collection
Remote sensing technologies provide attractive 

capabilities for land surface and other process monitoring 

in large, fragile, and logistically challenging environments 

like the Everglades (Jones and Desmond, 1998). They also 

yield synoptic views up to regional levels at relatively low 

cost and allow for non-destructive scientific sampling. 

However, the high-humidity and often cloudy subtropical 

climate of South Florida, combined with rapidly varying 

substrate conditions (e.g., changing water levels), makes 

the South Florida region a difficult one in which to use 

remote sensing. These difficulties are exacerbated by 

needs on the part of scientists and land managers to 

document very subtle differences in vegetation and water 

composition that are often difficult to recognize – even 

while standing on the ground. The fusion of multiple 

types of remotely sensed data may help to overcome these 

challenges and provide the best information possible for 

research aimed at adaptive resource management. The 

USGS is meeting these challenges through the collection 

and development of a multi-scale (Figure 5), multisensor, and multi-temporal (Figure 6) database for remote 

sensing research and ecosystem monitoring. Evaluating 

the efficacy of atmospheric corrections and minimum 

calibration requirements for particular data uses are 

primary research areas. As a result, a flexible, rich archive 

of image data is being developed.

Figure 3. A library of South Florida land cover spectra is being built 

using handheld and laboratory spectroscopy.

Figure 4. Examples of field-collected spectra.

Multi-scale, multi-sensor image data collection

B

A
Monitoring the progress of Everglades resource 

management requires the establishment of baseline 

information about current conditions, the development 

of intelligent (i.e. cost effective, but rigorous) ground 

sampling frameworks, and the capability to detect changes 

at regional scales for more detailed investigations. 

Through this project, a set of satellite image maps 

covering the entire South Florida area have been 

developed (Jones and Thomas, 2004). Interpretations of 

these image maps and other images in the database are 

providing baseline information. The multi-scale nature of 

the remote sensing database also provides a foundation 

for determination of sampling requirements (Jones, 2001). 

And, the multi-temporal aspect of the image database is 

increasing our understanding of land surface dynamics 

such as urban expansion (Figure 6), fire, and other 

disturbance events.

Landscape-scale modeling

 The interpretation of remotely sensed data and 

the spatial analysis of information derived from the 

imagery are improving the effectiveness of Everglades 

hydrodynamic models. Maps of land surface 

characteristics important in processes like resistance to 

surface water flow (Figure 7) are being analyzed using 

spatial statistics and concepts of landscape ecology 

(Figure 8) to yield more spatially and temporally complex 

model parameterizations. Once sufficient archives of 

calibrated data are developed, correlations among land, 

climate, social processes, and land surface characteristics 

will provide insights regarding impacts of natural and 

anthropogenic drivers and habitat characteristics.

Figure 5. The same location imaged at three different spatial 

resolutions (a: project-generated orthophotoquad – 1ft resolution; 

b: USGS digital orthophotoquad – 1m resolution; and c: projectgenerated satellite image map – 15m resolution).

Figure 6. Changes in land use west of Fort Lauderdale are captured 

in satellite data from 1992 (A) and 2001 (B).

Monitoring and change detection

C A

B
Figure 7. Vegetation classes grouped according to flow resistance 

characteristics have been mapped for various study regions using 

archived satellite imagery to parameterize hydrodynamic models.

Figure 8. Fine-resolution imagery (a) is being analyzed for directional 

spatial autocorrelation (b) to yield insights regarding spatial 

configuration of vegetation and (c) that can be used to parameterize 

hydrologic and other process models.

 USGS research is developing and applying innovative techniques to map the distribution of vegetation characteristics 

and related hydrologic, geologic, and biologic variables through space and over time in South Florida. With a goal of 

enabling more effective landscape monitoring, resource management, and restoration impact assessment, the USGS is 

developing and applying remote sensing, spatial analysis, and other geo-processing tools to increase our understanding of 

Everglades land surface dynamics, biophysical processes, and ecosystem functioning.

Jones, J.W. and J.C. Thomas, 2004. “Big Cypress/Pine Island”, 

 USGS Scientific Investigations Map Series, SIM-2842, 

 Reston, VA. 

Jones, J.W., 2001. Image and in-situ data integration to derive 

 sawgrass density for surface-flow modeling in the 

 Everglades Remote Sensing and Hydrology 2000, 

 IAHS 267, p. 507-512.

Jones, J.W. and G.B. Desmond, 1998. “Land characterization

 for hydrologic modeling in the Everglades.” In Proceedings 

 of the Fourth International Symposium on Remote Sensing 

 in Hydrology. IRI. p. 123-130.

For additional information contact: 

John W. Jones, Ph.D.

Phone: 703/648-5543

Fax: 703/648-4603

Email: jwjones@usgs.gov

-J.W. Jones and J. Neubauer

REFERENCES

SUMMARY

B

A

C