Initial Everglades Depth Estimation Network (EDEN) 

digital elevation model research and development 

By John W. Jones and Susan D. Price 

Open File Report 2007–1034 

U.S. Department of the Interior 

U.S. Geological Survey 
U.S. Department of the Interior 

Dirk Kempthorne, Secretary 

U.S. Geological Survey

Mark Myers, Director

U.S. Geological Survey, Reston, Virginia 2007 

Revised and reprinted: 2007 

For product and ordering information:

World Wide Web: http://www.usgs.gov/pubprod 

Telephone: 1-888-ASK-USGS

For more information on the USGS—the Federal source for science about the Earth,

its natural and living resources, natural hazards, and the environment:

World Wide Web: http://www.usgs.gov

Telephone: 1-888-ASK-USGS

Suggested citation:

Jones, John. W. and Price, Susan D., 2007, Everglades Depth Estimation Network (EDEN) digital

elevation model research and development: U.S. Geological Survey Open-File Report 2007-1034, 29 

pgs. 

Any use of trade, product, or firm names is for descriptive purposes only and does not imply

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual

copyright owners to reproduce any copyrighted material contained within this report.

ii
Contents 

Introduction ............................................................................................................................................................ 1


Model Input: Highly Accurate Elevation Data .................................................................................................. 1


Model Development Approach........................................................................................................................... 2


Results ..................................................................................................................................................................... 3


File naming conventions....................................................................................................................................... 4


Data documentation and distribution ................................................................................................................ 5


Future plans ............................................................................................................................................................ 5


Acknowledgements .............................................................................................................................................. 5


References Cited ................................................................................................................................................... 5


Figures ..................................................................................................................................................................... 6


Figures 

Figure 1. The airborne height finder (AHF) system as installed in a helicopter. ........................................ 6


Figure 2. Pre-mission testing of the AHF. .......................................................................................................... 7


Figure 3. Regional coverage of the high accuracy elevation database (HAED)........................................ 8


Figure 4. One 7.5-minute quadrangle area of HAED........................................................................................ 9


Figure 5. A regional DEM created using the TOPOGRID algorithm............................................................ 10


Figure 6. Modeled water depth for a small area of WCA3 using the TOPOGRID. ................................... 11


Figure 7. EDEN DEM processing sub-regions................................................................................................ 12


Figure 8. Modeled water depth using Radial Based Function. ................................................................... 13


Figure 9. Modeled water depth using kriging................................................................................................. 14


Figure 10. Standard error for WCA1 using ordinary kriging......................................................................... 15


Figure 11. The EDEN DEM as of summer 2006. .............................................................................................. 16


Figure 12. EDEN DEM released in January of 2007. ...................................................................................... 17


Figure 13. Boxplot of WCA1 vegetation verses elevation. ........................................................................... 18


 Tables 

Table 1. RMSE and R2created for WCA1 by different surfacing algorithms. .............................................. 3


Table 2. RMSE produced by ordinary kriging for each EDEN sub-area. ..................................................... 4


iii 
 Conversion Factors 

Inch/Pound to SI 

Multiply By To obtain 

Length 

inch (in.) 2.54 centimeter (cm) 

inch (in.) 25.4 millimeter (mm) 

foot (ft) 0.3048 meter (m) 

mile (mi) 1.609 kilometer (km) 

mile, nautical (nmi) 1.852 kilometer (km) 

yard (yd) 0.9144 meter (m) 

Area 

acre 4,047 square meter (m2) 

acre 0.4047 hectare (ha) 

acre 0.4047 square hectometer (hm2

) 

acre 0.004047 square kilometer (km2

) 

square foot (ft2

) 929.0 square centimeter (cm2) 

square foot (ft2

) 0.09290 square meter (m2) 

square inch (in

2

) 6.452 square centimeter (cm2) 

section (640 acres or 1 square 259.0 square hectometer (hm2

) 

mile) 

square mile (mi

2

) 259.0 hectare (ha) 

square mile (mi

2

) 2.590 square kilometer (km2) 

Volume 

barrel (bbl), (petroleum, 0.1590 cubic meter (m3) 

1 barrel=42 gal) 

ounce, fluid (fl. oz) 0.02957 liter (L) 

pint (pt) 0.4732 liter (L) 

quart (qt) 0.9464 liter (L) 

gallon (gal) 3.785 liter (L) 

gallon (gal) 0.003785 cubic meter (m3

) 

gallon (gal) 3.785 cubic decimeter (dm3

) 

million gallons (Mgal) 3,785 cubic meter (m3

) 

cubic inch (in3

) 16.39 cubic centimeter (cm3) 

cubic inch (in3

) 0.01639 cubic decimeter (dm3) 

iv
cubic inch (in3

) 0.01639 liter (L) 

cubic foot (ft3

) 28.32 cubic decimeter (dm3) 

cubic foot (ft3

) 0.02832 cubic meter (m3) 

cubic yard (yd

3

) 0.7646 cubic meter (m3) 

cubic mile (mi3

) 4.168 cubic kilometer (km3) 

acre-foot (acre-ft) 1,233 cubic meter (m3

) 

acre-foot (acre-ft) 0.001233 cubic hectometer (hm3

) 

Flow rate 

acre-foot per day (acre-ft/d) 0.01427 cubic meter per second (m3/s) 

acre-foot per year (acre-ft/yr) 1,233 cubic meter per year (m3/yr) 

acre-foot per year (acre-ft/yr) 0.001233 cubic hectometer per year 

(hm3

/yr) 

foot per second (ft/s) 0.3048 meter per second (m/s) 

foot per minute (ft/min) 0.3048 meter per minute (m/min) 

foot per hour (ft/hr) 0.3048 meter per hour (m/hr) 

foot per day (ft/d) 0.3048 meter per day (m/d) 

foot per year (ft/yr) 0.3048 meter per year (m/yr) 

cubic foot per second (ft

3

/s) 0.02832 cubic meter per second (m3/s) 

cubic foot per second per square 0.01093 cubic meter per second per 

mile [(ft3

/s)/mi2] square kilometer [(m3/s)/km2] 

cubic foot per day (ft

3

/d) 0.02832 cubic meter per day (m3/d) 

gallon per minute (gal/min) 0.06309 liter per second (L/s) 

gallon per day (gal/d) 0.003785 cubic meter per day (m3

/d) 

gallon per day per square mile 0.001461 cubic meter per day per square 

 [(gal/d)/mi2

] kilometer [(m3/d)/km2] 

million gallons per day 0.04381 cubic meter per second (m3

/s) 

(Mgal/d) 

million gallons per day per 1,461 cubic meter per day per square 

square mile [(Mgal/d)/mi2

] kilometer [(m3/d)/km2] 

inch per hour (in/h) 0 .0254 meter per hour (m/h) 

inch per year (in/yr) 25.4 millimeter per year (mm/yr) 

mile per hour (mi/h) 1.609 kilometer per hour (km/h) 

Mass 

ounce, avoirdupois (oz) 

pound, avoirdupois (lb) 

ton, short (2,000 lb) 

ton, long (2,240 lb) 

ton per day (ton/d) 

ton per day (ton/d) 

28.35 

0.4536 

 0.9072 

1.016 

0.9072 

0.9072 

gram (g) 

kilogram (kg) 

megagram (Mg) 

megagram (Mg) 

metric ton per day 

megagram per day (Mg/d) 

v 
ton per day per square mile 0.3503 megagram per day per square 

[(ton/d)/mi2

] kilometer [(Mg/d)/km2] 

ton per year (ton/yr) 0.9072 megagram per year (Mg/yr) 

ton per year (ton/yr) 0.9072 metric ton per year 

Pressure 

atmosphere, standard (atm) 101.3 kilopascal (kPa) 

bar 100 kilopascal (kPa) 

inch of mercury at 60oF (in Hg) 3.377 kilopascal (kPa) 

pound-force per square inch 6.895 kilopascal (kPa) 

(lbf/in2

) 

pound per square foot (lb/ft2

) 0.04788 kilopascal (kPa) 

pound per square inch (lb/in2

) 6.895 kilopascal (kPa) 

Density 

pound per cubic foot (lb/ft3) 16.02 kilogram per cubic meter 

(kg/m3

) 

pound per cubic foot (lb/ft3) 0.01602 gram per cubic centimeter 

(g/cm3

) 

Energy 

kilowatthour (kWh) 3,600,000 joule (J) 

Radioactivity 

picocurie per liter (pCi/L) 0.037 becquerel per liter (Bq/L) 

Specific capacity 

gallon per minute per foot 0.2070 liter per second per meter 

[(gal/min)/ft)] [(L/s)/m] 

Hydraulic conductivity 

foot per day (ft/d) 0.3048 meter per day (m/d) 

Hydraulic gradient 

foot per mile (ft/mi) 0.1894 meter per kilometer (m/km) 

Transmissivity* 

foot squared per day (ft

2

/d) 0.09290 meter squared per day (m2/d) 

Application rate 

pounds per acre per year 1.121 kilograms per hectare per year 

[(lb/acre)/yr] [(kg/ha)/yr] 

Leakance 

foot per day per foot [(ft/d)/ft] 

inch per year per foot [(in/yr)/ft] 

1 

83.33 

meter per day per meter 

millimeter per year per meter 

[(mm/yr)/m] 

vi 
Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 


°F=(1.8×°C)+32


Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 


°C=(°F-32)/1.8 


Vertical coordinate information is referenced to the insert datum name (and abbreviation) here for instance, “North


American Vertical Datum of 1988 (NAVD 88).” 


Horizontal coordinate information is referenced to the insert datum name (and abbreviation) here for instance, “North


American Datum of 1983 (NAD 83).” 


Altitude, as used in this report, refers to distance above the vertical datum. 


*Transmissivity: The standard unit for transmissivity is cubic foot per day per square foot times foot of aquifer thickness 


[(ft3

/d)/ft2]ft. In this report, the mathematically reduced form, foot squared per day (ft2/d), is used for convenience. 


Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25 °C). 


Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or micrograms per liter 


(μg/L). 


NOTE TO USGS USERS: Use of hectare (ha) as an alternative name for square hectometer (hm2

) is restricted to the 


measurement of small land or water areas. Use of liter (L) as a special name for cubic decimeter (dm3

) is restricted to 


the measurement of liquids and gases. No prefix other than milli should be used with liter. Metric ton (t) as a name for 


megagram (Mg) should be restricted to commercial usage, and no prefixes should be used with it. 


vii 
SI to Inch/Pound 

Multiply By To obtain 

Length 

centimeter (cm) 0.3937 inch (in.) 

millimeter (mm) 0.03937 inch (in.) 

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi) 

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Area 

square meter (m2) 0.0002471 acre 

hectare (ha) 2.471 acre 

square hectometer (hm2

) 2.471 acre 

square kilometer (km2

) 247.1 acre 

square centimeter (cm2

) 0.001076 square foot (ft2) 

square meter (m2

) 10.76 square foot (ft2) 

square centimeter (cm2

) 0.1550 square inch (ft

2

) 

square hectometer (hm2

) 0.003861 section (640 acres or 1 square 

mile) 

hectare (ha) 0.003861 square mile (mi2

) 

square kilometer (km2

) 0.3861 square mile (mi

2

) 

Volume 

cubic meter (m3) 6.290 barrel (petroleum, 1 barrel = 42 

gal) 

liter (L) 33.82 ounce, fluid (fl. oz) 

liter (L) 2.113 pint (pt) 

liter (L) 1.057 quart (qt) 

liter (L) 0.2642 gallon (gal) 

cubic meter (m3

) 264.2 gallon (gal) 

cubic decimeter (dm3

) 0.2642 gallon (gal) 

cubic meter (m3

) 0.0002642 million gallons (Mgal) 

cubic centimeter (cm3

) 0.06102 cubic inch (in3) 

cubic decimeter (dm3

) 61.02 cubic inch (in3) 

liter (L) 61.02 cubic inch (in3

) 

cubic decimeter (dm3

) 0.03531 cubic foot (ft3) 

cubic meter (m3

) 35.31 cubic foot (ft3) 

cubic meter (m3

) 1.308 cubic yard (yd

3

) 

viii 
cubic kilometer (km3

) 0.2399 cubic mile (mi3) 

cubic meter (m3

) 0.0008107 acre-foot (acre-ft) 

cubic hectometer (hm3

) 810.7 acre-foot (acre-ft) 

Flow rate 

cubic meter per second (m3/s) 70.07 acre-foot per day (acre-ft/d) 

cubic meter per year (m3/yr) 0.000811 acre-foot per year (acre-ft/yr) 

cubic hectometer per year 811.03 acre-foot per year (acre-ft/yr) 

(hm3

/yr) 

meter per second (m/s) 3.281 foot per second (ft/s) 

meter per minute (m/min) 3.281 foot per minute (ft/min) 

meter per hour (m/hr) 3.281 foot per hour (ft/hr) 

meter per day (m/d) 3.281 foot per day (ft/d) 

meter per year (m/yr) 3.281 foot per year ft/yr) 

cubic meter per second (m3

/s) 35.31 cubic foot per second (ft

3

/s) 

cubic meter per second per 91.49 cubic foot per second per square 

square kilometer [(m3

/s)/km2] mile [(ft3/s)/mi2] 

cubic meter per day (m3

/d) 35.31 cubic foot per day (ft

3

/d) 

liter per second (L/s) 15.85 gallon per minute (gal/min) 

cubic meter per day (m3

/d) 264.2 gallon per day (gal/d) 

cubic meter per day per square 684.28 gallon per day per square mile 

kilometer [(m3

/d)/km2] [(gal/d)/mi

2

] 

cubic meter per second (m3

/s) 22.83 million gallons per day (Mgal/d) 

cubic meter per day per square 0.0006844 million gallons per day per 

kilometer [(m3

/d)/km2] square mile [(Mgal/d)/mi2] 

cubic meter per hour (m3

/h) 39.37 inch per hour (in/h) 

millimeter per year (mm/yr) 0.03937 inch per year (in/yr) 

kilometer per hour (km/h) 0.6214 mile per hour (mi/h) 

Mass 

gram (g) 0.03527 ounce, avoirdupois (oz) 

kilogram (kg) 2.205 pound avoirdupois (lb) 

megagram (Mg) 1.102 ton, short (2,000 lb) 

megagram (Mg) 0.9842 ton, long (2,240 lb) 

metric ton per day 1.102 ton per day (ton/d) 

megagram per day (Mg/d) 1.102 ton per day (ton/d) 

megagram per day per square 2.8547 ton per day per square mile 

kilometer [(Mg/d)/km2

] [(ton/d)/mi2] 

megagram per year (Mg/yr) 1.102 ton per year (ton/yr) 

metric ton per year 1.102 ton per year (ton/yr) 

Pressure 

ix 
kilopascal (kPa) 0.009869 atmosphere, standard (atm) 

kilopascal (kPa) 0.01 bar 

kilopascal (kPa) 0.2961 inch of mercury at 60°F (in Hg) 

kilopascal (kPa) 0.1450 pound-force per inch (lbf/in) 

kilopascal (kPa) 20.88 pound per square foot (lb/ft2

) 

kilopascal (kPa) 0.1450 pound per square inch (lb/ft2

) 

Density 

kilogram per cubic meter 0.06242 pound per cubic foot (lb/ft3) 

(kg/m3

) 

gram per cubic centimeter 62.4220 pound per cubic foot (lb/ft3) 

(g/cm3

) 

Energy 

joule (J) 0.0000002 kilowatthour (kWh) 

Radioactivity 

becquerel per liter (Bq/L) 27.027 picocurie per liter (pCi/L) 

Specific capacity 

liter per second per meter 4.831 gallon per minute per foot 

[(L/s)/m] [(gal/min)/ft] 

Hydraulic conductivity 

meter per day (m/d) 3.281 foot per day (ft/d) 

Hydraulic gradient 

meter per kilometer (m/km) 5.27983 foot per mile (ft/mi) 

Transmissivity* 

meter squared per day (m2/d) 10.76 foot squared per day (ft

2

/d) 

Application rate 

kilograms per hectare per year 0.8921 pounds per acre per year 

[(kg/ha)/yr] [(lb/acre)/yr] 

Leakance 

meter per day per meter 

[(m/d)/m] 

1 foot per day per foot [(ft/d)/ft] 

millimeter per year per meter 

[(mm/yr)/m] 

0.012 inch per year per foot [(in/yr)/ft] 

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows: 


°F=(1.8×°C)+32


Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows: 


°C=(°F-32)/1.8 


Vertical coordinate information is referenced to the insert datum name (and abbreviation) here, for instance, “North


American Vertical Datum of 1988 (NAVD 88)” 


Horizontal coordinate information is referenced to the insert datum name (and abbreviation) here, for instance, “North


American Datum of 1983 (NAD 83)” 


x 
Altitude, as used in this report, refers to distance above the vertical datum. 


*Transmissivity: The standard unit for transmissivity is cubic foot per day per square foot times foot of aquifer thickness 


[(ft3

/d)/ft2]ft. In this report, the mathematically reduced form, foot squared per day (ft2/d), is used for convenience. 


Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25°C). 


Concentrations of chemical constituents in water are given either in milligrams per liter (mg/L) or micrograms per liter 


(μg/L). 


NOTE TO USGS USERS: Use of hectare (ha) as an alternative name for square hectometer (hm2

) is restricted to the 


measurement of small land or water areas. Use of liter (L) as a special name for cubic decimeter (dm3

) is restricted to 


the measurement of liquids and gases. No prefix other than milli should be used with liter. Metric ton (t) as a name for 


megagram (Mg) should be restricted to commercial usage, and no prefixes should be used with it. 


xi 
Initial Everglades Depth Estimation Network (EDEN) 

digital elevation model research and development 

By John W. Jones and Susan D. Price 

Introduction 

The Everglades Depth Estimation Network (EDEN) offers a consistent and documented dataset that 

can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, 

and to support biological and ecological assessments that measure ecosystem responses to the 

Comprehensive Everglades Restoration Plan (Telis, 2006). To produce historic and near-real time 

maps of water depths, the EDEN requires a system-wide digital elevation model (DEM) of the 

ground surface. Accurate Everglades wetland ground surface elevation data were non-existent 

before the U.S. Geological Survey (USGS) undertook the collection of highly accurate surface 

elevations at the regional scale. These form the foundation for EDEN DEM development. This 

development process is iterative as additional high accuracy elevation data (HAED) are collected, 

water surfacing algorithms improve, and additional ground-based ancillary data become available. 

Models are tested using withheld HAED and independently measured water depth data, and by 

using DEM data in EDEN adaptive management applications. Here the collection of HAED is 

briefly described before the approach to DEM development and the current EDEN DEM are 

detailed. Finally future research directions for continued model development, testing, and 

refinement are provided. 

Model Input: Highly Accurate Elevation Data 

No standard ground elevation data products are accurate enough for EDEN and other Everglades 

science and restoration requirements; therefore, the USGS had to create a new database of HAED. 

Following discussions with other scientists and potential users of HAED and considering the 

balance of hydrodynamic model development requirements and cost, a horizontal distance between 

collected elevation points of approximately 400m and a vertical accuracy of +/-15cm were 

specified for HAED. To create such a data base for the Everglades Region, two methods were used. 

Approximately 11,000 elevation points were collected by deploying surveyors on airboats. While 

conventional and easily understood, this approach has several disadvantages. First and foremost, 

its use throughout the entire Everglades system might cause significant negative impacts on 

Everglades vegetation and animals. In addition it is very time consuming and therefore expensive 

given a large region like the Everglades. As an alternative the USGS developed a system called the 

“Airborne Height Finder” (AHF – figures 1 and 2). The AHF has been deployed in a series of 

survey campaigns to collect over 43,000 HAED points covering the Everglades National Park and 

most of the Water Conservation Areas. These data were combined with the airboat-collected points 

to create an elevation data base for the Everglades region (figure 3). An example from one USGS 

quadrangle in which both airboat and AHF HAED data were collected is shown in figure 4. As data 

collection has occurred over a 10-year period, the AHF system has been refined, and specific 

1

attribute information collected for individual points has increased. Also some AHF data were 

collected to replace some elevation values measured using airboats or to address specific 

topography issues identified by hydrologic and biologic research. Given these developments and 

applications-specific data collection campaigns, disparate HAED files were created. Part of the 

EDEN DEM development included the reprocessing and quality assessment of these various data 

sets. As a result more than 70 separate HAED data files were merged into a single file using 

geographic information system (GIS) processing methods. During this process data for areas that 

were re-flown by the AHF were replaced, and a unified attribute file was produced. The resulting 

spatial and attribute data were processed through a final quality assurance and quality control 

process to produce consistent, high-quality input for EDEN DEM development. 

Model Development Approach 

The development of DEMs for use in EDEN applications, as well as other hydrologic and ecologic 

modeling and adaptive management, has been an iterative process. Prior to the EDEN project a 

DEM for an area beyond those of EDEN modeling (i.e., including coastal regions influenced by 

tides) was produced using the ESRI ARCGIS topogrid algorithm1

 (figure 5). This algorithm relies 

on spline interpolation that is modified to produce a “hydrologically correct” DEM. While visually 

pleasing and sufficient for regional scale analysis, this model is not suitable for the sub-regional 

and finer-scale quantitative analyses envisioned for EDEN outputs. For example while the spline 

approach honors the individual HAED values (i.e., spline surface elevations are exactly those of the 

input points at the input point locations), topogrid can generate false peaks and pits along regions 

where drastic changes in elevations occur and channels are not supported by actual ground 

measurements. Figure 6 depicts a small area in which water depth estimates have been created 

using the DEM produced by topogrid. While the dendritic drainage pattern depicted may seem 

plausible, it is not supported by field measurements and suggests resolution in the data that do not 

exist. Also when applied to the entire HAED dataset at once, the spline process fails to adequately 

represent topographic breaks that occur along the boundaries of Water Conservation Areas where 

levees, canals, and service roads interrupt the natural gradients presumably present prior to 

development of the water control infrastructure. 

To create a more realistic region-wide elevation model for EDEN purposes, the elevation data were 

segregated by Water Conservation Areas and National Park boundaries so that local trends could be 

isolated, sub-region specific interpolation models could be developed, and realistic breaks in 

elevation along sub-region boundaries could be imbedded in a final, region-wide DEM. For each 

EDEN sub-area (figure 7), several surfacing algorithms that are more conservative than topogrid 

(when interpolating between known elevations) were evaluated. Outputs from these different 

methods were evaluated through three approaches. First, for each sub-region 15 percent of the 

points were withheld from the model development for their respective area before numerous 

interpolation methods and parameters within interpolation methods were specified using the 

remaining 85 percent of HAED points. The withheld points were then used as a “check” of 

simulated elevation values by comparing generated surfaces against their values. Next all HAED 

were included in sub-area model development and cross-validation was applied. In this process the 

software iteratively compares modeled surfaces to those of the input points used to create the 

1

 Use of product and trade names is for illustrative and informational purposes only and does not represent an 

endorsement by the U.S. Government. 

2

surface2

. Water depths are created by subtracting the generated DEM from water surfaces that were 

interpolated from EDEN gage data; these depths are compared against estimated depths gathered 

by various principal investigators (i.e., “PI data”) during field campaigns. Based on these 

evaluations and consideration of the utility of other diagnostic surfaces that are created as byproducts of various interpolation processes, a “best” model is selected for each sub-area. Finally 

selected sub-area models are combined to create an EDEN regional DEM. The steps used in surface 

modeling can be summarized as follows: 

1) Subset the HAED by EDEN sub-areas. 

2) Randomly extract 15percent of the HAED points from the set of observations associated 

with each EDEN sub-area. 

3) Create numerous models for each sub-area using different surface interpolation methods. 

4) Compare elevations interpolated using each method against those of the data points 

withheld during model development. 

5) Based on error analysis select the "best" method for each EDEN sub-area. 

6) Given the best method selected use ALL available HAED points to generate numerous 

elevation models for each sub-area by varying within-method modeling parameters. 

7) Create depth layers for specific wet and dry days by subtracting modeled ground elevation 

surfaces from water height surfaces interpolated from recorded EDEN gage data. 

8) Compare modeled water depths against field-estimated water depths. 

9) Select the best performing elevation model for each sub-area. 

10) Combine the chosen sub-area models to create one single EDEN elevation model. 

11) As new HAED and field measurements of water depth become available, return to Step 1. 

Results 

Table 1 shows the different root mean square errors (RMSE) produced and variance in withheld 

data points explained by elevation models produced by three different methods for WCA 1. Table 2 

provides the RMSE generated through cross-validation by the best performing model used in each 

EDEN sub-region. The different water depth layers produced by three different elevation modeling 

methods for the same large-scale area are evident in figure 6, 8 and 9. 

Method RMSE (meters) R2 

Topogrid 0.1514 0.59 

RBF 0.1212 0.64 

Kriging 0.1056 0.69 

Table 1. Root mean squared error (RMSE) in meters and variance explained (R2

) in withheld data points by 

models created for Water Conservation Area 1 (WCA1) by different surfacing algorithms. “Topogrid” is the 

hydrologic surfacing algorithm incorporated in ESRI software. “RBF” references radial based function 

interpolation. “Kriging” references ordinary kriging with an anisotropic model. 

To-date the best performing DEMs for all sub-areas have been produced using the geostatistical 

approach called “anisotropic ordinary kriging”. This method consistently produced the lowest error 

2

 Cross-validation only applies to techniques that do not honor the actual elevation value at the observed point in 

creating the estimated surface. 

3

for the 15 percent of withheld points and during cross-validations. It has the added advantage of 

producing standard error maps (e.g., figure 10). The standard error map is one of several variables 

used to index the quality of water depth estimates both across space and through time. This helps 

users of EDEN output to assess the utility of depth estimates for particular places and time periods. 

Compartment RMSE (meters) Direction (degrees) 

WCA-1 0.1616 309 

WCA-2 0.1141 92 

WCA-3AN 0.0975 60 

WCA-3AS 0.1146 29 

WCA-3B 0.0887 32 

BCNP 0.1297 94 

ENP 0.1311 298 

Table 2. Root mean squared error (RMSE) in meters produced through cross-validation for ordinary kriging as 

applied to each EDEN sub-area. These models were anisotropic and the “Direction” column indicates the axis 

(in degrees) of anisotropy for each sub-area model. 

File naming conventions 

The modeling and evaluation approaches are repeatedly applied at the level of individual EDEN 

sub-areas (figure 7) as additional HAED or PI data become available. Revised sub-area DEMs are 

then merged into the regional EDEN DEM. This flexibility adds complexity that must be balanced 

through organized data management. To help track the various improvements to the EDEN DEM, a 

specific naming convention was developed. For example figure 11 shows an iteration of the EDEN 

DEM that is internally referenced by the EDEN research team as “EDEN_DEM_v2c”. This 

indicates that this DEM is the third modification (c) of the second generation (v2) of digital 

elevation modeling techniques (i.e., the HAED data available at the time of its creation and the 

application of kriging to each EDEN sub-area as described in this report). Following 

EDEN_DEM_v2c’s assessment and additional use in trial applications, EDEN collaborators felt 

that its performance was superior in all EDEN sub-areas except for WCA1 (figure 7), where the 

EDEN_DEM_v2a produced lower depth estimate errors across a variety of water level conditions 

(Lovejoy Palaseanu, per. com., 2006). Therefore, a “hybrid” DEM was created using the WCA1 

area model from EDEN_DEM_v2a and the EDEN_DEM_v2c models for all other sub-areas 

(internally named “EDEN_DEM_v2d”). While this naming convention is consistent and 

meaningful for EDEN team members, it is unnecessarily complex and not particularly meaningful 

for EDEN applications users. Release and operational versions of EDEN DEMs are therefore 

named according to the month and year they are put into operational use and officially released. 

Because commonly used GIS software impose a 13-character file name limit, a convention of 

“EDEN_EM_MMMYR” where EM stands for “elevation model”, MMM represents “month”, and 

YR designates the year, is used. Therefore, upon release EDEN_DEM_v2d was named 

“EDEN_EM_JAN07” (figure 12). 

4

Data documentation and distribution 

GIS cataloging capabilities are used to document every DEM created for EDEN. Each contains 

metadata that includes information on the intended use, date of production, modifications from 

previous DEM versions, and spatial characteristics. A separate metadata file that meets Federal 

Geographic Data Committee spatial metadata standards is associated with each release DEM 

version (such as EDEN_EM_JAN07). This open file report documents the approach used to create 

release EDEN DEMs. Future DEM releases will reference this file for those interested in the 

approach used to generate them. If EDEN DEM production methods change significantly from 

those documented here (e.g., with the use of satellite or airborne remote sensed data for DEM 

interpolation), a new open file report will be published to document the revised approach. Release 

DEMs are freely distributed through the South Florida Information Access website 

(http://sofia.usgs.gov/eden/models/groundelevmod.php). 

Future plans 

Because we are interested in simulating water depths at the sub-400m resolution, future plans 

include the development of pseudo-topography using statistical examination of more than 54,000 

HAED as a function of vegetation type. For some areas of the Everglades vegetation type has been 

noted at the location of each HAED measurement by the AHF operator. Results from preliminary 

analyses using approximately 3,500 elevation and vegetation points for WCA3 are provided in 

figure 13, a box-plot showing which land cover classes are separable by elevation (as provided by 

the AHF). Vegetation cover types mapped at approximately 30-meter spatial resolution can be 

derived using Landsat satellite data for the Greater Everglades region. In combination with 

modeled depth surfaces observed relationships between topography and these vegetation types may 

be used to synthesize higher-resolution elevation models. A primary challenge in this process will 

be the proper segmentation of land cover and HAED to derive meaningful local-area vegetationelevation relationships. 

Acknowledgements 

Funding for HAED collection was provided by the USGS’s Greater Everglades Priority 

Ecosystems Science Program, the U.S. Army Corp of Engineers, the National Park Service, and the 

South Florida Water Management District. Funding for EDEN DEM development was provided by 

the USGS’s Greater Everglades Priority Ecosystems Science Program and the Land Remote 

Sensing Program. 

References Cited 

Desmond, G.D., 2003, Measuring and mapping the topography of the Florida Everglades for 

Ecosystem Restoration. U.S. Geological Survey Fact Sheet 021-03, 4 p. 

Lovejoy Palaseanu , M., 2006, Personal communication regarding DEM performance in EDEN 

application tools. 

Telis, P.A., 2006, The Everglades Depth Estimation Network (EDEN) for Support of Ecological 

and Biological Assessments: U.S. Geological Survey Fact Sheet 2006-3087, 4 p. 

5

Figures 


Figure 1. The airborne height finder (AHF) system as installed in a helicopter. The system control panel is 

evident in the center of the photograph. The plate and plumb-bob system reside just forward of the seat. 

6

Figure 2. Pre-mission testing of the AHF just following lift-off. The operator calibrates and drops the plumb-bob 

from the leveling plate and when the cable reaches a specified resistance, the global positioning system (GPS) 

captures the horizontal and vertical position. Post-processing yields vertical accuracies that are better than +/- 

15cm. 

7

Figure 3. Regional coverage of the high accuracy elevation database (HAED) as of summer of 2006. Red points 

were collected via airboat (note missing values occur where upland/tree island areas are found). Blue points 

were collected using the AHF. Upland and highly vegetated areas do not pose a problem for the AHF. 

8 
Figure 4. Just one 7.5-minute quadrangle area of HAED extracted from the database. As in the previous figure 

red points denote elevation data collected via airboat, and blue points represent AHF observations. As 

additional funds became available the AHF was redeployed to fill in some areas not easily covered by airboat. 

9 
Figure 5. A regional DEM created using the TOPOGRID algorithm. While visually appealing and useful for 

region-wide analysis, this DEM is not suitable for higher-resolution EDEN applications. 

10

Figure 6. Modeled water depth for a small area of Water Conservation Area 3 (indicated with a star symbol on 

figure 7) produced by subtracting the DEM produced using the TOPOGRID algorithm from the water surface 

elevation model created from EDEN water level data for May 27, 2004. This DEM suggests a channel network 

that cannot be validated with available data. More conservative results from other approaches are depicted in 

the figures 8 and 9. 

11 
Figure 7. A location map depicting the Greater Everglades region, EDEN DEM processing sub-regions and the 

location of method comparison referenced in figures 6, 8, and 9 (shown by star). 

12 
Figure 8. Modeled water depth (for the location noted by a star in figure 7) for May 27, 2004 using the DEM 

produced through Radial Based Function - Multiquadratic approach from EDEN water surface lvel for May 27, 

2004 (compare with figures 6 and 9). 

13 
Figure 9. Modeled water depth using a DEM produced through anisotropic ordinary kriging subtracted from 

EDEN-water level for May 27, 2004 (compare with figures 6 and 7). 

14 
Figure 10. Standard error of estimates (in meters) produced for Water Conservation Area 1 (see figure 7 for 

WCA1's location) through ordinary kriging. With the highest relief and tree island coverage this area is the 

most difficult to model and yields the greatest standard errors. Ordinary kriging has the benefit of producing 

this information, which can be used in assigning confidence index values for depth estimates. 

15

Figure 11. The EDEN DEM (with units of meters) used in EDEN water depth and hydroperiod modeling 

research as of summer 2006. 

16 
Figure 12. EDEN DEM used in operational depth estimation/EDEN applications and released in January of 

2007. 

17 
Figure 13. Boxplot of Water Conservation Area 1 (WCA1) vegetation classes as interpreted by the AHF 

operator during collection ("VEG.FS" along the X-axis) verses elevation in meters ("ELEV.M" along the Yaxis). The total number of observations is 3,537. Generally the large number of points collected by the AHF 

allows for narrow 95 percent confidence intervals (shown as notches in the boxes) about the median elevation 

value for each vegetation class. These relationships may be exploited along with remote sensed vegetation cover 

maps to create higher-resolution, synthetic elevation information. 

18