Teresa Stephens, GIS Specialist, Paul Bechtel & Associates, Inc. and Andrew Weinberg, Geoscientist, Texas Water Development Board
Presented at the 2011 Texas GIS Forum
2. Playas in Texas
• Dominant hydrological
feature of High Plains
• ~20,000 mapped playas
• Important source of
groundwater recharge
• Stop-over points for
migratory waterfowl
• Used for irrigation, forage,
and grazing
• Over 50 years of studies
3. Playa Features
• Wetland area
defined by soil, Wetland area
plants, and
Water area
hydrology
• Many playas lie
within larger
topographic
depression
• Water area varies Topographic depression
seasonally
4. TWDB Playa Project
Background
Continued drawdown of High Plains Aquifer threatens
agricultural economy of the Texas Panhandle
Project Objectives
1. Determine volume and distribution of playa water
resources
2. Determine timing and magnitude of recharge under
current conditions
3. Assess playa modification strategies to increase
recharge
5. Objective 1: Determine Volume and
Distribution of Playa Water Resource
Two strategies:
• Field monitoring
– Instrument and survey
selected playas
• Remote sensing
– Estimate water
resource region-wide
7. Field Monitoring
PROs CONs
• Tailored to project objectives • Time consuming
• Continuous observations • Expensive
• Access to subsurface • Continual maintenance,
• Ground-truth remote/indirect recalibration
observations • Access agreements
• Extensive QC and data
management
8. Remote Sensing
PROs CONs
• Data available for free • Clouds
• Regional coverage • Limited resolution
• 30-year archive of imagery • Limited frequency of
• Work with desk-top tools observations
10. ESTIMATING WATER VOLUME
A 3-Part Process
1. CLASSIFY WATER AREAS IN PLAYAS USING REMOTE
SENSING METHODS
2. OBTAIN PLAYA WATER SURFACE ELEVATION AND BASIN
TOPOGRAPHY USING GIS METHODS
3. ESTIMATE PLAYA VOLUME USING GIS METHODS
12. CLASSIFY PLAYA WATER AREAS
EVALUATE AVAILABLE RS IMAGERY
• 21 Types of Remotely-Sensed Imagery were evaluated
• 17 were eliminated due to: lack of current data, wrong
scale, or simply not a good fit for the type of mapping
inherent to the project
• 4 selected for further consideration: Landsat-4, with
the Thematic Mapper sensor (TM), Landsat-5 TM, and
Landsat-5 Multi-Spectral Scanner (MSS), Landsat-7
13. CLASSIFY PLAYA WATER AREAS
EVALUATE AVAILABLE RS IMAGERY (cont.)
× Landsat-4 TM, decommissioned June 2001. (need current data)
Landsat-5 TM: improved spectral separation and
geometric fidelity, greater radiometric accuracy and
resolution than the MSS sensor. Used to monitor
changes in land surface over periods of months to
years—a near perfect fit for this project!
× Landsat-5 MSS: Landsat-5 TM better fit for this project.
× Landsat -7: The Scan Line Corrector (SLC) in the ETM+ instrument
failed in 2003; good data from 1999 – 2003.
14. CLASSIFY PLAYA WATER AREAS
Obtain Landsat-5 TM Image
Readily available from
landsat.gsfc.nasa.gov OR
glovis.usgs.gov
User friendly GUI allows
obtaining by coordinates,
satellite row/path, or by
interactively selecting an
area of interest. https://glovis.usgs.gov
16. CLASSIFY PLAYAS WATER AREAS
Evaluate TM Spectral Bands
Band Wavelength, µm Characteristics
1 0.45 to 0.52 Blue-green. No MSS equivalent. Maximum penetration of water,
which is useful for bathymetric mapping in shallow water. Useful for
distinguishing soil from vegetation and deciduous from coniferous
plants.
2 0.52 to 0.60 Green. Coincident with MSS band 4. Matches green reflectance peak
of vegetation, which is useful for assessing plant vigor.
3 0.63 to 0.69 Red. Coincident with MSS band 5. Matches a chlorophyll absorption
band that is important for discriminating vegetation types.
4 0.76 to 0.90 Reflected IR. Coincident with portions of MSS bands 6 and 7. Useful
for determining biomass content and for mapping shorelines.
5 1.55 to 1.75 Reflected IR. Indicates moisture content of soil and vegetation.
Penetrates thin clouds. Good contrast between vegetation types.
6 10.40 to 12.50 Thermal IR. Night time images are useful for thermal mapping and for
estimating soil moisture.
7 2.08 to 2.35 Reflected IR. Coincides with an absorption band caused by hydroxyl
ions in minerals. Ratios of bands 5 and 7 are potentially useful for
mapping hydrothermally altered rocks associated with mineral
deposits.
18. Composite and Detail Views of Enlarged Landsat-5 TM Subscene,
SE Quadrant, Floyd County (October 15, 2010)
19. CLASSIFY PLAYA WATER AREAS
Single, Band-5 Selected
• Initial evaluation indicated
single-spectral Band 5
classification provided best
results with minimal
processing
• Grid cells with a value of ≤60
indicate water area
• Field verification scheduled
Landsat-5 TM, Band 5
20. CLASSIFY PLAYA WATER AREAS
Field Verification - 11 May 2011
• Scheduled to coincide with
Landsat-5 image acquisition
• Cloud-free day
• Visual inspection of playas on
transect across study area
• 30 Playas in corridor classified
as wet, wet soil only, or dry
• Attributes overlaid on Landsat
imagery for further review.
Location Map of all
Field-Verified Playas
21. CLASSIFY PLAYA WATER AREAS
Field Verification—Wet Playas
DETAIL AREA
Landsat-5 TM, Spectral Detail of Wet Playa
Band 5 (Wet Playa)
22. CLASSIFY PLAYA WATER AREAS
Field Verification—Wet/Dry Playas
DETAIL AREA
Landsat-5 TM, Spectral Detail of Wet/Dry Playa
Band 5 (Wet/Dry Playa)
23. CLASSIFY PLAYA WATER AREAS
Field Verification--Dry Playas
DETAIL AREA
Landsat-5 TM, Spectral Detail of Dry Playa
Band 5 (Dry Playa)
24. CLASSIFY PLAYA WATER AREAS
Create Final Footprints
• Contour using Spatial
Analyst:
--input raster = Band 5
--contour interval = 60
• Isolines ≠60 removed
and non-playa water
areas clipped
• Remaining feature lines
converted to polygons
using Data Management
Tools in ArcToolBox
Wet Playa Footprints, October 15, 2010
26. PLAYA SURFACE ELEVATION AND
BASIN TOPOGRAPHY
Evaluate Available Elevation Data
• Five data-sets evaluated: National Elevation Dataset
(NED), Shuttle Radar Topography Mission (SRTM),
Digital Elevation Models (DEM), and Global 30-Arc-
Second Elevation Dataset (GTOP030).
• Major considerations included: seamless coverage,
matching scale, current data, and easily accessible
NED Data selected (http://seamless.usgs.gov)
• regularly updated composite of the latest DEM
• seamless
• 10-meter resolution – best available for study area
28. PLAYA SURFACE ELEVATION AND
BASIN TOPOGRAPHY
Obtain Playa Surface Elevations
• Project elevation data to UTM using ArcINFO Workstation
• Create Raster point file using ArcToolBox conversion tools
• Associate maximum surface elevation with individual
playa footprints using spatial join. However…
• Extremely long processing times (11 hours!) when using
the entire NED data set so an interim step was introduced
• Spatial query used to extract points inside or near playas,
then the spatial join was applied to the refined point data
set (spatial join processing time now <2 hours).
29. PLAYA SURFACE ELEVATION AND
BASIN TOPOGRAPHY
Create Final Elevation Data Set
SUMMARIZE ON PLAYA-ID
TO OBTAIN MIN/MAX
ELEVATION VALUES
30. PLAYA SURFACE ELEVATION AND
BASIN TOPOGRAPHY
Min/Max Elevation Attributes now
Associated with Wet Playa Footprints
32. ESTIMATE PLAYA WATER VOLUME
Work Directly with Raster Data
Top Surface: Generated using Polygon to Raster based
on Max Grid Elevation value
Bottom Surface: Obtained directly from projected NED
raster (no additional processing involved)
Volume Method: Use Spatial Analyst CutFill
35. ESTIMATED WATER VOLUME, FLOYD COUNTY, TEXAS
(OCTOBER 15, 2010)
Final Results
• Water identified in 741 of
the 1,721 mapped playas in
Floyd County
• Water area = 18,395 acres
(2.89% of Floyd County)
• Water volume = 97,216,952
cubic meters or 78,815 acre-
feet in Floyd County playas
on October 15, 2010.
37. Next Steps
Method Validation
• Playa surveys
• Area-volume and depth-
volume relationships
• Water level
observations
• Compare with remote
sensing
Bivins Playa -Elevation - Area
250
200
Area, acres
150
100
50
0
3238 3240 3242 3244 3246 3248
Elevation, ft msl
38. Method Validation
• Accuracy of RS estimates limited by:
– Image pixel size, pixel classification, and contouring
– Local accuracy of NED surface
– Landscape changes over time since underlying
topographic data collected
• Field data accuracy limited by:
– GPS accuracy (~ ½ inch vertical for Trimble R6)
– Number and distribution of grid points
– Access limitations
39. Method Validation
Floyd Crop Playa
• Single Floyd County
playa with field data for 2270500
970.2
10/15/2010 2270450
970.1
970
969.9
969.8
– RS volume estimate of 2270400 969.7
969.6
969.5
43,247 cubic meters 2270350
969.4
969.3
969.2
– Field volume estimate 2270300
969.1
969
968.9
of 51,218 cubic meters 2270250
968.8
968.7
968.6
based on 38 cm water 968.5
968.4
968.3
2270200
depth 968.2
968.1
968
– 16.9 relative percent 2270150
difference 340000 340050 340100 340150 340200 340250 340300 340350 340400 340450
0 50 100 150 200
40. Validation Data Set
• Scale up to area of
one Landsat image tile
• TWDB data
• No TWDB playas in
image area filled in
2011
• No data for 2010
• TTU/ARS data
• 16 playas monitored
in 2010
• Look at images from 9 TTU/ARS Playa
June, 25 June, 12 TWDB Playa
August, and 15 October
2010