1. DEVELOPING A MODEL TO VALIDATE THE USE OF LANDSAT AND MODIS DATA
TO MONITOR COASTAL MARSHES FOR PERSISTENT SALTWATER INTRUSION IN
SOUTHWESTERN LOUISIANA
Abraham Dailey
John C. Stennis Space Center
August 7, 2009
Reviewed by: _______________________
Callie M. Hall
Applied Sciences & Technology Project Office

2. Abstract
According to the USGS, Coastal Louisiana has lost approximately 1,900 square miles of
land between 1932 and 2000 (Steyer, Twilley, & Raynie, 2006). The threats of sea level rise,
subsidence, and land loss pose an ongoing threat to the health and vitality of the environment and
the economy of Coastal Louisiana. Kalcic et al. are investigating the impact of persistent
saltwater intrusion on coastal wetlands in Southwestern Louisiana to provide data products for
monitoring and restoring these wetlands. The enormous amount of data involved in this research
requires a data mining technique that is capable of sorting through data from many different
sources and combining the data in such a way that adjusts for spatial and temporal gaps in the
data. Modelbuilder in ArcGIS 9.2 was used to construct a series of tools that fulfill this
requirement. These tools were run on a sample of the data being used by Kalcic et al., and the
output was used to analyze a series of False Color Composites (FCCs) to determine how
wetlands are changing in response to changes in physical data describing the marsh. The results
show that this procedure is capable of fulfilling the requirements set by Kalcic et al.
Introduction
The Calcasieu-Sabine Basin is located in southwestern Louisiana, with the Sabine River
forming the border between Louisiana and Texas. This estuary was formerly two distinct
hydrologic units separated by the Gum Cove Ridge, with minimal saltwater intrusion from the
Gulf of Mexico. Dredging operations in the Calcasieu and Sabine Rivers improved the flow of
saltwater inland from the Gulf of Mexico, while the construction of the Gulf Intracoastal
Waterway (GIWW) and other inland canals connected the two basins and allowed saline waters
from the Gulf Coast to permeate and destabilize the freshwater equilibrium that had once existed
further inland (Louisiana Coastal Wetlands Conservation and Restoration Task Force, 2002).
These hydrological modifications have made the Calcasieu-Sabine Basin more vulnerable to
storm events, thereby exposing previously sheltered areas to the threat of saltwater inundation. If
subsidence and sea level rise outpace the rate at which vegetative communities are able to adapt
to the changing salinity gradient, this will exacerbate the current vulnerability as larger areas of
the coastal ecosystem are left barren and exposed to the threat of erosion. In light of these facts,
it is of critical importance to monitor the Calcasieu-Sabine Basin for persistent saltwater
intrusion.
Rodgers et al. and Steyer et al. have used indices derived from satellite imagery to
monitor changes in water, soil, and vegetation caused by saltwater inundation during Hurricanes
Katrina and Rita in Alabama and Louisiana respectively. Kalcic et al. have proposed to use
Landsat and MODIS time series data products, in combination with in situ data collected by
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3. USGS and NOAA monitoring stations, to identify areas of the coastal wetlands in the CalcasieuSabine Basin that are subjected to persistent saltwater flooding. These products will be used to
identify areas that are more vulnerable to the loss of habitat and environmental degradation
which may accompany a changing salinity gradient. Ultimately this will aid in assessing,
monitoring, and restoring the health of coastal estuaries to ensure that they will continue to
provide humanity with incalculable economic benefits, such as providing a buffer against storm
surge, filtering pollutants, and providing a spawning ground for marine life.
Problem
The problem with using data collected from various agencies with different agendas is
that there is no consistency in spatial or temporal coverage, attribute accuracy, or completeness
of the data. The USGS coastal monitoring stations were intended to collect a wide array of
attributes at the sites of various restoration activities and monitoring sites across the study area.
Unfortunately, some of these monitoring stations were abandoned when the associated
restoration activities were de-authorized, others were damaged or destroyed during extreme
weather events, and it appears that data from many stations simply was lost, possibly due to
faulty equipment or errors in post processing.
While it would be ideal to have complete data coverage, the reality is that we need a
procedure for working with the data that exists. This paper will examine the use of Model
Builder in ESRI’s ArcGIS 9.2 to develop a model that will synthesize remotely sensed and in
situ data to identify and validate the relationship that may exist between salinity, inundation, and
changes in vegetation, soil, and wetness indices derived from Landsat imagery. This model will
produce a database containing climate data, salinity, marsh water elevations, and pixel values
from the Landsat indices at the location of each USGS monitoring station. If an area of the study
area is persistently flooded the expected result will be a rise in the wetness index, followed by a
decline in the vegetation index with a corresponding rise in the soil index. The in situ data will
be used to confirm that changes in the wetness index were caused by saltwater intrusion, as
opposed to precipitation or storm-water run-off. This model will offer a powerful solution to the
problems outlined above, and the database it produces will significantly aid in the interpretation
and validation of the time series data products utilized by Kalcic et al.
Method of Solution
The first stage of the procedure involved downloading and assembling the USGS and
NOAA data into a comprehensive geo-spatial database. Continuous hourly data from every
USGS monitoring station in the vicinity of the Sabine-Calcasieu Basin were acquired for the
years 2004 - 2006 inclusive (Louisiana Department of Natural Resources, 2009). Attributes
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4. measuring salinity, water elevation relative to datum, and water elevation relative to marsh
surface were combined into one database. NOAA climate station data measuring daily
temperature and precipitation values around the perimeter of the study area was acquired from
the National Climatic Data Center Data measuring hourly tide heights (NOAA, 2009). Air and
water temperatures, barometric pressure, and wind speed and direction at stations around the
perimeter of the study area were acquired from NOAA's National Data Buoy Center (NOAA,
2009). The Landsat indices used in this study were provided by Kalcic et al (Kalcic, Estep, Hall,
& Steyer, 2009).
These data were loaded into ArcGIS and a model was developed to combine the various
sources into one database. Since there are many CS stations throughout the study area, their
spatial location provides the foundation for the final output of this model. Since the Landsat
indices are the primary focus of the study being conducted by Kalcic et al. the date and time of
each Landsat scene is used to determine the subset of data that will be selected from the USGS
CS / CRMS, NOAA climate, and NOAA NDBC databases. These data were combined using a
series of spatial joins, and the output of this procedure was filtered to remove invalid or
redundant data. A Detailed Operating Procedure (DOP) manual was written, which describes the
operation of this model in great detail (Dailey, 2009). This model was then used to extract data
for a series of dates before and after Hurricane Rita.
Discussion
An example of the output that is produced using the procedure described above is shown
in Figure 1. The left side of the chart shows data for USGS station CS-09, while the right side of
the chart shows data for station CS-15R (See Figure 2 for station locations). Figure 2 shows a
series of False Color Composites (FCC) of the Mud Lake area, in the southeastern quadrant of
the study area shown in Figure 1, which correspond to the dates used in Figure 1. These FCCs
have the NDWI assigned to the blue band, the NDVI assigned to the green band, and the NDSI
assigned to the red band. The USGS and NOAA data shown in Figure 1 depict the physical
conditions on the ground at the time that the Landsat scenes were acquired. Unfortunately, there
are some gaps in the available data.
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5. Figure 1: Example of output from model
As is shown in Figure 1, the only date that experienced a significant precipitation event
was September 24, 2006, which was the maximum value of all the days included in this study.
However, the daily precipitation data only represent conditions at the time of image acquisition,
and this ignores the effect of any significant precipitation events in the days prior to image
acquisition. Figure 3 is a chart of monthly precipitation totals as well as the difference from
normal precipitation. This chart shows that September 2005 was a month that had 5 inches above
normal precipitation, which is explained by Hurricane Rita. Figure 1 shows that between
September 21 and 29 tide height and water elevation relative to the marsh increased drastically at
station CS 20-15R, and the same may be inferred for station 20-09 based on its spatial proximity
to CS 20-15R. The increase in water elevation at these stations may be in part due to storm surge,
as suggested by the increased tide height, but it also may have been caused by the above average
precipitation for the month of September. This would explain why even as the area was
inundated with saltwater, the salinity values shown in Figure 1 actually declined between
September 21 and 29.
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6. Figure 2a-d (ordered left to right, top to bottom): FCCs for September 21, 2005, September 29, 2005, October 23, 2005,
and September 24, 2006 (Kalcic, Estep, Hall, & Steyer, 2009)
Figure 3 shows that September 2006 was a month with below average precipitation, and
it also shows that, with the exception of the months of June, July, and August, most of the period
between October 23, 2005 and September 24, 2006 received below average precipitation. While
salinity would be expected to increase during periods of drought, the precipitation experienced
during June, July, and August would be expected to dilute the salinity. Furthermore, as is shown
in Figure 1, the precipitation event that occurred on September 24, 2006 is the maximum
precipitation observed for any date considered in this study, and when combined with the
precipitation during the summer months, this may explain why salinity on September 24, 2006 is
lower than the value on October 23, 2005.
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7. Figure 3: Monthly Precipitation Data 2004 – 2006 (NOAA, 2009)
Figure 2a shows the study area on September 21, 2005, which was four days before
Hurricane Rita made landfall in southwestern Louisiana. Figure 1 indicates that the verified tide
height at the time of image acquisition was at a minimum value, while the water elevation
adjusted to the marsh surface and NDWI at station CS20-09 were at the maximum negative
values for the time period considered. This would explain why there is more land visible in
Figure 2a than in any of the other FCCs. The area immediately to the southeast of station CS2015R near Lake Calcasieu appears to have more water present in Figure 2a than in any of the
other FCCs. This would explain why the NDWI value at station CS20-15R was at a less negative
value on September 21, when there was more water visible in the image at the location of the
station, compared to the more negative NDWI value observed on October 23, when there was
less water visible in the image. Unfortunately, there is no water elevation adjusted to marsh
surface data available from station CS20-15R on October 23 to confirm this analysis.
Furthermore, the elevated water elevation observed at station CS20-15R on September 29 does
not seem to be confirmed by Figure 2b. While there is obviously a higher water elevation shown
just south of station CS20-15R in Figure 2b compared to Figure 2a, there does not appear to be
any change in water that is visible at the exact location of the station, and the NDWI values also
remain constant between September 21 and 29.
Figure 1 demonstrates that the NDVI values decreased between September 21 and
September 29, while NDSI values were at a maximum negative value on September 21 and
became less negative by September 29. Figure 2a shows that the area around station CS20-15R is
mostly green on September 21, 2005. Figure 2b shows an orange-yellow strip that originates
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8. from Lake Calcasieu just north of CS20-15R and moves in a southwesterly direction towards
Mud Lake. The orange would indicate that there is healthy vegetation intermixed with bare soil
or dead vegetation, while the yellow color may indicate that there is water intermixed with
healthy vegetation. The NDVI values shown in Figure 1 increased between September 29 and
October 23, while the NDSI values increased over the same time period. This implies that
healthy vegetation decreased after Rita, only to recover in the following month, while at the
same time there was a steady increase in dead plant matter and bare soil between September 21
and October 23.This is supported by Figure 2c, which shows that by October 23 this strip had
begun to recover, as indicated by the patches of green that appear in areas that had been orange
in Figure 2b. The increase in the NDSI shown in Figure 1 agrees with the orange areas that still
appear around station CS20-15R.
The patch to the southeast of CS20-15R remains a fairly constant green color in Figures
2a, 2b, and 2c, while it appears to be orange in Figure 2d. The difference between these two
areas could be explained if the vegetation to the north of CS20-15R was at a lower elevation than
the area to the southeast. If this was the case, it would have received the brunt of the saltwater
inundation and precipitation during Rita, and perhaps the vegetation was not able to tolerate the
changing conditions. There appears to be a ditch or canal visible in Figure 2 which connects
Lake Calcasieu and Mud Lake, and may confirm this hypothesis. The area to the southeast of
CS20-15R may have been at a higher elevation, or it may be a vegetation community that is
more tolerant of salinity. The reason this are appears orange in Figure 2d may be explained by
the long periods of below average precipitation during 2006.
Figure 1 shows that between September 21 and October 23, salinity values increased at
station CS-09 while water elevations may have increased between September 21 and 29, and
then decreased between September 29 and October 23, based on the height of the tides. Figure 2b
shows a rectangle to the right of station CS20-09 which highlights an area that experienced an
increase in the water levels to the east and to the south of the monitoring station between
September 21 and 29, 2005. The areas to the north and to the east of this rectangle have changed
from being mostly green in Figure 2a to being orange in Figure 2b. The NDVI value for station
CS20-09 shown in Figure 1 is at a maximum value on September 21 and gradually declines until
it reaches a minimum on October 23. This may indicate that storm surge from Lake Calcasieu
inundated this area of the marsh during Hurricane Rita and damaged the vegetation in this
community. This is confirmed by the fact that Figure 1 shows that the tide height was at the
maximum value of all dates considered, but unfortunately there is no data from this station to
confirm that the water levels were higher on September 29 compared to the 21. It can only be
assumed that the lack of data on the 29 was caused by a rapid increase in water levels, which
may have damaged the station and prevented it from collecting data.
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9. Figure 2c shows that after the water elevation in the vicinity of CS20-09 decreased
between September 29 and October 23 and salinity increased, the entire southeast quadrant of
this area of the marsh has changed from a mostly healthy green color to an orange color. This
indicates that there is more bare soil or dead vegetation in the vicinity of station CS20-09 on
October 23 than on September 21 or 29, which may be attributed to the increase in salinity over
this interval. This may support the claim that the higher water levels evident in Figure 2b are the
result of saltwater inundation, and the previous discussion of the health of the vegetation
communities around the vicinity of station CS20-15R demonstrate that this change is not entirely
due to seasonal variations.
Figure 1 shows that there is a decline in the NDVI values with a corresponding increase
in the NDSI values between September 21, 2005 and September 24, 2006 for station CS20-09.
Between October 23, 2005 and September 24, 2006 the NDVI values increase, but they do not
reach the same maximum value that was observed in September 2005. The NDSI value has
decreased, but it is still a less negative value than the NDSI in September 2005. This indicates
that there is less healthy vegetation and more bare soil and dead vegetation between September
2005 and September 2006, which may explain the greater presence of orange in Figure 2d,
particularly in the southeast quadrant of the image, compared to Figure 2a. While Figure 1 shows
that the salinity was actually lower on September 24, 2006 than on September 21, 2005, this may
be explained by the precipitation event that occurred on September 24, 2006. This precipitation
may disguise the fact that prior to this date salinity values may have been higher, and when
combined with the below average precipitation values for most of 2006, this may explain why
there is more bare soil in dead vegetation visible in Figure 2d than in Figure 2a. The initial shock
of saltwater inundation during Hurricane Rita may have weakened the vegetation communities,
while the below average precipitation in the following months may have caused the vegetation to
degrade even further. Unfortunately, it is not possible to arrive at a definite conclusion without
analyzing more data.
Conclusion
A database was constructed using the procedure outline in this paper, and this database
was used to analyze a series of images before and after Hurricane Rita. In some cases the data
confirmed the hypothesis that saltwater intrusion has a negative impact on the health of marsh
vegetation, but there were also several instances where the Landsat data did not agree with the
physical data from the monitoring stations. Some notable examples of this include when water
levels increased substantially at station CS20-15R between September 21 and 29, while the
NDWI values remained constant, or when salinity decreased while the health of the vegetation
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10. communities also decreased. The latter example was explained by an in-depth analysis of the
monthly precipitation data alongside the data generated by the model and the FCCs created from
the Landsat indices. This analysis suggested that salinity may have increased while precipitation
decreased over the course of the year, only to have been masked by an extreme precipitation
event on the date of image acquisition.
This paper revealed several improvements to the model that may be advisable before
implementing this algorithm on a larger scale. Specifically, it may be desirable to alter the
algorithm so that, if there are any missing physical data from the USGS monitoring stations on a
specific date, the algorithm will interpolate values based on data from nearby stations. This is
similar to the algorithm which joins data from the NOAA stations based on proximity to the
nearest USGS station. Furthermore, if there are any USGS stations that are missing all data
values on a particular day, it may be desirable to alter the procedure so that Landsat pixel values
are extracted at the location of the station and then the physical data is interpolated based on
available data from nearby stations. These changes would eliminate the gaps in data coverage
that were an obstacle in interpreting data in this paper, and they would provide a more uniform
picture of how conditions in the marsh are changing over time.
The model that was constructed using Model Builder in ArcGIS 9.2 successfully
assembled data from various sources and produced a database that could be used to aid in the
interpretation of the images; however some improvements may be needed before implementing
this model on a large scale. The evidence presented in this paper suggests that the model shows
promise as a solution to the original problem stated in this paper, but a more in-depth analysis of
the study area is required to confirm the hypothesis that there is persistent saltwater intrusion in
the study area, and that this intrusion has a negative impact on the health of the vegetation. To
this end, Kalcic et al. will use the model presented in this paper as they continue their research
into this problem.
Acknowledgments
I would like to think my mentors at NASA SSC, Callie Hall and Maria Kalcic, for
providing direction. I would also like to thank Nancy Borderlon, everyone in the Applied
Sciences & Technology Project Office, SSAI, and the entire SSC community. I also
acknowledge the funding provided by the Maine Space Grant Consortium.
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11. Bibliography
Allen, Y. C., Constant, G. C., & Couvillion, B. R. (2008). Preliminary Classification of Water Areas
Within the Atchafalaya Basin Floodway System by Using Landsat Imagery. U.S. Geological Survey Open-File
Report 2008-1320.
Baldwin, A. H., & Mendelssohn, I. A. (1998). Effects of Salinity and Water Level on Coastal Marshes: An
Experimental Test of Distrubance as a Catalyst for Vegetation Change. Aquatic Botany , 61, 255-268.
Dailey, A. (2009). Persistent Saltwater Tools Documentation. Stennis Space Center: NASA.
Howard, R. J., & Mendelssohn, I. A. (1999). Salinity as a Constraint on Growth of Oligohaline Marsh
Macrophytes. I. Species Variation in Stress Tolerance. American Journal of Botany , 86 (6), 785-794.
Kalcic, M. T., Estep, L., Hall, C. M., & Steyer, G. D. (2009). Monitoring Coastal Marshes for Persistent
Saltwater Intrusion. Earth Science Applications Directorate. Stennis Space Center: NASA.
Louisiana Coastal Wetlands Conservation and Restoration Task Force. (2002). Hydrologic Investigation of
the Louisiana Chenier Plain. Baton Rouge, LA: Louisiana Department of Natural Resources, Coastal Restoration
Division.
Louisiana Department of Natural Resources. (2009, 08 07). SONRIS GIS 15. Retrieved 08 07, 2009, from
http://sonris-www.dnr.state.la.us/gis/sonris/viewer.htm
Morris, J. T., Porter, D., Neet, M., Noble, P. A., Schmidt, L., Lapine, L. A., et al. (2005). Integrating
LIDAR Elevation Data, Multi-spectral Imagery and Neural Network Modelling for Marsh Characterization.
International Journal of Remote Sensing , 26 (23), 5221-5234.
NOAA. (2009, 08 07). Center for Oceanographic Products and Services. Retrieved 08 07, 2009, from
NOAA Tides and Currents: http://tidesandcurrents.noaa.gov/
NOAA. (2009, 08 07). NCDC: Record of Climatological Observations. Retrieved 08 07, 2009, from
NOAA National Climatic Data Center: http://cdo.ncdc.noaa.gov/dly/DLY
Rodgers, J. C., Cooke, W. H., & Murrah, A. W. (2009, May). The Impact of Hurricane Katrina on the
Coastal Vegetation of the Weeks Bay Reserve, Alabama from NDVI Data. Estuaries and Coasts , 496-507.
Rogers, A. S., & Kearney, M. S. (2004). Reducing Signature Variability in Unmixing Coastal Marsh
Thematic Mapper Scenes Using Spectral Indices. International Journal of Remote Sensing , 25 (12), 2317-2335.
Steyer, G. D., Barras, J. A., & Couvillion, B. R. Monitoring Coastal Louisiana Wetland Impacts From
2005 Hurricanes Using Multi-Temporal MODIS NDVI Data. Baton Rouge: USGS National Wetlands Research
Center.
Steyer, G. D., Twilley, R. R., & Raynie, R. C. (2006). An Integrated Monitoring Approach Using Multiple
Reference Sites to Assess Sustainable Restoration in Coastal Louisiana. USDA Forest Service Proceedings RMRSP-42CD, (pp. 326-333).
Twilley, R. R., & Barras, J. Chapter C.2 Formulation of the LCA Ecosystem Model.
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