Hurricane Track

NGOM Study Area

Hurricane Rita 

September 24, 2005

100

Miles

100

Kilometers

Louisiana Mississippi Alabama

Texas Florida

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Land Area Change and Fractional Water 

Maps in the Chenier Plain, Louisiana, 

Following Hurricane Rita (2005)

By Monica Palaseanu-Lovejoy, Christine Kranenburg, and John C. Brock

Pamphlet to accompany

Scientific Investigations Map 3141

U.S. Department of Interior

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

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2010

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

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Suggested citation:

Palaseanu-Lovejoy, M.E., Kranenburg, C.J., Brock, J.C., 2010, Land area change and fractional 

water maps in the Chenier Plain, Louisiana, following Hurricane Rita, (2005): U.S. Geological 

Survey, Scientific Investigations Map 3141, 6p. pamphlet.
i

Contents

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

Methodology ................................................................................................................................. 1

Discussion ..................................................................................................................................... 2

Conclusion ..................................................................................................................................... 5

Acknowledgments........................................................................................................................ 6

References .................................................................................................................................... 6

Figures

Figure 1. Example of fractional water map (2003 data), the Chenier Plain, Louisiana.......2

Figure 2. Methodology flowchart for creation of fractional water map...............................3

Figure 3. Change analysis map, the Chenier Plain, Louisiana................................................5

Tables

Table 1. Independent and dependent variable predictors......................................................4

Table 2. Evaluation indices for maximum Kappa coefficient of agreement (PCC, percent 

correctly classified; AUC, area under the curve).....................................................4

Table 3. Percent correctly classified (PCC) results for selection and validation points, 

and comparison with similar land/water classification maps for post-Hurricane 

Rita image.........................................................................................................................4
Land Area Change and Fractional Water Maps in the Chenier Plain, Louisiana, following Hurricane Rita (2005)

1Jacobs Technology, contracted to U.S. Geological Survey, St. Petersburg, FL, 2U.S. Geological Survey, Reston, VABy Monica Palaseanu-Lovejoy,1 Christine Kranenburg,1and John C. Brock2

Scientific Investigations Map 3141

Pamphlet accompanies map

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Hurricane TrackNGOM Study Area

Hurricane Rita September 24, 2005

100

Miles

100

Kilometers

Louisiana Mississippi Alabama

Texas Florida

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New Water Areas (9.4%)New Land Areas (2.6%)

Unchanged Land Areas (54.3%)Unchanged Water Areas (33.7%)

20032006

Land Area Change Following Hurricane Rita (2005)

Suggested citation:Palaseanu-Lovejoy, M.E., Kranenburg, C.J., and Brock, J.C., 2010, Land area change and fractional water maps in the Chenier Plain, Louisiana, following Hurricane Rita (2005): U.S. Geological Survey Scientific Investigations Map 3141, 1 sheet, 6 p. pamphlet.See pamphlet for references cited.

Image Sources:Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite imagery acquired on April 17, 2003, and Landsat 5 Thematic Mapper (TM) imagery acquired on February 12, 2006.IKONOS and QuickBird high resolution imagery acquired on May 23, 2003, and March 25, 2006, respectively.All imagery provided by USGS Earth Resources Observation and Science (EROS) Center.(The data are projected using the WGS 84 datum and the UTM Zone 15N coordinate system.)

Threshold PCC PCC Water PCC Land Kappa AUC Commission Omission

26% 98.96% 98.42% 99.52% 97.92% 99.92% 0.47% 1.58%

Threshold PCC PCC Water PCC Land Kappa AUC Commission Omission

51% 99.13% 98.73% 99.52% 98.26% 99.92% 0.52% 1.27%

U.S. Department of the Interior U.S. Geological Survey December 2010

Fractional Water

Land/Water Classification 

Maximum Kappa Threshold

Fractional Water

Land/Water Classification 

Maximum Kappa Threshold

Projected using the WGS 84 datum and the UTM Zone 15N coordinate system.

Percentage Correctly Classified (PCC) Area Under the Curve (AUC)Percentage Correctly Classified (PCC) Area Under the Curve (AUC) Northern Gulf of Mexico (NGOM) study area showing the track of Hurricane Rita in relation to the 

land change study area of this map.

Explanation

LandWater

100

 0

Percent Water
Introduction

In this study, we estimated the changes in land and water 

coverage of a 1,961-square-kilometer (km2) area in Louisiana’s 

Chenier Plain. The study area is roughly centered on the Sabine 

National Wildlife Refuge, which was impacted by Hurricane 

Rita on September 24, 2005. The objective of this study is 

twofold: (1) to provide pre- and post-Hurricane Rita moderateresolution (30-meter (m)) fractional water maps based upon 

multiple source images, and (2) to quantify land and water 

coverage changes due to Hurricane Rita.

Methodology

The USGS Earth Resources Observation and Science 

(EROS) Center provided the following imagery for this project: 

a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) satellite 

image acquired for Path 24, Row 39, on April 17, 2003, a 

Landsat 5 Thematic Mapper (TM) image acquired on February 

12, 2006, and one each of a QuickBird and IKONOS highresolution image acquired on May 23, 2003, and March 25, 

2006, respectively.

A series of vegetation and water indices (Horne, 2003; 

Zha and others, 2003; Wales, 2005; Yarbrough and others, 

2005; Navulur, 2006; Xu, 2006; Jenson, 2007) and transforms 

was created using the high-resolution imagery at a 4-m scale 

and thresholded to obtain a binary land/water map. Using a 

method developed in Yang and others (2003a,b), a 30-m grid 

was overlaid on the binary map, and the water pixels in each 

grid cell were counted to generate a water percentage map at 

30 m, henceforth referred to as the dependent variable. The 

areal extent of this percent water map corresponds to the extent 

of the high-resolution image. Using the National Land Cover 

Data (NLCD) Mapping Tool module for ERDAS IMAGINE 

(ERDAS, 2006), 100,000 stratified random sample points with 

a minimum of 1,000 points per stratum were selected from 

the dependent variable as training points for the independent 

variable layers. The independent variables consist of the 

six thematic bands of the Landsat imagery, Haralick texture 

(Haralick and others, 1973) features for each Landsat band 

(second moment, correlation, contrast, dissimilarity, entropy, 

homogeneity, mean, and variance), principal components 

(PCA), independent components (ICA), tasseled cap 

transformation (TCT) components, and an expanded set of 

water and vegetation indices derived from the Landsat image. 

More specialized water indices are possible at the Landsat scale 

because of the added availability of the two mid-IR bands, 

which are not present in the QuickBird/ and IKONOS imagery. 

Correlations between all independent variables were 

computed in order to minimize colinearity and overfitting. The 

rules for the regression tree were created for each combination 

of independent and dependent variables using the data-mining 

software RuleQuest Cubist v. 2.05 (RuleQuest Research, 2010). 

The resulting regression model was applied to the independent 

variables to obtain a fractional water map (fig. 1) whose aerial 

extent covers the entire study area. The methodology flowchart 

is presented in figure 2. 

To determine the best result from the more than 400 

parameter combinations run, over 6,000 random points 

were generated and unambiguously classified using 

corresponding aerial photographs. These map selection 

points and the percent water maps were analyzed by an 

R-language optimization procedure (Atkinson and Mahoney, 

2004; Wirtschaftsuniversität Wien, 2009) to determine the 

threshold at which the maximum Kappa coefficient occurs. 

The procedure calculates the confusion matrix, percentage 

correctly classified (PCC), Kappa coefficient of agreement, 

receiver operating curve (ROC), area under the curve (AUC), 

and p-values of differences in AUC between models for the 

optimized Kappa threshold and a fixed threshold of 0.5. The 

list of independent and dependent variables that were useful 

predictors and the results of the accuracy assessment for the 

final maps selected for 2003 and 2006 are shown in table 1 and 

table 2.

An accuracy assessment was done using 1,000 random 

points also classified using corresponding aerial photographs 

but separate from the points used for map selection. Whereas 

ambiguous (in terms of photo-interpretation) points were 

eliminated from the selection dataset, no restrictions were 

placed on the accuracy assessment points. For the postRita image (2006), we also had a classified land/water map 

developed by J. Barras, USGS (oral commun., 2009) and the 

National Oceanic and Atmospheric Administration (NOAA) 

Coastal Change Analysis Program (C-CAP) map (NOAA, 

2006), which was re-coded into a binary land/water map. The 

results of the comparison are presented in table 3.

Quantification of the percent land change from 

pre- to post-Rita land/water classification maps was done 

Land Area Change and Fractional Water 

Maps in the Chenier Plain, Louisiana, 

following Hurricane Rita (2005)

By Monica Palaseanu-Lovejoy, Christine Kranenburg, and John C. Brock
by subtracting one classification from the other. The 

accompanying change map (fig. 3) highlights all gains and 

losses that occurred during the comparison interval. It does 

not take into consideration differences in water levels (tidal or 

meteorological). The water levels were checked for imagery 

acquisition dates and times at six distinct water stations in the 

area, and the maximum difference in water levels does not 

exceed 15 centimeters (cm).

Discussion

The 2003 Landsat ETM+ image used for pre-Rita 

conditions can be considered a good baseline since in the 

previous 25 years only two low-intensity tropical storms made 

landfall within the study area, and all the major hurricanes 

in the vicinity (intensity greater or equal to 3 on the SaffirSimpson scale) were 100 km or more from the study area 

(NOAA, 2008). 

The Chenier Plain is a very complex area because 

it contains multiple, heterogeneous impoundments that 

encompass wetlands, pasture and cultivated fields, as well as 

industrial and residential areas. These areas can trap water 

for long periods of time, and drainage is often controlled by 

artificial means, contributing to the challenging interpretation 

of the land/water features and their persistence. Each of these 

distinct land-use types responds to and recovers differently 

from an extreme storm event. Thus, land/water changes 

detected after an extreme storm event reflect both permanent 

and temporary changes. Permanent new water areas are the 

result of direct removal of wetlands by storm surge and appear 

mostly connected to existing bodies of open water, or emerge 

in marsh fringing areas. However, new water areas unconnected 

Kilometers

0 5 10

2

Figure 1. Example of fractional water map (2003 data), the Chenier Plain, Louisiana. Sabine National Wildlife refuge is highlighted 

in red. The Five Lakes area is highlighted in violet. 

8 4 0 8

Kilometers Projected using the WGS 84 datum and the UTM Zone 15N coordinate system.
3

Multi-source remote 

sensing data

High-resolution

Index

@ 4 m

Regression tree

modeling

Model prediction

Model validation

Model accuracy

assessment

Threshold

Kappa max.

Change

Analysis

Dependent

variable

@ 30 m

Landsat Image

Landsat Ratios

Landsat Texture

PCA, ICA, TCT

CIR Imagery

QuickBird/IKONOS Imagery

Preprocessing

(radiometric, geometric, and

resolution resampling)

Ground Truth

Threshold land/water

% water @ 4 m

Degrade % water to 30 m

Landsat Imagery Preprocessing 

(radiometric and geometric)

Coefficients from Chander and 

others, 2009.

Tree pruning

Figure 2. Methodology flowchart for creation of fractional water map. Principal Component Analysis (PCA), Independent 

Component Analysis (ICA), Tasseled Cap Transformation (TCT).
to previous water bodies are also observed in historically dry, 

stable areas such as south of the Sabine National Wildlife 

Refuge, which is highlighted in red on figure 1 (Barras, 2009). 

Temporary new water bodies are usually the result of 

flooding and water entrapment in impounded areas, removal or 

scouring of floating and submerged vegetation, or are caused 

by water-level fluctuation due to tidal and (or) meteorological 

variations between images. A particularly difficult section to 

interpret is the Five Lakes area impoundment (highlighted in 

violet on fig. 1), in the center of the Chenier Plain study area. 

Discussions with local area managers revealed that this area 

is a freshwater marsh that was flooded by saltwater during 

Hurricane Rita. Compounding the problem, large mats of 

marsh wrack deposited by the hurricane into the canal on the 

southern edge of the impoundment prevented it from draining. 

As a consequence, the vegetation suffered severe salt burn and 

prolonged inundation. It represents about 2.16 percent of the 

total study area. It is clear that the marsh did not recover by 

February 2006. What is not clear is how much water actually 

drained from the impoundment. As previously mentioned, 

making a clear distinction between permanent and temporary 

changes to water bodies is a complicated task, which becomes 

even more difficult after a major storm event. Caution is needed 

in correctly assessing this area for any further interpretation.

Land gain in the Chenier Plain is mostly transitory and is 

the effect of storm-debris deposition, displacement of aquatic 

vegetation to different locations, and water-level fluctuations. 

The majority of the land gain seen along rivers, canals, and 

beaches is a direct result of variations in water levels due to 

tidal or meteorological influences. About 1 percent of the 

image area is affected in at least one of the inputs by clouds, 

cloud shadows, and contrails. Although they are not currently 

4

Table 3. Percent correctly classified (PCC) results for selection and validation points, and comparison with similar land/

water classification maps for post-Hurricane Rita image.

Table 2. Evaluation indices for maximum Kappa coefficient of agreement (PCC, percent correctly classified; AUC, area 

under the curve).

Year Independent variables Dependent variable

2003 Landsat bands: 1 to 6

Water Index: b1/b4 Texture: Variance bands 1 to 6

Water Index: (sum(vis)-sum (IR))/(sum(vis)+sum(IR))

2006 Landsat bands: 1 to 6 Water Index: TCT1or2 = Logical OR 

combination of tasseled cap transform 

bands 1 and 2. Texture: Homogeneity bands 1 to 6

Independent Components (3): bands 1 to 3 

Table 1. Independent and dependent variable predictors.

Year Threshold PCC PCC Water PCC Land Kappa AUC Commission Omission

2003 51% 99.13% 98.73% 99.52% 98.26% 99.92% 0.52% 1.27%

2006 26% 98.96% 98.42% 99.52% 97.92% 99.92% 0.47% 1.58%

2003 Selection: PCC (6804 points) Validation: PCC (1000 points)

Land Water Total Land Water Total

99.52% 98.73% 99.13% 96.37% 89.07% 93.70%

2006 Selection: PCC (6344 points) Validation: PCC (1000 points) 

Model Land Water Total Land Water Total

USGS 99.52% 98.42% 98.96% 91.35% 91.99% 91.60%

J. Barras (USGS, 2009) 99.94% 86.76% 93.24% 95.60% 76.74% 88.30%

C-CAP (NOAA) 99.87% 79.00% 89.27% 97.23% 72.87% 87.80%
masked out in the results, they are clearly not to be relied upon. 

A mask image is provided with the dataset.

Conclusion

The increase in water area of about 10 percent between 

2003 and 2006 is attributed to both permanent and temporary 

changes in land/water configuration. The majority of new 

water areas occurs around previous bodies of open-water and 

in marsh fringing areas, but they are also present in historically 

stable areas. The presence of scarring in stable marshes is 

associated with preexisting open water ponds, but in some 

areas it is difficult to discriminate between flooded marshes or 

marshes removed by storm surge. Storm-debris deposition can 

result in small new land areas, but land gain along waterways 

and beaches is considered the result of temporary variations in 

water levels due to tidal or meteorological influences.

Estimation of permanent losses (or gains) cannot be 

made until the transitory impacts are identified and quantified. 

However, it is expected that the permanent losses will be 

significant since the area did not have enough time to recover 

before the next extreme storm event, which occurred on 

Kilometers

0 5 10

5

Figure 3. Change analysis map, the Chenier Plain, Louisiana. 

Projected using the WGS 84 datum and the UTM Zone 15N coordinate system.
September 13, 2008, when Hurricane Ike made landfall as a 

Category 2 storm.

Acknowledgments

The USGS St. Petersburg Coastal and Marine Science 

Center acknowledges the support and assistance of the USGS 

Land Characterization team at the Earth Resources Observation 

and Science (EROS) Center, in Sioux Falls, S. Dak., and the 

USGS National Wetlands Research Center, in Baton Rouge, 

La., in the development of this dataset.

References

Atkinson, E.J. and Mahoney, D.W., 2004, Mayo Foundation 

for Medical Education and Research: S-Plus/R functions 

database, accessed online July 2008, at http://mayoresearch.

mayo.edu/mayo/research/biostat/splusfunctions.cfm.

Barras, J.A., 2009, Land area change and overview of major 

hurricane impacts in coastal Louisiana, 2004-08: U.S. 

Geological Survey Scientific Investigations Map 3080, scale 

1:250,000, 6 p. pamphlet. (Also available at: http://pubs.

usgs.gov/sim/3080.)

Chander, G.D., Markham, B.L., and Helder, D.L., 2009, 

Summary of current radiometric calibration coefficients for 

Landsat MSS, TM, ETM+, and EO-1 ALI sensors: Remote 

Sensing of Environment, v. 113, p. 893-903.

ERDAS IMAGINE 9.1, 2006, Atlanta GA, ERDAS Inc.

Haralick, R.M., Shanmugam, K.S., and Dinstein, I.H., 1973, 

Textural features for image classification: IEEE Transactions 

on Systems, Man and Cybernetics, v. 3, no. 6, p. 610-621.

Horne, J.H., 2003, A tasseled cap transformation for 

IKONOS images–Proceedings of the American Society of 

Photogrammetry and Remote Sensing, 2003: Anchorage, 

Alaska, one CD-ROM. 

Jenson, J.R., 2007, Remote sensing of the environment: An 

Earth resource perspective (2d ed.): Upper Saddle River, 

N.J., Prentice Hall, 544 p.

National Oceanic and Atmospheric Administration (NOAA), 

Coastal Services Center, 2006, C-CAP US (United States) 

Gulf Coast Zone 37/46 Area Post-Hurricane Katrina Land 

Cover Project, (also available at: http://www.csc.noaa.gov/

crs/lca/katrina/).

National Oceanic and Atmospheric Administration (NOAA), 

Coastal Services Center, 2008, Historical North Atlantic 

Tropical Cyclone Tracks, 1851-2007, (also available at: 

http://csc-s-maps-q.csc.noaa.gov/hurricanes/viewer.html).

Navulur, K.C.S., 2006, Multispectral image analysis using the 

object-oriented paradigm: Remote Sensing Applications 

Series, Boca Raton, Fla., CRC Press, 184 p.

RuleQuest Research, 2010, Cubist v. 2.05: St. Ives, NSW 2075, 

Australia, (also available at: http://www.rulequest.com),

Wales, P.M., 2005, Quantitative analysis of land loss in coastal 

Louisiana using remote sensing: Oxford, University of 

Mississippi, Master’s thesis, 91 p. 

Wirtschaftsuniversität Wien, 2009 [Department of Statistics 

and Mathematics], The comprehensive R archive network 

database, accessed July 2009 at http://cran.r-project.org/.

Xu, Hanqiu, 2006, Modification of normalised difference water 

index (NDWI) to enhance open water features in remotely 

sensed imagery: International Journal of Remote Sensing, v. 

27, no. 14, p. 3025-3033.

Yang, Limin, Huang, Chengquan, Homer, C.G., Wylie, B.K., 

and Coan, M.J., 2003a, An approach for mapping large-area 

impervious surfaces: Synergistic use of Landsat-7 ETM+ 

and high spatial resolution imagery: Canadian Journal of 

Remote Sensing, v. 29, no. 2, p. 230-240.

Yang, Limin, Xian, George., Klaver, J.M., and Deal, Brian, 

2003b, Urban land-cover change detection through subpixel imperviousness mapping using remotely sensed data: 

Photogrammetric Engineering and Remote Sensing, v. 69, 

no. 9, p. 1003-1010.

Yarbrough, L.D., Easson, G.L., and Kuszmaul, J.S., 2005, 

QuickBird 2 tasseled cap transform coefficients: A 

comparison of derivation methods–Proceedings of the 

American Society of Photogrammetry and Remote Sensing, 

2005: Sioux Falls, S. Dak., one CD-ROM. 

Zha, Yong, Gao, Jay, and Ni, Shaoxiang, 2003, Use of 

normalized difference built-up index in automatically 

mapping urban areas from TM imagery: International 

Journal of Remote Sensing, v. 24, no. 3, p. 583-594.

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