Louisiana

Prepared in cooperation with the National Aeronautics and Space Administration

Monitoring Coastal Inundation with Synthetic Aperture 

Radar Satellite Data

Open-File Report 2011–1208

U.S. Department of the Interior

U.S. Geological Survey

Monitoring Coastal Inundation with 

Synthetic Aperture Radar Satellite Data

By Yukihiro Suzuoki, Amina Rangoonwala, and Elijah Ramsey III

Open-File Report 2011–1208

U.S. Department of the Interior

U.S. Geological Survey

Prepared in cooperation with the National Aeronautics and Space Administration
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2011

Suggested citation:

Suzuoki, Yukihiro, Rangoonwala, Amina, and Ramsey, Elijah III, 2011, Monitoring coastal inundation with Synthetic 

Aperture Radar satellite data: U.S. Geological Survey Open-File Report 2011–1208, 46 p.

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iii

Contents

Abstract...........................................................................................................................................................1

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

Study Area..............................................................................................................................................2

Study Objectives ...................................................................................................................................3

Background............................................................................................................................................3

The Importance of Strategic Inundation Mapping in Coastal Louisiana............................3

Inundation Mapping with Optical Satellite versus SAR Data...............................................3

Point Measurements, Hydraulic Models, and Inundation Mapping ...................................3

Detection of Marsh Subcanopy Flooding by Using SAR.......................................................4

Spatial Coverage Extent Versus Spatial Resolution...............................................................4

Methods...........................................................................................................................................................5

Collecting, Calibrating, and Georeferencing Satellite Data...........................................................5

PALSAR and ASAR Scene-Collection Parameters ................................................................8

Collecting Coastal and Inland Hydrologic Data...............................................................................8

Assessing Hydrologic Station Locations ..........................................................................................8

Validating SAR Areal Data with Hydrologic Point Data..................................................................8

Mapping Inundation Extents...............................................................................................................9

Selecting a Reference Scene ...................................................................................................9

Delineating Permanent Water Bodies......................................................................................9

Determining of Change Detection Thresholds......................................................................11

Inundation Mapping with SAR-based Change Detection...................................................11

Results ...........................................................................................................................................................11

Coastal and Inland Water Levels .....................................................................................................11

Inundation Extent Mapping...............................................................................................................12

SAR Reference Scenes ............................................................................................................12

Inundation Thresholds ..............................................................................................................16

Assessments of SAR-based Inundation Maps .....................................................................17

Influence of Reference-Scene Selection on ASAR-Based Mapping Performance.......30

Acknowledgments 

The research described in this report was supported in part by the National Aeronautics 

& Space Administration (NASA) under the “Earth and Science for Decision Making: Gulf of 

Mexico Region” program (NNH08ZDA001N-GULF). Envisat Advanced Synthetic Aperture 

Radar data are owned by the European Space Agency and were provided via the Cat-1 7286 and 

2865 Projects. We thank Rebecca Sanches of NASA’s Alaska Satellite Facility, located in the 

Geophysical Institute of the University of Alaska at Fairbanks, for her kind and indispensable 

help in the acquisition of data used in this study. 

We also acknowledge the invaluable assistance of Thomas H. Cecere of the U.S. Geological 

Survey’s (USGS) National Center in Reston, Virginia, in helping us acquire data for this study. 

Gratitude is extended to Kari Cretini of USGS for her assistance in accessing water-level data 

records and to staff of the USGS Lafayette Publishing Service Center for help in preparation of 

this report. 
iv

Discussion.....................................................................................................................................................30

Complications of ASAR-Based Inundation Mapping ...................................................................30

Visual Comparisons of SAR-based Inundation Maps...................................................................35

Validating SAR-based Inundation Maps with Inland Water-Level Data ...................................35

Conclusion.....................................................................................................................................................43

References....................................................................................................................................................43

Figures

1. Map showing study area covering the coastal marshes of Louisiana,

as well as locations of nearshore water-level gages monitored by

the National Oceanic and Atmospheric Administration ..........................................................2

2. Diagram of swath coverage provided in generic scenes

captured by Synthetic Aperture Radar (SAR) sensors ............................................................5

3. Scenes captured by Synthetic Aperture Radar (SAR) sensors,

with orbital and look directions included ...................................................................................6

4. Examples of hydrologic stations in the Coastwide Reference

Monitoring System that were unsuitable for use in validating

Synthetic Aperture Radar-based inundation mapping.............................................................9

5. Hydrograph for 2008 dates at Coastwide Reference Monitoring System

station 0465....................................................................................................................................10

6. Monthly mean sea level measurements during 2008 at seven

coastal hydrologic stations included in the National Ocean Service’s

Tides & Currents program...........................................................................................................12

7. Map showing locations of hydrologic stations in the Coastwide Reference

Monitoring System within the region of the Louisiana coastal zone.

Marsh categories are also shown.............................................................................................13

8. Graph depicting sigma-nought change-detection results when comparing

inundation across reference-scene and target-scene data obtained

by an Advanced Synthetic Aperture Radar (ASAR) sensor aboard

the European Space Agency’s Envisat .....................................................................................16

9. Inundation maps created from Phased Array type L-band

Synthetic Aperture Radar (PALSAR) scenes acquired by the Japanese

Aerospace Exploration Agency’s Advanced Land Observing Satellite...............................17

10. Inundation maps created from Advanced Synthetic Aperture Radar

(ASAR) scenes exhibiting horizontal transmit and receive (HH)

polarization that were acquired by the European Space Agency’s Envisat.......................25

11. Inundation maps created from Advanced Synthetic Aperture Radar

(ASAR) scenes exhibiting vertical transmit and receive (VV) polarization

that were acquired by the European Space Agency’s Envisat.............................................28

12. Inundation mapping results derived by using an Advanced

Synthetic Aperture Radar (ASAR) target scene acquired on

September 17, 2008, by the European Space Agency’s Envisat.

A comparison of results derived from using two different ASAR

reference scenes ispresented ...................................................................................................34

13. Change-detection of inundation results compared with water-level

measurements at inland hydrologic stations within the Coastwide

Reference Monitoring System ...................................................................................................36
v

14. Change-detection of inundation results compared with water-level

measurements at inland hydrologic stations within the Coastwide

Reference Monitoring System ...................................................................................................39

15. Change-detection of inundation results compared with water-level

measurements at inland hydrologic stations within the Coastwide

Reference Monitoring System ...................................................................................................40

Tables

1. Collection date, orbital path, and coverages for scenes acquired

by the Phased Array type L-band Synthetic Aperture Radar (PALSAR)

sensor aboard the Japanese Aerospace Exploration Agency’s

Advanced Land Observing Satellite ............................................................................................7

2. Collection date, orbital track, and coverages for scenes acquired

by the Advanced Synthetic Aperture Radar (ASAR) sensor aboard

the European Space Agency’s Envisat .......................................................................................7

3. Landfall dates and times of tropical storms and hurricanes in the

Gulf of Mexico, 2006–9.................................................................................................................13

4. Mean sea level measurements along the western Louisiana coast

corresponding to dates when Phased Array type L-band Synthetic

Aperture Radar scenes were acquired during 2007–9 ..........................................................14

5. Mean sea level measurements along the eastern Louisiana coast

corresponding to dates when Phased Array type L-band Synthetic

Aperture Radar scenes were acquired during 2007–9 ..........................................................14

6. Mean sea level measurements along the western Louisiana coast

corresponding to dates when Advanced Synthetic Aperture Radar

scenes with horizontal transmit and receive polarization were

acquired during 2006–9 ...............................................................................................................15

7. Mean sea level measurements along the eastern Louisiana coast

corresponding to dates when Advanced Synthetic Aperture Radar scenes

with horizontal transmit and receive polarization were acquired

during 2006–9 ................................................................................................................................15

8. Mean sea level measurements along the western Louisiana coast

corresponding to dates when Advanced Synthetic Aperture Radar

scenes with vertical transmit and receive polarization were acquired

during 2007–9 ................................................................................................................................15

9. Mean sea level measurements along the eastern Louisiana coast

corresponding to dates when Advanced Synthetic Aperture Radar

scenes with vertical transmit and receive polarization were acquired

during 2007–9 ................................................................................................................................16

10. Inundation results for the western Louisiana coast compared with water-level 

measurements at inland hydrologic stations along the Louisiana coast............................23

11. Inundation results for the eastern Louisiana coast compared with water-level

measurements at inland hydrologic stations along the Louisiana coast............................24

12. Inundation results for the western Louisiana coast compared with water-level 

measurements at inland hydrologic stations along the Louisiana coast............................26

13. Inundation results for the eastern Louisiana coast compared with water-level

measurements at inland hydrologic stations along the Louisiana coast.. ...........................26
vi

Conversion Factors

SI to Inch/Pound

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2

) 0.3861 square mile (mi2)

14. Inundation results for the western Louisiana coast compared with water-level 

measurements at inland hydrologic stations along the Louisiana coast....................................29

15. Inundation results for the eastern Louisiana coast compared with water-level

measurements at inland hydrologic stations along the Louisiana coast....................................34
Monitoring Coastal Inundation with Synthetic Aperture 

Radar Satellite Data

By Yukihiro Suzuoki,1 Amina Rangoonwala,2 and Elijah Ramsey III3

1

 Formerly of ASci Corporation, Inc. (McLean, Virginia), for the

U.S. Geological Survey (currently of Japan Space Imaging Corporation, Tokyo, Japan)

2

 Five Rivers Services, LLC, for the U.S. Geological Survey

3

 U.S. Geological Survey

Abstract

Maps representing the presence and absence of surface 

inundation in the Louisiana coastal zone were created from 

available satellite scenes acquired by the Japanese Aerospace 

Exploration Agency’s Advanced Land Observing Satellite 

and by the European Space Agency’s Envisat from late 2006 

through summer 2009. Detection of aboveground surface 

flooding relied on the well-documented and distinct signature 

of decreased backscatter in Synthetic Aperture Radar (SAR), 

which is indicative of inundated marsh in the Gulf of Mexico. 

Even though decreases in backscatter were distinctive, the 

multiplicity of possible interactions between changing flood 

depths and canopy height yielded complex SAR-based 

representations of the marshes.

Validated by comparison to inland water levels, success 

of inundation mapping was primarily related to the operational 

frequencies of the SAR used to perform the mapping. Success 

of mapping was based on frequency of correspondence 

between satellite- and ground-based data. Overall, the most 

successful mapping (83 percent correspondence) was derived 

from Phased Array type L-band SAR (PALSAR), while 

mapping derived from C-band Advanced SAR (ASAR) was 

less successful (≤61 percent correspondence). Exceptions 

to the low performance of ASAR-based mapping (defined 

as >76 percent correspondence) occurred when water levels 

were well below or above ground, occurring over spatially 

extensive portions of the ASAR scene.

When mapping day-to-day coastal inundation extents, 

results indicate that SAR systems operating at C-band 

frequencies are not as effective as those operating at L-band 

frequencies; however, multiple factors not related to frequency 

also reduced the effectiveness of C-Band in detecting 

subcanopy inundation. C-band has performed and continues 

to perform exceedingly well in applications for response to 

dramatic events and when strategic collections are available; 

however, L-band seems to be more suitable for day-to-day 

mapping of coastal inundation.

Introduction

The use of satellite-based Synthetic Aperture Radar 

(SAR) to monitor coastal Louisiana inundation was evaluated 

by the Remote Sensing Applied Research group at the U.S. 

Geological Survey’s (USGS) National Wetlands Research 

Center. This initial applied research emphasized preparation 

of flood distribution maps for coastal Louisiana. The research 

included data collected by the Phased Array type L-band SAR 

(PALSAR) sensor onboard the Advanced Land Observing 

Satellite (ALOS) and the Advanced SAR (ASAR) sensor 

onboard Envisat. This combination of satellite-based SAR 

sensors provided the maximum temporal frequency of data 

collections covering the Louisiana coastal zone. An added 

advantage of the combination was the ability to compare 

the performance of two widely used SAR sensor systems 

operating at different frequencies or wavelengths—the 

L-band PALSAR and the C-band ASAR. SAR systems 

operating at both L- and C-band frequencies have repeatedly 

proven capable of mapping inundation in coastal and 

river floodplain systems.

Even though flood-extent mapping has become a 

routine and even operational activity in coastal marsh and 

forested wetland landscapes (Ramsey, 1998, 2005; Lu and 

Kwoun, 2008), especially during and after extreme storm 

events, satellite-based observations of long-term effects of 

flooding on natural vegetation are less common, and the 

usefulness of such observations requires further study. As 

reported in Ramsey and others (2009), the combined use of 

radar imagery (Envisat ASAR) and optical imagery (Landsat 
2 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Thematic Mapper [TM]) provided synergistic observations 

to document the extent of the coastal surge accompanying 

Hurricane Ike (Sept. 13, 2008) and a resultant marsh-dieback 

event. The dieback could be directly attributed to prolonged 

water logging and elevated salinity levels. This connection 

illustrated how inundation monitoring with frequent satellitebased radar data observations, combined with cloud-free 

optical data, can provide direct linkages between vegetation 

condition and the primary physical forces controlling it.

Study Area

The study area included coastal wetlands stretching from 

the western chenier to the eastern deltaic plains1 of coastal 

Louisiana, located in the north-central Gulf of Mexico (fig. 1). 

The deltaic plain was formed and is primarily sustained by 

the direct deposition of Mississippi River sediments, while 

the chenier plain is primarily dependent on current-related 

1

 The two major regions of the Louisiana coast are the chenier plain in 

the southwestern region and the deltaic plain in the southeastern region. See 

Saucier (1994) and Barrow and others (2007, p. 156) for further definitions.

reworking of river sediments from the Atchafalaya River 

(Coastal Louisiana Ecosystem Assessment & Restoration, 

2006). Relationships between water and sediment gave rise 

to highly permeable sand and shell (cheniers) barriers in the 

west and barrier islands in the east that protect extensive back 

barrier marshes that extend inland by 6–24 kilometers (km), 

commonly at less than 1.5 meters (m) above mean sea level 

and with slopes of less than 0.2 m per km (Chabreck, 1970).

The Louisiana coastal-marsh zone is dominantly 

underlain by frequently saturated soils. In this zone, 

subsurface faulting can produce surface subsidence that results 

in marsh submergence and fragmentation and, ultimately, 

the formation of permanent water bodies (Kiage and others, 

2005; Morton and others, 2005). In addition, hurricanes scour 

the marsh, creating small water bodies (Neyland, 2007), and 

push water with elevated salinity into freshwater marshes, 

causing salt burn in those areas (Neyland, 2007; Ramsey 

and others, 2009). Aggravating these detrimental impacts are 

channels and levies, as well as impoundments (constructed 

to provide transport conduits and waterfowl sanctuaries) that 

impede overland flow. These impediments can lengthen marsh 

exposure to elevated salinity water and, in the case of intense 

Figure 1. Map showing study area covering the coastal marshes of Louisiana, as well as locations of nearshore water-level gages 

monitored by the National Oceanic and Atmospheric Administration (NOAA) (2010).
Introduction 3

rainfall accompanying storm events, prolong inundation and 

promote water logging that can advance marsh alteration and 

deterioration. The combination of low topographic relief, 

poorly drained soils, tectonic activity, and flow impedance 

creates a spatially complex hydrological landscape.

Study Objectives

The goal of our applied research was to demonstrate 

the ability of SAR-based satellite imagery to monitor the 

distribution and occurrence of storm-related and tidal-related 

flooding in Louisiana coastal wetlands. Our demonstration 

is based on available SAR scenes that individually covered 

at least half the coastal zone from late 2006 to September 

2009 (fig. 1). Research goals included the following: (1) to 

derive inundation distributions from available SAR scenes, 

(2) to discover limitations and successes of SAR-based 

inundation mapping, (3) to assess the Coastwide Reference 

Monitoring System (CRMS) hydrologic water-level database 

appropriateness for validation of SAR-based mapping, and (4) 

to discover implications for continued applied research.

A successful research strategy should be based upon 

routine measurements that are cost effective and easily 

implemented into operational resource management. 

Furthermore, measurements should be verified against and 

calibrated with available, currently operational ground-based 

measurements (Nielsen and Werle, 1993).

Background

The Importance of Strategic Inundation Mapping 

in Coastal Louisiana

The coastal zone of Louisiana, located in the centralnorthern Gulf of Mexico, accounts for about 40 percent of 

the coastal tidal wetlands in the continental United States 

(Neyland, 2007). As in most coastal zones that include 

expanses of marsh, the zone is depicted by a gradual increase 

of elevation that starts at sea level at the coast and reaches 

1 to 1.5 m at the southern extent of upland prairie and 

forests. In addition to flood events (Kiage and others, 2005; 

Ramsey and others, 2009), the seasonal and interannual 

marsh phenology (Wang, 2004) and the ephemeral nature of 

many small water bodies (Kiage and others, 2005) in these 

marshes produce a highly dynamic landscape. In order to 

precisely identify the threshold duration of saturation required 

for wetland viability, these dynamics must be accounted for 

(Lang and others, 2008). While limited results do not allow 

immediate inferences to be made regarding the health of or 

trends in the marsh system, evidence suggests that episodic 

events can cause severe stress on marsh vegetation in the 

region (Ramsey and others, 2009; Ramsey, Werle, and others, 

in press).Produced systematically, maps portraying wetland 

vegetation condition and spatially distributed inundation can 

provide crucial information for linking wetland health with 

flood frequency and duration (Hess and others, 1995; Ramsey, 

1995; Werle and others, 2000; Toyra and Pietroniro, 2005).

Inundation Mapping with Optical Satellite

versus SAR Data

Passive remote sensing with optical sensors can 

adequately address many issues of coastal resource 

management (Klemas and others, 1993; Smith, 1997; Lunetta 

and others, 1998; Henry and others, 2006); however, map 

production based on optical systems are critically limited 

by their dependence on sunlight and favorable weather 

conditions when time-constrained collections are needed 

(Hess and others, 1995; Smith, 1997; Ramsey, 1998, 2005; 

Werle and others, 2000; Toyra and Pietroniro, 2005; Lang 

and others, 2008). Even when reliance on time-constrained 

collections is minimized, the restricted penetration of visible 

and near-infrared radiation into full cover canopies limits 

detection of subcanopy flooding with optical systems (Ormsby 

and others, 1985; Moghaddam and others, 2003; Toyra and 

Pietroniro, 2005).

Remote sensing systems using radar offer a viable 

alternative data source when timely and consistent collections 

are needed (Kasischke, 2003; Ramsey and others, 2006; 

Matgen and others, 2007; Lu and Kwoun, 2008). SAR 

sensors operating at centimeter-long wavelengths can collect 

information day and night and in most weather conditions, 

and they provide increased canopy penetration (Ormsby and 

others, 1985; Hess and others, 1995; Lewis and others, 1998; 

Ramsey, 1998; Toyra and Pietroniro, 2005). Satellite-based 

radar sensors, such as the C-band ASAR aboard the European 

Space Agency’s Envisat, the C-band SAR aboard the Canadian 

Space Agency’s Radarsat, the X-band SAR aboard the German 

TerraSAR-X, and the PALSAR aboard the Japanese Aerospace 

Exploration Agency’s ALOS, have proven to be valuable 

tools for surveying land and water surfaces during weatherrelated emergencies (Committee on Earth Observation 

Satellites, 2008).

Point Measurements, Hydraulic Models, and 

Inundation Mapping

Conventional contour mapping by using point 

measurements of water levels is hampered by the high spatial 

variability of flood occurrences, difficulties in timing field data 

collections with highly dynamic flood events, and inherent 

problems in predicting flood stages in marsh by using off-site 

gages (Leconte and Pultz, 1991; Ramsey, 1995). Hydraulic 

flow models can be used to predict inundation patterns (Smith, 

1997), alleviating many difficulties inherent in conventional 

contouring. Nevertheless, lack of the necessary spatial density 

of stage measurements and the prevalent disconnect between 

marsh and off-site measured flows diminish the capability to 
4 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

provide calibration and validation of hydraulic models (Werle 

and others, 2000; Matgen and others, 2007). To help overcome 

these difficulties in monitoring and simulating the spatially 

distributed and rapidly changing nature of coastal inundation, 

remote sensing systems are used.

Detection of Marsh Subcanopy Flooding

by Using SAR

Although L-band provides increased canopy 

transmittance of longer wavelengths, which implies superior 

mapping of subcanopy inundation (Ramsey, 1998; Kasischke 

and others, 2003; Toyra and Pietroniro, 2005), C-band has 

performed well in flood mapping of Louisiana marshes, as it 

has in other marshes occupying the northern Gulf of Mexico 

coasts (Ramsey, 1995; Kasischke and others, 2003; Kiage and 

others, 2005; Ramsey and others, 2009) and elsewhere (Hess 

and others, 1995). C- and L-band SAR data in horizontal 

transmit and receive (HH) and vertical transmit and receive 

(VV) polarizations (acquired by the Shuttle Imaging Radar-C 

[SIR-C] platform) were used by Pope and others (1997) to 

demonstrate that flooding of herbaceous vegetations can be 

exhibited as an increase or decrease in SAR backscatter. In 

effect, the nature of the change in water level and the ability to 

differentiate between flooded and nonflooded marshes depend 

on several biophysical variables, including marsh type, height, 

density, and stem orientation and size, as well as soil moisture, 

inundation depth and history, and SAR sensor parameters 

(Kasischke and others, 2003; Grings and others, 2005; Pope 

and others, 1997).

In coastal marshes occupying the northern and eastern 

Gulf of Mexico, the interrelation between biophysical 

variables and incident C-band SAR signals dominantly 

produce a decrease in backscatter from flooded versus 

nonflooded marshes. For example, C-band SAR data with VV 

polarization, which were acquired by the European Remote 

Sensing Satellite-1 (ERS-1), indicated that backscatter from 

flooded marsh was lower than that from nonflooded marsh 

in the northeastern Gulf of Mexico(data were field validated 

by Ramsey [1995] and modeled by Dobson and others 

[1996]). Similarly, while studying a variety of southeastern 

Gulf of Mexico marshes, Kasischke and others (2003) used 

C-band SAR data with VV polarization (acquired by ERS-2) 

to substantiate a progressive decrease in backscatter with 

increasing flood level and a positive relationship between 

soil moisture and SAR backscatter. In the north-central Gulf 

of Mexico, Kiage and others (2005) used C-band SAR with 

HH polarization (acquired by RADARSAT-1) to document 

decreased backscatter from hurricane surge-flooded saline, 

brackish, and fresh coastal marshes, compared to presurge 

backscatter intensities. Even the TerraSAR X-band system 

was used to successfully estimate water-level changes in south 

Florida marshes (Hong and others, 2010). In addition, any 

polarization influence on the effectiveness of flood mapping 

(for example, Grings and others, 2005) was expected to favor 

the copolarized (VV and HH) SAR data as applied in the 

current study as compared to cross-polarized (HV [horizontal 

send, vertical receive] and VH [vertical send, horizontal 

receive]) SAR data (for example, Smith, 1997). SAR data 

(HH and VV polarizations) trends for fresh to saline coastal 

marshes of the northern Gulf of Mexico have consistently 

indicated an association between decreased backscatter and 

flooded marshes, as compared to nonflooded marshes.

Spatial Coverage Extent Versus Spatial Resolution

Consistent and sequential (systematic) data collection 

is required for detecting and quantifying long-term 

environmental trends for resource monitoring and short-term 

dramatic change for emergency response. The frequency of 

data collection determines if the dynamics of the feature of 

interest are either captured or missed (Klemas, 2005). For 

instance, coastal resource phenologies should preferably be 

captured on a weekly basis, and coastal flooding should be 

captured every 2 h or better (Klemas, 2005). A 2-h revisit 

frequency is not feasible with any operational satellite system; 

however, if systematic sampling is available, higher frequency 

sampling may be approximated (Hager and others, 2009). 

For example, linking the tidal excursion (inland extent) to 

the flood stage over a single tidal cycle is an unreasonable 

observational requirement for orbiting satellites. If, however, 

the collections occur weekly or bimonthly over a long period 

of time, the tidal stages and excursions at the times of each 

collection may be aggregated to construct a series of tidal 

excursion extents that simulate a much higher collection 

frequency over a single tidal period. To obtain a reasonable 

hypsometric simulation, a bimonthly or better collection 

frequency is desired.

There is a tradeoff between spatial resolution and repeat 

frequency (for example, Schaber and Badeck, 2003; Fisher 

and Mustard, 2007). In essence, the higher the temporal 

frequency of scene captures, the coarser the ground spatial 

resolution. For example, large-format SAR collections 

made at bimonthly to weekly or higher repeat frequencies 

primarily provide moderate spatial resolutions (for example, 

150 m or less) (Hager and others, 2009). In addition to high 

reoccupation frequency, large-format satellite coverages 

provide a synoptic and regional collection perspective and 

continuity to the coastal inundation mapping. Regional and 

frequent SAR collections coupled with similar collections of 

optical satellite data promote the early detection of adverse 

regional impacts, improving the opportunity of remediation 

before irreparable loss of the coastal resource (Hager and 

others, 2009). In fact, research has shown that high-frequency 

collections at higher spatial scales (for example, 25 m or less), 

even in contiguous landcovers, can obscure the detection of 

change (for example, Schaber and Badeck, 2003; Fisher and 

Mustard, 2007).

Higher signal variability accompanying higher spatial 

resolution data could lower change-detection performance 
Methods 5

by concealing real differences within the signal noise (spatial 

variability). As will be discussed, scanning systems such as 

radar aggravate the issues related to spatial resolution and 

change-detection performance because of their dependency 

upon signal and scan angle, factors that can cause the 

appearance of change irrespective of whether the target 

changed or not. Because a greater number of high spatial 

resolution scenes are required to cover an extensive region 

(for example the entire Louisiana coastal zone), dependency 

upon the signal and scan angle is intensified relative to 

scenes acquired at lower spatial resolution but having greater 

regional coverage. 

While large-format monitoring provides a regional 

synoptic view, allows for rapid response, and promotes early 

detection of change, modes having a moderate swath (for 

example, 60 km) and higher spatial resolution have advantages 

as well. Such modes afford stand-level canopy information 

necessary for determining features such as the following: 

(a) the underlying wetland function, (b) canopy structures 

(for example, canopy gaps, subcanopy size, and species-type 

distributions), and (c) regeneration of and shifts in coastal 

wetland-species associations in response to storms, flooding, 

herbivory, fires, and climate changes (Hager and others, 2009). 

Because of the performance issues related to spatial scale and 

change-detection performance previously discussed, higher 

spatial resolution scenes would be most appropriately used to 

better define and mediate problems in the change-detection 

performance; while present in the regional scale products, 

such problems cannot be properly identified or examined 

at the coarser spatial resolution (Ramsey, Spruce, and 

others, in press).

Methods

Collecting, Calibrating, and Georeferencing 

Satellite Data

SAR coverages were acquired by the PALSAR sensor 

aboard ALOS and the C-band ASAR sensor onboard the 

Envisat. PALSAR scenes were collected in Wide Area 

Observation mode (Burst Mode 1 [WB1]) at a nominal 

spatial resolution of 100 m and incidence angles of 18° in 

the near-range to 43° in the far-range of the imaged swath 

(fig. 2). All PALSAR scenes used in this study were collected 

in the descending orbital direction (fig. 3A; table 1). To allow 

scene-to-scene comparability, PALSAR scenes with HH 

Figure 2. Diagram of swath coverage provided in generic scenes captured by Synthetic Aperture Radar (SAR) sensors. The example 

scene included in the diagram was captured by a Phased Array type L-band SAR sensor aboard the Japanese Aerospace Exploration 

Agency’s Advanced Land Observing Satellite. Orbital and look directions, look and local incident angles, and near range and far range 

are illustrated. 

Derived from data © 2009, JAXA METI
6 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

(horizontal send and receive) polarization were radiometrically 

calibrated to the sigma naught backscattering coefficient (σo

) 

with remote sensing software available at the Alaska Satellite 

Facility Website (2010). The 21 PALSAR scenes selected from 

January 2007 through September 2009 included all PALSAR 

scenes collected over coastal Louisiana (fig. 3A; table 1).

The C-band ASAR scenes were collected in Wide Swath 

mode at a nominal spatial resolution of 150 m and incidence 

angles of 17° in the near-range to 42° in the far-range of the 

imaged swath. The ASAR scenes were collected in both the 

ascending and descending orbits that had a variety of look 

directions (figs. 3B,C; table 2). ASAR scenes with HH and 

VV polarizations were transformed to σo

 estimates by using 

Next ESA SAR Toolbox (NEST) software and calibration 

coefficients provided by the European Space Agency 

(European Space Agency, 2007). The 24 ASAR scenes 

selected were collected from July 2006 to September 2009, 

and their times of collection corresponded to a wide range of 

sea levels and tidal flushing (figs. 3B,C; table 2).

All SAR data were registered to a Landsat Thematic 

Mapper (TM) Lambert Conformal Conic (LCC) base image 

having 25-m spatial resolution. Using the LCC projection 

eliminated problems due to the presence of multiple Universal 

Transverse Mercator (UTM) zones, and the LCC matched 

the State Plane Coordinate system for Louisiana. The LCC 

projection (WGA84 geoid) used two standard parallels 

separated by 1.5° latitude, a central meridian, and a false 

northing and easting defined by the State Plane Coordinate 

system for southern Louisiana. Applying State Plane 

parameters, we found that areas projected in the LCC and in 

Albers Equal Area Conic (AEAC) differed by <0.01 percent. 

In addition, the LCC base was assessed by comparison 

to direct USGS Digital Orthophoto Quarter Quadrangles 

(DOQQs). Rectification errors of the LCC base to the DOQQs 

were most often less than 0.5 pixels. Registration errors of the 

LCC base to all satellite-based SAR scenes ranged between 

0.2 and 0.5 pixels.

Figure 3. Scenes captured by Synthetic Aperture Radar (SAR) sensors, with orbital and look directions included. A, Scenes captured 

by the Phased Array type L-band SAR (PALSAR) sensor aboard the Japanese Aerospace Exploration Agency’s Advanced Land 

Observing Satellite (ALOS; derived from data ©2009, 2008, 2007 JAXA METI). B, Western region; scenes acquired by the Advanced 

Synthetic Aperture Radar (ASAR) sensor aboard the European Space Agency’s Envisat. C, Eastern region; scenes acquired by the ASAR 

sensor aboard Envisat. All ALOS PALSAR data are descending orbit (from north to south), western look direction. Envisat ASAR data are 

mixed with both descending orbit and ascending orbit (from south to north) and eastern and western look directions.

Derived from data ©2009, 

2008, 2007 JAXA METI

A

B C
Methods 7

Table 1. Collection date, orbital path, and coverages for scenes 

acquired by the Phased Array type L-band Synthetic Aperture 

Radar (PALSAR) sensor aboard the Japanese Aerospace 

Exploration Agency’s Advanced Land Observing Satellite (ALOS). 

PALSAR scenes were collected in Wide Area Observation Mode 

(Burst Mode 1 [WB1]).

 [Path, ALOS PALSAR apparent path over ground; coverage refers to the 

region of the Louisiana coastal zone covered by the scene]

Date Path Coverage Date Path Coverage

2007 2008

Jan. 7 487 East Jan. 10 487 East

Jan. 12 490 West Jan. 15 490 West

Feb. 22 487 East July 24 485 East

July 10 487 East Sept. 8 485 East

July 15 490 West Sept. 25 486 East

Aug. 25 487 East Oct. 7 484 East

Aug. 30 490 West 2009

Oct. 10 487 East Jan. 12 487 East

Oct. 15 490 West Jan. 17 490 West

Nov. 25 487 East Jan. 22 493 West

Nov. 30 490 West Sept. 16 488 East

Table 2. Collection date, orbital track, and coverages for scenes 

acquired by the Advanced Synthetic Aperture Radar (ASAR) 

sensor aboard the European Space Agency’s Envisat. ASAR 

scenes were collected in Wide Swath mode.

[Track, Envisat ASAR apparent path over ground; Pol., polarization; HH, 

horizontal transmit and receive; VV, vertical transmit and receive; coverage 

refers to the region of the Louisiana coastal zone covered by the scene]

Date Track Coverage Pol.

2006

Jul. 27 1

434 West HH

2007

July 25 1

119 East HH

July 31 1

205 West HH

Sept. 4 1

205 West VV

Sept. 14 1

348 East VV

2008

July 28 1

391 East VV

Aug. 3 1

477 West VV

Aug. 29 1

348 East VV

Sept. 1 1

391 East VV

Sept. 1 2

398 West VV

Sept. 14 2

83 West VV

Sept. 17 1

119 East VV

2009

Mar. 11 1

119 East VV

Mar. 30 1

391 East HH

Apr. 2 1

434 West HH

May17 2

83 West VV

May20 1

119 East VV

May20 2

126 West VV

May23 1

162 Central VV

May27 2

226 West VV

June 24 1

119 East HH

June 27 1

162 West HH

Aug. 30 2

83 West VV

Sept. 12 2

269 East VV

1

Scenes captured in ascending orbital mode.

2

Scenes captured in descending orbital mode.
8 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

PALSAR and ASAR Scene-Collection Parameters

The look and orbit directions were constant for all 

PALSAR scenes used in the inundation analyses (fig. 3A). 

Look-angle ranges were constant, and swath ranges were 

similar in PALSAR scenes covering the eastern and western 

regions (figs. 2 and 3A). In contrast, the ASAR scenes 

exhibited a high variety of orbit and look directions, lookangle ranges, and swath ranges (figs. 3B, C). Also in contrast 

to the PALSAR scenes, the ASAR scenes exhibited noticeably 

higher intensities in the near-range and a progressive decrease 

in intensities extending towards the far-range. Finally, ASAR 

scene polarizations varied between HH and VV, in contrast 

to the constant HH polarization of the PALSAR scenes. 

Comparability of the ASAR scenes was hindered by these 

variables, which also impeded the performance of ASARbased mapping of subcanopy inundation. 

Collecting Coastal and Inland Hydrologic Data

Records of coastal water-level timing and height were 

obtained for seven coastal hydrologic stations from the 

National Ocean Service’s Tides & Currents program (National 

Oceanographic and Atmospheric Administration, 2010). These 

are located along the eastern (Port Fourchon, West Bank1, 

Bayou Gauche, Freshwater Canal Locks and Lawma, Amerada 

Pass), and western (Sabine Pass North, Calcasieu Pass, Lake 

Charles, Freshwater Canal Locks [overlap]) regions of the 

Louisiana coast (fig. 1).

Inland water-level data for calibrating and validating 

the inundation maps were obtained from the Strategic Online 

Natural Resources Information System (SONRIS, 2009) from 

the Louisiana Department of Natural Resources. Hydrologic 

stations used for water-level measurements are included 

in the Coastwide Reference Monitoring System (CRMS). 

On December 1, 2009, we received a list of 212 hydrologic 

stations that were recommended by USGS personnel 

associated with the CRMS program (G. Steyer and G. 

Snedden, written commun.).

Stations included on the list had been confirmed 

to contain the best hydrologic records available for our 

inundation mapping. Before initiating the final phase of our 

mapping, we assessed these records. Our criteria included the 

following: (1) water-levels were referenced to the surrounding 

marsh surface (marsh ground-surface reference at 0-m) and (2) 

records displayed continuity and reliability of data. Continuity 

of data was exhibited by the station having continuous records 

over the study period, and reliability was expressed by the 

nature of the data through time. For instance, if the waterlevels became near constant for a continued time period, 

recordings at that site became suspect and were compared to 

other inland sites. If the suspect records were abnormal when 

compared to those for other sites, the station was excluded.

Assessing Hydrologic Station Locations

The location of many inland hydrograph stations did 

not allow direct comparison between recorded water-levels 

and SAR-based calculations of surface inundation. Although 

locations on the marsh platform were preferred, most 

sites were located in water channels that exhibit different 

flow dynamics than those of the marsh platform and were 

separated from the marsh platform by varying distances and 

obstructions, such as levies (fig. 4A-D). Many sites were 

located in a landscape of mixed marsh and forest stand (fig. 4A

and B) or were located in degrading marsh containing a high 

proportion of open water (fig. 4C–D). In many cases, direct 

validation of SAR-based flood mapping against hydrologic 

data was, thus, prevented by problems deriving from the 

decoupling of measured water levels from flooding occurring 

within the marsh platform and from the contamination of the 

SAR pixel by mixed nonmarsh land covers. Practically, in the 

most egregious cases this incompatibility required removal 

of sites or adjustment of the assessment location to an area 

as close as possible to the water-level recording location but 

within marsh exhibiting the necessary extent and uniformity. 

If moved, the assessment location was contained totally or 

partially within 100 or 150 m of the water-level recording site. 

Even though most hydrologic stations were unsuitable for 

direct validation of our SAR data, the occurrence or absence 

of elevated water levels at the remaining stations provided an 

indication of flooding in the surrounding marshes, and thus, a 

measure of the performance of our SAR-based flood detection.

Validating SAR Areal Data with Hydrologic 

Point Data

In general, the comparability of image products that are 

composites of spatially integrated ground elements (pixels 

of 100 and 150 m in this case) with point measurements 

(water-level recorder locations) can be an issue (for example, 

Fisher and others, 2006; Fisher and Mustard, 2007). This is 

true aside from specific issues regarding the unsuitability of 

some of the hydrologic station locations initially selected for 

our study. This incompatibility between different kinds of 

data may diminish the reliability of the assessment method, 

particularly when applied to highly dynamic situations (such 

as surge flooding and recession in complex marsh systems) 

occurring in heterogeneous environments. Alternatively, 

accurately determining surface flooding, particularly when 

surface-water levels are low (that is, <10 cm), requires fine 

resolution of data (for example, water-level recordings and 

average ground-surface elevations of marsh).The combined 

requirement of acquiring highly accurate ground-surface and 

water-level elevations and the need for accurate functioning 

of the water-level recorder may also limit the validity of the 

point measurements. In the case of this study, invalidity of the 

point measurements would decrease the perceived reliability 

of the SAR-based inundation products. In order to help 
Methods 9

Figure 4. Examples of hydrologic stations in the Coastwide Reference Monitoring System (CRMS) that were unsuitable for use in 

validating Synthetic Aperture Radar-based inundation mapping. CRMS station identification is provided in the lower right corner of each 

example. A, B, Sites where forests and shrubs were evident within marsh areas. C, D, Degraded and largely open-water sites.

improve the calibration and validation of the inundation maps 

by using inland water-level recordings, we inspected marsh 

areas adjacent to and surrounding hydrologic stations. These 

searches were limited to a 100-m (for PALSAR data) or 150-m 

(for ASAR data) radius surrounding hydrologic stations, 

within nominal ground resolutions. If there was a high density 

of flood occurrences within the pertinent 100- or 150-m radius, 

we concluded the site was flooded.

Mapping Inundation Extents

Selecting a Reference Scene 

To successfully validate our ability to detect change 

related to subcanopy flooding, we preferred to use a reference 

scene with limited or no flooding and soil water contents 

at less than saturated levels (Ramsey, 2005, 2008). When 

choosing reference scenes, we sought to avoid collection 

times that were closely following rain events, periods of 

atypically elevated sea levels (for example, Hurricane Dolly 

and Tropical Storm Edouard [fig. 5]), or during high tides 

(for example, spring tides). Additionally, SAR scenes used 

for reference were chosen as best as possible within the same 

season to minimize backscatter differences due to variations 

in vegetation phenology and canopy structure (Ramsey 

and others, 1999, 2004). These criteria for reference-scene 

selections helped ensure that our process of inundation 

mapping best captured subcanopy flooding.

Delineating Permanent Water Bodies

In order to minimize confusion between wind-roughened 

water surfaces, flooded marsh, and nonflooded marsh, 

permanent water bodies were defined within the study area 

(Ramsey and others, 1994; Ramsey, 2005, 2008).The locations 

and extents of permanent inland water bodies obtained from 

the Louisiana Oil Spill Coordinator’s Office (LOSCO) (2007) 
10 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Figure 5. Hydrograph for 2008 dates at Coastwide Reference Monitoring System (CRMS) station 0465 (as noted in fig. 4, this site was 

not used for inundation validation because mixed vegetation surrounded the site). Acquisition dates for Phased Array type L-band 

Synthetic Aperture Radar (PALSAR) and Advanced Synthetic Aperture Radar (ASAR) scenes are also provided. Abnormally high sea 

levels were associated with the impacts of Hurricanes Gustav and Ike and also with the passages of Hurricane Dolly and Tropical 

Storm Edouard. The influence of these storms on the water levels along the Louisiana coast impeded the ability to select SAR reference 

scenes appropriate for use in the inundation-change analyses. See table 3 for a list of storms influencing sea level off the Lousiana 

coast from 2006 to 2009.
Results 11

were updated with thirteen TM images collected from 2006 

to 2008 (western region: June 4, 2006; April 20 and August 

10 and 26, 2007; February 18, March 5, and July 27, 2008; 

central region: June 13, 2006, April 29, 2007, and June 18, 

2008; eastern region: June 6, 2006, April 6, 2007, and July 13, 

2008) before Hurricane Gustav made landfall on September 

1, 2008.These TM images were registered to the same 

LCC-projected TM-base image used to register SAR scenes. 

LOSCO open-water polygon coverage was superimposed on 

the suite of TM images, and the areas of omission between 

the vector coverage and water bodies on the TM images were 

determined. Water bodies visually identified on the suite of 

TM images were at least 40 square kilometers (km2

) in area 

and exhibited spatial and temporal consistency through 2006 

and 2008.These water bodies were classified as permanent 

water bodies and added to the LOSCO open water polygon 

coverage. The combined water polygon coverage was used 

to exclude all permanent water bodies from the SAR-based 

detection of inundation. In addition, a coastal-extent vector 

was used to exclude offshore waters (Louisiana Oil Spill 

Coordinator’s Office, 2007). Location errors in the coastal 

vector were found in association with rapidly changing and 

spatially complex deltaic marshes, such as in the bird’s foot of 

the Mississippi River Delta, the Atchafalaya River delta, and 

the Wax Lake delta.

Determining Change Detection Thresholds

To validate our SAR-based inundation maps, we needed 

to establish minimum inundation thresholds corresponding 

with reasonable predictions of flooded and nonflooded areas 

during “normal” conditions. The processing procedure to 

determine a minimum threshold for detecting reasonable 

changes in inundation was not an automated one and relied 

on operator intervention and judgment to determine the 

threshold (Ramsey, Werle, and others, in press). In principle, 

this procedure required consideration of radar parameters 

for imaging flooded and nonflooded terrains as well as 

background knowledge of flood condition and behavior 

within a specific geographic setting; both aspects were 

brought to bear in an informed trial-and-error procedure to 

determine the extent and configuration of flooded versus 

nonflooded boundary lines. We checked threshold flood 

extents for consistency by completing three steps, including 

the following:

1. by comparing inundation-change results with the 

original SAR data,

2. by comparing results with the closest date of TM-detected 

and inland-measured water levels, and 

3. by categorizing the terrain over a wide area by either 

oversaturating or undersaturating the imagery in such a 

way that known high ground was definitively excluded 

from contiguous “flooded” pixels and known low-lying, 

flood-prone areas were included.

Inundation Mapping with SAR-based

Change Detection

In order to successfully detect changes related to flooded 

marsh, each SAR scene was paired with a reference 

scene having the same HH or VV polarization in order 

to eliminate change artifacts associated with polarization 

differences. Calibrated SAR scenes were then subjected to 

a change-detection algorithm that incorporated an internal, 

5 by 5-pixel Kuan speckle filter to dampen noise while 

preserving edges and shape and retaining spatial continuity 

of detected inundation (Ramsey, Werle, and others, in press). 

A logarithmic ratio of the reference and the target scene data 

resulted in a measurement of decibel difference. A positive 

decibel difference indicated lower intensities in the target SAR 

scene, compared to those of the reference scene. If in excess 

of the predetermined threshold value, the difference was 

considered indicative of possible inundation. Once appropriate 

threshold values were obtained, inundation extents were 

mapped corresponding to the times at which SAR collections 

were made along the entire Louisiana coast.

Results

Coastal and Inland Water Levels

At all seven coastal hydrologic stations (Strategic Online 

Natural Resources Information System, 2009; National 

Oceanic and Atmospheric Administration, 2010), high sealevel variability was recorded from east to west along the 

Louisiana coast (fig. 6A–G). Many instances of elevated sea 

levels could be associated with the passage and impacts of 

tropical storms and hurricanes that occurred in the gulf from 

January 2006 to January 2010 (table 3). Elevated sea states 

prevailed in 2007 and 2008 and were more prominent in 

the eastern portion of the coast than in the western region. 

Consistently elevated sea levels in 2007 and 2008, particularly 

in the spring to fall months, made it difficult to select reference 

SAR scenes that did not exhibit surface flooding or saturated 

marsh soils.

Of the 212 CRMS hydrologic stations chosen for our 

study, only 67 were considered acceptable to validate the 

SAR-based inundation maps. Of these 67 stations, 12 were 

selected in the western region (fig. 7A) and 14 in the eastern 

region (fig. 7B) to provide adequate spatial dispersion for 

validation analyses. For these selected stations, there were 

comparatively consistent and reliable hydrologic records, 

and the stations were fairly well distributed across the coast 

at locations near areas of reasonably contiguous marsh 

without numerous trees or shrubs. Even though the selected 

stations represented a good validation set, their locations 

and associated water-level records were not without glitches 

that affected their comparability with SAR-based inundation 
12 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

mapping (see examples of unusable 

hydrologic stations in fig. 4).

As discussed in the Methods section, 

incompatibility between the point waterlevel recording and the SAR inundation 

mapping and other factors led to using 

a 100- or 150-m radius centered on the 

hydrograph point location to confirm 

the presence or absence of flooding. 

This spatially expanded criterion of 

correspondence was most often applied 

in the eastern region where changedetection results indicated scattered 

pockets of persistent flooding.

Inundation Extent Mapping

SAR Reference Scenes

For reasons already discussed, 

selection of SAR scenes for use as 

reference was complicated by a number 

of factors. In general, the choice of ASAR 

reference scenes was more challenging 

than that of PALSAR reference scenes. 

After extended comparisons, the best 

and most consistent selection criteria for 

reference scenes included the following: 

(1) scenes needed to correspond to the 

lowest coastal (tables 4–9) and inland 

water levels at the highest number of 

hydrologic stations in the eastern and 

western regions, (2) reference and target 

scenes needed to have the same HH and 

VV polarization, and (3) scenes needed 

to represent the dominant seasonality of 

the target scenes. Coastal water levels 

associated with PALSAR and ASAR 

reference scenes are included in tables 

4–9. Our reference-scene selection 

criteria most often allowed us to avoid 

detecting abnormal inundations but did 

not follow our preference for seasonal 

correspondence very well. These 

simple criteria, however, did provide 

a simulation of operational inundation 

mapping with satellite-based SAR, where 

the luxury to carefully scrutinize every 

detail involved in the reference-scene 

selection most often does not exist.

Figure 6. Monthly mean sea level measurements during 2008 at seven coastal 

hydrologic stations included in the National Ocean Service’s Tides & Currents 

program (National Oceanic and Atmospheric Administration, 2010). A, Sabine 

Pass North. B, Calcasieu Pass. C, Freshwater Canal Locks. D, Lake Charles. 

E, Lawma, Amerada Pass. F, Port Fourchon. G, West Bank 1, Bayou Gauche. See 

tables 4–9 for water levels at times when Synthetic Aperture Radar scenes were 

acquired.
Results 13

Table 3. Landfall dates and times of tropical storms and hurricanes in the Gulf of Mexico, 2006–9.

 [UTC, Coordinated Universal Time; NWR, National Wildlife Refuge]

Year Date Name Landfall

2006 June 10–14 Tropical Storm Alberto 0000 UTC June 13, near Apalachicola, Fla.

2007 Aug. 15–17 Tropical Storm Erin 1030 UTC Aug. 16, San Jose Island, Tex.

Sept. 12–14 Hurricane Humberto 0700 UTC Sept. 13, McFaddin NWR, Tex.

2008

July 20–25 Hurricane Dolly 1800 UTC July 23, South Padre Island, Tex.

Aug. 3–6 Tropical Storm Edouard 1200 UTC Aug. 5, McFaddin NWR, Tex.

Aug. 15–26 Tropical Storm Fay 1900 UTC Aug. 21, Flagler Beach, Fla. (3rd landfall)

Aug. 25–Sept. 4 Hurricane Gustav 1500 UTC Sept. 1, Cocodrie, La.

Sept. 1–14 Hurricane Ike 0700 UTC Sept. 13, Galveston Island, Tex.

Oct. 6–7 Tropical Storm Marco 1200 UTC Oct. 7, Misantla, Mexico

2009 Aug. 16–17 Tropical Storm Claudette 0530 UTC Aug. 17, Fort Walton Beach, Fla.

Nov. 4–10 Hurricane Ida 1800 UTC Nov. 9, Mississippi River, La. to Ala.

Figure 7. Map showing locations of 

hydrologic stations in the Coastwide 

Reference Monitoring System (CRMS) 

within the region of the Louisiana 

coastal zone. Marsh categories are 

also shown (Sasser and others, 2008). 

A, Western region. B, Eastern region.

A

B
14 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Table 5. Mean sea level measurements along the eastern Louisiana coast (see fig. 1; National Oceanographic and Atmospheric 

Administration, 2010) corresponding to dates when Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes were 

acquired during 2007–9.

[Sea level measurements are provided in meters. Measurements corresponding to acquisition of PALSAR data are highlighted in orange. The PALSAR reference 

scene had a horizontal transmit and receive polarization. PALSAR data were obtained by the Japanese Aerospace Exploration Agency’s Advanced Land 

Observing Satellite]

Eastern Coastal Region

Year Date Freshwater Canal locks Lawma, Amerada Pass Port Fourchon West Bank 1, Bayou Gauche

2007 Jan. 7 -0.262 -0.132 -0.245 0.120

Feb. 22 -.061 .021 -.082 -.205

Jul. 10 .132 .204 -.024 .016

Aug. 25 .329 .259 .228 .136

Oct. 10 .288 .155 .189 .243

Nov. 25 -.414 -.325 -.171 -.034

2008 Jan. 10 -.375 -.286 -.191 .056

July 24 .400 .165 .093 .161

Sept. 8 .252 .157 .204 .271

Sept. 25 .420 .282 .358 .321

Oct. 7 .065 .172 .159 .286

2009 Jan. 12 -.694 -.326 -.372 -.150

Sept. 16 .295 .433 .412 .356

Table 4. Mean sea level measurements along the western Louisiana coast (see fig. 1; National Oceanographic and Atmospheric 

Administration, 2010) corresponding to dates when Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes were 

acquired during 2007–9.

 [Sea level measurements are provided in meters. Measurements corresponding to acquisition of PALSAR data are highlighted in orange. The PALSAR 

reference scene had a horizontal transmit and receive polarization. PALSAR data were obtained by the Japanese Aerospace Exploration Agency’s Advanced 

Land Observing Satellite]

Western Coastal Region

Year Date Sabine Pass North Calcasieu Pass Lake Charles Freshwater Canal locks

2007 Jan. 12 0.083 0.167 -0.038 0.122

July 15 .233 .190 .291 .184

Aug. 30 -.023 -.124 .110 -.085

Oct. 15 .061 -.066 .267 .056

Nov. 30 -.080 -.218 -.053 -.214

2008 Jan. 15 -.053 -.051 -.257 -.009

2009 Jan. 17 -.153 -.117 -.336 .191

Jan. 22 -.474 -.383 -.463 -.183

Oct. 20 -.183 -.270 .156 -.256
Results 15

Table 6. Mean sea level measurements along the western Louisiana coast (see fig. 1; National Oceanographic and Atmospheric 

Administration, 2010) corresponding to dates when Advanced Synthetic Aperture Radar (ASAR) scenes with horizontal transmit 

and receive polarization were acquired during 2006–9.

[Sea level measurements are provided in meters. Measurements corresponding to acquisition of ASAR data are highlighted in orange. ASAR data were obtained 

by the European Space Agency’s Envisat. A, ascending orbital mode]

Western Coastal Region

Year Date Mode Sabine Pass North Calcasieu Pass Lake Charles Freshwater Canal locks

2006 July 27 A -0.054 -0.243 0.336 -0.113

2007 July 31 A -0.361 -0.452 0.047 -0.502

2009 Mar. 30 A -0.486 -0.610 0.002 -0.692

Apr. 2 A 0.186 0.130 0.363 0.167

Jun. 27 A -0.158 -0.219 0.177 -0.340

Table 7. Mean sea level measurements along the eastern Louisiana coast (see fig. 1; National Oceanographic and Atmospheric 

Administration, 2010) corresponding to dates when Advanced Synthetic Aperture Radar (ASAR) scenes with horizontal transmit 

and receive polarization were acquired during 2007–9.

[Sea level measurements are provided in meters. Measurements corresponding to acquisition of ASAR data are highlighted in orange. ASAR data were obtained 

by the European Space Agency’s Envisat. A, ascending orbital mode]

Eastern Coastal Region

Year Date Mode Freshwater Canal locks Lawma, Amerada Pass Port Fourchon West Bank 1, Bayou Gauche

2007 July 25 A 0.088 -0.050 -0.010 -0.064

2009 Mar. 30 A -.692 -.199 -.269 .294

Jun. 24 A -.642 -.170 -.278 .008

Jun. 27 A -.340 -.028 -.053 .075

Table 8. Mean sea level measurements along the western Louisiana coast (see fig. 1; National Oceanographic and Atmospheric 

Administration, 2010) corresponding to dates when Advanced Synthetic Aperture Radar (ASAR) scenes with vertical transmit and 

receive polarization were acquired during 2007–9.

[Sea level measurements are provided in meters. Measurements corresponding to acquisition of ASAR data are highlighted in orange. ASAR data were obtained 

by the European Space Agency’s Envisat. A, ascending orbital mode; D, descending orbital mode]

Western Coastal Region

Year Month Mode Sabine Pass North Calcasieu Pass Lake Charles Freshwater Canal locks

2007 Sept. 4 A 0.142 0.200 -0.172 0.053

2008 Aug. 3 A -.424 -.454 .063 -.431

Sept. 1 D -.195 -.447 .095 -1.132

Sept. 14 D 1.178 .721 .046 .593

2009 May17 D .113 .077 -.101 .106

May20 D .215 .279 .046 .209

May23 A -.030 .051 -.018 .011
16 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Table 9. Mean sea level measurements along the eastern Louisiana coast (see fig. 1; National Oceanographic and Atmospheric 

Administration, 2010) corresponding to dates when Advanced Synthetic Aperture Radar (ASAR) scenes with vertical transmit and 

receive polarization were acquired during 2007–9.

[Sea level measurements are provided in meters. Measurements corresponding to acquisition of ASAR data are highlighted in orange. ASAR data were obtained 

by the European Space Agency’s Envisat. A, ascending orbital mode; D, descending orbital mode]

Eastern Coastal Region

Year Month Mode Freshwater Canal locks Lawma, Amerada Pass Port Fourchon West Bank 1, Bayou Gauche

2007 Sept. 14 A -0.050 0.107 0.106 0.200

2008 July 28 A .101 .116 .080 .025

Aug. 29 A -.180 -.243 -.098 .000

Sept. 1 A -.101 -.143 .241 .045

Sept. 17 A -.111 .027 .322 .574

2009 Mar. 11 A -.017 -.049 .070 .081

May20 A .193 .150 .155 .056

May23 A .011 .074 .223 .174

May27 D .236 .448 .391 .198

Aug. 30 A .202 .062 .162 .132

Sept. 2 A -.178 -.233 .022 .052

Inundation Thresholds

In the results of the change-detection analyses used to 

map inundation distribution in the target scenes, the range 

and distribution of reference- and target-scene backscatter 

differences did not exhibit multimodal features but fairly 

continuous Gaussian-type distributions (an example is shown 

in fig. 8). This consistency in the analysis of reference and 

target SAR scenes has been noted previously (Ramsey and 

others, 2011). It is important to note that differences exhibited 

in the Gaussian distribution did not include permanent water 

bodies, as identified in our prechange-detection processing.

 As discussed in the “Methods” section, in order to 

minimize errors of commission (inclusion of nonflooded 

marshes) and omission (exclusion of flooded marshes) in 

SAR-based mapping, we used alternate data sources (for 

example, TM) to select appropriate inundation thresholds. 

Multiple thresholds were tested for each set of PALSAR 

and ASAR scenes covering the eastern and western regions 

of the coast. In the case of PALSAR scenes, all with HH 

polarization, a threshold value of 2.0 was chosen from 

a range of threshold values ranging from 1.5 to 2. After 

extensive testing, a threshold value of 1.0 for scenes with 

HH polarization was found to best reproduce the inundation 

patterns validated by our comparison of reference and target 

SAR scenes. For scenes exhibiting VV polarization, threshold 

values of 0.1for the eastern region and 1.0 for the western 

region were deemed most appropriate.

Figure 8. Graph depicting sigma-nought change-detection 

results when comparing inundation across reference-scene 

(acquired July 28, 2008) and target-scene data (acquired 

September 14, 2008) obtained by an Advanced Synthetic 

Aperture Radar (ASAR) sensor aboard the European Space 

Agency’s Envisat. Notice the highly Gaussian shape, with only a 

lightly positive skew, and the lack of any suggested bimodal or 

multimodal features.
Results 17

Assessments of SAR-based Inundation Maps

Inundation maps were produced (fig.9A, B) by using 

calibrated SAR scenes (along with pertinent reference scenes) 

collected from 2006 to 2009 that were subjected to the 

inundation-detection procedure. PALSAR-based inundation 

mapping exhibited good correspondences with coastal and 

inland water-level data. Correspondence rates of PALSARbased maps were 83 percent in the western region and 

85 percent in the eastern region (tables 10–11). In contrast, 

correspondence rates of ASAR-based maps were lower and 

more varied in comparison to PALSAR-based maps.

For the western coastal region, ASAR-based inundation 

mapping that used scenes with HH polarization yielded a 

correspondence rate of 53 percent (fig. 10A; table 12) when 

compared to the coastal and inland water-level data. In the 

eastern region, the correspondence rate increased to 81 percent 

(table 13); however, the three ASAR scenes with HH 

polarization were collected at times when coastal and inland 

water-level data indicated an absence of flooding throughout 

the eastern coastal region (fig. 10B).

For the western coastal region, ASAR-based inundation 

mapping by using VV polarization yielded a correspondence 

rate of 61 percent when compared to coastal and inland 

water-level data. This correspondence rate included scenes 

acquired on September 14, 2008 (associated with high waterlevels from storm surge) and August 3, 2008 (associated 

with regions of spatially extensive tidal or rainfall flooding) 

(fig. 11A, table 14).

Figure 9. Inundation maps created from Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes acquired by the 

Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. Inundation extents are expressed as a color overlay 

and were derived from interpretation of changes in decibels (σo

) between the target and reference PALSAR scenes (PCI, 2007; Ramsey, 

Werle, and others, in press). PALSAR scenes on the left are dated according to scene acquisition; color-adjusted scenes (right panel) 

indicate detected areas of inundation. A, Results for the western coastal region. The reference PALSAR scene was acquired on January 

15, 2008. B, Results for the eastern coastal region. The reference PALSAR scene was acquired on January 7, 2007.

A

Derived from data © 2008, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI
18 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Figure 9.—Continued Inundation maps created from Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes acquired 

by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference PALSAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). PALSAR scenes on the left are dated according to scene acquisition; color-adjusted scenes (right 

panel) indicate detected areas of inundation. A, Results for the western coastal region. The reference PALSAR scene was acquired on 

January 15, 2008. B, Results for the eastern coastal region. The reference PALSAR scene was acquired on January 7, 2007.

A

Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI

Derived from data © 2009, JAXA METI Derived from data © 2009, JAXA METI

Derived from data © 2009, JAXA METI Derived from data © 2009, JAXA METI
Results 19

Figure 9.—Continued Inundation maps created from Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes acquired 

by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference PALSAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). PALSAR scenes on the left are dated according to scene acquisition; color-adjusted scenes (right 

panel) indicate detected areas of inundation. A, Results for the western coastal region. The reference PALSAR scene was acquired on 

January 15, 2008. B, Results for the eastern coastal region. The reference PALSAR scene was acquired on January 7, 2007.

B

Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI
20 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Figure 9.—Continued Inundation maps created from Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes acquired 

by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference PALSAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). PALSAR scenes on the left are dated according to scene acquisition; color-adjusted scenes (right 

panel) indicate detected areas of inundation. A, Results for the western coastal region. The reference PALSAR scene was acquired on 

January 15, 2008. B, Results for the eastern coastal region. The reference PALSAR scene was acquired on January 7, 2007.

B

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI

Derived from data © 2007, JAXA METI Derived from data © 2007, JAXA METI
Results 21

B

Figure 9.—Continued Inundation maps created from Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes acquired 

by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference PALSAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). PALSAR scenes on the left are dated according to scene acquisition; color-adjusted scenes (right 

panel) indicate detected areas of inundation. A, Results for the western coastal region. The reference PALSAR scene was acquired on 

January 15, 2008. B, Results for the eastern coastal region. The reference PALSAR scene was acquired on January 7, 2007.

Derived from data © 2008, JAXA METI Derived from data © 2008, JAXA METI

Derived from data © 2008, JAXA METI Derived from data © 2008, JAXA METI

Derived from data © 2008, JAXA METI Derived from data © 2008, JAXA METI
22 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

B

Figure 9.—Continued Inundation maps created from Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes acquired 

by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference PALSAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). PALSAR scenes on the left are dated according to scene acquisition; color-adjusted scenes (right 

panel) indicate detected areas of inundation. A, Results for the western coastal region. The reference PALSAR scene was acquired on 

January 15, 2008. B, Results for the eastern coastal region. The reference PALSAR scene was acquired on January 7, 2007.

Derived from data © 2008, JAXA METI Derived from data © 2008, JAXA METI

Derived from data © 2008, JAXA METI Derived from data © 2008, JAXA METI

Derived from data © 2009, JAXA METI Derived from data © 2009, JAXA METI
Results 23

Figure 9.—Continued Inundation maps created from Phased Array type L-band Synthetic Aperture Radar (PALSAR) scenes acquired 

by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference PALSAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). PALSAR scenes on the left are dated according to scene acquisition; color-adjusted scenes (right 

panel) indicate detected areas of inundation. A, Results for the western coastal region. The reference PALSAR scene was acquired on 

January 15, 2008. B, Results for the eastern coastal region. The reference PALSAR scene was acquired on January 7, 2007.

BB

Table 10. Results of inundation-change analysis for the western Louisiana coast compared with water-level measurements at inland 

hydrologic stations along the coast. PCI image processing software was used to identify changes between the reference- and targetSynthetic Aperture Radar (SAR) scenes that represented inundation on the target scene (PCI, 2007; Ramsey, Werle, and others, in 

press). Phased Array type L-band SAR (PALSAR) data were acquired by the Japanese Aerospace Exploration Agency’s Advanced Land 

Observing Satellite. Inland water levels were measured against marsh-surface height.

[Water-level measurements are provided in meters. PALSAR; Phased Array type L-band Synthetic Aperture Radar; CRMS, Coastwide Reference Monitoring 

System; ID, identification; n.d., no data; PALSAR scenes used in the change-detection analysis exhibited horizontal transmit and receive polarization; CHDET, 

change-detection analysis]

PALSAR acquisition dates and inland water levels 

CRMS Station ID

2008 2007 2009

Jan. 15 Jan. 12 July 15 Aug. 30 Oct. 15 Nov. 30 Jan. 17 Jan. 22 

665 0.082 n.d. n.d. n.d. n.d. n.d. 0.149 0.134

682 -.189 n.d. n.d. n.d. n.d. -.009 -.101 -.140

588 n.d. n.d. n.d. .107 .189 n.d. n.d. n.d.

599 -.101 -.030 n.d. .055 .198 -.101 n.d. n.d.

609 -.140 n.d. n.d. n.d. n.d. n.d. -.125 -.143

576 n.d. n.d. n.d. n.d. n.d. n.d. -.119 -.189

508 n.d. n.d. n.d. n.d. n.d. n.d. -.268 -.268

2189 -.018 n.d. n.d. .085 .192 .012 .027 .000

680 .024 n.d. n.d. .058 .085 .055 n.d. n.d.

553 .195 n.d. .299 .274 .347 n.d. .186 .143

623 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

1277 n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d.

Water levels corresponding to acquisition of the PALSAR reference scene on January 15, 2008. 

CHDET results were consistent with water levels measured at CRMS stations.

CHDET results were not consistent with water levels measured at CRMS stations.

Either PALSAR data or site-specific water-level measurements were not available on the given date.

Derived from data © 2009, JAXA METI Derived from data © 2009, JAXA METI
24 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Table 11. Results of inundation-change analysis for the eastern Louisiana coast compared with water-level measurements at inland 

hydrologic stations along the Louisiana coast. PCI image processing software was used to identify changes between the reference- and 

target-Synthetic Aperture Radar (SAR) scenes that represented inundation on the target scene (PCI, 2007; Ramsey, Werle, and others, in 

press). Phased Array type L-band SAR data were acquired by the Japanese Aerospace Exploration Agency’s Advanced Land Observing 

Satellite. Inland water levels were measured against marsh-surface height.

 [Water-level measurements are provided in meters. PALSAR; Phased Array type L-band Synthetic Aperture Radar; CRMS, Coastwide Reference Monitoring 

System; ID, identification; n.d., no data; PALSAR scenes used in the change-detection analysis exhibited horizontal transmit and receive polarization; CHDET, 

change-detection analysis]

PALSAR acquisition dates and inland water levels

CRMS 

station 

ID

2007 2007 2008 2009

Jan. 7 Feb. 22 July 10 Aug. 25 Oct. 10 Nov. 25 Jan. 10 July 24 Sept. 8 Sept. 25 Oct. 7 Jan. 12 Sept. 16

374 -0.250 -0.113 -0.021 0.152 0.104 -0.311 -0.317 0.006 0.091 0.235 0.012 n.d. n.d.

311 -.137 -.030 .180 .283 .180 -.229 -.192 .140 .241 .351 .241 -.384 .375

338 -.168 -.034 .180 .311 .162 -.250 -.131 .113 .024 n.d. n.d. n.d. .451

4218 n.d n.d n.d. n.d. n.d. n.d. n.d. .088 .213 .296 .253 -.256 .363

261 n.d n.d .061 .162 .171 -.079 n.d. .128 .250 .283 .296 -.238 .390

251 -.018 -.122 .171 .274 .152 -.113 -.049 .125 .140 .472 .280 n.d. .466

3667 n.d n.d n.d. n.d. .207 .091 .006 n.d. n.d. .332 .381 -.140 .268

30 n.d n.d -.076 .131 .283 .290 .091 -.046 .351 .448 .466 -.003 .396

33 n.d n.d -.177 .058 .305 .122 .015 -.101 .341 .372 .329 -.149 .317

146 n.d n.d n.d. n.d. n.d. .238 -.037 -.064 .317 .427 n.d. n.d. n.d.

136 -.290 -.402 -.128 .058 .034 .000 -.320 -.219 .213 .530 .158 n.d. .226

4529 n.d n.d n.d. .274 .168 -.247 -.155 .058 .195 .247 .113 -.463 .475

147 -.326 -.183 -.009 .256 .216 -.262 n.d. -.162 n.d. .433 .101 -.280 .436

4572 n.d n.d n.d. n.d. n.d. .098 -.235 -.070 n.d. .472 .311 -.287 .494

Water levels corresponding to acquisition of the PALSAR reference scene on January 7, 2008.

Change-detection results were consistent with water levels measured at CRMS stations.

Change-detection results were not consistent with water levels measured at CRMS stations.

Either PALSAR data or site-specific water-level measurements were not available on the given date.
Results 25

Figure 10. Maps created from Advanced Synthetic Aperture Radar (ASAR) scenes exhibiting horizontal transmit and receive (HH) 

polarization that were acquired by the European Space Agency’s Envisat. Inundation extents are expressed as a color overlay and were 

derived from interpretation of changes in decibels (σo

) between the target and reference ASAR scenes (PCI, 2007; Ramsey, Werle, and 

others, in press). Images on the left are dated according to scene acquisition; color-adjusted scenes (right panel) indicate detected 

areas of inundation. A, Results for the western coastal region. The reference ASAR scene was acquired on July 31, 2007. B, Results for 

the eastern coastal region. The reference ASAR scene was acquired on March 30, 2009.

A
26 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Figure 10.—Continued Maps created from Advanced Synthetic Aperture Radar (ASAR) scenes exhibiting horizontal transmit and 

receive (HH) polarization that were acquired by the European Space Agency’s Envisat. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference ASAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). Images on the left are dated according to scene acquisition; color-adjusted scenes (right panel) 

indicate detected areas of inundation. A, Results for the western coastal region. The reference ASAR scene was acquired on July 31, 

2007. B, Results for the eastern coastal region. The reference ASAR scene was acquired on March 30, 2009.

B
Results 27

Table 12. Results of inundation-change analysis for the western 

Louisiana coast compared with water-level measurements at 

inland hydrologic stations along the Louisiana coast. PCI image 

processing software was used to identify changes between the 

reference- and target-Synthetic Aperture Radar (SAR) horizontal 

transmit and receive polarization scenes that represented 

inundation on the target scene (PCI, 2007; Ramsey, Werle, and 

others, in press). Advanced SAR (ASAR) data were acquired by 

the European Space Agency’s Envisat. Inland water levels were 

measured against marsh-surface height.

[Water-level measurements are provided in meters. ASAR; Advanced 

Synthetic Aperture Radar; CRMS, Coastwide Reference Monitoring System; 

ID, identification; n.d., no data; PALSAR scenes used in the change-detection 

analysis exhibited horizontal transmit and receive polarization; CHDET, 

change-detection analysis]

ASAR acquisition dates and inland water levels

CRMS station ID

2007 2009

July 31 Mar. 30 Apr. 2 June 27

665 n.d. n.d. 0.162 -0.122

682 n.d. -.006 -.012 -.247

588 .088 .131 .174 -.052

599 -.021 .055 .134 -.015

609 n.d. .049 .091 .027

576 n.d. .232 .201 -.055

508 n.d. .137 .131 -.055

2189 .079 .061 .204 n.d.

680 .043 .046 .030 n.d.

553 .326 .250 .280 -.012

623 n.d. .171 .183 -.076

1277 n.d. .128 .119 -.113

Water levels corresponding to acquisition of the 

PALSAR reference scene on July 31, 2007.

CHDET results were consistent with water levels 

measured at CRMS stations.

CHDET results were not consistent with water 

levels measured at CRMS stations.

Either PALSAR data or site-specific water-level 

measurements were not available on the

given date.

Table 13. Results of inundation-change analysis for the eastern 

Louisiana coast compared with water-level measurements at 

inland hydrologic stations along the Louisiana coast. PCI image 

processing software was used to identify changes between the 

reference- and target-Synthetic Aperture Radar (SAR) horizontal 

transmit and receive polarization scenes that represented 

inundation on the target scene (PCI, 2007; Ramsey, Werle, and 

others, in press). Advanced SAR (ASAR) data were acquired by 

the European Space Agency’s Envisat. Inland water levels were 

measured against marsh-surface height.

[Water-level measurements are provided in meters. ASAR; Advanced 

Synthetic Aperture Radar; CRMS, Coastwide Reference Monitoring System; 

ID, identification; n.d., no data; PALSAR scenes used in the change-detection 

analysis exhibited horizontal transmit and receive polarization; CHDET, 

change-detection analysis]

ASAR acquisition dates and inland water levels

CRMS station ID

2009 2007 2009

Mar. 30 July 25 June 24 June 27

374 n.d. -0.079 n.d. n.d.

311 -.198 -.052 -.195 -.034

338 -.287 -.027 -.32 -.082

4218 .006 n.d. -.094 .04

261 .064 -.116 .003 .131

251 -.091 -.07 -.094 .098

3667 .168 n.d. -.25 -.165

30 .125 .052 -.128 .101

33 .259 .009 -.259 -.067

146 -.061 n.d. n.d. n.d.

136 -.134 -.396 -.445 -.226

4529 -.274 n.d. -.235 0

147 -.344 -.192 n.d. n.d.

4572 -.18 n.d. -.329 -.04

Water levels corresponding to acquisition of the 

PALSAR reference scene on March 30, 2009.

CHDET results were consistent with water levels 

measured at CRMS stations.

CHDET results were not consistent with water 

levels measured at CRMS stations.

Either PALSAR data or site-specific water-level 

measurements were not available on the

given date.
28 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Figure 11. Inundation maps created from Advanced Synthetic Aperture Radar (ASAR) scenes exhibiting vertical transmit and receive 

(VV) polarization that were acquired by the European Space Agency’s Envisat. Inundation extents are expressed as a color overlay and 

were derived from interpretation of changes in decibels (σo

) between the target and reference ASAR scenes (PCI, 2007; Ramsey, Werle, 

and others, in press). Images in the left are dated according to scene acquisition; color-adjusted images (right panel) indicate detected 

areas of inundation. A, Results for the western coastal region. The reference ASAR scene was acquired on May 17, 2009. B, Results for 

the eastern coastal region. The reference ASAR scene was acquired on July 28, 2008.

A
Results 29

Figure 11.—Continued Inundation maps created from Advanced Synthetic Aperture Radar (ASAR) scenes exhibiting vertical transmit 

and receive (VV) polarization that were acquired by the European Space Agency’s Envisat. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference ASAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). Images in the left are dated according to scene acquisition; color-adjusted images (right panel) 

indicate detected areas of inundation. A, Results for the western coastal region. The reference ASAR scene was acquired on May 17, 

2009. B, Results for the eastern coastal region. The reference ASAR scene was acquired on July 28, 2008.

B
30 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Figure 11.—Continued Inundation maps created from Advanced Synthetic Aperture Radar (ASAR) scenes exhibiting vertical transmit 

and receive (VV) polarization that were acquired by the European Space Agency’s Envisat. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference ASAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). Images in the left are dated according to scene acquisition; color-adjusted images (right panel) 

indicate detected areas of inundation. A, Results for the western coastal region. The reference ASAR scene was acquired on May 17, 

2009. B, Results for the eastern coastal region. The reference ASAR scene was acquired on July 28, 2008.

B
Results 31

Figure 11.—Continued Inundation maps created from Advanced Synthetic Aperture Radar (ASAR) scenes exhibiting vertical transmit 

and receive (VV) polarization that were acquired by the European Space Agency’s Envisat. Inundation extents are expressed as a color 

overlay and were derived from interpretation of changes in decibels (σo

) between the target and reference ASAR scenes (PCI, 2007; 

Ramsey, Werle, and others, in press). Images in the left are dated according to scene acquisition; color-adjusted images (right panel) 

indicate detected areas of inundation. A, Results for the western coastal region. The reference ASAR scene was acquired on May 17, 

2009. B, Results for the eastern coastal region. The reference ASAR scene was acquired on July 28, 2008.

B
32 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

The highest number of inundation maps was created 

from ASAR VV scenes covering the eastern coastal region 

(fig. 11B). For this region, the overall correspondence rate of 

ASAR-based inundation maps with coastal and inland water 

levels was 61 percent, which included scenes acquired on 

August 29 and March 11, 2008, when water levels below 

marsh-surface dominated. The overall correspondence rate 

also included scenes acquired on May 23 and 27, 2009, when 

relatively high water levels above the marsh-surface were 

recorded (fig. 11B, table 15).Conversely, scenes in the eastern 

region that were acquired on September 1 and 17, 2009, when 

there were moderately high surface-water levels, yielded 

low correspondence with coastal and inland water-level data. 

On September 1 and May 20, 2009, marsh over-topping 

accompanied by wind or rain roughening of the surface water 

increased SAR backscatter, thereby reducing the effectiveness 

SAR-based inundation mapping in many near coastal locations 

(fig.11B) (Ramsey and others, 1994, 2009; Ramsey, Werle, and 

others, in press).

Table 14. Results of inundation-change analysis for the western Louisiana coast compared with water-level measurements at inland 

hydrologic stations along the Louisiana coast. PCI image processing software was used to identify changes between the referenceand target-Synthetic Aperture Radar (SAR) vertical transmit and receive polarization scenes that represented inundation on the target 

scene (PCI, 2007; Ramsey, Werle, and others, in press). Advanced SAR (ASAR) data were acquired by the European Space Agency’s 

Envisat. Inland water levels were measured against marsh-surface height.

[Water-level measurements are provided in meters. ASAR; Advanced Synthetic Aperture Radar; CRMS, Coastwide Reference Monitoring System; ID, 

identification; n.d., no data; ASAR scenes used in the change-detection analysis exhibited vertical transmit and receive polarization; CHDET, change-detection 

analysis]

ASAR acquisition dates and inland water levels

CRMS station ID

2009 2007 2008 2009

May 17 Sept. 4 Aug. 3 Sept. 1 Sept. 14 May 20 May 23

665 0.296 n.d. 0.158 0.162 1.548 0.302 0.113

682 -.064 n.d. -.140 .034 1.655 -.067 -.061

588 .128 .128 .040 -.034 1.180 .091 .107

599 .009 -.024 n.d. .015 .884 .101 .146

609 -.03 n.d. -.040 -.113 1.152 .018 .049

576 .101 n.d. .052 .070 1.189 .064 .113

508 -.131 n.d. -.064 -.195 1.228 -.006 .037

2189 .085 .040 .037 -.052 n.d. .055 .104

680 -.043 .027 n.d. .021 1.679 -.061 -.061

553 .354 .302 .271 .280 1.628 .317 .287

623 .064 n.d. .000 -.223 1.097 .024 .076

1277 .1463 n.d. -.354 -.213 .165 .104 .098

Water levels corresponding to acquisition of the PALSAR reference scene on May 17, 2009.

CHDET results were consistent with water levels measured at CRMS stations.

CHDET results were not consistent with water levels measured at CRMS stations.

Either PALSAR data or site-specific water-level measurements were not available on the given date.

A storm cell created high backscatter around CRMS0682 and CRMS0680. The flood peak was delayed at CRMS1277.

Although these known environmental conditions 

may have explained the low correspondence rates of the 

September 1 and May 20, 2009, scenes, the wind roughening 

of the water surfaces was not indicated as a factor in the 

low correspondence rates of the September 17, 2008, scene, 

which was also associated with relatively high water levels. 

Alternatively, low correspondence of the September 17, 2008, 

scene was likely due to a mixture of residual ponding of 

surface water and high water content in sediment following 

Hurricane Ike-induced coastal inundations that occurred in 

the east from about September 13 to 14, 2008. The ponded 

water would tend to decrease backscatter while the high 

water content in sediment at below saturation would tend to 

enhance backscatter (Ramsey and others, 1999). As in wind 

roughening, surface-water ponding would tend to lower the 

detection-based inundation mapping performance. The lower 

correspondences of the May 11and (in part) the May 23, 2009, 

scenes were mostly associated with near-surface water levels.
Results 33

Influence of Reference-Scene Selection on 

ASAR-Based Mapping Performance

 To fully review the performance of ASAR-based 

mapping of inundation, it was desirable to demonstrate how 

dependent the performance was on ASAR reference-scene 

selection. To provide this demonstration, we mapped the 

inundation extent on an ASAR scene (with VV polarization) 

acquired on September 17, 2008—three to four days after 

Hurricane Ike surge flooding and about 14 days after 

Hurricane Gustav flooding in the east (Ramsey, Werle, and 

others, in press). For comparison, we used two different 

ASAR reference scenes to calculate the inundation extent. 

The first reference scene, acquired on March 11, 2009, was 

Table 15. Results of inundation-change analysis for the eastern Louisiana coast compared with water-level measurements at inland 

hydrologic stations along the Louisiana coast. PCI image processing software was used to identify changes between the referenceand target-Synthetic Aperture Radar (SAR) vertical transmit and receive polarization scenes that represented inundation on the target 

scene (PCI, 2007; Ramsey, Werle, and others, in press Advanced SAR (ASAR) data were acquired by the European Space Agency’s 

Envisat. Inland water levels were measured against marsh-surface height.

[Water-level measurements are provided in meters. ASAR; Advanced Synthetic Aperture Radar; CRMS, Coastwide Reference Monitoring System; ID, 

identification; n.d., no data; ASAR scenes used in the change-detection analysis exhibited vertical transmit and receive polarization; CHDET, change-detection 

results]

ASAR acquisition dates and inland water levels

Station 

ID

2008 2007 2008 2009

July 28 Sept. 14 Aug. 29 Sept. 1 Sept. 17 Mar. 11 May 20 May 23 May 27 Aug. 30 Sept. 2

374 -0.101 -0.049 -0.265 -0.006 0.137 -0.061 0.043 -0.052 0.299 n.d. n.d.

311 -.055 .146 -.207 .073 .210 .012 .088 .006 .344 -.021 -.140

338 -.104 .189 -.216 -.064 .296 .070 .268 .055 .338 n.d. n.d.

4218 -.14 n.d. -.110 -.021 .207 .034 -.024 .094 .146 -.006 -.070

261 -.076 .195 -.073 -.018 .250 .030 -.024 .128 .183 .055 -.015

251 -.128 .189 -.140 -.018 .570 .067 .003 .064 .232 .049 -.079

3667 NA n.d. n.d. n.d. .229 -.076 .259 .418 .101 .076 .012

30 -.204 .101 -.027 .204 .351 -.021 .347 .500 .076 .091 .043

33 -.143 .104 -.091 .076 .418 -.070 .290 .399 .177 .088 .027

146 -.277 n.d. -.119 .277 .168 -.119 .192 .405 n.d. n.d. n.d.

136 -.564 -.277 -.411 .454 .283 -.192 .494 .539 .024 -.354 -.265

4529 -.11 .216 -.210 -.198 .183 .037 -.034 -.061 .326 .091 -.094

147 -.332 -.079 -.360 .701 .131 -.094 .091 .180 .094 n.d. n.d.

4572 -.305 n.d. -.351 .375 .241 -.070 .146 .216 .134 -.192 -.140

Water levels corresponding to acquisition of the PALSAR reference scene on July 28, 2008.

CHDET results were consistent with water levels measured at CRMS stations.

CHDET results were not consistent with water levels measured at CRMS stations.

Either PALSAR data or site-specific water-level measurements were not available on the given date.

chosen solely based on coastal water levels. The second 

reference scene, acquired on July 28, 2008, was selected based 

on a combination of coastal and inland water levels. The 

resultant inundation distributions calculated for September 

17, 2007, derived from the two different reference scenes 

highly diverged (fig. 12). The July 28 reference scene 

yielded a 43 percent correspondence between ASARbased inundation and measured water-levels, which was a 

significant improvement over the 21 percent correspondence 

rate derived from the March 11, 2009, reference scene. 

The improved correspondence of inundation mapping 

based on the July 28, 2008, reference scene demonstrated 

a high degree of sensitivity exhibited by the ASAR-based 

inundation mapping that was not exhibited by the PALSARbased inundation mapping.
34 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

Figure 12. Inundation mapping results derived by using an Advanced Synthetic Aperture Radar (ASAR) target scene acquired on 

September 17, 2008, by the European Space Agency’s Envisat. A comparison of results derived from using two different ASAR reference 

scenes is presented. A, Target scene paired with reference scene acquired July 28, 2008. B, Target scene paired with reference scene 

acquired March 11, 2009.

Discussion

SAR-based mapping of marsh inundation was compared 

to inland water-level records created when SAR scenes were 

acquired. Mapping success was determined by levels of 

correspondence between map and inland water-level records. 

Consistently higher mapping accuracies were obtained by 

using PALSAR (L-band) versus ASAR (C-band) data. Results 

for ASAR-based mapping also concluded that there were 

higher rates of correspondence when water levels were either 

much higher or much lower than the marsh-surface height. 

Additional comparisons were used to help explain variations 

in correspondence rates, particularly when using ASAR data.

Complications of ASAR-Based

Inundation Mapping 

Complications in the inundation mapping with the HHand VV-polarized ASAR data stemmed to some extent from 

a lack of high-quality ASAR reference scenes. This paucity 

resulted from the abnormally high sea states in 2007 and 2008 

that restricted the availability of ASAR reference scenes that 

completely fit the preferred criteria for reference selection. 

For the same reason, selection of appropriate reference scenes 

was also problematic in the PALSAR inundation mapping; 

however, high inconsistencies in ASAR scene collection 

geometries seemed to intensify these problems and further 

hinder conformance between the ASAR reference and target 

scenes; collection parameters of all PALSAR scenes were 

highly similar from scene to scene.

Variability in collection geometries and coverage 

extents might not directly cause substantial problems in 

marsh inundation mapping but could more likely affect 

mapping performance through associated changes in the 

local incident angle. Although the local incident angle is not 

functionally related to coverage or look direction, particularly 

in flat terrains, in the coastal region studied, the higher the 

differences in coverage and look direction between scenes, 

the more dramatic the change in local incident angle at 

equivalent locations on the scenes. Differences in the local 

incident angle (as referenced to a set ground location) from 

scene to scene led to differences in SAR backscatter that 

were not compensated by calibration. Added to this causal 

variability in local incident angle, ASAR scenes exhibited 

higher sensitivity to changes in look angle from the near to 

far range than exhibited in the PALSAR scenes. This added 

sensitivity to progressive changes in look angle compounds 

complexities related to inconsistencies in the local incident 

angle and indirectly changes the relationship between the 

tested threshold value and the commission and omission errors 

throughout the inundation maps. Those differences in SAR 

backscatter due to the variability in the local incidence angle 

and progressive backscatter increases related to look angle 
Discussion 35

would not represent differences in the presence or absence of 

inundation at that location. Because ASAR-based inundation 

mapping relied on scene-to-scene change detection, any 

anomalous, non-inundation-related changes confused the 

change-detection interpretation. 

In effect, these erratic changes in backscatter unrelated to 

changes in inundation diminished the ASAR-based inundation 

mapping performance in ways that were undeterminable in 

this study. The consistent choice of reference scenes and 

deliberate identification of change-detection thresholds did 

reduce these types of change-detection misclassifications. In 

the case of ASAR, however, the high variety of compounding 

influences could not be fully compensated, thus resulting 

in lowered ASAR-based inundation mapping performance 

compared to PALSAR-based inundation mapping that used 

scenes with near consistency in collection geometries. 

Visual Comparisons of SAR-based

Inundation Maps

Although there were no coincident collections of 

PALSAR and ASAR scenes for use in this study, several 

useful observations can be made by using the current study 

results. First, PALSAR coverage in the eastern and western 

coastal regions included a majority of scenes indicating 

extensive and spatially contiguous areas of inundation versus 

scenes indicating scattered areas of inundation (figs.9A,B).

In contrast, most ASAR scenes covering the eastern and 

western coastal regions (with either HH or VV polarization) 

indicated limited and scattered areas of inundation (figs.10A,B

and 11A,B).The possibility exists that the PALSAR scenes 

were collected at times of more spatially extensive inundation 

compared to the times when ASAR scenes were collected; 

however, comparisons of SAR-based mapping and inland 

water-level records indicated that correspondences were 

high when using PALSAR-based maps and low when using 

ASAR-based maps when water levels were near the surface 

(tables 10–15). Visually, the PALSAR-based inundation 

maps exhibited more often contiguous inundation extents 

than ASAR-based inundation maps and PALSAR-based 

inundations more often corresponded with inland recordings 

than did ASAR-based inundations. 

Validating SAR-based Inundation Maps with 

Inland Water-Level Data

Water-level data recorded at inland hydrologic stations 

were useful in elucidating the variable performance of SARbased inundation mapping. Modeling and field studies of 

Gulf of Mexico marshes have demonstrated that SAR (HH 

or VV polarization) backscatter decreases with increasing 

flood depth. Results of those studies have also confirmed that 

SAR backscatter can increase with increasing marsh biomass 

when the marsh is flooded, and backscatter can decrease with 

increasing biomass when marsh is not flooded. The decrease 

in backscatter when marsh is not flooded may be linked to 

the loss of enhanced backscatter associated with moist soils. 

These relationships between flooding and marsh biomass and 

backscatter are expected to be enhanced when using (L-band) 

PALSAR versus(C-band) ASAR systems because L-band has 

a higher potential for marsh penetration (Ramsey, 1998, 2005).

In order to add water-level data to the SAR-based 

inundation analyses, measurements recorded at inland 

hydrologic stations were depicted in separate graphics 

for each PALSAR and ASAR inundation map (figs. 13, 

14, and 15).For 17 of the 19 PALSAR-based inundation 

maps covering locations where more than one or two data 

points for water levels existed, correspondence rates ranged 

67–100 percent (figs. 13A, B). In one of the two cases where 

PALSAR results were lower than 85 percent (fig. 13B; 

scene from July 10, 2007), the correspondence was possibly 

lowered by contamination of the PALSAR pixel by nonmarsh 

land covers. In the second of these cases (fig. 9A,B), surface 

roughness of the surrounding inundated marshes seemed 

to lower mapping performance (Ramsey and others, 2009; 

Ramsey and others, 2011; Ramsey, Werle, and others, in 

press).Overall, inundation mapping by using PALSAR 

scenes with HH polarization performed exceedingly well. In 

nonflooded conditions, and at times of dominantly shallow 

or higher marsh flooding, the PALSAR-based inundation 

mapping corresponded with inland water-level recordings at 

rates exceeding 83 percent.

In contrast to PALSAR-based inundation mapping, 

ASAR-based mapping (using scenes with HH or VV 

polarization) did not perform as well. Most often, 

correspondence between ASAR-based inundation maps 

and inland water-level recordings was at rates around 60 

percent (figs. 14 and 15). At times, correspondence rates 

dropped much lower (fig. 15B; scenes from Sept. 17, 

2008, and Sept. 1,2009). In contrast, at a few times when 

water levels were ubiquitously below the ground surface 

(figs. 14B and 15B; scenes from July 25, 2007; June 24, 

2009; and August 29, 2008) or nearly 1 m or more above 

marsh-surface height (fig. 15A; scene from Sept. 14, 2008), 

correspondence rates increased to higher than 76 percent.
Figure 13. Change-detection (CHDET) of inundation results compared with water-level measurements at inland hydrologic stations within the CoastwSystem (CRMS). PCI image processing software was used to identify changes between the reference- and target-Synthetic Aperture Radar (SAR) sceinundation on the target scenes (PCI, 2007; Ramsey, Werle, and others, in press). The Phased Array type L-band Synthetic Aperture Radar (PALSAR) sJapanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. (Dates provided are acquisition dates of reference scene [first] and tasee tables 10 and 11 for associated data). A, Results for the western coastal region. B, Results for the eastern coastal region.
Figure 13.—Continued Change-detection (CHDET) of inundation results compared with water-level measurements at inland hydrologic stations withMonitoring System (CRMS). PCI image processing software was used to identify changes between the reference- and target-Synthetic Aperture Radarepresented inundation on the target scenes (PCI, 2007; Ramsey, Werle, and others, in press). The Phased Array type L-band Synthetic Aperture Radar acquired by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. (Dates provided are acquisition dates of reference sce[second]; please see tables 10 and 11 for associated data). A, Results for the western coastal region. B, Results for the eastern coastal region.
Figure 13.—Continued Change-detection (CHDET) of inundation results compared with water-level measurements at inland hydrologic stations withMonitoring System (CRMS). PCI image processing software was used to identify changes between the reference- and target-Synthetic Aperture Radarepresented inundation on the target scenes (PCI, 2007; Ramsey, Werle, and others, in press). The Phased Array type L-band Synthetic Aperture Radar acquired by the Japanese Aerospace Exploration Agency’s Advanced Land Observing Satellite. (Dates provided are acquisition dates of reference sce[second]; please see tables 10 and 11 for associated data). A, Results for the western coastal region. B, Results for the eastern coastal region.
Figure 14. Change-detection (CHDET) of inundation results compared with water-level measurements at inland hydrologic stations within the CoastwSystem (CRMS). PCI image processing software was used to identify changes between the reference- and target-Synthetic Aperture Radar (SAR) sceinundation on the target scenes (PCI, 2007; Ramsey, Werle, and others, in press). The Advanced Synthetic Aperture Radar (ASAR) scenes with horizonpolarization were acquired by the European Space Agency’s Envisat satellite. (Dates provided are acquisition dates of reference scene [first] and targsee tables 12 and 13 for associated data). A, Results for the western coastal region. B, Results for the eastern coastal region.
Figure 15. Change-detection (CHDET) of inundation results compared with water-level measurements at inland hydrologic stations within the CoastwSystem (CRMS). PCI image processing software was used to identify changes between the reference- and target-Synthetic Aperture Radar (SAR) sceinundation on the target scenes (PCI, 2007; Ramsey, Werle, and others, in press). The Advanced Synthetic Aperture Radar (ASAR) scenes with verticapolarization were acquired by the European Space Agency’s Envisat satellite. (Dates provided are acquisition dates of reference scene [first] and targsee tables 14 and 15 for associated data). A, Results for the western coastal region. B, Results for the eastern coastal region.
Figure 15.—Continued Change-detection (CHDET) of inundation results compared with water-level measurements at inland hydrologic stations withMonitoring System (CRMS). PCI image processing software was used to identify changes between the reference- and target-Synthetic Aperture Radrepresented inundation on the target scenes (PCI, 2007; Ramsey, Werle, and others, in press). The Advanced Synthetic Aperture Radar (ASAR) scenes receive polarization were acquired by the European Space Agency’s Envisat satellite. (Dates provided are acquisition dates of reference scene [first] please see tables 14 and 15 for associated data). A, Results for the western coastal region. B, Results for the eastern coastal region.
Figure 15.—Continued Change-detection (CHDET) of inundation results compared with water-level measurements at inland hydrologic stations withMonitoring System (CRMS). PCI image processing software was used to identify changes between the reference- and target-Synthetic Aperture Radrepresented inundation on the target scenes (PCI, 2007; Ramsey, Werle, and others, in press). The Advanced Synthetic Aperture Radar (ASAR) scenes receive polarization were acquired by the European Space Agency’s Envisat satellite. (Dates provided are acquisition dates of reference scene [first] please see tables 14 and 15 for associated data). A, Results for the western coastal region. B, Results for the eastern coastal region.
References 43

Conclusion

PALSAR-based inundation mapping performed 

consistently and exhibited high correspondence with inland 

water-level recordings, whereas ASAR-based mapping 

performed poorly to marginally, only improving to adequate 

performance rates when water levels were well below or 

above marsh-surface height over extensive areas throughout 

a scene. Overall, Phased Array type L-band SAR (PALSAR) 

based inundation mapping was the most successful resulting in 

higher than 83 percent correspondence with the presence and 

absence of inundation as recorded at inland hydrographic sites. 

Inundation mapping derived from C-band Advanced SAR 

(ASAR) was less successful with overall correspondences 

with inland recordings around 60 percent. Exceptions to 

the low performance of ASAR-based inundation mapping 

occurred when water levels were well below or above ground, 

occurring over spatially extensive portions of the ASAR scene.

Full assessment of ASAR-based inundation mapping 

was hindered by paucity of appropriate reference scenes. 

This paucity resulted from abnormally high sea levels in 

2007 and 2008 that increased the complexity of choosing 

reference scenes collected during times of minimum flooding. 

The use of a consistent set of reference selection criteria and 

thresholds related to inundation determination promoted 

inundation mapping but did not completely overcome the 

low performance of ASAR-based inundation mapping. Many 

factors contributed to lower performance of ASAR-based 

mapping in terms of correspondence of maps with inland 

water-level records; some examples of those factors include 

highly variable coverages and look directions (ascending 

or descending orbits) leading to variable local incident 

angles from scene to scene. An added complexity was what 

seemed to be an increased sensitivity of (C-band) ASAR to 

progressive changes in the look angle from far to near range as 

compared to (L-band) PALSAR. 

Results of the current study suggest that mapping of 

coastal inundation at high temporal frequency is highly 

successful when using PALSAR data. PALSAR and ASAR 

data are both useful but are most beneficial when collected 

within the constraints of a predetermined strategy; however, 

a problem is presented by a lack of availability of data. If 

data availability were overcome, PALSAR and ASAR data 

could be combined with ground-based field measurements 

to better define variable canopy-structure influences on 

inundation mapping performance. Furthermore, coupling 

SAR collections with satellite-based optical image collections 

(such as by Landsat Thematic Mapper) could improve 

inundation mapping, including detection of subcanopy 

inundation, as well as further development of water-level 

change mapping and canopy-structure mapping. An integrated 

system for satellite data collection will also provide for rapid 

emergency response, strategic resource management, and 

resource science advancement.

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46 Monitoring Coastal Inundation with Synthetic Aperture Radar Satellite Data

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