Using Landsat 7 TM data acquired days after a ﬂood event to …core.ecu.edu/geog/wangy/papers_in_pdfs/2004_Using Landsat... · 2003-12-16 · importance in immediate response, short-

Using Landsat 7 TM data acquired days after a flood event todelineate the maximum flood extent on a coastal floodplain

Y. WANG

Center for Geographic Information Science and Department of Geography,East Carolina University, Greenville, NC 27858, USA;e-mail: [email protected]

(Received 21 January 2002; in final form 25 April 2003 )

Abstract. In response to Hurricane Floyd, the Tar River crested at a recordheight of 4.30m above the flood stage at the river gauge station of Greenville(North Carolina, USA) on 21 September 1999. This resulted in a massiveflooding in the area. To delineate the maximum flood extent, an area of238.4 km2 along the Tar/Pamlico River, North Carolina, and within theoverlapped area of Landsat 7 Thematic Mapper (TM) path 14/row 35 and path15/row 35 scenes was studied. Three TM datasets of 28 July 1999 (path 15/row35), 23 September 1999 (path 14/row 35) and 30 September 1999 (path 15/row35) were analysed as pre-flood data, near peak data, and nine days after the peakdata, respectively. The 23 and 30 September flood extent maps were derived bychange detection and then verified by 85 nonflooded and flooded sites within thestudy area. The overall accuracies at the sites were between 82.5–99.3% on bothinundation extent maps. Although the recorded river surface level fell 2.62mfrom 23 to 30 September at the river gauge station of Greenville, comparison ofthe two flood extent maps on a pixel-by-pixel basis showed an agreement of90.7% in terms of regular river channels and waterbodies, flooded areas andnonflooded areas. The 30 September map captured over 90% of the flood extentas identified on the 23 September map. These results suggest that it is possible touse remotely sensed data acquired days after a river’s crest to capture most ofthe maximum extent of a flood occurring on a coastal floodplain, and shouldsomewhat reduce the requirement to have concurrently remotely sensed data inmapping a flood extent on a coastal floodplain.

1. Introduction

Hurricane Dennis visited Outer Banks, North Carolina, USA, on 2 September

1999. It hovered over the Atlantic Ocean close to Outer Banks for nearly two days,

and was downgraded as a tropical storm on 5 September. Dennis made its landfall

near Outer Banks and finally left the region on 6 September. Dennis dropped

significant precipitation across eastern North Carolina; the ground was saturated.

On 15 September 1999, Hurricane Floyd made landfall near the border between

South Carolina and North Carolina and proceeded to pass through eastern North

Carolina. Floyd dumped between 0.25–0.46m of rain in the region in less than three

days. On 17 September the Chowan, Roanoke, Tar/Pamlico and Neuse rivers of

North Carolina reached flood stage, and continued to rise for several days. Within

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2004 Taylor & Francis Ltd

http://www.tandf.co.uk/journalsDOI: 10.1080/0143116031000150022

INT. J. REMOTE SENSING, 10 MARCH, 2004,

VOL. 25, NO. 5, 959–974

a few days floodwaters covered over 50 000 km2, causing an unprecedented disaster

in the eastern region of the state. Estimated cost on damages and losses exceeded

$6 billion. A collection of articles documenting and studying the social, economic

and environmental impacts caused by the 1999 flood can be found in the bookedited by Maiolo et al. (2001).

During a flood event, to capture the maximum extent of the flood is of great

importance in immediate response, short- and long-term recovery, and future

mitigation activities. Remotely sensed data have been widely used to map the

extents of floods. For example, Imhoff et al. (1987) used Shuttle Imaging Radar—

Mission B (SIR-B) synthetic aperture radar (SAR) and Landsat 5 Thematic

Mapper (TM) data to delineate flood boundaries and assess flood damages caused

by monsoon rains in Bangladesh. Hess et al. (1995) used SIR-C SAR data to studythe inundation patterns on the Amazonian floodplain, Brazil. Pope et al. (1997)

employed SIR-C SAR data to identify seasonal flooding cycles in marshes of the

Yucatan Peninsula, Mexico. Melack and Wang (1998) derived the inundation

extent of the Balbina Reservoir (Brazil) by using Japanese Earth Resource

Satellite—1 (JERS-1) SAR data. Passive microwave data have also been used to

study the inundation area and the area/stage relationship in the Amazon River

floodplain (Sippel et al. 1998). Recently, using Landsat 7 TM data, Dartmouth

Flood Observatory (1999), Colby et al. (2000) and Wang et al. (2002) estimated theflood extent of the 1999 flood that resulted from Hurricane Floyd in eastern North

Carolina. The advantages of using satellite remotely sensed data in flood mapping

are the availability of the data, the effectiveness and robustness of the flood

mapping methods, and the relatively low cost for mapping a flood of large aerial

extent. However, due to a fixed satellite’s orbit, it is almost impossible to have

remotely sensed data concurrent with a flood event. This lack of timeliness may

undervalue the possible usage of the satellite data for flood mapping. For instance,

it takes a period of 16 days for the Landsat 7 TM sensor to revisit the same place,

and its closest visit day for most parts of eastern North Carolina was 30 September1999 (path 15). The Tar River surface water height fell from about 8.32m (above

mean sea level) on 21 September to 5.34m on 30 September 1999 at the Greenville

gauge station. (The flood stage at the Greenville gauge station is 4.02m.) Is it

possible to use the remotely sensed data, especially TM data, acquired several days

after the peak of a flood to map the maximum flood extent on a coastal floodplain?

If so, the concern about the concurrence of satellite data and a flood event on the

floodplain should be reduced. Mapping the flood extent by using TM data acquired

two and nine days after the peak of the 1999 flood occurred along Tar/PamlicoRiver in Pitt County and Beaufort County, North Carolina, will be used as an

example to at least partially answer this question.

2. Analytic approach

2.1. Study area and datasets

Pitt County is located at approximately the centre of the eastern coastal

floodplain of North Carolina. Beaufort County is easterly adjacent to Pitt. Tar

River flows into Pitt from the north-west corner, and exits from the most eastwardpart of the county into Beaufort. After passing beneath the bridge of US Highway

17 near the City of Washington, Beaufort County, Tar River is called Pamlico

River. Eventually, the water runs easterly into Pamlico Sound, North Carolina, and

finally into the Atlantic Ocean. A study area of 238.4 km2 along the Tar/Pamlico

River between the cities of Greenville, the largest city in Pitt County, and

960 Y. Wang

Washington, the largest city in Beaufort County, was selected and outlined

(figures 1 and 2). The curved dark signature, identified in figure 1(a), is the regular

river channel, and there are several small creeks/rivers whose water is drained into

the river. The remaining area consists mainly of the river’s primary and secondary

floodplains, as well as uplands where houses/buildings and other man-made

structures are found and agriculture activities are carried out. There are 14 land use

and land cover types within the study area. Bottomland forest/hardwood swamp

(83.7 km2, or 35.1% of the total study area) and cultivated land (71.1 km2 or 29.8%)

are the top two categories (table 1). Bottomland forests/hardwood swamps are areas

where deciduous trees are dominant and/or woody vegetation is over 3m tall, and

occur in lowland and seasonally wet or flooded areas. Tree crown coverage in these

areas is at least 25%. Cultivated lands are areas occupied by row and root crops

Figure 1. TM data of 23 September 1999. The Tar River enters from the north-west cornerand exits as the Pamlico River from the south-east corner. The expanded areas withdark or dimmed signatures, along and off the riverbanks, were flooded. (a)TM5zTM7; 15 nonflooded forest sites are shown. (b) TM4zTM8; 14 floodedforested sites are located.

Landsat 7 TM to delineate after-event flooding 961

that are planted in distinguishable rows and patterns. The soil is primarily clay

loam and poorly drained. Many parts of the study area have been extensively

drained and ditched, due to the high level of the ground water table, for mainly

agricultural use. The area is fairly flat with minimum, mode, median, mean and

maximum elevation values of 0.0, 1.5, 4.5, 5.0 and 22.4m (above mean sea level),

respectively. The standard deviation of the elevation is 3.7m.

This area is chosen because oblique aerial photographs taken during the 1999

flood and field data collected after the flood are available. The area is also a smallpart of the study area where integration of TM, digital elevation model (DEM) and

river gauge data has been done to map the flood extent on 30 September 1999 (e.g.

Wang et al. 2002). Most importantly, the area is within the overlapped zone of path

14/row 35 and path 15/row 35 TM scenes and both scenes are available. For path

14, the TM sensor visited the study area on 23 September 1999, two days after the

Figure 2. (a) TM5zTM7 data of 30 September 1999. Reduction of the flooded area isobserved. Twenty nonflooded fields are located. (b) TM4zTM8 of 30 September1999. Flood extent similar to that portrayed by TM5zTM7 in figure 1(a) orTM4zTM8 in figure 1(b) is noticeable. Ten nonflooded developed sites are indicated.

962 Y. Wang

Table 1. Summary of the land use and land cover types within the entire study area, and the selected sites of flooded and nonflooded forested areas, openfields and developed areas.

Land use and land covertypes defined in the NorthCarolina statewide landcover database

Entire studyarea

Forestedsites (km2)

Open fieldsites (km2)

Developedsites (km2)

(km2) (%) Nonflooded Flooded Nonflooded Flooded Nonflooded Flooded

Intensely Developed Area 7.30 3.1 0.89 0.98Less Intensely Developed Area 8.12 3.4 0.61 0.87Cultivated Land 71.05 29.8 4.86 3.14Managed Herbaceous Cover 7.66 3.2 0.45 0.12 0.05 0.22Evergreen Shrub land 19.36 8.1 0.20 0.54 0.16Deciduous Shrub land 1.66 0.7 0.14 0.09Mixed Shrub land 1.38 0.6 0.04Bottomland Forest/Hardwood Swamp 83.68 35.1 1.36 5.01Needleleaf Deciduous Tree 0.28 0.1Southern Yellow Pine 20.30 8.5 0.87 0.32Mixed Hardwood/Conifer 2.73 1.1 0.47 0.29Oak/Gum/Cypress 0.14 0.1Water Body 14.46 6.1Unconsolidated Sediment 0.19 0.1

Total 238.40 100.0 2.69 5.62 5.69 3.89 1.71 2.08

Landsat7TM

todelin

eate

after-even

tflooding

963

crest of the floodwater. The floodwater level was only about 0.36m below its

crested height at the Greenville gauge station (figure 1(b)). For path 15, the TM

sensor passed the area on 30 September 1999, and the floodwater level had dropped

2.62m at the Greenville gauge station since 23 September (table 2). Due to a wide

river cross-section, at the river gauge station of Washington (figure 1(b)), Pamlico

River crested at 1.68m on 21 September 1999. On 23 and 30 September, the surface

water height was 1.60 and 0.78m, respectively (table 2). If the flood extent map

derived from 30 September TM data agrees with that derived from 23 September

TM data, then the seven-day time lapse of the TM datasets is not crucial in

flood extent mapping on the coastal floodplain. If one can further verify the derived

flood maps on both dates, then there could be great value and significance in flood

mapping on floodplains using optical data (as well as radar data), especially using

remotely sensed data acquired days after the peak of a flooding event.

The datasets used in the analysis include TM data acquired on 28 July 1999

(path 15/row 35), 23 September 1999 (path 14/row 35) and 30 September 1999 (path

15/row 35), North Carolina statewide land use and land cover type data, oblique

digital aerial photographs taken on 23 September 1999, US Geological Survey

(USGS) colour infrared digital orthographic quarter quadrangles (DOQQ) acquired

in 1998, and field data collected after the flood. All digital datasets are geo-

referenced into a common Universal Transverse Mercator (UTM) coordinate using

the World Geodetic System—1984 (WGS 84) models for the spheroid and datum.

The 28 July, 23 September and 30 September TM data are used as the pre-flood,

near the peak of the flood, and nine days after the peak datasets. Ground data were

gathered in mid-October 1999, after the floodwaters completely receded but before

high-water marks on trees, vegetation, buildings and other landscape features

faded. The ground observation coupled with the oblique digital aerial photographs

taken during the flood event is used to verify the flooded/nonflooded areas on the

derived inundation maps and to address each map’s accuracy.

To facilitate the accuracy analysis, 40 flooded sites and 45 nonflooded sites are

chosen in the study area. Two factors are considered in the selection of the sites: (1)

flooded sites must be inundated on 23 September, and nonflooded sites must be dry

on 23 and 30 September; and (2) the land cover types within the sites should be

representative in the region. Using the USGS DOQQs, North Carolina statewide

land use and land cover type data, oblique aerial photographs and collected field

data, 15 nonflooded forest sites (figure 1(a)), 14 flooded forest sites (figure 1(b)), 20

nonflooded open fields (figure 2(a)), 13 flooded open fields (e.g. figure 3), 10

nonflooded developed sites (figure 2(b)) and 13 flooded developed sites (e.g.

Table 2. Surface water height (m) (above mean sea level) of the Tar/Pamlico River at theriver gauge stations of Greenville and Washington on selected dates in September 1999.

Date Greenville Washington

2 0.69 0.515 1.76 1.14

19 8.11 1.4920 8.30 1.6121 8.32 1.6822 8.18 1.6423 7.96 1.6030 5.34 0.78

964 Y. Wang

figure 4(a)) have been chosen. The forest sites include mainly bottomland forests/

hardwood swamps, as well as southern yellow pines, mixed hardwoods (e.g. oaks),

and conifers (e.g. loblolly pines). They are dominant types of forests on the

floodplain of eastern North Carolina. The open field sites include cultivated land,

managed herbaceous cover, and evergreen/deciduous shrub land. Agriculture is a

major activity in the rural areas of this region. The developed sites consist mostly of

intensely developed and less intensely developed areas that contain commercial/

industrial facilities, infrastructure, and houses for the majority of the human

population in the area. Total area of each category ranges from 1.71–5.69 km2

(table 1). Since the major developed areas are within or near the cities of Greenville

and Washington (figure 1(a)) and there is almost no substantial development

between the cities, the developed nonflooded and flooded sites are concentrated

near the two cites. For example, eight (out of 13) developed flooded sites

(figure 4(a)) are near Greenville, including, from left to right, four sites near PGV

Figure 3. (a) Ten (out of 13) flooded open field sites are identified on the TM4zTM8 dataof 30 September 1999. (b) A close-look of one open field site, as indicated by a whitearrow in (a), on the oblique aerial photograph of 23 September 1999.

Figure 4. (a) TM4zTM8 data of 30 September 1999 show the flooding at the airport andits surroundings. Eight (out of 13) flooded developed sites are shown. (b) Obliqueaerial photograph shows the flooding near the airport on 23 September 1999.


Airport, two industrial sites and two residential sites. During the peak flood period,

19–23 September 1999, the airport runways were almost totally submerged

(figure 4(b)), but only the southern portion of the runway was flooded on 30

September (figure 4(a)). Flooding in developed areas, even though its percentage of

coverage is only 6.5% in the study area (table 1), could cost lives, create significant

property damage, and interrupt daily life and commercial/industrial activities.

2.2. Analysis

The first step is to visually and qualitatively examine the inundation patterns

portrayed by the 23 September and 30 September TM datasets, coupled with the 28

July TM data as a reference to the pre-flood condition, and digital video

photographs and ground observations as verification for flooding or nonflooding

conditions. Various combinations of TM bands and combinations of band ratios,

differences and additions have been displayed and explored. In the end, TM band

5zTM band 7 (or TM5zTM7 for short) and TM band 4zTM band 8 (or

TM4zTM8) were used to study the September flood extent due to their ability to

identify the water–land (wet–dry) boundary. TM8 is also chosen because of its fine

15m615m spatial resolution (cf. TM 4, 5 or 7’s spatial resolution is 30m630m).

(TM4 data are magnified by a factor of 2 to match the spatial resolution of TM8 in

the analysis.) In figures 1 and 2, the dark curved signature is the regular river

channel. Dimmed and dark signatures along and off the river channel represent

flooded areas, and the flooded and nonflooded boundaries are quite evident.

TM5zTM7 (figure 1(a)) and TM4zTM8 (figure 1(b)) acquired on 23 September

show almost identical inundation patterns. Comparison of TM5zTM7 on 30

September (figure 2(a)) and TM5zTM7 on 23 September (figure 1(a)) reveals the

reduction of inundated areas due to the receding of floodwater (table 2). Also, there

are fewer dimmed (or flooded) areas on TM5zTM7 data (figure 2(a)) than on

TM4zTM8 data (figure 2(b)); this suggests different flood extents even though the

data were acquired on the same date, 30 September. Some noticeable differences are

pointed out by white arrows (figures 1(b), 2(a) and 2(b)). We further note the

following. First, the expanded dark and dimmed signatures on TM5zTM7

(figure 2(a)) show the flood extent on 30 September (Wang et al. 2002). Second, if

the ‘extra’ dimmed areas in figure 2(b) can be identified and verified as being

flooded, then the inundation patterns on TM4zTM8 of 30 September will be

larger than those portrayed by TM5zTM7 of 30 September and, most

importantly, the patterns may be very similar to those shown on TM4zTM8

(or TM5zTM7) data of 23 September (figure 1(b) or 1(a)). Next, a procedure using

TM4zTM8 datasets of 28 July and 23 and 30 September is designed to investigate

this quantitatively.

There are four major steps in the investigation. First, TM4zTM8 data acquired

on 28 July, 23 September and 30 September 1999 are used separately to classify the

study area into water or nonwater category for each of the three dates. Second, the

water and nonwater categories of 28 July and 23 September are compared to derive

the inundation map of 23 September by using a change detection method that

identifies the study area as: (1) waterbody (e.g. regular river channel, ponds, pools,

etc.) if the area is water in July (pre-flood) and September; (2) flooded area if the

area is assigned to the nonwater category in July but to the water category in

September; or (3) nonflooded area if the area is dry in July and September. The

inundation map of 30 September is derived in similar fashion. (Details about the

966 Y. Wang

classification, change detection, and inundation mapping methods can be found in

Wang et al. 2002.) Third, the two derived inundation maps are compared in terms

of sizes of the regular waterbodies, flooded areas and nonflooded areas, and spatial

distributions on a pixel-by-pixel basis. Lastly, the accuracy of each derived flood

map is verified at the 85 nonflooded and flooded sites.

3. Results3.1. 23 and 30 September inundation maps

Figure 5(a) is the 23 September 1999 inundation extent map. The regular river

channel and waterbodies are shown as black, the flooded areas as grey, and the

nonflooded areas as white (within the study area). Figure 5(b) is the 30 September

1999 inundation map. Comparison of the two maps reveals that: (1) the sizes of

river channel and waterbodies are almost identical, 13.23 km2 on the 23 September

map and 13.22 km2 on the 30 September map; (2) there is a reduction of 10.33 km2

in the flooded category on the 30 September map out of a total flooded area of

113.40 km2 on the 23 September map; and (3) an increase of 10.35 km2 in the

nonflooded category on the 30 September map from a total of 111.76 km2

nonflooded area on the 23 September map.

Further examination of both maps indicates that the spatial patterns of the

flood extent are similar (figure 5(a) and (b)). To quantify the similarity, a spatial

correlation is introduced and analysed on a pixel-by-pixel basis. If a pixel is

classified in the same category (regular river channel and waterbodies, flooded area,

or nonflooded area) on both inundation maps, the pixel is recoded as 1, otherwise

as 0. Out of a total of 1 059 538 pixels (each pixel 15m615m), 961 325 pixels

(216.30 km2) are recoded as 1, and 98 213 pixels (22.10 km2) as 0. Thus, the two

maps are spatially in agreement of 90.7%. Figure 5(c) shows the distribution of

agreement in white as well as the discrepancy in grey or black.

For the 22.10 km2 of discrepancy, 16.07 km2 (6.7% of the total study area)

involves the pixels that are classified as flooded on the 23 September map but as

nonflooded on the 30 September map. They are shown in grey (figure 5(c)). This

may indicate the underestimation of flooded areas and potentially warrants caution

if remotely sensed data acquired days after a flood event are used to map the

maximum extent on a coastal floodplain. Some surfaces will be dry after the

floodwater recedes and TM data acquired at that time will sense the surfaces as dry.

Also, it is interesting to note that even though the grey pixels are somewhat

scattered, there might be a pattern of concentration in the north-west corner

(figure 5(c)) near PGV Airport, and commercial and industrial facilities (figure 4).

This concentration can be attributed to the fact that these areas are located on

locally high ground, and the (mainly man-made) ground surface of these areas was

submerged on 23 September (e.g. figure 4(b)) but was dry on 30 September (e.g.

figure 4(a)) due to the receding of floodwater (table 2). The other 6.02 km2 areas

(2.6% of the total study area, in black, figure 5(c)) in discrepancy are areas that are

classified as nonflooded on the 23 September map but as flooded on the 30

September map. The scattered dark pixels could be attributed to the local pooling,

analyst’s error, or to some unknown factors that might cause more localized

flooding in the study area on 30 September than on 23 September.


Figure 5. Inundation maps of 23 September (a) and of 30 September 1999 (b).Black~regular river channels and waterbodies; grey~flooded areas; and white~nonflooded areas. (c) Comparison of the 23 and 30 September maps. White~nodifference; grey~derived as flooded areas on the 23 September map, but asnonflooded areas on the 30 September map; black~derived as nonflooded areas onthe 23 September map, but as flooded areas on the 30 September map.

968 Y. Wang

3.2. Verification of derived inundation maps

Using the selected 85 nonflooded and flooded forest, open field and developed

sites, flood map accuracies derived by TM4zTM8 data on 23 and 30 September

based on 28 July TM4zTM8 as pre-flood data are evaluated. For 14 flooded forest

sites, the producer’s accuracies are 79.0% and 82.2% on the 23 and 30 September

maps, respectively. Nearly 20% of the flooded forested areas are misclassified; this

leads to an underestimate of the flooded forest areas. This underestimation is

primarily due to the inability of the TM sensor to penetrate dense forest canopies

and to detect the water underneath the canopies. The user’s accuracies are about

98% for both maps. At the 15 nonflooded forest sites on both flood maps, the

producer’s accuracies are 96.3% and 97.0%, and the user’s accuracies are 68.7% and

72.3%. The overall accuracies are 84.6% on the 23 September map and 87.0% on

the 30 September map (table 3(a)).

At 13 flooded and 20 nonflooded open field sites, the producer’s accuracies are

over 90% and user’s accuracies are 94% or above on the 23 and 30 September maps,

respectively. The overall accuracies on both maps are over 96% (table 3(b)). The

high accuracies are mainly attributed to the following. First, in late September, the

crops of cotton, tobacco, soybean, etc. on the cultivated lands were near their

harvest time or might have been harvested. Thus, the percentage of vegetation

coverage in the fields was relatively low. Once there was water or no water in the

fields, the TM sensor could readily detect it. Second, for the flooded fields, the

floodwater might be high enough to totally inundate the crops in some cultivated

lands and vegetation in some herbaceous cover and shrubland areas on both dates.

Third, even though the floodwater level dropped significantly from 23 to 30

September (table 2), the open fields located away from the river channel might still

be covered with standing water, or the soil remained very wet or saturated with

water due to the very flat terrain, poorly drained soils and local pooling. Lastly, it

was possible that crops or vegetation on some fields was totally/partially under

floodwater on 23 September but came out of the floodwater on 30 September. A

period of inundation by the floodwater might cause damage to some (un-harvested)

crops and vegetation. Even though on 30 September there might be no water

present in these flooded areas, the reflectance from the damaged crops and

vegetation on TM4 and TM8 may be still low. These areas should have dark or

dimmed signatures on TM4 or 8 data; they could be categorized as being flooded.

The 96.1% overall classification accuracy in open fields on the 30 September map

shows that it is possible to use TM data acquired 7–9 days after the peak of the

flood to correctly map the flooding in agricultural fields, herbaceous cover area, and

evergreen/deciduous shrub lands on a coastal floodplain.

For 13 flooded developed sites, 88.4% and 71.2% of the area is categorized as

flooded areas on the 23 and 30 September maps, respectively (table 3(c)). The

receding of floodwater causes the decrease in the flooding percentage at the

developed sites. On 23 September, the developed sites were largely submerged under

the floodwater (e.g. upper left corners of figure 1(a)–(b), figure 4(b)). On 30

September, part of the developed area came out of the floodwater. For instance, the

middle to the northern portion of the runway of PGV Airport and its nearby

industrial and commercial areas became dry (at the upper left corner of figure 2(a),

figure 4(a)). Once the man-made surfaces are dry, the TM sensor will identify them

as nonflooded surfaces. Thus, to map the inundation in a developed area, timely

TM data are critical.


Table 3. Error matrix and classification accuracy derived by TM4zTM8 of 23 September1999 and TM4zTM8 of 30 September 1999 at the sites of forested areas, open fieldsand developed areas. Within producer’s and user’s accuracy sections, omission andcommission errors are in ( ) and [ ], respectively.

Forested areas Reference data

Classification Flooded (km2) Nonflooded (km2) Total (km2)

23 September1999

Flooded 4.44 0.10 4.54Nonflooded 1.18 2.59 3.77

Total 5.62 2.69 8.3130 September1999


Total 5.62 2.69 8.31

Overall accuracy: 84.6% of 23 September 1999 or 87.0% of 30 September 1999.

Producer’s accuracy (%) User’s accuracy (%)

23 September1999

30 September1999

23 September1999

30 September1999

Flooded 79.0 (21.0) 82.2 (17.8) 97.8 [2.2] 98.3 [1.7]

Nonflooded 96.3 (3.7) 97.0 (3.0) 68.7 [31.3] 72.3 [27.7]

Open fields Reference data


23 September1999


Total 3.89 5.69 9.5830 September1999


Total 3.89 5.69 9.58



23 September1999

30 September1999

23 September1999

30 September1999

Flooded 98.7 (1.3) 90.7 (9.3) 99.5 [0.5] 99.7 [0.3]

Nonflooded 99.6 (0.4) 99.8 (0.2) 99.1 [0.9] 94.0 [6.0]

Developed areas Reference data


23 September1999


Total 2.07 1.71 3.7830 September1999


Total 2.07 1.71 3.78



23 September1999

30 September1999

23 September1999

30 September1999

Flooded 88.4 (11.6) 71.2 (28.8) 92.4 [7.6] 94.9 [5.1]

Nonflooded 91.2 (8.8) 95.3 (4.7) 86.2 [13.8] 73.8 [26.2]

970 Y. Wang

4. Concluding remarks

For regular river/stream channels and waterbodies, flooded areas and

nonflooded areas, the analysis of flood maps using TM data acquired two days

and nine days after the floodwater crested on 21 September 1999 shows that: (1)although there is a significant drop in floodwater level from 23 to 30 September, the

30 September flood map is able to capture over 90% of the flooding extent

delineated on the 23 September flood map; (2) on a pixel-by-pixel basis, both 23

and 30 September maps are in agreement of 90.7%; and (3) for 29 nonflooded and

flooded forest sites, and 23 nonflooded and flooded developed sites, the overall

accuracy is between 82.5% and 89.7% on 23 and 30 September inundation maps.

The overall accuracy for 33 nonflooded and flooded open field sites is over 96%.

These results, as well as similar observed inundation patterns from the initial andvisual analysis of TM data on floodplains of other river systems (e.g. Chowan,

Roanoke and Neuse rivers) in eastern North Carolina within the overlapped area of

TM’s path 15/row 35 and path 14/row 35, suggest that it is possible to use remotely

sensed data acquired several days after a river’s crest to capture the most part of the

maximum extent of a flood on a coastal floodplain. There is especially a high

probability of success in mapping the flood extent in open fields that include

cultivated lands, herbaceous cover, evergreen shrub lands and deciduous shrub

lands where standing water, very wet or saturated soil, or damaged vegetationcaused by floodwater are present.

However, this research and Brivio’s study (using European Remote Sensing

Satellite (ERS)-1 SAR to map a flood extent in Italy; Brivio et al. 2002) also

suggests that timely remotely sensed (optical and SAR) data are crucial in

identifying flooding in areas with man-made surfaces such as parking lots, roads,

runways and roofs, and in areas where large topographic changes exist. Once the

floodwater recedes, these surfaces dry quickly, and the satellite will view them as

nonflooded surfaces. Additionally, the underestimate of flooded areas under forestcanopies is still a problem for optical data due to an optical sensor’s inability to

penetrate vegetation canopies. On the flood map, these undetected flooded areas

show up as scattered ‘holes’ or ‘islands’ within the primary/secondary floodplains

near the riverbanks (e.g. Wang et al. 2002). Alternative datasets such as radar,

DEM and river gauge data, or methods of using these datasets individually or

collectively (e.g. Muzik 1996, Brakenridge et al. 1998, Correia et al. 1998, Jones

et al. 1998, Colby et al. 2000, Nico et al. 2000, Siegel and Gerth 2000, Brivio et al.

2002, Wang 2002, Wang et al. 2002) should be used to identify flooding underneathcanopies so that the ‘holes’ can be filled in or the ‘islands’ be removed.

Although several limitations have been briefly mentioned here and possible

solutions are proposed, there is great potential for using the optical data, especially

TM data, in flood mapping on floodplains due to the availability, effectiveness and

low cost. (Satellite Probatoire d’Observation de la Terre (SPOT) of France and the

Indian Remote Sensing Satellite (IRS)-1C also provide commercially global

coverage of optical data and offer better spatial resolutions than the TM data.

However, the major advantages of Landsat data over SPOT data vhttp://www.spot.comw or IRS-1C data vhttp://www.euromap.de/prod_001.htmw are

the price and ground coverage per image.) The following arguments are made.

First, recent major severe flood events around the world occurred on the rivers’

floodplains or coastal floodplains, and were caused by heavy precipitation from

monsoons, cyclones and/or hurricanes. The 1993 flood in the middle portion of the

Mississippi River, USA, the 1998 flood in the middle and lower portions of


Changjiang Plain, China (http://www.chinapage.com/flood.htm), the 1998 flood in

the lower portions of the Ganges River and in most of the Brahmaputra River,

Bangladesh (http://www.bangladeshonline.com/gob/flood98), the 1999 flood in

eastern North Carolina, USA (e.g. Maiolo et al. 2001) and the 2001 flood in theLimpopo River, Mozambique (http://edcnts11.cr.usgs.gov/mozflooding) are some

instances. These floodplains are relatively flat in topography, and the lands are

mainly used for agriculture. Additionally, most of the soil on the floodplains is

primarily characterized as poorly or extremely poorly drained. Thus, the

floodwaters cannot flow out of the floodplains or percolate into the ground

quickly, which produces a relatively long duration (up to a couple of days) of high

flood surface water height. For example, the flood surface water height stayed near

its created height (8.32m) at Greenville for almost five days (table 2). These floodshave cost many people’s lives and resulted in huge loss and damage to the

countries’ economy. People’s daily life, commercial, industrial and agricultural

activities have been interrupted for months.

Second, DEM and river gauge data, and land cover information are of great

value in the hydrologic modelling and flood mapping on floodplains (e.g. Correia

et al. 1998, Jones et al. 1998, Colby et al. 2000, US Army Corps of Engineers 2000,

Brivio et al. 2002, Wang et al. 2002). These data are readily available to the public

in the USA and other developed countries. For example, with the integration ofDEMs and SAR data into a Geographical Information System (GIS), Brivio et al.

(2002) overcame the disadvantage of the lack of concurrent remotely sensed SAR

data with the peak of a flood event which occurred in Italy, and were able to map

up to 96.7% of flooded areas when compared with the officially reported inundation

extent. The inundation areas derived by the SAR data, acquired three days after the

peak of the flood, alone cover only about 20% of the flooded areas. However, in

other countries, especially developing countries, the DEMs, river gauge, and land

cover dataset may be unavailable to the public; the integration of the DEM, gauge,and land cover data with remotely sensed data into a GIS to map a flood extent

becomes infeasible.

Third, although SAR’s all-weather capability and ability to penetrate vegetation

canopies to some extent provides a unique advantage over optical data in flood

mapping, and SAR data have been successfully applied to map flooded areas in

forested environments (e.g. Imhoff et al. 1987, Hess et al. 1995, Pope et al. 1997,

Melack and Wang 1998, Miranda et al. 1998, Nico et al. 2000), there are concerns

about the current radar data in terms of sensor’s revisit cycle, global coverage, costof the data, as well as their system wavelengths. Only ERS-2 SAR and Radarsat

SAR are now collecting radar data regularly on a global scale. ERS-2’s and

Rardarsat’s repeat cycles are 35 and 24 days, respectively. Thus, their temporal

resolution may be low for a flood event. Since both sensors are active and rely on

solar radiation for charging their batteries, the SARs operate between an on and off

mode. As the SAR sensors orbit over the Earth they do not collect SAR data

continuously (whereas the optical sensors do). The price of ERS SAR data costs

from $1 940 to $2 810 depending on the order of a raw dataset or a map orienteddigital image covering a nominal area of 100 km6100 km vhttp://www.auslig.gov.

au/acres/prod_ser/ersprice.htmw. For a Radarsat image of standard beam mode

covering an area of y100 km6y100 km, the cost starts from $2 750 per image

vhttp://www.rsi.ca/storefront/radarsat/rsat_img_usd.htmw. Additionally, since

both SAR systems operate at C-band, their microwave energy’s ability to penetrate

tree canopies is limited (e.g. Hess et al. 1995, Wang et al. 1995, Wang 2002). Once

972 Y. Wang

the SAR sensors fail to penetrate the canopies, using the SAR data to detect

flooding underneath the canopies is then questionable. For instance, Wang (2002)

reported that the SIR-C’s C-HH and C-VV data were unable to penetrate the

canopies of forested areas along the Tar River, even though the radar’s incidence is

near 25‡.Fourth, with the potential success of using optical data collected days after the

peak of a flood to map the maximum flood extent on a floodplain where the

topography is relatively flat, its soil is poorly drained, and the area is less developed

with dominant land cover types of agriculture fields, shrub and herbaceous lands,

forested areas, etc., as demonstrated in this paper, the concern of the optical data’s

temporal resolution is somewhat reduced. Furthermore, with the successful launch

of the US Earth Observation System (EOS) AM satellite in 2000 and the Chinese

Earth Resource Satellite in 2002, and the future launch of the Advanced Land

Observation Satellite (ALOS) from Japan in 2004, a suite of optical sensors as well

as radars is collecting and will collect global data more frequently. These additional

data will definitely improve the temporal resolution of the remotely sensed data,

will make them more useful, accessible and affordable, and ultimately will facilitate

mapping the extent of future floods on floodplains in an effective way.

Acknowledgment

The author thanks two anonymous reviewers, whose comments have greatly

improved the quality of the paper.

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974 Landsat 7 TM to delineate after-event flooding

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