INT. J. REMOTE SENSING, VOL.
25,
NO.
10
MARCH,
2004,
5, 959–974
Using Landsat 7 TM data acquired days after a flood event to delineate 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 record height of 4.30 m above the flood stage at the river gauge station of Greenville (North Carolina, USA) on 21 September 1999. This resulted in a massive flooding in the area. To delineate the maximum flood extent, an area of 238.4 km2 along the Tar/Pamlico River, North Carolina, and within the overlapped area of Landsat 7 Thematic Mapper (TM) path 14/row 35 and path 15/row 35 scenes was studied. Three TM datasets of 28 July 1999 (path 15/row 35), 23 September 1999 (path 14/row 35) and 30 September 1999 (path 15/row 35) were analysed as pre-flood data, near peak data, and nine days after the peak data, respectively. The 23 and 30 September flood extent maps were derived by change detection and then verified by 85 nonflooded and flooded sites within the study area. The overall accuracies at the sites were between 82.5–99.3% on both inundation extent maps. Although the recorded river surface level fell 2.62 m from 23 to 30 September at the river gauge station of Greenville, comparison of the two flood extent maps on a pixel-by-pixel basis showed an agreement of 90.7% in terms of regular river channels and waterbodies, flooded areas and nonflooded areas. The 30 September map captured over 90% of the flood extent as identified on the 23 September map. These results suggest that it is possible to use remotely sensed data acquired days after a river’s crest to capture most of the maximum extent of a flood occurring on a coastal floodplain, and should somewhat reduce the requirement to have concurrently remotely sensed data in mapping 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 0.25–0.46 m 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 Sensing ISSN 0143-1161 print/ISSN 1366-5901 online # 2004 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/0143116031000150022
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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 book edited 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 study the 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 the flood 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 September 1999 (path 15). The Tar River surface water height fell from about 8.32 m (above mean sea level) on 21 September to 5.34 m on 30 September 1999 at the Greenville gauge station. (The flood stage at the Greenville gauge station is 4.02 m.) 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/Pamlico River 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 eastward part 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
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Figure 1. TM data of 23 September 1999. The Tar River enters from the north-west corner and exits as the Pamlico River from the south-east corner. The expanded areas with dark or dimmed signatures, along and off the riverbanks, were flooded. (a) TM5zTM7; 15 nonflooded forest sites are shown. (b) TM4zTM8; 14 flooded forested sites are located.
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 3 m 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
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Figure 2. (a) TM5zTM7 data of 30 September 1999. Reduction of the flooded area is observed. Twenty nonflooded fields are located. (b) TM4zTM8 of 30 September 1999. Flood extent similar to that portrayed by TM5zTM7 in figure 1(a) or TM4zTM8 in figure 1(b) is noticeable. Ten nonflooded developed sites are indicated.
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 22.4 m (above mean sea level), respectively. The standard deviation of the elevation is 3.7 m. 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 small part 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
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, open fields and developed areas.
Intensely Developed Area Less Intensely Developed Area Cultivated Land Managed Herbaceous Cover Evergreen Shrub land Deciduous Shrub land Mixed Shrub land Bottomland Forest/Hardwood Swamp Needleleaf Deciduous Tree Southern Yellow Pine Mixed Hardwood/Conifer Oak/Gum/Cypress Water Body Unconsolidated Sediment Total
Entire study area (km2)
(%)
7.30 8.12 71.05 7.66 19.36 1.66 1.38 83.68 0.28 20.30 2.73 0.14 14.46 0.19
3.1 3.4 29.8 3.2 8.1 0.7 0.6 35.1 0.1 8.5 1.1 0.1 6.1 0.1
238.40
100.0
Forested sites (km2) Nonflooded
Open field sites (km2)
Flooded
1.36
5.01
0.87 0.47
0.32 0.29
2.69
5.62
Nonflooded
Developed sites (km2)
Flooded
Nonflooded
Flooded
0.89 0.61
0.98 0.87
4.86 0.45 0.20 0.14 0.04
3.14 0.12 0.54 0.09
0.05 0.16
0.22
5.69
3.89
1.71
2.08
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Land use and land cover types defined in the North Carolina statewide land cover database
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Table 2. Surface water height (m) (above mean sea level) of the Tar/Pamlico River at the river gauge stations of Greenville and Washington on selected dates in September 1999. Date
Greenville
Washington
2 5 19 20 21 22 23 30
0.69 1.76 8.11 8.30 8.32 8.18 7.96 5.34
0.51 1.14 1.49 1.61 1.68 1.64 1.60 0.78
crest of the floodwater. The floodwater level was only about 0.36 m 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 2.62 m 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 1.68 m on 21 September 1999. On 23 and 30 September, the surface water height was 1.60 and 0.78 m, 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 georeferenced 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.
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Figure 3. (a) Ten (out of 13) flooded open field sites are identified on the TM4zTM8 data of 30 September 1999. (b) A close-look of one open field site, as indicated by a white arrow in (a), on the oblique aerial photograph of 23 September 1999.
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 4. (a) TM4zTM8 data of 30 September 1999 show the flooding at the airport and its surroundings. Eight (out of 13) flooded developed sites are shown. (b) Oblique aerial photograph shows the flooding near the airport on 23 September 1999.
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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 15 m615 m spatial resolution (cf. TM 4, 5 or 7’s spatial resolution is 30 m630 m). (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
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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. Results 3.1. 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 15 m615 m), 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.
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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~no difference; grey~derived as flooded areas on the 23 September map, but as nonflooded areas on the 30 September map; black~derived as nonflooded areas on the 23 September map, but as flooded areas on the 30 September map.
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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.
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Table 3. Error matrix and classification accuracy derived by TM4zTM8 of 23 September 1999 and TM4zTM8 of 30 September 1999 at the sites of forested areas, open fields and developed areas. Within producer’s and user’s accuracy sections, omission and commission errors are in ( ) and [ ], respectively. Forested areas
Reference data
Classification 23 September 1999 30 September 1999
Flooded Nonflooded Total Flooded Nonflooded Total
Flooded (km2)
Nonflooded (km2)
Total (km2)
4.44 1.18 5.62 4.62 1.00 5.62
0.10 2.59 2.69 0.08 2.61 2.69
4.54 3.77 8.31 4.70 3.61 8.31
Overall accuracy: 84.6% of 23 September 1999 or 87.0% of 30 September 1999. Producer’s accuracy (%)
Flooded Nonflooded
23 September 1999 79.0 (21.0) 96.3 (3.7)
Open fields
30 September 1999
23 September 1999 97.8 [2.2] 68.7 [31.3]
30 September 1999 98.3 [1.7] 72.3 [27.7]
Reference data
Classification 23 September 1999
30 September 1999 82.2 (17.8) 97.0 (3.0)
User’s accuracy (%)
Flooded Nonflooded Total Flooded Nonflooded Total
Flooded (km2)
Nonflooded (km2)
Total (km2)
3.84 0.05 3.89 3.53 0.36 3.89
0.02 5.67 5.69 0.01 5.68 5.69
3.86 5.72 9.58 3.54 6.04 9.58
Overall accuracy: 99.3% of 23 September 1999 or 96.1% of 30 September 1999. User’s accuracy (%)
Producer’s accuracy (%)
Flooded Nonflooded
23 September 1999 98.7 (1.3) 99.6 (0.4)
Developed areas
30 September 1999
23 September 1999 99.5 [0.5] 99.1 [0.9]
30 September 1999 99.7 [0.3] 94.0 [6.0]
Reference data
Classification 23 September 1999
30 September 1999 90.7 (9.3) 99.8 (0.2)
Flooded Nonflooded Total Flooded Nonflooded Total
2
Flooded (km )
Nonflooded (km2)
Total (km2)
1.83 0.25 2.07 1.49 0.58 2.07
0.15 1.56 1.71 0.08 1.63 1.71
1.98 1.81 3.78 1.57 2.21 3.78
Overall accuracy: 89.7% of 23 September 1999 or 82.5% of 30 September 1999. Producer’s accuracy (%)
Flooded Nonflooded
23 September 1999 88.4 (11.6) 91.2 (8.8)
30 September 1999 71.2 (28.8) 95.3 (4.7)
User’s accuracy (%) 23 September 1999 92.4 [7.6] 86.2 [13.8]
30 September 1999 94.9 [5.1] 73.8 [26.2]
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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 and visual 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 vegetation caused 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 forest canopies 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 underneath canopies 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
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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 the Limpopo 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 (8.32 m) at Greenville for almost five days (table 2). These floods have 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 of DEMs 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, cost of 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 oriented digital 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
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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. References BRAKENRIDGE, G. R., TRACY, B. T., and KNOX, J. C., 1998, Orbital SAR remote sensing of a river flood wave. International Journal of Remote Sensing, 19, 1439–1445. BRIVIO, P. A., COLOMBO, R., MAGGI, M., and TOMASONI, R., 2002, Integration of remote sensing data and GIS for accurate mapping of flooded areas. International Journal of Remote Sensing, 23, 429–441. COLBY, J. D., MULCAHY, K., and WANG, Y., 2000, Modeling flooding extent from Hurricane Floyd in the coastal plains of North Carolina. Environmental Hazard, 2, 157–168. CORREIA, F. N., REGO, F. C., SARAIVA, M. D. S., and RAMOS, I., 1998, Coupling GIS with hydrologic and hydraulic flood modeling. Water Resources Management, 12, 229–249. DARTMOUTH FLOOD OBSERVATORY, 1999, Flooding from Hurricane Floyd. DFO-1999-076, NASA-Supported Dartmouth Observatory, Dartmouth College, Hanover, NH, USA. HESS, L. L., MELACK, J. M., FILOSO, S., and WANG, Y., 1995, Realtime mapping of inundation on the Amazon floodplain with the SIR-C/X-SAR synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 33, 896–904. IMHOFF, M. L., VERMILLON, C., STORY, M. H., CHOUDHURY, A. M., and GAFOOR, A., 1987, Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat data. Photogrammetric Engineering and Remote Sensing, 4, 405–413. JONES, J. L., HALUSKA, T. L., WILLIAMSON, A. K., and ERWIN, M. L., 1998, Updating flood inundation maps efficiently: building on existing hydraulic information and modern elevation data with a GIS. US Geological Survey Open-File Report 98-200, Tacoma, WA, USA. MAIOLO, J. R., WHITEHEAD, J. C., MCGEE, M., KING, L., JOHNSON, J., and STONE, H., 2001, Facing Our Future: Hurricane Floyd and Recovery in the Coastal Plain, 1st edn (Wilmington, NC: Coastal Carolina Press). MELACK, J. M., and WANG, Y., 1998, Delineation of flooded area and flooded vegetation in
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Balbina Reservoir (Amazonas, Brazil) with synthetic aperture radar. Verhandlungen Internationale Vereinigung fu¨r Limnologie, 26, 2374–2377. MIRANDA, F. P., FONSECA, L. E. N., and CARR, J. R., 1998, Semivariogram textural classification of JERS-1 (Fuyo-1) SAR data obtained over a flooded area of the Amazon rainforest. International Journal of Remote Sensing, 19, 549–556. MUZIK, I., 1996, Flood modeling with GIS-derived distributed unit hydrographs. Hydrologic Processes, 10, 1401–1409. NICO, G., PAPPALEPORE, M., PASQUARIELLO, G., REFICE, A., and SAMARELLI, S., 2000, Comparison of SAR amplitude vs. coherence flood detection methods—a GIS application. International Journal of Remote Sensing, 21, 1619–1631. POPE, K. O., REJMANKOVA, E., PARIS, J. F., and WOODRUFF, R., 1997, Detecting seasonal flooding cycles in marshes of the Yucatan Peninsula with SIR-C polarimetric radar imagery. Remote Sensing of Environment, 59, 157–166. SIEGEL, H., and GERTH, M., 2000, Satellite-based studies of the 1997 Oder flood event in the Southern Baltic Sea. Remote Sensing of Environment, 73, 207–217. SIPPEL, S. J., HAMILTON, S. K., MELACK, J. M., and NOVO, E. M. M., 1998, Passive microwave observations of inundation area and the area/stage relation in the Amazon River floodplain. International Journal of Remote Sensing, 19, 3055–3074. US ARMY CORPS OF ENGINEERS, 2000, HEC-GeoRAS—an extension for support of HECRAS using ArcView, User’s Manual, version 3.0. Hydrological Engineering Center, Davis, CA. WANG, Y., 2002, Mapping extent of floods: what we have learned and how we can do better. Natural Hazards Review, 3, 68–73. WANG, Y., HESS, L. L., FILOSO, S., and MELACK, J. M., 1995, Understanding the radar backscattering from flooded and nonflooded Amazonian forests: results from canopy backscatter modeling. Remote Sensing of Environment, 54, 324–332. WANG, Y., COLBY, J., and MULCAHY, K., 2002, An efficient method for mapping flood extent in a coastal floodplain by integrating Landsat TM and DEM data. International Journal of Remote Sensing, 23, 3681–3696.
