Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing

llMmlm ELSEVIER

Geomorphology21 (1998) 295-312

Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing Philip A. Townsend *, Stephen J. Walsh D~Tartment of Geography, University of North Carolina, Chapel Hill, NC 27599-3220, USA

Received 15 June 1996; revised 15 February 1997; accepted 15 May 1997

Abstract Synthetic aperture radar images from multitemporal L-band JERS-1 and C-band ERS-1 satellites, a Landsat Thematic Mapper (TM) time-series, and GIS coverages were used in an integrative approach to model the potential of flood inundation within the lower Roanoke River floodplain, North Carolina. A digital elevation model (DEM) with one-meter vertical resolution was developed for the region from scan-digitized mylar separates of contour lines on USGS 7.5-min quadrangles. Several models representing potential wetness and potential flood inundation were generated from the DEMs using both raster (grid) and vecto:r (network) analyses. The potential inundation surfaces were derived from regression models that related known flood elevations to river position and floodplain location. The GIS models were assessed by comparison to classifications of flood change-detection achieved through the radar data. Statistical results indicate that the GIS-derived models successfully ide,ntified flooded areas as mapped by the radar change-detections. Further, statistical tests assessed the ability of individual radar and optical (Landsat TM) images to discriminate flooding as predicted by the GIS models. Both JERS-1 and ERS-1 images identified areas of inundation at different flood levels. © 1998 Elsevier Science B.V. Keywords: floodplain; inundation; terrain modelling; remote sensing; synthetic aperture radar

1. Introduction The spatial and temporal pattern of flood inundation is of critical importance to the distribution of vegetation in the floodplains of the southeastern U.S. Coastal Plain. Species composition and productivity of palustrine forested wetlands vary according to

* Corresponding author. Present address: Department of Geography, Texas A&M UnNersity, College Station, TX 77843-3147, USA. Tel.: + 1 (409) 845-7141; Fax: + 1 (409) 862-4487; E-mail: [email protected]

complex interactions between the fiver flow regime and floodplain geomorphology (Wharton et al., 1982; Sharitz and Mitsch, 1993). The spatial extent and duration of flooding, or the flood hydroperiod regime, have a crucial role in the ecology of floodplains. The ability to model potential flood inundation and map actual extent of inundation, timing, and intensity under different environmental conditions is central to understanding the dynamics of vegetation on the floodplain. Conventional modeling approaches using GIS and remote sensing have been of limited value in the floodplains of the southeastern Coastal Plain. Digital

0169-555X/98/$19.00 ~) 1998 Elsevier Science B.V. All rights reserved. PII S0169-555X(97)00069-X

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elevation models (DEMs) are either not available or too coarse to adequately capture the subtle variations in floodplain topography important in characterizing the spatial variability of the hydroperiod regime. Consequently, few DEM-based models have been developed to represent floodplain geomorphology in such environments. Additionally, analyses using remotely sensed imagery from optical sensors, such as Landsat Thematic Mapper (TM), are limited by the inability to detect standing water beneath forest canopies during partial or complete leaf-out conditions. Field data defining the areal extent, timing, and degree of flooding are generally limited to an inadequate number and distribution of point locations and/or transects that are seldom collected across multiple time periods or defined to be coincident with flood events. In addition, collection of suitable field data often is prohibitively expensive because of logistical problems associated with access and the need for samples across space and time. Such problems of in-situ data collection are further confounded by the need for floodplain-wide characterization and the difficulty in extending point measures (or limited areal samples) to area representation without the use of high quality DEMs. Radar remote sensing, however, has proven to be an effective tool for detecting flooding beneath forest canopies (Hess et al., 1990). Images from the increasing number of satellite-based synthetic aperture radar (SAR) sensors can be interpreted to map flood inundation over large areas of forested wetlands (Hess and Melack, 1994). For this research, we: (1) evaluated the ability of radar and optical Landsat TM image time-series to detect flooding within a forested wetland during a typical phenological cycle within the Roanoke River floodplain; (2) derived DEMs from the USGS 7.5rain quadrangles of the study area to generate a variety of models of potential flood inundation for the region using DEM-based topographic surfaces, FEMA-based hydrologic profiles, and Landsat TMand SAR-based image time-series; and (3) evaluated the sensitivity of each approach for characterizing flood inundation through statistical tests involving comparison of means for flood levels and image dates across a phenological cycle within the floodplain. Ultimately, the analyses allow the development of an optimal hydroperiod regime model using

DEM-based predictive variables and actual inundation data derived from SAR imagery.

2. Background The study area for this research is the floodplain of the lower Roanoke River, North Carolina (Fig. 1). This region includes some of the least-disturbed broad expanses of bottomland forests in the eastern United States. In recent years, much of the area has been protected by The Nature Conservancy (TNC), and managers have become increasingly interested in mapping the environmental gradients and potential changes in hydrology that influence vegetation distributions in the region. The floodplain has been substantially affected by natural and anthropogenic disturbances. In particular, the flooding regime has been altered as a consequence of dam construction on the Piedmont portions of the river during the past 50 years. The dams have served to greatly reduce maximum discharges on the river, leading to a decreased spatial extent of inundation. The duration of the small-to-medium scale floods, however, has increased, leading to the speculation that some low-lying areas may now be experiencing longer hydroperiods than in pre-dam periods (Konrad, 1997). DEM-based analyses of fluvial environments have been limited almost exclusively to areas of significant topographic relief. Pearlstine et al. (1985), however, employed a DEM to examine floodplain hydrology in a similar landscape. They hypothesized potential changes in vegetation composition because of proposed construction of a water diversion project along the Santee River in eastern South Carolina, and linked hydrological assumptions to data for flood transects from U.S. Army Corps of Engineers for a limited area. Other GIS-based studies of non-riverine wetland hydrology have either modeled hydrological characteristics of the wetlands at a much coarser resolution (Sklar et al., 1985), or have used fine-scale DEMs that were generated from field surveys for isolated and localized areas (Poiani and Johnson, 1993). For this research, we developed landscapescale DEMs with 25 m horizontal resolution and 1 m vertical resolution to characterize the entire lower Roanoke River basin. From these DEMs, we derived

P.A. Townsend, S.J. Walsh/ Geomorphology 21 (1998)295-312

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a series of models of potential inundation, linked them directly to published measurements of river stage, and evaluated the use of remotely sensed imagery for characterizing inundation through the integration of DEM data. Multispectral optical imagery has been used extensively to map fon~sted wetlands in the southeastern United States (Jensen et al., 1984, 1986, 1993; Hodgson et al., 198'7, 1988; Mackey, 1990; Walsh and Townsend, 1995; Townsend et al., 1995; Townsend and Butler, 1996). A few studies have indicated that optical remote sensing can be used to provide hydrologically relevant data about riparian landscapes, and to determine the extent of inundation during floods (Hewitt, 1990,; Muller et al., 1993). Optical remote sensing is limited, however, by its inability in forested wetlands to detect flooding because of the presence of dense canopies (Mackey and Riley, 1994). Older studies indicate that multivariate statistical techniques such as Principal Components Analysis (PCA) applied to a satellite timeseries may provide a means to distinguish differ-

ences between images related to inundation and phenology (Yamagata and Akiyama, 1988; Walsh and Townsend, 1995). Optical remote sensing has been used more commonly to generate hydrologically relevant vegetation maps through the use of multitemporal data (Price, 1986; Gurnell et al., 1993), and to infer hydrologic processes by integrating such information through a GIS (Gurnell et al., 1993). Without a distributed network of monitoring wells, the ability to measure pattern-process relationships over large areas within floodplains depends in part on the availability of f'me-resolution elevation data that can be used to model hydrologic processes. Consequently, the examination of vegetation-environment relationships across space is limited by the lack of available data to characterize hydrologic patterns. Recently, however, synthetic aperture radar (SAR) systems have emerged as a useful data source for mapping inundation in forested wetlands (Krohn et al., 1983; Place, 1985; Ormsby and Blanchard, 1985; Imhoff et ai., 1987; Hess et al., 1990; Sippel et al., 1994; Hess and Melack, 1994; Hess and Melack,

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1995; Wang et al., 1995). Satellite microwave data are now available from ERS-1, launched by the European Space Agency in 1991, from JERS-1, launched by the National Space Development Agency of Japan in 1992, and from RADARSAT, launched by the Canadian Space Agency in 1995. Data from ERS-1 and JERS-1 were used for this research because of the canopy-penetrating capability of the systems and the ability to assess characteristics of soil-moisture within forested environments. SAR operates in the microwave portion of the electromagnetic spectrum rather than the visible and infrared portions employed by optical sensors. Additionally, SAR data are collected by active remote sensing systems rather than passive sensors, such as used on the Landsat TM. Passive sensors exploit reflected solar radiation to record images, whereas active sensors provide sources of illumination. Radar imagery provides two distinct advantages for remote sensing: (1) microwave energy penetrates the atmosphere regardless of time of day and under virtually all weather conditions; and (2) microwave reflections from Earth materials bear little direct relationship to reflection in and near the visible spectrum, thereby providing independent environmental information concerning the same landscape features. SAR remote

A.

sensing records reflections or backscatter from Earth in the microwave part of the spectrum. The return signal sensed by the satellite is controlled by several factors: depression angle (or orientation) of the sensor to the surface, composition of the materials being sensed, surface roughness and topography, and the frequency and polarization of the microwave signal. Spatial resolutions of most satellite SAR systems range between 10 and 40 m, depending on sensor incidence angle with the surface. The frequency (or wavelength) and depression angle of a radar transmission determines the depth to which the signal will penetrate the surface. ERS-1 employs shorter C-band sensors (wavelength 3.757.5 cm), whereas JERS-1 utilizes the longer L-band sensors (wavelength 23.00 cm). Longer wavelengths are generally associated with greater penetration of forest canopies, particularly when wavelengths are substantially longer than leaf size (Pope et al., 1994; Wang et al., 1995). Research by Wang et al. (1995) compared the C- and L-bands with respect to the detection of flooding in Amazonian forests, and reported that L-band radar provided the best distinction of flooding in a forest. Other research indicates that C-band data, such as used by ERS-1, may provide useful soil-moisture information (Beaudoin et al.,

B.

Fig. 2. JERS-1 images for study area at high flow (A: March 29, 1994, 572 cms discharge) and low flow (B: August 8, 1994, 114 cms discharge). Bright tones on the images indicate flooded forests, whereas dark tones indicate open water and agricultural fields. The area shown covers approximately 15 × 15 km.

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A.

299

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Fig. 3. ERS-1 images for study area at high flow (A: April 14, 1993, 994 cms discharge) and low flow (B: September 1, 1993, 164 cms discharge). The area shown covers approximately 15 X 15 km.

1990; Pietroniro et al., 1993; Wang et al., 1994; Merot et al., 1994), particularly if used as part of a time-series encompassing different soil/canopy moisture conditions (Cihlar et al., 1992; Rignot and van Zyl, 1993). In general, radar backscatter returns are brighter over flooded forests, than over non-flooded forest because of double-bounce backscattering (Richards et al., 1987). This relationship is demonstrated in the JERS-1 images in Fig. 2. Calm, open water tends to have a dark response, whereas flooded forests have the brightest tones. Using images from multiple dates, areas of change in inundation extent between dates can be identified. Fig. 3 provides an ERS-1 image over the same area. F'rom a visual perspective, ERS-1 is less effective for identifying flooded forests because the shorter wavelength C-band microwave energy undergoes greater scattering from the forest canopy. In this research, we evaluate the abilities of ERS-1 and JERS-1 ,;ensors to detect flooding in the Roanoke River floodplain.

3. GIS database development We developed a comprehensive GIS database to support this research. All data were either input or derived within ARC/INFO (a vector-based GIS) or

ARC/INFO GRID (a raster-based GIS). ARC/INFO uses the network approach to structure locational information, and the relational approach to structure non-locational information. The base variables included hydrography, contours, watersheds, and transportation.

3.1. Digital elevation model The ability to model floodplain topography depends upon the availability of accurate elevation data. The eighteen topographic maps (7.5-min quadrangle map series) that cover the study area were developed by the USGS between 1962 and 1977 using photogrammetric methods. Fifteen of the quadrangles characterize topographic variation by using a five-foot contour interval and the other three quadrangles use a two-meter contour interval with supplemental one-meter contours in areas of low relief. DEMs are generated from the quadrangles by sampiing elevations into an altitude matrix using a 30 m horizontal resolution and 10 m vertical accuracy. Only three of the eighteen DEMs that cover the study area were available for acquisition from the USGS. Sensitivity analyses of the three DEMs indicate that the horizontal and vertical resolutions of the DEMs were below the resolution demands imposed

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by the study area. Specifically, the topographic subtleties critical to understanding the landforms, moisture variability, and corresponding distributions of vegetation were not well represented in the data or absent altogether from the elevation matrix. Additionally, the three available DEMs were characterized by distinct and regular patterns of striping across the elevation matrix caused by systematic sampling error at the time of DEM construction, and hence were not used in this research. DEMs were derived for all eighteen quadrangles in the study area by digitization of the contour lines and spot elevations from the USGS quadrangles. We scan-digitized the contour lines from mylar separates of the eighteen 7.5-rain quadrangles. The scanned contours were vectorized during the scanning process and subsequently imported into ARC/INFO GRID and then converted back to the vector data structure. The contour lines were smoothed to remove the stairstepped appearance that results from raster-to-vector conversion. The digital contour coverages were manually edited where the scanning and vectorization process did not adequately resolve separate contour lines. Elevation values were assigned to all of the contours in the coverages. Finally, point elevation values for benchmarks were manually digitized to provide additional elevation information for creation of the DEMs. We used the TOPOGR1D module in ARC/INFO to interpolate the digital elevation model from the contour data. TOPOGRID generates a DEM from contour lines, point elevation data, and hydrography information using an iterative technique of finite difference interpolation developed by Hutchinson (1989). The algorithm interpolates elevation values iteratively using a thin plate spline, thereby developing an optimal flow model that maintains the integrity of the input data while simultaneously ensuring surface continuity (Hutchinson, 1993). The algorithm is optimized by using vector hydrography data to constrain TOPOGRID to identifying the appropriate flow properties along drainages. We extracted the necessary digital hydrography data from the USGS 7.5-min quadrangle using the hydrography DLGs (Digital Line Graphs) for the study area. All hydrographic attributes, including rivers, lakes and streams, were represented as single-line features. Each feature was coded with the appropriate downstream flow

direction and those features with undefined flow characteristics were deleted. The final result of the TOPOGRID interpolation process was a DEM with hydrologically correct flow characteristics and a minimal number of sinks, or areas with internal drainage. In some places, especially areas where steep terrace slopes border broad flat bottomlands, TOPOGRID interpolated elevation values much lower than indicated on the topographic maps. In these cases, supplemental contour lines were added at the bottom of the slopes to constrain the interpolation process. The supplemental contour lines used the same elevation values as the lowest contour on the slopes. This limitation of TOPOGRID reflects the broader use of the algorithm for areas with more substantial and regular terrain variation. In a few flat areas where the TOPOGRID algorithm did not perform well, we manually edited the resulting DEM to provide the correct elevation values based upon the topographic maps. We selected a 25 m spatial resolution for the output DEM. This horizontal spacing represents the effective distance between resolvable contour lines in areas of significant relief on the topographic maps. Coarser contour resolution, such as 30-m grid cells, did not adequately capture the detail of the contour lines, whereas finer contour resolutions added detail as an artifact of the interpolation. All of the output data of digital elevation were converted to integer metric units, eliminating any false vertical detail that might be implied by keeping the sub-meter values produced in the interpolation process. Because the accuracy level of topographic maps is generally assumed to be one-half of a contour interval, we determined that the one-meter resolution constitutes the most appropriate rendering of the Roanoke River landscape based upon the characteristics of the input data. We assessed the accuracy of the DEM by comparing 90 randomly selected points (five points per quadrangle) with the contour maps. All 90 points on the DEM were within one meter of the expected value interpreted from the topographic maps. 3.2. Wetness Index

The DEM and digital hydrography data were used to derive a series of surfaces that represent the

P.A. Townsend,S.J. Walsh/ Geomorphology21 (1998)295-312 potential for a site to be inundated. One of the most common indices derived from DEMs, used to indicate potential wetness for a site, is the Topographic Convergence or Wetness Index (Beven and Kirkby, 1979; Moore et al., 1991; Wolock and McCabe, 1995). Although the index was designed to simulate areas of substantial topography, Phillips (1990) indicated that the Wetness Index can be used to accurately identify wetlands in Coastal Plain settings such as the lower Roanoke River Basin. The Wetness Index uses calculations of slope and upslope contributing area (the area that drains through a given location) to generate a dimensionless index of potential wetness. The general form of the index is: W,. = In( A/tan B)

(1)

where, Wi = wetness index; A = upslope contributing area; B = surface., slope. The Wetness Index should be interpreted as a representation of potential wetness based upon topographic position and upslope drainage rather than from direct hydrological processes. The Wetness Index conveys no information regarding flow characteristics of the Roanoke River and as such cannot be considered a predictor of flood inundation from riverine processes. With this model, upslope drainage into the lower Roanoke River from the upper basin is considered to be contained within the river channel, so the Wetness Index simply identifies potential wetness at a site as a consequence of the characteristics of local topography. Parameters for soil permeability can also be included in the calculations, but are not essential for calculating the model. Indeed, soil parameters are probably best not used in cases where the accurate estimation of soil characteristics is in question (Townsend and Walsh, 1996). We opted not to use soil parameters for any of the analyses in this research because of the incomplete coverage and lack of quality data from soil surveys. Additionally, soil classification units tend to be highly generalized in areas that are not suitable for agriculture, and thus contain less detail for the forested wetlands. Surface slope and upslope contributing area can be readily calculated within A R C / I N F O GRID for every grid cell in a DEM. The flat topography of the Roanoke River floodplain, however, poses two special problems for the, calculation of Wi. First, slope

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angle for most of the floodplain is near zero, which leads to the calculation of an undefined index value using Eq. (1) above. As such, all flat areas need to be provided with a slope value to facilitate the calculation of the Wi. We used a slope value of 0.00358 ° for all areas of zero slope. This value represents the total gradient of the Roanoke River from the fall line (elevation 13 m) to the mouth of the river at sea-level. The calculation of upslope contributing area posed a more difficult problem. Most algorithms of flow accumulation optimize flow paths by modeling contributing areas according to the most direct route from one grid cell to the lowest adjoining elevation grid cell. This approach does not operate well with broad zones of unchanging elevation. In such areas, drainage from any upslope contributing area is distributed across all grid cells that characterize a flat bottomland as long as no drainage channels are present. To accomplish this, all connected areas of equal elevation were treated as single elevation zones. Upslope drainage area, that area allocated to any single grid cell within the zone, was reallocated proportionally to the whole zone. Thus, all grid cells within a single flat area received identical values of the upslope contributing area. This produced a more realistic depiction of flow accumulation for flat areas. Using the modified slope and contributing area surfaces, we calculated a raster surface of Wi. This model represents potential wetness at a site because of to local topographic relief rather than riverine processes.

3.3. Models of potential flood inundation GIS models were designed to represent the potential for flooding from riverine sources at any location in the lower Roanoke River basin. These predictive surfaces were generated from a combination of vector (or network) modeling, using vector hydrography data and raster modeling using the DEM. The first index is a normative model of potential inundation based on the topographic position of a location with respect to the position of the river. This model assumes that the potential for any site to be inundated is directly related to the difference in elevation between that site and the river at its nearest hydro-

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logic link. This is the relationship between the geographic position on the river and the nearest hydrologic feature, such as a levee breach or tributary stream, that is capable of moving flood waters into the backswamps. Therefore, for every grid location it was necessary to calculate the associated elevation of the Roanoke River at that hydrologic link to determine the topographic position of the site with respect to the river. For many locations in the floodplain, this elevation is the elevation of the Roanoke River at its closest point. Most locations in the lower basin are drained, however, by hydrographic features that tend to transport water away from the river and into the backswamps during periods of high flow. The influence of the river at any given point may be more directly related to the location of a point with respect to a tributary of the Roanoke than to the Roanoke River itself. Therefore, flood characteristics for a point in the floodplain are dependent upon the elevation of the Roanoke River at its confluence with those tributaries that transport water in and out of the floodplain. We used topographic, Euclidean distance, and characteristics of the drainage basin to develop network-elevation models that determine the relative hydrologic position for every grid cell in the study area. Essentially, every location is allocated to a hydrographic feature based on three criteria: 1. Location within a certain drainage sub-basin--this prevents locations from being hydrologically associated with neighboring features that may be positioned in a different sub-basin. 2. Flow characteristics--flow information derived from the DEM ensures that a location is allocated to the proper hydrographic feature. 3. Euclidean distance--in areas where a location can reasonably be assigned to multiple hydrographic features, the shortest Euclidean distance is used to determine the associated feature. Once every grid cell is allocated to a certain hydrographic feature, we developed a raster model to determine the elevation from the DEM of the Roanoke River at the confluence of that feature with the river. All locations within the associated subbasins are assigned the elevation value of the Roanoke River at the identified confluence. Locations that are most closely linked with the river itself are assigned the elevation of the river at its nearest

point. This procedure is modified only slightly to accommodate the fact that some broad areas of equal elevation can be considered to be linked to the river at multiple locations. In these cases, elevation zones were assigned river elevations that were calculated as the mean of the elevations from the multiple hydrologic links. The normative model, the Position Above the River Index (PARI), is calculated as the difference in elevation between a location and the river at its nearest hydrologic link. Simply, for every location PARI describes its topographic position with respect to the elevation of low flow in the river. Low PARI values indicate a greater likelihood of flooding from the Roanoke River during enhanced flow periods. Alternatively, the PARI values can be used as a normative measure of the elevation that the river must rise for that site to become inundated. A second suite of floodplain indices were developed based upon the concept of the 100-year floodplain as defined by the U.S. Federal Emergency Management Agency (FEMA). FEMA has published a series of maps and flood insurance studies for all of the counties of the study area. The flood insurance studies include detailed information on the flood profile that provide river stages at certain locations along the lower Roanoke River for flood discharges associated with the 10-year, 50-year, 100-year, and 500-year floods (Federal Emergency Management Agency, 1980a,b, 1985, 1988, 1991, 1992, 1994, 1995a,b, 1996). Discharge levels associated with each of the flood intervals are listed in Table 1. Since closure of the upstream dams in the 1950s, sustained floods of 1000 cms (50-year flood) have occurred five times, whereas larger floods have ceased altogether. Prior to closure of the dams, discharges of 1000 cms occurred annually. Floods measuring 700 cms are more frequent, occurring in most years.

Table 1 Discharges on RoanokeRiver at Roanoke Rapids (river kilometer 216) for different FEMA flood magnitudes Hood type Discharge (cms) 10-year 708 50-year 991 100-year 1387 500-year 3738

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The elevations o1! flood stage are provided in English units according to the National Geodetic Vertical Datum of 1929, the same datum used in the construction of the contour maps and DEMs. The flood stages are reported for certain transects that are identified by the distance of the location in river miles (or feet) from the mouth of the Roanoke River at Albemarle Sound. We extracted values for flood elevations and river distances from all of the flood insurance studies for the region. This resulted in a data set of river distance and flood stage elevations at four representative discharge levels for a set of 33 locations along the lower Roanoke River. We converted the distance aad flood stage values to metric units for further analysis. To apply the information contained in this data to a spatial model of potential flood inundation, we developed a series of regression models that relate flood stage at a certain discharge to distance from the ~aaouth of the Roanoke River. We adapted the network modeling applications within A R C / I N F O to determine river distance (often called river mile) values for the single-line hydrography GIS coverage of the Roanoke River. Using the approximate mouth of the river as a starting point, the d y n a m i c seglmentation c o m p o n e n t of A R C / I N F O ' s Network Module calculated the distance of every location on the river from the mouth. The distance information was converted to point values of distance in ~aaeters. These point values were subsequently rasterized to create a grid coverage of the Roanoke River where each grid cell is coded with a value represe:ating the distance in meters at that point from the mouth of the river. This river distance was applied to the entire study area using the same methodology described above for the calculation of PARI, in w]aich river elevation is allocated to all locations within the basin. Consequently, a new grid was generated that provided the river distance at the nearest associated hydrological link or confluence with the Roanoke River for every location within the floodplain. Thus, every grid cell in the study area could be linked to a distance from the mouth of the Roanoke River and eventually an associated river flood stage level. Two important manipulations of the river distance data were required to prepare the data for subsequent analyses. First, values of river distance were scaled for distributaries near the mouth of the Roanoke

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River. The very lowest reaches of the river are characterized by a series of distributaries that transport water from one part of the river to another. In most cases, the distributaries represent a shortest path between river segments, and can be considered a part of the river channel for the purposes of network modeling. These distributaries carry a significantly lower volume of water, however, than does the Roanoke River, so river distance is probably best calculated via the Roanoke River. To accommodate the differences in river distance between the distributaries and the main channel of the Roanoke River, river distance along the distributaries was calculated as a scalar of the river distance at the upstream branch of the distributary to river distance at its downstream confluence with the distributary. River distance was no longer literally correct for the distributary; however, the river distance along the distributary would more closely match the associated river distance for the Roanoke River. Second, the information about river distance in the FEMA flood insurance studies needed to be matched to the river distance data generated in the GIS. River distance as measured on the ground does not precisely match river distance as modeled in a GIS environment. This discrepancy results from the selection of the location of the actual mouth of the river, and because the linear representation of the river in a GIS is bound to contain a certain amount of generalization. This relates to the fractal properties of linear features, in which measured distances along the same river increases as the scale of observation becomes finer (Burrough, 1986). We were able to scale the river distance measures from the FEMA documents to our GIS version of river distance using four paired distance measurements at known locations. Five bridges cross the Roanoke River between its mouth and the fall line 200 km upstream. Four of these bridges were used as transect locations for the FEMA profiles of flood elevation. We determined the associated river distance from our model by overlaying the USGS 7.5-rain transportation DLG over the grid of river distance and interactively extracting the distance values for the locations where the highways crossed the Roanoke River. We fit a least-squared regression line through the four data points to generate a scalar to convert FEMA distance to river distance measured within the

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GIS. Whereas a regression equation based on four points has limited power, few other opportunities existed for linking measures of fiver distances at known points along the river. Differentially corrected Global Positioning System (dGPS) technology could be used to determine the horizontal location of sample points with sufficient accuracy, but vertical resolutions from dGPS data were outside of the sensitivity for this study area and application. The developed regression equation is: GIS = -623.4974 + 0.991218-FEMA

(2)

The Y-intercept can be interpreted as the difference in distances between what our model determined to be the mouth of the fiver and the actual reference location used by FEMA. The location of the modeled river mouth was 623 m further upstream than that used by FEMA. This represents an error of 0.623 km for a total fiver distance of 216.9 km. The slope value indicates a rate of change between the opposing measurements of river distance. In this case, the slope of the equation indicates that every meter measured by FEMA corresponds to 0.991218 m of river distance in the GIS model. This equation

cannot be evaluated by the usual regression diagnostics because of the small number of data points; however, the residuals indicate a very close fit, ranging from 4.469 m to - 2 4 . 2 2 m. All of these residual values are less than the 25 m spatial resolution of the gridded data. Regression models were generated that relate flood stage to distance from the mouth of the river. These models were applied to the layer of river distance to indicate for every location in the floodplain a flood stage elevation at each of the four flood magnitudes. The data exhibit a slight curvilinear relationship between flood stage and river distance, because flood stages tend to level out as the fiver approaches sea-level. At the locations closest to the mouth of the river, flood elevations become nearly constant and somewhat insensitive to decreased distance from the Albemarle Sound. Fig. 4 displays the relationships between river distance and flood stage for each of the flood magnitudes. For the 10-year, 50-year, and 100-year flood magnitudes, we fit a piecewise regression line that provides two equations for flood elevation. For the 500-year flood, the data more closely approximated a second order polynomial fit.

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P.A. Townsend, S.Z Walsh/ Geomorphology 21 (1998)295-312

4. Satellite imagery

Table 2 Regression coefficients used to map flood inundation Flood mag. Distance 10-year 50-year 100-year 500-year

> 45,096 < 45,096 > 45,096 < 45,096 > 45,096 < 45,096 All

Intercept Slope (dist.) m - 1.685 0.0000777 m 1.366 0.0000123 m - 1.653 0.0000832 m 2.286 0 m - 1.899 0.000929 m 2.450 0 2.920 0.00000274 0.000000000454 a

305

R2-value 0.9996 0.9987 0.9972 0.9981

a Slope distance squared as part of the polynomial expression.

Table 2 lists the regression equations and associated diagnostics. Finally, flood potential was determined for every location. This was accomplished within the GIS by subtracting the flood stage for each magnitude flood from the corresponding elevation in the DEM. Negative values indicate that a site would be flooded at the given magnitude flood, whereas positive values suggest that the location would not be flooded. Because the flood stage models have a much finer vertical resolution than the DEM (centimeter resolution versus meter resolution), the values should be interpreted as flood potentials rather than absolute flood depths. The flood surfaces developed in this analysis can be considered predictors of flood potential at each magnitude, with increasingly negative numbers indicating areas more likely to be flooded, whereas increasingly positive numbers indicate areas less likely to be flooded. These data layers are identified as FLOOD10, FLOOD50, FLOOD100, and FLOOD500 in subsequent sections.

We evaluated the models described in the previous section to determine whether the GIS layers adequately identified flooded areas as predicted from a change-detection analysis of SAR data. We undertook a series of statistical tests to compare the relative abilities of individual SAR and optical satellite images for mapping flood inundation. Information about the satellite images and the associated hydrologic conditions used in the analyses are presented in Table 3. All discharge and flow data were provided by the USGS. The images were georeferenced to UTM (Universal Transverse Mercator) coordinates and spatially co-registered to the GIS layers for these analyses. Because of the large volume of satellite data, the analyses used transformations of the optical (Landsat Thematic Mapper) data rather than the raw imagery. Specifically, we employed the Normalized Difference Vegetation Index (NVDI), a widely used index of red and near-infrared reflectance that has been found to be highly correlated with vegetation biomass, but is also known to be very sensitive to the effects of soil and moisture backgrounds (Huete et al., 1985; Nemani et al., 1993; Qi et al., 1994). NDVI is calculated as: NDVI = ( T M 4 - TM 3 ) / ( T M 4 + TM3)

(3)

where TM 3 (channel 3) measures reflectance in the red wavelengths of the electromagnetic spectrum and TM 4 (channel 4) measures reflectance in near-infrared wavelengths, thereby compressing into a single output channel the effects of vegetation pigmen-

Table 3 Images used in the analyses and the hydrologic conditions at Roanoke Rapids, N.C. at time of sensing Date conditions

Sensor

Sensor type

Pixel size

Discharge (cms)

Flow

4/14/93 6/23/93 9/1/93 3/29/94 6/25/94 8/8/94 3/20/84 7/14/91 4/14/93 5/16/93

ERS- 1 ERS- 1 ERS- 1 ~.RS-1 JERS-1 JERS-1 TM TM TM TM

SAR (C) SAR (C) SAR (C) SAR (L) SAR (L) SAR (L) Optical Optical Optical Optical

12.5 m 12.5 m 12.5 m 12.5 m 12.5 m 12.5 m 30 m 30 m 30 m 30 m

994 119 164 572 150 115 563 77 994 260

50-year flood stage low after high prolonged at this rate prolonged at this rate low after spring high low summer prolonged at this rate prolonged at this rate 50-year flood stage > 20,000 prior week

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tation and vegetation structure associated with Landsat channels 3 and 4, respectively. Whereas the goal in image processing is often to reduce the influence of background reflectance, our goal was to determine whether the decreased reflectance associated with a flooded forest and expressed in NDVI values could be used to map flood inundation below a forest canopy. We also employed the Wetness Vegetation Index (WVI), which is calculated as the third orthogonal axis of the Kauth and Thomas (1976) 'tasseled cap' transformation (Crist and Kauth, 1986). The 'tasseled cap' indices transform the six original channels of TM data (the thermal-infrared channel was not used in the analysis) into uncorrelated, orthogonal axes (Crist and Ciccone, 1984). The first two axes are associated with soil brightness and vegetation greenness, whereas the third axis is associated with surface wetness (Crist and Kauth, 1986). The analyses sought to test the ability of NDVI and WVI for discriminating flood inundation. Both NDVI and WVI were scaled to an eight-bit data range (0-255) for these analyses. The radar data required minimal preprocessing. Both ERS-1 and JERS-1 data are calibrated by the distributors to remove distortion from receiver and processor error. The ERS-1 images do not have absolute calibration, so the digital numbers are used for analyses. The ERS-1 platform has been determined to be very stable thereby allowing comparisons among an image time-series (Rignot and van Zyl, 1993). Absolute calibration has been applied to the JERS-1 images. As such, the data values reported for individual JERS-1 scenes are expressed in dB (decibels) as the radar backscattering coefficients, or o-° (sigma-nought). Finally, we applied a 5 × 5 median filter to the SAR data to reduce random

Table 4 Ratios of radar images for the assembled time-series Sensor

Ratio

Change direction

JERS JERS JERS ERS ERS ERS

March/June March/August June/August April/June April/September June/September

wet to dry wet to dry dry to dry very wet to intermediate wet very wet to very dry intermediate wet to very dry

Table 5 Flood transition classes from ERS-1 and JERS-1 analyses 1. Flooded to not-flooded between the first and second dates. 2. Flooded to not-flooded between the first and third dates (no detectable change between the first and second or second and third dates). 3. Flooded to not-flooded between the second and third dates. 4. Unchanged or undetermined change.

speckle noise in the data and to produce a more smoothly varying image. The difference between flooded and non-flooded forests produces a well-documented response in radar backscattering (Richards et al., 1987; Hess et al., 1990; Hess and Melack, 1994; Wang et al., 1995). Image ratios are used to document this difference in backscattering between images. The ratios are conveniently expressed in dB (after Rignot and van Zyl, 1993) as: dB = 10 log 10(ratio of digital numbers)

(4)

When calculating the ratio, the image that is hypothesized to be 'wetter' is always used as the numerator. The six ratio images that were derived are listed in Table 4. The ratio images were reclassified to derive a nominal image representing change status. Flooded forests produce a 2 dB or greater increase in the backscatter ratio over non-flooded forests as detected by SAR sensors with longer wavelengths (L-band). We calculated SAR image ratios on a sensor-specific basis for the scenes listed in Table 5 (ratios were not applied among different sensors). All pixels with values greater than 2 dB represent areas of change from flooded to nonflooded, whereas areas with values below 2 dB represent areas of no-change (either remained flooded or remained non-flooded). A change-detection classification is derived from these images, in which different classes represent transition categories. These classes are listed in Table 5, and are the classes that are used to evaluate the effectiveness of the models of flood potential described in the previous section.

5. Statistical analyses From a statistical perspective, two questions can be asked regarding the relationship between the

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satellite imagery and GIS analyses. First, how well do the flood potential surfaces (PARI, FLOOD10, FLOOD50, FLOOD100, FLOOD500) and the Wetness Index (Wi) match the flooding classification generated by radar change-detections, and second, how well do individual satellite images (both radar and optical) identify inundated areas suggested by the GIS layers of flood potential? We used Analysis of Variance (ANOVA) and Tukey's multiple comparison test to evaluate the quality of the flood prediction surfaces. The analyses of the satellite images used an area subset covering approximately three quadrangles of the study area where all of the radar and Landsat images overlapped. A random sample of 1126 data points was selected from the images and GIS layers for all forested areas with elevations up to one meter above the 500-year floodplain. Forested regions were identified from a landcover classification of the region developed by Townt;end and Walsh (1997). We hypothesized that flood potential, as indicated by the GIS layers, should be predicted by the flood transition classes listed in Table 5. The only limitation is that areas that are perpetually flooded would be classified in the no-change class, meaning that such areas could not be discriminated from nonflooded, no-change classes. Such areas can be readily identified, however, based upon topographic position. The analyses indicate that all of the flood inundation potential models (PARI, FLOOD10, FLOOD50, FLOOD100, FLOOD500) perform well for identifying flooded areas. FLOOD50 is not reported in these analyses because it was highly correlated with FLOODIL0 ( r = 0.99981). Wi performed less well for the ERS-1 classification, but this is not surprising considering that Wi is not designed to predict flooding from riverine sources. The ANOVA statistics are listed in Table 6. The results suggest two conclusions. Fi:rst, the models based on FEMA best identify differences between areas that are flooded or non-flooded. Second, the higher F-values and lower confidence intervals reported for the JERS-l-based classification suggest a close relationship between JERS- 1 and the models of flood prediction, and indicate that JERS-1 more accurately identifies flooded areas. Although the ANOVA tests indicate relationships between the flood classes and the GIS models, a

307

Table 6 Results of ANOVA for radar analyses GIS layer

F-value

Pr > F

ERS- 1 change classes FLOOD10 FLOOD 100 FLOOD500 PARIM Wi

5.48 5.47 5.16 4.83 4.58

0.0010 0.0010 0.0015 0.0024 0.0034

JERS-1 change classes FLOOD10 FLOOD100 FLOOD500 PAR1M Wi

10.13 10.89 13.27 10.41 12.48

0.0001 0.0001 0.0001 0.0001 0.0001

more important concern is whether the mean index values (for FLOOD10, FLOOD100, FLOOD500, PAR/, and Wi) are significantly different from the important flood transition classes listed in Table 5. We used Tukey's multiple comparison test to determine whether the differences between the means of the multiple indices were statistically significant for each of the flood transition change classes. Tukey's method provides confidence intervals for the upper and lower limits of the possible difference between two means (Kleinbaum et al., 1988). If the confidence interval contains a value of zero, then the difference between means is not significant; if both limits are either above or below zero, then the difference between means is significant. Tukey's multiple comparison test indicates significant differences for the GIS indices at the 0.05 confidence interval. Specifically, for the JERS-1 classification, the means for all of the GIS layers for the unchanged class (class 4 in Table 5) were significantly different from the means of class 1 (flooded to non-fooded between the first and second date). These are the two most prevalent classes for the JERS-1 data, and the results are expected considering that discharge on the Roanoke River declined substantially between March 29 and June 25, but remained relatively constant between June 25 and August 8, 1994. Similarly, for the ERS- 1 classification, the means for all of the GIS layers for the unchanged class were significantly different from the means of class 2 (flooded to non-flooded between the first and third dates). Again,

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these results were expected. Although the discharge level on the Roanoke River is fairly low for June 23, the preceding three months had been marked by uncharacteristically high flows (at the 50-year flood level). It is very likely that many backswamps remained flooded on June 23, and subsequently drained during the low flow period between June 23 and September 1. These analyses indicate that the GIS variables adequately identify flooded areas when evaluated against SAR change-detection classifications. The second set of statistical analyses addressed the question as to whether individual satellite images could be used in conjunction with the GIS layers to map flood inundation at a specific time. For these analyses, the flood indices from FEMA were used as the independent variable and the radar backscatter values were used as the dependent variable. FLOOD10, FLOOD50, FLOOD100, and FLOOD500 were combined into a single classified layer (FLOODCLASS) as indicated in Table 7. We used ANOVA with Tukey's multiple comparison test to assess whether satellite reflectance values (Landsat TM) or backscatter coefficients (JERS-1 and ERS-1) vary as a function of flood class. The ANOVA results are presented in Table 8. Generally, FLOODCLASS predicted the differences in backscatter values for all of the radar images. For NDVI and WVI, FLOODCLASS best accounted for the variation in reflectance for the early season (March and April) images. These images correspond to the time periods associated with leaf-off and high flow conditions. During leaf-out and high water conditions (May 1993 TM scene), however, the optical imagery less consistently distinguished flooded areas. Tukey's test was also used to determine which Table 7 Classes in merged flood stage analysis 1. Never flooded 2. Flooded only at 500-year flood stage 3. Flooded only at 100-year flood stage 4. Flooded only at 50-year flood stage 5. Flooded only at 50-year flood stage, but within 50 cm of the 10-year flood stage 6. Flooded at 10-year flood stage (0-50 cm) 7. Flooded at 10-year flood stage (deeper than 50 cm)

Table 8 ANOVA results for individual satellite scenes Date and sensor

F-value

Pr > F

March 29, 1994 JERS-1 June 25, 1994 JERS-1 August 8, 1994 JERS-1 April 14, 1993 ERS-1 June 23, 1993 ERS-1 September 1, 1993 ERS-1 March 20, 1984 NDVI April 14, 1993 NDVI May 16, 1993 NDVI July 14, 1991 NDVI March 20, 1984 WVI April 14, 1993 WVI May 16, 1993 WVI July 14, 1991 WVI

26.75 5.00 4.64 25.49 7.88 9.31 15.49 29.42 4.64 6.06 45.87 67.13 1.66 4.20

0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001 0.1278 0.0004

classes in FLOODCLASS were best distinguished by the various images. For the JERS-1 images, Tukey's test indicated a distinct difference in means between class 7 and all of the other classes. No significant distinctions were identified among any of the other classes. This result is reasonable considering that the 572 cms discharge experienced on March 29, 1994 was well beneath the 10-year flood level. Only places within the 10-year floodplain should have been identified in the satellite image as flooded. For the June and August JERS-1 images, Tukey's test identified few differences between the means. Differences occurred between class 7 (the only areas where there would be any flooding) and some of the drier classes (1 and 2 for example). These results indicate that locations within the 10-year floodplain were unlikely to be flooded and, therefore, experience similar backscatter responses from the radar data. For the ERS-1 images, the multiple comparison tests also produced positive results. The results were particularly meaningful because ERS-1 has been documented to only have a limited capacity to identify flood inundation. For the April 1993 image (which was at the height of a 50-year flood), Tukey's test discriminated differences between flooded (classes 4-7) and non-flooded classes (classes 1 and 2) at the 50-year flood stage. Although the differences in means were significant, they were not as pronounced as compared to the JERS-1 images. For the optical imagery, the multiple comparison

P.A. Townsend, S.J. Walsh/ Geomorphology 21 (1998)295-312

tests produced more mixed results. For the early-season (March and April) images, both NDVI and WVI had significantly different means among the appropriate classes (between class 7 and the other classes). Additionally, Tukey~s test distinguished differences between classes 4, 5, and 6 and the drier classes for the April WVI image'. Tukey's test did not, however, distinguish significant differences between classes for the May and July NDVI and WVI images. These results support the hypothesis that optical imagery cannot be reliably used to map flood inundation during leaf-out periods within this study area setting.

6. Conclusion The synergistic use of radar and optical remote sensing in conjunction with GIS modeling is an effective method for delineating potential inundation in areas of subtle topographic relief. It is particularly important to develop a variety of means to characterize the floodplain landscape when field-based measures of inundation are unavailable or for limited geographic areas. The GIS-derived measures of inundation potential effectively represent the important components of flooding regimes: Wetness Index (Wi) considers slope and upslope contributing area, whereas PARIM and the FLOOD models relate potential inundation to topographic site and situation and the position of the river. The ability of V¢i to identify flooded areas was unexpected. On a broad scale, Wi is probably not an effective predictor of flood potential. The area that was used for our statistical tests, however, was near the upstream extent of the study area. This area contains more pronounced topographic variation and flat backswamps that are smaller in area than those of the lower reaches of the river. Wi and the FLOOD models are probably best used in tandem, with the FLOOD indices indicating potential wetness because of river flooding an6 W~ indicating wetness potential from topographic position and runoff. The JERS-1 images clearly identified differences between flooded and non-flooded regions. The success of ERS- 1 in identifying flooded areas was better than expected. This may result from the characteristically open canopies of the cypress-tupelo forests of the study area. Open canopies would permit greater

309

penetration to the surface below, thereby producing enhanced backscatter responses during periods of flooding. Some of the effects identified with the ERS-1 imagery may be related to phenology, although the impacts are thought to be minimal. Landsat TM data are less appropriate for mapping flood inundation than the tested radar data. Although early-season images can be used to effectively identify the extent of flood inundation, images collected during leaf-on periods demonstrate decreased ability to identify flooded areas. This was particularly evident from the poor performance of the May, 1993 NDVI and WVI images. These images represent a period of substantial wetness, yet Tukey's tests did not identify any significant relationships between the NDVI and WVI and the FLOODCLASS composite. Optical images are most appropriate for mapping flooding during leaf-off periods, and are best avoided if suitable SAR images are available. The most important use of the TM imagery for this research was the generation of the landcover classification that was used to stratify data for subsequent analyses. The ability to generate models of floodplain geomorphology and hydrology are essential to understanding the spatial distribution of vegetation in the region. DEMs can be developed from contour data to predict floodplain properties. The use of ancillary data, such as FEMA profiles of flood elevation, enables the analyses to be linked to real properties of the river flow regime. This is particularly relevant in areas where in-situ measurements of floodplain properties such as inundation are either sparse or non-existent. Radar imagery enhances the predictive power of such models by providing an independent remotely sensed source of validation. For many studies in areas of poor accessibility, this has the added benefit of limiting the expense that would be involved in installing water wells or other field-based sources of validation.

Acknowledgements This research was supported by grants from The Nature Conservancy to S.J. Walsh and P.A. Townsend. JERS-1 images were provided by the National Space Development Agency of Japan

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( N A S D A ) , and N A S D A retains o w n e r s h i p o f the J E R S - 1 images. All data processing for this research was c o m p l e t e d in the Spatial A n a l y s i s Lab, Departm e n t o f G e o g r a p h y , U n i v e r s i t y o f North Carolina. The authors thank R o b e r t Chastain, Graduate Research Assistant, Spatial A n a l y s i s Lab, for his assistance in processing the contour and D E M data.

References Beaudoin, A., Le Toan, T., Gwyn, Q.H.W., 1990. SAR observations and modeling of the C-Band backscatter variability due to multiscale geometry and soil moisture. IEEE Trans. Geosci. Remote Sensing 28 (5), 886-895. Beven, K.J., Kirkby, M.J., 1979. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. Bull. 24, 43-69. Burrough, P.A., 1986. Principles of Geographical Information Systems for Land Resources Assessment, Oxford, Oxford University Press, 194 pp. Cihlar, J., Pultz, T.J., Gray, A.L., 1992. Change detection with synthetic aperture radar. Int. J. Remote Sensing 13 (3), 401414. Crist, E.P., Ciccone, R.C., 1984. Application of the tasseled cap concept to simulated Thematic Mapper data. Photogramm. Eng. Remote Sensing 50 (3), 343-352. Crist, E.P., Kauth, R.J., 1986. The tasseled cap demystified. Photogramm. Eng. Remote Sensing 52 (1), 81-86. Federal Emergency Management Agency, 1980a. Flood Insurance Study, Halifax County, North Carolina (Unincorporated Areas). Washington, D.C., 24 pp. plus plates. Federal Emergency Management Agency, 1980b. Flood Insurance Study, Town of Weldon, North Carolina. Washington, D.C., 17 pp. plus plates. Federal Emergency Management Agency, 1985. Flood Insurance Study, Bertie County, North Carolina (Unincorporated Areas). Washington, D.C., 32 pp. plus plates. Federal Emergency Management Agency, 1988. Flood Insurance Study, Northampton County, North Carolina (Unincorporated Areas). Washington, D.C., 20 pp. plus plates. Federal Emergency Management Agency, 1991. Flood Insurance Study, Martin County, North Carolina (Unincorporated Areas). Washington, D.C., 28 pp. plus plates. Federal Emergency Management Agency, 1992. Flood Insurance Study, City of Roanoke Rapids, North Carolina. Washington, D.C., 27 pp. plus plates. Federal Emergency Management Agency, 1994. Flood Insurance Study, Washington County, North Carolina (Unincorporated Areas). Washington, D.C., 24 pp. plus plates. Federal Emergency Management Agency, 1995a. Flood Insurance Study, Bertie County, North Carolina (Unincorporated Areas). Washington, D.C., 28 pp. plus plates.

Federal Emergency Management Agency, 1995b. Flood Insurance Study, Town of Plymouth, North Carolina. Washington, D.C., 20 pp. plus plates. Federal Emergency Management Agency, 1996. Flood Insurance Rate Map, Town of Williamston, North Carolina. Washington, D.C. Gurnell, A.M., Edwards, P.J., Hill, C.T., 1993. The effects of management on heath and mire hydrology: a framework for a geographic information system approach. In: Haines-Young, R., Green, D.R., Cousins, S.H. (Eds.), Landscape Ecology and GIS. Taylor and Francis, London, pp. 212-222. Hess, L.L., Melack, J.M., 1994. Mapping wetland hydrology and vegetation with synthetic aperture radar. Int. J. Ecol. Environ. Sci. 20, 197-205. Hess, L.L., Melack, J.M., 1995. Delineation of inundated areas and vegetation in wetlands with synthetic aperture radar. Proceedings of the Wet-Dry Tropics Management Workshop, Jabirn, NT. Hess, L.L., Melack, J.M., Simonett, D.S., 1990. Radar detection of flooding beneath the forest canopy: a review. Int. J. Remote Sensing 11 (7), 1313-1325. Hewitt, M.J., III, 1990. Synoptic inventory of riparian ecosystems: the utility of Landsat Thematic Mapper data. For. Ecol. Manage. 33/34, 605-620. Hodgson, M.E., Jensen, J.R., Mackey, H.E. Jr., Coulter, M.C., 1987. Remote sensing of wetland habitat: a wood stork example. Photogramm. Eng. Remote Sensing 53 (8), 1075-1080. Hodgson, M.E., Jensen, J.R., Mackey, H.E. Jr., Coulter, M.C., 1988. Monitoring wood stork foraging habitat using remote sensing and geographic information systems. Photogramm. Eng. Remote Sensing 54 (11), 1601-1607. Huete, A.R., Jackson, R.D., Post, D.F., 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sensing Environ. 17, 37-53. Hutchinson, M.F., 1989. A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. J. Hydrol. 106, 211-232. Hutchinson, M.F., 1993. Development of a continent-wide DEM with applications to terrain and climate analysis. In: Goodchild, M.F., Parks, B.O., Steyaert, L.T. (Eds.), Environmental Modeling with GIS. Oxford University Press, New York, pp. 392-399. Imhoff, M.L., Vermillion, C., Story, M.H., Choudhury, A.M., Gafoor, A., Polcyn, F., 1987. Monsoon flood boundary delineation and damage assessment using space borne imaging radar and Landsat data. Photogramm. Eng. Remote Sensing 53 (4), 405-413. Jensen, J.R., Christensen, E.J., Sharitz, R., 1984. Nontidal wetland mapping in South Carolina using airborne multispectral scanner data. Remote Sensing Environ. 16, 1-12. Jensen, J.R., Hodgson, M.E., Christensen, E., Mackey, H.E. Jr., Tinney, L.R., Sharitz, R., 1986. Remote sensing inland wetlands: a multispectral approach. Photogramm. Eng. Remote Sensing 52 (1), 87-100. Jensen, J.R., Cowen, D.J., Althausen, J.D., Narumaiani, S., Weatherbee, O., 1993. An evaluation of the CoastWatch

P.A. Townsend, S.J. Walsh / Geomorphology 21 (1998) 295-312 change detection protocol in South Carolina. Phntogramm. Eng. Remote Sensing 59 (6), 1039-1046. Kauth, R.J., Thomas, GS., 1976. The tasseled cap--a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data, Purdue University, West Lafayette, Ind., pp. 4B41-4B51. Kleinbaum, D.G., Kupper, L.L., Muller, K.E., 1988. Applied Regression Analysis and Other Multivariable Methods. Duxbury Press, Belmont, CA, pp. 718 pp. Konrad, C.E., 1997. Hydroclimatology of the Roanoke River floodplain of Virginia and North Carolina. In: Walsh, S.J., Butler, D.R., Konrad, C.E., Peet, R.K. (Eds.), The Roanoke River Bioreserve: A i?reliminary Assessment of the Impact of Flow Modification ,an Hydrology, Geomorphological Processes, and Vegetation. The Nature Conservancy (in press). Krohn, M.D., Milton, N.M., Segal, D.B., 1983. SEASAT Synthetic Aperture Radar (SAR) response to lowland vegetation types in eastern Mar~,land and Virginia. J• Geophys. Res. 88 (C3), 1937-1952. Mackey, H.E., Jr., 1990. Monitoring seasonal and annual wetland changes in a freshwater marsh with SPOT HRV data. 1990 ASPRS/ACSM Annual Convention and Exposition, Tech. Pap. 4, pp. 283-292. Mackey, H.E., Jr., Riley, R.S., 1994. Mapping of flood patterns in a 10,000-acre southeastern river swamp with SPOT HRV data (abstract). ASPRS/ACSM Annual Convention and Exposition, Tech. Pap. 1, p. 375. Merot, P., Crave, A., Gascuel-Odoux, C., Louhala, S., 1994. Effect of saturated ~u~eaon backscattering coefficient of the ERS 1 synthetic aperture radar: first results. Water Resour. Res. 30 (2), 175-179. Moore, I.D., Grayson, P,.B., Ladson, A.R., 1991. Digital terrain modeling: a review of hydrological, geomorphological, and biological applications. Hydrol. Process. 5, 3-30. Muller, E., Decamps, El., Dobson, M.K., 1993. Contribution of space remote sensing to fiver studies. Freshwater Biol. 29, 301-312. Nemani, R., Pierce, L., Running, S., Band, L., 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed leaf area index estimates. Int. J. Remote Sensing 14 (13), 2519-2534. Ormsby, J.B., Blanchar¢l, B.J., 1985. Detection of lowland flooding using active microwave systems. Photogramm. Eng. Remote Sensing 51 (3)~ 317-328. Pearlstine, L., McKellai, H., Kitchens, W., 1985. Modelling the impacts of a fiver diversion on bottomiand forest communities in the Santee River floodplain, South Carolina. Ecol. Modelling 29, 283-302• Phillips, J.D., 1990. A saturation-based model of relative wetness for wetland identification. Water Resour. Bull. 26, 333-342. Pietroniro, A., Soulis, F,.D., Kouwen, N., Rotunno, O., Mullins, D.W., 1993. Extracting distributed soil moisture information from C-band wide swath SAR imagery. Can. J. Remote Sensing 19 (1), 77-132. Place, J.L., 1985. Mapping of forested wetland: use of Seasat

311

radar images to complement conventional sources. Prof. Geogr. 37 (4), 463-469. Poiani, K.A., Johnson, W.C., 1993. A spatial simulation model of hydrology and vegetation dynamics in semi-permanent prairie wetlands. Ecol. Appl. 3 (2), 279-293. Pope, K.O., Rey-Benayas, J.M., Paris, J.F., 1994. Radar remote sensing of forest and wetland ecosystems in the Central American tropics. Remote Sensing Environ. 48, 205-219. Price, M., 1986. The analysis of vegetation change by remote sensing. Progr. Phys. Geogr. 29, 261-281. Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y.H., Sorooshian, S., 1994. A modified soil adjusted vegetation index. Remote Sensing Environ. 48, 119-126. Richards, J.A., Woodgate, P.W., Skidmore, A.K., 1987. An explanation of enhanced radar backscatter from flooded forests. Int. J. Remote Sensing 8 (7), 1093-1100. Rignot, E.J,M., van Zyl, J., 1993. Change detection techniques for ERS-1 SAR data. 1EEE Trans. Geosci. Remote Sensing 31 (4), 896-906. Sharitz, R.R., Mitsch, W.J., 1993. Southern hardwood forests. In: Martin, W.H., Boyce, S.G., Echtemacht, A.C. (Eds.), Biodiversity of the Southeastern United States: Lowland Terrestrial Communities. Wiley, New York, pp. 311-372. Sippel, S.J., Hamilton, S.K., Melack, J.M., Choudhury, B.J., 1994. Determination of inundation area in the Amazon River floodplain using the SMMR 37 GHz polarization difference. Remote Sensing Environ. 48, 70-76. Sklar, F.H., Costanza, R., Day, Jr., J.W., 1985. Dynamic spatial simulation modeling of coastal wetland habitat succession. Ecol. Modelling, 29: 261-281. Townsend, P.A., Butler, D.R., 1996. Patterns of landscape use by beaver on the lower Roanoke River floodplain, North Carolina • Phys. Geogr. 17 (3), 253-269. Townsend, P.A., Walsh, S.J., 1996. Estimation of soil parameters for assessing potential wetness: comparison of model responses through GIS. Earth Surf. Process. Landforms 21, 307-326. Townsend, P.A., Walsh, S.J., 1997. Landcover of the Lower Roanoke River floodplain of North Carolina. In: Walsh, S.J., Butler, D.R., Kourad, C.E., Peet, R.K. (Eds.), The Roanoke River Bioreserve: A Preliminary Assessment of the Impact of Flow Modification on Hydrology, Geomorphological Processes, and Vegetation. The Nature Conservancy (in press). Townsend, P.A., Walsh, S.J., Butler, D.R., 1995. Beaver pond identification through a satellite-based ecological habitat classification. ASPRS-ACSM Annual Convention and Exposition, Charlotte, NC, Tech• Pap. 2, pp. 102-111. Walsh, S.J., Townsend, P.A., 1995. Comparison of change detection approaches for assessing a riverine flood hydroperiod. ASPRS-ACSM Annual Convention and Exposition, Charlotte, NC, Tech. Pap. 2, pp. 134-143. Wang, Y., Kasischke, E.S., Melack, J.M., Davis, F.W., Christensen, N.L. Jr., 1994. The effects of changes in loblolly pine biomass and soil moisture on ERS-1 backscatter. Remote Sensing Environ. 49, 25-31. Wang, Y., Hess, L.L., Melack, J.M., 1995. Understanding the

312

P.A. Townsend, S.J. Walsh / Geomorphology 21 (1998) 295-312

radar backscattering from flooded and nonflooded Amazonian forests: results from canopy backscatter modeling. Remote Sensing Environ. 54 (3), 324-332. Wharton, C.H., Kitchens, W.M., Pendleton, E.C., Sipe, T.W., 1982. The Ecology of Bottomland Hardwood Swamps of the Southeast: A Community Profile. U.S. Fish and Wildlife Service, Washington D.C., Biological Services Program, FWS/OBS-81/37, 133 pp.

Wolock, D.M., McCabe, G.J. Jr., 1995. Comparison of single and multiple flow direction algorithms for computing topographic parameters in TOPMODEL. Water Resour. Res. 31, 13151324. Yamagata, Y., Akiyama, T., 1988. Flood damage analysis using multitemporal Landsat Thematic Mapper data. Int. J. Remote Sensing 9, 503-514.