I
Monitoring Wetland Ditch Water Levels Using Landsat TM and Ground-Based Measurements D.H.A. Al-Khudhairy, C. Leemhuis, V. Hofhnann, I.M. Shepherd, R. Calaon, J.R. Thompson, H. Gavin, D.L. Gasca-Tucker, Q. Zalldls, G. Bilas, and D. Papadlmos
Abstract A methodology which makes use of Landsat Thematic Mapper
data to indirectly provide remotely sensed observations of water levels within channels and ditches in wetlands is presented. Using multi-temporal Landsat TM imagery and simultaneous ground-based measurements of water levels, statistical relationships are established between satellitederived effective wet-ditch widths and measured water levels in the drainage systems of three European wetlands. These relationships can thereafter be used to estimate historical ditch water levels and to monitor contemporary ditch water levels in the wetlands. The study shows that satellite imagery has much to offer in providing a historical perspective of wetland hydrology that otherwise would not be available, in monitoring changes in the hydrological regime of wetlands, and in providing complimentary approaches to field monitoring. (TM)
Introduction Wetlands have only infrequently been included within routine hydrological monitoring programs, and thus the availability of historical data, such as water levels, is characteristically limited (Hollisand Thompson, 1998).This represents a serious restriction to the establishment of baseline conditions against which current and future wetland hydrological conditions can be compared and assessed. It also limits the availability of input and calibration data for hydrological models (e.g.,Al-Khudhairy et al., 1999),which have evolved in the last few decades from addressing river basin management issues to continental water processes and global water balances (Engman, 1996). Satellite remote sensing has great potential for addressing some ofthe deficiencies of limited wetland hydrological data. There are numerous existing and soon-to-be launched spaceborne satellites operating in the visible and near-infra red range of the electromagneticspectrum as well as active and passive microwave sensors that are sensitive to open water surfaces. These sensors have significant potential for providing invaluable data for validating hydrological models, and for establishing recent historical (in the last two decades) and contemporary wetland hydrological conditions.
D.H.A. Al-Khudhairy,C. Leemhuis, V. Hoffman, I.M. Shepherd, and R. Calaon are with the Joint Research Centre, Cornmission of the European Communities, Ispra 21020 (VA),Italy
(
[email protected]). J.R. Thompson, H. Gavin, and D.L. Gasca-Tucker are with the Wetland Research Unit, Department of Geography, University College London, 26 Bedford Way, London, WCIH OAP, UK. G. Zalidis and G. Bilas are with the Laboratory of Applied Soil Science, School of Agriculture, Aristotle University of Thessaloniki, 54006 Greece. D. Papadimos is with the Greek BiotopeIWetland Centre, 14th Thessaloniki-Mihaniona, GR-570, Thermi, Greece. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
Through the development of non-linear relationships between satellite derived water surface area and ground measurements of river discharge, Smith et al. (1995; 1996) have proved that multi-temporal synthetic aperture radar (SAR) data from active microwave sensors are useful in estimating stream flow in three braided glacial rivers in Alaska and British Columbia, Canada. More recently, Sippel et al. (1998)used a linear mixing model, based on three end-members, that incorporates the contributions of water, non-flooded land, and inundated floodplain with passive microwave data to estimate temporal changes in flooded areas in order to derive predictive floodedarealriver-stagerelationships in large river floodplains. Space-borneradar altimetry has also been used to directly determine stage variations in large lakes (Birkett, 1995) and large rivers such as those in the Amazon Basin. Koblinsky et al. (1993) used the Geostat altimeter to estimate large river-stage change within an error of approximately 0.19 m to 1.20 m. Birkett (1998) used the TOPEXIPOSEIDONNASA radar altimeter to monitor water-level changes in large rivers with an RMS accuracy ranging from 0.11 m to 0.60 m. Unfortunately,because altimetry is a profiling and not an imaging technique, it is applicable only to water bodies greater than about a kilometer in . - - tho width. More recently, Alsdorf et al. (2000; 2001) improved resolution of this type of remotely sensed measurements using a different approach that is based on interferometric L-HH band SAR SIR-C radar data to provide centimeter-scale stage variations across 150-mto 2.75-km wide floodplain lakes and tributaries containing emergent vegetation within an accuracy that is generally on the order of k0.01 m. Landsat Multispectral Scanner (MSS) imagery has been used by Usachev (1983) and Xia et al. (1983)to develop positive relationships between satellite-derived estimates of inundated area and ground-based measurements of stage and discharge. Two shortcomings of Landsat MSS imagery compared to Landsat TM are the coarser spatial resolution (80-mpixels compared to a Landsat TM pixel of 30 m) and the smaller number of spectral bands. Landsat TM imagery, with its seven spectral bands covering the visible, near-infrared, mid-infrared, and thermal infrared of the electromagnetic spectrum, provides extra information in the mid-infrared and thermal infrared bands compared to Landsat MSS data which have only four bands. This multispectral nature of sensors such as Landsat TM provides an additional advantage over radar imagery. Of the seven spectral bands provided by Landsat TM, three (bands 4,5, and 7, which have, respectively, wavelengths equal to 0.76 to 0.90
Photograrnmetric Engineering & Remote Sensing VO~. 68, NO. 8, August 2002, pp. 809-818. 0099-lll2/02/6808-809$3.00/0
6 2002 American Society for Photogrammetry
and Remote Sensing August 2002
pm, 1.55 to 1.75 pm, and 2.08 to 2.35 pm) are particularly sensitive to the presence of water. However, with regard to remote sensing of water using Landsat TM data, most of the published studies are related to mapping the extent and frequency of river inundation (see Pope et al., 1992). Techniques using space-borne S A R imagery offer a major advantage over visible and near-infrared sensors for applications in hydrology due to their all-weather and daylnight capabilities. However, these approaches still suffer from two main drawbacks when compared with those based on visible and near-infrared sensors, such as Landsat TM.First, the presence of wind-induced waves or emergent vegetation can roughen the surface of open water bodies, making them difficult to discriminate from other non-flooded land surface types when using single frequency and polarization S A R data (Smith, 1997). More importantly, whereas the archives of visible and near-infrared sensors are almost 20 years long, most radar archives (i.e., Radarsat-1)capable of yielding information for channels narrower than 10 m, which characterise many wetlands, only date back as far as 1996. Shepherd et al. (2000) were among the first to exploit the use of the partial pixel approach with Landsat TM imagery and the digitized ditch positions to measure surface water area. Shepherd et al. (2000)tested their approach on part of the North Kent Marshes in southeast England and found a positive relationship between multi-temporal values of a ditch index (the proportion of an image pixel that is covered by water) and intermittent ground-based measurements of ditch water levels. The partial pixel approach employed by Shepherd et al. (2000)has been advanced as part of a three-year-longEuropean Union funded project, System for HYdrology using Land Observation for model Calibration (SHYLOG). The software developed as part of SHYLOG (Al-Khudhairy et al., 2001a) uses the ditch index to estimate the total water surface area and thereafter divides satellite-derived estimates of water surface areas by satellite-derived ditch lengths to evaluate effective wet-ditch widths (Smith et al., 1995; Smith eta]., 1996).This can be undertaken for various spatial scales ranging from short reaches of a ditch network to an entire ditch system. This paper presents the results of applying the SHYLOC softwareto three European wetlands, two of which are examples of English lowland wet grasslands while the third is a Greek wetland that has been heavily impacted by drainage for agriculture. It is worthwhile noting that the satellite-derived wet-ditch width is an effective value due to the spatial resolution (30 m) of the Landsat TM images used in this study. In other words, we are not estimating actual water surface area, ditch length, nor wet-ditch width from Landsat imagery. Instead, we develop predictive statistical relationships between satellite-derived data and groundbased measurements of ditch water level, which can be used to reconstruct historical water levels for drainage systems in wetlands using satellite-derived information alone. This implies that a short time series of observed ditch water levels could be extended by as much as 20 years in the case of Landsat-derived information (Al-Khudhairyet al., 2001b; Al-Khudhairy et al., 2001~).
The SHYLOC Methodology The General Appmach
The application of the SHYLOG software to the three wetland test sites required the acquisition of a time series of ditch water levels. These were obtained from records of existing instrumentation or from instruments installed within the sites as part of this study. The duration of the time series used in this paper varied between sites according to the availability of satellite imagery coinciding with dates of ground-based observations. 810
August
2002
Thereafter, the calibration phase consisted of developing statistical relationships between satellite-derived effective wetditch widths, calculated using the SHYLOG software (Al-Khudhairy et a].,2001a), and ditch water levels acquired from the field instrumentation. Principles of the SHYLOC Software
The SHYLOG method is based on the linear spectral unmixed model (see, also, Shepherd et al. (2000)).In this type of model, the reflectance, Ri,of a mixed pixel i (automatically identified by the SHYLOC software as a pixel that coincides with digitized ditch or stream positions) in a single band is the areally weighted linear sum of the pure reflectance, rj,of the number n of pure land-cover types occupying area ai, and that each of these land-cover types is linearly independent of each other: i.e., j= n
rjaj.
Ri= j= 1
The SHYLOG method assumes that a mixed pixel in the wetland environments studied herein comprises two components: ditch water and homogeneous land cover, which could be bare soil, grassland, or cultivated land (see Figure 1).In other words, SHYLOC uses a linear unmixed model with two endmembers as follows:
where a;,and (YLare the fractional areas of pure ditch water and land, respectively, and rwand rLare their reflectance values. The SHYLOG ditch index a;.of any mixed pixel i is derived by combining Equations 2 and 3: i.e., (Y.
=
Ri- r~ rw - r~
Knowing the values of Ri ,r,, and rL, the calculated ai(the ditch index) can be used to obtain estimates of the fractional area of
Figure 1. The shaded areas referto nonditchcarrying image pixels, whereas the white pixels crossed by ditches (continuous thick line) are referred to as ditchcarrying pixels. R, is the digital number or reflectance of the mixed ditchcarrying image pixel, which falls between two extremes in the nearand mid-infrared bands. The first extreme is due to the relatively high reflectance (r,) of the land cover surrounding the ditch, and the second one is due to the low reflectance of the ditch. The SHYLOC software estimates the proportion of water and non-water features creating this mixed reflectance,and converts them into appropriate surface areas.
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
ditch surface water in a mixed pixel. In SHYLOC, although the reflectance of the pure water component, r,, in Equation 4 is constant in a single band, it varies from one spectral band to another and with time as well, r, is determined by averaging the digital numbers of the reference freshwater body (relatively large rivers and reservoirs)pixels in each study site. On the other hand, the reflectance of the pure reference land component (automatically identified by the SHYLOC software as a non-ditch-carrying pixel that is not crossed by digitized ditch or stream positions), rL,is allowed to be variable or constant in each spectral band, and variable from one spectral band to another and over time. The SHYLOC technique offers eight methods to automatically determine the value of r~in Equation 4. The methods allow the calculation of rLusing either a moving average approach or a static one. In the moving average approach, rL(i3is a moving average of land digital numbers calculated over a template of 3 by 3 or 5 by 5 pixels around each ditch-carryingpixel i:i.e.,
xy
ri,j rL(i) = M
where ri,.is the digital number of the pure land pixel j contained in the moving window template around the ditch-carrying pixel i; Mindicates the total number of pure land pixels in the moving window template. In the static methods, rLis determined, depending on the particular method selected, using either the maximum or mean digital number of all land pixels in a user-defined control boundary, or the average or maximum digital number of all land pixels within a distance of one or two pixels that lie straight or diagonally to the ditch-carrying pixels under consideration in a user-defined control boundary (AlKhudhairy et al., 2001a). Ditch-carrying pixels are obviously excluded from both moving average and static land reference calculations. The SHYLOC software thereafter calculates the total ditch length by summing the length of all ditch segments that are covered by ditch-carrying pixels in a given control boundary (Hoffmannet al., 2001), thereby allowing the satellite-derived effectivewet-ditch width (Smith et al., 1995; Smith et al., 1996) to be estimated by dividing the satellite-derived ditch water surface area by the satellite-derived ditch length.
Study Areas and Ground-Based Observations The Elmley Marshes
The Elmley Marshes are located on the southern side of the Isle of Sheppey in southeast England. They have a complex drainage network that comprises fleets, runnels, and ditches inherited from pre-enclosure salt marshes but which have been extensively modified by human activities (Gavin,2000). The ditches drain by gravity through tidal sluices in the sea walls that allow water to be evacuated at low tide into the Swale, a tidal channel that separates the Isle of Sheppey from the mainland. The grazing marshes are managed by the Elmley Conservation Trust for low intensity grazing of cattle and sheep and in a way syrnpathetic to nature conservation (Willock, 1993; Gavin, 2000). Management includes the maintenance of high water in winter and early spring in order to create areas of surface water on specified areas of the marshes (Harpley, 1999).The ditches act as wet fences and provide drinking water for stock and wildlife (Harpley, 1999). Observations of ditch water levels within the Elmley Marshes were obtained from a network of instruments, comprising stage boards and automatic water level recorders (AWLR) installed as part of the SHYLOC project. Stage board observations were undertaken at 2- to %week intervals while the AWLRs were programmed to record hourly observations. The instruments were surveyed, enabling water levels to be expressed as 'HOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
meters above Ordnance Datum (m OD; Gavin, 2000). Surveys of the ditch cross sections show that their widths and maximum depths vary between sites (Figure 2a). The relationships between observed ditch elevation and wet-ditch width for three sites are shown in Figure 2b. It is clear that an increase in water level will lead to an increase in wet-ditch width. Pevensey Levels
The Pevensey Levels in East Sussex, southeast England comprise lowland grazing marshes that are highly managed with many of the ditch water levels controlled at a local scale through sluice structures on the gravity drained ditches (Gasca-Tucker,in prep.). On a larger scale, eight pump drainage schemes control ditch water levels to ensure that there is sufficient water in the summer to provide drinking water and wet fencing for the cattle that graze the marshes. In winter, the pumps only come into operation when there is a risk of flooding. Management of the Pevensey Levels aims to integrate the requirements of flood defense, agriculture, and conservation. In the gravity-drained ditches within the Pevensey Levels, daily records of ditch water level were available from Environment Agency (EA) stage boards. These were supplemented by hourly observations from AWLRs. Water level observations for pumped ditches were obtained from EA pumping station records. The surveying of all instrumentation enabled ditch water levels to be expressed as m OD. Figure 3a shows the cross sections of some of the ditches for which water level observations are available. It demonstrates that a range of widths and maximum depths characterize the ditch network. The relationships between ditch water levels and wet widths for these cross sections are shown in Figure 3b. It clearly shows that increasing wet-ditch widths are associated with higher water levels. Former Lake Karla
Lake Karla, situated in east-central Greece, was a former internationally important wetland that was progressively drained from the 1930s through the 1960s for agricultural purposes (Gerakis, 1992).This has resulted in numerous water, soil, ecological, and social problems, which were a direct result of the loss of the wetland's invaluable functions. A complex network of drainage and irrigation channels now characterizes the area. In winter and spring, water pumped from the Pinios River is diverted to the drainaee " network. and is thereafter u u m ~ e d from the network into several small reservoirs to store water for the irrigation period lasting from May until August. During these periods the drainage network is used for both drainage and irrigation. The remaining irrigation demands are provided by water abstracted from private and governmental abstraction wells. The area now experiences deficits in the supply of irrigation water due to the depletion of the underlying aquifers. Several restoration schemes are currently underway to reduce excessive groundwater abstractions and restore areas of open water. Water levels in the drainage and irrigation ditches were monitored at approximately 3-week intervals using a network of stage boards surveyed to provide observations as meters above sea level (m asl). Figure 4a shows that, as in the English sites, the widths and maximum depths of the ditches also vary throughout the Karla site. Similarly, there is a strong positive relationship between water levels in the ditches and the corresponding wet-ditch widths (Figure4b).
.
L
Image PrePreprocessing and Characteristics A total of 25 cloud-free multispectral Landsat TM images were acquired for this study. Nine images from between April 1995 and June 1999 were acquired for the Pevensey Levels, while eight images were obtained for the Elmley Marshes, and for the former Lake Karla. The images for the former site covered the period February 1998 through July 1999 while the dates of August 2002
2.0 1.8 1.6 1.4
5
1.2
-;
g 1.0 .- 0.8
LU
0.6 0.4 0.2 0.0 0
1
2
3
4 5 Wdth (rn)
7
6
9
8
0.0
(a)
0.5
1.5
1.0
2.0
Elevation (m OD)
(b)
Figure 2. (a) Channel cross sections at two stage boards (e and f) and an automatic water level recorder (AWLR) (h) in the Elmley Marshes, England. (b) Observed wet-ditch width-ditch water elevation relationships at cross-sections e, f , and h in the Elmley Marshes, England.
those images acquired for the former Lake Karla were between April 1998 and June 1999. The relatively small number of images for each wetland site was a result of the limited availability of cloud-free scenes during the period coinciding with the monitoring campaign and the availability of water level data. All the images underwent geometric and solar elevation angle corrections using the ENVI software package (Research Systems, 1999).The images were geometrically corrected using a simple rectification model such as the resampling, scaling, and translation model (Mather, 1999).This technique is suitable for relatively flat areas such as the Elmley Marshes, Pevensey Levels, and the plains of former Lake Karla. The least-squares method was used to transform image coordinates to map coordinates. Thirteen ground control points were used to calculate the
transformation between image and map coordinates for each site. The multi-temporal set of Landsat TM images was not corrected for atmospheric scattering for two reasons. First, atmospheric path radiance is significantly reduced in near- to midinfrared bands such as Landsat TM bands 4,5, and 7. Second, the ditch index, which forms the basis of the partial pixel method, expresses the difference between the digital numbers (see Figure 1)from the same band and the same image (ShepTherefore, atmosherd et al, 2000; Al-Khudhairy et 01,2001~). pheric correction cancels out and does not need to be considered. A sun elevation angle correction was applied to account for the variation in solar elevation angle from one image to another for each study site by normalizing the images to a constant sun angle of 90 degrees.
Barnhorn A
Gravity Field 2
o Horsebrldge +
Newbrrdge
-
Barnhorn
4Gravity 2
. --+- ---l_-.-.-
3
-
4
5 Width (m)
(a)
6
---
Horsebrldge Nowbridge
7
0 -1.0
-0.5
0.0
0.5
1.0
1.5
2.0
25
Elevation (m OD)
(b)
Figure 3. (a) Channel cross sections at the water level recording instrumentation used in the Pevensey Levels study site in England. (b) Observed wetditch width-ditch water elevation relationships at four cross sections in Pevensey Levels, England.
812
August 2002
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
57
54
--Cross
sectlon 10
+Cross -Cross
section 28
20 -
18
sectlon 30
-CmsEectlon -Crosssectlon -Cros;sctlon -Cros;~ect~on
-
16 -
51 14
3 VI E
-=; .m
11 18
26 30
-
- 12 -
4s
E
j 1 0 -
L -
*
W
8-
45
6
-
/
4-
42
2
38
-
0, 00
30
60
9D 120 W~dth(rn)
150
180
21 0
(a)
390
410
430
450
470 490 510 Eevatlon (m ad)
530
550
570
(b)
Figure 4. (a) Channel cross sections at the stage boards used in the former Lake Karla site in Greece. (b) Observed wet-ditch width-ditch water elevation relationships at four cross sections in the former Lake Karla, Greece.
Landsat TM spectral bands 4 , 5 , and 7 are known to be particularly sensitive to the presence of water bodies (Engmanand Gurney, 1991).Water has a relatively low reflectance, especially in the near-infrared (NR) to mid-infrared (MIR) portion of the electromagnetic spectrum. Vegetation, and particularly green grass, has relatively high reflectance for Landsat TM band 4 in comparison to bands 5 and 7. Soil also has a relatively high reflectance for Landsat TM band 4, although it is not as high as that of vegetation,while its reflectance for bands 5 and 7 is even higher. These spectral properties imply that the mixed-pixel approach that forms the basis of the SHYLOCsoftware will work particularly well using spectral band 4 in cases where water features are surrounded by vegetation, such as the grasslands of the Elmley Marshes and Pevensey Levels. They also imply that the SHYLOCmixed-pixel approach will work well using bands 5 and 7, in cases such as the former Lake Karla, where the ditches are predominantly surrounded by bare soil or other features such as gravel tracks which run alongside many of the ditches at this site.
Results and Discussion Using spectral bands 4 , 5 , and 7 of the Landsat TM images, the SHI'LOC software was applied to control boundaries defined around sites where water level recording instruments are located in the three study sites (Plates l a , lb, and lc). The width of the control boundary can influence the values, rL,of the reference pure land pixels in Equations 4 and 5 depending on the choice of the reference land calculation methods described earlier and if the land-cover type in the corresponding reference land pixels differs notably from the non ditch water component of the ditch-carrying pixels under consideration. The length of the control boundary can also influence rLif the land-cover changes significantly alongside the ditch section's length (Figure 5). Thus, the length of the control boundary serves to delineate the ditch section that should be taken into consideration in a SHYLOC calculation, whereas the width of the control boundary becomes important if we apply the land reference method "maximum or average digital number of reference land pixels within a defined control boundary." In general, the control boundary is selected to cover an entire ditch section or part PHOTOGRAMMETRICENGINEERING & REMOTE SENSING
of it (see Plate la) and would have a width in the range of 4 to 5 image pixels, including the ditch-carrying pixel. However, it is recommended that a priori sensitivity analysis should be carried out on the size of the control boundary and the type of reference land calculation method. The digitized ditch positions for the two English sites were acquired from the Ordnance Survey in England, whereas those for the Greek site were produced from 80 1:5000-scale orthophoto maps, which were acquired from the Greek Ministry of Agriculture. Because observations from the stage boards were only available at 2- to 8-week intervals, a linear interpolation between observations was used to evaluate water level on the day of each image acquisition. In the case of the automatic water level recorders, the mean daily water level on the day of image acquisition was derived from the hourly observations. On the other hand, the records for the pumping stations in the Pevensey Levels were used to determine the water level at the time of acquisition of each satellite image. Data from these pumped ditches were not used during periods of pumping due to the considerable fluctuations in water levels over a very short period which occurred at these times. When the pumps are not in operation, the ditch water levels are relatively stable and fluctuate in response to the prevailing climatic conditions. The strengths of the best-fit satellite-derived statistical relationships (a polynomial fit in the case of the English sites and a linear fit for the Greek site; both types of fits are in agreement with the best-fit relationships derived between observed wetditch width and ditch water levels) developed between satellite-derived effective wet-ditch width and water levels observed fiom field instrumentation are summarized in Table 1. A summary of the results of applying three different types of methods (moving 5 by 5 window, maximum digital number of all land pixels within the distance of one pixel that lie straight or diagonally to the ditch-carrying pixels, and maximum digital number in a defined control boundary) to calculate the reference land value in Equations 4 and 5 is also presented in Table 1.The table shows that the static "maximum digital number of all land pixels within a distance of one pixel that lie straight or diagonally to the ditch carrying pixels" reference land method August
ZOO2
813
2 L P c ma L1 C
* N
0 N 0
9
0
d Ex a
5
Z
P
6 E z
o Po
ES
rn
%
g C)
Plate 1. (a) Ditches (reproduced by kind permission of Ordnance Survey O Crown Copyright AL100028987) overlaid on spectral band 4 of Landsat-5 TM image of the Elmley Marshes, England (O ESA distributed by Eurimage, 1998). Sites e, f and h in the ditch network are where stage boards (e and f) and an automatic water level recorder (~WLR)(siteh) are installed. Rectangular boundaries correspond to control regions used in the SHYLoc analyses. (b) Ditches (reproduced by kind permission of Ordnance Survey O Crown Copyright AL100028987) overlaid on spectral band 4 of Landsat-5 TM image of the Pevensey Levels, England (O ESA distributed by Eurimage, 1998). Yellow squares indicate the location of ten ditch water-level recording instruments. (c) Irrigation and drainage ditches overlaid on spectral band 4 Landsat-5 TM image of the former Lark Karla, Greece (O ESA distributed by Eurimage, 1998). Full yellow circles indicate the location of 1 2 stage boards.
Figure 5. A schematic description of the complexity of the land cover surrounding the ditches in the former Lake Karla, Greece in Landsat TM ditchcarrying pixels.
provides, for the majority of the ditch sections, the strongest correlation for the best-fit relationships between satellite-derived effectivewet ditch and measured ditch water levels. In the sensitivity analysis, the width of the user-defined control boundary was selected to be greater than five image pixels. Note that when the static "maximum digital number of all land pixels within a distance of one pixel that lie straight or diagonally to the ditch carrying pixels" reference land method is applied,
the width of the control boundary is by default limited to a distance of one image pixel lying either side from the ditch-carrying pixel plus the ditch-carrying pixel itself. Table 1shows that band 4 provides the strongest satellitederived statistical relationships for the two English sites, whereas bands 5 and 7 provide the strongest fits in the case of the Greek site, with the exception of cross-section 18. The relatively poorer statistical relationships derived for the two English sites using bands 5 and 7 confirm that Landsat TM band 4 is the most appropriate when applying the SHYLOC mixed-pixel approach to ditches that are surrounded by grassland vegetation (Al-Khudhairyet al., Zooid), which has a relatively high reflectance in the NIR (band 4), whereas the reflectance from the water in the ditches is low. It is these two extremes in reflectance properties that explain the success of using Landsat TM band 4 within the SHYLOC approach in the English grazing marshes. On the other hand, in the case of many of the ditches analyzed in the former Lake Karla, the relatively weak statistical relationships developed using band 4 could be due to the reduced dominance of vegetation in the Landsat TM ditch-carrying pixels and the increased complexity of the land cover immediately surrounding the ditches (i.e., within the ditch-carrying image pixel) analyzed in this study (Al-Khudhairy et al., 2001d). This typically comprises a mixture of gravel tracks r u ~ i n alongside g both sides of the ditches, a bare soil or grass interface depending on the season, and cultivated crops (Figure 5). The best-fit satellite-derived statistical relationships between satellite-derived effective wet ditch width and ditch water levels observed from field instrumentation are illustrated in Figure 6. These results together with those summarized in Table 1also provide an indication that the partial pixel method may be approaching its limit of application for deriving wetditch widths for relatively narrow ditches (i.e., those where actual wet widths occupy less than 10 percent of an image pixel, e.g., Gravity Field 2 in Pevensey Levels and cross-section 11in former Lake Karla) using the 30- by 30-m pixels of Landsat TM imagery. The poor statistical fits observed for Gravity Field 2 and cross-section 11can also be attributed to the vegetation mats covering their surface water. Our results show that the strength of the relationship between satellite-derived wet-ditch width and observed water level is influenced by the shape, in particular, the width of the ditch cross sections. For example, Table 1shows that within the Elmley Marshes the relationship derived for site h was stronger (R2= 0.90) compared to the relationship for sites e and f (RZ= 0.71 and 0.68, respectively). The range of actual wet
OF CORRELATION (R2) BETWEEN GROUND MEASUREMENTSOF DITCHWATER LEVELSAND SATELLITE-DERIVED EFFECTIVE WET-DITCH WIDTHS TABLE 1. SUMMARY CALCULATED IN ~ N D S A TBANDS 4,5,AND 7 USING THREE DIFFERENT TYPES OF REFERENCELANDCALCULATION METHODS. DN DENOTESDIGITALNUMBER. THENUMBERS IN BOLD CORRESPOND TO THE BESTFITRELATIONSHIPSILLUSTRATED IN FIGURES 6a, 6b,AND 6~
Mean DN in moving 5 by 5 window Landsat band Site Elmley Marshes Elmley Marshes Elmley Marshes Pevensey Levels Pevensey Levels Pevensey Levels Pevensey Levels Former Lake Karla Former Lake Karla Former Lake Karla Former Lake Karla
Location
4
Stage board at e Stage board at f AWLR at h 0.17 Barnhorn 0.82 Newbridge 0.6 Gravity Field 2 No fit Horse Bridge 0.5 Cross-section 11 0.29 Cross-section 18 0.84 Cross-section 26 No fit Cross-section 30 -
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
5
No fit No fit 0.4
No No No No No
fit fit fit fit fit
Maximum DN in one pixel Landsat band
7
4
-
0.71 0.68 0.9 0.57 0.8 0.5 0.51
No fit No fit No fit No fit No fit No fit 0.58
0.46 0.44
No fit
No fit
-
0.18
Maximum DN inside control boundary ~~d~~~ band
5
7
4
0.47
No fit No fit No fit No fit No fit No fit No fit
-
No fit 0.75 0.16 0.4 0.5
No fit No fit No fit 0.72 0.66
7
0.77
No fit
-
No fit
0.58
0.42 0.4 0.1
0.62 0.78
0.36
0.5
5
-
No fit No fit
No fit No fit
No fit NO fit No fit NO fit NO fit 0.5 No fit NO fit 0.51
0.65
Observed wet-ditch width range on dates of image acquisition (m) 4.0-5.1 3.4-4.5 6.2-8.0 5.0-5.5 4.4-5.9 1.7-2.9 6.5-7.6 2.2-2.8 4.0-8.0 13.8-14.3 10.0-12.0
August 2002
815
20 -
o S~tee
15-
f f
C
=
P
3 01
+.t
m
15-
B
4p
lo-
0
Z
x
G I 3
= ti m
2
Barnhom o Horse Bridge + New Bridge A O w & Field 2
6
+S~teh
5
i
-
a S ~ t ef
-E
5B
25
5 -
to
-
/
aD
5-
A
0
0
1
10
12
14 16 18 Ground-measurements of ditch water levels (m OD)
1
0
20
0.5
1
1.5
2
Wound-based measurementsof ditch water levels (m OD)
(a)
(b) 45 +Cross section 30
40
o Cross section 26
E 35 a f
A
Cross section 18
+ Cross section 11
g
Figure 6. (a) Best fit relationship (polynomial) between satellite derived effective wetditch width and corrected observations of water level from stage boards at sites e and f and an AWLR at h in the Elmley Marshes, England. (b) Best fit relationship (polynomial) between satellitederived effective wet-ditch width and corrected observations of water level from pumping station records at sites Horse Bridge, New Bridge, and Barnhorn in the Pevensey Levels, England. (c) Best fit relationship (linear) between satellitederived effective wet-ditch width and corrected observations of water level from stage boards at crosssections 18, 26, and 30 in the former Lake Karla, Greece.
Z 25
4
20
$
e -d
lo
V
5
E3 15
00
0
390
41.0
43.0
45.0
47.0
49.0
51.0
530
Ground-messurements of ditch water levels (m asl)
(c)
55.0
57.0
2.5
ditch widths at site h was 6.2 m to 8.0 m compared with only 3.4 m to 4.5 m and 4.0 to 5.1 m, respectively at sites f and e. In addition, the complexity and type of the land cover (i.e., twocomponent-ditch water and grassland-image pixels encountered in the English sites compared to the three or more component-ditch water, gravel track, and cultivated cropimage pixels at the Greek site) surrounding the ditch determines the most appropriate band to use when applying the SHYLOC partial-pixel approach to Landsat TM data.
Conclusions The results of applying the ~HYLOCmixed-pixel approach to three European wetlands show that, when using Landsat TM band 4 in ditches that are predominantly surroundedby grassland (such as in the two English sites), meaningful statistical relationships can be developed between satellite-derived effective wet-ditch widths and ground-based measurements of ditch water levels. The strength of these relationships is influenced by the shape, in particular, the width of the ditch cross sections. However, as soon as the complexity of the land cover surrounding the ditches in a Landsat TM ditch-carrying pixel increases to include, for example, bare soil, 5- to 10-m-wide gravel tracks, and cultivated cropland, as encountered in the Greek test site, Landsat bands 5 and 7 provide relatively stronger statistical fits than does band 4. The most important conclusion is that the SHYLOCmixed partial-pixel method works extremely well when applied to two-componentLandsat TM ditch carrying pixels (i.e., comprising only water and a homogeneous non-water feature such as grassland or bare soil). In the case of relatively complex Landsat TM ditch-carryingpixels (i.e.,pixels comprising, in addition to water, two or more other non-water features), it may be worthwhile applying satellite imagery of finer spatial resolution (e.g., SPOT'S 20-m NIR and MIR bands, Landsat Enhanced Thematic Mapper's panchromatic 15-m channel, or Radarsat's fine beam mode of 10-mresolution which can also resolve the problem of ditches with dense vegetation mat cover).However, the main disadvantage of these sensors is their relatively short archive of satellite imagery in comparison to that of Landsat TM. Once statistical, predictive, relationships between satellite-derived effective ditch widths and ditch water levels have been established, the s m o c software could be used with archives of historical satellite data (almost 20 years in the case of Landsat TM) to provide historical water level data for the wetIand streams and ditches. This is an important development because it shows that remote sensing can contribute to important elements of wetland hydrological science and restoration programs such as the provision of (I) calibration data, in this case stream or ditch water level, for hydrological models and (2) a historical perspective of the hydrology of a wetland that would otherwise not have been available. Future work should seek to apply the sHYLoC software to different wetland environments in order to compare the strength and shape of the relationships between satellitederived effective wet widths and observed ditch water levels. This will indicate the potential of transferring these satellitederived predictive relationships from gauged to ungauged wetland ditch systems of similar morphology.
Acknowledgments The SHYLOC Project was partly funded by the European Commission under its Space Technology Programme under the Fourth Framework Programme. The authors dedicate the results of this study to their late friend and colleague, Dr. G. E. (Ted)Hollis, who inspired the conception of the sHYLoC project. The authors wish to acknowledgethe support of the Elmley Conservation Trust. Alison Berry, Emma Durham, and Alastair Graham provided fieldwork assistance. The authors are also grateful to the other partners of this project (DHI Water & PHOTOGRAMMETfUC ENGINEERING & REMOTE SENSING
Environment in Denmark) as well as to three anonymous reviewers for their specific suggestions and helpful comments. The executable of the SHYLOC software and SHYLOC user manual are both available free from the project's Web site: http:/l poplar.sti.jrc.it/public/iain/shyloc/home.html.
Al-Khudhairy, D.H.A., J.R. Thompson, H. Gavin, and N.A.S. Hamm, 1999. Hydrological monitoring of a drained grazing marsh under agricultural land use and the simulation of restoration management scenarios, Hydrol. Sci. J., 44(6):943-971. Al-Khudhairy, D.H.A., V. Hoffmann, and C Leemhuis, 2001a. SHYLOC User Manual, Version 2.001,EUR 19745 EN, European Communities, Ispra, Italy, 94 p. Al-Khudhairy, D.H.A., C. Leemhuis, V. Hoffmann, R. Calaon, LM. Shepherd, J.R. Thompson, H. Gavin, D. Gasca-Tucker, H. Refstrup Sorenson, A. Refsgaard, G. Bilas, G. Zalidis, and D. Papadimos, 2001b. Innovative technologies for scientific wetland management, conservation and restoration, Symposium Proceedings, Remote Sensing and Hydrology 2000,03-07 April, Santa Fe, New Mexico (IAHS Pub. No. 267), pp. 491-494. Al-Khudhairy, D.H.A., C. Leemhuis, V. Hoffmann, R. Calaon I.M. Shepherd, J.R. Thompson, H. Gavin, and D. Gasca-Tucker, 2001~.Monitoring wetland ditch water levels in the North Kent Marshes, UK, using Landsat TM imagery and ground-based measurements, Hydrol. Sci. J., 46(4):585-597. Al-Khudhairy, D.H.A., C. Leemhuis, V. Hoffmann, I.M. Shepherd, J.R. Thompson, H. Gavin, D.L. Gasca-Tucker, G. Bilas, G. Zalidis, H. Refstrup Sorenson, A. Refsgaard, D. Papadimos, and A. Argentieri, 2001d. SHYLOC Final Report, EUR 19755 EN, European Communities, Ispra, Italy, 342 p. Alsdorf, D.E., J.M. Melack, T. Dunne, L.A.K. Mertes, L.L. Hess, and L.C. Smith, 2000. Interferometric radar measurements of water level changes on the Amazon flood plain, Nature, 404(6):174-177. Alsdorf, D.E., L.C. Smith, and J.M. Melack, 2001. Amazon floodplain water level changes measured with interferometric SIR-C radar, IEEE %ns. Geosci. and Remote Sens., 39(2):423-431. Birkett, C.M., 1995. The contribution of TOPEXIPOSEIDON to the global monitoring of climatically sensitive lakes, J. Geophys. Res., 100, 25:179-204. , 1998. The contribution of TOPEX NASA radar altimeter to the global monitoring of large river and wetlands, Wat. Resour. Res., 34(5):1223-1239. Engman, E.T., 1996. Remote Sensing Applications to Hydrology: Future Impact, Special Issue, Hydrol. Sci. J., 41(4):637-648. Engrnan, E.T., and R.J. Gurney, 1991. Remote Sensing in Hydrology, Chapman and Hall, London, United Kingdom, 225 p. Gasca-Tucker, D.L., in prep. Hydrology and Hydrological Management of the Pevensey Levels, Ph.D. thesis, University College London, London, United Kingdom. Gavin, H., 2000. The Hydrology of the Elmley Marshes, North Kent, UK, Ph.D thesis, University College London, London, United Kingdom, 364 p. Gerakis, P.A., 1992. Former Lake Karla rehabilitation case study, Conservation and Management of Greek Wetlands (P.A. Gerakis, editor), IUCN, Gland, Switzerland, pp. 429-489. Harpley, J., (editor), 1999. Water Level Management Plan for Elmley and Spitend Marshes, Isle of Sheppey, Kent, Lower Medway IDM, Report May 1999, Maidstone, Kent, United Kingdom, 88 p. Hoffman, V., 2001. SHYLOC Technical Reference Manual, Technical Note No. 1.01.24, European Communities, Ipra, Italy, 72 p. Hollis, G.E., and J.R. Thompson, 1998. Hydrological data for wetland management, Journal of the Chartered Institution of Water and Environmental Management, 12:9-17. Koblinksy, C.J., R.T. Clarke, A.C. Bremer, and H. Frey, 1993. Measurement of river level variations with satellite imagery, Wat. Resour. Res., 29(6):1839-1848. Mather, P.M., 1999. Computer Processing of Remotely-Sensed Images, John Wiley & Sons, Chichester, United Kingdom, 292 p. August 2002
817
Pope, K.O., E.J. Shefher, K.J. Linthicum, C.L. Bailey, T.M. Logan, E.S. Kasichke, K. Bairney, A.R. Njogu, and C.R. Roberts, 1992. Identification of Central Kenyan rift valley fever versus vector habitats with Landsat TM and evaluation of their flooding status with airborne imaging radar, Remote Sensing of Environment, 40:185-196. Research Systems, 1999. ENVI User's Guide, Version 3.2, July 1999 Edition, Research Systems, Inc., Boulder, Colorado, 605 p. Shepherd, I.M., G. Wilkinson, and J.R. Thompson, 2000. Measuring surface water storage in the North Kent Marshes using LandsatTM images, Int. J. Remote Sens., 9:1843-1865. Smith, L.C., 1997. Satellite remote sensing of river inundation area, stage and discharge: A Review, Hydrol. Processes, 11:1427-1439. Smith, L.C., B.L. Isacks, R.R. Forster, A.L. Bloom, and I. Preuss, 1995. Estimation of discharge from braided glacial rivers using ERS 1 synthetic aperture radar: First results, Wat. Resour. Res., 31(5):1325-1329. Smith, L.C., B.L. Isacks, and A.L. Bloom, 1996. Estimation of discharge from three braided rivers using synthetic aperture radar satellite
imagery: Potential application to ungauged basins, Wat. Resour. Res., 32(7):2021-2034. Sipper, S.J., S.K. Hamilton, J.M. Melack, and E.M.M. Novo, 1998. Passive microwave observation of inundation area and the arealstage relation in the Amazon River floodplain, Int. J. Remote Sens., 19:3055-3074. Usachev, V.F., 1983. Evaluation of flood plain inundations by remote sensing methods, Symposium Proceedings, Hydrological Applications of Remote Sensing and Remote Data ZFansmission,August, Hamburg, Germany (IAHS Publ. 145), pp. 475-482. Willock, C., 1993. Farming wildfowl on Elmley Island, The Field, (February):82-83. Xia, L., Z. Shulin, and L. Xianglian, 1983. The application of Landsat imagery in the surveying of water resources of Dongting Lake, Symposium Proceedings, Hyrdrological Applications of Remote Sensing and Remote Data llansmission, August, Hamburg, Germany (IAHS Publ. 1451, pp. 483-489. (Received 25 July 2001; accepted 03 January 2002; revised 19 February)
Final Push to Retire the 4
-
- 1 Building Fund
You can help ASPRS retire the Building Fund. Our current goal is t o pay off the remaining mortgage balance in time for a victory celebration at the Annual Meeting in 2004-the Society's 70th anniversary. In 1999the mortgage balance stood at more than $360,000. By last
First Mortgage Balance
year it was down to approximately $220,000. Today that balance is saw,ow
less than $145,000 and the end is in sight. Special recognition goes
noa,ow
to sustaining members LSRI, ERDAS, Z/I Imaging, LH Systems, and
S6LX.m
sSW.ow
SAIC for their substantial contributions to the Fund over the past
1400,OW
three years.
SIO(I,~
Several regions are now matching their member's donations, including the Potomac Region, Rocky Mountain Region and the
s2w.wo slW'ooo
$0
Columbia River Region. Each of the regionally matched contribu-,wh"m,
tions are then matched again by the National matching Fund, with the result that each dollar contributed by a member in those re-
.
gions yields Four dollars to the Building Fund. This "double match" has collectively raised over $30,000 in Building Fund deposits.
As you consider tax management strategies throughout the year, please consider contributingto the ASPRS
Cumulative Building Fund Contributions
Bui,dingFundDlive toclayl
[Through April 20021 $5W.00(
$400.000
5300,000
Working together, we can all make the Society, and thereby the profession, stronger for the future. Let us hear from you today. We need your help to meet our goal.
$200.000
s:i!
$1 OO.OOO
Past President and
eq
Chair, Building Fund Drive 1
PHOTOGRAMMETRICENGINEERING & REMOTE SENSING
