Monitoring surface water storage in the north Kent marshes using ...

int. j. remote sensing, 2000, vol. 21, no. 9, 1843– 1865

Monitoring surface water storage in the north Kent marshes using Landsat TM images I. SHEPHERD European Commission, Institute for Systems, Informatics and Safety, Joint Research Centre, 21020 Ispra ( Va), Italy; e-mail: [email protected]

G. WILKINSON School of Computer Science and Electronic Systems, Kingston University, Kingston-upon-Thames KT1 2EE, England, UK

and J. THOMPSON Wetland Research Unit, Department of Geography, University College London, WC1H 0AP England, UK (Received 21 October 1997; in nal form 22 January 1999 ) Abstract. A system has been developed that uses remote sensing data for the determination of the quantity of surface water stored in a network of ditches. The method combines Landsat TM images with ditch positions obtained from maps in order to derive ‘Ditch Indices’. The results of the analysis show that the system is promising. The presence of the sub-pixel size ditches was clearly detected in the image and the monospectral ‘Ditch Indices’ correlated strongly with each other and with ground measurements.

1.

Background and objectives The last few decades have witnessed an enormous rise in awareness of the importance of wetlands. They cover approximately 6% of the Earth’s surface and provide human populations with a host of goods and services including food storage, water quality maintenance, agricultural production, sheries and recreation (Hollis and Acreman 1994 ). Wetlands are also believed to play a signi cant role in global climate change by acting as a source of atmospheric greenhouse gases such as methane and a sink for both carbon and nitrogen (Jansson et al. 1994 ). Global biodiversity is also enhanced by wetlands which are vital for the survival of a disproportionately large number of threatened and endangered species (Mitsch and Gosselink 1993 ). However, around the world wetlands are being lost and degraded as economic development results in increasing pressure to drain and re-claim land for agricultural, industrial and other uses. Hydrology is the key factor controlling the structure and functioning of any wetland which in turn determines speci c ecosystem responses. An understanding of wetland hydrology is therefore a vital prerequisite for successful wetland management. Hydrologists construct models of river catchments and wetlands in order to understand their functioning so that past events can be explained and future ones International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online © 2000 Taylor & Francis Ltd http://www.tandf.co.uk/journals

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predicted. The data required by the models can be divided into a number of classes — input data, calibration data and validation data. The input data includes such parameters as land cover, topography, root depth, soil permeability, leaf index and rainfall. The calibration and validation data are most frequently water storage and ow. With the obvious exception of rainfall the calibration and validation data are, arguably, more important than input data because some parameters, such as soil permeability, can only be estimated approximately and most hydrologists need to ‘tune’ this or other parameters in order that the results of the model match the ‘calibration’ measurements. The problem with traditional ground-based measurements is their uneven spatial coverage and their unreliability. Remote sensing measurements, on the other hand, oŒer area-averaged quantities, large area coverage and an archive of images that, in some cases, goes back 20 years. However remote sensing is not used as a matter of routine by hydrologists partly because research is still needed on the derivation of reliable quantitative information from remote sensing data for incorporating within numerical hydrological models (Whitelaw et al. 1994 ). When considering the possible contribution of remote sensing to hydrology it is salutary to understand the drawbacks as well as the advantages. Serious limitations are: 1. Resolution. The smallest multi-spectral pixel size available for at least the last ten years is 20 m. 2. Frequency of Images. Many sensors only produce an image of a particular area approximately once a fortnight. Furthermore, frequent cloud cover means that a small proportion— less than 10% in northern Europe (Kontoes and Stakenborg 1990 )—are actually usable. In some countries such as the UK there can be considerable variability in annual image availability plus additional problems from snow cover in winter (Fuller et al. 1994 ). The ability of imaging radar to penetrate cloud cover is one of the main reasons for the increasing use of synthetic aperture radar (SAR) imagery in hydrological studies. The other reason is the large in uence that the presence of water has on a SAR image —whether it be surface water, wet soil or wet vegetation. For instance, Kasischke and Bourgeau-Chavez (1997 ) showed how SAR imagery can be used to detect and monitor changes in wetland ecosystems. Nevertheless we do not consider SAR in this paper. Firstly because we are interested in using archives of images from as far back in the past as possible, most SAR images only date from the nineties onwards, and secondly because the multispectral nature of Landsat TM does give it some advantages for our purposes. In any case it is clear that remote sensing measurements on their own cannot provide enough information to determine how the hydrology of an area varies over the course of time. They can only provide snapshots that allow models to be calibrated or validated. In this paper we show how images from the Landsat Thematic Mapper instrument can be used to determine the amount of surface water in a wetland catchment. The area which has been studied is the north Kent marshes in England. Earlier work on the remote sensing of wetlands has demonstrated the potential of radar altimetry to monitor changes in water level (Cudlip et al. 1990 ), and the use of Landsat MSS and SPOT HRV imagery to inventory changes in aquatic vegetation as indicators of hydrological change (Jensen et al. 1995 ). A recent study has also been reported

Monitoring surface water storage using L andsat T M

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on the characterisation of mangrove wetland vegetation canopies from spectral features (Ramsey and Jensen 1996 ).

2.

The north Kent marshes The north Kent marshes are located in south-east England along the lower Thames estuary and on both sides of the Swale (a tidal channel which separates the Isle of Sheppey from the mainland ). They are largely the product of human activities which go back as early as Roman times. A series of sea defences have been constructed, in the form of embankments and walls, to isolate and protect former salt marshes form the sea. The majority of these are at least several centuries old although some small areas were enclosed as recently as the last 20 or 30 years. During the period 1960–1980 extensive drainage and eld amalgamation took place within the marshes to convert them into arable land. Since then little new drainage and conversion has taken place, re ecting the increasing concern of nature conservation organisations about the loss of traditional grazing marshes and the awareness of farmers of the unstable nature of heavy marsh soil after underdrainage. The drainage system in the north Kent marshes consists of a network of eets, runnels and ditches inherited from pre-enclosure salt marshes but extensively modi ed by human activity. These divide the marshes into elds of varying size. Along the sea walls there are a number of tidal aps (sluices) which let drainage water escape into the sea at low tide and largely exclude salt water in ows at high tide. At present, wetland management focuses on maintaining the traditional water regime of the marshes. The ecological importance of the north Kent marshes is re ected in their designation as Sites of Special Scienti c Interest (SSSI ), Ramsar Sites under the Convention on Wetlands of International Importance, National Nature Reserves (NNR) and Special Protection Areas (SPA) under the EEC Birds Directive (79/409 ). The marshes are also included within the Ministry of Agriculture, Fisheries and Foods (MAFF) Environmentally Sensitive Areas (ESA) scheme which recognises their importance for birds, nature conservation and archaeological sites. The hydrology of a large part of the north Kent marshes on and immediately south of the Isle of Sheppey was summarised by Hollis et al. in 1993 while AlKhudhairy et al. 1996, have developed a distributed hydrological model of the marshes. Al-Khudhairy and Thompson 1997, went on to apply this model for determining the impact of land-use changes on the wetland’s functioning. In this paper we develop a method using remote sensing images that can be used to calibrate or validate models such as this one.

3. Ground survey of the ditches 3.1. T he measurements Within the area examined by Hollis et al. (1993 ) ditches were surveyed in March and June 1993. The length of reaches were measured and the channel width and water depth determined for each reach. The survey points are shown in gures 1 and 2 overlain on a map of elevations supplied by Ordnance Survey (OS) and a map of streams digitised from 1::10 000 scale maps. The catchment boundaries identi ed by Hollis et al. (1993 ) are shown in gure 3. Table 1 shows the average values of the channel measurements for each of the catchments while the average

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Figure 1. Channel survey points south of the Swale. This and following maps use the British National Grid coordinates. The grid coordinates are marked on the edges of the maps at 1 km intervals (in this gure 591 and 592 are 1 km apart). The centre of this particular gure is approximately 0 ß 45¾ E 51 ß 24¾ N.

values over all the catchments are shown in table 2. This data shows: 1. The average channel width is around 4 m. 2. The maximum is 27 m. 3. The average width in catchments 3a (Neatscourt) and 6c (south Iwade) is rather less than in the other catchments. 4. There is consistently more water in the ditches in March 1993 than in June 1993. Since we know from the eld study that the sides of the ditches are not vertical then an increase in water depth in a channel will lead to an increase in the width of water surface which should be visible from satellite measurements. In this paper we aim to show that: 1. The ditches do aŒect the signal for certain channels of Landsat TM. 2. A diŒerence can be discerned between wet and dry periods.


Figure 2.

Channel survey points north of the Swale.

(b)

(a)

Figure 3.

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Catchment boundaries. (a) Studied by Hollis et al. 1993. (b) Studied in this paper. Table 1.

Average ditch measurements in the catchments (1993). Maximum values

Mean values

Depth (m) Catchment 1 Joan Fleet 3 Minster 3a Neatscourt 5 Ferry 6 Ridham 6b Iwade 6c South Iwade

Total length (m)

Width (m)

March

975 8020 5025 1332 7360 1710 1045

10.0 25.0 4.0 14.3 26.6 4.6 3.4

1.1 2.1 1.2 1.0 1.1 0.7 0.2

Depth (m)

June

Width (m)

March

June

1.0 2.0 1.1 0.7 0.6 0.6 0.1

4.70 5.31 1.96 5.60 4.51 3.07 1.86

0.40 1.28 0.58 0.44 0.49 0.41 0.13

0.34 1.03 0.48 0.31 0.25 0.36 0.07

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Overall average measurements for ditches (1993 ).

Variable Width (m) Length (m) Depth in June (m) Depth in March (m) Volume in March (m3 )

Maximum

Mean

26.6 2006.4 2.0 2.1 5500

4.4 158.2 0.52 0.71 620

3.2. Modi ed catchment boundaries The original Hollis et al. (1993 ) survey studied only the area likely to be aŒected by proposed roadworks. Since then attention has expanded to include other areas of interest to conservationists such as the Elmley marshes. For the present study we consider catchment boundaries as shown in the second map of gure 3. 4.

Detection of water with Landsat TM In this experiment we have used multi-spectral Landsat Thematic Mapper imagery. All bands have a spatial resolution of 30 m except band 6 which has a resolution of 120 m. It is well known (De Jong 1994 ) that incident infra-red wavelengths (greater than 0.7 mm) are almost completely absorbed by water so we will use bands 4 (0.76–0.90 mm), 5 (1.55–1.75 mm) and 7 (2.08–2.35 mm) to search for water. Con rmation of this for our test site is shown in gure 4 which shows band 5 of the Landsat TM image of 12 February 1988. We see the outline of the Isle of Sheppey

Figure 4. Band 5 of 12 February 1988 Landsat TM image ©Eurimage. All maps in this paper use the British National Grid Coordinates. The grid coordinates are marked on the edges of the maps at 1 km intervals.


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rather well. The next stage is to con rm that the re ectance on land is very diŒerent from that on water. We do this by selecting seven control areas (see gure 5). The odd numbered ones are in areas that we know are wet. The even numbered ones are on ‘dry’ land. The digital number of a pixel is eŒectively a relative intensity of electromagnetic energy measured for the ground resolution cell represented by that pixel. We expect this number to be much lower for the areas with water (1, 3, 5 and 7), especially for channels 4 and 5, but also for the other channels as well. In the analysis that follows we will be examining six Landsat TM images, each of them georeferenced on a 30 m by 30 m grid. The dates of these images were: E E E E E E

12 February 1988 1 November 1988 21 February 1989 3 August 1990 20 May 1992 15 November 1993

There is a considerable variation from image to image, especially of the ‘land’ values. Å k between the average value of the digital number on Figure 6 shows the diŒerence D Å k (squares 1, 3, 5 and 7) for each land ÅL k (squares 2, 4 and 6) and that on water W of the Landsat bands. D Å k =W Å kÕ ÅL k

(1)

In the analysis that follows it is the diŒerence between the ‘wet’ pixels and the ‘dry’ pixels that interests us. It is clear that:

Figure 5.

Control areas used to determine the average diŒerence between ‘land’ and ‘water’ pixels (digital elevation model ©Crown copyright).

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Figure 6.

E E E E

E

DiŒerences between average of ‘wet’ pixels and average of ‘dry’ pixels. The tick marks on the x-axis indicate the beginning of the year in question.

Å k between the ‘wet’ and ‘dry’ pixels is always considerable. The diŒerence D Overall bands 4 and 5 exhibit the most diŒerence between ‘wet’ and ‘dry’. For band 7 there is a consistent diŒerence, but it is less than for bands 4 and 5. Å k is generally negative. For bands 4, 5 and 7, as we would expect, the diŒerence D i.e. the number (eŒectively the re ectance) is greater for ‘dry’ pixels. Å k is not constant from image to image. In fact it varies The diŒerence D considerably.

Å k lower than for dry We have shown that the digital number of wet pixels is about D cells. It follows that if, for instance, a dry pixel size 30 m Ö 30 m has a stream of Å k /30 width 4 m running through it then the digital number of that cell is about 4 Ö D lower than an exactly similar pixel without a stream in it. We would not expect the depth of the stream to have a great in uence because, for the wavelengths considered here, the absorption depth in water is much less than the typical depths of water shown in table 1. 5.

The ditches Having shown that Landsat TM, particularly channels 4, 5 and 7, is useful for detecting the presence of water it would be useful to use such imagery to extract ditches and detect the amount of surface water stored in them. An examination of the TM image does in fact show a number of lines in the marshes that may correspond to ditches and, in principle, it might be possible to develop an algorithm to detect them based, for example, on edge or lineament detection approaches. This could be done using, for example, gradient-based multispectral edge detection (Drewniok 1994 ), the Hough transform method as used for geological linear features by Karnieli et al. (1996 ) or GIS-guided techniques as used by Van Cleynenbreugel et al. (1990 ) for road networks. However, we do not need to do this for the north Kent marshes, since the positions of the ditches are well known and are recorded on maps. Hence it is possible to integrate the background map information, in digital form, into the image analysis process. Such an integrated approach has been demonstrated in many


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studies to aid the exploitation of remotely sensed imagery, e.g. in classi cation (Jones et al. 1988, Kontoes et al. 1993 ) and in segmentation (Tailor et al. 1988 ). For the north Kent marshes we have vectors digitised from 1:10 000 scale maps. These are the lines drawn on gures 1, 2, 3 and 5. Examining the vectors in closer detail it is important to notice that they are not centreline ditch positions. Neither do they form a hydraulic network. The vectors sometimes mark the edges of the ditches rather than the centreline. There are also gaps in the lines where ditches pass through pipes. One could imagine some processing to make a correct hydraulic network from these vectors or checking whether the map is correct using aerial photographs but for our purposes the ‘edge’ vectors will be su cient. The aim of our exercise is to develop automatic procedures using readily available data. 6.

Method used We take advantage of the fact that in the wetland of interest, the water-carrying ditches are su ciently spectrally dissimilar to the background land cover that the spectral contrast can be used to derive a measure related to their width. This avoids the need to use more complex classi cation techniques such as fuzzy classi cation (Maselli et al. 1996 ), spectral unmixing (Van der Meer 1995 ) or multivariate linear regression (Van Kootwijk et al. 1995 ). Figure 7 shows a set of image pixels with a stream. The value rki is the digital number of channel k of the TM image for pixel i. Si is the set of eight neighbouring pixels. For the case shown in gure 7. S = N, S, E, W, NW, SE, SW, NE i We can then de ne an average neighbour value ai by

(2)

1 ak = i 8

(3) rkj j ×Si and the diŒerence value dki as the diŒerence between the number of a pixel and the number of its neighbours, i.e. dk = rk Õ ak i i i

(4)

rk = L k +dk i i i

(5)

Now we suppose that

Figure 7.

Illustration of neighbouring cells with a ditch passing through ve pixels.

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where dki marks the disturbance to the number L ki of pixel i by the ditch (for pixels without ditches dki = 0). We then make our rst assumption. If it were not for the stream, then the value of the pixel would be the average of its neighbours, i.e. L kÕ i

1 8

then

j ×S

i

L k# 0 j

(6)

1 8

(7) djk j ×Si If P is the set of N pixels that have streams in them then d, d are vectors of dimension N containing the values d and d for all elements of the set P. i i dk # dk Õ i i

dk # (I Õ C)dk # Kdk

(8)

where I is the identity matrix and C is a square connectivity matrix whose members c obey the relation: i,j ½ if pixel i is connected to pixel j and both pixels contain streams c = (9) i,j 0 otherwise

G

The members of matrix K are ki,j . Clearly matrix C is symmetric (i.e. ci,j = cj,i ). The number of ditch cells can be expressed as a weighted mean of the land and water values so the disturbance dik can be calculated as: dik = ai W ki +(1 Õ ai ) L ki Õ L ki = a (W k Õ L k ) i i i

Dk (10) i i where W ki is the number per pixel of water and ai is a weighting factor that should measure the fraction of the pixel covered in water. This equation indicates, as we expect, that the in uence of the stream on the digital number of the pixel depends partly on the fraction of the pixel that is covered by the stream (ai ) and partly on the diŒerence of digital number between land and water (Dki ). The di culty in the analysis comes from separating the in uence of each of these. We now make our second assumption. We assume that temporal changes in Dik are more important than spatial ones, i.e. Dk # D Å k (11) i =a

Å k was de ned in equation (1) as an average value over the catchment. So where D using this approximation, substituting equation (10) in (8) gives. dk # KDk a

(12)

We can then calculate a mean value for dÅ k for all N stream pixels: dÅ k =

1 N D Å k N

i ×P

dk i

(13)

(14) ki,j aj i ×P j ×P and de ne hi as the number of pixels adjacent to stream pixel i that themselves have #


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streams in them. In gure 7 hi = 4. Then the set of all stream pixels P can be divided into nine subsets Ph where Ph is the set of Nh pixels that are connected to h other pixels. Clearly the intersection of the sets Ph is zero and the aggregate is P. 8

P=

h= 0

N=

8

h= 0

Ph

(15)

Nh

(16)

We now make our third assumption. We assume that the average value of ai is constant for any su ciently large group of stream pixels. In other words the fraction of a pixel that is covered in water ai does not depend on hi , the number of ‘connections’, i.e. 1 aÅ # N

1 Nh

(17) ai i ×P i × Ph so the assumption on the right-hand side of equation (14) can be evaluated as:

m ×P n ×P

k

m,n an

ai =

8 h ai h = 0 8 i × Ph m ×P 8 h # Na N h aÅ Å Õ h= 0 8 hÅ # Na Å 1Õ 8 =

am Õ

A B

(18)

where hÅ is the average number of neighbouring cells that have streams in them. 1 8 (19) hNh 8N h 0 = Combining equation (14) with (18) gives a relation between dÅ and aÅ and it is this relation that forms the basis of the subsequent analysis. hÅ =

A B hÅ 8

dÅ k # aÅ D Å k 1Õ

(20)

In this paper we will use two monospectral ditch indices. The rst of these dÅ k is de ned from equation (13). The second, the ‘normalised ditch index’ nÅk is de ned by: dÅ k nÅk = D Å k substituting from equation (20)

(21)

A B

nÅk # aÅ 1 Õ

hÅ 8

(22)

so nÅk is proportional to the average fraction of the ‘ditch pixels’ that are covered in water and hence to the average width of the ditches. We should make it clear at this point that the method we describe in this paper does not require this relation to be absolutely correct and for all our assumptions to be valid. The value for a is a global

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value for the area being studied that can be correlated to surface water storage. Our analysis has shown why this should be so. 6.1. Neighbouring cells As well as calculating d¯ ki and dÅ k we calculate d¯ kc . This is calculated on the same basis as equation (4) but, instead of performing the analysis on the set P of pixels with ditches in, we analyse a set whose pixels have a constant displacement with respect to the pixel which, according to the map, should contain the stream. We de ne values of d¯ kc for the following sets: c ×X

Stream is in the cell

c ×N

One cell to the north of stream cell

c ×2S

Two cells to the south of stream cells

c ×SW

Cell to the south-west of stream cell

etc.

(23)

6.2. Atmospheric correction It is well known that the atmosphere selectively scatters light and that for TM images band 1 has the highest component of scattered light and band 7 the least. There are various techniques for correcting images for this eŒect (Sabins 1996 ) that are especially useful if images from diŒerent dates and atmospheric conditions are being compared. These techniques all boil down to subtracting a number from the digital numbers for each band. This correction is constant over the entire image. Fortunately this correction is not necessary in our method. The algorithm we Å k in equahave developed is based on the calculation of d¯ ik in equation (4) and D tion (1). Both these quantities express the diŒerence between digital numbers from the same band and the same image. Therefore the atmospheric correction cancels out and does not need to be calculated. 7.

Analysis of one image To check our method we analysed the area shown within the frame in gure 8. We rst considered the image of 20 May 1992. Figure 9 shows the value of d¯ ck for various values of c and k. This graph con rms that the technique is functioning. The signal ‘all’ is zero as it should be. This is de ned by: E

dÅ = all N

E E

1

dk i

(24) total i ×P where PT is the set of all Ntotal pixels in the area of interest (not just those with streams in). The main peaks are negative as they should be. The strongest signal (for both channel 4 and channel 5) is from c ×SE. This implies that the georeferencing of the image is not quite correct and that there is a shift. T

We then examined this shift. Figure 10 shows a small portion of the Isle of Sheppey. The stream vectors are overlaid with the raster map of pixel sets X and SE with medium grey pixels i ×X, light grey pixels i ×SE, black pixels i ×X.SE (intersection of the two sets), gure 11 shows, for the same area, the ditches overlaid on channel


Figure 8.

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De nition of area of interest (digital elevation model ©Crown copyright).

Figure 9.

dÅ k for 20 May 1992 image ( bands 4, 5 and 7). c

5 of the TM image of 20 May 1992. A careful visual examination of this image does indeed suggest a georeferencing error in the Landsat TM image. The darker areas in the image are slightly to the south-east of where the map indicates the stream should be. In order to check that this was the case the Landsat image was shifted 30 m north and 40 m west (approximately one pixel in each direction) and the operation repeated. Figure 12 shows the superposition of the shifted image with the ditch positions and gure 13 shows the new result of applying the statistics.

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Figure 10.

Illustration of sets X, SE and X.SE.

Some iteration was then performed with all the images in order to obtain the maximum value of d¯ kc for c = X. This was achieved with the shifts shown in table 3. These gures give an accuracy of the geo-referencing performed by the image supplier. Generally the accuracy is correct to within one pixel. The error in the 15 November 1993 image was an exception—it is wrong by about three pixels. It is also interesting to see the distribution of values of d Xk as well as the mean ¯d k . This is plotted as gure 14 for the (correctly georeferenced ) 20 May 1992 image X where the x-axis shows the value of dkX and the y-axis shows the number of pixels with a value between dkX Õ 0.1 and dkX +0.1. Even though the mean value d¯ kX is negative the mode of the distribution (the point with the most values) is zero re ecting the fact that the map may contain some dried-up or very narrow streams. From this point onwards we shall use the most accurately geo-referenced images. The conclusions of the following analysis would not have changed if we had used the ‘as-received’ images. 8.

Multi-temporal analysis of images The aim of the study is to observe changes in surface water storage over time. In this section we describe the results of a preliminary analysis. Figure 15 shows, for bands 4, 5 and 7, and for each image the value of a monospectral ‘ditch index’. Mk = Õ min d¯ k a c c

(25)


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Figure 11. Ditches overlaid on band 5 of Landsat TM image (20 May 1992 ).

where d¯ kc was de ned in equation (23). We also plot ( gure 16) the equivalent normalised monospectral ‘ditch index’ values from equation (21). 100d¯ kc Mk = max nÅk = Õ min n c Dk c c Comparing gures 15 with 16 one can see that:

(26)

1. The normalising does make a diŒerence. The magnitude of the unnormalised diŒerence decreases between the rst (February 1988 ) and second (November 1988 ) images whereas it increases for the normalised value. The behaviour of the last two images also shows an inverse proportionality. 2. The normalised values for channels 5 and 7 are similar whereas the value for channel 4 behaves diŒerently. 9.

Spatial variation As well as calculating d¯ kc for the whole of the area enclosed in the frame in gure 8 we calculated it for a number of the catchments shown in gure 3. Both the monospectral ditch indices Mka and Mkn were calculated for each of the six images. For each image the Elmley, Minster, Neatscourt and Ridham catchments were calculated separately in addition to the streams inside the frame in gure 8 (the ‘Isle of Sheppey’). Thus a total of 30 values were obtained for each wavelength. The values for each wavelength (channels) were then correlated with each other

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Figure 12. Ditches overlaid on band 5 of Landsat TM image (20 May 1992), shifted 40 m north and 40 m west.

Figure 13. Åd kc (ditch index) for 20 May 1992 image ( bands 4, 5 and 7) shifted 40 m north and 40 m west.

using Pearson correlations in order to determine which wavelengths were the best indicator for water. The results shown in gure 17 con rm that channels 5 and 7 correlate the best and may be a result of the increasing absorption of water as the wavelength lengthens.

Monitoring surface water storage using L andsat T M Table 3.

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Shifts required to georeference images. Shift

Date

North (m)

12 February 1988 1 November 1988 3 August 1989 21 February 1989 20 May 1992 15 November 1993 Õ

40 10 20 30 40 90

East (m)

Õ

Õ

Õ

35 0 20 30 40 40

Figure 14. Distribution of values of dkX for 20 May 1992 image ( bands 4, 5 and 7), shifted 40 m north and 40 m west.

Figure 15.

Values of ditch indices (Mka ) for Isle of Sheppey for bands 4, 5 and 7.

A mean ditch index was then de ned by: M Å = n

(M4n +2M7n +2M5n ) 5

(27)

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Figure 16. Values of normalised ditch indices Mkn for Isle of Sheppey for bands 4, 5 and 7.

Å for the diŒerent catchments. The signals are all higher than the Figure 18 shows M n average ‘Isle of Sheppey’ value. Neatscourt shows the strongest signal, then Minster, then Elmley, then the mean value. The Ridham marshes are anomalous. They start fairly high for the rst two images then (after 1990 ) the values are consistently lower. This may indicate a change in water management strategy at this time. These results are not borne out by the ditch survey summarised in table 1. In that survey Neatscourt had narrower ditches than Minster. Indeed from equation (22) we can see that the ditch index depends on h¯ as well as aÅ . This value may well vary spatially. Furthermore our assumption expressed in equation (11), that the Å k is constant, may not be valid for diŒerent catchments. This indicates quantity D that our method is better used to compare one catchment at diŒerent times rather than to compare diŒerent catchments. 10.

Comparison with ground measurements Unfortunately there are few historical records of water levels in the marshes. Hollis et al. (1993 ) conducted an extensive search for data but found little. In fact this was a motivation for the present study. A limited set of measurements were made in the Elmley catchment. Figure 19 shows the ditch water levels at six positions measured on an approximately monthly basis. The thicker line shows the average water level and the vertical lines indicate the dates of those Landsat TM images that we have analysed that coincide with the water level measurement period. There are three of these images. Å k for bands 4, 5 and 7 plotted against the Figure 20 shows a correlation of M interpolated average measured ditch level in this period. Obviously we would have preferred more images and a longer measurement period but the results are encouraging. They indicate a positive correlation between water levels measured on the ground and those deduced from remote sensing. 11.

Conclusions The system that we have developed and explained in this paper is an innovative use of remote sensing for the determination of surface water stored in a network of ditches. The method is diŒerent from many remote sensing methods in that:


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Figure 17. Cross-correlations of the monospectral unnormalised and normalised ditch indices. The symbols on the graphs indicate the date of the image: 1. 12 February 1988; 2. 1 November 1988; 3. 21 February 1989; 4. 3 August 1990; 5. 20 May 1992; 6. 15 November 1993.

E

E

E

The end result is a number (a ‘ditch index’) not a map. The method is designed to measure features that are much smaller than a pixel (typically 10% of the area). The method does not search for water but uses information from commercially available maps to locate it.

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Figure 18.

Figure 19.

Å in the diŒerent catchments. Values of M n

Ditch water levels in Elmley: September 1989–December 1993.

The system is completely automatic:

E

E

E

E

The user only needs to import an image into a Geographical Information System and delineate the boundary surrounding the area of interest. All the statistics are calculated automatically. The image should be geo-referenced but the method can accept errors of one or two pixels. No eŒort was made to con rm whether or not the streams marked on the map and used in study were the most important ones, whether they had dried up or whether they were the only ones.


Figure 20.

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Correlation between signal and ground measurements for bands 4, 5 and 7.

The results of the analysis show that the system is promising. The presence of the sub-pixel size ditches was clearly indicated in the image and in fact the signal obtained from them was used in a feedback loop to improve the accuracy of the geo-referencing. Three monospectral ‘Ditch Indices’ were de ned that correlated strongly with each other and with ground measurements. The next step will be to improve the calibration of the system against ground measurements or aerial photography. Better control areas could be used to de ne the diŒerence between ‘land’ and ‘water’. Our ‘water’ control sites were in the sea. It would have been more appropriate to use areas of shallow fresh water. When the relation between ‘ditch indices’ and water stored in ditches is understood a multitemporal analysis can be used to determine the water stored at a number of instants over the past years. It is not clear yet whether a calibration of the system for one area will necessarily be valid for another but one can imagine a system, rather more sophisticated than the one we used here, that takes into account parameters such as soil moisture de cit or vegetation indices. There are also a number of possible re nements of the method itself. It would Å rather than one averaged over the be feasible to use a local normalising factor D whole catchment. Most of the data we used, including the images, is purchased from data suppliers. At the moment the prices are expensive and this is partly because one is forced to buy data in packages and sometimes, as was the case here, only a small fraction of the data is useful. As the market becomes more mature one might expect the pricing policy to become more exible and the data to become cheaper. Hydrological models have a requirement for calibration and validation data over as long a period as possible. The possibility of using archives of remotely sensed images to provide historical records will lead to a signi cant advance in our ability to produce accurate hydrological predictions. Acknowledgments We would like to thank our colleagues, Delilah Al-Khudhairy and Christian Kaiser who reviewed a rst draft of this manuscript, Karen Fullerton who helped

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