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FRINGE '96 Workshop: ERS SAR Interferometry, 30 September - 2 October 1996
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FRINGE 96

Assessment of land cover mapping potential in Africa using tandem ERS interferometry

B. Mumford, J-P Muller (mailto:jpmuller@ps.ucl.ac.uk), A. Mandanayake, Department of Photogrammetry & Surveying, University College London, Gower Street, London. WC1E 6BT, U.K.

Abstract

Accurate mapping of land cover types is essential to a number of scientific disciplines, in particular environmental monitoring. This goal relies on accurately calculating the extent of changes in surface cover such as forest. Recent research shows that ERS tandem interferometry products, especially phase coherence may be related to land cover type.
Traditional ground based methods of land cover mapping are prohibitively expensive due to the large areas involved. Optical satellite remote sensing methods are more appropriate but require cloud-free conditions for data to be useful. In tropical areas cloud free acquisitions can be rare reducing these sensors' applicability to such studies. ERS interferometry data can be acquired day and night irrespective of weather conditions and the introduction of ERS-2 means tandem scenes are imaged just 24 hours apart. These factors coupled with the wide coverage available means that ERS tandem interferometry offers an exciting alternative to optical sensors for land cover mapping.
In this study ERS tandem data are acquired and processed over the HAPEX-SAHEL area in Niger to which SPOT XS, Landsat TM and ATSR2 data are also available. The geocoded coherence maps are compared to land cover maps produced from the above sensors and a classified image produced. A quantitative analysis of the land cover maps shows high correlation between the vegetated classes. This analysis demonstrates clearly the viability of using tandem interferometry data in the evaluation of land cover types.
The study shows the huge potential of ERS tandem interferometry not only as a source of topographical information but also as a way of accurately monitoring changes in surface cover.
Keywords: tandem interferometry, phase coherence, land cover, optical methods

Introduction

The production of land cover maps from remotely-sensed images has always been perceived as one of the greatest contributions which satellite earth observations could make to both scientific and commercial exploitation of these data.

Since the launch in 1972 of the US ERTS-1 (renamed LANDSAT) programme, scientists have been using optical and near-infrared data to obtain 30m-100m resolution land cover maps. More recently from the LANDSAT-TM and SPOT-XS sensors.

The objectives here are to assess the potential of ERS tandem interferometry to provide an alternative source of land cover information for a semi-arid environment. Existing optical-NIR have poor land cover mapping quality in these environments due to their inability to deal with low Leaf Area Index areas (LAI<2). The clear advantage of this approach is it could permit land cover maps to be produced at anytime of year irrespective of cloud cover which is very high particularly during the rainy season when the Inter-Tropical Convergence Zone is present. It may also be used to validate land cover maps produced using coarse resolution sensors such as AVHRR [Loveland et al., 1991] and ATSR-2 [Higgins, 1995] as well as exploring scaling and generalisation issues [Barnsley et al., 1995]

Results are presented of a quantitative comparison between a land cover map derived using SPOT-XS, LANDSAT-TM and ERS SAR interferometric results. The method used to obtain the SAR interferometric results is briefly described together with an analysis of the results.

Data-Set Description

Both the SPOT-XS and LANDSAT-TM scenes were acquired on 25 September 1992. Details of these scenes as well as the HAPEX-SAHEL experiment can be obtained from Prince et al., 1995. Unfortunately, due to the limited availability of ERS tandem data from the Libreville, Gabon Receiving Station, only a dry season data-set was acquired which would have much greater amounts of bare earth present (see later). The location of the HAPEX-SAHEL test site is shown below (Figure 1) as well as a close up showing the processed area and the location of the anciliary data (Figure 2).

Figure 1. Overview map of the HAPEX-SAHEL study area using DCW map vectors

 

Figure 2. Close up of area in Figure 1 showing data sources used and test area


The details of the ERS tandem pair are given below in Table 1.

Area label
ERS
Orbit
Frame
Date
Latitude extent
Longitude extent
Bperp in m
HAPEX-SAHEL
ERS-1
24974
3339
24-April-96
12.67-13.82N
1.80-3.00E
102
HAPEX-SAHEL
ERS-2
5301
3339
25-April-96
12.67-13.82N
1.80-3.00E
102

Table 1. Characteristics of the ERS tandem pairs used for land cover mapping.

Method

An area of the full ERS-1,2 scene was selected for processing based upon the availablity of ground truth data from the HAPEX-SAHEL experiment. The commercial EDS-PulSAR system was used to focus the RAW data into an SLC product and further software was used to produce an interferogram and coherence image. In-house UCL software [Muller et al., this proceedings] was used to flatten the interferogram and geocode to a (lat,lon) grid using a 30 arc-second (~1km) DEM together with the precision state orbital vectors from the D-PAF. The tandem pair had a perpendicular baseline seperation (Bperp) of 102m which meant the elevation difference implied by a whole phase cycle was 92m. This resulted in well spaced fringes which appeared easily unwrappable (see Figure 3).

Figure 3. UCL-IfSAR geocoded flattened fringe pattern using 30 arc-second DEM
(C) UCL/ESA 1996

The phase coherence image (Figure 4) and histogram (Figure 5) computed show that the phase coherence values are high enough for our application.

Figure 4. Phase coherence image for test area (C) UCL/ESA 1996

Figure 5. Phase coherence histogram for test area (C) UCL/ESA 1996

The UCL-IfSAR system for unwrapping is similar to that described by [Goldstein et al., 1988] with the additional use of the coherence image to prevent unwrapping in areas of very low coherence. The residues were first calculated and then opposite-signed residues were linked to produce branch cuts which must not be crossed during unwrapping. Although it is possible to unwrap an interferogram using just a single phase seed point, some 25 seed points were chosen throughout the interferogram. These seeds were chosen by allocating a 'zeroth' fringe and then counting up or down the fringes depending on the grey scale gradient. Having joined the residues and produced a seed point file the algorithm was used to integrate the phase values throughout the interferogram and produce absolute values without the 2PI ambiguity inherent in the interferogram. The unwrapped interferogram was analysed visually in order to determine if any obvious blunders had occurred which would need further seed points to correct. Since there were no obvious blunders the unwrapped phase values were converted to heights using the coarse DEM and the orbital trajectory data and another UCL algorithm (see Figure 6).

shaded ifsar dem

Figure 6. UCL-IfSAR hill shaded DEM using 30 arc-second DEM for phase flattening (with overlay box showing test area) (C) UCL/ESA 1996

The ERS-1 and ERS-2 amplitude images were terrain geocoded to the same (lat,lon) coordinates of the coherence image (see Figures 7 and 8). A rectangular section enclosing all available data sources was selected for further analysis (see Figure 6 for location).

 

Figure 7. ERS1 amplitude image for test area (C) UCL/ESA 1996

 

Figure 8. ERS2 amplitude image for test area (C) UCL/ESA 1996

 

An amplitude difference image was then computed and is shown in Figure 9.

Figure 9. Amplitude difference image for test area (C) UCL/ESA 1996

The considerable speckle noise present in these images was considered potentially damaging to classification accuracies. Pratt (1978) suggested the use of a median filter in order to reduce speckle. A 5x5 kernel was selected after investigating the speckle-supression and edge-retention characteristics of a 3x3 and 7x7 kernel. Wegmueller and Werner, 1995 suggest the construction of a false colour composite using the coherence, a single data amplitude and amplitude difference images in order to facilitate visualisation of the thematic information contained within the various SAR products. A schematic diagram of this proposed method is shown in Figure 10.

Figure 10. Wegmueller and Werner proposed false colour composite procedure

Such a composite was produced using the geocoded SAR products described above (see Figure 11).

Figure 11. False colour composite using colour scheme shown in Figure 10. (C) UCL/ESA 1996

 

The false colour composite (Figure 11) showed several distinct classes and presented good evidence on its own of the land cover capabilities of IfSAR. A 50-class ISODATA unsupervised classification was then performed using ERDAS-IMAGINE on the dataset in order to enhance the class information present in the image in line with the classification procedure shown below in Figure 12.

Figure 12. Overview of classification procedure

Figure 13. Unsupervised classification of SAR datasets (C) UCL/ESA 1996

The result of this classification helped in the selection of 4 training areas for a maximum likelihood supervised classification shown in Figure 14.

Figure 14. Supervised land cover classification map of SAR with colour key (C) UCL/ESA 1996

A land cover map produced in 1992 using SPOT-XS data acquired on September 25th 1992 as part of the HAPEX-SAHEL experiment [Prince et al., 1994] was downloaded from the HSIS www site (http://www.orstom.fr/hapex/index.htm) (acknowledgement J.M d'Herbes, C. Valentin, B. Mougenot). The map which was derived using supervised classification was originally in 6 classes as shown in Table 2 below.

Class
Description
% woody
%grassy
1
Woody vegetation
25-75
10
2
Bare soil
0
0
3
Shrubby savanna
10-35
25-75
4
Grassy vegetation
<5
20-50
5
Light vegetation
<10
<10
6
Free water
N/A
N/A

Table 2. Original classes of the HSIS map

The 6-class map was reduced to 4 classes which correspond to the IGBP classification scheme [Belward and Loveland, 1995]. Classes 3, 4 and 5 were merged, producing the final classification scheme shown in Table 3 and in Figure 15.

Class
Description
%woody
%grassy
1
Woody savannas
25-75
10
2
Barren
0
0
3
Open shrublands
5-35
10-50
4
Water bodies
N/A
N/A

Table 3. HSIS classes after merging to 4-class IGBP scheme

Figure 15. Supervised land cover classification map of HSIS with colour key

The map was registered to the radar imagery using a cubic transformation with some 10 ground control points. A section of a 7-band Thematic Mapper scene was available and this was registered to the same (lat,lon) coordinates as the IfSAR products in the same way as the HSIS map. A 50-class ISODATA unsupervised classification was performed on the 6 non-thermal bands in order to help select 4 training areas for a maximum likelihood supervised classification (Figure 16).

Figure 16. Supervised land cover classification map of LANDSAT-TM with colour key

The footprints of the three data sources were not identical which necessitated the selection of a rectangular area enclosing all three sources for further analysis. The HSIS map was treated as a reference source and the common areas in the radar and TM classifications were extracted.

Congalton, (1991) recommended random stratified sampling in order to fairly assess the accuracy of classifications based on remotely sensed data. 40,000 random stratified samples ( representing approx. 5% of the pixels present in the area selected for quality assessment) were selected from the reference dataset. Error matrices were then produced using the radar and TM classifications (see next section). The correlation between the radar and TM classifications was also directly assessed. The construction of error matrices allowed a simple statistical analysis of the classification accuracies to take place.

Results

WS= woody savannas, BA= barren, OS=open shrublands, WB=water bodies.

Table 4 below shows the error matrix computed for the SAR classification using HSIS as the reference source. HSIS classes are along the top, SAR classes are down the side.

WS
BA
OS
WB






6697
2724
4316
36
13773
343
1247
767
15
2372
2797
1623
18834
130
23384
3
1
143
365
512





9840
5595
24060
546

Table 4. Error matrix for radar classification using HSIS as reference

PRODUCER'S ACCURACIES: 68%,22%,78%,67%
USER'S ACCURACIES : 49%,53%,81%,71%
OVERALL ACCURACY: 68%

The producer's accuracies show the proportion of reference pixels correctly identified by the radar classification. The user's accuracies show how many pixels labelled a particular class by the radar classification actually are that class in the reference data. The two measures together are extremely useful as they give the commision and omission errors. For example, a map could be produced where every single pixel was labelled as `barren'. A simple test of the producer's accuracy for `barren' would give a figure of 100% - no omissions. However, the user's accuracy would reveal the true nature of the classification by showing the huge commision errors.

The figures for the radar classification are reasonable with the exception of `barren' - with producer's and user's accuracies of 22% and 53% respectively. The overall accuracy of 68% is encouraging however despite being weighted considerably by the good results for the large `open shrublands' class. The HSIS map was produced using imagery acquired during September, when vegetation is at a maximum. The radar imagery was acquired at the height of the dry season and therefore the classification results are expected to differ considerably.

WS
B
OS
WB






7078
3417
4053
8
14556
1920
1860
3530
3
7313
842
318
16471
130
17761
0
0
6
364
370





9840
5595
24060
505

Table 5. Error matrix for TM classification using HSIS as reference

Producer's accuracies: 72%,33%,68%,72%
User's accuracies : 49%,25%,93%,98%
Overall accuracy: 64%

Table 5 above shows that the barren class is again very badly represented in both omission and comission errors. The user's accuracies for `open shrublands' and `water bodies' are extremely high indicating that these classes had a very distinct profile in the TM imagery.

Table 6 below shows the error matrix computed for the SAR classification using TM as the reference source.

WS
B
OS
WB






8631
2589
2552
1
13773
1162
939
271
0
2372
4732
3775
14789
47
23343
31
10
149
322
512





14556
7313
17761
370

Table 6. Error matrix for radar classification using TM as reference data

Producer's accuracies: 59%, 13%, 83%, 87%
User's accuracies : 63%, 40%, 63%, 63%
Overall accuracy : 62%

This error analysis indicates that there is virtually no correlation between the areas marked as barren in the TM and radar imagery. The other classes are well correlated and the overall accuracy is fair.

Discussion and conclusions

The land cover maps produced using SAR interferometry show high correlation with both the LANDSAT-TM and SPOT-XS results for the vegetated classes. The differences may partly be due to the different times of year (this is particularly true for the barren class which are much higher for the SAR than the other two satellite-produced maps) and partly due to the fact that the SAR senses the volume scattering from within the vegetation canopy [Gatelli et al., 1994] as opposed to the response of the vegetation at different spectral wavelengths at optical/NIR wavelengths.

SAR interferometry has been shown to be an extremely effective method, from a single date of obtaining land cover information in a semi-arid environment. A quantitative assessment was made of SAR-derived land cover using the IGBP classification scheme using both SPOT-XS and LANDSAT-TM as "ground" truth. For vegetated and water classes the correlation was high whereas for barren the SAR consistently shows a greater coverage.

Further studies are underway to assess the land cover potential of IfSAR over other areas in Africa including wetlands (Morley & Muller, this proceedings), savannah and tropical forested areas as well as within Europe. Reports on these experiments will be made in upcoming Florence meeting.

Acknowledgements

Ben Mumford acknowledges receipt of a NERC Advanced Course studentship (GT3/95/140/E0) for a Master of Science programme in Remote Sensing. This work was supported by BNSC under the Application Development Programme with Logica Space Systems and by NERC under the framework of the TIGER-SVATS project. Finally, ERS raw data was supplied under the ESA data grant (AOL.UK201).

References

Barnsley, M.J., S.L. Barr, and T. Tsang. (1995). Producing large-area land cover maps from satellite sensor images: scaling issues and generalisation techniques., in Environmental Remote Sensing from regional to global scales, edited by G. Foody, and P. Curran, John Wiley & Sons.

Belward, A. and Loveland, T. (1995). The IGBP-DIS 1Km Land Cover Project. Proc. 21st annual conference of the remote sensing society, Southampton, UK. Remote sensing society, Nottingham. 1099-1106.

Congalton, R. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Rem. Sens. Env., Vol.37. 35-46.

Gatelli, F., A.M. Guarnieri, F. Parizzi, P. Pasquali, C. Prati, and F. Rocca. (1994). The wavenumber shift in SAR interferometry., IEEE Trans. Geosci. Rem. Sensing, 32, 855-865.

Goldstein, R. M., Zebker, H. A. & Werner, C. L. (1988). Satellite radar interferometry: Two dimensional phase unwrapping". Radio Science, Vol.23, No.4, 713-720.

Higgins, N.A. (1995). Potential of the ATSR-2 on ERS-2 for monitoring land cover change., in 21st Annual Conference of the UK Remote Sensing Society, edited by P.J. Curran, and Y.C. Roberston, pp. 320-324, Remote Sensing Society, University of Southampton, 11-14 September.

Loveland, T.R., J.W. Merchant, D.O. Ohlen, and J.F. Brown (1991). Development of a Land Cover Characteristics Database for the Conterminous US, Photogrammetric Engineering and Remote Sensing, 57 (11), 1453-1463.

Pratt, W. K. (1978). Digital Image Processing. Wiley, New York.

Prince, S. D., Kerr, Y. H., Goutorbe, J.-P., Lebel, T., Tinga, A., Bessemoulin, P., Brouwer, J., Dolman, A. J., Engman, E. T., Gash, J. H. C., Hoepffner, M., Kabat, P., Monteny, B., Said, F., Sellers, P., & Wallace, J. (1995). Geographical, biological and remote sensing aspects of the Hydrologic Atmospheric Pilot Experiment in the Sahel (HAPEX-SAHEL). Rem. Sens. Env., 51(1), 215-234.

Wegmueller, U. and Werner, C. L. (1995). Land surface analysis using ERS-1 SAR interferometry. ESA Bull., No.81, Feb. 1995. 30-37

Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry