CONTINUOUS MAPPING OF THE ALQUEVA REGION OF PORTUGAL USING SATELLITE IMAGERY Célia Gouveia 1,2 and Carlos DaCamara 2 1 Escola Superior de Tecnologia, Instituto Politécnico de Setúbal, Setúbal. Portugal; 2 Centro de Geofísica da Universidade de Lisboa, Portugal Abstract Located in the South of Portugal, the Alqueva dam has given rise to one of the largest artificial lakes of Europe. Therefore it is not surprising that reliable information is more and more required about the climate impact of the Alqueva and its effects at the agricultural and socio-economic levels. However recent land cover maps such as Corine2000 and GLC2000 still do not show the location of the Alqueva, whose extent is large enough to be identifiable in MSG imagery. In the present study we show how MSG imagery may be used to monitor the Alqueva region. The developed approach was applied for the year 2005 and the results are validated by comparing with those obtained from the VEGETATION instrument for the year of 2005. INTRODUCTION The Alqueva dam is one of the largest dams in the Iberian Peninsula (with a full storage level at 152 m) and will lead to the largest artificial lake in Europe, with an area of 250 km 2 (35 km 2 in Spain) and a total capacity of 4,150 hm3, of which 3,150 hm 3 are usable during regular operation. The Alqueva (Fig 1) was conceived as a regional development project, with the aim of meeting the needs for a strategic water reservoir in the Alentejo, a semi-arid region in southern Portugal. Furthermore, its strategic importance relates with the needs of guaranteeing the water supply to population and industry, of changing the crop model in Alentejo's agriculture, of fighting against physical desertification and climate change, of intervening in the domains of the environmental and national heritage and of encouraging the regional employment market. Figure 1: The Alqueva basin. (source: http://poaap.quaternaire.pt). Therefore it is not surprising that reliable information is more and more required about the climate impact of the Alqueva and its effects at the agricultural and socio-economic levels. However recent land cover maps such as Corine2000 and GLC2000 still do not show the Alqueva basin. The purpose
of this work is to present a methodology allowing the mapping of the Alqueva water body based on data from the SEVIRI and VEGETATION instruments. For arid or semi-arid regions, water bodies and wetlands play a very important role due their importance for the human activities. Several authors have developed methodologies to produce water body cartographies from high resolution satellite imagery (Frazier and Page, 2000, Baghadi et al 2001, Chopra at al, 2001). Recently, Xiao et al (2002) developed an algorithm to identify paddy rice fields, using temporal profiles of LSWI and NDVI data derived from 10-day composite VGT images Rice paddies were identified as areas where LSWI values increased (due to the greater surface moisture during the flooding period) and were temporarily greater than NDVI values (Xiao et al., 2002). Gond et al (2004) presented a robust methodology to monitoring the state of small water bodies and wetlands on dry regions, using satellite imagery from SPOT4/VEGETATION. DATA The data consist of B2, B3, and SWIR (i.e. red, near infrared (NIR), and short-wave infrared) 10-day composites of VEGETATION data (VGT-S10 products) available at http://free.vito.vgt.be and covering the whole year of 2005 and images of Channels 1, 2 and 3 (centred on 0.6, 0.8 and 1.6 µm) from SEVIRI, covering the period from May to October of 2005, provided by the LSA SAF. The reference map used is the Corine Land Cover Map (CORINE2000), available at http://www.iambiente.pt with a resolution of 250. The Corine Land Cover is a key database for integrated environmental assessment and provides a pan-european inventory of biophysical land cover information, using a 44 class-nomenclature. This thematic Map was re-projected onto Geographic Coordinates and pixels were geo-coded to the size of 1,000 m, by applying a criterion based on the most frequent class and considering a pixel to be water if there are at least 9 pixels classified as water inside a box of 16 pixels (Fig 2a). Figure 2: Corine Land Cover Map, version 2000 (CORINE2000) for Portugal. Left panel: CORINE2000 geo-coded to the size of 1,000m, using a criterion based on the most frequent class. Right panel: water in CORINE2000, selecting the classes labelled from 35 to 43 of nomenclature. METHODS AND RESULTS The method presented takes benefit of the advantages of the availability of the short wave infrared channel for VEGETATION (1.58 1.75 μm, SWIR) and the near infrared channel for SEVERI (centered in 1.6 μm, NIR). These channels are absorbed by water, being it free water bodies or water contained in plant cells.
In the vegetative areas, the reflectance of the NIR band is the highest. The radiance of the SWIR band is absorbed by water contained in leaves but not in the NIR band (Tucker, 1980). In order to assess water content in a normalised way, NDWI (normalised difference water index, (NIR- SWIR)/(NIR+SWIR)) was introduced by Gao (1996). This index increases with vegetation water content or from dry soil to free water. It was assumed that if SWIR is larger than NIR then the land cover in that area is non or sparsely vegetated (Sato, 2004). The NDVI is helpful if ponds are characterised by well-developed vegetation contrasting with the surrounding dryland. Using VGT Database Pixels contaminated by clouds, snow and striping caused by blind and/or aberrant MIR detectors were removed using the information from status maps provided by VITO. RGB (NDWI, NDVI, SWIR) images were produced for each decade and visually checked. On colour composites of derived channels, water bodies may be easily recognised by visual analysis due to the local contrast (Fig 3). Based on percentiles, computed for each decade, of NDWI, NDVI and SWIR we have developed a dynamical threshold technique appropriate to the detection of water bodies (namely the Alqueva basin), estuaries and coastal lagoons. The number of decades with water classification was counted in each pixel and the spatial distribution of this number in the study area was examined. Figure 3: Identification of water bodies and estuaries using VGT Dataset. Left panel: Rgb (NDWI, NDVI and SWIR) images for Alqueva basin. Right panel: Rgb (NDWI, NDVI and SWIR) images for estuaries of Tagus an Sado. The green lines are contaminated pixels by aberrant MIR detectors. Figure 4 shows the spatial distribution of the number of decades classified as water. Such pixels were identified as those classified as water pixels in more than 15 for estuaries and coastal lagoons and more than 20 decades for water bodies, namely Alqueva Basin. Obtained classification for Portugal is shown in Figure 5 Figure 4: Identification of water bodies and estuaries in VGT dataset. Left panel: rgb (NDWI, NDVI and SWIR) images for Alqueva basin (bottom) and estuaries of Tagus an Sado (top). Right panel: composite of 36 decades where water pixels are those classified as water in more than 15 decades.
Figure 5: Water classificationas obtained from VGT-S10 products, based on the developed technique. In order to assess the quality of the classifications performed we have computed four accuracy measures, using as reference the CORINE2000. A given pixel was classified as water in the ground truth if belonging to classes 35 to 43 of CORINE2000 Map (Fig 2b). A global measure of classification quality is given by the overall accuracy (A) and the quality of each individual class may be assessed based on three different measures; the Producer s accuracy (PA), the User s accuracy (UA) and the Comparison Index (CI). These measures are defined as follows: Correctly Classifieded Pixels A= Total Number of Pixels (1) Correctly Classifieded Pixels PA= Total Detected Pixels from Reference (2) Correctly Classifieded Pixels UA= Total Detected Pixels from Classified data (3) CI = UA PA (4) Obtained results are given in Table 1 and indicate that pixels classified as water have a probability of 71% of belonging to this class in ground truth. Overal Accuracy Producer s Acuracy User s Accuracy Comparison Index 0.97 0.60 0.71 0.65 Table 1: Quality parameters of performed classifications. Using MSG Database With the aim of minimizing cloud and snow contamination, MVC (Maximum Value Composite) for the MSG images were computed, using 10 day-composites. On colour composites on derived channels, water bodies may be easily recognised by visual inspection because of the local contrast (Fig 6). RGB (NDWI, NDVI, Ch1.6) images for each composite were visually checked and a dynamic threshold technique for detecting ocean, water bodies and estuaries was developed, based on percentiles of NDWI, NDVI and Ch1.6 for each decade.
Figure 6: Identification of water bodies and estuaries using the MSG Dataset. Rgb (NDWI, NDVI and SWIR) images for: Right circle: Alqueva basin. Left circle: for estuaries of Tagus an Sado. Pixels of land are defined as those pixels classified as water for 0 (zero) decades. Pixels with water are defined as those classified as water in more then 2/12 for Alqueva/Estuaries (Figure 4). Figure 7 shows the spatial distribution of the number of decades classified as water. Pixels of land are defined as those pixels classified as water for 0 (zero) decades (left). Pixels with water are defined as those classified as water in more than 12 for Estuaries (right) and more than 2 for the Alqueva dam. Figure 7: Identification of ocean, water bodies and estuaries in MSG imagery. Top panel: composite of 14 decades with land identified as pixels never classified as water in the 10-day composites. Bottom panel: composite of 14 decades where water pixels are those classified as water in more than 2 decades. Obtained classification for Portugal, geocoded to 5 x 5 km of latitude and longitude, is shown in Figure 8. Because of their different resolutions, VGT and MSG datasets are expected to lead to classifications capturing different land cover characteristics. Taking into acount the fact that the usage of CORINE2000, geocoded to 5 km will introduce considerable errors in the accuracy measures, we have decided to analyse the resulted classification by visual inspection. As shown in Figure 8, it is possible to recognize the main water bodies in Portugal: Tagus and Sado estuaries, the Aveiro coastal lagoon and especially the Alqueva basin.
Figure 8: Water classification as obtained from the MSG dataset, using the developed technique. CONCLUSIONS AND FUTURE WORK In the present study we have relied on MSG and VGT images to monitor the Alqueva region. The developed technique takes benefit of the advantages of the availability of the SWIR channel for VGT and the NIR channel for SEVERI. These channels are absorbed by water, being it free water bodies or water contained in plant cells. The developed method that allows mapping water bodies, estuaries and coastal lagoons, using VGT dataset, has led to good results, with a probability larger than 70% of a given pixel being correctly classified as water. When applied to the MSG dataset, the technique led to recongnition of the main water bodies in Portugal, in particular the Alqueva basin. If separately applied to single 10-day composites, the developed threshold technique based on dynamic percentiles may allow a continuous monitoring of the Alqueva basin. Obtained results for the VGT dataset may be incorporated in a future update of the GLC 2000 map for Portugal. ACKNOWLEDGEMENTS The authors are grateful to Dr. Isabel Trigo from LSA SAF (Portugal) and to VITO for kindly providing the raw data. The authors are also grateful to the Portuguese Environmental Institute for kindly providing the Corine Land Cover Map 2000, for Portugal. REFERENCES 1. Baghdadi, N., Bernier, M., Gauthier, Y., and Neeson, I., 2001, Evaluation of C-band data for wetland mapping. International Journal of Remote Sensing, 22, 71 88. 2. Chopra, R., Kerma, V. K., and Sharma, P. K., 2001, Mapping, monitoring and conservation of Harika wetland ecosystem, Punjab, India, through remote sensing. International Journal of Remote Sensing, 22, 89 98. 3. Frazier, P., and Page, K., 2000, Water body detection and delineation with Landsat TM data. Photogrammetric Engineering and Remote Sensing, 66, 1461 1467. 4. Gao, B. G., 1996, NDWI a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257 266. 5. Gond, V., Bartholome, E., Ouattara, F., Nonguierma, A. and Bado L., 2004, Surveillance et cartographie des plans d eau et des zones humides et inondables en re gions arides avec l instrument VEGETATION embarque sur SPOT-4.International Journal of Remote, 25, 987 1004.
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