LARGE SCALE LAND COVER MAP GENERATION BASED ON LOW AND MEDIUM RESOLUTION ENVISAT ASAR DATA, APPLICATION TO POYANG LAKE AREA (JIANGXI, P.R. CHINA).

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1 LARGE SCALE LAND COVER MAP GENERATION BASED ON LOW AND MEDIUM RESOLUTION ENVISAT ASAR DATA, APPLICATION TO POYANG LAKE AREA (JIANGXI, P.R. CHINA). Andreoli Rémi (1), Shifeng Huang (2), Li Jiren (2), Yésou Hervé (1), Desnos Yves-Louis (3). (1) SERTIT, Université Louis Pasteur, Bld Sébastien Brant, BP F Illkirch-Graffenstaden, France, (2) IWHR, Ministry Water Resource, Beijing, China (3) European Space Agency - ESRIN, Italy Yves-louis.desnos@esa.int ABSTRACT Located in Jiangxi Province, Poyang Lake is the largest freshwater lake in China and constitutes a major hydrological subsystem of the middle Yangtze River (Changjiang) basin in central China. An impressive amount of low to medium resolution ENVISAT data covering the Poyang Lake s 2004 and 2005 hydrologic years was acquired and analysed within the framework of the Flood DRAGON Project ID Land cover mapping was realized synergistically using: 1) a land cover map derived from a colour composition containing seasonal ENVISAT ASAR Global Monitoring Mode image sums, and; 2) a land cover map derived from a colour composition containing filtered, seasonal ENVISAT ASAR Wide Swath Mode image sums. Confronted and validated with the hydrodynamic characterization derived from Landsat reference data, these preliminary classifications lead to a land cover map with 13 classes covering 20,000 square kilometers (170 km from North to South and 120 km from East to West) and was realized at a 1/ scale, but can be used from 1/ to 1/ The results highlight the great potential of ASAR medium and low resolution products for wide-area mapping. Worldwide, large archives of such data already exist which should enable access to the prerequisite amount of data required for such large-scale, land cover characterization. Keywords: Land cover, ENVISAT Global Monitoring Mode, ENVISAT Wide Swath Mode, Poyang Lake, China 1. INTRODUCTION Located in the Jiangxi Province, Poyang Lake is the largest freshwater lake in China and constitutes a major hydrological subsystem of the middle Yangtze River (Changjiang) basin in central China. Playing a key role in Changjiang basin's flood regulation and control, Poyang Lake undergoes very significant, seasonal, water level variations: lake elevation varies about 9 m and his size fluctuates from less than 1,000 km² during the dry winter period to more than 4,000 km² during the wet summer season [1]. Generally, the Poyang Lake area is one of the most frequently flooded areas in China. Seven major floods have occurred in the past fifty years (1931, 1954, 1973, 1977, 1983, 1992, 1995, 1998) and the most severe ever recorded was in 1998 [2, 3]. Poyang Lake was selected as the principal Flood DRAGON Project test site, part of the MOST-ESA DRAGON Programme, whose the principal aim is to explore the potential of ENVISAT's ASAR and MERIS data's spatial and temporal resolution in rapid flood mapping and monitoring [4, 5]. Accordingly, the monitoring and forecasting of floods and the monitoring of Poyang Lake's hydrological dynamics require a good knowledge of its landscape and land cover. Over a wide area composed of very varied hydro-dynamics, geomorphology and environments, this information is not easily accessible. Following studies carried out on the assessment of ENVISAT ASAR Global Monitoring Mode for land applications [6, 7], this work presents a synergistic use of ENVISAT ASAR low and medium resolution time-series for large scale land cover map generation. 2. DATABASE AND METHODOLOGY 2.1. Database The database over Poyang Lake area includes, firstly, a set of 2 optical high resolution reference Landsat data, covering two different hydrological periods of Poyang Lake (Table 1), and a 3 arc second (90 m) SRTM Digital Elevation Model. Landsat reference data were used for a preliminary hydrodynamic characterization of Poyang Lake s annual water level variations and a land cover mapping (Fig. 1). Water bodies and wetland areas were extracted by thresholding each Landsat images. Lowlands, including lands below 20 m above mean sea level and frequently flooded overbanks, have been discriminated using SRTM DEM and medium scale landscape analysis. The resulting classification shows the annual water level dynamics while differentiating low water levels, from reference data, areas of seasonal water variations (lakeshore) and wetland soils associated with lowlands (Fig. 1). Proc. Envisat Symposium 2007, Montreux, Switzerland April 2007 (ESA SP-636, July 2007)

2 Figure 1: Lowland landcover in the Lake Poyang area derived from Landsat data The ENVISAT ASAR low and medium resolution database includes 30 images acquired between January 2004 and June 2006: 14 ASAR Global Monitoring Mode (GMM) images with a spatial resolution of 1 km Table 1: Reference Landsat data Hydrological satellite Type date period Landsat 5 High water Multispectral 15/07/1989 TM level Landsat 7 Multispectral 10/12/1999 Low water level ETM+ for a pixel spacing of 500 m [8]; and 16 ASAR Wide Swath Mode (WSM) images, with a spatial resolution of 150m for a pixel spacing of 75 m [8]. Field survesy carried out during the 2006 flood season were conducted to validate and precise preliminary land cover maps derived from the ENVISAT datasets. Poyang Lake looks like an inland sea bordered by flat lakeshore fringes comparable to tidal areas (meadows and muddy areas). The lowlands are mainly occupied by paddy fields with aqua-culture farms (pearl, water-lily, fish) separated by copses or hedges. In the Gan and Fuhe delta, paddy fields are characterized by several culture cycles: ploughed fields, crop growth, crop maturity, and harvesting. The hills and mountainous areas surrounding Poyang Lake are occupied by a mixed forest on the slopes (deciduous trees and conifers), dry crops (cotton, leguminous, sunflowers) and paddy fields. The entrance to the channel connecting Poyang Lake to Changjiang is edged by inherited dunes of fine sands (Duchang County). These dunes form parallel hills of m high on both sides of the lake. Each dune sector extends 10 km from north to south and 3-5 km east-west (Fig. 2) Methodology The land cover mapping methodology has four major steps: data pre-processing, data filtering, landscape analysis and finally land cover mapping (Fig. 3). The first step consists of ENVISAT GMM and WSM data calibration, and geo-referencing. The antenna pattern was corrected using the Rough-range calibration tool for the GMM dataset and the Backscattering Image Generation tool for the WSM dataset implemented in the BEST software. In addition, calibrated ENVISAT GMM data were standardized to make them comparable because the Rough-range calibration tool doesn t correspond to a true calibration [9]. Figure 2: Upper images from left to right, Fuhe delta, pearl farm in the Gan and Fuhe delta, paddy fields in the Gan and Fuhe delta and water-lily in the Gan and Fuhe delta; bottom images from left to right, hilly landscape of north eastern of Poyang Lake, sand dunes near Duchang.

3 Geometric correction was performed using a second order polynomial model for ENVISAT ASAR GMM in ERDAS Imagine V8.7; whereas a Wide Swath Mode data orthorectification was performed, using a SRTM DEM, with PCI Geomatica v9.1's orthorectification tool. whereas the infilling period is only covered by VV data [7]. Speckle reduction was realized on ENVISAT Wide Swath Mode data performing a multi-channel averaging over all images based on a pixel-to-pixel summation developped by Lee, Bruniquel and Lopez, [10, 11]. Seasonal summing for landscape analysis was realized on a subset of 7 filtered data (Table 2) from the 16 ENVISAT Wide Swath Mode in the database. Due to differences in terms of polarization and orbit mode (ascending or descending), seasonal summing was computed only taking into account one hydrological period to enhance radiometry according to the season. This resulted in mixing different polarizations and modes, increasing the complexity of the results. Also, a difference image between low-water seasonal summing and high-water seasonal summing was performed. 3. LANDSCAPE ANALYSIS, VALIDATION AND CORRECTIONS 3.1. Landscape analysis Figure 3: Land cover mapping methodology. Landscape analysis required data filtering and radiometric enhancement. So, similar GMM data in terms of hydrological period (acquisition time) and polarization, have been summed according to the season (Table 2). It is noticeable that images in ascendant mode with polarization HH are well represented in the dataset, The multitemporal GMM colour composite of the seasonally summed products firstly led to the classification of four major land cover types which were extracted by visual interpretation: permanent water bodies between January 2004 and April 2005, areas of seasonal water level variations (lakeshore), wet lowlands (mostly paddy fields and marshes), and urban areas [7] (Fig. 4.A). On the WSM multitemporal colour composite, land cover appears more complex with different types of water, moisture, heterogeneity in the lakeshore being seen. So, a rich and detailed land cover map of 22 classes was elaborated from the filtered WSM dataset through a supervised classification (Fig. 4.B). Table 2: Data used for radiometric enhancement by seasonal summing Hydrological period ENVISAT GMM ENVISAT WSM polarization mode date polarization mode date 09/12/2004 HH A 29/01/2004 Low water level HH A VV D 20/12/ /12/2004 VV A 20/02/ /03/2004 HH A 30/04/2004 Infilling VV A 27/04/ /05/2004 VV D 20/04/2005 High water level HH 25/07/2004 HH D 21/08/2004 A 26/08/2004 HH A 11/09/2004 D 06/09/2004 Draw-off HH A 19/10/ /11/

4 3.2. Landscape analysis validation and corrections As previously observed with the GMM land cover map [7], comparisons of the ENVISAT land cover maps with the reference map show that the cartographic results concord well. The shape of the major perennial water bodies is well represented. The mapped delta plain of Gang and Fuhe rivers, from Nanchang to Poyang Lake are similar to the wet lowlands identified on the Landsat reference classification. Quantitative comparison of water bodies, lakeshore and lowland classes between the ENVISAT land cover maps and the Landsat reference map, re-sampled respectively to 500m and 75 m, with the aim of eliminating resolution effects, shows that: 69,7% of water bodies, 57,9% of seasonally flooded areas and 74,4% of lowlands and ordinary flooded overbanks are both recognized on GMM and Landsat classifications; 64,8% of water bodies, 63,3% of lakeshore and 45,1% of lowlands and frequently flooded overbanks are recognized jointly on WSM and Landsat classifications. Doing the same between WSM land cover map resampled to 500m and the GMM derived classification shows that 60,2% of water bodies, 77,8% of lakeshore and 47,5% of low-lands and frequently flooded overbanks are recognized by both classifications. There are three main explainations for the differences between the classifications: different resolutions, the difference in time and period between ENVISAT data time-series and reference Landsat data, and different land cover discrimination methodologies. The difference in resolution between the datatsets can explain that finer elements like streams and the morphology of the channel linking the Poyang depression to Changjiang are not well mapped on the GMM land cover map, but also to a lesser extent on the WSM land cover map. The differences in date and period between ENVISAT data time series aquired between 2004 and 2006 and the Landsat references aquired in 1989 and 1999 can explain the extensive differences concerning areas of seasonal water variations between ENVISAT and Landsat classifications. In fact, the July 1989 floods appear more pronounced than those observed during the ENVISAT monitoring period. Finally, the lowlands of the Gan and Fuhe River delta and frequently flooded overbanks have been discriminated based on geomorphological considerations exploiting landscape interpretation and topographic information in the reference land cover map. Whereas, in the low and medium resolution ENVISAT land cover map, only ASAR radiometry was taken into A B Figure 4: A. Land cover map derived from GMM colour composition; B. Initial land cover map (22 classes) derived from ENVISAT WSM filtered products and seasonal summing products.

5 Table 3: Land cover analysis over the Poyang Lake area. Area Class name Perennial water bodies Intermittent water bodies in sandy river beds/not very deep water Lakeshore Area Parent classes 902,3 % of the total 4,5 905,5 4,5 Poyang Lake 2595,2 12,8 Km² Wetlands /swamps 1112,2 5,5 Dry crops (cotton, vegetables) Valley crops (mainly paddy fields) Delta plain crops (mainly paddy fields, pearls, waterlilies) 2564,6 12,7 1623,2 8,0 1598,9 7,9 Forest (conifer, broad-leaved trees, bamboo) 8606,5 42,5 Dunes Urban area (very high backscattering) Urban area (high backscattering) Urban area (medium backscattering) Urban area (low backscattering) TOTAL 50,9 7,2 76,3 75,5 132, ,56 0,3 0,0 0,4 0,4 0,7 100 Km² % % within classes 20,5 4403,0 21,7 20,6 58,9 Wetlands / swamps 1112,2 5,5 44,3 Crops 5786,7 28,6 28,1 27,6 Forest (conifer, broad-leaved trees, bamboo) Dunes Urban areas 8606,5 42,5-50,9 0,3 291,3 1,4 2,5 26,2 25,9 45,4 account. But, differences in vegetation composition, soil roughness and moisture lead to differences in backscattering behaviour which might explain some of the differencies between ENVISAT land cover maps and the reference classification. The high level ENVISAT ASAR GMM and WSM land cover classes were corrected and completed by field observations. This validation and correction step enabled the combination and simplification of the classifications leading to a 13 class land cover map (Fig. 6): Perennial water bodies; intermittent water in sandy river beds/not very deep water (foreshore); Poyang lake shorre (lakeshore); wetlands/swamp; dry crops (cotton, vegetable); valley crops (mainly paddy fields); delta plain crops (mainly paddy fields, pearl farms, water-lily harvesting); forest (Conifer, broad-leaved trees, bamboo); dunes; urban areas (from high to low backscattering). It is also noticeable that sand dunes in Duchang area were firstly misclassified as areas of seasonal water level variation on WSM landscape analysis and were corrected by field observations. 4. RESULTS AND DISCUSSION The Poyang Lake s land cover map covers about square kilometres (170 km from North to South and 120 km from East to West) and was realized at 1/ scale, but can be used from 1/ to 1: This land cover analysis (Table 3) shows that the Figure 6:Land cover map dervied from low and medium resolution ENVISAT ASAR products and field survey

6 entire lake area, including perennial water bodies, intermittent water bodies in sandy river beds and/or not very deep water, and lakeshore, reaches 4 403,0 km². 58,9% of this corresponds to areas of water level variation. Perennial water bodies account for 902,3 km². On the whole, forestry is the major land cover type in the area with 8 606,5 km² (42,5%) covering much of the uplands. Agricultural fields cover 28,6% of the analysed area (5 786,7 km²) and wetland farming is the main agricultural activity around Poyang Lake extending to about 3 222,1 km² (55,7% of crops area). These major landscape types are completed by: 1 112,2 km² of wetlands / swamps; 50,9 km² of dunes; and 291,3 km² of urban areas. Since the catastrophic 1998 floods, in the middle reaches of the Changjiang, wetland restoration policies were set up by the central government in order to reduce flood impacts. Studies on wetland restoration monitoring around Poyang Lake have already been carried out using 1998 and 2004 optical EO data [12]. The use of an ENVISAT ASAR database over Poyang Lake area should greatly complement and continue wetland restoration monitoring. These are also very promising results for the use of ENVISAT ASAR low and medium resolution data in global landscape characterisation over wide areas. Large archives of these data already exist which should enable access to the prerequisite amount of data required for such large-scale, land cover characterization of hydrodynamically complex areas. But it s necessary to take resolution into account as to the result's degree of confidence but also in their confrontation with other kinds of data. 5. ACKNOWLEDGEMENT The authors are grateful to ESA because this work was realized within the framework of the Sino-European joint research (ESA-NRSCC) DRAGON project 2551 and supported by ESA's Support Training of Young European Scientists through a grant. The authors would also like to thank Dr Christine KING (BRGM) for her support through the Sino-French WARM cooperation programme. 6. REFERENCES 3. Yongqiang, Z., Xiqing, C., The 1998 Flood on the Yangtze, China. Natural Hazards 22: pp Yésou, H., Li, J., Li, J., Wang, X., Yida, F., Wang, Y., Huang, S., Xin, J., de Fraipont, P., Assessment of the Synergistic Exploitation of ENVISAT ASAR and MERIS Data for Plain Flood Rapid Mapping: a Part of the Dragon Flood Project. ENVISAT Symposium Salzburg 6-10 September Li J., Yésou H., Huang S., Li J., Li X., Xin J., Wang X., Andreoli R., ENVISAT ASAR medium and high resolution images for Near Real Time flood monitoring in China during the 2005 flood season. Proc Dragon Symposium Mid-Term Results, Santorini, Greece 27 June 1 July 2005, ESA SP-611: pp Andreoli, R., Assessment of the Synergistic Exploitation of ENVISAT ASAR and MERIS for Rapid Flood Mapping and Monitoring, Flood DRAGON Project Young Scientist Final Report: 194p. 7. Andreoli, R. and Yésou, H., Monitoring water level seasonal variations of large natural lake exploiting ENVISAT ASAR low resolution time series: application to Poyang Lake (P.R. China) during the hydrological period. Proc Dragon Symposium Mid-Term Results, Santorini, Greece 27 June 1 July 2005, ESA SP- 611: pp ESA, ASAR product Handbook: 543 p. 9. ESA, Telespazio, BEST Basic Envisat SAR Toolbox User Manual Version : 189p. 10. Lee, J-S., Grunes, M.R., Mango, S.A., Speckle reduction in multipolarization, multifrequency SAR imagery, IEEE Transaction on Geoscience and Remote Sensing, Vol 29, n 4: pp Bruniquel, J., Lopes, A., Multi-variate optimal speckle reduction in SAR imagery, Int. J. Remote Sensing, Vol 18, n 3: pp Jiang, L., Yu, X., Zhao, H., Zhou, Y., China s Wetlands Restoration around Poyang Lake, Middle Yangtze: Evidences from Landsat TM/ETM Images. Proc. of Geoscience and Remote Sensing Symposium, IGARSS '05, IEEE International Volume 4, 25-29: pp Shankman, D. and Liang, Q., Landscape Changes and Increasing Flood Frequency in China s Poyang Lake Region. The Professional Geographer, 55(4): pp Shankman, D., Keim, B. D., Song, J., Flood Frequency in China s Poyang Lake Region: Trends and Teleconnections. Int. J. Climatol. 26: pp