Remote Sensing Applications for Water Resources Management of the Tisza River Basin B. Mueschen, V. Hochschild Friedrich-Schiller-University (FSU) Jena, Institute of Geography, Department for Geoinformatics, Hydrology and Modelling, Loebdergraben 32, D-07743 Jena, GERMANY –
[email protected] Abstract – Tisza River basin includes a unique riparian wetland ecosystem which has to be protected and revitalized. The EU project “The Tisza River Project Real-life scale integrated catchment models for supporting water- and environmental management decisions” deals with integrated catchment modelling to help solving the most critical water- and environmental problems in the Tisza River basin. Amongst the project’s objectives is the derivation of spatial and temporal distributed parameter sets for modelling by means of remote sensing. The whole Tisza River basin is to be modelled in 250 m grid size whereas three selected sub-catchments are to be modelled in 50 m grid size. Oxbow water regime modelling will be performed in wetlands on 1 : 2000 scale. For that purpose Earth Observation (EO) data such as Terra MODIS, Landsat-7 ETM and QuickBird are investigated to classify landcover, and to derive vegetation indices. Keywords: remote sensing, land cover, vegetation indices, water resources management. 1.
INTRODUCTION
The EU project “The Tisza River Project - Real-life scale integrated catchment models for supporting water- and environmental management decisions” (EU Contract No. EVK1-CT2001-00099, http://www.tiszariver.com) deals with the use of integrated catchment modelling tools to help solving the most critical water- and environmental problems in the Tisza River basin in line with the relevant EU policy objectives, aiding amongst others the introduction of the Water Framework Directive of the EU in the countries of the Tisza Basin. Tisza River is the largest tributary to the Danube River (basin size 157.200 km2) and is located almost exactly in the geographic centre of Europe. Multiple water and environmental issues have to be handled in this large basin because it is seriously affected by water pollution from both point and non-point sources including accidents like the catastrophic cyanide spill in 2000. Furthermore floods are the main quantitative issue in almost all flatland regions of the basin. In recent years the annual floods have exceeded the former records (ever observed highest levels) at several stations in subsequent three years. Several flood raptures happened in Ukraine and a serious dike breech happened at Tarpa, Hungary in the year 2001. The societies of the five European countries Ukraine (Tisza headwaters), Slovak Republic, Hungary, Romania, and Yugoslavia (Tisza mouth into Danube River) are deeply concerned with the solution of these problems. There is also a growing international concern about the state of the aquatic ecosystem with special regard to wetlands of the floodplain (Jolankai, 2003).
2.
OBJECTIVES
The overall project objective is to help saving the water resources and ecological values by developing a “real-life” scale integrated catchment model system for supporting water and environmental management decisions. For that purpose spatial and temporal distributed parameter sets for modelling shall be derived by exploiting the synergistic potential of remote sensing and GIS (Geographic Information System) integration. The specific objectives of work package “GIS and database development, remote sensing and parameterization” led by the Friedrich-Schiller-University (FSU) Jena are: generation of a hybrid, internet-accessible database, identification of the model key parameters and variables to be parameterized, classification of the identified physiographic properties of the catchment by means of combined remote sensing techniques, delineation of the spatial distribution of the classified areas as thematic GIS layers. 3.
METHOD
The whole Tisza River basin is to be modelled in 250 m grid size whereas three selected sub-catchments are to be modelled in 50 m grid size: Zagyva catchment in Hungary with an area of 5680 km2, Hornad catchment in the Slovak Republic with an area of 4870 km2, and Lapus catchment in Romania with an area of 1820 km2. Oxbow water regime modelling will be performed in wetlands on 1 : 2000 scale. According to the different modelling scales, medium resolution satellite data such as Terra MODIS and Landsat ETM are investigated to classify landcover by pixel-based approaches, and furthermore to derive leaf area index (LAI). Wetland vegetation, especially macrophytes, are distinguished by objectoriented classification of high resolution QuickBird data. 4.
LAND USE CLASSIFICATIONS
4.1 Data Processing The overall processing scheme for remote sensing data with respect to model parameterization that is applied for the Tisza River project is presented in Fig. 1: Image registration to Universal Transverse Mercator (UTM) as common projection and World Geodetic System 1984 (WGS 84) as common ellipsoid is applied to all individual scenes. If necessary, relative atmospheric correction or haze reduction precedes geometric correction.
atmospheric correction
index images
geometric correction
spectral signatures
topographic normalization
classification
accuracy assessment
visual interpretation
parameterisation Fig. 1: Processing steps for remote sensing data with respect to model parameterization.
Budapest
producer’s accuracy 14,0
user’s accuracy 65,5
water
81,0
100,0
decid. and mixed forest coniferous forest grassland/ pasture agriculture
90,2
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settlement Danube
Legend: cloud settlement water deciduous and mixed forest coniferous forest grassland/pasture agriculture
km 0
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Projection: Ellipsoid: Geodetic Datum:
UTM, Zone 34 WGS84 WGS84
Satellite: Sensor: Acquisition Dates :
Terra MODIS June 20, 2002 May 27, 2003
Fig. 2: Terra/MODIS land cover classification of the entire Tisza Basin (250 m) and accuracy assessment.
In mountainous regions topographic normalization using a Digital Elevation Model (DEM) is applied. Visual assessment of land use and regional patterns is followed by derivation of index images (e.g. Normalized Difference Vegetation Index, NDVI), supervised classification and accuracy assessment. 4.2 Data Acquisition Data of the Moderate Resolution Imaging Spectroradiometer (MODIS) with 250 m resolution serve to perform a six classes land cover classification of the entire Tisza basin. Two scenes with minor cloud coverage were acquired on June 2002 and May 2003. Both scenes were classified separately while as final product the two derived land cover maps were merged, thus reducing the effects of misclassifications due to cloud coverage (Fig. 2). Landsat-7 Enhanced Thematic Mapper (ETM) scenes acquired in August 2000 were processed for the three sub-catchments Zagyva, Hornad and Lapus. Additionally a Landsat-5 Thematic Mapper (TM) scene acquired in 1988 was purchased for change
detection analysis in Zagyva catchment. A ground campaign to obtain reference data for Hornad sub-catchment was carried out by FSU in August 2002. A high resolution QuickBird scene (2.44 m) acquired July 15, 2002 is currently under investigation for the discrimination of wetland vegetation. A ground campaign carried out in July 2002 by the Austrian project partner Institute of Ecology and Conservation Biology, University of Vienna, provides reference information on different macrophytes.
4.3 Results For the entire Tisza Basin six land cover classes: settlement, water, deciduous and mixed forest, coniferous forest, grassland/pasture, and agriculture were classified (Fig. 2). The accuracy assessment was performed in areas with existing reference data such as Zagyva and Hornad sub-catchments. The producer’s and user’s accuracies listed in Fig. 2 show that the 250 m pixel size did not allow to classify small rural villages.
Legend: unclassified settlement water forest grassland/pasture
Projection: UTM, Zone 34 Ellipsoid: WGS84 Geodetic Datum: WGS84 Satellite: Landsat-7 Sensor: ETM Acquisition Date: August 20, 2000
agriculture
Fig. 3: Landsat-7 ETM land cover classification of Zagyva sub-catchment (50 m).
settlement water
Legend: unclassified settlement water deciduous and mixed forest coniferous forest grassland/pasture agriculture
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Projection: Ellipsoid: Geodetic Datum:
UTM, Zone 34 WGS84 WGS84
Satellite: Sensor: Acquisition Date:
Landsat-7 ETM August 20, 2000
km
decid. / mixed forest conif. forest grassland/ pasture agriculture
producer’s accuracy 78,0
user’s accuracy 96,1
94,5
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96,5
95,8
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88,6
74,1
89,1
90,4
Fig. 4: Landsat-7 ETM land cover classification of Hornad sub-catchment (50 m) and accuracy assessment. For Zagyva sub-catchment six land cover classes: settlement, water, deciduous and mixed forest, coniferous forest, grassland/pasture, and agriculture were classified. To produce the final end product the forest classes were merged to one class, and the pixel size was resampled to 50 m (Fig. 3). The accuracy assessment was carried out in a qualitative way by using an elder CORINE landcover classification as reference data (with a much coarser resolution of 250 m). Water, forest and agriculture show high classification accuracy. Due to the acquisition date at the end of August a lot of agricultural fields are already harvested and show similar spectral signatures as sealed/urban areas resulting in misclassifications, i.e. in overrepresentation of urban pixels. To minimize the misclassified urban pixels in the agricultural areas a filter was applied (eliminating all areas smaller than 4 pixels) which lead to a good result concerning the elimination of misclassified pixels in agricultural areas; however at the same time it reduced the settlements themselves. The class grassland/pasture shows overall good accuracy but also some mixture with agriculture due to the acquisition date in August. For Hornad sub-catchment six land cover classes: settlement, water, deciduous and mixed forest, coniferous forest, grassland/pasture, and agriculture were classified, and the quantitative accuracy assessment was performed using ground truth data (Fig. 4). Water and the two forest classes show high classification accuracy. As in Zagyva sub-catchment
agricultural fields show similar spectral signatures as sealed/urban areas resulting in over-representation of urban pixels, and the class grassland/pasture shows overall good accuracy but also some mixture with agriculture. 5.
CONCLUSION AND OUTLOOK
The results obtained so far confirm the benefits expected from the use of satellite data for model parameterization. Land cover information can be classified with 250 m MODIS and 30 m ETM data. Derivation of distributed parameter information of the physical catchment properties is currently under investigation by the Dept. of Hydrology and Hydraulic Engineering of the Free University Brussels. Future work of FSU Jena will concentrate on the classification of wetland vegetation from high resolution QuickBird data by an object-oriented classification approach. REFERENCES Jolánkai, 2003, Report of the activities of the project in the period 01 January 2002 – 31 December 2002, Section 1: Management and resource usage summary, related to the reporting (unpublished), 14 pp.
