APPLIED REMOTE SENSING AND GIS INTEGRATION FOR MODEL PARAMETERIZATION (ARSGISIP)

Transcription

1 Final Report APPLIED REMOTE SENSING AND GIS INTEGRATION FOR MODEL PARAMETERIZATION (ARSGISIP) CONTRACT NO. ENV4-CT Period: 01/01/98 12/31/00 by Prof. Dr. Wolfgang-Albert Flügel and Bettina Müschen Lehrstuhl für Geoinformatik Geographisches Institut Löbdergraben 32 D Jena, GERMANY Tel.: (0) FAX: (0) arsgisip@geogr.uni-jena.de

2 ARSGISIP Final Report CONTENTS i CONTENTS List of Tables List of Figures Definitions, Acronyms, Abbreviations iii v x SUMMARY 1 A HIGHLIGHTS... 1 B OBJECTIVES... 2 C CONTEXT OF WORK... 2 D OUTLINE OF METHODOLOGY... 3 E RESULTS... 5 F CONCLUSIONS... 6 I INTRODUCTION 8 A OBJECTIVES... 8 B CONTEXT... 9 C BRIEF DESCRIPTION OF THE CONSORTIUM II WORK ACHIEVED 14 A OVERVIEW A.1 Method...14 A.2 Description of catchments...16 A.3 Description of models...19 B MILESTONES REACHED MS 110: Databases for test catchments of the individual end users 21 MS 210: Flow chart o f system s dynamics 21 MS 220: Definition of model key parameters and variables to be identified by remote sensing 21 MS 230: De finition of associated physiographic catchment properties 21 MS 240: Definition of required resolution in space and time 21 MS 311: Land use/crop classification and LAI 22 MS 312: Thematic maps of land use and crop pattern in space and time/linear and point sources 23 MS 321: Forest type classification 23 MS 322: Thematic maps of forest types/clearcuts 24 MS 331: Assessment of land degradation 24 MS 332: Thematic maps of source areas for sediment contribution and surface runoff 25 MS 341: Identification of appropriate methods for soil moisture estimation 25 MS 342: Thematic maps of soil moisture distribution 25 MS 351: Thematic maps of quantified chlorophyll content/eutrophication/algae growth/sediment distribution 25 MS 352: Freezing dynamics of lakes 26 MS 361: Thematic maps of flood plains surface roughness/inundation areas 26 MS 371: Ground truth data for all classifications carried out in WT 310 to WT 360 and for parameterizations carried out in WP MS 372: Thematic maps presenting field work results 27 MS 410: Delineation of Response Units (RUs) 28 MS 420: Thematic maps and GIS coverages from RUs 28 MS 510: Parameterization of model used by the individual RETs 28 MS 520: Thematic maps and GIS coverages of parameterized model key parameters and variables 29 MS 610: Validated parameterizations of the individual RET s models 29 MS 620: Cost-benefit analyses for EO applications 31 C RESULTS BY WORK PACKAGE C.1 Generation of databases (WP 100)...32 WT 110: Acquisition of satellite images 32 WT 120: Generation of GIS coverages 34

3 ARSGISIP Final Report CONTENTS ii C.2 Identification of parameters (WP 200)...36 WT 210: System s analysis - description of relevant transport dynamics and its governing processes 36 WT 220: Definition of model key parameters 40 C.3 Classification (WP 300) Spatial-temporal land cover Settlement detection SPOT 1-3 and SPOT-4 MIR classification Enhancement methods in land cover classification due to the MIR channel on SPOT Potential of SAR coherence imagery for classification improvement Complementarity between amplitude and coherence Leaf Area Index 50 WT 310: Agriculture WT 320: Forest WT 330: Erosion and land degradation 57 WT 340: Soil moisture 59 WT 350: Water quality 62 WT 360: Flood plains 64 WT 370: Ground survey 65 C.4 Delineation (WP 400)...68 WT 410: Production of thematic maps 68 WT 420: Delineation of response units 69 C.5 Parameterization (WP 500)...72 WT 510: Quantification of parameters and variables required by end user 72 C.6 Validation (WP 600)...77 WT 610: Verification analyses 79 WT 620: Cost-benefit analyses 80 D DEVIATIONS FROM THE WORK PLAN III RESULTS AND CONCLUSIONS 89 IV REFERENCES 92 V ANNEXES V-1 A B C D E COPIES OF SPECIFIC DELIVERABLES, TABLES AND FIGURES...V-1 REPORTS ON WORKSHOPS...V-83 B.1 Kick-off, March V-83 B.2 Second workshop, May V-93 B.3 Third workshop, 7-8 Oct V-99 B.4 Fourth workshop, Dec V-102 B.5 Fifth workshop, 4-8 Sept V-105 B.6 Sixth workshop, 1-2 Dec V-112 PUBLISHED MATERIAL...V-113 C.1 Reviewed Paper... V-113 C.2 Proceedings Paper... V-113 C.3 Graduate Theses... V-114 C.4 Workshops... V-115 C.5 Internal Reports... V-115 PUBLICITY MATERIAL...V-116 D.1 Project newsletter... V-116 D.2 Remote sensing method pool... V-117 D.3 Parameterization pool... V-153 PRESENTATION OF PROJECT WEB PAGE...V-182

4 ARSGISIP Final Report LIST OF TABLES iii LIST OF TABLES Table 1 Satellite image data available for the ARSGISIP testsites. V-1 Table 2 ERS scenes available for the Alsace Region, France. V-2 Table 3 ERS scenes available for the Camargue Region, France. V-2 Table 4 GIS layer of the Weida-Zeulenroda catchment, Germany. V-2 Table 5 Datasets obtained for the Innbach catchment, Upper Austria. V-2 Table 6 Datasets obtained for the Gurk catchment, Carinthia, Austria. V-3 Table 7 GIS layers of the Alsace region, France. V-3 Table 8 GIS layers of the Camargue region, France. V-3 Table 9 GIS layer of the Flumendosa River catchment, Italy. V-3 Table 10 GIS database of the Siuruanjoki River catchment, Finland. V-3 Table 11 GIS database of the Flisa study area, Norway. V-3 Table 12 GIS database of the Genevadsån and Lagan catchment, Sweden. V-4 Table 13 Accurracy assessment for the 1999 multitemporal Landsat-5 TM classification of the Weida-Zeulenroda catchment, Germany. V-4 Table 14 Accurracy assessment for the classification of the Landsat-7 ETM scene acquired June 16, 2000 in the Weida-Zeulenroda catchment, Germany. V-4 Table 15 Accuracy of crop type classification for the Innbach catchment, Austria. V-5 Table 16 Accurracy assessment for the multitemporal classification of Landsat-5 TM scenes in the Mulargia catchment, Italy. V-5 Table 17 Improved Landsat-5 TM land use classification of Siuruanjoki basin, Finland. V-5 Table 18 Confusion matrix for JERS classification of Siuruanjoki basin, Finland. V-6 Table 19 Confusion matrix for ERS classification of Siuruanjoki basin, Finland. V-6 Table 20 Accuracy assessment for the single date Landsat-5 TM classification of the Flisa catchment, Norway. V-6 Table 21 Classification accurracy in % for training areas with the SMAP procedure, Lagan catchment, Sweden. V-7 Table 22 Results of the combined hydrologic and erosive WEPP model for 20 hillslope profiles in the Rio Uvini subcatchment, Italy. V-7 Table 23 Complete information on rainfall simulation test sites for 20 of the total 49 pedological units in the Flumendosa and Mulargia catchments, Italy. V-8 Table 24 Results of the combined hydrologic and erosive WEPP model for each land unit in the Flumendosa catchment, Italy. V-9 Table 25 Analytical data of the sediments from the Flumendosa Reservoir, Italy. V-9 Table 26 Analytical data of the sediments from the Mulargia Reservoir, Italy. V-10 Table 27 Mean water quality during July-September 1999 in the drainage basin of Siuruanjoki, Finland. V-10 Table 28 Comparison of the results obtained from the three optimum selection methods. V-11 Table 29 Mean SPOT 4 values per band for each of the target image features for a May image. V-11 Table 30 Coefficients of the thematic axes after the SPOT 4 Gram-Schmidt transformation. V-11 Table 31 Sketch of the Flumendosa Soil Map legend. V-11 Table 32 Strickler coefficients of WASPI model. V-12 Table 33 Estimated equations (fourth degree polynomial) for the SI curves. V-12 Table 34 Age groups defined according to clearcut-history. indicates any forest class other than clearcut. V-13 Table 35 Age at breast height for different species and growth conditions. V-13 Table 36 Height classes from age classes based on the equations derived. V-13

5 ARSGISIP Final Report LIST OF TABLES iv Table 37 Differentiation of forest stands older than 23 years according to historical development. V-13 Table 38 Description of classes in the SI dataset. V-14 Table 39 Rules used to derive the 5 different height classes. V-14 Table 40 Regression coefficients for the relation between species and different vegetation indices. V-15 Table 41 Equations used to derive LAI. V-15 Table 42 Average height and basal area taken from forest inventory data from Hedemark. V-15 Table 43 LAI classes distributed according to SI and height class. V-15 Table 44 Simulated and computed discharge [m 3 /s] for the hydrological years V-15 Table 45 Simulated and computed discharge [m 3 /s] for the hydrological years for the Zeulenroda catchment, Germany (with radiation data). V-16 Table 46 Simulated and computed discharge [m 3 /s] for the hydrological years for the Mulargia catchment, Italy. V-16

6 ARSGISIP Final Report LIST OF FIGURES v LIST OF FIGURES Fig. 1 Location of the Weida-Zeulenroda-Lössau reservoir system, Germany. V-17 Fig. 2 Overview of Austrian test catchments: Innbach Upper Austria, Gurk Carinthia. V-17 Fig. 3 Localisation of the Ried Center Alsace and Potassic basin test sites, France. V-18 Fig. 4 Localisation of the Camargue experimental test area, France. V-18 Fig. 5 Localisation and shaded relief of the Italian catchment Mulargia, Sardinia. V-19 Fig. 6 Localisation and shaded relief of the Siuruanjoki catchment, Finland. V-19 Fig. 7 Location of the Jostedalen and Flisa catchments, Norway (above left) and overview of the Jostedalen catchment and sub-catchments (right). V-20 Fig. 8 Overview of the Lagan and Genevadsån catchment, Sweden. V-20 Fig. 9 Input data for the WASMOD model that can be drived in general by remote sensing data (marked in yellow) and that where derived by remote sensing data in the ARSGISIP project (marked in green). V-21 Fig. 10 Input data for the PRMS/MMS model that can be drived in general by remote sensing data (marked in yellow) and that where derived by remote sensing data in the ARSGISIP project (marked in green). V-21 Fig. 11 Input data for HBV and HBV-N model. V-22 Fig. 12 Schematic structure of the WASMOD water and solute transport model (Reiche, V-23 Fig. 13 Scheme of WASPI flood model. V-23 Fig. 14 Model components of PRMS (Leavesley et al. 1983) V-24 Fig. 15 Model components of V3MOD (Lauri 1999). V-24 Fig. 16 Model structure in LANDPINE (Rinde 1999). V-25 Fig. 17 Schematic structure of the present HBV model (Arheimer & Brandt 1998). V-25 Fig. 18 Flow chart of system s dynamics in the Weida-Zeulenroda catchment area, Germany. V-26 Fig. 19 Flow chart of system s dynamics in the Innbach catchment area, Austria. V-26 Fig. 20 Flow chart of system s dynamics in the Upper Rhine Basin - Alsatian plain, France. V-27 Fig. 21 Flow chart of system s dynamics in the Flumendosa catchment area, Italy. V-27 Fig. 22 Flow chart of system s dynamics in the Siuruanjoki catchment area, Finland. V-28 Fig. 23 Flow chart of system s dynamic in the Flisa and Jostedalen catchment areas, Norway. V-28 Fig. 24 Flow chart of system s dynamics in the Lagan catchment area, Sweden. V-29 Fig. 25 Modelled nitrogen transport from land-based sources in southern Sweden. V-30 Fig. 26 Basic principle of parameterization by Earth Observation and ground survey data. V-31 Fig. 27 Multi-temporal classification flowchart for SPOT data. V-32 Fig. 28 NDVI signatures of different crops and grassland in the Weida-Zeulenroda catchment, Germany, for three Landsat-5 TM scenes acquired V-33 Fig. 29 NDVI signatures of different crops and grassland in the Weida-Zeulenroda catchment, Germany, for four Landsat-5 TM scenes acquired V-33 Fig. 30 Landsat-5 TM 1999 multitemporal land use classification of the Weida-Zeulenroda catchment, Germany. V-34 Fig. 31 Land use classification of Landsat-7 ETM acquired June 19, 2000 for the Weida-Zeulenroda catchment, Germany. V-35 Fig. 32 Settlement mask digitized from IRS-1C PAN data, Weida-Zeulenroda, Germany. V-36 Fig. 33 IDM texture analysis of IRS-1C PAN data, Weida-Zeulenroda, Germany. V-36

7 ARSGISIP Final Report LIST OF FIGURES vi Fig. 34 Settlement mask derived from IDM texture analysis of IRS-1C PAN data acquired Oct. 24, 1997, and reclassified with 1999 Landsat TM data, Weida-Zeulenroda V-36 Fig. 35 Thematic map of 1998 multi-temporal Landsat-5 TM land use classification (above), together with cadaster overlay (below, detail); testsite Innbach, Austria. V-37 Fig. 36 Thematic map presenting land use classification - Gurk catchment, Austria. V-38 Fig. 37 Thematic map presenting land use classification Gurk catchment, Austria (detail). V-38 Fig. 38 Multi-temporal classification detail of the Ill River and its afforested banks, France. V-39 Fig. 39 Theme signature behaviour of SPOT4 data for the Ried Centre Alsace, France. V-39 Fig. 40 Spectral signatures, expressed in exo-atmospheric reflectance values for the Ried Centre Alsace testsite, France. V-39 Fig. 41 Multi-temporal land use classification of SPOT 4 MIR data from the Alsace Region, France. V-40 Fig. 42 Grassland dynamics in the Ill River flood zone (delimited by the blue line), France. V-40 Fig. 43 Complementarity between radar amplitude and coherence over Ried Center Alsace. V-41 Fig. 44 Average backscatter coefficient plotted as a function of the coherence for thematic classes over Ried Centre Alsace, France. V-41 Fig. 45 Multitemporal Landsat-5 TM classification of the Mulargia catchment, Italy. V-42 Fig. 46 Landsat-5 TM land use classification of the Siuruanjoki catchment, Finland. V-43 Fig single date Landsat-5 TM classification for the Flisa catchment, Norway, with mixture filtering before resampling; cloud pixels are substituted with 1985 single date Landsat-5 TM classification. V-44 Fig single date Landsat-5 TM classification for the Flisa catchment, Norway, with mixture filtering before resampling; cloud pixels are substituted with mixed coniferous forest dominated by pine and forested bog. V-45 Fig. 49 Comparison of raw radar data (a), and data filtered with Lee (b), Frost (c) and EPOS (d) filter. V-46 Fig classification of multitemporal ERS-2 SAR data (6 dates) and green, red and near infrared bands of IRS data from May 1998; Genevadsån catchment, Sweden. V-47 Fig classification of multitemporal Landsat-5 TM data (5 dates); Genevadsån catchment, Sweden. V-47 Fig classification of multitemporal EPOS filtered ERS-2 SAR data (6 dates); Genevadsån catchment, Sweden. V-48 Fig. 53 Classification of single date optical with multitemporal radar data; Genevadsån catchment, Sweden. V-48 Fig. 54 Land use and crop map for Genevadsån catchment, Sweden, based on multitemporal Landsat TM data from April 5, April 21, May 7, August 2 and October 14, V-49 Fig. 55 Land use and crop map for Lagan catchment, Sweden, based on Multitemporal Landsat TM data from April 5, May 7 and October 14, V-49 Fig. 56 Classification accuracy for the Swedish catchments with SMAP procedure for different data sets. V-50 Fig. 57 Classification accuracy for test areas in the Swedish catchments for multitemporal radar data. V-51

8 ARSGISIP Final Report LIST OF FIGURES vii Fig. 58 Comparison of classification accuracy for different EPOS filter sizes and classification procedures; smap: SMAP procedure, ml(s): Maximum likelihood classification with one spectral class per class, ml(m): Maximum likelihood classification with spectral subclasses. V-51 Fig. 59 Crop area as percent of the agricultural area according to ground truth data (train_data) and classification results (classif_data) of multitemporal radar data. V-52 Fig. 60 LAI map of the Mulargia catchment, Sardinia, based on the transformation of NDVI values derived from Landsat TM data with an empiric equation. V-52 Fig. 61 NDVI values derived from Landsat-5 TM data versus LAI measured with the canopy analyzer CA100 in the Mulargia catchment, Sardinia. V-53 Fig. 62 Classification of forested area around the MDPA testsite, Alsace, France. V-53 Fig. 63 A colour composite image of the three NDVI s for July 1984, 1992 and 1999 provided only for the forested area. RED = Landsat 5 TM 1984, GREEN = Landsat 5 TM 1992 and BLUE = SPOT4 1999; MDPA testsite, Alsace, France. V-54 Fig. 64 A colour composite image of three logarithmic vegetation indices for July 1984, 1992 and 1999 provided only for the forested area. RED = Landsat 5 TM 1984, GREEN = Landsat 5 TM 1992 and BLUE = SPOT4 1999; MDPA testsite, Alsace, France. V-54 Fig TM classification (30 m x 30 m) before post-classification and resampling, Flisa catchment, Norway. V-55 Fig single date Landsat-5 TM classification for the Flisa catchment, Norway, with majority filtering before resampling; cloud pixels are substituted with 1985 single date Landsat-5 TM classification. V-56 Fig single date Landsat-5 TM classification for the Flisa catchment, Norway, with majority filtering before resampling; cloud pixels are substituted with mixed coniferous forest dominated by pine and forested bog. V-56 Fig. 68 False colour composite representing the Soil, Vegetation and Humidity axes respectively in RGB. Blue colours represent wet surfaces while bright red colours represent dry surfaces. Green colours represent chlorophyll-active vegetation; Ried Centre Alsace, France. V-57 Fig. 69 SPOT 4 Soils Product for Spring 1999; Ried Centre Alsace, France. V-57 Fig. 70 Bare soils classification using SERTIT s SPOT 4 indices performed on early spring data with high ambient humidity; Ried Centre Alsace, France. V-57 Fig. 71 Average backscattering coefficient per soil type obtained from a modified ARAA (Association pour la Relance Agronomique en Alsace) Soil Map,1994 V-58 Fig. 72 Average backscattering coefficient per soil type elaborated from a spring 1999 SPOT 4 data classification. V-59 Fig. 73 Example of WEPP output from a rainfall simulation test site in the Flumendosa catchment, Italy. V-60 Fig. 74 Pigment content in the Weida-Zeulenroda catchment, Germany, derived from Landsat-5 TM data acquired August 4, V-60 Fig. 75 Water surface temperature for the Weida-Zeulenroda catchment Germany, derived from Landsat-5 TM data acquired July 3, V-60 Fig. 76 Thematic maps of surface roughness parameterization Gurk catchment, Austria (detail); a) April, b) June, c) November. V-61 Fig. 77 Autumn 1998 flood of minor extent, Ried Centre Alsace, France. V-62 Fig. 78 Early Spring flood of average extent, Ried Centre Alsace, France. V-62 Fig. 79 Widespread Autumnal flood 1999, Ried Centre Alsace, France. V-62 Fig. 80 Flood extent synopsis map of the Ried Centre Alsace, France. V-63

9 ARSGISIP Final Report LIST OF FIGURES viii Fig. 81 Superposition of the land use classification by the flood extent synopsis map, Ried Centre Alsace, France. V-63 Fig. 82 Land use flood impact assessment, Ried Centre Alsace, France. V-63 Fig. 83 Camargue flood analysis image processing flowchart. V-64 Fig. 84 Colour composite of two SAR images, with a pre-flood and an image taken immediately after the major flood event s beginning and a ratio of the two images; Camargue Region, France. V-65 Fig. 85 Overlay of the change detection derived, flood extent mapping onto the 12 SAR image temporal fusion, which is used as a detailed cartographic; reference; Camargue Region, France. V-65 Fig. 86 Training and verification sites for crop patterns; information delivered by the end user TTV for 1998, 1999 and 2000 land use classifications in the Weida- Zeulenroda catchment, Germany. V-66 Fig. 87 Water sampling points (marked as yellow dots) of the Weida-Zeulenroda- Lössau field campaign performed on August 20, 1999 for water quality assessment. V-66 Fig. 88 Thematic map presenting field work results Innbach catchment, Austria (detail). V-67 Fig. 89 Map presenting GPS located ground truth points - Gurk catchment, Austria (detail). V-67 Fig. 90 Mapping of samples of recognised grassland parcels (modified from Agricultural Management documents, CRA 1998); Ried Centre Alsace, France. V-68 Fig. 91 Soil-Type Maps of the Central Alsace Plain on bare soil surfaces (modified from ARAA Soil Map, 1994); Ried Centre Alsace, France. V-68 Fig. 92 Delineation of important past flood extents of the Ill River, France. Cyan 1978 flood, Purple 1982 flood, Dark Blue 1983 flood (modified from Alsace flood zone cartography, DDAF 1992). V-69 Fig. 93 Soils in the Mulargia catchment, Italy, with field sample points and the corresponding land units. V-69 Fig. 94 Location of 1998 and 1999 field sites in the Siuruanjoki catchment, Finland. V-70 Fig. 95 Distribution of samples during the 1999 field campaign in the Flisa catchment, Norway. V-70 Fig. 96 Routes and points sampled during the 2000 field campaign in the Flisa catchment, Norway. V-71 Fig ground truth in the Genevadsån and Fylleån catchment, Sweden. V-71 Fig. 98 Ground truth data for 1999 in the Genevadsån catchment, Sweden. V-72 Fig. 99 Thematic map representing 29 Hydrological Response Units (HRUs) for the Zeulenroda catchment, Germany. V-72 Fig. 100 Thematic map of Response Units for impact modeling - Innbach catchment, Austria. V-73 Fig. 101 Thematic map representing 35 Hydrological Response Units (HRUs) for the Mulargia catchment, Italy. V-73 Fig. 102 Type and grade of erosion representing 42 Erosion Response Units (ERUs) for the Mulargia catchment, Italy. V-74 Fig. 103 Thematic map of erosion risk, delineated from the ERUs as shown in Fig. 102, Mulargia catchment, Italy. V-74 Fig. 104 Nitrate balance estimated by simple input/output balance based on interviews With farmers in the Weida-Zeulenroda catchment, Germany. V-75 Fig. 105 Conductivity of bedrock derived from geological and hydrogeological maps in the Weida-Zeulenroda catchment, Germany. V-75

10 ARSGISIP Final Report LIST OF FIGURES ix Fig. 106 Field capacity of the upper meter of the whole soil profile for the Weida-Zeulenroda catchment, Germany. V-76 Fig. 107 WASPI flood line simulation per cross-section considering surface roughness in the Gurk catchment, Austria. V-76 Fig. 108 Joining roughness coefficients with the land use codes within the land use map. V-77 Fig. 109 Vegetation coverage parameter, derived from a dataset where cloudy pixels are substituted with pine and forested bog; Flisa catchment, Norway. V-77 Fig. 110 Vegetation height parameter; Flisa catchment, Norway. V-78 Fig. 111 Site index for the Flisa area, Norway. V-78 Fig. 112 Site index curves for spruce (G), pine (F) and birch (B) indicating the relation between site index, height and age in breast height. V-79 Fig. 113 LAI parameter for the Flisa catchment, Norway. V-79 Fig. 114 PRMS model simulation for the hydrological year 1976, Zeulenroda catchment, Germany (best year); computed discharge as blue line, observed values in red; r = 0,96. V-80 Fig. 115 PRMS model simulation for the hydrological year 1977, Zeulenroda catchment, Germany (worst year); computed discharge as blue line, observed values in red; r = 0,73. V-80 Fig. 116 PRMS model simulation for the hydrological year 1995, Mulargia catchment, Italy (best year); computed discharge as blue line, observed values in red; r = 0,94. V-80 Fig. 117 PRMS model simulation for the hydrological year 1997, Mulargia catchment, Italy (worst year); computed discharge as blue line, observed values in red; r = 0,58. V-80 Fig. 118 Computed (black line) and observed (red line) discharge in the Vääräjoki basin, Finland, in validation period (Y-scale m 3 /s); r 2 = 0,587. V-81 Fig. 119 Validation run of Siuruanjoki basin, Finland , r 2 = V-81 Fig. 120 Nitrogen load based on SOIL-N results and reclassification of the crop and land use classes. V-82

11 ARSGISIP Final Report DEFINITIONS, ACRONYMS, ABBREVIATIONS x DEFINITIONS, ACRONYMS, ABBREVIATIONS AIF Adaptive Image Fusion CI Canopy Imager DEM Digital Elevation Model EO Earth Observation EPOS Edge Preserving Optimized Speckle filter EREC European Research End User Consortium GIS Geographic Information System HBV Hydrologiska Byråns Vattenbalansavdelning (Hydrological Bureau Waterbalance-section, former section at SMHI) IEC Idealized European Catchment IGMI Istituto Geografico Militare Italiano IRS Indian Remote Sensing Satellite LAI Leaf Area Index LIA Local Incidence Angle MSS MultiSpectral Scanner NDVI Normalized Difference Vegetation Index NV Naturvårdsverket (Environmental Protection Agency) PRMS Precipitation-Runoff Modelling System RET Research End User Team SDR Sediment Delivery Ratio SI Site Index SLU Sveriges Lantbruksuniversitet i Ultuna (Swedish Agricultural University) SMAP Sequential Maximum Á Posteriori estimation SMHI Swedish Meteorological and Hydrological Institute TM Thematic Mapper V3MOD EIA Ltd. Grid based hydrological model WASMOD Water- and Solute Transport Modelling System WFD Water Framework Directive

12 ARSGISIP Final Report SUMMARY A Highlights 1 SUMMARY A Highlights - Classification of spatially distributed land cover information from remote sensing. - Identification of hydro-pedological soil units with SPOT4 MIR data. - Development of SAR imagery processing methods for flood mapping and monitoring. - Derivation of spatially distributed surface roughness parameters. - Derivation of tree height from optical EO data time series imagery, based on age distribution and growth conditions. - Derivation of Leaf Area Index (LAI) from optical EO data, based on species, height and basal area information. - Automatic generation of a settlement mask from IRS-1C PAN data with texture analysis. - GIS delineation of Response Units (RUs) for hydrological, solute transport and erosion modeling. - Improved parameterization of pollution risk model, flood model, hydrological and solute transport models. - ARSGISIP end users have been supplied with accurate and well consistent spatial data for parameterization of their respective basin simulation. - Cost-benefit analyses reveal that remote sensing methods provide improved spatial information and related systems analyses if compared with point measurements and field mapping. - Evidence is given that these methods provide data for lower costs if compared with conventional methods. - Intensive and fruitful co-operation between partners resulting in a significantly increased knowledge about image processing methods and hydrological modelling. - Assembly of the gained interdisciplinary knowledge in two method pools for remote sensing analyses and model parameterization by means of GIS. - Distribution of remote sensing software developed by the Austrian partner concerning data fusion, topographic correction, texture analysis, and postclassification to the whole ARSGISIP partner consortium. - Strong feedback of the project to Austrian authority: Adoption of the ARSGISIP approach by political responsibilities of Upper Austria with some consequences for the administration publications in total, thereof 6 reviewed papers, 13 proceedings papers, and 1 workshop contribution; additionally 2 graduate theses and 11 internal reports (unpublished). - Arrangement of six fruitful project workshops. - Eight very effective working visits between partners for common field campaigns as well as software installation and training, whereas five visits lasted at least one week.

13 ARSGISIP Final Report SUMMARY B Objectives/ C Context of work 2 B Objectives The ARSGISIP project addresses contentious environmental challenges for sustainable land and water resources management in trans European eco-regions. The general project objective was to promote the application of remote sensing techniques and GIS integration in representative European regions by demonstrating and verifying the cost-effective implementation of Earth Observation (EO) data for the parameterisation of hydrological, erosion, and solute transport models. The project can be seen as another step towards the upgrade of a European Remote Sensing User Community applying EO data and remote sensing techniques for identifying and classifying source areas which generate runoff, erosion or pollution. This information was urgently demanded by the projects end users to evaluate environmental pressures and for prognostic simulation of management scenarios. The project was carried out jointly in a trans-national collaboration by seven Research End User Teams (RETs) from Germany (coordinator), Austria, France, Italy, Finland, Norway and Sweden comprising researchers specialized in remote sensing, GIS, modelling, software engineering and systems analyses. Various test catchments are included for methodological studies located in different major climatic zones of Europe, and differing considerably in scale and in respect to their physiographic properties. However, they share common environmental problems which can be differentiated into water pollution, land degradation, and floods. They have been dealt with, by combining the classification potential of high resolution EO with the powerful spatial analysis available from GIS to identify the complex systems dynamics of the river catchments. Therefore the objectives of the ARSGISIP project are addressing the following research subjects: (i) (ii) (iii) exploite the synergetic potential of remote sensing and GIS integration for model parameterisation; developing and applying such techniques in modelling studies of test catchments in different European climates (mediterranean, humid moderate, cold boreal); evaluate their cost benefit analyses in comparison to standard field methods applied so far. C Context of work European regions such as those represented in ARSGISIP have in common that environmental protection cannot be seen without considering the importance of agriculture and forestry, which both are essential for the development of the Community welfare. Agencies concerned about water related environmental management in all European countries have in common to apply physical based models to prognostic simulate catchment hydrology, erosion or solute transport dynamics. On the other hand, these European regions differ considerably in terms of climate (cold-boreal in Scandinavia; moderate-humid in central Europe; semi-arid mediterranean in southern Europe), geology, topography, soils and land use. The individual composition of the Vegetation-Soil-Topography Interface (VSTI) of any catchment is resulting in a specific environmental potential for the regional development of agriculture and forestry, which in

14 ARSGISIP Final Report SUMMARY C Context of work / D Outline of methodology 3 turn have subsequent impacts on the hydrological, erosion and solute transport dynamics of the river catchments. Environmental problems resulting from inadequate management of agriculture and forestry in Europe (but not only in Europe) must therefore be understood as being primarily related to the hydrological transport dynamics of river catchments. The benefits of applied remote sensing techniques for improving the parameterisation of prognostic simulation models are obtained by the fact that: (i) (ii) (iii) (iv) each of the major European climatic zones is represented by a local Research-End User Team (RET) dealing with individual water related environmental problems of their respective test catchment; all RETs are integrated via common Work Packages (WPs) into the project=s European Research End User Consortium (EREC), thereby generating the projects synergism; within the EREC improved insight for integrated hydrological systems analyses, water resources management and environmental protection is developed; regionalisation is obtained by reflecting the principles of interactive process dynamics extracted from this insight towards the Idealized European Catchment (IEC), which can be seen as a conceptualised synergetic pool of the project comprising scientific expertise, methodologies and data. The deliverables and milestones produced by the WPs contribute towards a methodological pool of applied, verified and validated remote sensing and model parameterization techniques. As these tools are disseminated, the synergistic effect of the project as well as the deliverables produced are beneficial to all researchers and enterprises carrying out related work within the European Community. D Outline of methodology There are many physically based catchment models describing the transport of water and solute derived from rainfall and man made input. They have in common to be of a hybrid structure by using physical and empirical process algorithms. The latter use parameters describing the complex nature of the respective process in a simplified way. Such parameters often have a different physical meaning but those related to the Vegetation-Soil-Topography- Interface (VSTI) can be quantified from analysing the physiographic properties of the catchment by means of remote sensing and GIS. Consequently the research carried out by the ARSGISIP partners focussed considerable on the characterisation of the VSTIs in their respective test catchments, on remote sensing application to describe properties of the VSTI and the design of appropriate GIS data bases to use the informations derived for spatial GIS analyses. To a large extent the model input data can be supplied by existing and future Earth Observation (EO) techniques and can be grouped as follows: (i) (ii) (iii) mapping the spatial-temporal distribution of land cover; derivation of vegetation/agricultural crop pattern, canopy development, and soils characteristics such as surface roughness and surface soil moisture; derivation of land use;

15 ARSGISIP Final Report SUMMARY D Outline of methodology 4 (iv) derived information from EO data such as leaf area index (LAI), combined with standard surveys and/or expert knowledge such as seasonal management practices and evapotranspiration. The first two points can be derived directly from EO applying digital image processing techniques. The last two points typically will be handled in a GIS or in models combining input information derived from different sources. The physically based simulation models WASMOD (REICHE 1991), MMS/PRMS (LEAVESLEY et al. 1983), WASPI, HPP, V3MOD (LAURI 1999), LANDPINE (RINDE 1999), and SOIL-N (JOHANSSON & HOFFMANN 1997) together with HBV-N (NATURVÅRDSVERKET 1997) were selected for the test catchments. A preliminary review of these models revealed that they parameterize similar physiographic basin properties controlling their hydrological and solute transport dynamics. Such properties are land use (i.e. land cover and management), topography, geology, and soils comprising the VSTI. The latter in turn can be described and quantified by parameters such as slope gradient, aspect, soil moisture, surface roughness, or LAI. Systems analysis was carried out within the catchments to identify their major hydrological subsystems, physiographic properties, and management practices controlling their interlinked water and solute transfer dynamics. From this exercise, key model parameters and variables were identified, which are directly related to the VSTI and can be quantified spatially by means of image processing. Standard remote sensing techniques as well as GIS analyses were applied to derive the above mentioned key parameters from optical and microwave data by classifying source areas for runoff, nutrient leaching and erosion within the basins. The methodological steps are as follows: (i) (ii) (iii) (iv) (v) A multitemporal, multispectral, and multisensoral approach has been chosen to improve the classification accuracy by enhancing the discrimination capabilities. Data obtained from optical and microwave sensors such as Landsat-5 TM, Landsat-7 ETM, Landsat MSS, IRS-1C LISS, IRS-1C Pan, SPOT-4 MIR, ERS-1/2 SAR and JERS-1 SAR were processed and evaluated. EO data were georeferenced and radar data filtered to reduce speckle. Different classification and preprocessing methods were tested with single and multidate optical and radar data and combinations of optical and radar data to determine data sets which supply land use and crop information with sufficient accuracy. Response Units (RUs) were delineated by GIS analyses for hydrological, solute transport and erosion modelling based on the defined parameters. Model simulations were performed and evaluated. Qualitative and quantitative cost-benefit estimations were done by estimation of the costs per square kilometre for providing model input information derived from EO techniques in comparison with traditional methods).

16 ARSGISIP Final Report SUMMARY E Results 5 E Results (i) (ii) (iii) The intensive co-operation between partners resulted in a significantly increased knowledge about image processing methods and hydrological model parameterization. The interdisciplinary scientific expertise gained from the different RETs during the project was assembled in two synergetic method pools for remote sensing analyses and model parameterization by means of GIS. The method pools are open to the public in analog and digital form via the internet (xml script) on the project s home page ( Cost-benefit analyses reveal that remote sensing methods provide improved spatial information and related systems analyses if compared with point measurements and field mapping. Evidence is given that these methods provide data for lower costs if compared with conventional methods. A GIS has been built up by each RET with a DEM, georeferenced remote sensing data, topographic information, and results of field campaigns performed during the three years. The data sets will be further applied by the projects end users for water management at catchment scale. (iv) Time differentiated classification of land cover, agricultural crops, grassland dynamics, forest types, canopy development, and classification of runoff and nutrient source areas was done. Multitemporal Landsat Thematic Mapper data allow classification of agricultural crops with fairly high accuracy. General characteristics of the land use pattern and crop distribution are clearly visible in each classification, but with characteristic differences between the various data sets. Results for multitemporal radar data are not quite as good, but still useful, considering their monitoring potential under poor weather condition. (v) (vi) (vii) Specific indices derived from SPOT4 MIR permit the identification of hydropedological soil units. The complementarity with SAR data has been investigated and shows a characteristic behaviour for very wet soils. SAR processing methods were developed for flood mapping and monitoring. A precise map of flood area can be provided by EO data. MIR adds to this precision. Optical and SAR data are complementary, SAR providing all weather capabilities. Multi-temporal indices derived from NIR/MIR enables mapping of the effects of potassium leaching on the health of forest stands. (viii) Improved parameterization of pollution risk model, flood model, hydrological and solute transport models by EO data: - spatially distributed surface roughness parameters were derived; - tree height was extracted from optical data time series imagery, based on age distribution and growth conditions; - Leaf Area Index (LAI) was derived from optical data, based on species, height and basal area information; - a settlement mask was automatically derived from high resolution optical data by texture analysis. (ix) (x) (xi) Response Units (RUs) were delineated by GIS analyses for hydrological, solute transport and erosion modeling. By applying the ERU concept the derived information are used as modelling entities for regionalization of erosion processes as well as to identify areas subject to different erosion processes. Consequently risk classes of soil erosion could be derived. ARSGISIP end users have been supplied with accurate and well consistent spatial data for parameterization of their respective basin simulation.

17 ARSGISIP Final Report SUMMARY F Conclusions 6 F Conclusions The project s results clearly demonstrate the paramount importance of remote sensing and GIS technology for parameterisation of trans European hydrological benchmark models demanded by the recently released Water Framework Directive (WFD). Such techniques provide improved spatial information and related systems analyses if compared with point measurements and field mapping, and moreover they offer complete coverage independent of international borders. Classification of distributed land use is of paramount importance for simulating runoff generation, solute transport and erosion as it controls the amount of water distributed towards the interflow component, groundwater aquifer and surface runoff. Application of remote sensing technologies for identification of different agricultural land cover types offers a new quality in the identification of non-point agricultural sources. In combination with selected ground based information (e.g. geomorphology, soil characteristics, farming practices, draining areas) it is now possible to identify the most relevant and crop related non-point agricultural sources in the catchments under study. Under consideration of technical aspects and the costs it is now possible to integrate this method with a repetition time of 1 to 10 years into the general water quality management system. The use of Earth Observation Data results in a number of improvements in the input data for hydrological models. The spatial distribution of land use classes and agricultural crops can be monitored using classification of remote sensing data. Due to the weather conditions in Scandinavia data collection during crucial periods of the growing cycle can not be guaranteed with optical data. Multitemporal radar data, which are weather independent, allow the identification of major land use classes and major crop classes based on typical changes in backscattering characteristics during a year. Data collection over successive years allows monitoring of crop rotation and thus a significant extension of the modeling possibilities for nitrogen load. Data collected in late fall and early spring allow also to estimate the extent of vegetation cover during winter. Bogs and wetlands can be mapped effectively with Landsat TM data. Temporal profiles of backscatter coefficients for agricultural crops need further investigation to verify their suitability for crop identification with limited ground information. Problems exist still for a detailed subdivision of e.g. small grains and for the reliable separation of specific classes. Crops covering only a few percent of the agricultural area pose the problem of collecting sufficient ground truth data for the representative statistical description of their spectral properties and for accuracy assessment. For operational applications it might be better not to consider rare classes, covering e.g. less than 1% of the agricultural area. For general hydrological models, which require only information about major land use classes like water, forest, agricultural and built up areas, input data can be derived through multispectral classification of e.g. single date Landsat or SPOT data with very high accuracy and limited ground information. More detailed classification of agricultural crops requires multitemporal data and at this stage comparatively extensive ground information. With multitemporal satellite data an average classification accuracy for crop types of more than 80% is feasible.

18 ARSGISIP Final Report SUMMARY F Conclusions 7 Land use classification of satellite images is an effective way to obtain surface roughness information for flood modeling. Including information on phenology and climate conditions as well as on cultivation characteristics allows the provision of flood model with varying roughness coefficients during the vegetation period. The possibility to classify land use and agricultural utilization every year allows also to consider the variation in agricultural cultivation due to crop rotation habits and thus different roughness condition as well as different roughness variation conditions. The assessment of the validity of roughness coefficients is subject of the calibration and application process of the model. According to the gained experience regarding agricultural practices there is not only one roughness coefficient for the respective crop types or growth stages due to different seed quality and growing conditions. An evaluation of the results can thus only be performed by comparing the model outputs to the effects of a real flood. For derivation of trees height and LAI, the current method is based on vegetation types, age and site index (SI) data. In the future it should be put more emphasis on deriving a relation between EO data and SI. At present these data can be purchased at very high costs, therefore development of methods producing SI from time-series EO data is needed. It should also be possible to develop a method based on a combination of remote sensing and a plant sociological /ecological approach. Antecedent soil moisture conditions cannot be obtained so far but are required to predict infiltration and runoff generation. The end user Environmental Protection Agency of Sweden is planning to use remote sensing data more extensively in their future activities. Emphasis will be placed on regional and local studies e.g. in support of county administrations. This will include the use of detailed models to evaluate alternative approaches to reduce the environmental impact of e.g. nitrogen. Methods to monitor major parts of Sweden are also of interest. Typically the Environmental Protection Agency will subcontract remote sensing data evaluation as well as parts of GIS analysis and model calculations. The benefit of increasing model resolution can be considered in potential future projects. However, as resolution - both spectral and spatial - increase, also the costs increase. The level of detail and the quality of parameterisation can increase with increasing resolution of EO data. Therefore, resolution has to be considered as a function of costs and benefits. Compared to conventional data, EO data - at any resolution - have the advantage to cover large areas. Cloud coverage is a problem in boreal zones and radar data has to be considered.

19 ARSGISIP Final Report I INTRODUCTION A Objectives 8 I A INTRODUCTION Objectives The ARSGISIP project is addressing environmental challenges for sustainable land and water resources management throughout Europe. The general project objective is to promote the application of remote sensing techniques and GIS integration in representative European regions by demonstrating and verifying the cost-effective implementation of Earth Observation (EO) data for the parameterisation of hydrological, erosion, and solute transport models. The project can be seen as another step towards the upgrade of a European Remote Sensing User Community applying EO data and remote sensing techniques for identifying and classifying source areas which generate runoff, erosion or pollution. This information is highly demanded by end users to detect and evaluate related pressures, and prognostively simulate environmental change. The collaborating ARSGISIP end users are organisations responsible for the management of land and/or water resources, and require decision support through the application of physical based models on a catchment scale. The end users apply environmental simulation models, which typically require intensive and costly parameterisation when dealing with river catchments. The innovative approach to use EO derived information as input for catchment models shall facilitate decision support for land- and water managers throughout Europe in a cost-effective way. ARSGISIP is carried out in various test catchments which are located in different major climatic zones of Europe, and which differ considerably in scale and in respect to their physiographic properties. However, they share common environmental problems which can be differentiated into water pollution, land degradation, and floods. They shall be dealt with, by combining the classification potential of high resolution EO with the powerful spatial analysis available from GIS to identify the complex systems dynamics of the river catchments. Therefore the objectives of the ARSGISIP project are addressing the following research subjects: (i) (ii) (iii) exploite the synergetic potential of remote sensing and GIS integration for model parameterisation; developing and applying such techniques in modelling studies of test catchments in different European climates (mediterranean, humid moderate, cold boreal); evaluate their cost benefit analyses in comparison to standard field methods applied so far. Those objectives are carried out through six Work Packages (WPs) which have been defined with particular reference to operational needs of the respective end users. The quantifiable results expected of those WPs can briefly listed as follows: (i) generation of individual remote sensing and GIS databases; (ii) identification of the end users models key parameters and variables to be parameterised; (iii) classification of the identified physiographic properties of the catchments by means of combined remote sensing techniques; (iv) delineation of the spatial distribution of the classified areas as thematic GIS coverages; (v) validation of the parameterisation by cost-benefit analyses after verification.

20 ARSGISIP Final Report I INTRODUCTION B Context 9 B Context European regions such as those represented in ARSGISIP have in common that environmental protection cannot be seen without considering the importance of agriculture and forestry, which both are essential for the development of the Community welfare. Agencies concerned about water related environmental management in all European countries have in common to apply physical based models to prognostic simulate catchment hydrology, erosion or solute transport dynamics. On the other hand, these European regions differ considerably in terms of climate (cold-boreal in Scandinavia; moderate-humid in central Europe; semi-arid mediterranean in southern Europe), geology, topography, soils and land use. The individual composition of the Vegetation-Soil-Topography Interface (VSTI) of any catchment is resulting in a specific environmental potential for the regional development of agriculture and forestry, which in turn have subsequent impacts on the hydrological, erosion and solute transport dynamics of the river catchments. Environmental problems resulting from inadequate management of agriculture and forestry in Europe (but not only in Europe) must therefore be understood as being primarily related to the hydrological transport dynamics of river catchments. The benefits of applied remote sensing techniques for improving the parameterisation of prognostic simulation models are obtained by the fact that: (i) (ii) (iii) (iv) each of the major European climatic zones is represented by a local Research-End User Team (RET) dealing with individual water related environmental problems of their respective test catchment; all RETs are integrated via common Work Packages (WPs) into the project=s European Research End User Consortium (EREC), thereby generating the projects synergism; within the EREC improved insight for integrated hydrological systems analyses, water resources management and environmental protection is developed; regionalisation is obtained by reflecting the principles of interactive process dynamics extracted from this insight towards the Idealized European Catchment (IEC), which can be seen as a conceptualised synergetic pool of the project comprising scientific expertise, methodologies and data. At present standard survey informations are used by the German end user Thüringer Talsperren-Verwaltung (TTV) to simulate nutrient leaching and drainage from agricultural fields. However, for an adequate parameterisation of the hydrological and solute transport models used by the TTV these surveys are lacking detail in space and time. More appropriate surveys and their update are to cost intensive for an operational application. EO data are offering a cost-effective alternative for improving the parameterising of the VSTI by classifying, delineating and quantifying the spatial variability of land cover, topographical entities related to soil catenae and underlaying bedrock, Response Units (RUs) such as Hydrological Response Unist (HRUs) generating runoff and solute leaching, and eutrophication within reservoirs to identify and control their respective trophic level. The problems of the Austrian end user Upper Austrian Water Authority refer to very heavy loads of organic and chemical fertilizer application due to intensive agricultural utilization in the Innbach catchment. To estimate the impact of agricultural land use practices to river pollution there is an obvious need for detailed information on land-use related to the particular