FAME System Specification Description

Transcription

1 FAME System Specification Description SPATIAL APPLICATIONS DIVISION (SADL) K.U.LEUVEN RESEARCH & DEVELOPMENT Vital Decosterstraat 102, B-3000 LEUVEN TEL.: FAX: URL: HYDRAULICS LABORATORY K.U.LEUVEN Kasteelpark Arenberg 40, B-3001 LEUVEN TEL.: FAX: URL: SARMAP s.a. Cascine de Barico CH-6989 Purasca, Switzerland Tel Fax:

2 Colofon Title: FAME System Specification Description Author: Patrick Willems (LH), Sylvia Thompson (SADL), Massimo Barbieri (SARMAP) Version: Printed: File location: K.U.Leuven Research & Development Hydraulics Laboratory (LH): Kasteelpark Arenberg 40 B-3001 LEUVEN TEL.: FAX URL: SADL: Vital Decosterstraat 102 B-3000 LEUVEN TEL.: FAX URL: Currently six university research units participate in SADL: the Institute for Urban and Regional Planning; the Laboratory for Soil and Water Research; the Laboratory for Forest, Nature and Landscape Research; the Institute for Social and Economic Geography; the Laboratory for Experimental Geomorphology and the PSI-VISICS research group of the department of electrical engineering

3 FAME System Specification Description TABLE OF CONTENTS Table of contents... 1 Table of Figures... 1 Table of Tables Introduction System Methodology and Resources River Cross-Sectional Data Integrated with High resolution DEM General Product Specifications Methodology and Resources Flood Maps Detected by SAR Images General Product Specifications Methodology and Resources Flood Simulation Models Validated by SAR Derived Flood Maps General Product Specifications Methodology and Resources Composite Hydrographs on the Basis of Discharge/Duration/Frequency Distributions General Product Specifications Methodology and Resources Updated Land Use and Infrastructure Maps General Product Specifications Methodology and Resources Flood Maps of Different Return Periods General Product Specifications Methodology and Resources Conclusion References TABLE OF FIGURES Figure 1: Functional Elements of the FAME Service... 4 Figure 2: DEM and Cross-Section Data Flow... 7 Figure 3: SAR Processing Figure 4: Flood Simulation Model Validation Procedure Figure 5: Composite Hydrograph Calibration Figure 6: IKONOS Imagery Classification Procedure Figure 7: IKONOS Imagery Alternate Classification Procedure Figure 8: Landsat ETM+ Classification Procedure Figure 9: Flood Maps Production

4 FAME System Specification Description TABLE OF TABLES Table 1: DEM and Cross-Sectional Data Flow... 6 Table 2: SAR Processing Table 3: Flood Simulation Model Validation by SAR derived flood maps Table 4: Composite Hydrographs Table 5: IKONOS Classification Table 6: IKONOS Alternate Classification Table 7: Landsat ETM+ Classificaiton Table 8: Flood Risk Maps Table 9: Summary Table of Procedures andresources

5 1 INTRODUCTION The FAME System Specification Description (SSD) describes the architectural design of the end-to-end information service chain, which is supported by proven and widely used system analysis and design tools commonly used for information system modelling. This document will repeatedly make reference to the following FAME documents: FAME Product Specifications Document (PSD) FAME Service Definition Document (SDD) FAME Service Development Plan (SDP) (SQAP) The FAME Product Specifications Document (PSD) and FAME Service Definition Document (SDD) outlined the scope of the FAME project. To recap, the objectives of the FAME project are to demonstrate the following: Improvements in flood modelling performance by use of earth observation products; and, A spatial information service for flood modelling and mapping. The deliverables proposed by FAME and described in the FAME PSD are: River cross-sectional data integrated with high resolution digital elevation models (DEM); Historical flood maps detected by SAR images; Flood simulation models calibrated by SAR images; Composite hydrographs on the basis of discharge/duration/frequency distributions; Updated land use and infrastructure maps; and Flood maps of different return periods and flood risk maps. The flow diagram of the functional elements of the FAME Service, to produce GIS based flood and flood risk maps is provided in Figure 1. The SQAP outlined a plan to assess the products and services discussed in the PSD and SDD. The quality assessment of the FAME Service will be based on three case study areas; the Demer and Dender Catchments in Belgium and the Entella Catchment in Italy; which have been selected by the direct end users. As was mentioned in the SDP report, the evolutionary nature of the FAME project will allow for improvements in the FAME service, once the results of the case studies in Belgium and Italy have been assessed. Hence, the procedures and resource simulations outlined in this report may alter slightly. 2/10/2003 3

6 Hydrological Analysis and River Hydraulic Modelling GIS Development and Digital Terrain Modelling Flood Detection Mapping using Satellite Radar Imagery Calibration and Validation of Flood Plain Mapping Model Product Evaluation GIS Overlay Analysis GIS Based Flood and Flood Risk Maps Land Cover Mapping using IKONOS and Landsat Imagery Figure 1: Functional Elements of the FAME Service At the FAME Interim meeting in November 2002 in Antwerpen, the user was represented with the original objectives, the procedures adapted for the Dender catchment study, the triumphs and hurdles encountered during the working phase and the end user was already confronted with different alternatives and options of proceeding the work on the other catchments. Continuous interaction with the end user aims at providing largely user defined results and creates a less restrictive sphere for progress. At hand of the results obtained for the Dender catchment and the conclusions drawn from the Interim meeting, procedures were refined and adapted where necessary for the Demer and Entella catchments. This report will draw on the experience gained from the work conducted on the Dender catchment and will refer to the procedures used for the Demer and Entella catchments. 2/10/2003 4

7 2 SYSTEM METHODOLOGY AND RESOURCES The FAME System Specification Description will aim at reflecting a concise description of the software development (methodology) and resources usage for every product defined under the FAME project. The following discussion points will be evaluated for every product: The general product specifications for a given deliverable, The methodology followed to obtain the given deliverable and the corresponding resources required in order to obtain the FAME product. 2.1 River Cross-Sectional Data Integrated with High resolution DEM In the case of the Dender and Demer catchments of Belgium, the LiDAR DEM was used for the respective studies. The minimum spatial resolution of LiDAR elevation data is 4m for the Dender catchment and 10 m for the Demer catchment. The DEM, derived by LiDAR, needs to be corrected by integrating it with detailed river cross-sectional measurements or by other high resolution and accurate elevation data along dike line elements. The aim of this correction is to remove errors caused by different land uses, and the poor representation of dike levels and river depths by LiDAR. The accurate representation of the dike levels in the DEM is important for flood mapping applications (extrapolation of the river water levels outside the river bed, to map the water in the floodplains). When the grid size of the DEM is smaller than the dike width or similar, the dikes may be incompletely represented which can cause erroneous extrapolations in the flood mapping. It is clear that this task can only be carried out when more detailed and/or accurate dike elevation data (in comparison with the LiDAR data) is available. In the case of the Entella catchment in Italy, for instance, the FAME cases study has shown that the cross-sectional data were not more detailed then the high resolution DEM. It also was seen that the Entella does not have dikes, so that the flood mapping procedures will not have large problems. In that case, the need for the integration of the DEM with the cross-sectional data is less strong. Irrespective, the DEM forms an integral part of the FAME service process, not just for improving the boundary conditions of flood plain models, but also the processing of satellite images (see 2.2). In order to guarantee a service of dependency, an evaluation of the DEM needs to be undertaken and corrections using cross-sectional data have to be applied were necessary General Product Specifications Within the scope of the FAME service, reliability and dependency are directly related to the quality of the DEM and the river cross-sectional data. Due to the DEM and cross-sectional data s co-dependency, they should be assessed and were necessary, adapted in respect to the other. The entire project procedure and success depends on the accuracy of these two data sets. 2/10/2003 5

8 2.1.2 Methodology and Resources A first evaluation of the DEM with respect to the cross-sectional data, and possibly, other reference sources, should not be taken for granted. The example of the Entella catchment demonstrates the importance of good controls. The user expected the crosssectional data of the Entella to be more accurate than the DEM, but on evaluation, it was discovered that the DEM data were more accurately geocoded in comparison with the cross-sectional data. The cross-sectional data also had insufficient cross-sections in order to conduct any corrections, were it is necessary. Also for the Demer catchment, the cross-sectional data were insufficient to make corrections, especially when these data were compared with other data on the dike crest elevations. Along the line elements in the 10 m resolution DEM of the Demer, the end user had more detailed LiDAR observations available with an average spatial resolution of approximately 2 m. In cases where the integration of the DEM with the cross-sectional data is considered appropriate and useful to obtain an improved seamless DEM, the following steps have to be followed: 1. Extract the x,y,z values of the left and right bank tops, and the river thalweg (i.e. deepest point between left and right bank), 2. Create poly Z-line shape files on the basis of the 3D points, 3. Create a River Channel DEM by a TIN interpolation of all the river crosssection points occurring between the tops of the left and right bank, 4. Intersect the River Channel DEM with the original DEM to make a seamless DEM. Figure 2 gives an overview of the product chain and Table 1 gives and overview of the resources involved. For the procedure, whereby the cross-sectional data has to be corrected, no experience has been gathered so far during the FAME service and the exact methodology and resources are mere speculation. Note that resources are dependent on the state and the format of the data. Table 1: DEM and Cross-Sectional Data Flow Methodology and Resources for DEM or Cross-sectional Data Integration Step Description Software Specifications 1 Quality Control ArcView or Erdas Imagine Resources in Working Days 0.5 to 1.0 If the Quality Control necessitates DEM correction, go to step 2. If the Quality Control necessitates Cross-Section correction, go to Step 6. If no correction is necessary, the flood map detection may commence ( 2.2). 2 Extraction of x,y,z Values EditPlus or Excel 0.2 to Creation of Poly Z-line ArcView 3D Analyst 0.2 to Creation of River Channel DEM by TIN Interpolation 5 Intersect River Channel DEM with original DEM Custom Programmed Sediment Module for ArcView Custom Programmed Sediment Module for ArcView 0.2 to to This process could not be explored during the FAME service as yet and is therefore based on ArcView 0.8 to /10/2003 6

9 TOTAL speculation: Extraction or correction of Cross- Section data using DEM DEM or Cross-Sectional Data Correction 1.3 to 2.25 DEM Cross- Sectional Data Quality Control Are any Corrections to either DEM or Cross-Sectional Data necessary? Use supplied DEM and Cross- Sectional Data YES: DEM YES: Cross-Sections Extraction of Left Bank, Right Bank and Thalweg This case has not been explored as yet: Extraction of Correct Cross-Sectional Data Creation of Poly Z- Lines Creation of River Channel DEM by a TIN interpolation Intersection of DEM and River Channel DEM Corrected DEM Corrected Cross- Sectional Data Figure 2: DEM and Cross-sectional Data Flow 2/10/2003 7

10 2.2 Historical Flood Maps Detected by SAR Images Historical flood depth maps and flood volume maps were to be calculated by integrating the SAR derived flood maps and the corrected digital elevation model. SAR derived flood maps are useful for: - The identification of the potential zones at flood risk; the flood plains along these areas have to be considered when setting up a flood simulation model; - The identification of the potential zones at flood risk for areas where no flood simulation models are available or where the construction of these models is not appropriate (e.g. in insurance flood risk calculations for large areas, such as the complete territory of a country); - The validation of flood simulation models (see 2.3); and - When created on-line: for flood warning applications General Product Specifications A full-resolution RAW or SLC SAR image has ground resolution ranging from 5 m in azimuth and 20 m in range for ERS; 5 m in azimuth and 10 m in range for Radarsat Fine Beam and something in between for ENVISAT ASAR Image Mode (in this case the range resolution is also depending on the selected incidence angle). After processing, (through focussing, multi-looking and geo-coding procedures) a square pixel of ground resolution (the resolution of the land use map) of 20 m is obtained. The SARscape processing chain, from the RAW data ingestion to the geocoded product generation, is mostly automated. The operator intervention is required just to define the range and azimuth multi-looking factors (which are fixed for ERS data, but are depending on the viewing geometry for ENVISAT and Radarsat images and they have to be calculated accordingly) and to eventually select a single GCP (the SARscape Range-Doppler geocoding approach allows to get very accurate results by means of a single, precisely located GCP) in case a reference source (topographic maps, GPS survey, etc.) was available; otherwise the nominal geocoding procedure, which is based on the satellite orbital parameters stored in the scene header file, allows to get a good accuracy (within few pixels) at least for those products, such as ERS and ENVISAT, which are characterised by reliable orbit information. In both cases (calculation of the multi-looking factors and GCP selection) the additional time required to get a geocoded product can increase of no more than 1 hour with respect to the standard SAR processing procedure as presented in table 2. In case multi-pass satellite acquisitions (ascending and descending orbits), or even images acquired over adjacent satellite tracks, are processed together for flood map generation, a DEM of comparable spatial resolution has to be used to correct the different SAR imagery geometry in order to properly geocode the whole data set. The Services Cases report will illustrate this case applied on the Demer catchment. Same considerations apply also in the case of Radarsat or ASAR images acquired with different incidence angles. In terms of processing time there is basically no change if a DEM is used or not in the geocoding step, what makes the big difference is of course whether the DEM has to be generated from scratch (in this case, approximately from 1 to 2 days additional work can be expected). Over mountainous zones (which is usually not the case, since floods usually happen on flat areas) a DEM is also useful to avoid possible commission errors that could arise in shadow areas in particular in case of unavailability of a SAR reference frame acquired during normal river conditions. Indeed if a reference acquisition is available and the satellite data processed are acquired over a common track, those features (steady water or shadow areas) which remain the same over time are not classified as flood in any case. 2/10/2003 8

11 By using SAR geocoded products for flooded area classification purposes, accuracy is expected at more than 80 % confidence level. As pointed out in previous FAME reports, a lower reliability of the flood maps derived from SAR data, has to be expected, especially with ERS imagery, in case of strong wind conditions or in proximity of barriers, which generate both water turbulence and consequently water surface roughness, making it difficult or even impossible to discriminate water from land. This problem is dramatically reduced by acquiring imagery with less steep incidence angles or by using horizontal polarisation modes (both possibilities can be exploited by ENVISAT and Radarsat SAR sensors). The flood maps, which can be derived from ERS SAR data, are comparable to cartographic products at a scale ranging from 1: and 1: , in case of Radarsat and ENVISAT data it is possible to reproduce on a graphical scale higher than 1: To be compliant with the user requirements the SAR maps are provided at a scale of 1:25.000, giving two classes: flooded and non-flooded. The flood depth maps were to be provided at a scale of 1:25000, giving 6 depth classes: 0, 0.5, 1, 1.5, 2, >2 m. It was confirmed for the Belgian site that, when using Envisat ASAR and Radarsat in Tandem, the temporal frequency is 2 days. In this way, flood duration maps can distinguish between 5 duration classes: <1, 1-2, 2-3, 3-4, > 5 days. Unfortunately, no Radarsat archive data were available over the project test sites during any of the examined flood events. The same applies to the 2 additional flood events studied in the FAME framework: Dresden, August 2002 (investigated by means of ERS and ENVISAT data) Dender, January 2003 (under investigation by means of ERS and ENVISAT data) Methodology and Resources Figure 3, on the next page, gives an overview of the SAR processing chain and Table 2 gives and overview of the resources involved to produce a flood map starting from a SAR RAW product. 2/10/2003 9

12 Figure 3: Table 2: SAR Processing Procedure SAR Processing Procedure Methodology and Resources for SAR Processing Step Description Software Specifications Resources in Working Days 1 Focussing and multi-looking SARscape Co-registration SARscape Speckle filtering SARscape Geo-coding and radiometric Calibration SARscape Classification Mosaicing SARscape 0.2 TOTAL SAR Processing 1.5 These processing time scan be reduced in case of emergencies due for example to the need of quasi-real time monitoring during flood crisis periods. On the other hand it must be mentioned that the generation of a flood map does not consist only of the SAR data 2/10/

13 processing, but it also involves two main issues related to SAR imagery acquisition and delivery: SAR acquisition planning and relevant data selection through online and offline catalogues (i.e. EOLI and DESCW for ESA data). In case of Radarsat data, the Swath Planning Application (SPA) can be used. Based on this SPA for Radarsat and DESCW for ENVISAT/ASAR, it has been obtained for the Belgian test site that the largest gap between consecutive acquisitions made by Radarsat or ENVISAT/ASAR is 2 days (revisiting period 3 days), even if in most cases we have a revisiting period of 2 days. The acquisition planning step usually requires from 1 to 3 hours depending on the number of satellites one wants to plan. The request for planning a new satellite acquisition over a certain area must arrive at least 1 week before the acquisition date in order to be accepted both by ESA and CSA.. Special agreements can be signed with data providers in order to reduce at the minimum these two time frames and consequently meet the user requirements. Currently, for disaster management purposes, it is possible to ask for planning a new satellite acquisition both for ERS/ENVISAT (no later than 48 hours before the requested satellite acquisition the service is not operative on weekends) and for Radarsat (no later than 29 hours before the requested satellite acquisition the service is always operative). Time delay between satellite data acquisition and product delivery represents often a crucial problem to apply satellite data for flood monitoring purposes (this is especially true when the aim is a quasi real time observation of a flood event). For emergency response, there are already ongoing procedures (most of them are usually adopted in the International Charter on Space and Major Disasters ) to use ftp transfer in order to reduce the time delay for data delivery up to less than two hours from the acquisition time. The International Charter can be activated for the case of floods by authorized users but the data is provided free up to level one i.e. raw and Precision Image (PRI). For Radarsat, the issues of satellite tasking, acquisitions, processing and real time delivery have also been addressed through the Radarsat Emergency Response Service, and used by the International Charter. For the identified set of procedures, see: Otherwise a rush procedure is foreseen both for ESA and Radarsat data, which enables to get the ordered product within 24 hours after the satellite acquisition (the cost is between 1.5 and 2 times higher than the same data delivered by standard procedure in around 5 days ). Automatic coregistration procedure for multi-temporal data sets: The automatic coregistration procedure cannot be performed in a reliable way (pixel accuracy) in case of images acquired with different incidence angles (i.e. different acquisition modes, different satellite tracks). In this case the images are coregistered during the geocoding step in SARscape, where a single ground control point is selected to resample one image onto the other following a rangedoppler approach, which allows to achieve a very high accuracy (see Service Cases report that will follow for Demer river basin, 1995 flood event) On the other hand, also working with images acquired over the same satellite track and in the same acquisition mode, when very large floods occur (which introduce lots of homogenous zones in SAR imagery), the result of an automatic coregistration procedure, based only on the image cross-correlation function, could be not as good as expected. In this case SARscape provides two alternatives: - Working with ENVISAT data, hence with very precise orbital information, a specific fully automatic procedure, based on the state vector accuracy, is implemented. In this case the coregistration is not depending on the cross-correlation between image pixels and thus it is not influenced by the presence of large and homogeneously distributed flooded areas. - Working with Radarsat and/or ERS data (state vectors not enough precise to be used for coregistering with pixel accuracy), a procedure to manually locate the windows to be used for the coregistration is provided, so that an operator can visually identify the best portion of the image, which are possibly not affected by floods. This option shall start an automatic coregistration process, where the cross- 2/10/

14 correlation function is computed only on pixels located inside the windows selected by the operator. 2.3 Flood Simulation Models Validated by SAR Derived Flood Maps General Product Specifications Flood simulation models were to be validated based on historical flood maps. By comparison of the flood model simulation results with the flood maps for a number of historical events, the spatial extent of the simulated flood and the flood depths can be validated. By making this comparison at different time moments, the accuracy of the flood duration as simulated by the model can also be validated. Because historical flood information is often not available, or only in a very limited or inaccurate state, the SAR derived flood maps are very useful. They can be used in addition to the existing information on historical floods. In some catchments, SAR derived flood maps might be the only source of historical flood information available. For the Dender and Demer catchments in Belgium, existing quasi 2D hydrodynamic models were used. They were implemented using the MIKE11 hydraulic modelling software. For the Entella catchment in Italy, an existing 1D hydrodynamic HEC-RAS model was used. In the three cases, maps were available with information on the maximum spatial extent of historical floods. The maps are, however, for some areas very inaccurate and do not provide information on the flood duration (the spatial extent of the floods at different time moments), the flood depths, and the increase in time of the area flooded. Based on an overlay between the simulation results of the flood model and the different flood maps, the model can be evaluated and shortcomings/inaccuracies identified. Improvements can be made to update the model. When specific areas are shown to be flooded in the model while this does not match with the historical flood information, it becomes clear that the dike levels or the top levels of flood barriers have to be checked and improved. When the opposite occurs (the models do not predict flooding while there is flooding in reality) for specific zones, one has to look for the presence of culverts or other structures that can release flooded water. Based on information on the flood duration, the infiltration capacity of the soil after the flood can be validated. When large deviations occur in the spatial extent of the flood or the flood depths, the accuracy in the prediction of the inflow discharges, the water levels in the river bed, and/or the flooded volumes have to be checked Methodology and Resources Figure 4 gives an overview of the product chain and Table 3 gives and overview of the resources involved. Table 3: Flood Simulation Model Validation Procedure Methodology and Resources for Flood Simulation Model Validation by SAR Derived Flood Maps Step Description Software Specifications Resources in Working Days 1 Processing of rainfall time series and rainfall input calculation for the historical events to be considered in the validation Custom software in Excel-Visual Basic 1.5 2/10/

15 2 Simulation of the rainfall input series in the hydrological models for the sub-catchments 3 Simulation of the hydraulic flood model 4 Mapping of the simulation results (extrapolation of the water levels in the calculation nodes of the model to determine the spatial extent of the flooding at the time moments of the SAR derived flood maps, and at the peak moments of the flood) Hydrological modelling software (e.g. NAM- MIKE11) + hydrological models for the different subtcatchments 1D Hydraulic modelling software (e.g. MIKE11, HEC-RAS) + hydraulic model of the river ArcView + MIKE11 or HEC-RAS interface Overlay of the simulation results with the SAR derived flood maps, and other historical flood maps + evaluation ArcView Model improvement, based on the evaluation in 5. TOTAL Flood Simulation Model Validation 1D Hydraulic modelling software + hydraulic model of the river (e.g. MIKE11, HEC-RAS) Depending on the model shortcomings /10/

16 Rainfall input series processing historical events Simulation hydrological models subcatchments Simulation hydraulic flood model Other sources of historical flood information Historical flood maps simulated by the model SAR derived flood maps Overlay and comparison Identification of inaccuracies + improvement flood model Validated flood simulation model Figure 4: Flood Simulation Model Validation Procedure 2.4 Composite Hydrographs on the Basis of Discharge/Duration/Frequency Distributions On the basis of a statistical analysis of a long-term discharge time series and the use of hydrological models, discharge/duration/frequency (QDF) relationships can be calculated. These relationships can then be used to construct representative hydrographs (so-called composite hydrographs ) for different return periods. The composite hydrographs are of use for the calculation of flood maps for different return periods, using the validated flood simulation model General Product Specifications The return periods for the hydrographs as specified by the end user are: 1, 2.5, 10, 25, 50, 100, 250, 500, 1000, 2500, 5000, years (or limited by the maximum return period for which the flood plain model has been set up; this means up to the spatial extent of the flood plain areas implemented). For all upstream boundaries in the flood simulation model (thus for the discharges at each tributary inflow point and the rainfallrunoff discharges from each sub-catchment), frequency distributions of rainfall-runoff discharges are constructed for a range of aggregation-levels from 1 hour to 15 days. 2/10/

17 The relation between the parameters of these distributions and the aggregation-level is analysed and calibrated. Based on that analysis, QDF-relationships are constructed, which form the basis of the composite synthetic hydrographs for the different return periods. After this analysis is done for each sub-catchment in the river basin, time shifts have to be determined between the hydrographs from the individual sub-catchments. This is done based on a comparison of the QDF-relationships downstream of the confluence of each tributary or sub-catchment with the main river. The most optimal time shift is determined by minimizing the difference between the QDF-relationships derived from a long-term simulation (or simulation of selected extreme events), and the QDFrelationships simulated on the basis of the composite hydrographs. The same comparison is also used to check the accuracy of the composite hydrograph method for flood risk modelling, and to check whether the underlying assumptions of this method are valid in the particular case considered. The composite hydrographs are used at the upstream boundaries of the flood simulation models. For the downstream boundary condition, composite limnigraphs are constructed on the basis of downstream water level series Methodology and Resources Figure 5 gives an overview of the product chain and Table 4 gives and overview of the resources involved. Table 4: Composite Hydrographs Calculation Procedure Methodology and Resources for Composite Hydrographs Step Description Software Specifications Resources in Working Days 1 Analysis of the river network structure as implemented in the model, together with the upstream and downstream boundary conditions 2 Simulation of long-term rainfall series in the hydrological models of the different subcatchments 3 Frequency analysis of the discharge time series, and the long-term hydrological simulation results (at the different boundaries of the flood simulation model) 4 Calculation of QDFrelationships for the time series at the different boundaries of the flood simulation model (HDFrelationships at the downstream boundary) 1D Hydraulic modelling software (e.g. MIKE11, HEC-RAS) + hydraulic model of the river Hydrological modelling software (e.g. NAM- MIKE11) + hydrological models for the different sub-catchments Custom software in Excel-Visual Basic Custom software in Excel-Visual Basic (When processed long-term rainfall input series are available); 3 (when not) 3 to 5 (depending on the number of model boundaries) 2 to 3 (depending on the number of model boundaries) 2/10/

18 5 Construction of composite hydrographs (and limnigraphs for the downstream boundary) 6 Long-term simulation with the hydraulic flood model, using the long-term hydrological results, or simulation of the selected events 7 Statistical analysis of the longterm simulation results downstream of each confluence 8 Simulation of the composite hydrographs in the hydraulic flood model, and overlay of the results with the results of 7. 9 Calculation of the time shifts between the composite hydrographs/limnigraphs of the different sub-catchments 10 Simulation of the shifted composite hydrographs in the hydraulic flood model, and analysis of the accuracy of the composite hydrograph method Custom software in Excel-Visual Basic 1D Hydraulic modelling software (e.g. MIKE11, HEC-RAS) + hydraulic model of the river Custom software in Excel-Visual Basic 1D Hydraulic modelling software (e.g. MIKE11, HEC-RAS) + hydraulic model of the river; Custom software in Excel-Visual Basic Custom software in Excel-Visual Basic 1D Hydraulic modelling software (e.g. MIKE11, HEC-RAS) + hydraulic model of the river; Custom software in Excel-Visual Basic 1 to 2 (depending on the number of boundaries) 0.5 (to set up the simulation; simulation time not included) 2 to 3 (depending on the number of confluences) 1 (computer simulation time not included) 1 to 2 (depending on the number of model boundaries) 1 (computer simulation time not included) TOTAL Composite Hydrographs 12.5 to /10/

19 Long-term rainfall input series Simulation long-term rainfall series Hydrological models subcatchments Schematisation of the river network + boundaries Long-term series of rainfallrunoff discharges Extreme value analysis Discharge / water level series at model boundaries Flood simulation model Calculation of QDFrelationships Construction of composite hydrographs and limnigraphs Simulation in hydraulic flood model Simulation in hydraulic flood model Extreme value analysis downstream confluences Calculation of time shifts in composite hydrographs Validation Validated composite hydrographs and limnigraphs Figure 5: Composite Hydrograph Calculation Procedure 2/10/

20 2.5 Updated Land Use and Infrastructure Maps The general objectives were to update existing land use and infrastructure maps using satellite remote sensing. To this end, FAME would develop land cover maps using IKONOS and Landsat ETM+ imagery. However, both types of imagery were not used for all three study areas: For the Dender catchment in Belgium, IKONOS and Landsat ETM+ imagery was used, since this was the first study to be conducted in the scope of the FAME project. Therefore, for the Dender, an evaluation was done on the suitability of the imagery as well. For the Demer catchment in Belgium, only IKONOS imagery was used, since the Landsat ETM+ imagery proved to represent too low a resolution for damage assessments conducted by the direct end user AWZ, and did not adequately capture city infrastructure. For the Entella catchment in Italy, only Landsat ETM+ imagery was used. Land cover classes were defined according to user needs and according to existing land use maps General Product Specifications Dender Catchment For the Dender, two separate land use classifications were conducted: A land use classification of the Dender catchment using Landsat ETM+ and IKONOS images, whereby land use classes have been defined by AWZ; and, A land use classification to update the VLM Land Use Map (1995) using a Landsat ETM+ image. The existing VLM land use map had a resolution of 20 m. However, the land use map developed from the Landsat ETM+ image had a resolution of 15 m and the land use map developed from the IKONOS image had a resolution of 4 m Demer Catchment For the Demer catchment, a land use classification was based on the AWZ land use classes. However, the land use classes were adapted in accordance to experience gained and results obtained from the Dender catchment. The resulting classification had a resolution of 4m Entella Catchment D Appolonia supplied a land use map, whose classes were adapted in order to derive a land use map from a Landsat ETM+ image. Hence, the resulting map had a resolution of 15 m Methodology and Resources The methodology and resources for developing a land use map differ in accordance to the imagery used: 2/10/

21 Landsat ETM+ imagery is high resolution imagery, where most classification methods are well established and automated. IKONOS imagery is very high resolution imagery, where classification methods are still evolving and not fully automated. Hence, this classification is usually more time consuming. The complexity, irrespective of the imagery used, of conducting a land cover classification, varies in accordance to the reference data that is available for the classification. The resulting quality and accuracy of the classification is also directly proportional to the quality and accuracy of the reference data, and the abundance thereof. The principal methodology for the classification will remain the same, but the complexity of the model will change in accordance to the reference data that is available. In the following two chapters, activities that are denoted to require 0.0 resources imply, the resources are a matter of minutes, instead of hours Classification using IKONOS Imagery Figure 6 gives an overview of the product chain and Table 5 gives and overview of the resources involved. This procedure is greatly dependant on the software and the reference information availability. If a detailed two-dimensional road network and regional plan are available, no digitizing work is necessary. However, in most situations, such data is not available and has to be extracted by digitizing. It should be noted however, that if a FAME service is set up for a given catchment or catchment area, data such as road infrastructure and other infrastructure only requires updating on a ten year basis. Hence, once the model input data has been created, steps 4 will not have to be repeated for every classification. Note that the procedure is devised for multi-spectral IKONOS imagery, without the support of the panchromatic band. Table 5: IKONOS Imagery Classification Procedure Methodology and Resources for IKONOS Classification Step Description Software Specifications Resources in Working Days 1 Geo-Coding Erdas Imagine 1.0 to Supervised Classification: Defining Training Areas and Running Classification 3 Mosaic Images (if IKONOS consists of consecutive acquisitions) Erdas Imagine 3.0 to 5.0 Erdas Imagine 0.0 to Digitizing Infrastructure & Water ArcView 10.0 to Define and Run Model (includes import, conversion and preparation of input data) The import, conversion and preparation of input data may well occur before Geo-coding. ArcView, ArcGIS and Erdas Imagine 1.0 to 10.0 Total IKONOS Classification 15.0 to /10/

22 Regional Plan IKONOS Ortho Images Agricultural Units Geo-coding Define Training Areas and Run Supervised Classification Mosaic Images Digitize Water, Roads and Built-up Areas Define and Run Model to Refine Classification Shapefile of Water, Roads and Built-up Areas Land Cover Classification Figure 6: IKONOS Imagery Classification Procedure Alternate Classification Method using IKONOS Imagery Figure 7 gives an overview of the product chain and Table 6 gives and overview of the resources involved. This procedure outlines the previous procedure of conducting a land cover classification on IKONOS imagery, in the case where a panchromatic band is present. Furthermore, it assumes the availability of an algorithm such as the ROADMAPS system, developed by Carnegie Mellon University (US), and the Automated Linear Feature Information Extraction (ALFIE) system, developed by QinetiQ in the UK. Such a system enables semi-automated road extraction in a matter of hours instead of days. However, again, vector data defining built-up areas rarely exist and might still have to be digitized. In the case of the Demer and Dender, classes such as Built-up Area 1, 2, 3 and 4, Infrastructure etc necessitated digitizing. In the case of the Entella, however, such detailed classes were not required and digitizing was not necessary. Table 6: IKONOS Imagery Alternate Classification Procedure Methodology and Resources for IKONOS Alternate Classification Step Description Software Specifications Resources in Working 2/10/

23 Days 1 Resolution Merge Erdas Imagine 0.0 to Geo-Coding Erdas Imagine 1.0 to Supervised Classification: Defining Training Areas and Running Classification 4 Mosaic Images (if IKONOS consists of consecutive acquisitions) 5 Extraction of Line Infrastructure & Water and Digitizing Built-up Areas Erdas Imagine 3.0 to 5.0 Erdas Imagine 0.0 to 0.0 ArcView 1.0 to Define and Run Model (includes import, conversion and preparation of input data) The import, conversion and preparation of input data may well occur before Geo-coding. ArcView, ArcGIS and Erdas Imagine 1.0 to 10.0 Total IKONOS Alternate Classification 6.0 to /10/

24 Regional Plan IKONOS Ortho Images Agricultural Units Resolution Merge and Geo-coding Define Training Areas and Run Supervised Classification Mosaic Images Automatic extraction of line features (rivers and roads) & Digitzing Built-up classes Define and Run Model to Refine Classification Shapefile of Water, Roads and Built-up Areas Land Cover Classification Figure 7: IKONOS Imagery Alternate Classification Procedure Classification using Landsat ETM+ Imagery The procedure of classifying a Landsat ETM+ image does incorporate the presence of a panchromatic band. Figure 8 gives an overview of the product chain and Table 7 gives and overview of the resources involved. Table 7: Landsat ETM+ Classificaiton Procedure Methodology and Resources for Landsat ETM+ Classification Step Description Software Specifications Resources in Working Days 1 Extract Area of Interest Erdas Imagine 0.0 to Resolution Merge Erdas Imagine 0.0 to Geo-Coding Erdas Imagine 1.0 to 2.0 2/10/

25 4 Supervised Classification: Defining Training Areas and Running Classification Erdas Imagine 3.0 to Define and Run Model (includes import, conversion and preparation of input data) The import, conversion and preparation of input data may well occur before Geo-coding. ArcView, ArcGIS and Erdas Imagine 1.0 to 10.0 Total Landsat ETM+ Classification 5.0 to 17.0 Land Use Map Landsat ETM+ Image DEM Ground Truthing or Other Relevant Data Extract Area of Interest Ortho Imagery Resolution Merge Topographic Maps Geo-coding Define Training Areas and Run Supervised Classification Define and Run Model to Refine Classification Land Cover Classification Figure 8: Landsat ETM+ Classification Procedure 2/10/

26 2.6 Flood Maps of Different Return Periods and Flood Risk Maps Flood maps of different return periods were created using the synthetic composite hydrographs, and the flood simulation model. Based on these maps and making use of the updated land use map, also flood damage assessment maps can be created by the end user General Product Specifications The synthetic composite hydrographs are simulated in the hydraulic flood model to calculate the water levels along the river for different return periods. Based on these simulation results, the high resolution DEM integrated with the cross-sectional data, and an interface software between the hydraulic flood model and the GIS, flood maps are calculated for different return periods. The maps are validated by comparison with the historical flood maps (used for validation of the flood simulation model in 2.3) and the return periods calculated for the historical floods. The maps are provided at the same resolution as the DEM, as the flood depths will be calculated for each pixel in the DEM. The classes for high, medium and low risk areas will be determined in consultation with the end user. On the basis of the flood maps, the end user can make an integration with the updated land use/infrastructure map and stage-damage nomgraphs to derive flood damage assessment maps Methodology and Resources Figure 9 gives an overview of the product chain and Table 8 gives and overview of the resources involved. 2/10/

27 Composite hydrographs Simulation hydraulic flood model Corrected high resolution DEM Flood mapping Flood maps for different return periods (Updated) land use map Flood damage assessment and/or risk level identification Flood risk maps Figure 9: Flood Risk Maps Production Procedure 2/10/

28 Table 8: Flood Risk Maps Production Procedure Methodology and Resources for Flood Maps of Different Return Periods and Flood Risk Maps Step Description Software Specifications Resources in Working Days 1 Simulation of the composite hydrographs in the hydraulic flood model 2 Creation of flood maps for the different return periods, making use of the high resolution DEM integrated with the cross-sectional data + validation 1D Hydraulic modelling software (e.g. MIKE11, HEC-RAS) + hydraulic model of the river 1D Hydraulic modelling software (e.g. MIKE11, HEC-RAS) + hydraulic model of the river; ArcView; Interface software between the hydraulic flood model and ArcView (e.g. MIKE-GIS) 1 (computer simulation time not included) 3 (computer simulation time not included) 3 Mapping of the areas for different flood risk classes (different levels of flood risk or flood return period) ArcView 2 Total Flood Risk Maps 5 2/10/

29 3 CONCLUSION Table 9 gives an overview of the expected resources required for the consecutive components of the FAME service: Table 9: Summary Table of Procedures andresources Overview of Resources for System Chapter System Component 2.1 DEM and Cross- Section Integration or Correction Quality Control Component Procedures Extraction of x,y,z Values Creation of Poly Z-line Creation of River Channel DEM by TIN Interpolation Intersect River Channel DEM with original DEM This process could not be explored during the FAME service as yet and is therefore based on speculation: Resources 1.3 to 2.3 Extraction or correction of Cross-Section data using DEM 2.2 SAR Processing Focussing and multi-looking Flood Simulation Model Validation Co-registration Speckle filtering Geo-coding and radiometric Calibration Classification Mosaicing Processing of rainfall time series and rainfall input calculation for the historical events to be considered in the validation Simulation of the rainfall input series in the hydrological models for the sub-catchments Simulation of the hydraulic flood model Mapping of the simulation results (extrapolation of the water levels in the calculation nodes of the model to determine the spatial extent of the flooding at the time moments of the SAR derived flood maps, and at the peak moments of the flood) Overlay of the simulation results with the SAR /10/

30 2.4 Composite Hydrographs IKONOS Land Cover Classification derived flood maps, and other historical flood maps + evaluation Model improvement, based on the evaluation in 5. Analysis of the river network structure as implemented in the model, together with the upstream and downstream boundary conditions Simulation of long-term rainfall series in the hydrological models of the different subcatchments Frequency analysis of the discharge time series, and the long-term hydrological simulation results (at the different boundaries of the flood simulation model) Calculation of QDF-relationships for the time series at the different boundaries of the flood simulation model (HDF-relationships at the downstream boundary) Construction of composite hydrographs (and limnigraphs for the downstream boundary) Long-term simulation with the hydraulic flood model, using the long-term hydrological results, or simulation of the selected events Statistical analysis of the long-term simulation results downstream of each confluence Simulation of the composite hydrographs in the hydraulic flood model, and overlay of the results with the results of 7. Calculation of the time shifts between the composite hydrographs/limnigraphs of the different sub-catchments Simulation of the shifted composite hydrographs in the hydraulic flood model, and analysis of the accuracy of the composite hydrograph method Geo-Coding Supervised Classification: Defining Training Areas and Running Classification Mosaic Images (if IKONOS consists of consecutive acquisitions) Digitizing Infrastructure & Water Define and Run Model (includes import, conversion and preparation of input data) The import, conversion and preparation of input data may well occur before Geo-coding to to Alternate IKONOS Land Cover Classification Resolution Merge 6.0 to /10/

31 Classification Geo-Coding Supervised Classification: Defining Training Areas and Running Classification Mosaic Images (if IKONOS consists of consecutive acquisitions) Extracting Line Infrastructure and Digitizing Built-up Areas Define and Run Model (includes import, conversion and preparation of input data) The import, conversion and preparation of input data may well occur before Geo-coding Landsat Land Cover Classification Extract Area of Interest Resolution Merge Geo-Coding Supervised Classification: Defining Training Areas and Running Classification Define and Run Model (includes import, conversion and preparation of input data) The import, conversion and preparation of input data may well occur before Geo-coding. 5.0 to Flood Risk Maps Simulation of the composite hydrographs in the hydraulic flood model 5 Creation of flood maps for the different return periods + validation Mapping of the areas for different flood risk classes (different levels of flood risk or flood return period) However, it should be mentioned again in summary, that the procedures followed and resources required for a given component are greatly dependent on the following factors: Accuracy of DEM and Cross-Section Data, The Availability of Feature Extraction Algorithms such as QinetiQ ALFIE, Abundance of Reference Information and Reference Data, Format, Quality and Accuracy of Reference Information and Reference Data, The Availability and Applicability of a Hydrological Model, and the rainfall input series, The Availability and Timely Delivery of Imagery (SAR, Landsat & IKONOS). 2/10/