exploitation of new data types to create digital surface models for flood ...

EXPLOITATION OF NEW DATA TYPES TO CREATE DIGITAL SURFACE MODELS FOR FLOOD INUNDATION MODELLING

Dr Martin J Smith Earl P Edwards Dr Gary Priestnall Prof Paul Bates

University of Nottingham University of Nottingham University of Nottingham University of Bristol

June 2006 FRMRC Research Report UR3 Project Web: www.floodrisk.org.uk

Data types for digital surface models FRMRC Research Report UR3

FRMRC Partners The FRMRC Partners are: • University of Bristol • Heriot Watt University • HR Wallingford • Imperial College, London • University of Lancaster • University of Manchester • University of Nottingham • University of Sheffield

Project Secretariat ARP Directorate of Planning and Academic Services University of Manchester Sackville Street, Manchester PO Box 88 M60 1QD Tel: +44 (0)161 306 3626 Fax: +44 (0)161 306 3627 Web: www.floodrisk.org.uk

UR3_data_types_for_digital_surface_models_WP5_2_v1_1.doc

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Summary Digital Elevation Models form the primary data input for the generation of flood inundation models, the results of which constitutes a critical part of any flood risk management system. Several methods exist for the generation of DEMs, each with its own set of merits and limitations, and these have been presented in section 2. The choice of a particular method is often a difficult one and may be influenced by budgetary constraints and the size of the project area as opposed to accuracy and spatial resolution of the DEM. It would be ideal to have the most accurate DEM with a high spatial resolution that realistically characterizes the floodplains. This, however, may result in large data volumes incapable of being executed on existing software and hardware systems. In the end, a compromise will have to be sought between technical demands, efficiency and economics. In this study, comparisons were carried out between the various methods. A Preliminary assessment was first conducted by comparing the requirements of the flood modeller, as identified in section 3, with the capabilities of the DEM methods in section 2 and the characteristics of the available products in section 5. This led to the short listing of three methods that may be most suitable for the required flood inundation modelling: digital photogrammetry, airborne InSAR and LiDAR. Further investigations were carried out on these methods and the results were presented in section 7. From these results it can be concluded that LiDAR provides a good method for generating an accurate DEM that characterizes the landscape representing the floodplain and therefore maybe suitable for use in flood inundation modelling. However, the quality of a DEM produced by photogrammetry is influenced significantly by the scale of the imagery used as discussed in section 7.5.1 and by the method of measurement where manual measurement (semi-automatic) can be an advantage. It must be stated that the automatically generated DEMs by photogrammetry in this study have not been manually quality controlled. It is common practice for automatically generated DEMs to be stereoscopically inspected and any erroneous points manually corrected. This would considerably enhance the quality of the DEMs as qualities similar to manual measurements discussed here might be expected. Where flood inundation modelling is being considered at the national or global level and quality of elevations are not so critical then other methods not considered in section 7, such are spaceborne InSAR (for example SRTM), can be valuable and cost effective alternatives. Combining datasets from different technologies as identified in section 3 has a great deal of potential, particularly the combining of photogrammetry and LiDAR. This is not just for DEM generation but also for feature identification and providing information for determination of roughness coefficients (see Smith et al. 2004). As many Ordnance Survey map products have been produced by photogrammetry (e.g. OS MasterMap) the use of these products could essentially mean the use of photogrammetric techniques. The future promises an explosion of new sensors as outlined in section 6, and using a synergistic approach may significantly improve present capabilities. Future spaceborne optical systems like Ikonos-2 and QuickBird 2 will have sub-metre image resolution, but its use will still be restricted by cloud cover problems over the UK. These systems, however, can be complemented with the future Radar mission of Tandem-X. Of great promise will be the digital airborne imaging systems and improvements made to LiDAR. LiDAR with a full waveform system should be capable of generating additional datasets for use in DTM generation and higher accuracy point cloud data. In addition to improvements in technology, it is expected to see improvements in processing algorithms which again will benefit the quality of the information available for flood risk management.


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Document Details Document History Version

Date

Lead Authors

Institution

Dr Martin J Smith Earl P Edwards Dr Gary Priestnall Prof Paul Bates J Bushell

Universities: Nottingham Nottingham Nottingham Bristol HR Wallingford Ltd

J Bushell

HR Wallingford Ltd

Aug 2005

June 2006

7 Mar 2007

Joint authors

Comments Final draft

Formatting for publication; change of filename from ‘UFMO_5_2_Repo rt Final-DraftCorrections.doc’ Correction of definition of CFMP

End user approval: Dr Kate Scott, Environment Agency, 23 January 2007

Acknowledgement This research was performed as part of a multi-disciplinary programme undertaken by the Flood Risk Management Research Consortium. The Consortium is funded by the UK Engineering and Physical Sciences Research Council under grant GR/S76304/01, with co-funding from: • Defra and the Environment Agency through their Joint R&D programme on Flood and Coastal Erosion Risk Management, • UKWIR • NERC • The Scottish Executive • Rivers Agency Northern Ireland

Disclaimer This document reflects only the authors’ views and not those of the FRMRC Funders. This work may rely on data from sources external to the FRMRC Partners. The FRMRC Partners do not accept liability for loss or damage suffered by any third party as a result of errors or inaccuracies in such data. The information in this document is provided “as is” and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and neither the FRMRC Funders nor any FRMRC Partners is liable for any use that may be made of the information.

©

Copyright 2006

The content of this report remains the copyright of the FRMRC Partners, unless specifically acknowledged in the text below or as ceded to the funders under the FRMRC contract by the Partners.


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Table of Contents Title page

i

FRMRC Partners ........................................................................................................................... ii Summary

.................................................................................................................................. iii

Document Details ......................................................................................................................... iv Table of Contents .......................................................................................................................... v

1. Introduction............................................................................................................................. 1 1.1 Background ..................................................................................................................... 1 1.2 Project Objectives ........................................................................................................... 1 1.3 Organization of Report.................................................................................................... 1 2. Fundamentals of Surface Modelling ....................................................................................... 2 2.1 Terminologies ................................................................................................................. 2 2.2 Digital Surface Modelling Techniques ........................................................................... 4 2.2.1 Cartographic ........................................................................................................ 4 2.2.2 Ground Surveying................................................................................................ 5 2.2.3 Digital Aerial Photogrammetry ........................................................................... 6 2.2.4 Interferometric SAR .......................................................................................... 12 2.2.5 LiDAR ............................................................................................................... 14 2.2.6 Summary of Merits and Limitations of DEM Techniques ................................ 16 2.3 Quality Assessment of DEMs ....................................................................................... 19 2.4 Vertical Datums ............................................................................................................ 20 3. Flood Inundation Modelling (FIM)....................................................................................... 22 3.1 Overview of Existing FIM Practices............................................................................. 22 3.2 Data Requirements for FIM .......................................................................................... 24 3.3 Critical Landscape Features and Environments ............................................................ 26 3.4 Current Issues................................................................................................................ 26 3.5 Summary ....................................................................................................................... 27 4. Metadata Issues..................................................................................................................... 28 4.1 Importance of Metadata for Flood Inundation Modelling............................................. 28 4.2 Metadata Standards ....................................................................................................... 28 4.3 Future Directions........................................................................................................... 29 5. Generally Available Topographic DEM Datasets................................................................. 29 5.1 LandMap Elevation Data .............................................................................................. 30 5.2 Shuttle Radar Topographic Mission.............................................................................. 30 5.3 Ordnance Survey LandForm Profile ............................................................................. 31 5.4 NextMap Britain InSAR ............................................................................................... 32 5.5 LiDAR........................................................................................................................... 32 5.6 UK Perspectives ............................................................................................................ 33 5.7 BlueSky’s Point-Z......................................................................................................... 34 5.8 Summary ....................................................................................................................... 34 6. Emerging Data Capture Systems .......................................................................................... 36 6.1 Leica Airborne Digital Sensor (ADS) 40...................................................................... 36 6.2 Vexcel’s Digital Camera - UltraCamD ......................................................................... 38 6.3 Wide Swath SONAR Bathymetry................................................................................. 39 6.4 TanDEM X.................................................................................................................... 43 7. Evaluation of Appropriate DEM Techniques ....................................................................... 43 UR3_data_types_for_digital_surface_models_WP5_2_v1_1.doc

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7.1 DEM Requirements for FIM......................................................................................... 44 7.2 Appropriate DEM Techniques ...................................................................................... 44 7.3 Study Areas and Datasets.............................................................................................. 45 7.3.1 Newark on Trent ................................................................................................ 45 7.3.2 Upton Upon Severn ........................................................................................... 45 7.4 Experiments and Results for Newark on Trent Site ...................................................... 46 7.4.1 Methodology...................................................................................................... 46 7.4.2 Visual Comparisons of DSMs ........................................................................... 46 7.4.3 Quality Evaluation Using Landscape Profiles ................................................... 51 7.4.4 Summary............................................................................................................ 54 7.5 Experiments and Results for Upton-Upon-Severn site ................................................. 55 7.5.1 Digital Photogrammetric DSM.......................................................................... 55 7.5.2 LiDAR DSM and DTM ..................................................................................... 56 7.5.3 Visual Comparisons of DSMs ........................................................................... 57 7.5.4 Comparisons Using Landscape Profiles ............................................................ 58 7.5.5 Accuracy of the Surface..................................................................................... 60 7.5.6 Spatial Distribution of Elevation Errors ............................................................ 60 7.5.7 Detection of Critical Landscape Features .......................................................... 62 7.5.8 Summary............................................................................................................ 65 8. Discussion ............................................................................................................................. 65 9. Conclusion ............................................................................................................................ 70 10. Acknowledgements............................................................................................................... 72 11. References............................................................................................................................. 72 Table of Tables Table 2.1 Summary of LiDAR Characteristics (adopted from El-Ashmawy 2003) ................ 16 Table 2.2 Summary of Merits and Limitations of Available DEM Techniques ...................... 18 Table 5.1 Summary of Characteristics of Generally Available DEMs .................................... 35 Table 7.1 Summary of Statistics for Absolute Orientation ...................................................... 55 Table 7.2 Summary of Achievable Height Accuracy from Large Scale Photography ............ 55 Table 7.3 Comparison of Photogrammetric and LiDAR with GPS Points.............................. 60 Table of Figures Figure 2.1 Distinction between DSM (red) and DTM (blue) surfaces........................................ 2 Figure 2.2 Examples of Data Structures Used in Elevation Modelling (Priestnall 2005)........... 4 Figure 2.3 Example of a Rectangular Block of Aerial Photos (Leica 2003)............................... 7 Figure 2.4 Internal Geometry (Leica 2003)................................................................................. 8 Figure 2.5 Exterior Orientation Elements (Leica 2003).............................................................. 8 Figure 2.6 Space Intersection Approach (Leica 2003).............................................................. 10 Figure 2.7 Digital Photogrammetric Workstation ..................................................................... 11 Figure 2.8 InSAR Geometry (Dowman 2004) .......................................................................... 12 Figure 2.9 Concept of Terrain Mapping Using InSAR (Li et al. 2004) .................................... 13 Figure 2.10 Typical LiDAR System and its Main Components (Smith 2005) ........................... 14 Figure 2.11 Scheme to Generate DEM using LiDAR (Wehr and Lohr 1999)............................ 15 Figure 2.12 Ellipsoidal height H and Orthometric height h of two points A and B related by a model of Geoid-ellipsoid separation N (after OS 2002)....................................... 21 Figure 5.1 Coverage of LandMap data over the United Kingdom (source: Landmap.ac.uk) ... 30 Figure 5.2 SRTM 90m data coverage by region (source: jpl.nasa.gov).................................... 31 Figure 5.3 LandForm Profile Examples. Contours (left) and Perspective view of DTM (right) ....................................................................................................................... 31 UR3_data_types_for_digital_surface_models_WP5_2_v1_1.doc

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Figure 5.4 Figure 5.5 Figure 6.1 Figure 6.2 Figure 6.3 Figure 6.4 Figure 6.5 Figure 6.6 Figure 6.7

InSAR coverage for England and Wales (source: www.intermap.com).................. 32 EA LiDAR coverage shown in blue (Modified from EA diagram)......................... 33 Schematic of the ADS40 Sensor (courtesy Leica Geosystems)............................... 36 Schematic of the ADS40 Sensor (courtesy Leica Geosystems)............................... 37 Sample DSM (left) and ortho-image (right) (courtesy Leica Geosystems) ............. 37 Vexcel’s UltraCamD digital aerial camera (source: www.vexcel.com) .................. 38 Example Colour Image from the UltraCam D (© Simmons Aerofilm Ltd) ............ 38 Typical Detail that can be resolved by Zooming in (© Simmons Aerofilm Ltd) .... 39 Comparing the number of successful matches in a pair of scanned film images (a) and a pair of UltraCam-D images(b) (after Leberl and Gruber 2005)............... 39 Figure 6.8 Typical Components for a Wide Swath SONAR Bathymetry System .................... 40 Figure 6.9 Combining LiDAR (a) with SONAR Bathymetry (b) to form a Merged Dataset (c) (Courtesy The Environment Agency)................................................................. 41 Figure 6.10 Potential for Deriving Cross-Sections from Combined LiDAR and Bathymetry Datasets (Courtesy The Environment Agency) ....................................................... 42 Figure 6.8 Illustration of Proposed Tandem-X Satellites (www.terrasar.de)..................... 43 Figure 7.1 Conceptual Framework for DTM and Feature extraction from DSM ..................... 44 Figure 7.2 Oblique Aerial Photography of Flooding at Upton-Upon-Severn ........................... 46 Figure 7.3 1:25,000 aerial photography DSM in a residential area(Asal 2003)........................ 47 Figure 7.4 1:10,000 aerial photography DSM in a residential area (Asal 2003)....................... 48 Figure 7.5 LiDAR DSM in a residential area (Asal 2003)........................................................ 48 Figure 7.6 Hill-shaded map created from 1:10,000 photogrammetry DSM ............................. 49 Figure 7.7 Hill shaded Map Created from LiDAR DSM (Asal 2003) ...................................... 49 Figure 7.7 Hill shaded Map Created from LiDAR DSM (Asal 2003) ...................................... 50 Figure 7.8 Longitudinal profile in a road bridge on the River Trent located in a position close to the middle area of the 1:10,000 stereo model (Asal 2003)......................... 52 Figure 7.9 Profiles (A – B) in grassland surface of Cleveland Square located at the lower right part of the 1:10,000 stereo model (Asal 2003) ................................................ 52 Figure 7.10 Longitudinal profiles (A – B) in a store building in the shopping area ................... 53 Figure 7.11 Longitudinal profile in a church building in the shopping area ............................... 54 Figure 7.12 Colour Coded Photogrammetric DSM Generated Using LPS DPW ....................... 56 Figure 7.13 Ortho-Photo Generated based on Photogrammetric DSM....................................... 56 Figure 7.14 Colour Coded LiDAR DSM (a) and DTM (b) for Upton Upon Severn .................. 57 Figure 7.15 Ortho Image (a), Colour Coded LiDAR DSM (b) and Photogrammetric DSM (c) for Part of the Town of Upton Upon Severn ...................................................... 58 Figure 7.16 Profile#1 Taken Across Section of the Floodplain .................................................. 58 Figure 7.17 Profile#2 Taken Across large Building.................................................................... 59 Figure 7.18 Profile#3 Taken Over Bare Earth in Agricultural Area ........................................... 59 Figure 7.19 Profile#4 Taken Across and Embanked Feature...................................................... 60 Figure 7.20 Ortho Image (a), colour Coded Photogrammetric DSM (b), and Colour Coded LiDAR DSM (c) for Part of an agricultural/rural area of Upton Upon Severn ....... 61 Figure 7.21 Ortho Image (a), colour Coded Photogrammetric DSM (b), and Colour Coded LiDAR DSM (c) for Part of the town of Upton Upon Severn ................................. 61 Figure 7.22 Orthophoto (left) and DSM Difference Image (right) for the Agricultural/Rural Area.......................................................................................................................... 61 Figure 7.23 Orthophoto (left) and DSM Difference Image (right) for Part of the town of Upton Upon Severn.................................................................................................. 62 Figure 7.24 Orthophoto (a); Photogrammetric DSM (b); and LiDAR DSM (c)......................... 62 Figure 7.25 Hill shaded Photogrammetric DSM (a); LiDAR DSM (b) and LiDAR DTM (c) ... 63 Figure 7.26 Orthophoto Overlaid with Landline Buildings (blue) (left) and LiDAR DSM Overlaid with Landline Building Outlines (white) (right) for Part of Town of Upton Upon Severn.................................................................................................. 63 Figure 7.27 Colour Coded Photogrammetric DSM (left) and Orthophoto subset (right) ........... 64 Figure 7.28 Orthophoto (a); Photogrammetric DSM (b); and LiDAR DSM (c)......................... 64 UR3_data_types_for_digital_surface_models_WP5_2_v1_1.doc

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Figure 7.29 Profile Comparing Photogrammetry, LiDAR and GPS along Road Centreline ...... 65 Figure 8.1 DEM Generated from Contours Showing Artefacts (OS LandForm Profile).......... 66 Figure 8.2 SAR Digital Surface Model (DSM) (after Kerridge 2005)...................................... 68 Figure 8.3 SAR Digital Terrain Model (DTM) (after Kerridge 2005)...................................... 68


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1.

Introduction

1.1

BACKGROUND

Fundamental to high quality flood inundation modelling is the need for high quality information about the landscape. Existing new technologies for the collection of information about the landscape provide a catalyst for new research into hydrodynamic modelling to fully utilise this new resource. Often hydrodynamic modelling requires different information about the landscape dependent upon the type and relationship of landscape features and there influence on the risk of flooding. Thus embankments alongside a river are often the first critical feature to control flow if and when a flood occurs. Once an embankment is overtopped features on a lowland floodplain might have little influence on the water flow and contain no features of significance or economic importance. In this situation the characteristics of the embankment would be require to a high level of detail while the floodplain would require little detail. Remote sensing techniques available today offer a cost effective method of capturing landscape information. The level of quality now obtainable from these techniques will enable more sophisticated landscape modelling and therefore enable more sophisticated hydrodynamic models to be produced. High quality data describing the landscape and the activities taking place on the surface are fundamental to flood risk management. A key parameter in landscape modelling for flood risk management is height information. This normally takes the basic form of a digital surface model of the ground surface. Features on the landscape, manmade or natural, provide additional information that influences the flow of water over the landscape. One of the aims of remote sensing is to create the digital surface model to enable these features to be identified.

1.2

PROJECT OBJECTIVES

This report aims to provide a review of the merits and limitation of various survey and mapping techniques including newly available data types for the creation of digital surface models for flood inundation modelling. The target end users are consultants undertaking modelling, risk assessment and mapping at a range of scales on behalf of the Environment Agency or Defra and those responsible for developing and implementing CFMPs (Catchment Flood Management Plans) and Shoreline Management Plans (SMP). It is also hoped it will be of value to the researchers involved in the EPSRC Flood Risk Management Research Consortium (FRMRC).

1.3

ORGANIZATION OF REPORT

Considering the target end users, this report provides an introduction to a number of topics including references to enable the reader to find further information. It focuses on issues considered to be important or to maintain the flow of the content. The report starts with introducing the fundamentals of surface modelling and presents practices in hydrodynamic modelling. Important to data collection is a specification; this has been developed through discussion with consultants and hydraulic modellers in the FRMRC. This is expected to be a dynamic specification as new hydraulic modelling techniques are developed and data collecting methods are improved. An introduction is provided into metadata which is becoming an increasingly important issue as we strive towards establishing standards and more rigorous quality assurance. For flood modellers this is critical because data and information is often collected from a variety of sources and can be of varying ages. Metadata could help track the history of information and enable the ‘fitness for purpose’ to be assessed long after its creation.


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Selections of the most appropriate generally available topographic datasets are reviewed as these can be a very cost effective way of obtaining digital surface models. Although one of their advantages may be ease of availability there could be limitations in terms of the fitness for purpose in hydrodynamic modelling. To complete the background information a review of emerging data collection methods is provided. This is limited to horizon scanning those technologies that are presently in limited use or expected to be available in the near future. Out of the review of the technologies will come a recommended list of methods and data types of value to the hydrodynamic flood modelling community. The major test site chosen, partly for the ‘richness of data’ and partly for its familiarity to the flood modelling community, is the area of Upton Upon Severn. Not all data types are available for this area and this area does not provide the range of landscape types that are of interest, so some selected alternative sites have been used. Finally, some conclusions have been given.

2.

Fundamentals of Surface Modelling

2.1

TERMINOLOGIES

When dealing with technology that is used in various parts of the world, there can be terms that vary in usage from continent to continent. For the purposes of this report the following terms, definitions and concepts will be adopted.

DEM, DSM and DTM A Digital Surface Model (DSM) is a representation of any surface by using three dimensional (3D) coordinates, normally X, Y, Z Cartesian coordinates. The surface might be part of a small object, for example a vase, or it may be a very large object such as the surface of the Earth. When related to the Earth’s surface, these coordinates are often converted into Easting, Northing and Height (E, N and H). A Digital Elevation Model (DEM) specifically relates to elevation and therefore height, and so will be defined as a DSM of the Earth’s surface. It is used generically to define both the ground surface, also called a Digital Terrain Model (DTM), and the ground surface plus the tops of features above the ground surface such as artificial structures and vegetation. The DSM is therefore the first surface that many airborne and spaceborne sensors will interact with. A DTM is normally created by stripping off all above ground surface features from the DSM to reveal a ‘bald-earth’ model (see Figure 2.1).

constant N, E spacing ground surface

Figure 2.1 Distinction between DSM (red) and DTM (blue) surfaces (modified from Russell 2001)


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Traditionally, the term DEM was often used to describe elevation models derived from satellite remote sensing where it was not easy to distinguish between ground and non-ground points. Today with the many advances in instrumentation and techniques, this distinction can be made more easily and therefore there is the need to have terminologies that distinctly identify the surfaces being considered. The method of data collection (see section 2.2) will ultimately decide the term that will be used to best describe the surface created.

Filtering The process of eliminating non-ground points from a DSM to obtain a DTM is often referred to as filtering or stripping. A variety of methods have been proposed for this task (Sithole and Vosselman 2003) which can be grouped into two main categories: labelling approach and adjustment approach. Despite these approaches and the fact that DTM products can be prepared by many providers of elevation data, difficulties still remain in undertaking this task. Further research is therefore required to refine existing techniques or develop new techniques that can be optimized to automatically and reliably filter a DSM to generate a DTM. Moreover, we still need a comprehensive understanding of the performance of the filtering process and the quality of the resultant DTM.

Data Structures The most common DEM data structure is the raster or grid structure. Graphically, these normally consist of a matrix of square grid cells with the mean cell elevation stored in a two dimensional array. Location of a cell in geographic space is implicit from the row and column location of the cell within the array, provided that the boundary coordinates (geo-referencing) of the array are known. Grid DEMs are widely available and used because of their simplicity, processing ease and computational efficiency (Martz and Garbrecht 1992). Limitations include: grid size dependency of certain computed topographic parameters (Fairfield and Leymarie 1991) and inability to locally adjust the grid size to the dimensions of topographic land surface features. Other DEM data structures, such as the Triangulated Irregular Network (TIN, also called mesh) and contour-based structures, have overcome some of the disadvantages of grid DEMs; however, they have shortcomings of their own and are not as widely available as grid DEMs. The raw data from some techniques tend to have the elevation in E, N and H forming ‘point clouds’ of either randomly spaced points or those positioned using a regular pattern; these do not form a continuous surface and would eventually have to be converted to either a grid or TIN/mesh before use in most modelling software. The Choice of data structure will depend on data availability, nature of surface being modelled and the techniques that will be used to analyse the problem and manipulate model scale and resolution of the data. Although the grid DEM is the most commonly used data structure it is rarely the data capture structure. The raw data is normally captured in a way that is appropriate to the technology being used and at the end of processing a regular grid is then interpolated. The regular grid interval should be commensurate with the density of the data capture so there is minimal reduction in quality produced by the interpolation process. Of course, the data capture density should be commensurate with the required level of detail. Figure 2.2 illustrates the common data structures in use today.


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N, E and H Point Cloud

Figure 2.2

Triangular Irregular Network (TIN) or ‘mesh’

Gridded Elevation Model

Examples of Data Structures Used in Elevation Modelling (Priestnall 2005)

Spatial Resolution The concept of spatial resolution is well developed in the field of remote sensing, where it is defined in terms of the ground dimensions of the element or pixel making up the dataset. It therefore refers to the size of the grid cell used to represent the surface being modelled and relates to the size of the smallest feature that can be represented and detected in the elevation model. In digital photogrammetric terms, the ground representation of the size of an image pixel is often called the Ground Space Distance (GSD) whilst image resolution relates to the minimum feature size that can be identified in the image. So resolution is influenced by the quality of the optical system of the imager, atmospheric effects, etc.

Interpolation When the topographic data is collected it may not represent a continuous surface or represent the area of interest. The method of converting the discrete points collected into a continuous surface is referred to as surface interpolation. Several algorithms exist for this purpose and the most popular are: kriging, nearest neighbour, inverse distance weighted and spline. Many elevation modelling packages offer a choice of interpolation methods, however the user should be aware that the accuracy of a selected method will depend on accuracy of initial point measurements, density and distribution of the raw data. The reader is directed to Burrough and McDonnell 1998 and Watson 1994 for more information on the different interpolation algorithms.

2.2

DIGITAL SURFACE MODELLING TECHNIQUES

Several approaches exist for the collection of data and the generation of models of the earth’s surface with each method having its own merits and limitations. In addition, each area of interest has its own specific set of conditions which will render certain techniques more suitable than others. Sometimes, it may be necessary to use a combination of different methods or even specifically design methods in some extreme cases. The selection of a particular technique will rely on: the accuracy required; the extent of the area of interest; the budget; time frame; the modelling objective; etc. The following subsections outline some of the common methods used for elevation data collection and modelling.

2.2.1 Cartographic Essentially, the cartographic method involves the production of elevation models by digitizing or scanning the contour lines and spot heights on an available topographic map of the area of interest. Once this data has been converted to a digital format it can be interpolated using one the methods mentioned in section 2.1. Since these contour lines and spot heights normally represent bare earth


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surfaces, the product generated is a DTM. This approach is usually relatively economical; however, its accuracy depends greatly on the quality of the map and the skill of the operator digitizing the data. Also, the contours are widely spaced in low lying floodplain areas which do not give a good representation of the surfaces in these areas. Further information on the Cartographic technique can be found in Kennie and Petrie 1990.

Merits Normally based on widely available topographic maps. Easy to create if the digital contours are available. Only areas of interest need to be digitized.

Main Sources of Errors and Limitations Errors in the topographic map (shrinkage, positional, generalization). Digitizing errors. Interpolation errors. Highly dependent on the quality and scale of the base map. Does not accurately characterize low lying areas such as floodplains.

2.2.2 Ground Surveying These techniques require that observations for elevation models be made directly in the field. The current approaches normally considered for achieving this are: conventional total station or spirit levelling surveys; and Global Positioning Systems (GPS). The objective is to acquire appropriate measurements at every point requiring 3D national grid coordinates (Easting, Northing and Height). These points are usually conveniently placed across the terrain and will have to be re-sampled into a regular grid using one of the interpolation methods in section 2.1. These techniques are the most accurately available to date, however they can require lengthy field work making them potentially costly and therefore unsuitable for DSM/DTM generation of large areas. They however play an important support role for other techniques by providing control information, validation data and filling in gaps in data.

Total Stations Modern theodolites with integrated distance measurement devices and built in computation and storage functionality are called total stations. These instruments are highly accurate with the ability to position points better than ±5mm. Angles and distances are measured on the total station which is usually mounted over a point whose position is known in a local coordinate system. Using well established trigonometric procedures, the 3D coordinates of the point is determined. Further 3D points are then interpolated and gridded to form a DEM. Further information on the use of total stations can be found in any standard surveying text, for example Kavanagh 2003 ; Bannister et al. 1998. •

Spirit Levelling Very high quality elevations (better than 1mm) can be obtained by spirit levelling using appropriate quality equipment (optical or digital level and staff) and measurement techniques. The technique does not provide plan position (E, N), only heights, therefore when a DEM is required the intersections of the grid will have to be set-out in the field before levelling is performed. This technique can be useful in determining the elevation of linear features that are easily identifiable on maps, for example, ditches or crest of embankments, and used for enforcing elevations in DEMs/DTMs. The technique can also be useful in determining cross-sections of river channels for use in 1D modelling applications. Further information can be found in any standard surveying text (see for example Bannister et al. 1998).


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Kinematic GPS The principles of GPS are well documented in many book and papers for example: Hoffman-Wellenhof et al. 1994; Kaplan 1996; Leick 1995; Wells 1989 and Rizos 1997. A useful starting point would be Peter Dana’s web material at colorado.edu/geography/gcraft/notes/gps/gps_f.html. GPS is a system developed by the US military to provide continuous all weather positioning, velocity and timing measurements. The system is based on a constellation of more than 24 satellites, whose positions are accurately known, transmitting continuous signals on two frequencies in the microwave band - L1 (1575.42 MHz) and L2 (1227.60 MHz). Specially designed receivers record the signals and using well established computational procedures (Leick 1995) the 3D position of the point of interest is determined. The two main observing modes used in surveying are differential static positioning and kinematic positioning each requiring a minimum of two GPS receivers; a base and a roving receiver. Typically with static observations the receiver is required to be stationary at the required position for more than 30 minute, depending on the distance between the base and rover, in order to resolve the ambiguities that are characteristic of GPS measurements and usually yield accuracies better than ±2cm. The Kinematic GPS mode, either post processed (PPK) or real time (RTK) requires less observation time as observations at a single epoch are used to compute position. In RTK, corrections are transmitted via a radio link from the base to the rover. The 3D position of a point can be determined and provided in the field with a potential accuracy of better than ±5cm. PPK is the mode of choice when establishing points for the creation of DTMs since it can be a rapid method of survey. The 3D positions of points are initially computed with reference to the WGS84 datum and often coordinates have to be transformed and projected to a national or local coordinate system; in the UK this is often OSGB36 and the British National Grid (OS 2002). The 3D positions could then be interpolated to create a regular grid DTM. Merits

Extremely accurate. Total stations and spirit levelling can acquire elevations under forest canopy and other vegetated areas. Provide ground control for almost all airborne and spaceborne sensors. Acquisition of quality control information. Provides measurements for filling in data voids in DEMs. Limitations

Expensive and time consuming to collect for large areas. GPS systems do not provide reliable results in heavily vegetated areas and urban canyons (receivers need line of sight to satellites). Access is required to measure points. Safety issues – area of interest may include a dangerous or hostile environment. Line of sight is required for total station surveys. GPS requires clear view to at least 4 satellites at all times (5 for RTK). For RTK GPS, line of sight required for radio link between base and rover receivers. This could be solved by using more advanced radios or GSM cellular phone technology.

2.2.3 Digital Aerial Photogrammetry Photogrammetry is generally defined as the art and science of making accurate measurements using photographs or images, the Manual of Photogrammetry 5th edition gives a modern description (ASPRS 2004). For the purposes of this report, aerial photography only will be considered and limited to ‘vertical photographs’ (tilt of less than 3o) taken from an aircraft platform. Photogrammetry is a passive system which detects the reflected solar radiation from ground surface and records the returns digitally or on analogue film. Classical aerial photographs for surveying purposes are taken with a camera specifically designed for photogrammetry called a metric camera. These are normally of 152.4 mm focal length and large (22.5cm x 22.5cm) format, very closely representing perspective geometry.


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Perspective geometry is fundamental to many photogrammetric processes and a stereo-pair of photographs (images in digital photogrammetry) are used to generate 3D coordinates (ASPRS 2004). The steps for generating a DEM using digital photogrammetric techniques are well documented in modern photogrammetry textbooks, for example ASPRS 2004; Mikhail et al. 2001; Wolf and Dewitt 2000; Schenk 1999 and are summarised as follows: acquisition and pre-processing of aerial photos; interior orientation; exterior orientation – involving aerial triangulation or relative and absolute orientations; automatic and semi-automatic DEM generation; and DEM editing. A brief outline is given in the following sections. •

Image Acquisition and Pre-Processing Analogue photographs or digital images are usually acquired using a specially equipped survey aircraft. Most of the modern systems have GPS for positioning and an Inertial Measurement Unit (IMU) for orientation of the platform. A flight plan is usually developed to assist the pilot in executing the project and to ensure that the 60% forward overlap and 20-30% sidelap is achieved. The photos are usually flown in strips which are later combined to form a rectangular block as shown in Figure 2.3. If the photos were taken with a film camera, diapositives need to be created from the film and then scanned using a photogrammetric scanner at an appropriate resolution (typically in the range of 10-30 µm) before use in the digital photogrammetric process. The optimum image resolution will depend on the desired output accuracy but will influence the file storage size. It is important to consider scanner resolution (for film cameras) or pixel resolution (for digital cameras) since image resolution is one of the principal factors contributing to the overall accuracy of the extracted 3D position of points. Some image enhancement may be necessary to try and remove haze and other atmospheric effects.

Figure 2.3 Example of a Rectangular Block of Aerial Photos (Leica 2003) •

Interior Orientation The objective here is to define the internal geometry of the camera as it existed when the photos/images were taken and is usually specified using the following parameters as shown in Figure 2.4: location of principal point (xo, yo); focal length of camera (f); x, y location of fiducial marks, usually 4 or 8 points to define the image coordinate system; and lens distortion polynomial. These parameters are normally referred to as interior orientation elements and derived from a camera calibration exercise Wolf and Dewitt 2000.


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Figure 2.4 Internal Geometry (Leica 2003) •

Exterior Orientation In this step, it is necessary to determine the position and angular orientation (attitude) of the camera at the instant each photo/image was taken. Six parameters, three for position (Xo, Yo and Zo) defining the perspective centre (O) and three for orientation: Omega (ω) - a rotation about the photo x axis; Phi (ϕ) – a rotation about the y axis; and Kappa (κ) – a rotation about the z axis are used to define the exterior orientation elements as illustrated in Figure 2.5.

Figure 2.5 Exterior Orientation Elements (Leica 2003) In modern aircraft systems the positional elements are determined using an onboard GPS while the angular orientations are derived with the aid of an IMU. In traditional systems the elements would have to be derived with the aid of ground control points (GCPs) which can be time consuming and costly to establish for large projects. The objective of exterior orientation is to form a relationship between the image space coordinates (x, y, z) and ground space coordinates (X, Y, Z or x’, y’, z’). Although GPS and IMU integrated with imaging systems can produce ‘direct geo-referencing’ of the image there are two traditional photogrammetric methods to determine exterior orientation:


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Aerial Triangulation To determine the exterior orientation elements for photogrammetry it is expected that there would be at least three GCPs (3 height, 2 plan -theoretical minimum) in the 60% overlapping portion of each stereo-pair of photographs. So for a project with thirty or more images at least ninety GCPs are required, which is rarely available and even if some GCPs are common between images thus reducing their number it would still be a time consuming and costly to identify and measure each one. To keep the cost of the project to a minimum, an optimum number of GCPs are utilized which is dependent on the number of photographs being considered and high accuracy is achieved using a technique referred to as Aerial Triangulation. This process determines the relationship between the photos/images in a stereo-pair or block (formed from overlapping stereo-pairs), and the ground control coordinates. Aerial Triangulation can also be used to refine the initial exterior orientation obtained from systems using GPS and IMU to produce direct measurement of position and attitude (direct geo-referencing) of images. Once accurate exterior orientation elements have been determined the 3D position of points in the stereo model can be collected. •

Relative and Absolute Orientations Exterior orientation of a stereo-pair of images can be broken into two processes: relative and absolute orientation. Relative orientation allows the relationship of the two images to each other, at the instance of exposure, to be determined. This enables a ‘stereo-model’ of the landscape to be created which can be viewed and measured through the principles of stereoscopy. The measurements then need to be related to the map projection coordinates through the process of absolute orientation (ASPRS 2004).

•

Automatic and Semi-Automatic DEM Generation In generating elevation data from stereo-pairs it is necessary to first locate corresponding image points that are in the overlapping areas, for example P1 and its corresponding image point P2 as shown in Figure 2.6. This process is either done semi-automatically, where the plan location is automatically measured but an operator measures the height; or automatically, using a technique called image matching with one of three methods: area-based matching; feature-based matching; or relation-based matching (Schenk 1999). The semi-automatic process depends on the operator visually inspecting the images and placing a measuring mark in the surface of the stereo image (model). The semi-automatic process will not be considered further here as the automatic process is the modern approach having clear economic benefits. An image pyramid (Leica 2003) is used during the matching process to reduce the computation time and increase the matching reliability. Two image points are considered to be a match if the value from the functional model comparing the two locations is above some predefined threshold.


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Figure 2.6

Space Intersection Approach (Leica 2003)

The result of the image matching process is a disparity file containing the image coordinates of the corresponding points covering the majority of the overlap area. If the interior and exterior orientations were carried out correctly, then based on Figure 2.5, O1-p1-P-p2-O2-O1 will form a plane surface; implying that O1, p1 and P all lie on the same line (i.e. collinear), similarly O2, p2 and P. A set of equations identified as collinearity equations (Wolf and Dewitt 2000) enforces this relationship and forms part of the space intersection approach, illustrated in Figure 2.6, that is used to solve for the 3D positions of point P from all measurements in the disparity file. These 3D positions are interpolated into a regular grid using one of the methods mentioned in section 2.1. Since photogrammetric images record the first surface visible from the air, the model formed is essentially a DSM. Software modules are available on most Digital Photogrammetric Workstations (DPWs) to edit the DSM by semi-automatic techniques to remove blunders and in some cases filter it to create a DTM. Most of these editing procedures involve human interaction and can be very time consuming. Figure 2.7 shows an example of a DPW, where the operator can use special goggles to see the stereomodel in 3D and has the potential to manually extract 3D coordinates of points with elevation accuracy dependent on the scale of the photograph and ability of the observer. Manual measurements might play an important role in extracting elevations of embankments and other critical features and also for collecting 3D data to fill in voids in other DEM datasets.


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Figure 2.7

Digital Photogrammetric Workstation

Main Sources of Errors and Limitations

• • • • • • • • •

In manual measurements − Observers ability to see stereoscopically. − Skill in measuring the 3D stereo model created from pairs of photographs. Definition of camera geometry. Scale of photographs/images. Identification of ground control points used to provide the relationship between the imagery and mapping coordinate system. Limitations in stereo matching algorithm for automatic DSM generation. Difficult to generate bare earth models in densely vegetated areas. Image quality – resolution. Delays in obtaining photographs/ images - restricted by weather conditions and environmental conditions. Quality control required for automatically derived DEMs.

Merits

• • • • • •

It is a proven and well understood approach. The photos can be used for other purposes - Provides an optical image of the landscape for interpretation and measurement. Relatively economical for surveys of large areas. Aerial photographs can provide a good historical record of actual inundation extents. Potential for high accuracies in plan and height measurement. Digital cameras provide high quality radiometric characteristics and may include an infra-red band.


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2.2.4 Interferometric SAR The Interferometric Synthetic Aperture Radar (InSAR, also IFSAR) technique for DSM generation uses two side-looking SAR antennae onboard a platform (satellite, space shuttle or aircraft) separated by a known baseline to image the terrain. Two main configurations exist for the acquisition of data: Repeat pass interferometry, which is usually associated with satellite systems, where the data is acquire from two passes of the sensor in similar orbits; and single pass interferometry, which is common with aircrafts and the Space Shuttle, where the data is acquired in a single pass from two antennas separated by a fixed baseline. The Interferometric process has been widely discussed in the literature, in the case of repeat pass see Goldstein et al. 1988 and for single pass see Madsen et al. 1991. The sensor geometry necessary to extract elevations is illustrated in Figure 2.8. If the two antennas at A1 and A2, separated by a baseline B receive the backscattered signal from the same terrain point, there will be a difference in path δp for the two signals. The baseline angle α is determined from the sensor’s inertial navigation system (INS), the height H is accurately determined from differential GPS and the distance to terrain point is the slant range P. It is a matter of trigonometry to compute the terrain height h using these quantities. For full explanations on the procedure see Zebker and Goldstein 1986 and Hanssen 2001. Figure 2.9 shows the mapping process using airborne InSAR

technique.

Figure 2.8

InSAR Geometry (Dowman 2004)


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Figure 2.9

Concept of Terrain Mapping Using InSAR (Li et al. 2004)

The elevation measured for any pixel (resolution cell) results from a combined signal of scatterers located in the resolution cell (sample area). Elevations are determined based on ‘volume scatter’, i.e. there will be some penetration into the canopy and the range recorded will not depict the true height of the tree (first surface). Therefore, areas covered by vegetation will include more height measurement noise than areas covered by specular scatters (i.e. buildings). The wavelength of the radar will determine the penetration of the signal into the vegetation, X band will not penetrate as far as L band. In addition, the surface area represented by one pixel may consist of a combination of different scatterers. Height measurements could be biased due to an interaction of the backscatter from these surface features. The backscattered signal (radar response) is integrated over a square footprint (resolution cell) somewhat larger (about 50%) than the 5m DSM sample distance (Dowman 2004). Therefore, the elevation measured for any DSM sample (resolution cell) will result from a combined signal of scattering objects located in this sample area. If hedges and shrubs are closely located to a road, both, the raised objects and the road itself (bald earth) will contribute to the elevation value measured for this DSM sample. InSAR has been widely used from spaceborne platforms; the ERS Tandem mission and the Shuttle Radar Topography Mission (SRTM) are the two prime examples. The main airborne InSAR is the Intermap STAR-3i. This is a single-pass across-track InSAR system operating commercially since January 1997. The system is an X-band SAR interferometer carried on board a LearJet 36. The two antennae are separated by a 1m baseline. Accurate positioning and orientation is achieved through the use of an on-board laser-based inertial navigation system and an on-board differential GPS (Mercer and Schnick 1999). Main Sources of Errors and Limitations

• • • •

Volume scattering in vegetated areas leading to poor coherence. Confusing height data in water body regions. Height errors within the vicinity of bright targets are quite common. Radar shadow, foreshortening and layover in severe terrain.


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• • • •

Trees and valley's can cover drainage sites, making it difficult to accurately represent the drainage. Elevation models may not be an accurate representation of the water flow. InSAR performance can be degraded in urban areas due to bright targets and no data due to shadowed areas. Artifacts due to topography or atmospheric propagation. Coarse spatial resolution and vertical measurement accuracy from satellite InSAR systems.

Merits

Can “see through” clouds and operate in almost any weather condition. Generates its own ‘illumination’ and therefore has the potential to acquire data both day and night. Has shown to be capable of measuring deformations (changes in height) of the land surface to a high accuracy.

2.2.5 LiDAR Since 1994, a new technology for terrain modelling has been made available to the surveying and mapping community and is commonly referred to as LiDAR (Light Detection And Ranging). A LiDAR system (see Figure 2.10) is based on the combination of three different data collection tools: a laser scanner mounted on an aircraft or helicopter platform; a Global Positioning System (GPS) used in kinematic mode to provide the sensor position; an Inertial Measurement Unit (IMU or INS) to provide the orientation; and a data processing and storage unit. Z

GPS Y

Aircraft or helicopter

X

X Laser

Y IMU

Z Flying heights 200-2500m

20o - 40o

Laser pulses up to 100,000 per second Accuracy 5 – 25 cm 0.25 - 5m spacing

H N E

Multiple reflectances laser pulse

GPS ground base station per

Figure 2.10 Typical LiDAR System and its Main Components (Smith 2005) The laser provides the light source, at a specific frequency, which is directed towards the surface of the earth where it reflects off features back towards the aircraft. When a laser pulse is sent to the terrain it can hit more than one object, for example, in vegetated areas the pulse first encounters the foliage while the rest hits the bare earth. Dependent on the system configuration the receiver can collect both pulses, commonly called the first return (the portion striking the foliage) and the last return (the portion striking the bare earth). In some systems it is possible to collect the complete wave form thus


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recording multiple returns from a single pulse. The intensity of the reflected pulse can provide useful information about the object or surface on the terrain it interacted with. For DEM generation most clients are usually only interested in the last return which we would expect represents the bare earth or most closely represents this surface. Upon capture by the receiver unit, the reflectance from the bare earth, tops of vegetation, tops of buildings and other structures are relayed to a discriminator and a time interval meter which accurately measures the elapsed time between the transmitted and received signal. Using the velocity of light and half the round trip time of the laser pulse, the distance (range) between the laser and the terrain feature can be easily computed. For each point a unique time stamp is recorded which will allow the interpolation of the aircraft position from the GPS data and the determination of the aircraft attitude (roll, pitch and heading) from the IMU data. With the range, scan angle, aircraft position and attitude, and GPS base station data the 3D position of all data points can be computed in the GPS datum (WGS84) and can then be transformed into the local map projection (for example, British National Grid). Table 2.1 summarizes the main characteristics of the system while Figure 2.11 shows a simplified processing scheme to generate a DEM using LiDAR.

Figure 2.11 Scheme to Generate DEM using LiDAR (Wehr and Lohr 1999) In typical standard conditions, taking into account the flight (speed 200 250 km/hour, altitude 500 2,000 m) and sensor characteristics (scan angle ± 10 20 degrees, emission rate 2,000 100,000 pulses per second), terrain elevations are collected within a density of at least one point every 0.25 5 m. The technology allows therefore, the generation of accurate and high resolution DEMs suitable for many applications. Further reviews of LiDAR can be found in Fowler 2001; Flood 2001; Baltavias 1999; and Wehr and Lohr 1999.


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Table 2.1 Summary of LiDAR Characteristics (adopted from El-Ashmawy 2003) Characteristic

Min Value

Max Value

Typical Values

Scan Angle ( )

14

75

20 – 40

Pulse Rate (kHz)

5

83

5 – 15

Scan Rate (Hz)

20

630

25 – 40

Flying Height (h) (m)

20

6100

200 – 300 (H), 500 – 1000 (A)a

GPS Frequency (Hz)

1

10

1–2

IMU Frequency (Hz)

40

200

50

Beam Divergence (mRad)

0.05

4

0.3 – 2

Swath Width (m)

0.25 h

1.5 h

0.35 – 0.7 h

Across-Track Spacing (m)

0.1

10

0.5 – 2

Along-Track Spacing (m)

0.06

10

0.3 – 1

Angle Precision (roll, pitch/yaw)(o)

0.004/0.008

0.05/0.08, 0.5/0.2b

0.02 – 0.04 / 0.03 – 0.05

Range Accuracy (cm)

2

30

5 – 15

Height Accuracy (cm)

10

60

15 – 20

Planimetric Accuracy (m)

0.1

3

0.3 – 1

o

a: H = helicopter, A = airplane b: For systems with no true IMU, using 3-4 GPS antennas for attitude determination.

Main Sources of Errors and Limitations

• • • • • • • • • •

Atmospheric delay on laser pulse. Range errors due to inaccurate measurement of pulse or variation in the speed of the oscillation of the mirror. Time synchronization between laser, GPS and IMU. Physical offset between recording centres of Laser, GPS and IMU. GPS associated errors (e.g. geometry of satellite constellation; multipath; troposphere; distance to base station). IMU associated errors. Transformation from WGS84 to local map projection and geoid measurements. Often a narrow swath width, so many flight lines are required for extensive areas. Cannot work in all weather conditions (e.g. strong winds, fog, clouds). May require complementary data, such as aerial photo, if interpretation of points is necessary.

Merits

• • • •

Potential for high heighting accuracy when compared to other airborne DEM techniques. Can generate DEM for a surface with little or no texture. Could measure vegetation heights when set to record first and last pulse. Works both day and night making it a flexible acquisition system.

2.2.6 Summary of Merits and Limitations of DEM Techniques There is no one technique that provides the ideal solution giving accuracy, completeness and optimum economy. Clearly, ‘fitness-for-purpose’ needs to be investigated on a project by project basis. Ground survey techniques can provide potentially the very highest accuracies, but they may not be economically viable. InSAR can provide DEM over large areas either day or night and through cloud cover; yet, the volume scattering mode of signal acquisition causes an averaging of the terrain which ultimately leads to a smoothing of important features and the geometric characteristics of the sensor


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may cause displacement of some terrain objects. Photogrammetry, while having the potential for accurate manual point extraction is limited by the automatic matching algorithms and shadowed areas in the scene. LiDAR, while being able to work day or night, is limited like photogrammetry by weather conditions and is heavily dependent on the quality of the GPS and IMU measurements. Table 2.2 gives a summary of the main merits and limitations of available DEM generation techniques.


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Cartographic

Simple to generate if digital contours are available Economical for large areas

Highly dependent on the scale and quality of the base map. Does not accurately characterize low lying areas such as floodplains Influenced by the skill of operator digitizing the map

Ground Surveying

Extremely accurate Total Stations can acquire elevations under canopy Provides measurements for filling in voids in other datasets

Expensive and time consuming to collect for large areas GPS does not provide reliable heights under canopy Access required to property for measurement of heights

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Digital Aerial Photogrammetry

It is a proven and well understood approach Potential for high accuracies in plan and height Provides an optical image for interpretation Relatively economical for surveys of large areas

Delay between acquisition of images and production of DEM Dependent on scale and quality of imagery Limitations in the automatic matching algorithm Manual measurements require an experienced observer

Can “see” through clouds and operate day or night Rapidly map very large areas.

Volume scattering in vegetated areas lead to poor coherence Performance can degrad in urban areas due to bright targets and shadows Artefacts in the DEM due to topography or atmospheric propagation

Methods

Interferometric SAR

LiDAR

Merits

Limitations

May require a lot of flying time for extremely large areas Potential for high accuracy Cannot operate in cloudy, rainy or windy conditions Can generate DEM for surface with little or no texture Could measure vegetation height when set to record first and May requires complementary data, such as photo, if interpretation of points is necessary last pulse



Table 2.2 Summary of Merits and Limitations of Available DEM Techniques

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2.3

QUALITY ASSESSMENT OF DEMS

Understanding the quality of the DEM datasets is crucial to their use in many studies and also in the detection of change obtained from the comparison of DEMs acquired at different temporal resolutions. Wise 1998 and Cooper 1998 noted that the quality of DEM data is too often overlooked, which can have serious implications for hydrological studies. Lane et al. 2004 have put forward a few reasons why it is important to have some understanding of the quality of DEMs. First, there has been increased growth in the availability of digital data sources, some of which have unknown or poorly specified data quality. Second, digital data derived from numerous processes tend to have errors propagated at each stage of the process leading to magnified errors in DEM-derived hydrological or geomorphological parameters. Third, new methods of data generation have lead to increased automation for the production of DEMs which ultimately have led to a significant increase in the data whose quality is to be determined with a substantial reduction in manual quality control. Lastly, the volume of data generated automatically is extremely large in comparison to the available check data. The ability to assess errors in the dataset is therefore reduced to the point of becoming unreliable. It is a fundamental principle of surveying that a measurement is not useful if it does not have a quality measure and this is supported by Caspary and Joos 2002; unless the quality of a DEM is well known, the dataset should not be used for any application. Information on DEM quality is of great importance since it affects the reliability of any flood inundation modelling exercise and can impact on the credibility of any decisions made from those results. What therefore do we mean by quality as it refers to spatial data? Quality may mean different things to different users depending on their respective needs or applications. Veregin 1999 surmises that quality refers to the difference between the actual characteristics of the dataset and the relevant specifications that define it or the claims made about it. For the purposes of this report we will refer to the quality of a DEM as it’s fitness for a particular application (i.e. “fitness-for-purpose”). How then do we determine the quality of a DEM? Veregin 1999 identified several components of quality which included: accuracy, precision, spatial resolution, consistency and completeness. In surveying and mapping it is common to associate the quality of a DEM with the level of errors that the dataset contains. There is no such thing as an error-free DEM. Following traditional error analysis in surveying, Cooper and Cross 1988 identified three categories of such errors: systematic errors, random errors and gross errors (or blunders) which are thought to have a direct influence on data accuracy, precision and reliability respectively. Systematic errors have a fixed pattern and are usually related to the sensor or the observing procedures used for the data collection. Such errors may not be easily detectable and if not eliminated can introduce significant bias or artefacts into the DEM being generated. If these errors can be identified, they can then be modelled, reduced or even eliminated (Daniel and Tennant 2001). System calibration and mathematical modelling are often used to aid the minimization of systematic errors. Random errors are due to the unpredictable variations in the sensors or the environment and the observing procedures in use. They are therefore due mainly to inconsistencies in the measurement process and cannot be removed, even after eliminating systematic errors. They are usually dealt with through the use of statistics and rigorous adjustments such as leastsquares (Mikhail 1976). Blunders or mistakes are often caused by human error and will affect the reliability of the DEM. The occurrence of these can normally be minimized by the use of good measurement/survey practices. In assessing the quality of a DEM it is common practice (see for example: Shearer 1990; Lane et al. 2004 and Edwards et al. 2005) to compare height values of random points derived from the DEM with height values of co-located points obtained by using a more accurate instrument/method than that used to derive the DEM being assessed. As an example, a DEM generated from LiDAR can be assessed using points observed with GPS or a total station survey. The difference between the DEM points and the GPS points will give residuals, which could be positive or negative, at these locations. By conducting statistical analysis on the residuals, one can arrive at estimates of the quality of the DEM under consideration. The statistical measures normally used are: Mean Error; Standard Deviation and the Root Mean Square Error. It is important to note that these are ‘global’ statistical measures and only


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represent the error at the sample locations and may not representative of the quality of the entire DEM, which may have systematic error trends due to the interaction of the sensor with different types of landscape features. •

Mean Error (ME) This statistical measure determines the extent to which the DEM is free from systematic errors or bias. The sign of the residuals are taken into account and will tend to zero if there are similar magnitudes of positive and negative values i.e. there is no systematic error. If a significant positive or negative value was determined then this would indicate the evidence of systematic error i.e. one surface is systematically higher or lower than the other. The Mean is computed as follows: n

− ∆H =

∑ν

i

i =1

n

− Where ∆H is the mean error, ν is the difference between the DEM and known height and n the

number of check points used. •

Standard Deviation (SD) Standard deviation is another commonly used statistical expression based on the dispersion of random errors in the DEM. It shows how the values being considered vary with respect to the mean and gives a sense of the precision of the DEM. SD is computed as follows: n

SD = ±

∑ (ν

i

− − ∆H ) 2

i =1

n

This statistic is also useful for detecting blunders or outliers as it relates to a probability density function where SD defines the area under the curve and the 68% probability of any observation occurring within ±SD from the mean. It is common practice to reject as a blunder, any point that is not within ±3×SD from the mean residuals. •

Root Mean Square Error (RMSE) The most widely used measure for reporting accuracy is the Root Mean Square Error (RMSE). Its wide spread use may be due to the ease of computations and the ease with which the concept can be understood by most data users. It is a dispersion measure being approximately equivalent to the average deviation between two datasets. The RMSE is computed as follows: n

RMSE = ±

∑ν i =1

2 i

n

The most common use of the RMSE is to provide a single global measure of deviation. Consequently, there is no indication of spatial variation over the surface of the DEM. As the name contains the word ‘error’ it is expected that there is no bias in the residuals.

2.4

VERTICAL DATUMS

A vertical datum is a base reference level or surface for establishing the origin of the vertical dimension of elevation above the ‘earth's surface’. A datum can depend on the ellipsoid, the Earth model defined by gravity called the geoid, or the definition of sea level. So before elevations can be


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determined we must first decide on a reference surface from which the heights must be measured. The elevation of any point is then the ‘vertical’ distance above this chosen reference surface. In the UK the vertical datum most often used is called the Ordnance Datum Newlyn (ODN). It has been determined by taking the average of the tide-gauge readings at Newlyn, Cornwall between 1915 and 1921. ODN heights have become a national standard in the UK being used for contours, spot heights and bench marks (BMs). There are likely to be differences between ODN heights, modern geoidal heights and ellipsoidal heights; it is important to understand why this is so. Heights measured in relation to gravity are often called orthometric heights; an example is ordinary spirit levelling. OS 2002 outlines three reasons for differences in ODN and geoidal heights: Firstly, the ODN model assumes that mean sea level (MSL) at Newlyn coincides with the Geoid at that point. This is not true; the true Geoid is the level surface which best fits global mean sea level not MSL at a particular place and time. MSL around the UK lies below the global Geoid by approximately 80 cm. This effect is important only in applications which require a relationship between Orthometric heights in more than one country; normally for applications conducted in the UK it is unimportant as long as ODN is used. Secondly, since the ODN model is related to MSL only at Newlyn it is susceptible to slope errors as the lines of spirit levelling progress a great distance from this point. This error probably amounts to no more than 20 cm across the whole 1000km extent of the model. Thirdly and most importantly, errors can be incurred when using BMs to establish ODN heights. Of the more that 440,000 BMs across the UK, the majority have not been rigorously checked since the 1970s. There is some evidence of subsidence at BMs of the order of several metres where mining has caused collapse of the ground. We must be aware of errors due to the limitation in the original computations of the height network and due to possible movement of the BMs since they were established. Individual BMs therefore should not be trusted without first verifying their quality. The wide spread use of high precision GPS makes it possible to establish ODN height without reference to BMs using the National GPS Network coupled with the National Geoid Model OSGM02. In fact, the OS recommends that for the establishment of all high precision height control that this approach be adopted. In this way anyone equipped with GPS should have access to the vertical datum at any point without having to visit traditional BMs. Figure 2.11 shows the relationship between GPS ellipsoidal heights (based on WGS84 ellipsoid) and Orthometric heights related to ODN. The accuracy of the OSGM02 at this time is approximately ±2cm. This obviously has repercussions for mixing heights (DSMs) from different sources and ages.

Figure 2.12 Ellipsoidal height H and Orthometric height h of two points A and B related by a model of Geoid-ellipsoid separation N (after OS 2002)


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3.

Flood Inundation Modelling (FIM)

Flood events across Europe in the summer of 2002 and during previous years have raised public, political and scientific awareness of flood risk and flood protection (Becker and Grunewald 2003). Flooding is now widely acknowledged as an issue of strategic importance at a transnational level, with major economic and social implications for citizens of many European countries (Samuels 2003; Collier 2003). Given the physical evolution of floodplain zones, it is unsurprising that floods continue to be a natural occurrence or that these areas play a crucial role in the routing and storage of floodwaters. The risks posed during these episodes of flooding are wide ranging but, from society’s perspective, the main focus is the risk to people and property. Flood risk has two components – the chance (or probability) of an event occurring and the impact (or consequence) associated with the event (Sayers et al. 2002). While the recurrence probability of a particular flood event can be determined through statistical analysis of historical data (NERC 1975; Institute of Hydrology 1999) or continuous numerical simulation (Calver et al. 1999; Cameron et al. 1999), there is still a relative paucity in observations of distributed inundation extent that allow the spatial impact of flooding to be mapped. In such circumstances numerical hydraulic models are the only feasible methodologies available to estimate flood inundation resulting from particular recurrence interval events (see for example Bates et al. 1998; Bates and De Roo 2000; Werner 2001; Werner 2002; Horritt and Bates 2002). Such models solve equations representation mass and momentum conservation over userdefined control volumes and time steps to yield estimates of dynamically changing water depths and velocities at these spaces and time scales. These equations may be one, two or three-dimensional, and the choice of equation type and numerical solution technique determines the geometric properties of the discretization scheme, the required data and the outputs obtained from the model. For a comprehensive review of flood inundation and flow routing models see Bates 2005;Bates et al. 2005a.

3.1

OVERVIEW OF EXISTING FIM PRACTICES

Flood inundation models require four key data items: (1) topographic data to construct the model grid (normally in the form of a DTM); (2) bulk flow data to provide model inflow and outflow boundary conditions; (3) an estimate of the grid square effective friction parameter for each model cell (often based on ground surface nature/features) and (4) a source of validation data. Until relatively recently, topographic data could only be acquired by ground survey. This is expensive and time consuming to collect, and is typically only available as a series of cross-sections perpendicular to the channel and with spacing of between 100 and 1000m. At the cross section, the heights could be of millimetric accuracy, but any Digital Elevation Model generated from such a source will incur significant interpolation errors as one moves further away from the measured data. Formerly, the only alternative to survey data was to use topographic data available on national survey maps; however this tends to be of limited accuracy and detail for FIM in floodplain areas. For example, in the UK nationally available contour data are only recorded at 5m spacing to a height accuracy of ±1.25m. Bulk flow data comes typically from nationally maintained gauging stations and friction parameters are usually calibrated. Lastly, until recently the only source of model validation data has been the same bulk flow sources used to assign model boundary conditions. As nationally maintained gauging networks were typically designed with flood warning rather than hydraulic model validation in mind, few such data points are available (typical gauge spacing is 10 to 60km or more apart) and flood inundation predictions have until recently been almost impossible to validate. The key areas in which distributed data are required by flood inundation models are for topography parameterization, friction parameterization and for wide-area model validation. In all these areas, recent advances in airborne and satellite remote sensing has generated data sets that can, for the first time, at least begin the process of parameterizing and validating flood inundation models in a more distributed fashion than has hitherto been possible. For topography data, much recent interest has focussed on the use of airborne laser altimetry data or LiDAR to parameterize flood inundation models (see for example Gomes-Pereira and Wicherson 1999; Marks and Bates 2000; Bates et al. 2003; French 2003; Charlton et al. 2003), although other authors have also examined the use of aerial stereo-photogrammetry (Baltavias E.P


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1999; Lane S.N 2000; Westway R.M et al. 2003) and airborne Synthetic Aperture Radar interferometry (Hodgson et al. 2003). LiDAR data also yields information on surface objects such

as vegetation and buildings which can be used to constrain the parameterization of surface roughness (e.g. Mason et al. 2003) and the mesh discretization (Cobby et al. 2003). For example, Mason et al. 2003 present a methodology to calculate time and space distributed friction coefficients for flood inundation models directly from LiDAR data. Using this method for each mesh node in an unstructured grid, an instantaneous friction factor is calculated at each time step, given the vegetation heights in the neighbourhood of the node and the current water depth and flow velocity at this point. A node’s neighbourhood is defined automatically as a polygon whose vertices are the centroids of the elements surrounding the node. Hence, if the discretization changes, so do the area-effective parameter values assigned for each mesh node, although this does make an assumption of linear scaling that has yet to be adequately tested. Whilst much further work is required in this area, such studies are beginning to provide methods to calculate important elements of frictional resistance explicitly for particular flow routing problems. This leads to the prospect of spatially distributed grid scale effective parameters and, hopefully, a reduced need for calibration of hydraulic models. The next step in this process is to begin to design the model discretization around the data sets to ensure that optimal use is made of the information content available and to reduce spatial lumping of parameter values. For example, instead of assigning topographic data to a pre-defined unstructured grid in an a posteriori step, Bates et al. 2003 first identify significant breaks of slope and maxima and minima that are then ‘hard-wired’ into the mesh discretization. Cobby et al. 2003 take the problem of mesh generation and parameter assignment even further and present an automatic mesh generator that decomposes an unstructured mesh according to both topographic gradients and vegetation features on the floodplain as determined from high resolution LiDAR surveys. The advantage of this approach is that the friction assigned to each computational node can be made much more representative of material properties in the surrounding neighbourhood to give a model grid where elements represent homogeneous rather than heterogeneous regions. In practical terms, this could mean, for example, that the impact of zones of high friction but low spatial extent (e.g. hedges) are not ‘smeared’ over a wide area and so the degree to which parameter values are physically appropriate for the regions they represent will improve. For mesh elements that include a mix of material types, the only certainty is that the ‘average’ value will always be wrong! Parameters will remain grid-scale effective values, as the frictional resistance generated by individual plants will still be aggregated, but the unrealistic spatial lumping of different zones (and hence processes) is reduced. If it proves possible to generate flood inundation models with a physically appropriate and more disaggregated discretization of data and model parameters, we may then potentially have a degree of confidence in the spatial predictions made by the particular code. Until recently, validation data for hydraulic models consisted primarily of bulk flow measurements, either at the catchment outlet or at gauging stations internal to the model domain. Measurements of internal state variables, where made, tended to consist only of data from a small number of points. However, considerable difficulties exist in actually measuring the quantities predicted by distributed numerical models because of the (necessarily) discrete way in which they treat time and space. Unsurprisingly therefore, comparison of small numbers of point state variable measurements to grid-scale model predictions showed only mixed success (e.g. Lane S.N et al. 1999). Model validation thus relied on bulk flow data that represented the aggregate response of the catchment to that point (e.g. Bates et al. 1998). However, for any given model and discretization, many different spatial patterns of grid square effective parameter values can lead to the same aggregate response, but give different spatial predictions and thus process inferences. In fact, replication of aggregate catchment response only requires single values of model parameters spatially lumped at the catchment scale and representing aggregate conditions. The consequences of a lack of distributed validation data are therefore equifinality and a tolerance of the physically unrealistic spatial lumping of parameter values and processes. Assessment of distributed models therefore requires distributed data at a scale commensurate with the model predictions, and in the case of flood inundation models this could be wide-area synoptic maps of inundation extent, water depth or flow velocity. Of these, inundation extent is perhaps the easiest to


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determine over scales appropriate to flood routing and forecasting problems. Inundation data provides a potential solution to the problem of hydraulic model validation as whilst these data are 0D in time their 2D spatial format could provide ‘whole reach’ data for both distributed calibration and validation of distributed predictions. Inundation extent can also be seen as a sensitive test of a hydraulic model, as small errors in predicted water surface elevation would lead to large errors in shoreline position over flat floodplain topography. Much interest in defining inundation extent has focussed on the use of satellite Synthetic Aperture Radars (SAR’s) due to their day-night, all-weather capability and high resolution. Satellite SAR data has a resolution of 12.5m, whilst airborne SAR data may have a resolution down to 0.5m and both are of an appropriate scale for hydraulic model validation if the data can be processed to accurately delimit the shoreline. Unfortunately, this may not be straightforward (see for example Horritt et al. 2001), and such processing tends to be prone to misclassification errors as a result of the limited range of frequencies and polarizations available on satellite radar instruments. Yet, such data are at least wide area and distributed and therefore have good potential for use in hydraulic model validation. More recently, work on EPSRC grant GR/S17161 “Towards the next generation of flood inundation models for predicting water level and extent” has examined the use of fine spatial resolution airborne SAR data as a way of overcoming some of these problems. Availability of a sequence of four airborne SAR scenes of the November 2000 flooding on the lower River Severn has allowed analysis, for the first time, of the dynamic performance of flood inundation models and the impact of particular landscape features on the development of flood inundation (see for example Bates et al. 2004).

3.2

DATA REQUIREMENTS FOR FIM

The above studies and discussions with FIM consultants and academics have yielded a number of important insights into the data requirements for flood inundation modelling which are summarised below. The principal data need for flood inundation modelling is, as noted above, a high quality Digital Elevation Model. This needs to represent the ‘bare earth’ and not be contaminated with vegetation or building artefacts, which need to be filtered into separate GIS layers for possible future use in mesh generation and friction parameterization. Over natural terrain, vertical accuracy and spatial scale need only be consistent with the amplitude of small scale topographic variability. Hence, accuracy greater than small scale natural variability of the floodplain surface (i.e. minor ground variations such as furrows in a ploughed field or tyre tracks) is likely to be spurious. Beyond the scale of topographic ‘noise’, most floodplains typically have a detailed micro-topography of relict channels, terraces, and drainage ditches

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