stitching elevation and bathymetry data for the murray ... · stitching elevation and bathymetry...

[Report number [xx/xx]

Report to South Australian Department of Water, Land and Biodiversity

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Jenet Austin and John Gallant

October 2010

Water for a Healthy Country Flagship Report series ISSN: 1835-095X

ISBN 978 0 643 10430 3 (PDF)

Australia is founding its future on science and innovation. Its national science agency, CSIRO, is a powerhouse of ideas, technologies and skills.

CSIRO initiated the National Research Flagships to address Australia’s major research challenges and opportunities. They apply large scale, long term, multidisciplinary science and aim for widespread adoption of solutions. The Flagship Collaboration Fund supports the best and brightest researchers to address these complex challenges through partnerships between CSIRO, universities, research agencies and industry.

The Water for a Healthy Country Flagship aims to provide Australia with solutions for water resource management, creating economic gains of $3 billion per annum by 2030, while protecting or restoring our major water ecosystems. The work contained in this report was undertaken by CSIRO as a consultancy with the South Australian Department of Water, Land and Biodiversity Conservation.

For more information about Water for a Healthy Country Flagship or the National Research Flagship Initiative visit www.csiro.au/org/HealthyCountry.html

Citation: Austin, JM and Gallant, JC. 2010. Stitching elevation and bathymetry data for the Murray River and Lower Lakes, South Australia. CSIRO: Water for a Healthy Country National Research Flagship. 76 pp

Copyright and Disclaimer:

© 2010 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO.

Copyright of Data Provided to CSIRO:

Data provided to CSIRO by the Government of South Australia are licensed under a Creative Commons Attribution 2.5 Australia Licence. The copyright owner of this data is Government of South Australia, through the Department of Water 2011.

Important Disclaimer:

CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

Cover Photograph:

Description: Murray River near Mannum, South Australia. 3 September 2008.

Photographer: John Gallant

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Page iii

CONTENTS Acknowledgments ...................................................................................................... vii

Executive Summary................................................................................................... viii

1. Introduction ......................................................................................................... 1 1.1. Context...................................................................................................................... 1 1.2. Literature review ....................................................................................................... 1 1.3. Data and location...................................................................................................... 1

2. DEM to DEM stitches .......................................................................................... 4 2.1. Katarapko DEM to Pyap-Lock1 DEM ....................................................................... 5 2.2. Chowilla DEM to Katarapko DEM........................................................................... 10 2.3. Pyap-Lock 1 DEM to Lock 1-Wellington DEM ........................................................ 15 2.4. Lock1-Wellington DEM to Lower Lakes DEM......................................................... 18 2.5. Lower Lakes-Coorong DEM to Southeast region DEM.......................................... 22

3. Bathymetry to Bathymetry Stitch..................................................................... 29 3.1. Channel to Lower Lakes bathymetry ...................................................................... 29

4. DEM to Bathymetry Stitches ............................................................................ 33 4.1. Lock 1-Wellington DEM to Channel bathymetry..................................................... 33 4.2. Lakes DEM to Channel bathymetry ........................................................................ 40 4.3. Lower Lakes DEM to Lower Lakes bathymetry ...................................................... 41 4.4. Lakes and Southeast region DEMs to Coorong bathymetry .................................. 51

5. Final Dataset ...................................................................................................... 57 5.1. Section descriptions................................................................................................ 57 5.2. Quality Assessment ................................................................................................ 63

6. Recommendations ............................................................................................ 65 6.1. Data collection ........................................................................................................ 65 6.2. Data manipulation................................................................................................... 65

7. Conclusions....................................................................................................... 66

References .................................................................................................................. 67

LIST OF FIGURES Figure 1.3-1. DEM locations .................................................................................................... 2

Figure 1.3-2. Bathymetry locations .......................................................................................... 2

Figure 2.1-1. Location of the Katarapko and upstream-end Pyap datasets ............................ 7

Figure 2.1-2. a) difference between the unmodified Katarapko and Pyap DEMs (2m resolution). b) difference between resampled Katarapko and Pyap DEMs (1m resolution). c) difference after modification of Katarapko dataset to shift grid origin (2m resolution). Blue-purple = Katarapko > Pyap; pale yellow = +/- 10cm; orange-red = Pyap > Katarapko............ 8

Figure 2.1-3. Hillshade of the Pyap-Lock1/resampled-Katarapko mosaic............................... 9

Figure 2.1-4. Final mosaic of the Pyap-Lock1 DEM with the resampled Katarapko DEM (Part A to C-downstream)................................................................................................................. 9

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Page iv

Figure 2.2-1. Location of Katarapko and Chowilla DEMs along the Murray River................. 10

Figure 2.2-2. Difference between the resampled Chowilla and Katarapko DEMs................. 11

Figure 2.2-3. Measured difference between Katarapko and Chowilla in a single grid cell column from north to south (blue line) and the model created to correct for the difference (red line) ........................................................................................................................................ 12

Figure 2.2-4. a) adjustment surface for Chowilla based on modelled difference, decreasing to zero with increasing distance from overlap edge. b) difference between adjusted Chowilla and Katarapko Part D. ........................................................................................................... 13

Figure 2.2-5. Hillshade of adjusted-Chowilla-Katarapko-Part-D mosaic................................ 14

Figure 2.2-6. Final mosaic of the adjusted-Chowilla-Katarapko-Part-D with Katarapko-upstream-Part-C .................................................................................................................... 14

Figure 2.3-1. Location of the overlapping DEMs at Lock 1 on the Murray River ................... 15

Figure 2.3-2. a) difference between the lidar and photogrammetry (Block 1) DEMs. b) final mosaic of the modified DEMs. c) difference between the test and final mosaics. d) hillshade of the final mosaic.................................................................................................................. 17

Figure 2.4-1. Input datasets for Lock 1 to Lower Lakes......................................................... 18

Figure 2.4-2. Test mosaic of the lidar tiles and Block 8 DEM, with the parallel polygons along the join outlined in black. a) western side of the river; b) eastern side. ................................. 19

Figure 2.4-3. Inverse distance weighted interpolation of the differences between the lidar and Block 8 DEMs. a) western side of the river; b) eastern side. ................................................. 20

Figure 2.4-4. Input points for the TOPOGRID interpolation along the join-edge between the datasets. a) western side of the river; b) eastern side. .......................................................... 21

Figure 2.4-5. Final mosaic of the unmodified Lower Lakes lidar DEM, the TOPOGRID interpolation, and Block 8 of the Lock 1-Wellington DEM. a) western side of the river; b) eastern side. .......................................................................................................................... 22

Figure 2.5-1. Location of DEM datasets in the Coorong and Southeast. The red line indicates the eastern edge of data supplied for this project.................................................................. 23

Figure 2.5-2. Spatial shift between the Coorong and Southeast datasets, shown by the lines of opposing large-difference values either side of various landscape features ..................... 24

Figure 2.5-3. Southern Coorong dataset ............................................................................... 24

Figure 2.5-4. Slight offsets between flight lines within a) the Southeast dataset (vertical arrows); and b) the Coorong dataset (diagonal arrows) ........................................................ 25

Figure 2.5-5. East (right) and west (left) sides of the overlap between the Coorong and Southeast region DEMs, at the northern end of the Southeast dataset. The gap between the two sides is not to scale. a) shows the Coorong clipping boundary and b) the Southeast clipping boundary................................................................................................................... 26

Figure 2.5-6. Mosaic boundary and the clipped Southeast region DEM (a) and the clipped Coorong DEM (b)................................................................................................................... 26

Figure 2.5-7. Mosaic of the Coorong and Southeast datasets, a) western side, b) eastern side ........................................................................................................................................ 27

Figure 2.5-8. Preliminary Lakes Section 9, created from the final merge of the overlap area mosaic, the clipped Coorong and Southeast DEMs, and Lake Albert of the Lower Lakes.... 27

Figure 2.5-9. Final DEM sections including all of the Southeast DEM (not including bathymetry data).................................................................................................................... 28

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Page v

Figure 3.1-1. Location of bathymetry and DEM datasets at the northern end of the Lower Lakes ..................................................................................................................................... 29

Figure 3.1-2. Point datasets used in TOPOGRID interpolation ............................................. 31

Figure 3.1-3. Clipped version of the interpolation produced by TOPOGRID ......................... 31

Figure 3.1-4. Final version of the upper lakes bathymetry (mosaic of the interpolated surface and clipped lakes datasets) ................................................................................................... 32

Figure 4.1-1. Location of DEM datasets between Lock1 and the Lower Lakes..................... 34

Figure 4.1-2. A section of channel bathymetry in Block 1 showing detached cells ............... 35

Figure 4.1-3. Block 2 DEM showing channel values of less than 0.13 m set to nodata, and the 150m bathymetry buffer polygon ..................................................................................... 36

Figure 4.1-4. Intermediate join of the Block 7 DEM and the channel bathymetry. Note the nodata areas (white) between the two datasets that need to be filled................................... 37

Figure 4.1-5. Example TOPOGRID input datasets: points from Block 4 and Block 5, and the 160.m-buffered polygon mask for Block 4 ............................................................................. 37

Figure 4.1-6. Final join of the Block 7 DEM and the channel bathymetry with the TOPOGRID interpolation used to fill the gaps ........................................................................................... 38

Figure 4.1-7. Final merged sections of the river, a) River Section 5 and b) River Section 6 . 38

Figure 4.1-8. Block 8 final processing.................................................................................... 39

Figure 4.1-9. Artefact removal, a) before and b) after ........................................................... 39

Figure 4.2-1. Intermediate join of the Lower Lakes lidar DEM tiles and the southern end of the channel bathymetry ......................................................................................................... 40

Figure 4.2-2. Final join of the lakes lidar DEM tiles and the southern end of the channel bathymetry, with the TOPOGRID interpolation used to fill the gaps...................................... 41

Figure 4.3-1. Location of bathymetry and DEM datasets in the Lower Lakes ....................... 42

Figure 4.3-2. The Lakes-Coorong DEM tiles were divided into seven Lakes parts and two Coorong parts for the join to the bathymetry ......................................................................... 43

Figure 4.3-3. Processing of DEM tiles: a) resupplied tile with interpolation over water areas; b) raw point data; c) grid created from points; d) polygon coverage created from grid; e) dissolved and buffered polygon coverage; f) DEM clipped using buffered coverage. ........... 44

Figure 4.3-4. Part 5 bathymetry, a) original grid with the bathymetry points extent outlined in red; b) clipped bathymetry grid .............................................................................................. 45

Figure 4.3-5. Example of DEM buffers (10 m, 30 m, and 500 m) .......................................... 46

Figure 4.3-6. Examples of a) interpolation extent coverage; b) bathymetry clip coverage and c) final mask coverage........................................................................................................... 47

Figure 4.3-7. a) Gaps to be filled by interpolation at the boundary between Parts 6 (south) and 7 (north); b) polygon created to clip points for additional interpolation input (red line) ... 47

Figure 4.3-8. a) Bathymetry converted to points (blue) with DEM (greyscale) for reference; b) bathymetry grid (blue) set to nodata within 30 m of the DEM edge; c) DEM data with elevation values of less than or equal to 0.5 m converted to points (orange); d) example of additional bathymetry and DEM points for edge matching during interpolation (bathymetry – Part 6 in blue and Part 7 in green, DEM – Part 6 in purple and Part 7 in yellow). ................. 49

Figure 4.3-9. a) Input points for the Part 1 interpolation; b) interpolation output; c) merge of the Part 1 DEM with the 30 m-buffered bathymetry grid and the interpolation from TOPOGRID............................................................................................................................ 49

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Page vi

Figure 4.3-10. a) Lakes Section 7 (Parts 1, 2, and 3); b) Preliminary Lakes Section 8 (Parts 4 and 5); c) Preliminary Lakes Section 9a (Parts 6 and 7) ....................................................... 50

Figure 4.4-1. Input data locations .......................................................................................... 51

Figure 4.4-2. a) The area of overlap between Section 8 old and the Coorong bathymetry; b) the difference between the two surfaces (Section 8 old minus bathymetry).......................... 52

Figure 4.4-3. Artefact bridge (black) that was removed from the Section 9 DEM.................. 53

Figure 4.4-4. Differences between the additional Section 8-9 edge interpolation and the original interpolations for a) Section 8 and b) Section 9. The clip boundary (black line) was based on these differences.................................................................................................... 55

Figure 4.4-5. Final version of Lakes Section 8 with Coorong bathymetry included ............... 55

Figure 4.4-6. Final version of Lakes Section 9 with Coorong bathymetry included ............... 56

Figure 5.1-1. River Section 1, Chowilla and Katarapko DEMs .............................................. 57

Figure 5.1-2. River Section 2, Katarapko and Pyap DEMs.................................................... 58

Figure 5.1-3. River Section 3, Pyap to Lock 1 DEM .............................................................. 58

Figure 5.1-4. River Section 4, Pyap to Lock 1 DEM .............................................................. 59

Figure 5.1-5. River Section 5, Pyap to Lock 1 and Lock 1 to Wellington DEMs with channel bathymetry ............................................................................................................................. 59

Figure 5.1-6. River Section 6, Lock 1 to Wellington DEM with channel bathymetry.............. 60

Figure 5.1-7. Lakes Section 7, Lock 1 to Wellington and Lower Lakes DEMs with channel, Pomanda and Lower Lakes bathymetry ................................................................................ 60

Figure 5.1-8. Lakes Section 8, Lower Lakes DEM with Lower Lakes and Coorong bathymetry............................................................................................................................................... 61

Figure 5.1-9. Lakes Section 9 and 9b, Lower Lakes and Southeast region DEMs with Lower Lakes and Coorong bathymetry............................................................................................. 61

Figure 5.1-10. Section 10, Southeast region DEM ................................................................ 62



LIST OF TABLES Table 1. Input datasets ............................................................................................................ 1

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Page vii

ACKNOWLEDGMENTS This work was funded by the South Australian Department for Water as part of their Imagery Baseline Data Project. We acknowledge the support of Mr Russell Flavel, who championed the project in the South Australian Department for Water.

Mark Thomas (CSIRO Land and Water) initiated and managed the project.

Data were supplied by the Government of South Australia, through the Department for Water, the Department of Environment and Heritage, and the Southeast Natural Resources Management Board.

SA government inputs to the report “Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia” have been provided to CSIRO under Creative Commons-Attribution license (CC-BY).

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Page viii

EXECUTIVE SUMMARY This project created a seamless elevation model for the Murray River from the South Australian border to the Murray Mouth, including Lakes Albert and Alexandrina and the Coorong and, where data was available, the lakes and river beds. Six digital elevation models (DEMs) and four bathymetry data sets were combined using a variety of methods to resolve inconsistencies between the various data, including:

differences related to the methods of data acquisition (lidar, photogrammetry and sonar),

systematic elevation errors in one DEM,

differences in grid alignments,

misleading data resulting from extrapolation of sonar data to a deemed shoreline elevation, and

conflicting bathymetric data from different dates probably due to changes in channel bed form.

The completed DEM provides a uniform and consistent coverage over a 450 km length of river and about 600 km of lake shoreline.

It was beyond the scope of the current project to undertake rigorous field checking and quality assurance of the final dataset. However, limited quality assessment was conducted with the main objective of ensuring that the data quality had not been compromised during the stitching process, and that the final product maintained the standard of (at worst) the least accurate input dataset for each stitch

During the project a number of issues with the source data were identified that complicated the stitching process. The following recommendations are proposed to avoid these issues in future data acquisition and processing.

New data acquisitions should always overlap (not just abut) existing data so that any differences between elevations can be assessed and adjusted.

Raw data, such as point data from sonar and lidar acquisitions, should be retained so that differences between gridded products and other data sets can be understood and corrected.

In gridded products, documentation should show where the data is more or less reliable using mask or reliability layers. In particular, extrapolation of the surface beyond the extent of the measured data should be clearly identified.

The project demonstrated that DEMs and bathymetry from various sources can be stitched together to create large, seamless, high resolution data sets, and that the stitching process could be streamlined if the acquisition and processing of data is conducted with stitching in mind.

Stitching Elevation and Bathymetry Data for the Murray River and Lower Lakes, South Australia Page 1

1. INTRODUCTION

1.1. Context In 2007 the South Australian Government’s Image Baseline Data Project (IBDP) included a Digital Elevation Model (DEM) component. The DEM technical working group identified the South Australian River Murray system as a key region of interest, and a need for up to date river channel/wetland and Lower Lakes water level data for management purposes. The objectives were to:

acquire DEMs of sufficient resolution to enable detailed investigation of water levels, backwater/wetland form and connectivity;

to join (stitch) newly acquired data to existing DEMs;

stitch in bathymetric data where it exists

The purpose of this technical report is to document the steps taken to produce a seamless DEM-bathymetry surface.

1.2. Literature review A thorough review of the existing literature via library databases and Google searches found some articles that mentioned joining or stitching DEMs, but they did not include detailed descriptions of the methods used. The one exception was an article that discussed joining DEM datasets to bathymetry in Florida by Gesch and Wilson (2001). The authors produced a seamless dataset for use in coastal and near-shore modelling by bringing them into a common horizontal reference frame, transforming them to a common vertical datum and interpolating a surface at the boundary of the terrestrial and bathymetric layers using data from both sources.

1.3. Data and location Six high resolution DEMs and four bathymetry datasets were available for this project and are described in Table 1. The DEMs are shown in Figure 1.3-1, covering the Chowilla floodplain at the state border, the Murray River channel, the Lower Lakes and Coorong, and the south eastern corner of South Australia. The bathymetry datasets extend from Lock 1 to Wellington in the river channel, and cover the Lower Lakes and the Coorong (Figure 1.3-2).

Table 1. Input datasets

Dataset Acquisition method Acquisition Date Resolution (m)

Chowilla DEM Lidar 2003 2

Katarapko DEM Lidar 2007 2

Pyap to Lock 1 DEM Lidar 2008 2

Lock1 to Wellington DEM Photogrammetry 2008 2

Lower Lakes DEM Lidar 2008 2

Southeast DEM Lidar 2008 2

Murray channel bathymetry Sonar 2007-2008 5

Pomanda bathymetry Multibeam sounder 2006 5

Lower Lakes bathymetry Sonar 2004 50 - 100

Upper Coorong bathymetry Sonar 2004 10


Figure 1.3-1. DEM locations

Figure 1.3-2. Bathymetry locations

The various datasets were collected at different times using different methods, including lidar, photogrammetry and sonar. The datasets vary in their extent, have different resolutions and were collected with different objectives for their use. Based on the intrinsic accuracy of each data collection method, the resolution of the data and a visual assessment of the level of detail in each data set, the following priority order was used to determine which dataset was more reliable in any given join:

1. Lidar DEM


2. Photogrammetric DEM

3. Channel bathymetry

4. Pomanda bathymetry

5. Coorong bathymetry

6. Lower Lakes bathymetry

The methods described in this report are divided into parts based on the type of stitch. Section 2 describes the DEM to DEM stitches, Section 3 covers bathymetry to bathymetry, and Section 4 the DEM to bathymetry. Section 5 describes the division of the stitched data into 13 sections for delivery, and quality assessment. Sections 6 and 7 discuss recommendations and data issues, and conclusions.


2. DEM TO DEM STITCHES For the DEM datasets in this project, two basic types of overlap existed where they met. Either the data overlapped by only a small distance (e.g. a few grid cells), or there was a large overlap (hundreds of metres). These situations were dealt with differently, as broadly outlined below. Following the general descriptions of the two methods used to join the DEMs, Sections 2.1 to 2.5 describe each of the individual stitches in more detail.

In the description of methods the term ‘merge’ means combining two data sets using a priority order while ‘mosaic’ means combining two overlapping data sets using a smooth transition from one to the other in the overlap area. This corresponds to the merge and mosaic functions in ESRI’s GRID package (http://resources.arcgis.com).

Small Overlap Method

An example of a small overlap is at the boundary between the photogrammetric DEM and lidar DEM near Wellington, where the two datasets abut without a gap or overlap. There are a few cells along this edge with values in both datasets as well as some gaps where neither DEM has a data value. The following method was developed to join data along edges like this:

1. Resolve any issues with projections and no data values (e.g. replacing zeros with no data where required)

2. Create a difference grid from the two datasets

3. If the difference is noticeable and systematic (similar difference along the entire join), indicating a level difference between the two DEMs, adjust for the difference by either:

a. creating an adjustment surface for each DEM (or one DEM if there is reason to believe the other is more reliable) for some distance from the join so that the systematic difference is removed at the join and the alteration is spread over a large enough area that the surface form is not damaged, or

b. altering the level in both DEMs by adding a constant value to each to make them consistent at the join, or

c. seeking corrected data from the data supplier

4. For differences that are small and/or local, remove the discrepancy by adding the difference back into the edge area:

a. Define the edge by manually creating narrow (1-2 cell width) parallel polygons (this step would be automated if this situation occurred frequently)

b. Create an inverse distance weighted (IDW) surface from the difference grid cells (from step 2)

c. Choose how to allocate the difference between the two datasets – in this case we judged that the lidar DEM was more reliable so made alterations only to the photogrammetric DEM.

d. Add the difference to the original elevations (decreasing away from the edge), facilitated by the parallel polygons.

e. Convert the modified elevations to points

f. Use TOPOGRID to create a new surface from original and modified points that extends either side of the edge

5. Merge the original and modified surfaces with priority, e.g. original lidar, then modified photogrammetry, then unmodified photogrammetry.


Large Overlap Method

The following method was developed to stitch together DEMs where the overlap consists of hundreds of metres or more:

1. Resolve any issues with projections and no data values

2. Create a difference grid from the two datasets

3. Mosaic as a trial stitch

4. Create additional difference grids by subtracting the trial mosaic from the originals

5. Look at the differences between the surfaces:

a. Examine for spatial mis-registration

b. Does the difference along the overlap cause problems?

c. Is there an overall shift of one DEM?

d. Identify any specific issues such as poor interpolation either side of a weir, or areas that don’t fit with the general trend of the overlap

e. Can the differences be modelled?

f. Justifications for choosing to adjust one dataset or both.

6. Make changes determined in previous step: choose which areas of each dataset are considered more reliable, and develop a model of differences if needed

7. Mosaic the modified datasets and check to ensure the results are satisfactory

In general, independent data of higher quality is not available to test the overlap area, since the data being processed is typically the best available data. The assessments of the quality of the stitching therefore rely on visual examination of the surface looking for artefacts that are noticeably different from natural terrain.

2.1. Katarapko DEM to Pyap-Lock1 DEM The input datasets for this stitch were the upstream end of the Pyap to Lock1 lidar-derived DEM tiles, and the downstream three quarters of the Katarapko lidar-derived DEM (Figure 2.1-1). The overlap for these datasets was approximately 10 km so the steps for this stitch followed the large overlap method. The initial processing steps were:

1. Created a clip coverage for the overlap area

2. Merged Pyap-Lock1 tiles in the overlap area

3. Calculated the difference between Pyap-Lock1 and Katarapko at the overlap (Figure 2.1-2a)

4. Resampled both to 1 m (cubic) and recalculated the difference (Figure 2.1-2b)

Figures 2.1-2a and 2.1-2b show a positional shift between the two datasets of approximately 1 m, despite the datasets being supplied in the same projection. This shift was due to a difference in the grid origins rather than an actual spatial shift in the data, whereby calculation of the difference between the unmodified versions shifted one of the grids to coincide with the other creating an appearance of a spatial discrepancy. The solution was to reconstruct one of the DEMs to coincide with the other DEM’s grid structure. This was achieved by interpolating the 2 m resolution Katarapko DEM to 1 m, then resampling back to 2 m with the correct grid origin. Given the Pyap DEM tiles matched the grid cells in the downstream photogrammetry-derived DEM, this DEM was left unmodified and the Katarapko DEM was the one chosen to be modified to achieve the match.

Katarapko processing:


1. Divided the Katarapko DEM into four parts with 2-3 cell overlaps due to ESRI software size restrictions when processing (parts named ‘A’ to ‘D’, with ‘A’ at the downstream end)

2. Resampled the four Katarapko parts to 1 m using the cubic option to create a smooth interpolation to the finer resolution

3. Resampled the four Katarapko parts with 1 m resolution back to 2 m using the bilinear option, with the snap-grid set to the Pyap-Lock1 DEM to ensure the re-resampled grid cells were centred correctly

4. Created a new difference surface between Part A of the corrected Katarapko DEM and the Pyap-Lock1 DEM (Figure 2.1-2c). Compared to the difference grid in Figure 2.1-2a, the new height differences were minimal. Some larger differences were still present along the river bank - however the patterns suggested that different vegetation removal algorithms had been used in earlier processing rather than further positional issues being the cause for the elevation differences.

After the Katarapko DEM was shifted, the mosaic function was used to join Part A to the Pyap-Lock1 DEM. A hillshade was created from the mosaic output to check for obvious lines at the edges of the overlap (Figure 2.1-3). No artefacts were visible. Part C of the Katarapko DEM was then split in half, with the downstream piece of Part C joined to Part B and to the Part-A-Pyap-Lock1 mosaic to form DEM River Section 2 of the final dataset (Figure 2.1-4). Part-C-upstream was joined to the Katarapko-Part-D-Chowilla mosaic, as described in Section 2.2. The split of Part C of Katarapko was done in order to ensure a seamless join for users of the final product.

Figure 2.1-4 shows that the Katarapko DEM had nodata values on water surfaces while the Pyap-Lock1 DEM had elevation values. This did not cause any issues for the join because the location on the main channel were these would have met, i.e. there would have been a line across the channel where data would begin, was at the edge of the datasets’ extents where there was no data anyway.


Figure 2.1-1. Location of the Katarapko and upstream-end Pyap datasets


Figure 2.1-2. a) difference between the unmodified Katarapko and Pyap DEMs (2m resolution). b) difference between resampled Katarapko and Pyap DEMs (1m resolution). c) difference after modification of Katarapko dataset to shift grid origin (2m resolution). Blue-purple = Katarapko

> Pyap; pale yellow = +/- 10cm; orange-red = Pyap > Katarapko.


Figure 2.1-3. Hillshade of the Pyap-Lock1/resampled-Katarapko mosaic

Figure 2.1-4. Final mosaic of the Pyap-Lock1 DEM with the resampled Katarapko DEM (Part A

to C-downstream).


2.2. Chowilla DEM to Katarapko DEM The input datasets for this stitch were Part-C-upstream and Part D of the Katarapko lidar-derived DEM, and the Chowilla lidar-derived DEM (Figure 2.2-1). The overlap for these datasets was approximately 12 km north-south and 2km east-west, tapering to a point at the northern end. The methods for this stitch followed the steps outlined above for large overlaps. The initial processing steps followed those used for the Katarapko DEM grid origin shift (Section 2.1), this time shifting the Chowilla DEM origin to conform to the rest of the DEMs. The steps included:

1. Resampled the 2 m Chowilla DEM to 1 m using the cubic option

2. Resampled the 1 m Chowilla DEM back to 2 m using the bilinear option, with the snap grid set to Part D of Katarapko to ensure the 2 m grid cells had the correct origin

3. Calculated the elevation differences in the overlapping areas between the resampled Chowilla DEM and Katarapko Part D (Figure 2.2-2)

Figure 2.2-1. Location of Katarapko and Chowilla DEMs along the Murray River


As shown in Figure 2.2-2, the difference between the two surfaces varies from north to south but is reasonably consistent east to west. Overall the Chowilla elevation values were higher than the Katarapko values, with the differences decreasing from north to south. The Chowilla dataset was considered less reliable partly because it is older, but also because the Katarapko values matched well with the Pyap data in the overlap at its downstream end. Thus the Chowilla dataset elevations were modified in the steps below and Katarapko were not. The modifications lowered the Chowilla elevations slightly, however from a modelling perspective this would not affect the overall direction water would flow across the landscape because the Chowilla dataset has higher elevation values generally.

Figure 2.2-2. Difference between the resampled Chowilla and Katarapko DEMs

After completion of this stitching project, it was discovered that an independent assessment of the Chowilla LiDAR DEM found that elevations in the DEM were too high by 15 – 30 cm due to an error in ground reference. This finding supports our decision to modify the elevations in the Chowilla DEM rather than the Katarapko DEM. Ideally the whole Chowilla DEM should be corrected and then re-stitched to the Katarapko data, but this was not possible in the scope of this project.

The next steps involved modelling the difference between Katarapko Part D and the resampled Chowilla dataset:

1. Using a single column grid running north-south along the overlap, created a grid from the y coordinate as follows: ymod = ( ycoord – 6230000 ) / 10000. This produces values roughly in the range 0 to 1.


2. Exported the values from ymod and the corresponding Katarapko-Chowilla difference values to a text file

3. Using Excel, modelled the differences between Katarapko and Chowilla in the single column (Figure 2.2-3) and then exported the modelled values

4. Used the modelled values to create an ascii grid covering the overlap area and extending 3 km eastwards into the Chowilla dataset

5. Created an ArcInfo grid (diffmap) from the ascii grid

Figure 2.2-3. Measured difference between Katarapko and Chowilla in a single grid cell column from north to south (blue line) and the model created to correct for the difference (red line)

The modelled difference grid (diffmap) needed to be applied to the Chowilla using a weighting surface that tapered to zero so that there would be no abrupt changes resulting from the adjustment. The following steps describe the creation of the weighting surface that tapered to zero moving perpendicularly (roughly eastwards) out from the overlap edge:

1. Created two grids with the same extent as Chowilla and with cell values populated from either the x or y coordinate (xgrid and ygrid respectively)

2. Created a weighting grid using xgrid and ygrid, where a value of 1 was given to the cells along the eastern edge of the overlap, cells to the west within the overlap area were assigned values greater than 1, and cells to the east values less than 1. The weightings were decreased to zero at ~3 km from the overlap edge. The equation used to create the weighting grid was as follows:

w = 1 - ( ( xgrid / 2 + ( 8439415 - ygrid ) / -9.1299 ) * 4.6732 / 3000 )

3. Created an adjustment grid for the Chowilla dataset (Figure 2.2-4a) derived from the weighting grid (w) in the previous step and the difference grid (diffmap) based on the modelled average difference. The output adjustment grid (ch_offset) values were calculated using these rules:

where diffmap = null, adjustment = 0;

where w => 1, adjustment = diffmap;

where 1 > w > 0, adjustment = diffmap * w;


and where w <= 0, adjustment = 0.

4. The resampled Chowilla dataset was then added to the adjustment grid (ch_offset) to produce a new version (adjusted Chowilla)

5. A new difference grid was then calculated using Katarapko section D and the adjusted Chowilla data. Figure 2.2-4b shows that the larger differences between Chowilla and Katarapko have been eliminated, leaving only finer scale variations either side of zero.

Figure 2.2-4. a) adjustment surface for Chowilla based on modelled difference, decreasing to

zero with increasing distance from overlap edge. b) difference between adjusted Chowilla and Katarapko Part D.

The final steps to join the Katarapko and Chowilla DEMs were as follows:

1. Used the mosaic function to join Katarapko Part D and the adjusted Chowilla DEM

2. Created a hillshade from the mosaic output to check for artefacts along the edges of the overlap (Figure 2.2-5).

3. Used the mosaic function to join the Katarapko-Part-D-Chowilla DEM to the upstream end of Katarapko Part C) (Figure 2.2-6). The inclusion of the upstream Part C of Katarapko ensured the Chowilla-Katarapko section of river was seamless with the Katarapko-Pyap section of river. This output is River Section 1 of the final dataset.


Figure 2.2-5. Hillshade of adjusted-Chowilla-Katarapko-Part-D mosaic

Figure 2.2-6. Final mosaic of the adjusted-Chowilla-Katarapko-Part-D with Katarapko-upstream-

Part-C


2.3. Pyap-Lock 1 DEM to Lock 1-Wellington DEM The input datasets for this stitch were the Lock 1-end tiles of the lidar-derived Pyap-Lock 1 DEM and Block 1 of the photogrammetry-derived Lock 1-Wellington DEM (Figure 2.3-1). A river channel boundary shapefile was used in the processing, and some imagery from Google Earth™ was used as a reference.

Figure 2.3-1. Location of the overlapping DEMs at Lock 1 on the Murray River

A difference grid was calculated from the two datasets (Figure 2.3-2a) followed by a test mosaic. Additional difference surfaces were created for comparison: lidar minus the test mosaic, and the Block 1 photogrammetry minus the test mosaic.

The grid cells from these DEMs matched exactly and the Lock 1 weir structure aligned very closely in the original versions of these two datasets. This indicated the registration was quite a good match and no shifting of either dataset was required. In Figure 2.3-2a the largest differences between the lidar and Block 1 elevation values were along the steep banks on the western side of the overlap area. This was not considered to be a cause for concern because the terrain is very steep and in these areas small registration differences or differences in vegetation removal algorithms can result in large differences in elevation. In these steep locations we allowed the mosaic function to determine which input dataset had the greatest contribution to the elevation values.


Along either side of Lock 1 in the river channel the lidar DEM showed unreliable interpolation patterns with unrealistic values, whereas the Block 1 data showed the expected sharp change in the elevation of the water surface (Figure 2.3-2a). The lidar channel values were therefore masked, and only the Block 1 data were allowed to contribute to the water surface values near the Lock.

There was an area in the middle of the overlap to the east of the main channel where the Block 1 data showed higher elevations than the lidar DEM (the non-channel purple patch in Figure 2.3-2a). This contrasted with what appeared to be a trend for the rest of the overlap area where the lidar DEM generally had slightly higher elevations. As this appeared to be an anomaly in the photogrammetric data, the area was masked to allow only the lidar DEM to contribute values in the final output mosaic.

The next steps involved creating coverages to mask the river channel below Lock 1 and the area with ‘high’ Block 1 values in the middle of the overlap. Grid versions of these masks were then used to set the lidar DEM to nodata in the channel below the Lock and the Block 1 values to nodata in the ‘higher elevation’ area.

The mosaic function was used to join the modified DEMs together (Figure 2.3-2b). The difference between this mosaic and the test mosaic was also calculated and Figure 2.3-2c shows that the only differences between the mosaics are in the masked areas. Finally, a hillshade was created to look for any obvious artefact, e.g. linear features, and none were found (Figure 2.3-2d).


Figure 2.3-2. a) difference between the lidar and photogrammetry (Block 1) DEMs. b) final

mosaic of the modified DEMs. c) difference between the test and final mosaics. d) hillshade of the final mosaic.


2.4. Lock1-Wellington DEM to Lower Lakes DEM This stitch was the only one in the project with a small overlap between the input DEMs. In some places along the overlap edge there are cells without data from either source, sometimes the data abut, and in others there is a few-cell overlap. Hence the small overlap method as outlined above in Section 2 was used to join these DEMs together.

The input datasets were Block 8 of the photogrammetry-derived Lock1-Wellington DEM and two tiles from the Lakes lidar-derived DEM (Figure 2.4-1), one on each side of the Murray River channel. Additionally, the boundary shapefiles for these DEMs were used as masks.

Figure 2.4-1. Input datasets for Lock 1 to Lower Lakes.

In this stitch the photogrammetry-derived DEM was considered less reliable than the lidar-derived DEM, so modifications to the data were solely on the Block 8 side; the lidar DEM tiles were not changed.

The first step was to create a test mosaic grid to use as a reference (Figure 2.4-2). Then 0.3 m and 1 m contours were created from the test mosaic to look for discontinuities between the DEMs. Difference values were calculated for the few overlapping cells by subtracting Block 8 from each of the lidar-derived tiles.


For each side of the river a coverage was created to mask the edge along which the two DEMs touched. A copy of the first coverage was edited to consist of three parallel polygons beginning at the join and continuing into the Block 8 side (Figure 2.4-2).

Figure 2.4-2. Test mosaic of the lidar tiles and Block 8 DEM, with the parallel polygons along

the join outlined in black. a) western side of the river; b) eastern side.

Point coverages were created from the height difference values of the overlapping grid cells. The Inverse Distance Weighting (IDW) function was used to produce interpolated difference surfaces from the point coverages (Figure 2.4-3). These difference surfaces were then clipped using the parallel polygon coverages.

Next, the Block 8 DEM was modified using the clipped interpolated difference surfaces adding less of the difference values (i.e. ¾ to ¼) progressively, away from the shared edge. This resulted in three narrow modified Block 8 grids on each side of the river. The resulting 6 grids were then converted to create 6 Block 8 point coverages.


A small area along the join-edge of each of the lidar tiles was also clipped and converted to point coverages. The lidar points and the 6 Block 8 point coverages (Figure 2.4-4) were used as input for a TOPOGRID interpolation.

Finally, the mosaic function was used to combine the data in the following order of priority: the unmodified lidar DEM tiles, the TOPOGRID interpolation along the join-edge, and then the Block 8 DEM (Figure 2.4-5).

Figure 2.4-3. Inverse distance weighted interpolation of the differences between the lidar and

Block 8 DEMs. a) western side of the river; b) eastern side.


Figure 2.4-4. Input points for the TOPOGRID interpolation along the join-edge between the

datasets. a) western side of the river; b) eastern side.


Figure 2.4-5. Final mosaic of the unmodified Lower Lakes lidar DEM, the TOPOGRID

interpolation, and Block 8 of the Lock 1-Wellington DEM. a) western side of the river; b) eastern side.

2.5. Lower Lakes-Coorong DEM to Southeast region DEM The data joined together in this stitch were from the Southeast region lidar DEM, the Coorong lidar DEM and the Lower Lakes DEM (Figure 2.5-1). The Southeast region data supplied for this project extended eastwards only to the red line shown in Figure 2.5-1.

Differences were calculated for the overlap area between the Coorong and Southeast DEMs. At the central to northern end of the overlap there was a spatial shift of possibly one grid cell between the datasets (Figure 2.5-2). This shift was not as obvious at the northern-most end where the overlap was quite narrow. The overlap was minimal because the Coorong data were clipped to a poorly specified boundary and for most of its extent only covered a very narrow strip between the water and the sand dunes (Figure 2.5-3).


Figure 2.5-1. Location of DEM datasets in the Coorong and Southeast. The red line indicates

the eastern edge of data supplied for this project.

There are flight line related differences visible in both datasets, manifesting as obvious joins running north-south for the Southeast region dataset (Figure 2.5-4a) and southeast-northwest for the Lakes-Coorong dataset (Figure 2.5-4b). These lines are most obvious in the southern part of the overlap and much less so in the north. Additionally, there appear to be some differences in the vegetation removal, although this issue may be somewhat masked or exacerbated by the shift shown in Figure 2.5-2.

Given these issues it was decided the best course was to clip the Coorong data to have only a small overlap (~10 cells) with the Southeast dataset, leaving out of the join the remainder of the Coorong data to the south. Further reasoning concluded that the Southeast region dataset had a much greater extent and was more recent, and as there were errors in both datasets there was little point in introducing more errors into the Southeast region data for what was a narrow overlap for most of its length.


Figure 2.5-2. Spatial shift between the Coorong and Southeast datasets, shown by the lines of

opposing large-difference values either side of various landscape features

Figure 2.5-3. Southern Coorong dataset


Figure 2.5-4. Slight offsets between flight lines within a) the Southeast dataset (vertical

arrows); and b) the Coorong dataset (diagonal arrows)

Stitching together the DEMs at the northern end of the Southeast region dataset required creation of DEM boundary masks (striped polygons in Figure 2.5-5), and a small mosaic mask (outlined in black in Figure 2.5-6). The DEMs were then clipped to their boundary masks and the mosaic mask (Figures 2.5-5a and 2.5-6a for the Southeast DEM and Figures 2.5-5b and 2.5-6b for the Coorong DEM).

The mosaic tool in ArcInfo was used to combine the clipped DEMs within the mosaic mask area (Figure 2.5-7). The output from the mosaic was then merged with the Coorong and Southeast region DEMs, in that order, to create an intermediate Lakes Section 9b grid. The preliminary Lakes Section 9 grid was created using the Raster Calculator in ArcMap to join the intermediate Lakes Section 9b with Lakes Section 9a (see Methods 4.3 and 4.4 below for Lakes Section 9a creation) (Figure 2.5-8).

Finally, the remaining Southeast region DEM tiles were joined together into a number of sections (Figure 2.5-9). Lakes Section 9 was finalised later when the Coorong south lagoon and bathymetry were joined to this preliminary version (see Section 4.4 below).


Figure 2.5-5. East (right) and west (left) sides of the overlap between the Coorong and

Southeast region DEMs, at the northern end of the Southeast dataset. The gap between the two sides is not to scale. a) shows the Coorong clipping boundary and b) the Southeast clipping

boundary.

Figure 2.5-6. Mosaic boundary and the clipped Southeast region DEM (a) and the clipped

Coorong DEM (b)


Figure 2.5-7. Mosaic of the Coorong and Southeast datasets, a) western side, b) eastern side

Figure 2.5-8. Preliminary Lakes Section 9, created from the final merge of the overlap area mosaic, the clipped Coorong and Southeast DEMs, and Lake Albert of the Lower Lakes.


Figure 2.5-9. Final DEM sections including all of the Southeast DEM (not including bathymetry

data)


3. BATHYMETRY TO BATHYMETRY STITCH The four bathymetry datasets supplied for this stitch were (i) the Murray River channel from Lock 1 to the Lower Lakes, (ii) Pomanda in the upper lakes, (iii) the Lower Lakes, and (iv) the Coorong. The aim of this stitch was to join together these bathymetry datasets at the northern end of the Lower Lakes while maintaining data quality to at least that of the least reliable dataset, without creating any visible joins, and at the same time giving priority to the more accurate data.

3.1. Channel to Lower Lakes bathymetry Listing the input datasets in order of reliability, the Murray River channel was the most recent and most accurate, (5 m resolution), followed by Pomanda in the upper lakes (2006, intermediate accuracy, 5 m resolution), the north Coorong lagoon (2004, 10 m resolution) and the Lower Lakes (2004, least accurate, 50-100 m resolution) (Figure 3.1-1).

Figure 3.1-1. Location of bathymetry and DEM datasets at the northern end of the Lower Lakes


The channel and Pomanda datasets did not overlap but there was complete overlap between Pomanda and the Lower Lakes. Given the relative ages and accuracy of the latter two, the Lower Lakes data was removed in the overlap area and the data sets were treated as non-overlapping. There was some evidence that the lake floor had changed between the two acquisition dates so the data were merged where the bathymetry appeared to be consistent.

In the final DEM-bathymetry surfaces the majority of the channel and lakes input data were unmodified. Hence, for consistency, the raster versions of these datasets were converted to points for use in the interpolations around dataset edges and at overlaps, rather than using the point versions which were also supplied. The Pomanda dataset was received only as points.

The channel grid was clipped to a small area adjacent to the Pomanda dataset and then converted to points for use in the interpolation. A subset of the Pomanda points was created that included only points with values of less than -1 m in elevation, and some additional points were removed where they contrasted sharply with the Lower Lakes elevations. From the modified Pomanda points, an extent polygon coverage and 25 m and 100 m buffer polygon coverages were produced.

The Lower Lakes data were clipped to surround the Pomanda data, and then bilinearly resampled to a 2 m cell size (to match the output from the interpolation, the channel bathymetry and the DEM to which it was later stitched). The clipped grid was converted to points and an extent polygon coverage was produced as well.

An interpolation bounding box was created with the modified Pomanda points extent polygon in the centre. A second version of this was created that consisted of the bounding box polygon and the Pomanda 25 m buffer polygon. This second version was then used to clip the Lower Lakes points, thereby excluding them from the central Pomanda 25 m buffer polygon.

Using TOPOGRID with the clipped channel points, the modified Pomanda points, and the clipped Lower Lakes points as input (Figure 3.1-2), a new bathymetry grid for the Pomanda area was created (Figure 3.1-3). The TOPOGRID output was clipped using the Pomanda 100 m buffer polygon coverage, and moved one metre south and one metre west to account for a shift that appeared to be introduced by TOPOGRID when a boundary extent was specified.

The 2 m Lower Lakes grid was clipped using the bounding-box-25-m-buffer polygon coverage to ensure all Lower Lakes cells within 25 m of the Pomanda data were set to nodata. Finally, the clipped-shifted TOPOGRID interpolation was joined to the 2 m clipped Lower Lakes grid by mosaic (Figure 3.1-4).


Figure 3.1-2. Point datasets used in TOPOGRID interpolation

Figure 3.1-3. Clipped version of the interpolation produced by TOPOGRID


Figure 3.1-4. Final version of the upper lakes bathymetry (mosaic of the interpolated surface

and clipped lakes datasets)


4. DEM TO BATHYMETRY STITCHES The basic method used to join the DEMs to the bathymetry followed that of Gesch and Wilson (2001). That is, a seamless dataset was produced by interpolating a surface at the boundary of the terrestrial and bathymetric layers using data from both sources.

In comparison to the DEM datasets, the bathymetry data were relatively coarse in resolution due to their acquisition methods. Consequently, the lidar and photogrammetry DEMs were given priority in all DEM-bathymetry stitches. The only exception to this was the water surface elevations in the Lock 1-Wellington DEM, which were removed before joining to the channel bathymetry.

In the Lower Lakes, the lake bed bathymetry had been extrapolated beyond reliably measured data to a notional shoreline elevation that was inconsistent with the adjacent lidar DEM. In these areas the lidar data were given precedence, a decision that was supported by the fact that the bathymetry data collection was only reliable in depths of more than 0.5 m. Thus any Lower Lakes elevation values above -0.5 m were ignored (the water level elevation was approximately 0 m), and in the North Coorong lagoon elevations above 0 m were ignored.

4.1. Lock 1-Wellington DEM to Channel bathymetry The input datasets for this stitch were the Murray River channel bathymetry, the eight blocks of photogrammetry DEM between Lock 1 and Wellington, the lidar DEM tiles stitched to Blocks 1 and 8 of the photogrammetry DEM (see methods 2.3 and 2.4) (Figure 4.1-1), and a Murray River channel boundary polygon.

The supplied channel bathymetry included isolated grid cells separated by nodata values from the bulk of the channel elevations (Figure 4.1-2). These were removed using Focalvariety and then the data were resampled (bilinear) to 2 m to match the surrounding DEMs. The resampled bathymetry was reprojected using ArcToolbox and a WGS84 to GDA94 transformation. The river channel boundary polygon was edited to match the extent of the bathymetry and converted to a grid for later use as a mask.


Figure 4.1-1. Location of DEM datasets between Lock1 and the Lower Lakes


Figure 4.1-2. A section of channel bathymetry in Block 1 showing detached cells

For each of the eight DEM blocks, the following steps were followed:

1. Using the river channel mask grid, set all DEM values within the channel with an elevation less than 0.13 m to either nodata or -999 (Figure 4.1-3 shows the nodata version). The 0.13 m threshold was used because it was an obvious break: the water surface values were almost always lower than this elevation and the land elevations were higher.

2. Clipped the reprojected bathymetry grid into sections corresponding to each DEM block

3. Created a 150 m buffer mask grid from the clipped bathymetry for each block. A 150 m buffer was used because it included sufficient DEM data for a smooth transition between the datasets in TOPOGRID, whilst also not being too large for GRID to process.

4. Produced an intermediate join grid for each block from the appropriate DEM and bathymetry (Figure 4.1-4)

5. Clipped the intermediate DEM-bathymetry join grid with the 150 m buffer and then created a polygon mask and a point coverage to use as input for TOPOGRID (Figure 4.1-5)

6. Extended the bathymetry mask coverages another 10 m outwards from their existing edge (to 160 m) (Figure 4.1-5)

7. Created additional up- and downstream point coverages (10 m width) for use as TOPOGRID inputs to ensure a smooth join between the DEM blocks (e.g. for the Block 5 interpolation, 10 m of DEM and bathymetry points from Block 4 and Block 6 were included)

8. Used TOPOGRID to create an interpolated surface from the DEM and bathymetry points (including 10 m of up- and downstream points). Tolerance values were set very low (tol. 1 – 0.5 and horizontal_std_err – 0.01) to minimise smoothing (given the 2 m input resolution) and the effect of higher elevation points bordering nodata areas in the channel. The output was moved one metre south and one metre west to account


for a shift that appeared to be introduced by TOPOGRID when a boundary extent was specified.

9. Combined the intermediate DEM-bathymetry join grid with the TOPOGRID interpolation, using the channel mask grid to ensure interpolated values were only used where the intermediate join grid was nodata (Figure 4.1-6)

Excluding Block 8, the DEM-bathymetry-interpolation grids were merged to form the final versions of River Sections 5 and 6 (Figure 4.1-7).

Block 8 had some remaining patches of nodata that were located outside the channel mask grid (Figure 4.1-8a). The TOPOGRID interpolation did include these areas so the final DEM-bathymetry-interpolation grid was recreated using the 150 m channel buffer grid rather than the channel mask grid. This final version of Block 8 (Figure 4.1-8b) was not merged with the upstream blocks into River Section 6; instead it was added into the DEM-Lower-Lakes bathymetry stitch.

Finally, there were some artefacts due to the choice of an elevation threshold of less than 0.13 m when masking water surface heights in the block DEMs. These artefacts were removed using the following steps:

1. Created boundary coverages from the River Section 5 and 6 grids.

2. Manually edited the boundary coverages to include a polygon surrounding each artefact in the channel (Figure 4.1-9a).

3. Converted the edited boundary coverage to a grid and set the values within the artefact polygons to nodata.

4. Created new versions of River Sections 5 and 6 by conditionally replacing the artefacts in the old versions with bathymetry values (where they existed) (example shown Figure 4.1-9b).

Figure 4.1-3. Block 2 DEM showing channel values of less than 0.13 m set to nodata, and the

150m bathymetry buffer polygon


Figure 4.1-4. Intermediate join of the Block 7 DEM and the channel bathymetry. Note the nodata

areas (white) between the two datasets that need to be filled.

Figure 4.1-5. Example TOPOGRID input datasets: points from Block 4 and Block 5, and the

160.m-buffered polygon mask for Block 4


Figure 4.1-6. Final join of the Block 7 DEM and the channel bathymetry with the TOPOGRID

interpolation used to fill the gaps

Figure 4.1-7. Final merged sections of the river, a) River Section 5 and b) River Section 6


Figure 4.1-8. Block 8 final processing

Figure 4.1-9. Artefact removal, a) before and b) after


4.2. Lakes DEM to Channel bathymetry The method used for joining the Lakes DEM to the southern end of the channel bathymetry was essentially the same as in Section 4.1. The input datasets included the two lidar DEM tiles either side of the southern end of the river channel and the channel bathymetry.

The lidar tiles were merged and the output was joined with the resampled-reprojected bathymetry from Part 4.1 to form an intermediate DEM-bathymetry join grid (Figure 4.2-1). A 0 m elevation threshold was used for the bathymetry within the lc3526084 DEM tile and a -0.5 m threshold within the lc3506084 DEM tile so that elevations from both sources were used in the -0.5 to 0 m range, ensuring a smooth transition between the datasets.

A polygon mask coverage and a point coverage were created from the intermediate join grid. TOPOGRID was used to produce an interpolation from the point and polygon coverages and the output was moved one metre south and one metre west to account for a shift that appeared to be introduced by TOPOGRID when a boundary extent was specified.

Finally, the intermediate DEM-bathymetry join grid was combined with the interpolation so the interpolation filled any nodata areas within the intermediate grid, using the polygon coverage as a mask. The final output from this join (Figure 4.2-2) was an input for Section 4.3.

Figure 4.2-1. Intermediate join of the Lower Lakes lidar DEM tiles and the southern end of the

channel bathymetry


Figure 4.2-2. Final join of the lakes lidar DEM tiles and the southern end of the channel

bathymetry, with the TOPOGRID interpolation used to fill the gaps

4.3. Lower Lakes DEM to Lower Lakes bathymetry The input datasets for this stitch included (i) the Lower Lakes lidar DEM tiles, (ii) the Block 8 photogrammetry stitched to two Lakes lidar DEM tiles and the channel bathymetry (see Figure 4.1-8c), (iii) the southern end of the channel bathymetry joined to the two adjacent Lakes lidar DEM tiles (Figure 4.2-2), (iv) the final version of the Pomanda-Lower Lakes bathymetry dataset (Figure 3.1-4), and (v), the unmodified Lower Lakes bathymetry dataset (not used where it overlaps with the joined Pomanda-Lower Lakes). The extent for the Lower Lakes DEM tiles and bathymetry are shown in Figure 4.3-1.

The Lower Lakes lidar DEM tiles were resupplied by the survey company because clipping issues in the first version resulted in many small areas of missing data. The second version was only clipped to an external boundary by the supplier, which unfortunately retained large patches of interpolated values over water that should have been designated nodata. Consequently the first steps in this part of the methods were creating masks and clipping the DEM tiles.

The Lower Lakes area was divided into seven parts and the Coorong into two to make processing easier given the extent and 2 m resolution of the data (Figure 4.3-2). The Lower Lakes bathymetry data did not extend far into the Coorong so the majority of the processing described here concerns only the seven parts into which the Lakes data were divided.


Figure 4.3-1. Location of bathymetry and DEM datasets in the Lower Lakes

The initial processing for this stitch involved clipping the DEM tiles over water areas. The resupplied DEM data included the lidar ground points as 3D shapefiles. The shapefile for the lc3805992 tile was corrupt so had to be recreated from the corresponding text file which listed x,y,z values. The shapefiles were converted to point coverages and then the following steps were performed for each tile (Figure 4.3-3):

1. In GRID the window was set to the tile extent and the cell size to 6 m (larger than the distance between most points to minimise cell gaps and nodata during processing)

2. The point coverage was converted to a grid

3. The grid was then converted to a polygon coverage, with internal polygons dissolved

4. The polygon coverage was then buffered to a distance of 8 m out from the edge

5. The buffered coverage was converted to a grid with a 2 m cell size

6. The buffer grid was then used as a mask to clip the DEM for that tile

For some tiles the 8 m buffer was not large enough to remove all nodata gaps around the edges (due to sparse data at the edge of the data region) so step 3 above was repeated using a 10 m buffer (for tiles lc3006078, lc3386048, lc3446046) or a 12 m buffer (for tiles lc3486070, lc3366050, lc3526072).


Point data were not supplied for tile lc3366070. Close inspection of the DEMs for this tile, from the original dataset and the resupplied dataset, showed very smooth interpolations as though the values were derived from the edges of the neighbouring two tiles rather than lidar measurements in the area covered by the tile. As this tile is mostly water (nodata) and the project was constrained for time, the issue was flagged but the original tile was used anyway.

The clipped DEM tiles were then merged into the seven parts over the Lakes area and two parts for the Coorong shown in Figure 4.3-2. The Lakes Part 1 merge included the Block 8-lidar-DEM-channel-bathymetry grid (Figure 4.1-8c) and the lidar-DEM-channel-bathymetry grid (Figure 4.2-2).

Figure 4.3-2. The Lakes-Coorong DEM tiles were divided into seven Lakes parts and two

Coorong parts for the join to the bathymetry


Figure 4.3-3. Processing of DEM tiles: a) resupplied tile with interpolation over water areas; b)

raw point data; c) grid created from points; d) polygon coverage created from grid; e) dissolved and buffered polygon coverage; f) DEM clipped using buffered coverage.


The Lower Lakes bathymetry also required some modifications. The dataset was resampled to a 2 m resolution to match the DEM and clipped to the extents of the seven parts of the Lakes area. The Lakes Part 1 (cf. Figure 4.3-2) bathymetry datasets were joined so that the Pomanda-Lower Lakes data (Figure 3.1-4) had priority within their extent. Next the bathymetry for each Lakes part had values greater than -0.5 m elevation set to nodata. Values greater than this threshold were deemed to be unreliable given the constraints of sonar data collection. Additionally there was often an overlap with the DEM in this elevation range, and the lidar was considered to be more accurate. The Lakes Part 5 contained areas of bathymetry with a very smooth interpolation that appeared to be a spurious result produced by allowing the interpolation to extend beyond the extent of the input data (the extent for the Lower Lakes bathymetry points was much smaller than that of the gridded version of the data). Consequently a clip coverage was created and the extended areas in the bathymetry surface were removed (Figure 4.3-4).

Figure 4.3-4. Part 5 bathymetry, a) original grid with the bathymetry points extent outlined in

red; b) clipped bathymetry grid


A number of buffer polygon coverages were required for this stitch (Figure 4.3-5). For each of the seven Lakes parts, the following steps were performed:

1. Window and cell size set to the DEM

2. Created 10 m, 30 m, and 500 m buffer grids

3. Converted each buffer grid to a polygon coverage (internal polygons were dissolved)

DEM extent polygon coverages were also needed, and these were created from the DEM for each of the seven Lakes parts. Interpolation extent coverages were created by joining the DEM extent coverages with the 500 m buffer coverages (Figure 4.3-6a). Bathymetry clipping boundary coverages were created from the DEM extent coverages and the 30 m buffer coverages (Figure 4.3-6b). A mask coverage for each of the final stitched DEM-bathymetry-interpolation grids was created from the DEM extent coverage for each Lakes part, enclosing the Lakes area within the part and preventing the interpolated data from extending beyond the DEM extent (Figure 4.3-6c).

Edge clip coverages were needed at the boundaries between some Lakes parts. An example is shown in Figure 4.3-7a where a polygon coverage was created to clip additional input data from the adjacent Lakes part for inclusion in the interpolation, thereby preventing any discontinuities between the parts. These additional interpolation inputs were needed at the boundaries between Lakes Parts 4 and 5, Parts 6 and 7, and Parts 2 and 7.

Figure 4.3-5. Example of DEM buffers (10 m, 30 m, and 500 m)


Figure 4.3-6. Examples of a) interpolation extent coverage; b) bathymetry clip coverage and c)

final mask coverage

Figure 4.3-7. a) Gaps to be filled by interpolation at the boundary between Parts 6 (south) and 7

(north); b) polygon created to clip points for additional interpolation input (red line)


Prior to the interpolation, the following processing steps were performed for each of the seven Lakes parts:

1. Converted the interpolation extent boundary (Figure 4.3-6a) to a grid

2. Using the 500 m buffer grid and the interpolation extent boundary grid, converted the bathymetry grid to a point coverage (Figure 4.3-8a). The distance from the DEM edge to where the bathymetry points began was set to 15 m for Parts 5 and 6, and to 20 m for the other parts. The narrower 15 m gap between DEM and bathymetry points for Parts 5 and 6 was used because of the greater prevalence of channels and shallow water within these parts. A 20 m gap would have resulted in too little bathymetry data contributing to the interpolation between the two datasets.

3. Created a bathymetry buffer grid from the bathymetry grid, with a distance of 1000 m for Lakes Part 6 and 500 m for all the other Lakes parts.

4. Used the bathymetry clip coverage (Figure 4.3-6b) to clip the bathymetry grid to begin 30 m from the DEM edge (Figure 4.3-8b)

5. Using the bathymetry buffer grid to limit the extent to 500 m beyond the bathymetry (1000 m for Lakes Part 6), clipped the DEM grid to less than or equal to 0.5.m elevation and then converted it to a point coverage (Figure 4.3-8c)

6. Using the edge clip coverage (Figure 4.3-7b), clipped both sides of each edge between Parts 4 and 5, Parts 6 and 7, and Parts 2 and 7, to produce additional bathymetry and DEM point inputs for the interpolation (Figure 4.3-8d)

Some manual editing of the interpolation input points at the end of the river channel in Lakes Part 1 was required. Points were removed from the Lakes bathymetry point coverage at the end of the river channel to allow greater smoothing between the Lakes bathymetry and the channel bathymetry. The related bathymetry clipping boundary was edited to reflect these changes, and the clipped bathymetry grid (buffered 30 m from the DEM edge) was recreated.

TOPOGRID was used to produce an interpolated surface between the DEM and bathymetry for each of the Lakes parts (Figure 4.3-9a shows an input example, Figure 4.3-9b shows output).Tolerances were set quite small to minimise smoothing since the input points were at a resolution of 2 m, and much finer than the resolution of the Lakes bathymetry.

For each of the Lakes parts, the DEM was then merged with the 30 m clipped bathymetry grid and the interpolation, in that order of priority (Figure 4.3-9c). The merge for Lakes Part 2 included the original grid for tile lc3326082 because point data for this tile were not included with the resupplied version of the data, and therefore it could not be clipped with the other tiles.

As described in Section 4.1, some artefacts needed to be removed from the southern end of the channel bathymetry. The same process was used to remove these as previously, and an example is shown in Figure 4.1-9.

Finally, the seven Lakes parts were merged into larger sections to form final data sections. Lakes Part 1 was merged with Parts 2 and 3 to create Lakes Section 7 (Figure 4.3-10a). Parts 4 and 5 were merged to become Preliminary Lakes Section 8 (Figure 4.3-10b). Parts 6 and 7 became Preliminary Lakes Section 9a (Figure 4.3-10c).


Figure 4.3-8. a) Bathymetry converted to points (blue) with DEM (greyscale) for reference; b) bathymetry grid (blue) set to nodata within 30 m of the DEM edge; c) DEM data with elevation values of less than or equal to 0.5 m converted to points (orange); d) example of additional

bathymetry and DEM points for edge matching during interpolation (bathymetry – Part 6 in blue and Part 7 in green, DEM – Part 6 in purple and Part 7 in yellow).

Figure 4.3-9. a) Input points for the Part 1 interpolation; b) interpolation output; c) merge of the

Part 1 DEM with the 30 m-buffered bathymetry grid and the interpolation from TOPOGRID.


Figure 4.3-10. a) Lakes Section 7 (Parts 1, 2, and 3); b) Preliminary Lakes Section 8 (Parts 4 and

5); c) Preliminary Lakes Section 9a (Parts 6 and 7)


4.4. Lakes and Southeast region DEMs to Coorong bathymetry The inputs for this stitch were (i) Preliminary Section 8 (Figure 4.3-10b), (ii) Preliminary Section 9a (Figure 4.3-10c and the joined Southeast tiles, Figure 2.5-8), and (iii) the Coorong north lagoon bathymetry (Figure 4.4-1). These input preliminary Section 8 and 9 DEM-bathymetry grids are referred to as Section 8 old and Section 9 old below.

The Preliminary Section 9a dataset was too large for processing in GRID, so a mask was created to limit its extent to surround the Coorong bathymetry. The remainder of Section 9 was added back in after the DEM-bathymetry stitching.

The Coorong bathymetry was resampled to 2 m and clipped into two parts to match the extents of Sections 8 and 9. All elevation values greater than 0 m were set to nodata and difference grids were calculated with the DEMs.

Figure 4.4-1. Input data locations

From the DEM-bathymetry difference grids a couple of things were noted. There was an area of complete overlap between the Preliminary Section 8 DEM-bathymetry and the Coorong bathymetry at the northern end (Figure 4.4-2a and 4.4-2b). In this area, the largest differences where Section 8 had higher elevations are generally around the edges, i.e. where the bathymetry data was likely to be unreliable. The largest differences where the Coorong bathymetry values were higher (implying the water is shallow) varied across the channel width and length. Given that we did not know which dataset was more reliable and there weren’t any obvious patterns to suggest which one was better, it was decided to ignore the


overlapping Coorong data and leave the data as it was from the Lower Lakes bathymetry in Section 8.

Additionally, use of a 0 m threshold appeared to remove most of the incorrect bathymetry data while retaining many of the shallow areas where Preliminary Sections 8 and 9 did not already have values. This was higher than the -0.5 m threshold used in the Lower Lakes DEM-bathymetry stitching methods (Section 4.3), however the Coorong data seemed to extend with real values further than the Lower Lakes bathymetry data.

Figure 4.4-2. a) The area of overlap between Section 8 old and the Coorong bathymetry; b) the

difference between the two surfaces (Section 8 old minus bathymetry)

Buffer and boundary polygon coverages were needed for further processing, so the following steps were used for each section, except where specified:

1. Created 10 m and 30 m buffer grids

2. Created a 1000 m buffer grid (for Section 8) and a 500 m buffer grid (for Section 9)

3. Converted the buffer grids from 1 and 2 above into coverages and dissolved any internal polygons


4. Created a coverage from the old Section 8 DEM-bathymetry grid and dissolved any internal polygons to produce a boundary coverage

5. Edited the Section 8 boundary coverage to become a TOPOGRID interpolation extent coverage (the Section 9 clip-to-bathymetry-extent mask will be used as an interpolation extent coverage)

6. Created a Coorong bathymetry extent coverage and combined it with the 30 m buffer coverage to produce a bathymetry clip coverage, and then converted the clip coverage to a grid

7. Edited the boundary coverage (step 4 above) to include arcs from the bathymetry extent coverage where the DEM has islands rather than continuous land area, to produce a final DEM-bathymetry-interpolation mask coverage. Converted the mask coverage to a grid.

8. Along the boundary between Sections 8 and 9, created an edge polygon coverage for use in clipping point data that needed to be included in the other Section’s interpolation. Modified this edge coverage to create a second version that covered only the western side of the join and extended north and south along the gap to be filled by the interpolation.

The old Section 9 DEM-bathymetry grid required some editing to remove a small artefact bridge (Figure 4.4-3).

Figure 4.4-3. Artefact bridge (black) that was removed from the Section 9 DEM

To produce an interpolated surface using TOPOGRID, these steps were followed for each Section, except where specified:

1. Clipped the < 0 m elevation Coorong bathymetry grid to the interpolation extent coverage and to within 20 m of the DEM edge, then converted it to a point coverage


2. Created a bathymetry buffer grid, and converted it to a coverage

3. Clipped the < 0 m elevation Coorong bathymetry grid using the 30 m buffer coverage to remove any values within 30 m of the DEM edge

4. Modified the old Section grid to include elevation values 2 m or less and converted to a point coverage

5. Created edge point coverages for the DEM and bathymetry, with the western side of the join being larger

6. Split the Section 9 point coverages into three parts because it was too large for TOPOGRID, and also re-created interpolation extent coverages, edge polygon coverages and edge point coverages

7. Ran TOPOGRID using DEM-bathymetry points, Coorong bathymetry points, edge points and interpolation extent masks. The tolerances were set to small values to minimise smoothing.

8. Merged together the DEM-bathymetry, the 30 m buffered < 0 m Coorong bathymetry, and the interpolation grid (in that order) for Section 8 and the three Section 9 parts. Merged together the three Section 9 parts.

An obvious line in the interpolation on the western side of the join between Sections 8 and 9 resulted in a rerun of TOPOGRID within the area of the larger edge boundary coverage. Then differences were calculated between this new interpolation and the merged DEM-bathymetry-Coorong-bathymetry-interpolation grids for both Section 8 and Section 9 (Figure 4.4-4). Based on the difference surface, a clip coverage was created that extended from the join into both Sections 8 and 9, ending where the difference between the merged grid and the new interpolation was very close to zero. At this point the new small interpolation surface would fit seamlessly with the original.

Final merging for the two Sections was a repeat of the previous, except that it included the new small interpolation at the edge of Sections 8 and 9. So for Section 8 (Figure 4.4-5) the data were merged in the following order: old Section 8 DEM-bathymetry, the 30 m buffered < 0 m Coorong bathymetry, the small Section 8-9 edge interpolation, and the large initial TOPOGRID interpolation.

The Section 9 merging was a little more complicated and was completed in the following order: old Section 9 DEM-bathymetry, the 30 m buffered < 0 m Coorong bathymetry, the small Section 8-9 edge interpolation, and the three Section 9 parts with DEM-bathymetry-interpolation. Finally the excluded parts of Section 9, which were well beyond the Coorong bathymetry extent, were added. The final version of Lakes Section 9 is shown in Figure 4.4-6.


Figure 4.4-4. Differences between the additional Section 8-9 edge interpolation and the original

interpolations for a) Section 8 and b) Section 9. The clip boundary (black line) was based on these differences.

Figure 4.4-5. Final version of Lakes Section 8 with Coorong bathymetry included


Figure 4.4-6. Final version of Lakes Section 9 with Coorong bathymetry included


5. FINAL DATASET

5.1. Section descriptions The final DEM-bathymetry dataset for the Murray River and Southeast region of South Australia was divided into 13 sections, shown in Figures 5.1-1 to 5.1-12. The fine spatial resolution of this dataset and the file size constraints associated with the ESRI ArcGIS software meant that the division into sections was somewhat arbitrary. There are no discontinuities at section edges so the full dataset can be reconstituted with no edge effects when needed.

Figure 5.1-1. River Section 1, Chowilla and Katarapko DEMs


Figure 5.1-2. River Section 2, Katarapko and Pyap DEMs

Figure 5.1-3. River Section 3, Pyap to Lock 1 DEM


Figure 5.1-4. River Section 4, Pyap to Lock 1 DEM

Figure 5.1-5. River Section 5, Pyap to Lock 1 and Lock 1 to Wellington DEMs with channel

bathymetry


Figure 5.1-6. River Section 6, Lock 1 to Wellington DEM with channel bathymetry

Figure 5.1-7. Lakes Section 7, Lock 1 to Wellington and Lower Lakes DEMs with channel,

Pomanda and Lower Lakes bathymetry


Figure 5.1-8. Lakes Section 8, Lower Lakes DEM with Lower Lakes and Coorong bathymetry

Figure 5.1-9. Lakes Section 9 and 9b, Lower Lakes and Southeast region DEMs with Lower

Lakes and Coorong bathymetry


Figure 5.1-10. Section 10, Southeast region DEM




5.2. Quality Assessment It was beyond the scope of the current project to undertake rigorous field checking and quality assurance of the final dataset. Some quality assessment was conducted to ensure that the data quality had not been compromised during the stitching process, and that the final product maintained the standard of (at worst) the least accurate input dataset for each stitch.

In general, limited range colour stretches and close scrutiny were used to determine whether unnatural or linear features existed, with particular attention paid to the edges of the final data sections to ensure seamlessness.

For the DEM to DEM stitches, manual inspection of a hillshade derived from the final version of each join was the primary method used to look for artefacts.

In the DEM to bathymetry stitches, interpolated values were used to fill an enforced buffer zone between the datasets. This method did not result in the creation of any linear features, so the manual inspection determined whether the interpolation-filled buffer zone was consistent with the quality of the adjacent bathymetric data. The DEM data in these stitches was unmodified in all cases.

The Chowilla DEM was lowered by 0.15 – 0.35 m within the overlap with the Katarapko DEM. It may be case that the whole Chowilla dataset needed lowering, however fixing Chowilla was not within the scope of this work and there was no information at the time to guide a more general modification. The change to the Chowilla data preserved correct flow directions in channels.

The Lower Lakes DEM had a few small issues of note. Tile lc3366070 did not come with point data and the original gridded version was made up of smooth values that appeared to be derived from the adjacent tiles. Given its area was mostly water the tile was used as supplied. A buffer was used to clip out water areas in the resupplied version of the Lower Lakes DEM, either 8 m or 12 m from the edge of the points. This meant that some interpolated data representing water areas was retained in the final product, however since additional interpolated data were added between this edge and the bathymetry the overall impact on the accuracy of the final dataset was small. Finally, there were some flight lines


visible in the Lower Lakes DEM (Figure 2.5-4a). They were most obvious in the west and the southeast (the Coorong south lagoon), with the latter being of concern when considering the stitch to the Southeast region DEM. These flight lines, and the narrow extent of the south lagoon DEM data, were the main reasons for the decision not to mosaic the two DEMs for the full length of the Coorong data within the Southeast DEM.

The Southeast region DEM had some visible flight lines when the difference with the Coorong south lagoon of the Lower Lakes DEM was calculated (Figure 2.5-4b). It was not within the scope of this project to fix this problem.

The metadata for the Lower Lakes and Coorong bathymetry data indicated that the sonar becomes unreliable at water depths of less than half a metre. Given this, all elevation values in the Lower Lakes dataset above -0.5 m were removed, and all values above 0 m were removed from the Coorong bathymetry. This decision was supported by the fact that the bathymetry interpolation was obviously extended to an arbitrary boundary which often overlapped with the Lower Lakes DEM, giving conflicting elevation values.


6. RECOMMENDATIONS

6.1. Data collection There are three main recommendations regarding data collection. Firstly, given the relative ease with which stitches between DEMs having large overlaps were completed compared to those with small overlaps, it is recommended that the extent of all proposed datasets be specified such that they have a significant overlap with any existing data. This would ensure that the influence of small differences between datasets can be minimised, and where large differences existed, there would be sufficient information available that any decision regarding modifications to one or both could be easily justified.

Secondly, where possible, the extent of any proposed dataset should go beyond the immediate needs of the stakeholders. Quality datasets are regularly used for things that they were not originally considered for, often with the wish that ‘if only it covered this area too’. This is a particular issue where the omitted area is small and unlikely to be covered by a separate data acquisition. It is recommended that the extent be discussed with the data supplier to obtain all data that will be acquired, including areas immediately outside the proposed extent. Naturally there are constraints associated with time and funding for data collection, however these can sometimes be overcome if additional stakeholders are included in the planning phase. The recently developed National Elevation Data Framework provides a mechanism for coordinating such activity. Lastly, all raw point data should be retained. It is often useful for checking extents and errors, and for other purposes such as preventing extrapolation beyond the actual extent of the data as described in this report.

6.2. Data manipulation The data manipulations performed in this project followed some pragmatic guidelines that would also be relevant in other similar activities. These include:

Try to minimise changes to the data

If one dataset is less reliable, modify it first

Use consistent methods

Document all steps so decisions can be justified, products described and methods repeated at later dates

Recognise that although the cell size of a final dataset may be at a fine resolution, it is likely that some of the input data were collected at a much coarser level


7. CONCLUSIONS Stitching together disparate DEM and bathymetry data is a valid option for creating large, seamless, high resolution datasets. The output from this work will be used for modelling and resource management related work in the Murray region of South Australia.

Data acquisition needs to be well planned to ensure appropriate extents for possible data uses and useful overlaps with adjacent datasets, and extrapolation of surfaces beyond the extent of the raw data, as was done in the Lower Lakes bathymetry, should be avoided where possible and clearly documented when it is done. The methods developed for this project could also be relevant to other work such as merging SRTM DEM with higher resolution data.


REFERENCES

Gesch, D and Wilson, R (2001) Development of a seamless multisource topographic/bathymetric elevation model of Tampa Bay. Marine Technology Society Journal 35 (4): 58-64.

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