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Developing a Digital Elevation Model using ANUDEM for the CSHC, China

Developing a Digital Elevation Model using ANUDEM for the Coarse Sandy Hilly Catchments of the Loess Plateau, China QinKe Yang, Tom G. Van Niel, Tim R. McVicar Michael F. Hutchinson and LingTao Li CSIRO Land and Water Technical Report 7/05

CSIRO Land and Water Technical Report 7/05

www.csiro.au

www.csiro.au

Developing a Digital Elevation Model using ANUDEM for the Coarse Sandy Hilly Catchments of the Loess Plateau, China

QinKe Yang, Tom G. Van Niel, Tim R. McVicar, Michael F. Hutchinson, and LingTao Li

CSIRO Land and Water Technical Report 7/05 September 2005

Copyright and Disclaimer © 2005 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 Land and Water. 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. Authors: a* b c d e QinKe Yang , Tom G. Van Niel , Tim R. McVicar , Michael F. Hutchinson , and LingTao Li a

Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, 26 Xinong Road, Yangling, 712100, Shaanxi Province, China, Tel.: +86-29-8701-9577, e-mail: [email protected]

b

CSIRO Land and Water, Private Bag No. 5, Wembley, 6913,WA, Australia Tel.: +61-8-9333-6705, e-mail: [email protected]

c

CSIRO Land and Water, GPO Box 1666, Canberra, 2601, ACT, Australia Tel.: +61-2-6246-5741, e-mail: [email protected]

d

Centre for Resource and Environmental Studies, Australian National University, Canberra, 0200, ACT, Australia Tel.: +61-2-6125-4783, e-mail: [email protected]

e

CSIRO Land and Water, GPO Box 1666, Canberra, 2601, ACT, Australia Tel.: +61-2-6246-5809, e-mail: [email protected] Cover: The main photograph is a typical landscape of the Coarse Sandy Hilly Catchments of the Loess Plateau, China. Severe gullying is present, some of which almost reach the ridges, to reduce erosion rates large areas of re-vegetation can be seen; this re-vegetation also reduces the water yield from this landscape. Summer cropping is conducted on the terraces and extensive grazing of sheep is undertaken throughout the landscape. This photo was taken by XianMo Zhu near Yan’an City, Shaanxi Province, May 1985. The bottom left insert shows a worker tending 5-year old pines; photo by Tim McVicar, Pianguan County, Shanxi Province, 7th October 2004. The top right insert shows the resulting digital elevation model with the red line defining the Coarse Sandy Hilly Catchments study site. For bibliographic purposes, this document may be cited as: Yang, Q.K., Van Niel, T.G., McVicar, T.R., Hutchinson, M.F. and Li, L.T. (2005) Developing a digital elevation model using ANUDEM for the Coarse Sandy Hilly Catchments of the Loess Plateau, China. CSIRO Land and Water Technical Report 7/05, Canberra, Australia, 74 pp. A PDF version is available at: http://www.clw.csiro.au/publications/technical2005/tr7-05 ISBN 0 643 09236 6

CSIRO Land and Water

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Contents Acknowledgements ................................................................................................ iv Executive Summary................................................................................................. v 1. Introduction....................................................................................................... 1 2. Correcting the CSHC input data .................................................................... 10 2.1.

Contour and spot height correction ......................................................................... 13

2.2.

River and lake correction ......................................................................................... 16

2.3.

Impact of data corrections on the resulting DEM quality ......................................... 21

3.

2.3.1.

Calculating sink number and frequency distributions ...................................... 21

2.3.2.

Results for the iterative improvement of the input datasets ............................ 23

Optimising key ANUDEM parameters ........................................................... 25 3.1.

Determining the optimum spatial resolution ............................................................ 25

3.2.

Determining the number of iterations ...................................................................... 26

3.3.

Determining the relative amounts of profile and total curvatures ............................ 29

4.

3.3.1.

Assessing changes at specific locations in the landscape .............................. 30

3.3.2.

Stream network analysis.................................................................................. 33

3.3.3.

New lines, drainage enforcement, and sinks analysis..................................... 35

3.3.4.

Determining changes in elevation and slope................................................... 37

3.3.5.

Using independent higher quality data as a validation dataset ....................... 38

Minimising positional difference between YHB and CSHC datasets .......... 43 4.1.

Spot height comparison ........................................................................................... 46

4.2.

Contour comparison ................................................................................................ 48

4.3.

Pooled spot height and contour centroid comparison ............................................. 50

5.

Application and assessment of the ANUDEM output................................... 53 5.1.

Generating sub-catchments for the CSHC .............................................................. 53

5.2.

Comparing outputs from the ANUDEM and TIN algorithms.................................... 55

6. 7. 8. 9. 10. 11. 12. 13.

Conclusions .................................................................................................. 62 References..................................................................................................... 66 Appendix A – check_fnodes.aml ................................................................. 68 Appendix B – centreline_using_circle.ave .................................................. 69 Appendix C – multipnt_to_pnt.ave............................................................... 71 Appendix D – pnt2multipnt.ave.................................................................... 72 Appendix E – multip2plyline.ave.................................................................. 73 Appendix F – sum_grid.ave.......................................................................... 74


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Acknowledgements The research was funded by Australian Centre for International Agricultural Research (ACIAR), specifically project LWR1/2002/018, Chinese Academy of Sciences (CAS) Institute of Soil and Water Conservation, and CSIRO Land and Water, and CAS Innovation Project (中国科学院知识创新重要方向项目：黄土高原水土保持的区域环境效应研究，合同号 KZCX3-SW-421 / Impacts of Soil and Water Conservation on Environmental Factors in Loess Plateau of China: Project of Knowledge Innovation Program of CAS, KZCX3-SW-421). We received helpful comments from Dr John Gallant and Mr Trevor Dowling, both from CSIRO Land and Water Canberra, while performing this research. Mr Cade McTaggart, from Centre for Resource and Environmental Studies, Australian National University, Canberra, provided assistance by ensuring that we had the latest version of ANUDEM. Dr Yun Chen and Mr Ron DeRose are to be thanked for assisting us by using some of the SedNet suite of tools to determine the sub-catchments for the study site.


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Executive Summary Current and planned re-vegetation programs to assist the reduction of soil erosion in the Loess Plateau, China are likely to result in decreases of regional water yield, as perennial vegetation (trees, shrubs and grasses) use more water then the annual vegetation they replace. A hydrologically correct digital elevation model (DEM) forms the basis of successfully monitoring and modelling this impact. For example, the DEM is critical for modelling the water balance, modelling surface climate, and mapping vegetation suitability for replanting schemes. These are all key objectives in the Australian Centre for International Agricultural Research (ACIAR) project LWR1/2002/018. Therefore, ensuring that a high quality DEM was available for the project study site, the Coarse Sandy Hilly Catchments (CSHC), was critical for project success. In this technical report, we inspect two central issues concerning the quality of the CSHC DEM. These include: (1) assessing and improving the quality of the 1:250,000 source topographic data (i.e., contours, spot heights, rivers, lakes held in Geographical Information System (GIS)); and (2) optimising the parameters used in the interpolation algorithm (ANUDEM Version 5.1). An iterative process involving the careful analysis of the ANUDEM output and diagnostic files identified problems that were subsequently corrected (where possible) in the input topographic data. The ANUDEM parameters that were optimised for the CSHC included: (a) output grid resolution; (b) number of iterations; and (c) the amount of profile curvature used as governed by the 2nd roughness penalty. To optimise the 2nd roughness five DEM related attributes were assessed; the first four involved analysis of the output DEM, whereas the final assessment was an independent validation using higher resolution (1:100,000) spot height data available for part of the CSHC, specifically the YanHe Basin (YHB). The spot height analysis required the minimisation of positional differences between the CSHC and YHB data. Important improvements in the quality of the CSHC DEM were achieved by: (1) fixing over 1,100 errors in the input topographic data; and (2) optimising key ANUDEM parameters. As a result of these CSIRO Land and Water

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procedures, we have high confidence in the quality of subsequent modelling exercises that will use this DEM.

摘要多年的水土治理，特别是近年来退耕还林（草）工程的实施，对区域土壤侵蚀和地表径流具有重要影响。数字高程模型（DEM）是区域水文地貌定量分析（流域划分、流水线提取、流域出口面积量测等）、流域径流和侵蚀产沙模拟、气候要素空间插值、太阳辐射计算（主要与坡度和坡向有关）、区域植被适宜性评价和水土保持措施优化配置等研究的基础。为了满足澳大利亚农业研究中心项目（区域植被恢复的水环境效应研究， LWR1/2002/018）和中国科学院知识创新方向项目（黄土高原水土保持的区域环境效应， KZCX3-SW-421）研究工作需要，我们引进专业化 DEM 插值软件 ANUDEM，以多种比例尺数字地形图上的地形信息（等高线，高程点，河流、湖泊和水库）为基本数据源，在世界上水土流失最为严重的黄土高原，开展了 DEM 建立方法的研究。本研究报告报道了该项研究的部分内容，即利用 1:25 万数字地形图建立黄土高原多沙粗沙区中等分辨率 DEM 的方法。主要内容包括： (1) 源数据质量错误检验和校改；(2) ANUDEM 运行参数优化，包括：最佳栅格尺寸、插值叠代次数、地形粗糙度等；（3）与大比例尺测绘资料（延河流域 1:10 万地形图）对比进行 DEM 定位精度评价；（4）黄土高原多沙粗沙区 100 米栅格 DEM 应用说明和示范。该研究取得了三个方面研究结果：（1）建立了基于数字地形图和 ANUDEM 软件建立水文地貌关系正确 DEM 的工作流程；（2）提出了基于 1:25 万地形图建立丘陵地区中分辨率 DEM 的关键参数；（3）建立了黄土高原多沙粗沙区 100 米栅格 DEM（包括经过错误校改的矢量地形图）。本研究对基于 1:25 万地形图建立更大区域 DEM，和基于更大比例尺数字地形图建立高分辨率 DEM，均具有参考和指导意义，同时也对 ANUDEM 的使用方法做出了示范和介绍。 CSIRO Land and Water

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1. Introduction In many developing countries, including China, moderate resolution (10 to 250 m planar resolution or ≈ 1:10,000 to 1:250,000 scale) digital elevation models (DEMs) are not routinely available from government mapping agencies, or if they are available, then the quality of the DEM does not often meet the user requirements. Given that in most spatial environmental research (e.g., hydrology, geomorphology, vegetation prediction, and land-use monitoring, see Wilson and Gallant 2000) hydrologically correct DEMs are critical input, many researchers in developing countries are forced to generate their own DEMs to meet research needs.

While a DEM can be considered any digital representation of the topographic landscape, it must also meet the criteria for which it will be used. While there are potentially many relatively new data sources from which to generate moderate resolution DEMs (ranging from ground survey with kinematic GPS to airborne photogrammetry, interferometry, and radar or laser altimetry, see Hutchinson and Gallant 2000), by far the most popular data source is digital topographic data used in an interpolation algorithm to generate a DEM. These data typically include contours, spot heights, rivers and lakes, and while this use of such topographic data appears straightforward, results can vary greatly as a function of both the quality of the input data and of the processing algorithm used. For example, while a DEM can be generated using a Triangular Irregular Network (TIN) approach, the resultant DEM will often not be hydrologically correct. This can be seen by artificial features being present such as spurious triangular-shaped sinks or peaks, resulting in incorrect derived stream networks, contributing areas and catchment boundaries calculations. Additionally, surfaces of slope and aspect (produced as the first and second derivatives from the DEM) can contain obvious errors.


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To meet the challenging demand of creating hydrologically correct DEMs from digital topographic data (e.g., contours, spot heights, rivers and lakes), an algorithm developed by the Australian National University (called ANUDEM) has evolved over the last 20 years (Hutchinson 1988; Hutchinson 1989; Hutchinson 2004a). In many developing countries these digital topographic data required by ANUDEM are relatively accessible, especially for projects addressing major national concerns that include personnel from the developing country.

In the Loess Plateau of China major soil erosion (of the order of 20,000 t/km2/year, Xiang-zhou et al. 2004) has resulted in the Chinese central government developing a “Green Hills / Clean Rivers” policy whereby large areas of upland catchments will be re-vegetated with perennial species with the aim to reduce soil erosion (see http://www.novexcn.com/environmental_protec_law.html). The positive impact of this development has been shown in many small experimental plots but it is yet to be calculated over large regions. One important consideration associated with this reduction in regional soil erosion is the coupled reduction in regional water yield due to increased regional evapotranspiration of perennial vegetation (trees, shrubs and grasses) compared to the annual vegetation they replace (Xiubin et al. 2003; Huang and Zhang 2004). With the average annual water yield from the Yellow River already overcommitted (Xiubin et al. 2003), with the river less frequently reaching the ocean (specifically the Bohai Sea Li 2003), balanced planning of the timing, area and location of ongoing re-vegetation programs in the Loess Plateau is a major concern for local-, regional-, and national-land and water resource managers. Models that estimate the interactions between water balance, soil erosion, meteorological surfaces, and vegetation suitability across the landscape are all based on a DEM. Having access to a high quality, hydrologically correct DEM is very important for generating accurate models that will ultimately be used to make management decisions and determine environmental policy.


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In this technical report we discuss two main issues regarding the improvement of the DEM used for our research project: (1) quality control and error checking performed on the original digital topographic data obtained by the Chinese Central Government; and (2) methods for optimising key ANUDEM parameters. These are introduced in turn in the following paragraphs.

An iterative process was used to improve the quality of the input topographic data (i.e., contours, spot heights, rivers, lakes) with careful analysis of the ANUDEM output and diagnostic files undertaken to identify problems that were subsequently corrected (where possible) in the input topographic data. This process had to be iterative as large errors can mask smaller errors, and not all errors will be detected in a single run of ANUDEM. Intensive use was made of the output ANUDEM diagnostics files; these are aimed at assisting users to generate the highest quality DEM possible by identifying locations of errors in the input topographic data (it is then up to the user to correct these errors). If mechanisms can be established so that errors fixed in the ANUDEM processing by research organisations can be communicated to the central government mapping agencies this will be beneficial to all other users of this data.

Additionally, to address topic 2 above three key ANUDEM parameters were optimised, including: (a) output grid resolution; (b) number of iterations; and (c) the amount of profile curvature used as governed by the 2nd roughness penalty. The ability to change the amount of profile curvature is new in ANUDEM Version 5.1 and careful analysis of its impact was needed. To perform this optimisation, five DEM related attributes were assessed; the first four involved analysis of the output DEM, whereas the final assessment was independent validation. To perform this independent validation, we made use of higher resolution (1:100,000) spot height data available for the YanHe Basin (YHB). The YHB lies within the 1:250,000 Coarse Sandy Hilly Catchments (CSHC – see Figure 1). Prior to conducting the spot height analysis, we


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minimised the positional differences between the CSHC 1:250,000 input data and the 1:00,000 YHB data.

Conducting the input data quality improvement in an iterative manner, using the ANUDEM output data-files and diagnostic log-files, meant that over 1,100 input data errors were identified and corrected. This, coupled with optimisation of several key ANUDEM parameters, means that the resultant DEM for the CSHC will be of high quality, as it will have been generated using the current best available topographic and best practice algorithm. It should also be noted that the ANUDEM algorithm will continue to be iteratively improved (as has been done in the past) with new releases capturing the enhancements to the algorithm. The best available input topographic data set developed here can be used in the future with new releases of ANUDEM to develop a new (and hopefully improved) DEM. Also the future releases of ANUDEM may contain improved error checking diagnostics, thereby allowing the input topographic data to be further improved.

The validation spot height analysis will also allow for multi-resolution comparison studies to be conducted (specifically with the YanHe Basin and CSHC DEMs, see Figure 1 and Table 1). A large positional offset would prohibit a direct comparison between two independent datasets because it forces matching landscape positions in the two datasets (e.g., hill top-to-hill top, mid slope-to-mid slope, or valley bottom-to-valley bottom) to be in different geographical space, and thus potentially causes a mismatch between these features (e.g., hill top-to-mid slope, or mid slope-to-valley bottom). This means that when one dataset is, for example, subtracted from the other, the differences in elevation values are not very realistic, as they are at least partly (and sometimes mostly) caused by positional offset between the two datasets. However, if this positional offset is measured and subsequently minimised to within an acceptable distance (e.g., within 1 pixel), then any direct comparison will more closely match associated map features on the two separate


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datasets and the results will be more attributable to actual differences than to the confounding influence of mis-matched landscape positions. The methods developed in this report can also be used when generating the other higher resolution contour and spot height based DEMs, see Table 1. While the 1:250,000 input databases used to generate the CSHC DEM cover the entire Loess Plateau (Figure 1), this report only discusses the DEM development for the CSHC study area.

2

Figure 1. The insert map shows the location of the 623,586 km Loess Plateau (shaded) on the middle reaches of the Yellow River that supports a population of 82 million people (Xiubin et 2 al. 2003). The main map shows the location of the 112,728 km Coarse Sandy Hilly Catchments (CSHC), with the location of the YanHe Basin (YHB) test area shown by the dark grey rectangle overlaid on the main map.


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Table 1. Listing of contour based datasets from which DEMs can be derived; similar data used for the CSHC has been obtained for the entire Loess Plateau (see Figure 1). The column labelled CI is the contour interval and in the comments column, NGCC represents the National Geographic Centre of China, and ISWC is the Institute of Soil and Water Conservation; the name of the lead authors’ organisation. Note that the ‘area’ column is the rectangle that the DEMs will be developed in to cover the entire catchment or study area (which is given in brackets below). Name of Dataset (Abbreviation) Coarse Sandy Hilly Catchments (CSHC) * YanHe Basin (YHB)

Area 2 (km ) 215,055 (112,728)

Map Scale 1:250,000

Publication Date 1975-1986

CI (m) 100

20,169 (7,673)

1:100,000

1986

40

Comments Data obtained from NGCC.

Contours, spot heights and rivers digitised by ISWC. Yan’an Demonstration Area 2,475 1:50,000 1977 20 Contours digitised by (YDA) (1,200) ISWC, spot heights and rivers still to do. JiuYuanGou Area 1 369 1:50,000 1977 20 Contours digitised by (JYG_1) (369) ISWC, spot heights and rivers still to do. ChaBaGou Catchment 456 1:10,000 1977 10 Contours digitised by (CBG) (211) ISWC, spot heights and rivers still to do. JiuYuanGou Area 2 101 1:10,000 1977 10 Contours digitised by (JYG_2) (101) ISWC, spot heights and rivers still to do. YanGou Catchment 120 1:10,000 1977 10 Contours digitised by (YG) (47) ISWC, spot heights and rivers still to do. XianNanGou Catchment 140 1:10,000 1977 5 Contours, spot (XNG) (44) heights and rivers digitised by ISWC. * Over 85% of the contours in the CSHC have a 100 m CI; see Table 2 for more information.

Figure 2 shows the locations of a series of photos illustrating typical terrain of the CSHC (and also areas where more detailed analysis are performed in this report); Figure 3 shows the photos themselves. The report is organised as follows: the topic was introduced in Section 1; Section 2 discusses the processes used to correct the 1:250,000 input topographic data from which the CSHC DEM is generated; Section 3 describes the optimisation of key ANUDEM parameters; Section 4 discusses minimising of positional accuracy for the higher resolution YHB 1:100,000 data that were used for independent validation in Section 3; and Section 5 discuses the outputs, including the generation of a set of 42 sub-catchments compromising the CSHC and


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qualitative comparisons between DEMs generated using the ANUDEM and TIN algorithms. Conclusions are drawn in Section 6.

Figure 2. Locations of photos presented in Figure 3 are shown, and the shaded areas are the locations of detailed areas used in Figure 4, Figure 8 and Figure 16. The dotted box is the YHB test area, and the boundary of the CSHC is given by the solid line.

Figure 3. Twelve photos showing the typical landforms of the CSHC, see the following 2 pages.


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(a) Typical landform in the hill-gully portion of the CSHC, showing 30 year old trees planted on the ridge, with farmer houses built into the hill-side, soybean and vegetable crops in the foreground, with the meteorological instrumentation of Ansai field station being examined by Tim McVicar. Ansai, th 5 October 2004, photo by Tom Van Niel.

(b) The Suide Bureau of Soil and Water Conservation meet the ACIAR project team in a sediment earth dam over-flow channel. Apples are grown on the dam wall, with high yielding crops (corn, sorghum millet, and market garden th vegetables) grown on the flat land behind the dam. Suide, 8 Oct 2004, photo by Tim McVicar.

(c) Re-vegetation occurring on the steeper slopes in the th Yan’an area, 4 Oct 2004, photo by Tim McVicar

(d) Re-vegetation planting in a steep narrow (< 50m) gully. th Jingbian, 5 Oct 2004, photo by Tim McVicar.

(e) Dune fields subject to wind erosion typical of the flatter (f) Re-vegetation to stabilise dunes. Crop stubble is bound Ordos Plateau landscape; northwest of the CSHC study area. into a rope that is laid out in a grid, and individual plants are th th Yulin, 5 Oct 2004, photo by Tim McVicar. planted at each grid intersection. Yulin, 5 Oct 2004, photo by Tim McVicar.


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(g) Typical gullied landscape in Suide county, modified by (h) An extremely steep slope (almost vertical for some 200m) terraces to reduce the speed of surface runoff andthhence that meets the base rockth at the lower level of the loess reduce water erosion and fertilizer loss. Suide, 8 Oct 2004, deposition. Jingbian, 5 Oct 2004, photo by Tim McVicar. photo by Tim McVicar.

(i) Typical landscape showing how aspect is important to determining the location of re-vegetation. The person (Ms Liu YongMei) in the left mid-ground provides relative scale. th Pianguan, 7 Oct 2004, photo by Mu Xingmin.

(j) A typical hilly-gully landscape, showing agricultural activity near the village in the valley, with some steep gullies th extending to almost the ridge line. Suide, 8 Oct 2004, photo by Tim McVicar.

th

(k) Typical land colour near and after the harvest season in the (l) Sediment trapped in a small earth-dam. Jingbian, 5 Oct th heavily cultivated Loess Plateau. Suide, 8 Oct 2004, photo 2004, photo by Tim McVicar. by Tim McVicar.


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2. Correcting the CSHC input data Creation of the CSHC DEM consisted of two basic steps: (1) quality control and correction of the input topographic data (i.e., contours, spot heights, rivers, and lakes) and testing the impact of these various versions on the quality of the output DEM; and (2) running ANUDEM and testing the impact of various ANUDEM parameters on the quality of the output DEM. This current section concerns the first step, while section 3 deals with the second step.

All corrections made to the CSHC source datasets were performed over the entire CSHC area. However, when optimising the ANUDEM parameters meant that multiple runs of ANUDEM were performed. The entire CSHC site required 8 hours of processing time to generate the full CSHC DEM at 100 m x- and y- resolution (using a PC with a 1.6 GHz processor, and 768 MB of random access memory (RAM)). Therefore, to assess the influence of input data corrections and the optimising of ANUDEM parameters, we limited our processing to the YHB test area, see Figure 1 and 2. The YHB test area is representative of the overall landscape, and is a small enough area to create a 100 m resolution DEM using ANUDEM in less than 30 minutes on a computer with the above specifications. Using a small test area allowed for multiple tests to be run relatively rapidly and greatly reduced the disk space required to store these various tests. Any mention of the term ‘test’, ‘test area’, or ‘YHB area’ in this section is thus associated with the processing of the CSHC data over the YHB area only.

The input data densities of the key input datasets for the YHB test area are slightly higher than those for the entire CSHC (see Table 2) due to the relatively flat Ordos Plateau (see Figure 3e and Figure 3f) that is located in the north-west of the CSHC (and extends past the catchment boundary of the CSHC to the north-west). In Table 2 data density summaries of similar data from Canberra, Australia reveal that the CSIRO Land and Water

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Canberra data densities of contours and spot heights are less than the same metrics for both the entire CSHC and the YHB test area. However, for the river network it should be noted that the density from the Canberra region was higher than for both China areas, see Table 2. Given that the local relative relief in the Loess Plateau is higher than that for the Canberra region this river density result was surprising, but is thought to be due to the higher quality of the original Australian river network maps (i.e., most, possibly all, 1st order streams were captured in the original paper maps) than that of the mapping of the river network for the Loess Plateau (i.e., it is likely that not all 1st order streams were captured on the paper maps). For both countries the river network on the original paper maps were fully captured digitally. Results presented later (Figure 21) empirically confirm that the relatively lower data density of the river network in the input CSHC 1:250,000 dataset compared to the stream network derived from ANUDEM is mainly driven by the data density of the input contour data. The input source data to ANUDEM consisted of four basic datasets. These were: (1) contours; (2) spot heights; (3) rivers; and (4) lakes and reservoirs. Throughout the many iterations of processing data over the entire CSHC area, a few basic problems with the source datasets were observed and subsequently resolved. Primarily, these consisted of: 1. contours containing the wrong elevation attribute; 2. contours that were digitised incorrectly (e.g., where writing on the source map obscured the contour below it, sometimes contours were connected when they should not have been or alternately unconnected when they should have been connected); 3. spot heights containing the wrong elevation attribute; 4. spot heights in the wrong position; 5. rivers and lakes were originally not separated; 6. rivers flowing in the wrong direction (i.e., flowing uphill); CSIRO Land and Water

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7. man-made canals included in the river network resulting in spurious DEM values; and 8. large rivers were represented by two lines (one on each bank) instead of a single line down the centre (all smaller rivers were represented as single lines). Table 2. Comparing the data densities of the input datasets for the: (1) entire CSHC; (2) YHB Test Area; and (3) Canberra, Australia. For each area the statistics for the contours, spot 2 heights and rivers datasets are given. The units of the column ‘density’ are km/km for the 2 contours and rivers, and points/km for the spot heights. For spot heights it is not applicable (n/a) that there be a length statistic reported. Data type

Number

Length (m)

Density

64,897

507,318,441

2.359

100m

55,612

450,387,154

2.094

50m

9,285

56,931,288

0.265

Spot heights

4,903

n/a

0.023

Rivers

17,204

55,684,294

0.259

2

Entire CSHC (215,055 km ) Contours (All)

*

2

YHB Test Area (20,169 km ) Contours

11,684

78,947,458

3.914

Spot heights

461

n/a

0.023

Rivers

3,267

8,720,177

0.432

**

2

Canberra, Australia (15,099 km ) Contours

3,570

26943199

1.784

Spot heights

251

n/a

0.017

Rivers

3,703

10088606

0.668

* For the CSHC dataset the contours with an interval of 100 m are 85.69% of the total number and 88.78% of the total length. There are no 50 m interval contours in the YHB test area. st

** From Geoscience Australia http://www.ga.gov.au/bridge/products.jsp (accessed 31 March 2005), the Map Sheet Code is I-55-16, the map sheet covers the area from 148.5°E to 150.0°E and -35.0°S to -36.0°S, the data layer identification codes are: contours (i5516cld); spot heights (i5516eld) and drainage (i5516dld). The analysis was performed in an Albers projection using st nd the Australian National spheroid, with a central meridian of 132°E and 1 and 2 standard parallels of -18.0°S to -36.0°S, respectively. There was no false easting or northing. The map scale was 1:250,000 and the contour interval was originally 50 m. However in this research, to compare the Australian dataset with the CSHR dataset (1:250,000 and contour interval of 100 m), we only used contours that were a multiple of 100 m.

As the resultant DEM generated using ANUDEM will be used: (1) to define the sub-catchments of the study area; (2) to define the river network; (3) to define the areas contributing to measurements made at the hydrology stations located on the CSIRO Land and Water

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river network; (4) as a covariate for interpolating surface meteorological variables; (5) in net radiation modelling using the SRAD model that takes into account slope and aspect (Moore et al. 1993; Gallant 1997; Wilson and Gallant 2000); and (6) as a basis for re-vegetation site selection, it is concluded that the DEM is a fundamental dataset; improving the DEM will improve all subsequent spatial analysis conducted for the CSHC in our ACIAR project (and for many other projects). Due to this, strategies were designed to ensure that the DEM was of the highest quality as possible by identifying, and either correcting or deleting, the incorrect features. These issues are addressed below.

2.1. Contour and spot height correction The various contour and spot height errors were mainly addressed by two assessments. The first was to compare large residual points (output by ANUDEM in the log file) to the DEM value at the same location; this would allow for identification of mislabelled spot heights or contours as well as misplaced spot heights and incorrectly digitised contours. The second consisted of identifying inordinately large slopes from the output DEM in order to detect additional mislabelled contours; as these large slopes were not necessarily near a spot height.

ANUDEM outputs a text file containing the locations of any input spot heights that have large residual error (Hutchinson 2004a); we refer to these points as ‘residuals’ or ‘residual points’. These large residual points were imported into ArcInfo by using the ArcInfo generate command. Once the points were converted to a GIS dataset, they were linked to the original spot height coverage based on their location, which allowed the original spot height value of all the residuals to be accessed by a field in the topographic dataset; we refer to this field as the ‘original elevation’. Another field was added to the GIS dataset which contained the ANUDEM elevation value at all of the residual points by using the ArcInfo latticespot command; this field is called the ‘DEM elevation’ here. We then selected all of the residuals that had a difference between CSIRO Land and Water

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the original elevation and the DEM elevation of greater than or equal to one-half of the contour interval or 50 m in this case. We decided on one-half the contour interval as this is the expected vertical accuracy as stated in Australian and United States of America (denoted US in the following) map accuracy standards. Chinese standards were not known, but were expected to be the same or very similar to the Australian and US values.

For the entire CSHC area, 2,097 residuals were defined by ANUDEM. Of these, 155 had a difference between the original elevation and the DEM elevation of greater than or equal to 50 m. Each of the 155 points were manually inspected for errors. In addition to checking the elevation of the points, the surrounding contours were also assessed. If, with respect to the surrounding contours, the spot height was in the correct position (or a possibly correct position), and labelled correctly, and all the contours progressed in elevation value incrementally as expected, then no action was taken. This result was fairly common as the rather coarse output pixel size of the DEM (100 m x- and y-resolution was used) in combination with the relatively high contour interval density would sometimes not allow for the ANUDEM algorithm to change elevation values enough to match both the contour and spot height data. That is, sometimes the spot height elevation at the top of a hill had too much vertical relief between itself and its closest contour when compared to the length of an output pixel (100 m) for it to be adequately reflected in the output DEM. This means that while the residual error was greater than 50 m there were no problems with any of the input data; this was the case for 67 of the 155 residuals manually checked.

When there was an inconsistency between the position of the spot height and the surrounding contours, as long as the contours progressed as expected, the spot height was deleted; this action was required 82 times. When the high residual error was caused by a mislabelled contour, the contour attribute was corrected; this occurred 7 times (i.e., at 7 residual points). Finally, there was only 1 residual that was caused by


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an incorrectly digitised contour. This contour was corrected based on the general shape of the nearest neighbour contours. This amounts to 90 actions for the 155 points. This included one residual point that required both the spot height be deleted and a contour attribute be fixed, and one high residual where some contour digitisation was required as well as an adjustment to a contour attribute. Hence, there were 2 extra actions as compared to the number of areas requiring some editing.

(a)

(b)

(c)

(d)

Figure 4. The slope surfaces were used to detect contour errors. The uncorrected slope data are shown in (a) where several incorrectly labelled contours were identified by extremely high slopes (bright white contours). This allowed the mislabelled contours to be selected in (b) where the thick red contours were labelled 1,050 m but should have been labelled 1,400 m – the thick blue contour was labelled 1,700 m but should have been labelled 1,650 m. These mislabelled contours were then corrected in (c), which allowed for a corrected DEM to be calculated as evidenced by the realistic slope surface in (d). The slope units are degrees above the horizontal ranging from 0° (black) to 57° (white) for both (a) and (d), whereas the contours have units of metres above mean sea level increasing from green (1,050 m), though yellows to pink (1,800 m) for both (b) and (c). The location of this area is shown in Figure 2. CSIRO Land and Water

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Additionally, slope of the output DEM for the entire CSHC area was calculated and inspected for areas of exceedingly large slopes, defined here as slopes > 39° from horizontal (this threshold would obviously change for higher resolution DEMs which would better represent the extreme slopes present in the CSHC). These very large slopes invariably identified mislabelled contour lines, as these very large slopes predominantly occurred in the same shape as the shape of a contour line (or part of a contour line). This assessment was particularly good at finding mislabelled contours in areas that had no spot height data near it. Figure 4a is an example of a slope surface where several mislabelled contours are clearly evident. After selecting the mislabelled contours in the input data (Figure 4b) and correcting them (Figure 4c), a more realistic DEM and slope surface can be produced, see Figure 4d. This analysis of extreme slopes found several additional contour lines requiring correction of the labelled contour elevation that had not been identified in the previous check of residual points.

2.2. River and lake correction It was seen in the ANUDEM tests (see section 2.3. below) that running ANUDEM without separating the rivers and lakes into separate inputs, resulted in inappropriate processing of the DEM. Therefore, one of the first corrections made was to simply separate these two data sources. The lines comprising the boundaries of lakes were determined manually in ArcEdit, and given a unique identifier (cov-id = 72). These lake lines were selected and saved to a separate topographic dataset, in which they were made to contain polygon topology in preparation for exporting (using the ArcInfo ungenerate command). Next, it was noticed that there was a great deal of man-made canals along some of the bigger rivers. These canals, being man-made, should not be used to enforce drainage by the ANUDEM algorithm (Hutchinson 2004a), and were manually selected and removed from the rivers topographic dataset.


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Next we noticed that some of the rivers (or certain segments of the rivers) were flowing in the wrong direction. An attempt was made to identify these manually, and correct them using the ArcEdit flip command. However, since there were so many lines to check (> 24,000) it was not feasible to perform this manually for the entire 215,055 km2 rectangle area containing the CSHC, see Table 1. Therefore an arc macro language (aml) program (called check_fnodes.aml) was written to identify many possible locations where parts of the river could be flowing in the wrong direction; see Appendix A and Figure 5a. The aml finds any river intersection points that contain more than one ‘from node’ (FNODE). The occurrence of more than one FNODE at a single point indicates that the specific intersection has more than one segment flowing out of it (Figure 5b). It is possible for this combination to be correct (if a single stream splits into two), but is most often a result of an error (in most common confluence intersections). Exactly 1,057 occurrences were identified as possible errors, each was manually checked, 805 were confirmed as errors (66 of these were located in the YHB test box) and all 805 were flipped, the remaining 252 were single streams that then split. These, then, were not errors and hence no action was needed. Many of these splitting streams were located in the relative flat Ordos Plateau in the north-west of the CSHC (see Figure 3 photos e and f). Another way of identifying streams that flow in the wrong direction would be to overlay sinks on the stream network. Sinks located at the end of a stream are likely to indicate that the stream is flowing in the wrong direction; we did not perform this check, but will do so for any subsequent processing of the higher resolution datasets.


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(a)

(b)

(c) Figure 5. In (a), a hypothetical example of a river flow direction error is shown. In this example, the red river segment is flowing in the wrong direction. “From nodes” (FNODEs) are represented by circles and arrow heads represent “To nodes” (TNODEs). FNODEs are the start of the line and TNODEs are the ends of lines. The check_fnodes.aml searches each river intersection and identifies those that have more than one FNODE, like the intersection depicted by the red filled circle above. In (b), a hypothetical example of an error not identified by the check_fnodes.aml due to a ‘dangling’ arc is shown. This results in only one FNODE at any intersection even though there is a river segment still flowing in the wrong direction. In (c), a hypothetical example of an error not identified because of a missing node intersection is shown. The red rectangle identifies the likely location of where the arc should be split (i.e., insert a node).


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There are some known flow direction errors that were not identified by check_fnodes.aml, but were fixed whenever they were identified by chance. For example, the check_fnodes.aml, program would not identify the flow direction error if the river segment in error was not actually intersecting the river network on both ends (these are also called ‘dangling arcs’ in ArcInfo, see Figure 5b). That is because no river intersection in Figure 5b has more than one FNODE. Also, if a river segment is missing a node (i.e., the single line actually represents two river segments and should be split), then the error is not identified because, again, it does not result in an instance of multiple FNODEs – see Figure 5c.

Finally, many large rivers in the river network were represented by two lines (one on each bank) as opposed to a single centreline, as was the case with all of the smaller rivers making up the vast majority of lines in the network. This posed a problem with ANUDEM processing and needed to be fixed. We attempted to use the ArcInfo centreline command, but the resulting single-line rivers were not sufficient. Therefore a series of avenue scripts were created in order to help define these centrelines. The main script is called ‘centreline_using_circle.ave’ – see Appendix B. Before running the script, all the dual-line rivers were manually selected and given a unique identifier (cov-id = 711). These dual-line rivers were exported to a separate topographic dataset and lines on opposing sides of the river were given unique attributes. In this case, a field called ‘side’ was added to the database and opposing sides of the river were arbitrarily labelled as either a ‘1’ or a ‘2’ (but in each case an entire side was labelled 1 and the entire opposite side was labelled 2). The script then separated all lines whose side was equal to 1 into equal 100 m segments; these points are referred to as the ‘originating side’ or ‘originating side points’. Then, all lines with a side equal to 2, were separated into equal 10 m segments; these are referred to as the ‘opposing side’ or ‘opposing side points’. For each originating side point, a circle was created with a radius of 50 m. This circle was iteratively grown by adding 10 m to the radius until it intersected with any point(s) from the opposing side. The locations of all


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intersecting opposing side points were averaged to represent a single location for the opposing side. Then, the originating side point and the single opposing side point were averaged to define the centre point. This was repeated for every originating side point in turn, so the density of centre points was basically 100 m (the same as the originating side density).

The centre points were exported to a ‘multipoint’ GIS dataset. This multipoint dataset was exported as a point topographic dataset using the script called ‘mp2pnt.ave’ (Appendix C) so each point could be accessed individually. A field called ‘attribute’ was added to this point dataset and all points belonging to a single river reach were selected and given a unique attribute number (up to 187 reaches were manually selected). Any points that were outside of the original dual-lines were not selected. These points were converted back to 187 multipoints using the script called ‘pnt2mp.ave’ (Appendix D) and finally to their associated 187 ‘polylines’ using the script called ‘mp2plyline.ave’ (Appendix E). The polyline dataset was then edited where required to remove any extraneous lines, and merged with the other single-line rivers. The new single-line topographic dataset (shapefile) was converted to an ArcInfo coverage using the ArcInfo shapearc command. Exactly 12,047 nodes in this new single-line coverage were snapped to their closest arc within 400 m using the ArcEdit snap command (after selecting ‘dangling’ arcs). This single-line rivers coverage was then merged with the lakes and the dual rivers coverages so that all of the original data were retained. The check_fnodes.aml macro was run again to ensure that any of the new single-line rivers that were running in the wrong direction were flipped (33 multiple fnode intersections were found and manually checked and corrected if necessary). Each different kind of water body was given a unique identifier; cov-id for single rivers equals 71, cov-id for lakes equals 72, and cov-id for dual rivers equals 711. Only the single rivers and lakes coverages were ungenerated in ArcInfo for input into ANUDEM.


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2.3. Impact of data corrections on the resulting DEM quality Without assessing the impact of correcting the input datasets on the resultant DEM quality, it would not be known whether performing such laborious and time consuming actions were necessary. Therefore, after correcting for the abovementioned input data errors, it was necessary to somehow measure DEM data quality improvement. We decided that calculating the number of single-cell and multiple-cell sinks and some summary statistics of their frequency distribution was a good metric (or diagnostic) of the resultant DEM data quality. That is, if after each step in our data correction procedure, above, the number of sinks and the mean depth of these sinks decreased, then we would feel confident that the DEM quality was improving. However, we note that this metric is probably only really useful for estimating coarse-level changes, where smaller changes might not be detectible because all sinks are not a result of an error in the DEM. First we describe how the sink numbers and frequency distributions were calculated and then we report these values for the various steps in the data correction procedures documented above.

2.3.1. Calculating sink number and frequency distributions As a standard output of ANUDEM, a text file including the location of sinks is created. These sink locations can be imported into a GIS dataset using the ArcInfo generate command; these are referred to as ‘ANUDEM sinks’ or ‘ANUDEM sink points’ hereafter. It was decided to use the number of sinks in every output DEM as the main indicator of data quality, and as the area used for testing ANUDEM remained the same (i.e., the YHB test area), quantifying the number of sinks allowed for a direct comparison between various tests. However, after inspection of some output DEMs, we realised that many of the ANUDEM sinks were very small (some on the order of < 1 μm to < 1 m). As a consequence, we decided to also describe the frequency distribution of these sinks as the overall number of sinks was probably inadequate on its own in describing the data quality of the output DEMs. That is, considering that the


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accuracy of the input contour data is around 50 m and ANUDEM considers sinks to a precision of μm, there was a mismatch between the accuracy of the input data and the precision to which errors were being summarised in ANUDEM.

Therefore, we have not used the ANUDEM sinks as the indicator of data quality, but rather use ArcInfo to calculate some sink diagnostics. Also, we not only can report the overall number of sinks in ArcInfo, but can also summarise the numbers of sinks based on a few critical thresholds which are more meaningful when compared to the accuracy of the source data. In order to do this, it required using the ArcInfo fill command (the output is called the ‘filled DEM’). We then compared each filled DEM to the input ANUDEM DEM (called the ‘original DEM’). The difference between the filled DEM and the original DEM (called the ‘difference DEM’) gave us the boundary of all of the sinks for that particular test. Since the difference DEM needs to be integer data to summarise the frequency distribution, sinks smaller than some predetermined value were eliminated in the process (e.g., < 1 mm was used in this research – see below). Since many sinks in the original DEM would not just be defined by a single-cell, the sink boundaries defined from ArcInfo (called ‘ArcInfo sinks’) contain both single- and multiple-cell sinks. These ArcInfo sinks were then summarised based on a zonalstats command in ArcView using the sum_grid.ave avenue script (see Appendix F). These statistics were summarised in excel separately for single-cell sinks, multiple-cell sinks, and all sinks together. The multiple-cell sinks were summarised based on both the mean depth of each sink and the maximum depth of each sink. The main steps of this process are listed below: 1. Fill the original DEM using ArcInfo (results in ‘filled DEM’); 2. Subtract original DEM from filled DEM, multiplied by 1,000 and integerised (i.e., [[[filled DEM - original DEM]*1,000].int]; results in ‘difference DEM’ – in this way, the number of sinks less than 1 mm are not counted in the final summary – they are removed here;


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3. Discretise the difference DEM to 1s and 0s in order to define boundaries of sinks (i.e., sinks are not always single cell); results in ‘boundary GRID’; 4. Convert this boundary GRID to boundary polygons; results in a shapefile called ‘boundary GIS dataset’; 5. Use of sum_grid.ave (Appendix F) to determine statistics for each ArcInfo sink; and 6. Export the dbf of the boundary GIS dataset to an excel file to summarise the frequency distribution.

2.3.2. Results for the iterative improvement of the input datasets Table 3 shows the improvements by reducing errors in the input CSHC 1:250,000 data sets when generating a DEM for the YHB test area. From these results we see that, as expected, including the river network has a major impact on the resultant DEM quality. Also, separating the rivers and lakes resulted in a big reduction in the number of sinks and an improvement in DEM data quality. The other data error corrections did not have as much of an impact as these first two over the entire site. Some of the summary statistics changed in the ‘wrong’ direction after a correction (e.g., Num. of Single-cell Sinks after fixing the 187 double river reaches to become single river arcs). However, as discussed above, using the sink metric as the only measure of DEM data quality is inadvisable because sinks are not always caused by an error in the DEM. Therefore, the small increases and decreases in the number of sinks for the last few rows of Table 3 are probably not hydrologically significant and show that the sink metric is not as useful for measuring small-level difference over the entire area. These corrections however should make a relatively large (yet localised) difference to the quality in those areas. Carefully finding as many errors (as possible) in the input datasets and either fixing them (or deleting them) iteratively improved the final CSHC DEM. Next we discuss the optimisation of several key ANUDEM parameters.


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Table 3. Number of single- and multi-cell sinks (and some summary statistics for each case) as a function of reducing errors in the input datasets to develop the DEM for the YHB test area using the CSHC data are shown. For the ‘single-cell sinks’ cases Num., Min., Max., and Num. > 10 m represent the number, minimum, maximum, and number where the difference between the sink and its 8 neighbouring cells is greater than 10 m. For the ‘multi-cell sink’ cases average depth of all cells in the sinks are calculated first and then descriptive statistics are then generated. All ANUDEM parameters were default values (i.e., number of iterations = 20 and nd 2 roughness = 0.0) and a spatial resolution of 100 m was used in all cases. Single-cell Sinks Num.

Min.

Max.

Mean

Multi-cell Sinks Std

Num. > 10m

Num.

Max. Avg Depth

Mean Avg Depth

Std Avg Depth

Avg Num. Cells

8.09

5.88

7.76

4.33

3.61

7.90

Original Data as obtained from NGCC (12,840 Contours and 446 Spot Heights) 7,138

2.89

57.31

22.29

8.60

6,898

14,260

44.83

Original Data with the additional 3,610 waterways arcs, and 96 polygons 4,081

1.51

42.10

15.25

9.74

2,472

7,506

38.98

Waterway arcs separated into rivers (3,208 arcs), lakes (24 polygons) and canals (378 arcs not used) 2,785

1.37

42.09

9.93

4.23

1,110

7,147

19.57

3.95

2.65

7.70

Flow Direction for 66 rivers arcs and id (river or lake) for 55 arcs have been corrected 2,576

1.38

42.07

9.40

3.47

952

7,041

17.13

3.90

2.62

7.36

3.89

2.60

7.31

3.89

2.60

7.48

Elevation attributes for 45 contours corrected and 2 spot heights deleted 2,567

1.35

42.07

9.43

3.42

957

7,058

18.67

The 187 double river reaches edited to become single river arcs 2,618

1.65

42.06


9.40

3.44

959

7,036

16.69

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3. Optimising key ANUDEM parameters Three ANUDEM parameters were tested in order to define optimal values for the CSHC dataset. These were tested in the YHB test area and include: (1) spatial resolution; (2) the number of iterations; and (3) the relative contribution of profile and total curvatures to the roughness penalty (governed by the ANUDEM 2nd roughness penalty parameter). The default values provided in the ANUDEM Version 5.1 manual were used for all other parameters. The three optimisation analyses are described in turn.

3.1. Determining the optimum spatial resolution A key factor in most spatial modelling exercises is determining the optimal resolution. A heuristic rule (also termed a ‘rule of thumb’) developed by one of the authors (M. F. Hutchinson) is that the output DEM x- and y-resolution is 10-3 of the map scale. For example, using contour data at a scale of 1:100,000 the output grid resolution would be 100 m, whereas contour data generated from a 1:250,000 scale map would result in a 250 m resolution grid cell. Hutchinson and Gallant (2000) provide a method to analyse the information content of the input data to assist in selecting the final output grid cell resolution. This analysis, conducted on the YHB test area is shown in Figure 6a, where a distinctive break-point was not present in these data (contrary to the example of Hutchinson and Gallant). As the CSHC is a landform of rapidly changing slopes (see Figure 3) we wanted to generate a DEM with the highest x- and y-resolutions that the input data could readily support; to enable better modelling of slopes and meteorological surfaces. Consequently we assessed the rate of change between successive spatial resolutions (see Figure 6b) and also calculated the number of sinks (see Figure 6c). From this analysis it can be concluded that an output resolution of either 50 m or 100 m provided the optimal information content. While this finding is a much higher x- and y-resolution than the previously introduced heuristic rule, given the high input data density present for the CSHC (see Table 2 CSIRO Land and Water

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above) it seems sensible that the data can support a 100 m x- and y-resolution DEM. For geomorphic analysis it could be argued that the data support a 50 m x- and y-resolution DEM (see Figure 6b and 6c). However, such a high resolution DEM generated from this dataset might contain more ‘noise’ than is acceptable and this would largely depend upon local data densities. Additionally the DEM would be 4 times as large to store on disk. This becomes a significant issue when generating monthly meteorological surfaces (not averages) for 21 years (McVicar et al. 2005). In the following sub-sections all runs of ANUDEM were performed at 100 m x- and y-resolution.

(a)

(b)

Figure 6. Impact of spatial resolution for the YHB test area on: (a) the RMS of the slope; (b) the rate of change (or slope) of the RMS slope data presented in (a); and (c) the number of sinks.

(c)

3.2. Determining the number of iterations ANUDEM produces a statistic (called the ‘number of new lines’) that is an indicator of the resultant DEM stability, for a specific data set and selection of ANUDEM input CSIRO Land and Water

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parameters. The ‘number of new lines’ are the inferred ridge and stream lines from the contour data, and is essentially a measure of numeric stability of the ANUDEM algorithm. ANUDEM initially produces a low resolution DEM for the spatial extent (this is stored in memory). A series of higher resolution DEMs are also produced in memory (by progressively halving the resolution of the output pixels), resulting with a DEM possessing the user input x- and y-resolution that is written to disk. The theory underpinning the use of this ‘nested grid SOR (successive over-relaxation) iterative method’ is provided by Hutchinson (2000). For each grid resolution, the ‘number of new lines’ generated from the penultimate and ultimate iteration is reported in the ANUDEM log file. Small values indicate that the resultant DEM is stable as consecutive iterations produced similar DEMs, and therefore, few new lines were added to describe the profile between ridgelines and streamlines. If this value is not small (a typical low value that may be deemed appropriate is less than 10) then the ANUDEM user specified ‘maximum number of iterations’ may need to be increased. At each grid resolution, the user specified ‘maximum number of iterations’ governs the number of iterations available to ANUDEM to produce a stable DEM before progressing to the next finer resolution DEM.

For results performed in the YHB test area we found that the ‘number of last new lines’ using 20 iterations (the default value suggested in the ANUDEM Version 5.1 manual) was approximately 400. This showed that a stable solution was not being generated; we expect this to be solely due to the complexity of the terrain in the CSHC. Consequently we examined the impact of changing the ANUDEM ‘maximum number of iterations’ user input; the iteration number was varied from 10 to 120, in increments of 10. Figure 7a illustrates that from 40 iterations onwards provides a satisfactorily low number of ‘last new lines’. It should be noted that the improvement from 10 to 20 iterations was the most dramatic (see Figure 7a), the improvements beyond the default value of 20 are marginal, yet important for this case. Given that the number of iterations is linearly related to the computational time – see Figure 7b – we decided


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that setting the ANUDEM maximum number of iterations user input to 40 was necessary to allow the representation of the landscape to become stable given the complexity of the landform within the CSHC.

(a)

(b)

(c)

(d)

Figure 7. Impact of ANUDEM ‘maximum number of iterations’ user input on: (a) the number of last new lines; (b) running time; (c) number of drainage enforcements; and (d) number of sinks.

Further analysis of the impact of the ‘maximum number of iterations’ user input on two ANUDEM characteristics, the number of drainage enforcements and number of sinks, are shown in Figure 7c and Figure 7d, respectively. From the results presented in Figure 7a to Figure 7d we see that using 40 iterations is a pragmatic balance that minimises the ‘number of last new lines’ and sinks, while enforcing drainage, while not taking too long to process. In the following sub-sections all runs of ANUDEM are performed using 40 iterations.


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3.3. Determining the relative amounts of profile and total curvatures The profile curvature, defined as the curvature of the fitted surface in the downslope direction (Gallant and Wilson 2000), is newly introduced in Version 5.1 of ANUDEM (Hutchinson 2004a). The profile curvature, which is locally adaptive (Hutchinson 2000), can be used to partly replace the total curvature. This is controlled by the user specifying the 2nd roughness penalty. Values for the 2nd roughness can range from 0.0 to 0.9; a value of 0.0 (the default) means only total curvature is used (there is no profile curvature used), whereas a value of 0.9 means that 0.1 of total curvature and 0.9 of profile curvature is used.

Given that profile curvature is newly introduced in Version 5.1 of ANUDEM with users being encouraged to make their own assessment of the optimum value for their data sets, and that profile curvature allows better representation of rapid changes in gradient (a common phenomena in the CSHC), we examined the impact of the 2nd roughness penalty on the resultant DEM in 5 specific ways. They were: (1) assessing changes at specific locations in the landscape; (2) conducting stream network analysis; (3) performing ‘new line’, drainage enforcement and sink analysis from the ANUDEM diagnostic log-file; (4) determining the changes in the elevation and slope for the entire DEM; and most importantly (5) using independent higher resolution 1:100,000 spot height data as a validation dataset. The first four cases are essentially careful analysis of the resultant output DEM or diagnostic log-file, while the last case (5 above) is an independent validation using higher resolution data. Each will be discussed in turn. In all cases the number of iterations was set at 40, the spatial resolution was 100 m, and the highest quality input data set was used; all have been discussed previously.


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3.3.1. Assessing changes at specific locations in the landscape In the southern portion of the CSHC (specifically in southern part of YanHe Basin – see Figure 8, near Yan'an) an area covering 487 km2 (25.29 km north-south and 19.29 km east-west) was selected for detailed examination of changes in elevation of specific landscape groups resulting from changing the 2nd roughness penalty. The five landscape groups are: (1) hill tops (HT); (2) saddle points (SP); (3) heads of gullies (HG); (4) stream outlets (SO); and (5) ridges (R). The locations of the 122 points are shown in Figure 8.

Figure 8. The locations of the points identifying the five landform types: (1) hill tops (HT); (2) saddle points (SP); (3) heads of gullies (HG); (4) stream outlets (SO); and (5) ridges (R) are shown. The blue lines represent the input river network and the brown lines represent the input contour data with a 100 m interval. The background grey surface is the elevation, which ranges from 925 m to 1,450 m above mean sea level. The location of this area is shown in Figure 2, and the red rectangle (located near the right edge of the figure) is the extent of the zoomed area shown in Figure 11 below.

The 2nd roughness value was incremented in 0.1 steps from 0.0 to 0.9 and the average and standard deviation of the five landform types was calculated, with results are presented in Table 4. For each of the 5 landform groups the divergences from the


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average value are shown in Figure 9 when only total curvature is used (i.e., when the user specified 2nd roughness penalty is 0.0).

nd

Table 4. Impact of changing the 2 roughness penalty on the mean and standard deviation (Stdev) of the 5 landform types (HT = hill tops, SP = saddle points, HG = head of gullies, SO = stream outlets and R = ridges) shown on Figure 8. All units are in m above mean sea level. Landform

Stat.

Type

2nd roughness penalty

No. 0.0

HT

Mean Stdev

R

0.4

0.5

0.6

0.7

0.8

0.9

85.32

84.65

84.93

84.95

84.87

84.79

85.10

84.62

85.31

85.96

84.84

84.82

84.77

84.61

84.59

84.06

83.84

83.48

82.51

81.31

Mean 12 1,188.04 1,188.08 1,188.04 1,187.96 1,187.94 1,188.21 1,187.71 1,187.14 1,186.54 1,185.78 Stdev

SO

0.3

Mean 36 1,274.30 1,274.28 1,274.23 1,274.14 1,274.05 1,273.30 1,273.31 1,272.77 1,271.30 1,269.50 Stdev

HG

0.2

Mean 58 1,323.75 1,324.06 1,324.09 1,324.38 1,324.74 1,325.46 1,326.31 1,327.88 1,329.70 1,333.33 Stdev

SP

0.1

82.68

82.74

82.79

82.89

83.14

82.92

81.85

83.13

82.62

82.47

6 1,031.18 1,031.26 1,032.45 1,033.07 1,034.12 1,035.56 1,037.60 1,038.43 1,041.17 1,040.24 38.92

38.96

39.65

39.77

40.56

42.33

42.83

43.64

47.13

44.75

Mean 10 1,258.06 1,259.40 1,259.48 1,259.61 1,259.96 1,260.41 1,261.02 1,261.68 1,263.11 1,265.14 Stdev

68.85

67.43

67.41

67.31

67.23

67.04

66.85

66.88

66.31

66.81

outlets

ridges

hill tops head gullies

saddles

nd

Figure 9. Divergence from the average elevation value when no profile curvature is used (i.e., 2 nd roughness = 0.0), with increasing profile curvature governed by increasing the 2 roughness for the 5 landform types introduced in Figure 8 (above). CSIRO Land and Water

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Results from Table 4 and Figure 9 show that increasing the 2nd roughness penalty results in an increase of the average elevation of the hill tops, stream outlets and ridges, whereas the average elevation of saddle points and gully-heads decrease. This means that the local relative relief, and hence slopes, represented by the DEM will increase slightly as more locally adaptive profile curvature is used by ANUDEM (by the user specifying a higher 2nd roughness penalty). However, in Figure 9 the average elevation of ‘outlets’ decreases when using a 2nd roughness penalty of 0.9 compared to when 0.8 was used; this suggests instability when using a 2nd roughness penalty of 0.9. Given the complex landform of the CSHC (Figure 3) higher relief (more locally adaptive profile curvature) will most likely provide a better representation of the terrain. To further illustrate this, a detailed analysis of the impact of the degree of profile curvature was performed. Two resulting profiles are illustrated: (1) from a ridge to a shoulder; and (2) from an upper hill slope to a stream outlet (shown in Figure 10a and Figure 10b respectively). The location of the points is shown in Figure 11; this is the red rectangle box shown on Figure 8.

0.9 0.8 0.7 0.6 0.5 0.3 0.0

(a)

0.9 0.8 0.7 0.6 0.5 0.3 0.0

(b) nd

Figure 10. Detailed examination of the elevation for two profiles for seven different 2 roughness penalties: (a) shows the profile from the ridge to the shoulder; and (b) from an upper hillslope to a stream outlet. The locations of the points are detailed in Figure 11, which is shown on Figure 8.


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Figure 11. Location of the detailed sample points used in Figure 10a and Figure 10b is shown. The blue line represents an input river and the brown lines represent the input contour data with a 100 m interval. The background grey surface is the elevation, which ranges from 970 m to 1,235 m above mean sea level.

From the analysis of the two profiles we see that the more profile curvature used the steeper the landform becomes. However, it should be noted that where contours exist (elevations of a multiple of 100 m – 1,200 m in Figure 10a and 1,000 m in Figure 10b), the elevation value of the pixel is not greatly influenced by the amount of profile curvature as there are input data controlling the resulting value of the DEM at this particular location; this result is entirely expected. These analyses indicate that a high 2nd roughness should be used (e.g., a 2nd roughness of 0.8 or 0.9) in order to increase the slope of the profile and better match the relief of the site.

3.3.2. Stream network analysis The influence of the total stream length and river density as a function of the 2nd roughness penalty was determined. The results are shown in two ways. Firstly the actual values are provided, and secondly results are provided relative to the length and density when the 2nd roughness was calculated using the default value of 0.0, see Table 5. That is, the relative difference in length and the relative difference in density were calculated for each successive 2nd roughness by: rel_diffx = Outputx − Output0.0 CSIRO Land and Water

(1) Page 33

where rel_diffx = relative difference in ANUDEM output; x = is the 2nd roughness value between 0.1 and 0.9 (incremented by 0.1); Outputx = ANUDEM output produced when the 2nd roughness of x is used; and Output0.0 = ANUDEM output produced when the default 2nd roughness of 0.0 is used. In this sub-section ‘ANUDEM output’ refers to either total stream length and river density.

Negative values in the difference columns (denoted diff. in Table 5) reveal that there is a decrease in either river length or density when compared to the same attribute using the default 2nd roughness value of 0.0. Therefore, the results presented in Table 5 show that there is a gradual decrease in river length and river density as a function of increasing 2nd roughness penalty from 0.0 to 0.5. From 0.5 to 0.9 the direction reverses; with increasing 2nd roughness, the river length and river density starts to increase (but are still lower relative to the default value). These practically reach the default values again at a 2nd roughness of 0.8. A ‘breakpoint’ is evident between 0.8 and 0.9 by a large difference in both length and density; this breakpoint indicates that using a 2nd roughness of 0.9 would likely produce unstable results. Careful analysis of the stream network derived from the resultant DEMs illustrated that for the 2nd roughness of 0.9, there were many more spurious small streams derived than when using a 2nd roughness of 0.8. The positive values of ‘length diff.’ and ‘density diff.’ for a 2nd roughness of 0.9 indicate that the value is now higher than the optimal value. Using a 2nd roughness anywhere between 0.0 and 0.8 makes very little difference to either river length or density. Since a value of 0.8 has minimal impact on the stream metrics, yet greatly changes the relative relief (see Figure 9 and Figure 10), this analysis indicates that a 2nd roughness penalty of 0.8 provides a good representation of the landscape using this input data.


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Table 5. The river network and density statistics and their relative differences (compared to nd the default 2 roughness penalty of 0.0) are listed for the YHB Test area. The input river data 2 length was 8,720,177.75 m with a density of 432.35 m/km – see Table 2. The length and nd density relative differences (denoted ‘diff.’ in the column headings) were calculated for each 2 nd roughness from 0.1 to 0.9 with respect to the ANUDEM default 2 roughness value of 0.0, see equation 1. nd

2

roughness

Length (m)

Length diff. (m)

Density 2

(m/km )

Density diff. 2

(m/km )

0.0

24,283,023

—

1,204

—

0.1

24,265,204

-17,819

1,203

-1

0.2

24,264,912

-18,111

1,203

-1

0.3

24,254,638

-28,385

1,203

-1

0.4

24,253,711

-29,312

1,203

-1

0.5

24,241,220

-41,803

1,202

-2

0.6

24,268,426

-14,597

1,203

-1

0.7

24,272,279

-10,744

1,203

-1

0.8

24,279,780

-3,243

1,204

0

0.9

24,387,855

104,832

1,209

5

3.3.3. New lines, drainage enforcement, and sinks analysis A similar analysis was run to inspect the impact of changing the 2nd roughness on the difference in the number of last new lines, the number of drainage enforcements, and the number of sinks. As was done in section 3.3.2., each successive value of 2nd roughness was run through ANUDEM and both: (i) the actual output values; and (ii) the relative output values compared to the default value results (2nd roughness = 0.0) were reported. The goal was to minimise both the number of new lines (as this shows a stable result) and the number of sinks (as this is a metric of general improvement in data quality). While the goal is not necessarily to maximise the number of drainage enforcements, a high number shows that the output river network should be well connected and hydrologically correct as ANUDEM is enforcing drainage at places other than where the input stream data exists. Given: (1) our concerns about the input data density of the rivers relative to the contours (Table 2); and (2) the purposes for which the resultant DEM will be used, then generating a well connected and hydrologically correct DEM was a desirable characteristic.


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Table 6. Actual values and the relative difference values for last new lines, drainage enforcements and sinks for the YHB test area. Relative difference values (denoted ‘diff.’ in the nd column headings) were referenced to the output when a default 2 roughness value of 0.0 was used, see equation 1. No. last

No.

No. last

new lines

new lines

0.0

No.

No.

No.

Drainage

Drainage

sinks

sinks

diff.

Enf.

Enf. diff.

10

—

415,187

—

12,606

—

0.1

16

6

415,431

244

12,565

-41

0.2

11

1

415,249

62

12,560

-46

0.3

12

2

415,399

212

12,584

-22

0.4

15

5

415,110

-77

12,569

-37

0.5

12

2

414,593

-594

12,529

-77

0.6

15

5

415,645

458

12,455

-151

0.7

11

1

417,514

2,327

12,353

-253

0.8

4

-6

422,347

7,160

12,271

-335

0.9

14

4

432,828

17,641

11,994

-612

nd

2 roughness

diff.

Table 6 reveals that the number of drainage enforcements and number of sinks are reciprocally related. The first increases as the 2nd roughness penalty increases, while the latter decreases. When the 2nd roughness penalty is > 0.4 the ratio (number of drainage enforcements / number of sinks) becomes larger. This means that more gullies and ridges will be extracted from the input data (i.e., contours, spot heights and streams), so the resultant DEM can better represent the complex landforms found in CSHC. The minimum number of last new lines, an indicator of algorithm stability, was generated with a 2nd roughness penalty of 0.8, whereas the minimum number of sinks was found at 0.9. The number of drainage enforcements was high for both 0.8 and 0.9. As the previous stream analysis precludes that 0.9 be chosen, the results from Table 6 suggest, again, that a 2nd roughness of 0.8 is best for this scale dataset in this landscape.


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3.3.4. Determining changes in elevation and slope The difference in the elevation and slope statistics were calculated using the same two approaches as before: (i) actual values are shown (Table 7), and (ii) relative values with reference to the default 2nd roughness = 0.0 were calculated (Table 8). These statistics reveal that the mean elevation and slope increase with an increasing 2nd roughness; the standard deviation of the elevation remains approximately constant and the standard deviation of the slope decreases with increasing 2nd roughness (Table 7 and Table 8). The more important metric for this analysis is the slope. The increase in mean slope was expected given the changes in elevation at various landform positions discussed above (i.e., as the amount of profile curvature used increases, average elevation of hill tops, stream outlets and ridges increases, while the average elevation of the gully heads and saddles reduces – see section 3.3.1 above), and is desirable given the landscape extremely steep landscape encountered in the CSHC; see Figure 3. As several times before, a 2nd roughness of 0.9 is suggested as the best value. However, given that the stream analysis (see section 3.3.2 above) found that using a 2nd roughness of 0.9 was unstable as the resultant stream network derived from the DEM contained many small spurious streams, we again opt for a 2nd roughness of 0.8. Table 7. Summary statistics for the resultant DEM produced using ANUDEM when varying the nd 2 roughness penalty. There were 1,882,632 100 m resolution cells (1,128*1,669) in the YHB test area, the minimum slope was always 0.0° and hence it is not shown in the table. Min., Max., and Stdev represent the minimum, maximum and standard deviation, respectively. nd

2

Elevation (m)

roughness

Min.

Stdev

Max.

Mean

Stdev

0.0

420.4 1,813.5 1,172.7 212.9

40.1

10.3

6.9

0.1

420.4 1,813.5 1,172.7 212.9

40.1

10.3

6.9

0.2

421.5 1,813.5 1,172.7 213.0

40.0

10.3

6.9

0.3

408.7 1,813.6 1,172.8 212.9

40.0

10.3

6.9

0.4

429.6 1,813.6 1,172.8 213.0

39.8

10.4

6.8

0.5

420.6 1,813.7 1,172.9 212.9

39.7

10.4

6.8

0.6

420.5 1,813.8 1,173.1 212.8

39.4

10.4

6.7

0.7

412.6 1,814.4 1,173.3 212.9

39.6

10.5

6.6

0.8

422.6 1,818.0 1,173.6 212.9

39.6

10.7

6.5

0.9

417.9 1,824.5 1,174.0 212.9

41.1

11.0

6.4


Max.

Mean

Slope (°)

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Table 8. Relative difference values for the resultant DEM produced using ANUDEM when nd nd varying the 2 roughness relative to the output when using the default 2 roughness of 0.0. Min., Max., and Stdev represent the minimum; maximum and standard deviation using the data presented in Table 7 (see equation 1). nd

2

Elevation diff. (m)

Slope diff. (°)

roughness

Min.

Max.

Mean

Stdev

Max.

Mean

Stdev

0.0

—

—

—

—

—

—

—

0.1

0.0

0.0

0.0

0.0

0.0

0.0

0.0

0.2

1.1

0.0

0.0

0.1

-0.1

0.0

0.0

0.3

-11.7

0.1

0.1

0.0

-0.1

0.0

0.0

0.4

9.2

0.1

0.1

0.1

-0.3

0.1

-0.1

0.5

0.2

0.2

0.2

0.0

-0.4

0.1

-0.1

0.6

0.1

0.3

0.4

-0.1

-0.7

0.1

-0.2

0.7

-7.8

0.9

0.6

0.0

-0.5

0.2

-0.3

0.8

2.2

4.5

0.9

0.0

-0.5

0.4

-0.4

0.9

-2.5

11.0

1.3

0.0

1.0

0.7

-0.5

3.3.5. Using independent higher quality data as a validation dataset To independently validate the influence of the 2nd roughness we assessed the differences between spot heights from a 1:100,000 data set (covering just the YHB – introduced in Table 1 and Figure 1 and 2) with the maximum elevation of a 3 by 3 pixel window centred on the same locations as the spot heights for each resultant DEM. Of the 1,594 spot heights in the YHB 1:100,000 data there were 120 that were common with the spot heights in the CSHC 1:250,000 data. The spot heights common to the two data sets were identified as being within 100 m of each other horizontally (post positional accuracy correction of the YHB 1:100,000 dataset – see Section 4 below) and having the same elevation. The remaining 1,474 non-common spot heights in the YHB 1:100,000 dataset were used as independent data to assess the impact of the 2nd roughness penalty on the resultant ANUDEM generated output using the corrected 1:250,000 data as input. In this analysis we assume a ‘spot height’ represents the landscape local maxima; hence we have taken the difference between the non-common spot heights and the maximum of a 3 by 3 pixel window centred on the same locations to test if any of its neighbours are higher than the spot height. We selected a 3 by 3 pixel window as the RMS error in the x- and y-directions after


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assessing the positional accuracy between the two data sets and shifting the YHB data to minimise the difference were 57.10 m and 63.74 m, respectively (or within a 1 pixel radius). See Section 4 below for full details on how the assessment of the positional accuracy between the 1:250,000 CSHC and 1:100,000 YHB data sets was performed.

Results presented in Table 9 show that the bias (or average error) between the two sets of height data (calculated as YHB 1:100,000 spot height minus the maximum elevation from ANUDEM output using 1:250,000 data) reveal that the 1:100,000 spot heights are higher than the maximum in the 3 by 3 pixel window derived from the 1:250,000 data. This is to be expected in this landscape (see the photos presented in Figure 3) as given no control it would be difficult for any terrain algorithm to model the true surface. This bias reduces as the amount of profile curvature increases (i.e., increasing the 2nd roughness penalty user specification), this pattern is also seen in the root mean squared difference. Table 9. Summary statistics of the difference between the 1,474 spot heights from the 1:100,000 data set for the YanHe Basin minus the maximum elevation in each 3 by 3 pixel window (centred on the same location) of the ANUDEM output using the 1:250,000 CSHC data nd as input as a function of 2 roughness. All units are m above sea level, with the calculations being YHB spot height – CSHC DEM elevation value. nd

2 roughness penalty

Statistic 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Bias

35.79

35.76

35.62

35.27

34.89

34.10

33.01

31.50

28.48

22.95

RMSD

42.21

42.11

42.09

41.78

41.62

40.98

40.16

39.16

37.25

34.58

Where the bias (also known as the mean error) is calculated as

[

1 n

is the root mean squared difference, given as

[

1 n

n

∑ (P − O ) and RMSD i

i

i =1

n

∑ (P − O ) ] , where Pi is the set of 1,474 2

i

0.5

i

i=1

non-common spot heights from the 1:100,000 YHB data and Oi is the maximum elevation from a 3 by 3 window from the resultant ANUDEM output generated from the 1:250,000 topographic source data centred on the corresponding locations. Table 9 shows that there little difference when using a 2nd roughness penalty between 0.0 and 0.5.


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While Table 9 shows that the minimum difference is shown for a 2nd roughness penalty of 0.9, as before, the 2nd roughness of 0.8 provides the second best results, and given our concerns with the stability of the stream network derived from the DEM generated when using a 2nd roughness of 0.9, using a 2nd roughness of 0.8 is probably the better choice. Therefore, although the results of this and some of the previous analyses indicate that a 2nd roughness of 0.9 might be the appropriate parameter value, the instability shown in the stream analysis means that using a 2nd roughness of 0.8 is probably a better choice given that the 2nd roughness of 0.8 had very good statistics in each case and did not show this instability in relation to the derived stream network. Additionally, the number of new lines indicate that 2nd roughness of 0.8 is the most appropriate, and when metrics using a 2nd roughness of 0.8 are compared to those when using a 2nd roughness of 0.9, it is apparent that only marginal improvements are made for some aspects of the DEM when using 2nd roughness of 0.9, compared with the all round capacity of a 2nd roughness of 0.8 to produce good results. Consequently the 2nd roughness value of 0.8 will be used in addition to the other non-default ANUDEM parameters decided in the previous sub-sections: (1) the ‘maximum number of iterations’ was set to 40 (see Section 3.2 above); and (2) the output resolution of 100 m (see Section 3.1 above). The full final set of ANUDEM parameters used to develop the CSHC DEM is given in Table 10.

Table 10. The following user directives were applied to ANUDEM Version 5.1 algorithm using the corrected 1:250,000 data to construct the DEM for the CSHC. The ANUDEM user directives that were explored in the report are bolded, input files and output files are not included in this listing. ANUDEM User Directive

Value

Comments

Drainage option

1

Drainage enforced where possible

Contour data

1

Data mainly consists of contours

Discretisation error factor

1.0

Vertical standard error

0.0

st

1 roughness penalty

0.0

Determines how much planar (or contour) curvature is used in addition to the default total curvature. Set to 0.0 for contour data.


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ANUDEM User Directive nd

2 roughness penalty

Value

Comments

0.8

Explored in Section 3.3 (above), determines how much profile curvature is used in addition to the default total curvature

Elevation tolerance

50

Half the contour interval of 100m

Maximum number of

40

Explored in Section 3.2 (above)

Elevation units

1

Elevation units in m

Height minima

225.0

Data points lower than this value ignored, and

iterations

fitted cells cannot be lower than this. Height maxima

2840.0

Data points higher than this value ignored, and fitted cells cannot be higher than this.

Centring option

1

Grid points located at the centre of pixels, for use in ARC-INFO

Position units

1

Position units in m

X lower *

-134000.0

Data points below this value are ignored

X upper *

271000.0

Data points above this value are ignored

Y lower *

3739000.0

Data points below this value are ignored

Y upper *

4270000.0

Data points above this value are ignored

Grid spacing

100

Final grid resolution is 100 m, explored in Section

Grid margin

2000

3.1 (above) This corresponds to twenty 100 m resolution output grid cells. Will vary depending on the user required output resolution. * This specifies the spatial extent of the output DEM, in this case data are in metres based on the following Albers Projection; these neat lines would obviously change for different study areas.

The Albers Equal Area Conic projection uses the following parameters: 1st standard parallel:

36° 30’ 0.000’’ N

2nd standard parallel:

37° 10’ 0.000’’ N

Central meridian:

109° 30’ 0.000’’ E

False easting (meters):

0.00000

False northing (meters):

0.00000

Latitude of projection's origin:

0° 0’ 0.000’’

Spheroid:

KRASOVSKY

Xshift:

0.0000000000

Yshift:

0.0000000000

While the input data and key ANUDEM parameters were optimised, there were still sinks present in the output DEM. As we did not know if these were valid sinks, we assumed they were artefacts due to the combined interaction between the input CSIRO Land and Water

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topographic data and the parameter-optimised ANUDEM algorithm. Consequently the sinks were filled, as discussed previously, prior to the DEM being used in any following analysis. Table 11 lists the number of sinks associated with the changes made to the select ANUDEM parameters (see Table 3 for changes in sinks as the data quality improved). We see that in Table 11 the number of sinks increases when using a 2nd roughness penalty of 0.8; this may be due to the lower input river density in the YHB test area. The value of the 2nd roughness penalty is obviously a pragmatic balance between several metrics describing the ability of the DEM to best represent the landform.

Table 11. Number of single- and multi-cell sinks (and some summary statistics for each case) as a function of selecting key ANUDEM parameters for the YHB test area using the 1:250,000 CSHC data are shown. For the ‘single-cell sinks’ cases Num., Min., Max., and Num. > 10 m represent the number, minimum, maximum, and number where the difference between the sink and its 8 neighbouring cells is greater than 10 m. For the ‘multi-cell sink’ cases average depth of all cells in the sinks are calculated first and then descriptive statistics are then generated. In all cases if ANUDEM parameter(s) are not explicitly specified then the default values were used; in all cases the spatial resolution was 100 m. Single-cell Sinks Num.

Min.

Max.

Mean

Multi-cell Sinks Std

Num. > 10m

Num.

Max. Avg Depth

Mean Avg Depth

Std Avg Depth

Avg Num. Cells

16.69

3.89

2.60

7.48

2.58

7.28

3.92

5.22

ANUDEM default value with ‘best’ quality data (see Table 3) 2,618

1.65

42.06

9.40

3.44

959

7,036

Optimising the ‘number of iterations’ parameter (iterations = 40 was used) 2,563

1.47

42.01

Optimising the 2

nd

4,587

41.95

1.39

9.36

3.28

roughness penalty (2 12.57

5.22

938 nd

7,037

15.59

3.88

roughness = 0.8 and iterations = 40) 3,053

8,925

26.35

5.44

The following section describes the methods and results used to assess and minimise the positional accuracy between the 1:100,000 40 m contour interval (CI) YHB dataset and the 1:250,000 100 m CI CSHC dataset (as used in Section 3.3.5). This was only performed for the YHB (not the entire CSHC Area) as the 1:100,000 40 m CI data only covered the YHB. In Section 5 we then illustrate and discuss the output ANUDEM for the entire CSHC, where qualitative comparison with output generated from a TIN is also shown. CSIRO Land and Water

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4. Minimising positional difference between YHB and CSHC datasets Higher resolution contour and spot height data (than the 1:250,000 scale data with a contour interval primarily of 100 m) existed (see Table 1) over several portions of the CSHC. We decided to use the 1:100,000 map scale spot height and 40 m contour interval data covering the YHB to independently validate the influence of the 2nd roughness penalty (see Section 3.3.5 above). Prior to performing this analysis the positional differences between the CSHC and YHB data needed to be assessed, and minimised if the differences were larger than currently accepted international positional mapping standards.

While processing the 40 m contour interval YHB DEM, a shift (appearing to be systematic in nature) was identified between the CSHC source data (contours, spot heights, rivers and lakes) and the source data for the YHB. In this section, we quantify and minimise this positional offset to allow more meaningful comparison between the YHB and CSHC DEMs (and their derivatives) for future studies. A large locational offset between the CSHC and YHB data would greatly reduce the ability to directly compare these datasets, especially in the fast-changing relief that typifies the study site (see Figure 3). To avoid such effects, other multi-resolution DEM analyses have simulated various coarse resolution data from a single fine resolution source DEM (Gallant and Dowling 2003). However, simulating coarse scale data rather than using independent native coarse scale data could possibly introduce dependencies between the datasets that might be due strictly to the simulation (i.e., not due to true structure in the landscape).

This means that in any multi-resolution DEM analysis, there exists at least two general concerns that could greatly influence the results of the analysis. These are: (1) positional effects if the various resolutions are based on independent datasets created CSIRO Land and Water

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from different native scales; or (2) possible effects introduced by simulating various coarse-scale data from a single fine-scale source. In this study, and particularly in this section, we concentrate on 1 above by assessing the positional difference between these two datasets (the 1:250,000 CSHC and the 1:100,000 YHB) in order to determine both how different the positions of common map features are in these data, and if this positional difference can be minimised to an ‘acceptable’ level. A future study may address 2, above, depending on these findings. To determine the positional offset, we used common point-based techniques for estimating positional accuracy and guidelines defined for map and digital data accuracy in both Australia and the US. A very brief description of positional accuracy as well as accuracy standards follows.

Positional accuracy estimates of any particular GIS dataset are often assessed by comparing locations of well-defined points found on the dataset in question to associated points on an independent dataset of higher accuracy (NMCA 1975; FGDC 1998a; FGDC 1998b; Van Niel and McVicar 2000; Van Niel and McVicar 2001). This higher accuracy dataset is referred to as the ‘reference’ and the dataset being tested is referred to as either the ‘input’ or ‘test’ dataset. For GIS applications, a horizontal accuracy assessment tool was created in ArcView for a previous study and is downloadable from the internet (Van Niel 2000). This tool is described extensively in Van Niel and McVicar (2000). For the purposes of assessing the positional accuracy between the CSHC and YHB datasets, the ability to calculate the bearing between the input and test datasets was added to the original positional accuracy extension. This new functionality calculates the direction of the vector difference between associated points in the test and reference datasets (a new version of the ArcView extension including this functionality is available upon request).

Australian map accuracy standards state that not more than 10% of the points used for testing the accuracy of a dataset should exceed an allowable distance (calculated as


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0.5 mm on the source map scale) (NMCA 1975). For example, a 1:250,000 scale map will have an allowable error distance (distance between the reference and the test datasets) of 125 m. Well-defined points are limited to those map features that are both easily visible on the ground and plotable within 0.25 mm on the scale of the source map (e.g., 62.5 m for a 1:250,000 scale map); see NMCA (1975) and Van Niel and McVicar (2000) for more details. Since the binary way of reporting accuracy in the map standards as either ‘passing’ or ‘failing’ is not always very pertinent for digital geospatial data, the US has defined National Standards for Spatial Data Accuracy (NSSDA). The NSSDA is defined by a 95% confidence interval around the root-mean-squared error (RMSE) between at least 20 associated well-defined reference and test points – see FGDC (1998b) and Van Niel and McVicar (2000) for more details. In the accuracy assessments described below, we report statistics with regard to both Australian map accuracy standards and the US NSSDA.

A complication to directly measuring and subsequently minimising positional differences is in defining common well-defined points between independent elevation source data, especially if spot heights are not comparable and/or the difference in scale is considerable. We concentrate on two separate methods for determining well-defined points, and subsequently estimating positional differences between the two elevation source datasets: delineating well-defined points from (1) matching spot heights; and (2) centroid points of matching small enclosed contours. The second method is particularly useful if only contour data is available as matching locations are particularly difficult to accurately identify between non-intersecting line features on datasets of disparate scales (i.e., it is easier to identify common points if line features intersect like in a road network, but harder when the lines never cross, like contour data). Details of the methods used and their results are given below for both of these alternatives (spot height comparison and contour centroid comparison).


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4.1. Spot height comparison Fifty six (56) matching pairs of spot heights distributed throughout the YHB, were arbitrarily chosen to estimate the positional difference between the 1:100,000 YHB and 1:250,000 CSHC datasets (see Figure 12). Due to its coarse scale, less spot heights were available from the CSHC dataset, so this dataset limited the number of matching pairs that could be identified. However, matches were easily identified and we were able to greatly exceed the number of pairs necessary based on Australian and US accuracy standards (20 pairs are needed according to the standards of both countries). Points were matched by linking the elevation fields in the separate spot height topographic point datasets (i.e., linking the elevation field in the YHB to that of the CSHC). This allowed for semi-automated matching of pairs. As a CSHC point of interest was selected, since the databases were linked based on elevation, any YHB spot heights with the same elevation were then also selected. When more than one YHB spot height was selected for a single CSHC spot height, the closest point was retained as the matching point. In every case where multiple YHB points were initially selected, there was always only a single point that was within a kilometre radius of the selected CSHC point. If there were no YHB points selected for an individual CSHC point, or if all the YHB points initially selected were very far away (i.e., >1 km) from the selected CSHC point, then the point was rejected. Points were selected until a relatively equal distribution across the whole YHB was attained. These points were matched through a common field containing an integer identifier between 1 and 56.


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N

20

0

20

40 km

Figure 12. Within the YHB, the relatively few available CSHC spot heights are shown as hollow squares, while the more numerous YHB spot heights are shown as filled grey circles. Location of CSHC-YHB spot height pairs used for positional difference estimation (56) are shown as filled black squares.

The positional and bearing statistics were generated from these matching points using the positional accuracy extension to ArcView with the YHB serving as the ‘reference’ dataset and the CSHC serving as the ‘input’ or ‘test’ dataset. The mean difference was around 125 m (which is the allowable error distance for a 1:250,000 scale dataset such as the CSHC), so the distances measured were near the expected value (see column labelled ‘S’, Table 12). More importantly, the NSSDA value was around 250 m, which signified that 95% of well-defined point-pairs between the YHB and the CSHC would be expected to be less than this value. The mean bearing was calculated to be near 235°, with a standard deviation of 47°, both from grid North increasing clockwise. That is, the YHB is at a bearing of 235° from the CSHC dataset, and with a low standard deviation, a simple x- and y-offset applied to the YHB dataset


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would be expected to significantly reduce the error. Before applying any shift, the contour data was also assessed.

4.2. Contour comparison The contour data was inspected for identifiable features that could be matched. This proved to be difficult given both the positional shift between the datasets and the generalisation differences due to the disparate scales of the datasets (see Figure 13). However, small enclosed contours served as good places to match the two datasets as their shape was less affected by scale (i.e., the size was different, but the shape remained relatively the same). Also, the centroids of these polygons could be defined objectively using ArcInfo, and thus eliminated user subjectivity in defining the points. First, contour data that were not a multiple of 100 m were eliminated from the YHB dataset (which has a contour interval of 40 m). This made the selection of matching contours easier and, based on the range of elevations in the YHB, the following contour intervals were used: 200; 400; 600; 800; 1,000; 1,200; 1,400; and 1,600 m. From these available contours, 112 matching pairs of small, enclosed contours were arbitrarily chosen within and around the YHB until roughly an even distribution was apparent (Figure 14).

It was ensured that any gaps in the ‘enclosed’ contours were truly closed by using the ArcInfo snap command and any dangling arcs were also eliminated. These line datasets were converted to polygon datasets by using the ArcInfo build command, and labelpoints were created at the centroid of these polygons using the ArcInfo centroidlabels command. These centroid points were exported to an ArcView shapefile and, as before, were linked with a common identifier field (integers from 1 to 112) and difference and bearing statistics were generated using the positional accuracy ArcView extension (again, YHB = ‘reference’ and CSHC = ‘test’).


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N

300

0

N

300

600 m

300

#

(a)

0

300

600 m

#

#

#

(b)

Figure 13. An example of matching contour intervals (1,200 m) for the YHB (grey lines) and the CSHC (black lines) are shown before the shift was applied (a) and after the shift was applied (b). Identification of well-defined points is difficult along most of the line due to differences in detail related to scale and the positional offset, but are easily seen at the centroids of small enclosed contours. An example of one of the centroid pairs is shown near the bottom of the figures; the centroid of the small enclosed YHB contour is shown as a grey filled circle and the centroid of the small enclosed CSHC contour is shown as a black filled circle. The shift that was applied to (a) to produce (b) is a block offset of 121.33 m and 238.17 degrees from North, as calculated using the pooled spot height and contour analysis presented in Table 12 and discussed in full below.

The results of the contour centroid assessment were very similar to those of the spot height assessment (see column labelled “C” in Table 12). The mean distance was around 120 m, with an NSSDA (95% RMSE) estimate of 220 m, and a mean and standard deviation bearing of around 240 degrees and 32 degrees, both from grid North. A relatively small standard deviation around the mean bearing of the 112 point-pairs again suggested that a single x-, y-offset applied to one of the datasets should significantly reduce the difference distance. Also, the similarity between all of the statistics measured in both the spot height and contour centroid assessments suggested that: (1) they could be pooled (or combined) for this analysis; and (2) for other analyses, they were sufficiently alike that performing just one of these should be sufficient (e.g., if no spot heights are available, the contour centroid method should give a reliable estimate of the differences by itself, or vice versa).


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4.3. Pooled spot height and contour centroid comparison Based on these results, we then pooled the 56 spot height point-pairs and the 112 contour centroid point-pairs and reran the positional difference program with the entire 168 point-pairs after matching these pooled YHB and CSHC datasets with a common identifier. The results are shown in Table 12 in the column labelled ‘S_C_p’, where statistics fall in between the original two analyses. With regard to the CSHC DEM resolution, this relates to (at well-defined points) a mean difference of around 1.21 pixels. At 95% of these points, the difference should be less than 2.31 pixels. Using the mean pooled distance and the mean pooled bearing (121.33 m and 238.17 degrees), all the YHB DEM source datasets were shifted with the ArcInfo move command, including: (1) spot heights; (2) contours; (3) rivers; and (4) lakes and reservoirs. Although the finer scale YHB data is considered to be the reference, we shifted the YHB source data to the ‘CSHC data-space’. The YHB data was shifted to the CSHC data-space because the CSHC dataset covers the entire study site and thus, all other resolution DEMs (see Table 1) will be compared to it in the future. Additionally, the 1:250,000 data was obtained from the National Geographic Centre of China (NGCC) we expect it data to meet a higher data quality standard.


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N

20

0

20

40 km

Figure 14. Within and around the YHB, the contour centroid point pairs used for positional difference estimation (112) are shown as filled black circles. The boundary of the YHB is given by the thin black line; it is sub-catchment 29 on Figure 15.

The positional accuracy statistics were summarised again using the shifted YHB pooled spot heights and contour centroids to determine how close the ‘corrected’ YHB datasets were to the CSHC dataset (see column labelled ‘S_C_p_s’ in Table 12). This simple shift reduced the pooled mean difference by 49.57 m and the NSSDA estimate by 82.79 m. This shows that the mean difference between the ‘corrected’ YHB and CSHC DEMs (at well-defined points, at least) is now 0.72 of a CSHC pixel, and that we expect 95% of such points in the DEM to be less than 1.47 of a CSHC pixels. Note the x- and y- resolution of a ‘CSHC pixel’ is 100 m.


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Table 12. Summary statistics of various positional difference analyses described in the text are shown. The mean distance and bearing used to shift the YHB data are bolded. There are 56 point pairs used for the spot height analysis and 112 point pairs used for the contour centroid analysis. Statistic S C S_C_p S_C_p_s Mean Difference Distance (m)

124.58 119.70 121.33

71.76

75.64 48.46 58.74

46.76

Upper Limit for 95% Confidence Interval of Difference (m)

272.84 214.68 236.46

163.41

RMS Difference Distance (m)

145.40 129.06 134.72

85.58

RMS X (m)

110.84 106.77 108.15

57.10

RMS Y (m)

94.10 72.50 80.35

63.74

Allowable Error Distance (m)

125.00 125.00 125.00

125.00

NSSDA (m)

250.81 219.40 230.68

147.89

Mean Bearing (° from grid North)

234.72 239.90 238.17

162.83

Standard Deviation of Difference Distance (m)

Standard Deviation of Bearing (° from grid North) 47.35 32.35 37.95 113.35 S = spot height analysis; C = contour centroid analysis; S_C_p = pooled spot height and contour centroid analyses; S_C_p_s = pooled spot height and contour centroid analyses after shifting by mean distance and bearing (i.e., 121.33 m and 238.17 ° from grid North, respectively); RMS = Root Mean Square error; NSSDA = US National Standard for Spatial Data Accuracy positional accuracy estimate; this estimate is calculated as the 95% confidence interval of the RMS error.

Next we define the exact area comprising the CSHC, and following this, we discuss the improvements of the ANUDEM Version 5.1 generated DEM compared to the original DEM produced with the TIN algorithm that was previously available for the CSHC.


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5. Application and assessment of the ANUDEM output 5.1. Generating sub-catchments for the CSHC ANUDEM Version 5.1 was run with the best quality datasets (discussed in Section 2) and using the parameters documented in Table 10. Sub-catchments were then defined from this output DEM using the ‘Stream Define’ tool that is part of the SedNet program (for full details see Wilkinson et al. 2004). From this analysis we see that the CSHC consists of 42 catchments ranging from 127 km2 to 31,460 km2 (total area is 112,728 km2, Figure 15 and Table 13.

Figure 15. On the main map the CSHC sub-catchment boundaries are overlaid on the resultant DEM, with each sub-catchment numbered (with additional information provided in Table13). In the inset map the CSHC location in China is represented by the shaded area. CSIRO Land and Water

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Table 13. The 42 sub-catchments in the CSHC are listed. For the sub-catchments identified with the term ‘River’ the watercourse is named, for the other small sub-catchments the rivers are unnamed on the 1:250,000 source map, so they are given the township name (from the province-county-township hierarchy used in China). Rec #

Catchment Name

流域名称

1

Honghe River

红河

2

Lamawan

喇嘛湾

470

3

Longwanggou River

龙王沟

1,777

Huangfuchuan River

皇甫川

3,509

Yangjiachuan River

杨家川

1,114

Kuyehe River

窟野河

9,050

Pianguanhe River

偏关河

2,078

8

Qingshuichuan River

清水川

968

9

Hequ

河曲

585

10

Xianchuanhe River

县川河

1,595

11

Gushanchuan River

孤山川

1,318

12

Zhujiachuan River

朱家川

2,919

Baode

保德

Wudinghe River

无定河

31,460

Tuweihe River

秃尾河

3,333

Wujiazhuang

武家庄

1,007

17

Huashuta-Luzihe River

化树塔-芦子河

18

Lanyihe River

岚漪河

2,219

19

Weifenhe River

蔚汾河

1,645

20

Zhongzhuang

中川

21

Jialuhe River

佳芦河

1,206

22

Yangjiapu-Zhaojiaping

杨家铺-赵家坪

2,365

Qiushuihe River

湫水河

1,984

Nuanqushan

暖渠山

218

Sanchuanhe River

三川河

4,139

26

Hedi-Mutouyu

河底-木头峪

1,816

27

Chengjiazhuang

程家庄

295

28

Jinjiazhuang

靳家庄

461

29

YanHe River

延河

7,712

30

Qingjianhe River

清涧河

4,074

31

Lijiashan-Yanchasi

李家山-眼岔寺

Quchanhe River

屈产河

1,227

Yonghe River

雍河

2,070

Xinshuihe River

昕水河

4,351

Anhe-Guandao

安河-关道

4 5 6 7

13 14 15 16

23 24 25

32 33 34 35


2

Area (km ) 5,698

242

583

127

781

382

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36

Fenchuanhe River

汾川河

1,880

37

Xigelou

西葛沟

331

38

Wencheng-Wangjiayao

文城-王家窑

495

39

Zhouchuan River

州川河

712

40

Shiwanghe River

仕望川

2,375

Ehe River

鄂河

1,023

Sili-Jiyizhen

寺里-集义镇

41 42 Total

1,133 2 112,728 km

5.2. Comparing outputs from the ANUDEM and TIN algorithms Finally to illustrate the worth of using ANUDEM Version 5.1, over the more widely available TIN method to generate a DEM, for a selected focus area we compared several DEM derived outputs. Figure 16 shows the input data; the location of the focus area in the CSHC is given in Figure 2. As the TIN algorithm does not use the river network we used ANUDEM in two ways: (1) without the river network (to aid direct comparison with the TIN algorithm); and (2) with the river network. For both ANUDEM cases the final optimised parameters (Table 10) were used to generate the DEM. To generate the TIN, the ArcInfo TINLATTICE command was used; the following parameters were set: weed tolerance = 1, proximal_tolerance = 0.5, and no z_factor was applied. For both the ANUDEM and TIN cases we used the highest quality input data available.

(a)

(b)

Figure 16. Source data used for TINLATTICE and ANUDEM comparison, with (a) showing the contours and spot heights (red dots) and (b) the river network (used by ANUDEM only). This focus area is shown on Figure 2.


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The TIN and ANUDEM (no rivers and rivers) outputs compared are: 1. Elevation; units are m above sea level (Figure 17); 2. slope (or the 1st derivative of elevation); units are ° from horizontal (Figure 18); 3. aspect (or the 1st derivative of slope or the 2nd derivative of elevation); units are ° from grid North increasing clockwise (Figure 19); 4. hillshade; the DEMs are illuminated from an azimuth of 315 ° from grid North (i.e., north-west) and an altitude of 45° above the horizon (Figure 20); 5. output river network derived from the DEM using an area threshold of 20 grid cells; each grid cell is 100 m x- and y-resolution (Figure 21); 6. sine of the aspect; this transformation highlights east-west structure (Figure 22); and 7. cosine of the aspect; this transformation highlights north-south structure (Figure 23).

(a)

(b) 1,749 m

1,210 m

Figure 17. Output elevation surfaces in metres above sea level for (a) TIN, (b) ANUDEM (with no rivers) and (c) ANUDEM (with rivers). The input data are shown in Figure 16. (c)


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(a)

(b) 39°

0°

Figure 18. Output slope surfaces in ° from horizontal for (a) TIN, (b) ANUDEM (with no rivers) and (c) ANUDEM (with rivers). The input data are shown in Figure 17. (c)

(a)

(b) 360°

0°

(c)


Figure 19. Output aspect surfaces in ° from grid North (with North set to 0° and increasing clockwise) for (a) TIN, (b) ANUDEM (with no rivers) and (c) ANUDEM (with rivers). The input data are shown in Figure 18.

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(a)

(b) 246

55

Figure 20. Output hill shade surfaces, using azimuth = 315°, altitude = 45°) for (a) TIN, (b) ANUDEM (with no rivers) and (c) ANUDEM (with rivers). The input data are shown in Figure 17. (c)

(a)

(b)

(c)

(d)

Figure 21. Output rivers from DEM for (a) TIN, (b) ANUDEM (with no rivers) and (c) ANUDEM (with rivers). Input rivers are shown on (d).


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(a)

(b) 1

-1

Figure 22. Output sine (aspect) surfaces for (a) TIN, (b) ANUDEM (with no rivers) and (c) ANUDEM (with rivers). The input data are shown in Figure 19. (c)

(a)

(b) 1

-1

Figure 23. Output cosine (aspect) surfaces for (a) TIN, (b) ANUDEM (with no rivers) and (c) ANUDEM (with rivers). The input data are shown in Figure 19. (c)


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The results clearly illustrate the ‘blocky’ nature of resultant DEMs generated by TINLATTICE compared to those generated from ANUDEM. That is, compare (a – TIN) with both (b – ANUDEM no rivers) and (c – ANUDEM rivers) from Figure 17 to Figure 23. The elevation in the valleys when using ANUDEM (Figure 17c) is lower than for the TIN (Figure 17a), and there is much finer structure in the two ANUDEM outputs compared to the TIN DEM. For elevation (Figure 17) and slope (Figure 18) we see that the TIN DEM is very discontinuous when compared to the outputs from ANUDEM. It appears that the TIN algorithm has great difficulty dealing with the steep gullies with relatively gentle hill tops and ridges. This is especially evident in the aspect (Figure 19), and hillshade (Figure 20) surfaces. Additionally the river network created from the TIN is least reliable; it contains many discontinuous and multiple-line rivers (Figure 21a), when compared to the two ANUDEM outputs (Figure 21b and Figure 21c). It is interesting to note that only small changes can be seen when not using the river network as input to ANUDEM (Figure 21b) compared to when the river network was used as input (Figure 21c); this indicates the high degree of control exhibited on the ANUDEM algorithm from the contour data, and reflects the complex landforms of the CSHC. However in both cases the density of the output river network is greater than that of the 1:250,000 input data (Figure 21d); this confirms our previous suspicions that the river network (especially some of the 1st order streams) was not well captured in the original 1:250,000 paper maps that were the basis for the digital river network database compared to the contour data; first noted when discussing the input data density (Table 2). The sine of the aspect (highlighting the east-west structure (Figure 22), and the cosine of the aspect (revealing the north-south structure Figure 23) again reinforce the better representation of the terrain surface derived when using ANUDEM compared to the TIN.

In addition to using the DEM to define the sub-catchments of the study area (as discussed above), the DEM will also be used to: (1) define the river network; (2) define areas contributing to measurements made at the hydrology stations located on the


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river network; (3) as a covariate for interpolating some surface meteorological variables; (4) used in net radiation modelling (using the SRAD model) that takes into account slope and aspect; and (5) as a basis for re-vegetation site selection. Consequently we needed the DEM to represent the terrain as well as possible (given current data and algorithms). This was the motivating factor behind generating this fundamental dataset for the CSHC, as it underpins most subsequent spatial analysis.


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6. Conclusions This report documents quality control methods and subsequent improvements made to the input data to generate a DEM, using ANUDEM Version 5.1, of the CSHC in the middle reaches of the Yellow River Basin in China. The improvement of the topographic input data quality was performed iteratively using the ANUDEM output data-files and diagnostic log-files. Additionally, careful selection of a variety of key ANUDEM parameters improved the quality of the final DEM over previous DEMs generated using less sophisticated methods.

The resultant DEM has already been used to define the boundary of the study area and the 42 sub-catchments that define the CSHC. In our ACIAR project the DEM will ultimately be used to define: (1) the river network; and (2) the polygon of contributing area for each hydrology station. Additionally, the DEM will be used to: (3) ensure elevation dependence is properly captured when using ANUSPLIN (Hutchinson 2004b) to generate surfaces of monthly meteorological variables (not monthly averages) from Jan. 1980 until Dec. 2000 (McVicar et al. 2005); (4) model monthly surfaces of net radiation, and it shortwave and longwave incoming and outgoing components, in complex terrain taking elevation, slope and aspect into account using the SRAD model (Moore et al. 1993; Gallant 1997; Wilson and Gallant 2000); and (5) as a basis for re-vegetation site selection. Also, having access to such a high quality DEM, over previous versions, means that any future regional erosion modelling performed for the CSHC study site will be improved. The methods and experience developed here while generating the DEM for the CSHC could be used with the higher resolution contour and spot height datasets (introduced in Table 1) to generate best practice DEMs from these datasets.

Given that the previous sections provided a great deal of specific detail, as part of the conclusions it is critical to reiterate the general strategy used for generating a CSIRO Land and Water

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hydrologically correct DEM. Presenting the four general steps this facilitates technology transfer; see figure 24 and the following descriptions below.

Figure 24. The four-major steps to develop, and use, a hydrologically correct DEM using the ANUDEM algorithm and best available topographic data are highlighted.

1. Data pre-processing: this step includes data acquisition, including purchasing the digital products from relevant survey or mapping agencies, digitising paper topographic maps by technicians, or the numerous other ways to obtain digital CSIRO Land and Water

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topographic data. To follow the steps outlined in this report, the data should include contours, spot heights, and stream networks and these should be in a format that can be used by ANUDEM (e.g., ArcInfo ungenerate file). Note, when joining many map sheets together for large extents, matching available computer memory to that needed to process these large datasets is critical at this stage. 2. Input data correction: the main tasks in this step include: (1) detection and correction of errors in the source data (e.g., location and value of spot heights, shape and value of contours, direction of rivers, reducing dual line rivers to single lines); and (2) minimising positional differences between different source datasets if multiple resolution DEMs are being produced. The former involves an iterative process that uses the log files from ANUDEM to detect errors as well as various other procedures (e.g., extreme slope analysis, or check_fnodes.aml), and then fix these errors in ArcInfo. The later is mainly based on ArcView programs. The best-practice (‘perfect’) input datasets are prepared from this step. 3. ANUDEM parameter optimisation: Since this was the first time that ANUDEM has been applied over the complex landforms of the Loess Plateau, it was critical to optimise some key ANUDEM parameters to improve the quality of the final DEM. These parameters include grid cell size of the output DEM, the number of iterations, and the 2nd roughness penalty. From the ANUDEM log files, the number of last new lines, the number of drainage enforcements, and the number of sinks can be used to optimise these parameters. Representation of the landform profile within the resultant DEM is largely governed by the 2nd roughness penalty used. 4. Generating final DEM for various applications: Once the best-practice (‘perfect’) data is prepared and the ANUDEM parameters are optimised, the final DEM should be generated. For this study, the final DEM has 100 m resolution for the entire CSHC. This final DEM will be used in a variety of


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environmental applications including vegetation suitability mapping, interpolating surface meteorological data, determining contributing areas, and defining the river network.


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7. References FGDC (1998a) 'Geospatial positioning accuracy standards. Part1: Reporting methodology.' Federal Geographic Data Committee, FGDC-STD-007.1-1998, Reston, Virginia, USA. FGDC (1998b) 'Geospatial positioning accuracy standards. Part3: National standard for spatial data accuracy.' Federal Geographic Data Committee, FGDC-STD-007.3-1998, Reston, Virginia, USA. Gallant JC (1997) 'Use of SRAD Version 3.1 to model solar radiation - release notes.' Australian National University, Centre for Resource and Environmental Studies, Canberra. Gallant JC, Dowling TI (2003) A multiresolution index of valley bottom flatness for mapping depositional areas. Water Resources Research 39, 1347-1347. Gallant JC, Wilson JP (2000) Primary Topographic Attributes. In 'Terrain Analysis'. (Eds JP Wilson and JC Gallant) pp. 51-85. (John Wiley & Sons, Inc.: New York) Huang M, Zhang L (2004) Hydrological responses to conservation practices in a catchment of the Loess Plateau, China. Hydrological Processes 18, 1885-1898. Hutchinson MF (1988) Calculation of Hydrologically Sound Digital Elevation Models. In 'Proc. Third International Symposium on Spatial Data Handling, August 17-19'. Sydney, Australia pp. 117-133 Hutchinson MF (1989) A new procedure for Gridding Elevation and Stream Line Data with Automatic Removal of Spurious Pits. Journal of Hydrology 106, 211-232. Hutchinson MF (2000) Optimising the degree of data smoothing for locally adaptive finite element bivariate smoothing splines. Australian and New Zealand Industrial and Applied Mathematics Journal 42 (E), C774-C796. Hutchinson MF (2004a) ANUDEM Version 5.1 User Guide, http://cres.anu.edu.au/outputs/software.php. Hutchinson MF (2004b) ANUSPLIN Version 4.3 User Guide, http://cres.anu.edu.au/outputs/software.php. Hutchinson MF, Gallant JC (2000) Digital Elevation Models and Representation of Terrain Shape. In 'Terrain Analysis'. (Eds JP Wilson and JC Gallant) pp. 51-85. (John Wiley & Sons, Inc.: New York) Li GY (2003) 'Ponderation and Practice of the Yellow River Control.' (Yellow River Conservancy Press: Zhengzhou) McVicar TR, Li LT, Van Niel TG, Hutchinson MF, Mu XM, Liu ZH (2005) 'Developing Monthly Meteorological Surfaces from 1980 to 2000 for the Coarse Sandy Hilly


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Catchments of the Loess Plateau, China.' CSIRO Land and Water Technical Report, 8/05, Canberra, Australia. Moore ID, Norton TW, Williams JE (1993) Modelling Environmental Heterogeneity in Forested Landscapes. Journal of Hydrology 150, 717-747. NMCA (1975) 'Standards of map accuracy.' National Mapping Council of Australia. Van Niel TG (2000) Horizontal accuracy extension. http://gis.esri.com/arcscripts/details.cfm?CFGRIDKEY=C03BD2F2-680F-11D4-94320 0508B0CB419

Van Niel TG, McVicar TR (2000) 'Assessing and improving positional accuracy and its effects on areal estimation at Coleambally Irrigation Area.' Cooperative Research Centre for Sustainable Rice Production, P1-01/00, Yanco, NSW, Australia. Van Niel TG, McVicar TR (2001) Assessing positional accuracy and its effects on rice crop area measurement: an application at Coleambally Irrigation Area. Australian Journal of Experimental Agriculture 41, 557-566. Wilkinson S, Henderson A, Chen Y (2004) 'SedNet User Guide. Client Report for the Cooperative Research Centre for Catchment Hydrology.' CSIRO Land and Water, Canberra, Australia. Wilson JP, Gallant JC (Eds) (2000) 'Terrain Analysis.' (John Wiley & Sons, Inc.: New York) Xiang-zhou X, Hong-wu Z, Ouyang Z (2004) Development of check-dam systems in gullies on the Loess Plateau, China. Environmental Science and Policy 7, 79-86. Xiubin H, Zhanbin L, Mingde H, Keli T, Fengli Z (2003) Down-scale analysis for water scarcity in response to soil-water conservation on Loess Plateau of China. Agriculture, Ecosystems and Environment 94, 355-361.


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8. Appendix A – check_fnodes.aml /* ------------------/* N: check_fnodes.aml /* F: check how many fnodes there are at any intersection of streams. /* D: April 20, 2004 /* by TVN, Canberra /* /* TVN - grabbed some code from drain_direction.aml and modified it to check /* how many fnodes there are at any intersection of streams. /* the idea is, that if there are more than 1 fnode at any one intersection, /* then there is at least 1 stream in the wrong direction. /* /* there could still be other errors, /* ================================================================== /* Copy linkcov to a new cover, add some items. Convert the nodes to /* points, get the elevation values at each point. We renode the /* linkage cover to ensure that the node-id matches the arc /* FNODE# and TNODE#. &args linkcov &s link %linkcov% build %link% arc nodepoint %link% %link%_pt build %link%_pt point &if [iteminfo %link%_pt -point fn_type -exists] &then dropitem %link%_pt.pat %link%_pt.pat fn_type /*&if [iteminfo %link% -arc fn_type -exists] &then /* dropitem %link%.aat %link%.aat fn_type additem %link%_pt.pat %link%_pt.pat fn_type 5 5 c /*additem %link%.aat %link%.aat fn_type 5 5 c /* Find intersections that have >1 FNODE (these would have a stream direction problem). arcplot aselect %link% arcs &s numarc [extract 1 [show select %link% arc]] /* find number of arcs to assess &do i = 1 &to %numarc% &ty Arc %i% of %numarc% /* systematically process each arc for $RECNO 1 to numarc aselect %link% arcs reselect %link% arcs $RECNO = %i% /* for $RECNO i, find out what the FNODE# is - this is in FNODE# &s FNODE_NUM [extract 1 [show select %link% arc 1 ITEM FNODE#]] /* now, select all the arcs that have this FNODE# aselect %link% arcs reselect %link% arcs FNODE# = %FNODE_NUM% /* and put the number of selected arcs in the variable fnode &s fnode [extract 1 [show select %link% arcs]] /* finally, if the number of arcs at that intersection is /* greater than 1, there is a problem, so put 'error' in fn_type item of points coverage &if %fnode% gt 1 &then &do aselect %link%_pt points reselect %link%_pt points %link%_pt# = %FNODE_NUM% moveitem %link%_pt point 'error' to fn_type &end /*&else /* &do /* &ty here /* aselect %link%_pt points /* reselect %link%_pt points %link%_pt-id = %FNODE_NUM% /* moveitem %link%_pt point 'okay' to fn_type /* &end &end q &return


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9. Appendix B – centreline_using_circle.ave ' ---------------------------------------------------------------------' DiskFile : centreline_using_circle.ave : calculate centreline ' Programmer : Tom Van Niel ' Created : 27-May-2004 ' Revisions : ' ' Function : calculates centre line of a dual lines coverage (e.g., dual ' river lines for the yellow river) for each point in one side ' of the coastline by growing a circle until it inters with the ' other side. ' ' References : None ' Called By : GUI ' Calls : None ' ---------------------------------------------------------------------' initialise variables theView = AV.GetActiveDoc theProjection = theView.GetProjection theLineTheme = theView.GetActiveThemes.Get(0) theLineTheme.ClearSelection 'theDEM = Grid.Make(Grid.MakeSrcName("D:\cia2\Dem")) theLineFTab = theLineTheme.GetFtab theLineShape = theLineFTab.FindField("Shape") theLineIdFld = theLineFTab.FindField("Side") theDisplay = theView.GetDisplay densifyValue = 100 count = 0 totalparts = 0 partsflipped = 0 nth_point = 4 def = av.GetProject.GetWorkDir.MakeTmp("smooth","shp") def = SourceManager.PutDataSet(ftab, "Output Shapefile Name", def, true) if (def = nil) then return nil end theOutFTab = FTab.MakeNew(def, MultiPoint) theFElevField = Field.Make("FrElev",#FIELD_DECIMAL,8,3) theTElevField = Field.Make("ToElev",#FIELD_DECIMAL,8,3) theOutFTab.SetEditable(TRUE) theOutFTab.AddFields({theFElevField,theTElevField}) shapeField = theOutFTab.FindField("Shape") ' Check to make sure user has write permission to table If (theOutFtab.CanEdit) then theOutFtab.SetEditable(TRUE) Else MsgBox.Info("You do not have write permission to "+theOutFtab.GetBaseTableFileName.GetBaseName.AsString,"Exiting") Return Nil End ' Query each side of the river count = 0 For Each side in {1,2} aQueryString = "( [side] = "+side.asstring+" )" if (side = 2) then densifyValue = densifyValue/10 end aBitmap = theLineFtab.GetSelection theLineFTab.Query (aQueryString, aBitmap, #VTAB_SELTYPE_NEW) theNewPointsList = {} aPartCount = 0 For Each record In theLineFTab.GetSelection thePolyLine = theLineFTab.ReturnValue(theLineShape, record).ReturnDensified (densifyValue) thePolyLinePartsList = thePolyLine.Split (thePolyLine.AsMultiPoint) 'put all the points in a list For each aPart in thePolyLinePartsList aPoint = aPart.ReturnCenter theNewPointsList.Add(aPoint) End End


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theMultiPoint = MultiPoint.Make (theNewPointsList) 'arec = theOutFtab.AddRecord 'theOutFtab.SetValue (ShapeField, arec, theMultiPoint) if (side = 1) then mp1 = theMultiPoint else mp2 = theMultiPoint end count = count + 1 End 'Now difference the shapes 'mp1 = theOutFtab.ReturnValue(shapefield, 0) 'mp2 = theOutFtab.ReturnValue(shapefield, 1) pl2 = polyline.Make({mp2.asList}) anIncrement = 10 theNewPointsList = {} For each aPnt in mp1.aslist ' aradius = 50 While (true) aRadius = aRadius + anIncrement aCircle = circle.make(aPnt,aradius) If (aCircle.Intersects(mp2)) then break End End 'now get the centre position of this first intersecting circle and the line tmpPolyLine = aCircle.ReturnIntersection (pl2) tmpMPnt = tmpPolyLine.AsMultiPoint pntCount = 0 cumX = 0 cumY = 0 For each pnt in tmpMPnt.AsList cumX = cumX + pnt.GetX cumY = cumY + pnt.GetY pntCount = pntCount + 1 End tmpPnt = Point.Make(cumX/pntCount, cumY/pntCount) 'calculate the average between the original point and the center of the intersection meanPntX = (aPnt.GetX + tmpPnt.GetX)/2 meanPntY = (aPnt.GetY + tmpPnt.GetY)/2 meanPnt = Point.Make(meanPntX,meanPntY) theNewPointsList.Add(meanPnt) End arec = theOutFtab.AddRecord theOutFtab.SetValue (ShapeField, arec, mp1) arec = theOutFtab.AddRecord theOutFtab.SetValue (ShapeField, arec, mp2) arec = theOutFtab.AddRecord theMultiPoint = MultiPoint.Make (theNewPointsList) theOutFtab.SetValue (ShapeField, arec, theMultiPoint) theOutFtab.SetEditable(FALSE)


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10. Appendix C – multipnt_to_pnt.ave ' ---------------------------------------------------------------------' DiskFile : multipnt_to_pnt.ave : convert multipoint to point ' Programmer : Tom Van Niel ' Created : 27-May-2004 ' Revisions : ' ' Function : Converts multipoint features to point features.' ' References : None ' Called By : GUI ' Calls : None ' ---------------------------------------------------------------------' ' Initialise variables theView = av.getactivedoc theMPthm = theView.FindTheme("Intscn_hyp_space.shp") mpFtab = theMPthm.GetFtab mpShpFld = mpFtab.FindField("Shape") For each rec in mpFtab pntList = mpFtab.ReturnValue(mpShpFld,rec).AsList end 'Make new Shapefile outFN = av.GetProject.GetWorkDir.MakeTmp("outlut","shp") outStrng = msgbox.Input("Enter Output ShapeFile Name","out shp NAME",outFN.asString) If (outStrng = NIL) then return NIL End outStrng = outStrng.Trim.Substitute(" ","") outFN = FileName.Make(outStrng) outFtab = FTab.MakeNew(outFN,Point) outAttFld = Field.Make("Attrib",#FIELD_BYTE,1,0) outFtab.AddFields({outAttFld}) outShpFld = outFTab.FindField("Shape") For each pnt in pntList newRec = outFtab.AddRecord outFTab.SetValue (outShpFld, newRec, pnt) End


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11. Appendix D – pnt2multipnt.ave 'PNT2MULTIPNT - TVN - 31-MAY-2004 ' theView = av.getactivedoc thePTthm = theView.GetActiveThemes.Get(0) pntFtab = thePTthm.GetFtab pntShpFld = pntFtab.FindField("Shape") 'Make new Shapefile outFN = av.GetProject.GetWorkDir.MakeTmp("out_mp","shp") outStrng = msgbox.Input("Enter Output ShapeFile Name","out shp NAME",outFN.asString) If (outStrng = NIL) then return NIL End outStrng = outStrng.Trim.Substitute(" ","") outFN = FileName.Make(outStrng) outFtab = FTab.MakeNew(outFN,MultiPoint) outAttFld = Field.Make("Attrib",#FIELD_BYTE,1,0) outFtab.AddFields({outAttFld}) outShpFld = outFTab.FindField("Shape") For Each Num in 1..187 pntList = {} aQueryString = "( [Attrib] = "+Num.asstring+" )" aBitmap = pntFtab.GetSelection pntFTab.Query (aQueryString, aBitmap, #VTAB_SELTYPE_NEW) For Each record In pntFTab.GetSelection pnt = pntFtab.ReturnValue(pntShpFld,record) pntList.Add(pnt) end If (aBitmap.Count 0) then mp = multipoint.Make(pntList) newRec = outFtab.AddRecord outFTab.SetValue (outShpFld, newRec, mp) End End


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12. Appendix E – multip2plyline.ave 'TVN - 31-May_2004 'Convert MultiPoint features to PolyLines ' theView = AV.GetActiveDoc theProjection = theView.GetProjection theMPTheme = theView.GetActiveThemes.Get(0) theMPTheme.ClearSelection theMPFTab = theMPTheme.GetFtab theMPShape = theMPFTab.FindField("Shape") theDisplay = theView.GetDisplay def = av.GetProject.GetWorkDir.MakeTmp("ply_ln","shp") def = SourceManager.PutDataSet(ftab, "Output Shapefile Name", def, true) if (def = nil) then return nil end OutFTab = FTab.MakeNew(def, PolyLine) outAttFld = Field.Make("Attrib",#FIELD_BYTE,1,0) outFtab.AddFields({outAttFld}) OutFTab.SetEditable(TRUE) shapeField = OutFTab.FindField("Shape") ' Check to make sure user has write permission to table If (OutFtab.CanEdit) then OutFtab.SetEditable(TRUE) Else MsgBox.Info("You do not have write permission to "+OutFtab.GetBaseTableFileName.GetBaseName.AsString,"Exiting") Return Nil End For Each record In theMPFTab theMP = theMPFTab.ReturnValue(theMPShape, record) arec = OutFtab.AddRecord thePolyLine = PolyLine.Make ({theMP.AsList}) OutFtab.SetValue (ShapeField, arec, thePolyLine) End OutFtab.SetEditable(FALSE)


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13. Appendix F – sum_grid.ave ' TVN - sum_grid.ave - summarise grid by polygons ' Initialize Variables theView = av.GetActiveDoc thethemes = theView.GetThemes GrList = {} FList = {} aPrj = theView.GetProjection ' Put themes in separate lists for feature and Grid themes For each Thm in theThemes If (Thm.Is(GTheme)) then GrList.add(Thm) Elseif( Thm.Is(fTheme)) then FList.add(Thm) End End ' ERROR CHECK LISTS If (Flist.Count = 0) then System.Beep MsgBox.info("No feature Themes to Select From, must add Paddock Theme to the View","ERROR") Return NIL End If (GrList.Count = 0) then System.Beep MsgBox.info("No Grids to Select From, must add a Grid to the View","ERROR") Return NIL End ' Get info from User and Error Handle ' Get Paddock Theme Padd_theme = MsgBox.Choice(FList, "Choose the Paddock Theme","Paddock Selection") ' Check for Cancel, Exit if true If (Padd_theme = NIL) then System.Beep Return NIL End ' Make sure there is a feature selection on Paddock Theme PaddFtab = Padd_Theme.GetFtab OrigPaddBit = PaddFtab.GetSelection.Clone If (OrigPaddBit.Count = 0) then System.Beep MsgBox.info("No Paddock Theme features selected, please try again.","ERROR") Return NIL End ' Get Index (GRID) theme to summarize IndexLst = MsgBox.MultiList(GrList, "Choose the Index (Grid) Theme(s) to Summarize","Index Selection") ' Check for Cancel or no selection, Exit if true If ((IndexLst = Nil) or (IndexLst.Count = 0)) then System.Beep Return Nil End ' Run ZonalStats on Index Grid For Each Index_Theme in IndexLst 'Export paddock theme to a new shapefile so it can be joined with zstats later basename = Index_Theme.GetGrid.GetSrcName.GetFileName.GetBaseName newPaddFtab = Padd_Theme.ExportToFTab (av.GetProject.GetWorkDir.MakeTmp(basename,"shp")) zoneField = newPaddFTab.FindField("Id") aFN = av.GetProject.GetWorkDir.MakeTmp("zstat","dbf") zt = Index_Theme.GetGrid.ZonalStatsTable(newPaddFtab, aPrj, zoneField, TRUE, aFN) if (zt.HasError) then return NIL end ztJoinFld = zt.FindField("Id") ' Join Shape file's FTab to zt newPaddFtab.Join(zoneField,zt,ztJoinFld) ' Add new padd shapefile to view newPaddFTheme = FTheme.Make(newPaddFTab) theview.addtheme(newPaddFTheme) End


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Developing a Digital Elevation Model using ANUDEM for the CSHC, China

Developing a Digital Elevation Model using ANUDEM for the Coarse Sandy Hilly Catchments of the Loess Plateau, China QinKe Yang, Tom G. Van Niel, Tim R. McVicar Michael F. Hutchinson and LingTao Li CSIRO Land and Water Technical Report 7/05

CSIRO Land and Water Technical Report 7/05

www.csiro.au

www.csiro.au