38, Tripoleos Str., Athens , Greece, 23, Velvedou Str., Athens , Greece,

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1 A Feature Based Accuracy Evaluation of GTOPO30 Miliaresis, G. Ch 1 & Paraschou, C. V. E. 2 Remote Sensing Laboratory, National Technical University of Athens, 1 38, Tripoleos Str., Athens , Greece, miliaresis@ .com, 2 23, Velvedou Str., Athens , Greece, paraschou@ .com Abstract The aim is the accuracy evaluation of the information content of GTOPO30 on the basis of the parametric representation of mountains. A comparison of the maximum elevation of the mountain objects extracted from the GTOPO30 to the elevation measured from the aeronautical maps of scale 1: (maximum elevation figures produced by the UK Military Survey) is performed. It was found that: The mean absolute difference was m. If a weighted estimation of the difference is computed on the basis of the area extent of the mountains then the mean difference was equal to m (largest in size mountains have greatest differences). The scattergram of the maximum elevation determined from the DEM versus the map heights indicates that the mountain features with elevation greatest than 3000 m in the map were forced to be equal to the value 3000 m in the DEM. The spatial distribution of the differences showed that the greatest differences were in North-NW where the highest and greatest in size mountains are evident. We conclude that a systematic error exists in the dataset. Either the maximum cell elevation value was set equal to 3000 m during the resampling stage or the contour lines of height greater than 3000 m were not included in the production of DEM. 1. Introduction The mountains are of great significance in geological-geomorphological interpretation, in military mapping and in navigation of airplanes and missiles. The global digital elevation model (DEM) GTOPO30 at 30 arc second spacing (US Geological Survey 1998) was used for the extraction of global hydrologic features (Verdin and Jenson 1996) and the extraction of physiographic features (Miliaresis and Argialas 1998a, 1999a), while coarse low-quality DEMs like GTOPO30 are used in the InSAR processing chain (Seymour and Cumming 1999). In a previous research effort (Miliaresis 2001), a method for regional geomorphometric mapping and landscape characterisation was developed on the basis of GTOPO30. The method was implemented in Zagros Ranges (Iran) of size 330,000 km 2 (Figure 1). More specifically: Figure 1. The study area in the map of Asfar et al. (1976). The GTOPO30 DEM of the study area (geographic co-ordinates) was converted to a RSPS2001 Proceedings Terrain Modelling 203

2 rectangular grid with 926 m spacing (Figure 2) and resampled by the nearest neighbour rule. The DEM-to-Mount transformation, a region-growing algorithm for terrain segmentation was implemented for the extraction of mountain objects (Miliaresis 2000a). The landscape of the study area was decomposed to a) two terrain classes named mount and non-mount (Figure 3) and b) isolated mountain objects that were numerically represented on the basis of appropriate geomorphometric attributes (Miliaresis 2000b). Figure 2. GTOPO30 DEM of the study area. The elevation (1 to 3,000 m) was rescaled to the interval 255 to 0 (the brightest pixels have lowest elevation). The extracted mountains were found to be in accordance to the features interpreted visually from the (GTOPO30) shaded relief map (Miliaresis 2001). The evaluation procedure is actually an evaluation of the algorithm performance. The aim of this research is to evaluate the accuracy and the information content of GTOPO30 on the basis of the parametric representation of mountains, in order to verify its potential uses and applications to various scientific fields (Pike 1995, 1999). Figure 3. The pixels labelled white (within the study area) represent the mountain terrain class identified by the algorithm. 2. Methodology At first, the attribute representation of the mountain features, the accuracy characteristics of GTOPO30 and the availability of regional scale topographical maps are discussed. Finally attributes are selected and measured from maps and compared to the attribute representation of mountain objects derived from GTOPO30. RSPS2001 Proceedings Terrain Modelling 204

3 2.1 Attribute based representation of mountains The most typical representation of a mountain is performed on the basis of its maximum elevation (Hmax) while its area size and orientation are important attributes too. The maximum elevation of a mountain is identified with great positional and hypsometric accuracy (spot heights) in topographical maps. For example the elevation of the mountain feature KOH-E-DERA (Figure 4) is well defined in the topographic map of Zagros Ranges (Military Survey 1998). On the contrary the other attributes are not well defined since their computation depend either on certain thresholds required by the region growing segmentation algorithm that delineates the mountains Figure 4. The elevation of the from DEMs (Graff and Usery 1993, Miliaresis and mountain-top (KOH-E- Argialas 1998a, 1999a, Miliaresis 2000a, Miliaresis DERA) is 5596 feets. 2001) or the expertise of a certain photointerpreter who delineates the mountains manually from topographic maps. For example if the gradient region-growing criterion is increased then a) the size of the mountain features extracted from DEMs will decrease and b) the elevation of the mountain base (Hmin) will increase (Argialas and Miliaresis 2001). The maximum elevation participates to the computation of the most significant attributes used for the parametric description of the mountains (Miliaresis and Argialas 1998b, 1999b, 1999c, 2002) like Local Relief (LR), Massiveness (M) and Mean Elevation (Hmean), see equations 1 and 2. LR= Hmax-Hmin (1) M= (Hmax-Hmean) / (Hmax-Hmin) (2) Local relief determines the height of the mountain above its base and massiveness determines the amount of material that was removed by erosion (Mark 1975) while Hmean corresponds to the mean mountain volume per pixel above sea level (Miliaresis 2001). All of them are very important factors for the geomorphologic characterization of mountains (Miliaresis 2000b). We conclude that the maximum elevation of a mountain feature is well defined in a topographic map and definitely is well preserved within the corresponding mountain object extracted from a DEM. Thus in this research effort we shall evaluate the accuracy and information content of GTOPO30 by comparing the maximum elevation values computed for the mountain objects extracted from the GTOPO30 to the values determined from military topographical maps. The TPC series of aeronautical maps of scale 1: with basic and intermediate contour interval 500 and 250 feet respectively, produced by the United Kingdom Military Survey were selected. These maps were partially revised and recompiled in 1998 (Military Survey 1998) and contain maximum elevation figures (MEF) while at least the highest mountain-top per mountain feature is marked by a spot height (Figure 4). Note that MEF is based on mean sea level and on information available concerning the highest known feature including terrain and obstructions RSPS2001 Proceedings Terrain Modelling 205

4 (trees, towers, antennae, etc.) Figure 5. GTOPO30 was derived from several raster and vector sources of topographic information ( 2.2 GTOPO30 Accuracy Specifications The global 1-kilometer DEM's including GTOPO30, are based primarily on data derived from two sources: a) Digital Chart of the World (DCW) a vector dataset and b) Digital Terrain Elevation Data (DTED), a raster dataset (Gesch and Larson 2000). The data source of GTOPO30 for the test site area (Zagros Ranges) is the raster topographic database DTED with a horizontal grid spacing of 3-arc seconds (Figure 5). The 3-arc second data (DTED) were generalised to 30-arc seconds by selecting one elevation value to represent the area covered by 100 (a 10 by 10 matrix) full resolution grid cells (US Geological Survey 1998). There are several options for aggregating the 100 full resolution grid cells into 1 generalised, reduced resolution grid cell. For the Eurasia DTED selection of the representative 30-arc second value was being by calculation of the median value. Such an approach is ideal for low relief areas, but significant smoothing of features can occur in high relief areas and prominent topographic features may not be represented. The absolute vertical accuracy of the Eurasia DEM is according to the source data. DTED have a vertical accuracy of ± 30 meters linear error at the 90 percent confidence level and can also be described as a RMSE of 18 meters (US Geological Survey 1998). The areas of the Eurasia DEM derived from DTED retain that same level of accuracy because through generalisation an actual elevation value from one full resolution DTED cell is chosen to represent the area of the reduced resolution cell (although the area on the ground represented by that one elevation value is now much larger than the area covered by the full resolution cell). Note that the GTOPO30 grid derived from DCW have a vertical accuracy (linear error at the 90 percent confidence level) of ± 160 meters or ½ of the contour interval (305 m), described as an RMSE of 97 meters (US Geological Survey 1998). A quality assessment of GTOPO30 on the basis of both spaceborne laser and radar altimetry was performed by Muller et al. (1999). In bare regions laser and radar altimetry data appear to be close to the GTOPO30 (regions that were produced from DTED) having an error 5.8m ± m. RSPS2001 Proceedings Terrain Modelling 206

5 2.3 Data Analysis Table 1 Mountain attributes. ID Area Height (m) (km 2 ) DEM Map Figure 6. Mountain objects ID. RSPS2001 Proceedings Terrain Modelling 207

6 The mountain objects that were extracted by the segmentation algorithm (Figure 6) were also delineated on the topographic maps of scale 1: (Military Survey 1998). For example, the mountain object 85 (see Table 1 and Figure 6) corresponds to the mountain of Figure 4 in the topographic map. Additionally, the mountain object 112 (see Table 1 and Figure 6) corresponds to the mountain of Figure 7. For each mountain objects delineated by the algorithm, corresponds a mountain feature in the map. On the contrary, due to the size thresholds selected (Miliaresis 2000a, 2001) some very small mountain features observed in the map, were omitted by the algorithm. Figure 7. The object 112. RSPS2001 Proceedings Terrain Modelling 208

7 The size and the maximum elevation of the mountain objects computed from GTOPO30 in addition to the elevation computed from the map is listed in Table 1. Statistical analysis indicated the followings: The mean maximum elevation and standard deviation of the mountain objects computed from the GTOPO30 is 2017 ± 728 m. The mean maximum elevation and standard deviation of the mountain features computed from the maps is 2090 ± 834 m. The difference in the mean values tends to be insignificant and did not indicate any major difference between the two datasets while more severe difference exists in standard deviation. The mean absolute difference H DEM -H MAP was 120 ± 207 m. If a weighted estimation of the mean absolute difference is computed on the basis of the size of the mountains H DEM -H MAP * (Mountain_size / Total_size_of_mountain_terrain_class) then the mean difference was found to be equal to m (largest in size mountains have greatest differences in elevation). Figure 8. The scattergram of the maximum elevation determined from the GTOPO30 (DEM) versus the map heights RSPS2001 Proceedings Terrain Modelling 209

8 In order to further explore the situation the scattergram (Figure 8) of the maximum elevation determined from the GTOPO30 versus the map was constructed. It was concluded that the mountain features with elevation greatest than 3000 m in the map were forced to be equal to the value 3000 m in the GTOPO30. Table 2. Mean and standard deviation of elevation differences for four intervals. Interval Mean St. Dev m m m Number of objects A [ 369, 1000 ) B [ 1000, 2000 ) C [ 2000, 3000 ) D [ 3000, 4409 ] Total [369, 4409] A systematic distribution of error in certain intervals exists, thus the mean elevation difference was computed for four intervals (Table 2). It is concluded that the most accurate objects on the basis of mountaintop-test are those in the interval [1000, 2000) meters while those in intervals [369, 1000) and [2000, 3000) retain the expected level of accuracy by US Geological Survey (1998). The mountain features in the interval [3000, 4409] retain a level of accuracy that can be compared to the error assumed for areas of GTOPO30 derived from the DCW (US Geological Survey 1998). In order to examine the spatial distribution of errors and detect clusters of mountain objects with some degree of spatial arrangement, a map was constructed as follows: Table 3. Equalize occurrence between mountain classes. Class Shading Height Difference (m) DEM-MAP Occurrence % Number of objects 1 ( 0, 29 ] ( 29, 125 ] ( 125, 943 ] ( 943, 1409 ] The domain of the height difference ( DEM-MAP ) was sliced into intervals by taking into account a) the spatial occurrence (percentage area of the mount terrain class occupied by objects in a certain interval) (Table 3) and b) the frequency (the number of mountains that belong in a certain interval) (Table 4). The objects of a certain class were depicted with a unique shade of gray (Figure 9 and 10). RSPS2001 Proceedings Terrain Modelling 210

9 Table 4. Equalize frequency between mountain classes. Class shading Difference (m) DEM-MAP Occurrence % Number of objects 1 ( 0, 18 ] ( 18, 46 ] ( 46, 166 ] ( 166, 1409 ] Figure 9. The spatial distribution (equalize occurrence) of the 4 mountain classes with different range of height differences (Table 3). Figure 10. The spatial distribution (equalize frequency) of the 4 mountain classes with different range of height differences (Table 4). The spatial distribution of the differences (Figure 9 and 10) showed that the greatest differences were in North-NW where the highest and greatest in size mountains are evident. On the contrary in South-SE, minimum differences are observed. RSPS2001 Proceedings Terrain Modelling 211

10 3. Conclusion and Prospects We conclude that a systematic error exists in the dataset. Either the maximum cell elevation value was set equal to 3000 m during the resampling of GTOPO30 (3 arc sec DTED was resampled to 30 arc sec) or the contour lines of height greater than 3000 m were not included in the production of DTED dataset (used for the production of GTOPO30 DEM in Zagros). On the other hand, the mountain objects that are not affected by this systematic error fulfil the GTOPO30 accuracy specifications for DTED. The height accuracy of the mountaintop affects the computation of the most important attributes used for the parametric description of a mountain. Thus, the mountain-tops accuracy test is of great importance in geomorphologic and geomorphometric studies. It is suggested that DEMs should be evaluated on the basis the mountain-top accuracy test and this test should be included among the quality assessment measures of DEMS. 4. References ARGIALAS, D., and MILIARESIS, G., Human factors in the Interpretation of Physiography by Symbolic and Numerical Representations within an Expert System, In «Human factors in the Interpretation of Remote Sensing Imagery» by R. R. Hoffman and A. B. Markman (Eds) (Lewis Publishers, New York), pp ASFAR, A., ESHGHI, M., and ODOULI, K., Geological history and stratigraphy of Iran. In Proceedings, Well Evaluation Conference (Schlumberger: Tehran, Iran), pp GESCH, D., and LARSON, K., Techniques for Development of Global 1-km Digital Elevation Models. GRAFF, L., and USERY, E., 1993, Automated classification of generic terrain features in digital elevation models. Photogrammetric Engineering & Remote Sensing, 59(9), MARK, D., 1975, Geomorphometric parameters, a review and evaluation, Geographiska Annaler 57A(10), MILIARESIS, G., 2000a, The DEM to mountain transformation of Zagros Ranges, In Proceedings of the 5th International Conference on GeoComputation, (University of Greenwich: Greenwich), 8 pages. MILIARESIS, G., 2000b, Landscape characterisation of Zargos Ranges, In Proceedings of the 26th International Conference of the Remote Sensing Society, (University of Leicester: Leicester), 8 pages. MILIARESIS, G., 2001, Geomorphometric mapping of Zagros Ranges at regional scale. Computers & Geosciences, 27, MILIARESIS, G., and ARGIALAS, D., 1998a, Physiographic feature extraction from moderate resolution digital elevation data, In Proceedings of the 24th International Conference of the Remote Sensing Society (University of Greenwich: Greenwich), pp MILIARESIS, G., and ARGIALAS, D., 1998b, Parametric representation and classification of mountain objects extracted from moderate resolution digital elevation data, In Proceedings of the 4th International Conference of the Association for Mathematical Geology (University of Firenze: Ispra-Naples), pp MILIARESIS, G., and ARGIALAS, D., 1999a, Segmentation of physiographic features from the Global digital elevation model / GTOPO30. Computers & Geosciences, 25(7), MILIARESIS, G., and ARGIALAS, D., 1999b, Fuzzy pattern recognition of compressional mountain ranges in Iran., In Proceedings of the 5 th International Conference of the Association for Mathematical Geology (IAMG: Trondheim), pp MILIARESIS, G., and ARGIALAS, D., 1999c, Formalization of the photo-interpretation process by a fuzzy set representation of mountain objects in the geomorphic context of the great basin section, In Proceedings of the 25 th Conference of the Remote Sensing Society, RSPS2001 Proceedings Terrain Modelling 212

11 (University of Wales: Cardiff) pp MILIARESIS, G., and ARGIALAS, D., 2002, Quantitative representation of mountain objects extracted from the global digital elevation model (GTOPO30). International Journal of Remote Sensing, [to appear in 2002]. MILITARY SURVEY, TPC Aeronautical Map Series of scale 1: (Iran, Zagros Ranges). Map sheets: H-6A, H-6B, H-6C, H-7A, H-7D, G-5C, G-5D, G-4C. Produced by the Military Survey, Ministry of Defence, United Kingdom, Edition 7-GSGS (Crown Copyright 1998). MULLER, J-P., KIM, J., and MORLEY, J., 1999, Quality assessment of global cartographically -derived DEMs using spaceborne altimetry, In Proceedings of the 25 th Conference of the Remote Sensing Society. (University of Wales, Cardiff) pp PIKE, R., 1995, Geomorphometry-process, practice and prospects. Zeitshcrift f. Geomorphologie N.F. suppl. Bd., 101, PIKE, R., A bibliography of geomorphometry, the quantitative representation of topography-supplement 3, (U.S. Geological Survey, Open-File Report : Menlo Park). SEYMOUR, M., and CUMMING, I., InSAR Terrain Height Estimation Using Low- Quality Sparse DEMs. lowresdem.html US GEOLOGICAL SURVEY, GTOPO 30 arc-seconds Digital Elevation Model VERDIN, K., and JENSON, S., 1996, Development of continental scale digital elevation models and extraction of hydrographic features, In Proceedings of the 3 rd International Conference on Integrating GIS and Environmental Modeling (NCGIA, Santa Fe), RSPS2001 Proceedings Terrain Modelling 213