On the topographic entity-oriented digital elevation model construction method for urban area land surface

doi:10.1007/s11707-020-0859-4

Front. Earth Sci.

2021, Vol. 15

Issue (3) : 580-594 https://doi.org/10.1007/s11707-020-0859-4

RESEARCH ARTICLE

On the topographic entity-oriented digital elevation model construction method for urban area land surface

Mingwei ZHAO^1,²(

), Ling JIANG¹, Chun WANG¹, Cancan YANG¹, Xin YANG³

¹. College of Geographic and Tourism, Chuzhou University, Chuzhou 239000, China
². State Key Laboratory of Resources and Environmental Information System, Beijing 100101, China
³. College of Foreign Languages, Chuzhou University, Chuzhou 239000, China

Download: PDF(3672 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Human activity transforms a land surface into a complex surface where artificial and natural landforms coexist and continuous and emergent landforms merge. In this background, the problems of conventional digital elevation models (DEMs), such as morphological distortion, complicated updates, and lack of information, are increasingly prominent. This study proposes a new idea of DEM construction based on the concept of geographic ontology. First, landforms with common features are abstracted into a certain type of topographic entity based on their morphologies and semantics. For each type of topographic entity, a DEM was constructed independently based on the available elevation information and other information about the semantics and spatial relationships. Second, individual DEMs were merged into a complete DEM following certain rules. A 1 km² area located in the suburb of Nanjing, Jiangsu Province, China, was selected as the experimental area. The effectiveness of the model construction method proposed in this study was verified. The results show that the DEM constructed according to the idea of this study has a significantly better performance than the conventional DEMs. The constructed DEM in this study can well represent ground objects, such as slopes, farmland, and ditches. In particular, the constructed DEM ensures the morphological accuracy of the ground objects.

Keywords topographic entity DEM urban area artificial strip terrain morphological accuracy

Corresponding Author(s): Mingwei ZHAO

Online First Date: 13 April 2021 Issue Date: 17 January 2022

Cite this article:

Mingwei ZHAO,Ling JIANG,Chun WANG, et al. On the topographic entity-oriented digital elevation model construction method for urban area land surface[J]. Front. Earth Sci., 2021, 15(3): 580-594.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-020-0859-4
https://academic.hep.com.cn/fesci/EN/Y2021/V15/I3/580

Fig.1 Concept image of topographic entity.

Fig.2 (a) Satellite photo and (b) Topographic image of the study area.

Fig.3 Terrain classification in the study area.

Fig.4 DEM construction process of artificial strip terrain.

Fig.5 Bidirectional densification and vertical abnormal line.

Fig.6 Schematic diagram of treatment results of the anomalies at a road curve.

Fig.7 (a) Relief image and (b) slope image of the study area.

Fig.8 Relief images of DEMs from different methods.

Tab.1 Elevation error statistics of research area/m

Fig.9 Error distribution from different methods.

1	F J Aguilar, F Agüera, M A Aguilar, F Carvajal (2005). Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogramm Eng Remote Sensing, 71(7): 805–816 https://doi.org/10.14358/PERS.71.7.805
2	Y An, F L Bian, J H Guan (2006). Design and comparision of Geo Ontology in GIS. Geomatics and Information Science of Wuhan Unversity, 31(12): 1108–1111 (in Chinese)
3	E P Baltsavias (1999). Airborne laser scanning: basic relations and formulas. ISPRS J Photogramm Remote Sens, 54(2–3): 199–214 https://doi.org/10.1016/S0924-2716(99)00015-5
4	E P Baltsavias, E Favey, A Bauder, H Bosch, M Pateraki (2001). Digital surface modelling by airborne laser scanning and digital photogrammetry for glacier monitoring. Photogramm Rec, 17(98): 243–273 https://doi.org/10.1111/0031-868X.00182
5	S Bhaskaran, S Paramananda, M Ramnarayan (2010). Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data. Applied Geography, 30(4): 650–665
6	P A Burrough, R A McDonnell (1998). Principles of Geographical Information Systems. New York: Oxford University Press
7	C F Chen, L Y Chang (2012). Rapid change detection of land use in urban regions with the aid of pseudo-variant features. J Appl Remote Sens, 6(1): 3574 https://doi.org/10.1117/1.JRS.6.063574
8	C F Chen, W Li, M F Li, H Dai (2013). A robust multiquadratic method and its application to DEM construction. Journal of Geo-Information Science, 15(6): 840–845 https://doi.org/10.3724/SP.J.1047.2013.00840
9	C F Chen, F Y Liu, C Q Yan, H L, Dai J Y Guo, G L Liu (2016). A huber-derived robust multi-quadric interpolation method for DEM construction. Geomatics and Information Science of Wuhan University, 41(6): 803–809 (in Chinese)
10	C F Chen, D Y Zheng, T X ( Yue 2010). Efficient fusion of ASTER and SRTM based on fast fourier transform. Remote Sensinc for Land and Resources, (4): 19–22
11	J J Chen, C H Zhou, J G ( Wang 2006). Advances in the study of the geoontology. Earth Science Frontiers, 13(3): 081–090
12	I Colomina, P Molina (2014). Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPS J Photogramm Remote Sens, 92: 79–97 https://doi.org/10.1016/j.isprsjprs.2014.02.013
13	R A Diaz-Varela, P J Zarco-Tejada, V Angileri, P Loudjani (2014). Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. J Environ Manage, 134: 117–126 https://doi.org/10.1016/j.jenvman.2014.01.006 pmid: 24473345
14	M Favalli, M T Pareschi (2004). Digital elevation model construction from structured topographic data: the DEST algorithm. J Geophys Res Earth Surf, 109: F04004 https://doi.org/10.1029/2004JF000150
15	P Fisher (1991). First experiments in viewshed uncertainty: the accuracy of the viewshed area. Photogramm Eng Remote Sensing, 57(10): 1321–1327
16	D W Henderson (1998). Differential Geometry. London: Prentice-Hall
17	M F Hutchinson (1989). A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. J Hydrol (Amst), 106(3–4): 211–232 https://doi.org/10.1016/0022-1694(89)90073-5
18	M F Hutchinson, J C Gallant (2000). Digital elevation models and representation of terrain shape. In: Wilson J P, Gallant J C, eds., Terrain Analysis: Principles and Applications. New York: Wiley, 29–50
19	M Javanmardi, E Javanmardi, Y L Gu, S Kamijo (2017). Towards high-definition 3D urban mapping: road feature-based registration of mobile mapping systems and aerial imagery. Remote Sens, 9(10): 975 https://doi.org/10.3390/rs9100975
20	L Jiang, M W Zhao, T X Yue, N Zhao, C Wang, J Sun L(2018). A modified HASM algorithm and its application in DEM construction. Earth Sci Inform, 11(3): 423–432 https://doi.org/10.1007/s12145-018-0341-y
21	V Karathanassi, C Iossifidis, D Rokos (1999). A thinning-based method for recognizing and extracting peri-urban road networks from SPOT panchromatic images. Int J Remote Sens, 20(1): 153–168 https://doi.org/10.1080/014311699213668
22	M Karkee, B L Steward, S A Aziz (2008). Improving quality of public domain digital elevation models through data fusion. Biosyst Eng, 101(3): 293–305 https://doi.org/10.1016/j.biosystemseng.2008.09.010
23	D Kawabata, J Bandibas (2010). Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology, 113(1–2): 97–109
24	J H Li, W J Chen (2005). A rule-based method for mapping Canada’s wetlands using optical, radar and DEM data. Int J Remote Sens, 26(22): 5051–5069 https://doi.org/10.1080/01431160500166516
25	A J D Mcnally, S J P McKenzie (2011). Combining multispectral aerial imagery and digital surface models to extract urban buildings. J Maps, 7(1): 51–59 https://doi.org/10.4113/jom.2011.1152
26	I D Moore, R B Grayson, A R Ladson (1991). Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Processes, 5(1): 3–30 https://doi.org/10.1002/hyp.3360050103
27	P N C Murphy, J Ogilvie, F R Meng, P Arp (2008). Stream network modelling using lidar and photogrammetric digital elevation models: a comparison and field verification. Hydrol Processes, 22(12): 1747–1754 https://doi.org/10.1002/hyp.6770
28	M Ortner, X Descombes, J Zerubia (2008). A marked point process of rectangles and segments for automatic analysis of digital elevation models. IEEE Trans Pattern Anal Mach Intell, 30(1): 105–119 https://doi.org/10.1109/TPAMI.2007.1159 pmid: 18000328
29	L G Palamuleni, N N Ndou (2014). Developing remote sensing methodology to distinguish urban built-up areas and bare land in Mafikeng town, South Africa. In: Geoscience and Remote Sensing Symposium. IEEE
30	H Papasaika, E Kokiopoulou, E Baltsavias, K Schindler, D Kressner (2011). Fusion of digital elevation models using sparse representations. Photogrammetric Image Analysis, 6952: 171–184 https://doi.org/10.1007/978-3-642-24393-6_15
31	R J Pike (2000). Geomorphometry—diversity in quantitative surface analysis. Prog Phys Geogr, 24: 1–20
32	T Podobnikar (2005). Production of integrated digital terrain model from multiple datasets of different quality. Int J Geogr Inf Sci, 19(1): 69–89 https://doi.org/10.1080/13658810412331280130
33	G Priestnall, J Jaafar, A Duncan (2000). Extracting urban features from LiDAR digital surface models. Comput Environ Urban Syst, 24(2): 65–78 https://doi.org/10.1016/S0198-9715(99)00047-2
34	W ( Reinartz2005).Understanding customer loyalty programs. Heidelberg: Springer, 361–379
35	A Roth, W Knpfle, G Strunz, M Lehner, P Reinartz (2002). Towards a global elevation product: combination of multi-Source digital elevation models. In: Proceedings. of Joint International Symposium on Geo-spatial Theory, Processing and Applications, Ottawa, Canada, 675–679
36	H Schultz, E M Riseman, F R Stolle, D M Woo (1999). Error detection and DEM fusion using self-consistency. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision. Los Alamitos, CA: IEEE Computer Society, 2: 1174–1181
37	K Slatton, S Teng, M ( Crawford 2002).Multiscale fusion of InSAR data for hydrological applications. In: Symposium on Terrain Analysis for Water Resources Applications, University of Texas, Austin, USA
38	D Somasundaram (2005). Differential Geometry. Harrow: Alpha Science International Ltd.
39	D J Song, T X Yue, Z P Du (2012). A new method of DEM generation from contour line. Geomatics and Information Science of Wuhan University, 37(4): 472–476 (in Chinese)
40	V A Toponogov (2006). Differential Geometry of Curves and Surfaces. New York: Birkhaeuser Boston
41	C Wang, G A Tang, X J Liu, Y Tao (2009). The model of terrain features preserved in grid DEM. Geomatics and Information Science of Wuhan University 34(10): 1149–1154 (in Chinese)
42	K Wang, P F Xiao, X Z Feng, G P Wu, H Li (2013). Extraction of urban rivers from high spatial resolution remotely sensed imagery based on filtering in the frequency domain. Journal of Remote Sensing, 17(2): 269–285
43	B Yang, W Shi, Q Li (2005). An integrated TIN and Grid method for constructing multi-resolution digital terrain models. Int J Geogr Inf Sci, 19(10): 1019–1038 https://doi.org/10.1080/13658810500391156
44	Q K Yang, W J Shi, . R McVicar, T G Van Niel, L T Li (2007). On constructing methods of hydrologically correct DEMs. Science of Soil and Water Conservation, 5(4): 1–6
45	H F, Yin P, Lv C W Zheng, X H Hu (2012) A Multi-source spatial data fusion method used for terrain simulation. In: Qian Z H, Cao L, Su W L, Wang T K, Yang H M, eds. Recent Advances in Computer Science and Information Engineering, vol 124. Heidelberg: Springer
46	J Youn, J S Bethel, E M Mikhail, C Lee (2008). Extracting urban road networks from high-resolution true orthoimage and lidar. Photogramm Eng Remote Sensing, 74(2): 227–237 https://doi.org/10.14358/PERS.74.2.227
47	L Yue, H Shen, L Zhang, X Zheng, F Zhang, Q Yuan (2017). High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations. ISPRS J Photogramm Remote Sens, 123: 20–34 https://doi.org/10.1016/j.isprsjprs.2016.11.002
48	T X Yue (2011). Surface Modeling: High Accuracy and High Speed Methods. New York: CRC Press
49	T X Yue, C F Chen, B L Li (2010a). An adaptive method of high accuracy surface modeling and its application to simulating elevation surfaces. Trans GIS, 14(5): 615–630 https://doi.org/10.1111/j.1467-9671.2010.01213.x
50	T X Yue, Z P Du, D J Song, Y Gong (2007). A new method of surface modeling and its application to DEM construction. Geomorphology, 91(1–2): 161–172 https://doi.org/10.1016/j.geomorph.2007.02.006
51	T X Yue, D J Song, Z P Du, W Wang (2010b). High-accuracy surface modelling and its application to DEM generation. Int J Remote Sens, 31(8): 2205–2226 https://doi.org/10.1080/01431161003668982
52	T X Yue, N Zhao, Y Liu, Y Wang, B Zhang, Z Du, Z Fan, W Shi, C Chen, M Zhao, D Song, S Wang, Y Song, C Yan, Q Li, X Sun, L Zhang, Y Tian, W Wang, Y Wang, S Ma, H Huang, Y Lu, Q Wang, C Wang, Y Wang, M Lu, W Zhou, Y Liu, X Yin, Z Wang, Z Bao, M Zhao, Y Zhao, Y Jiao, U Naseer, B Fan, S Li, Y Yang, J P Wilson (2020). A fundamental theorem for eco-environmental surface modelling and its applications. Sci China Earth Sci, 63(8): 1092–1112 https://doi.org/10.1007/s11430-019-9594-3
53	N Zhao, T X Yue, M W Zhao, Z P Du, Z M Fan, C F Chen (2014). Sensitivity studies of a high accuracy surface modeling method. Sci China Earth Sci, 57(10): 2386–2396 https://doi.org/10.1007/s11430-014-4926-0
54	W D Zhao, W Zhou, G A Tang, L Ma, J T Zhao (2015). Study on grid-tin hybrid DEM-based numerical simulation model of terraced dryland. Geography and Geo-Information Science, 31(3): 38–43 (in Chinese)

[1]	Ziyang DAI, Fayuan LI, Mingwei ZHAO, Lanhua LUO, Haoyang JIAO. Extraction of lacunarity variation index for revealing the slope pattern in the Loess Plateau of China[J]. Front. Earth Sci., 2021, 15(1): 94-105.
[2]	Bartłomiej SZYPUŁA, Małgorzata WIECZOREK. Geomorphometric relief classification with the k-median method in the Silesian Upland, southern Poland[J]. Front. Earth Sci., 2020, 14(1): 152-170.
[3]	Hongchun ZHU, Yuexue XU, Yu CHENG, Haiying LIU, Yipeng ZHAO. Landform classification based on optimal texture feature extraction from DEM data in Shandong Hilly Area, China[J]. Front. Earth Sci., 2019, 13(3): 641-655.
[4]	Guiyun ZHOU, Hongqiang WEI, Suhua FU. A fast and simple algorithm for calculating flow accumulation matrices from raster digital elevation[J]. Front. Earth Sci., 2019, 13(2): 317-326.
[5]	Jianjun CAO, Guoan TANG, Xuan FANG, Jilong LI, Yongjuan LIU, Yiting ZHANG, Ying ZHU, Fayuan LI. Terrain relief periods of loess landforms based on terrain profiles of the Loess Plateau in northern Shaanxi Province, China[J]. Front. Earth Sci., 2019, 13(2): 410-421.
[6]	Xin YANG, Jiaming NA, Guoan TANG, Tingting WANG, Axing ZHU. Bank gully extraction from DEMs utilizing the geomorphologic features of a loess hilly area in China[J]. Front. Earth Sci., 2019, 13(1): 151-168.
[7]	Parham PAHLAVANI, Behnaz BIGDELI. A mutual information-Dempster-Shafer based decision ensemble system for land cover classification of hyperspectral data[J]. Front. Earth Sci., 2017, 11(4): 774-783.
[8]	Yue LIU,Qiuming CHENG,Qinglin XIA,Xinqing WANG. The use of evidential belief functions for mineral potential mapping in the Nanling belt, South China[J]. Front. Earth Sci., 2015, 9(2): 342-354.
[9]	Huan LI,Dingyi XU,Yan ZHAO. Peatland area change in the southern Altay Mountains over the last twenty years based on GIS and RS analysis[J]. Front. Earth Sci., 2014, 8(4): 558-563.
[10]	Maotian LI,Jianzhong GE,Jens KAPPENBERG,Dagmar MUCH,Ohle NINO,Zhongyuan CHEN. Morphodynamic processes of the Elbe River estuary, Germany: the Coriolis effect, tidal asymmetry and human dredging[J]. Front. Earth Sci., 2014, 8(2): 181-189.
[11]	Shouke WEI, Albrecht GNAUCK, Alin LEI. Simulation analysis of domestic water demand and its future uncertainty in water scarce areas[J]. Front Earth Sci Chin, 2009, 3(3): 349-360.

Viewed

Full text

Abstract

Cited

Shared

Discussed