Geomatics and Environmental Engineering, vol. 18, no. 4

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Vol. 18 No. 4 (2024): Geomatics and Environmental Engineering

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Submitted
May 4, 2024
Accepted
Jul 10, 2024
Published
Sep 17, 2024
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Advanced Method for Determining Land Plot’s Physical Surface

https://doi.org/10.7494/geom.2024.18.4.73
Mykola Malashevskyi
The Institute of Land Management of National Academy of Agrarian Sciences of Ukraine, Department of Problems of Land Development and Land Cadastre, Kyiv, Ukraine
https://orcid.org/0000-0001-7171-8835
Olena Malashevska
National University of Life and Environmental Sciences of Ukraine, Geodesy and Cartography Department, Kyiv, Ukraine
https://orcid.org/0000-0002-5387-5674

Corresponding Author(s) : Olena Malashevska

olenamalashevska@gmail.com

Geomatics and Environmental Engineering, Vol. 18 No. 4 (2024): Geomatics and Environmental Engineering

Abstract

This study examines the issue of the accuracy of physical surface calculations for land plots with complex configurations and reliefs. The goal of the study was to develop a methodology for determining the surface area of land plots with complex configurations and reliefs. The presented model was based on the finite element method. The developed method allows one to evaluate a relief’s complexity by using a dimensionless mean physical surface complexity factor; a Fortran program was developed for the methodology. Experiments that proved the effectiveness of the methodology and a comparative analysis of those areas that were calculated by the presented method and TIN model were carried out. The research findings proved the practicability of the methodology for calculating the physical surfaces of land plots with complex configurations. The presented methodology can be used for flood modeling, landscape and vertical planning, etc.

Keywords

DEM area measurement finite element method TIN model GRID model
Malashevskyi, M. ., & Malashevska, O. (2024). Advanced Method for Determining Land Plot’s Physical Surface. Geomatics and Environmental Engineering, 18(4), 73–96. https://doi.org/10.7494/geom.2024.18.4.73
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References
  1. Windarni V.A., Sediyono E., Setiawan А.: The evaluation of land area measurement using GPS technology. Jurnal Ilmiah Kursor, vol. 9(1), 2017, pp. 1–8. https://doi.org/10.28961/kursor.v9i1.120.
  2. Carletto G., Gourlay S., Murray S., Zezza A.: Land Area Measurement in Household Surveys: A Guidebook. World Bank, Washington DC, 2016.
  3. Sandeep K.: GPS based handheld system for land area measurement. International Journal for Research in Applied Science & Engineering Technology, vol. 9, 2021, pp. 1619–1624. https://doi.org/10.22214/ijraset.2021.35302.
  4. FAO: FAO Training Series. 10. Measurement of Areas. https://www.fao.org/fishery/static/FAO_Training/FAO_Training/General/x6707e/x6707e10.htm [access: 30.03.2023].
  5. Kumar L.A., Jebarani M.R.E., Krishnan V.G., Ahmad M.W.: Multitemporal change detection and irregular land shape area measurement from multispectral sensor images through BSO algorithm. Mathematical Problems in Engineering, 2022, 3090074. https://doi.org/10.1155/2022/3090074.
  6. Hao X., Pan Yu.: Accuracy analysis of earthwork calculation based on triangulated irregular network (TIN). Intelligent Automation & Soft Computing, vol. 17(6), 2011, pp. 793–802. https://doi.org/10.1080/10798587.2011.10643188.
  7. Kim J., Kim H., Tanoli W., Seo J.: 3D earthwork BIM design and its application in an advanced construction equipment operation. Architecture and Engineering, vol. 4(2), 2019, pp. 22–26. https://doi.org/10.23968/2500-0055-2019-4-2-22-26.
  8. Nguyen V.T.: Building TIN (triangular irregular network) problem in topology model. [in:] 2010 International Conference on Machine Learning and Cybernetics (ICMLC 2010), 11–14 July 2010, Qingdao, China, vol. 1, IEEE, Piscataway 2010, pp. 14–21. https://doi.org/10.1109/ICMLC.2010.5581100.
  9. Li Z., Wu L., Zhang Z., Yang Y.: CD-TIN based urban inundation simulation method and its experiment. Geomatics and Information Science of Wuhan University, vol. 39(9), 2014, pp. 1080–1085. https://doi.org/10.13203/j.whugis20120098.
  10. Skorkovská V., Kolingerova I., Benes B.: Hydraulic erosion modeling on a triangular mesh. [in:] Růžičková K., Inspektor T. (eds.), Surface Models for Geosciences, Lecture Notes in Geoinformation and Cartography, Springer, Cham 2015, pp. 237–247. https://doi.org/10.1007/978-3-319-18407-4_20.
  11. He L., Li X., Lei S., Bi B., Chen S.: A front advancing adaptive triangular mesh dynamic generation algorithm and its application in 3D geological modeling. Sustainability, vol. 15(9), 2023, 7214. https://doi.org/10.3390/su15097214.
  12. Wilson J.P., Gallant J.C.: Digital terrain analysis. [in:] Wilson J.P., Gallant J.C. (eds.), Terrain Analysis: Principles and Applications, John Wiley & Sons, 2000, pp. 1–27.
  13. Masini N., Coluzzi R., Lasaponara R.: On the airborne lidar contribution in archaeology: From site identification to landscape investigation. [in:] Wang C.-C. (ed.), Laser Scanning: Theory and Applications, InTech, 2011, pp. 263–290. https://doi.org/10.5772/14655.
  14. Maliqi E., Penev P., Kelmendi F.: Creating and analysing the Digital Terrain Model of the Slivovo area using QGIS software. Geodesy and Cartography, vol. 43(3), 2017, pp. 117–123. https://doi.org/10.3846/20296991.2017.1376445.
  15. Rasmus J.C., Hjelle Ø.: Multiresolution spline models and their applications in geomorphology. [in:] Evans I.S., Dikau R., Tokunaga E., Ohmori H., Hirano M. (eds.), Concepts and Modelling in Geomorphology: International Perspectives, TERRAPUB, Tokyo 2003, pp. 221–237.
  16. Chen Ch., Li Ya., Cao X., Dai H.: Smooth surface modeling of DEMs based on a regularized least squares method of thin plate spline. Mathematical Geosciences, vol. 46(8), 2014, pp. 909–929. https://doi.org/10.1007/s11004-013-9519-5.
  17. Wang Y.G., Zhu C.Q., Wang Z.W.: A surface model of grid DEM based on Coons surface. Acta Geodaetica et Cartographica Sinica, vol. 37(2), 2008, pp. 217–222.
  18. Zhang J., Lin X.: Filtering airborne LiDAR data by embedding smoothnessconstrained segmentation in progressive TIN densification. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 81, 2013, pp. 44–59. https://doi.org/10.1016/j.isprsjprs.2013.04.001.
  19. Ali T.: Building of robust multi-scale representations of LiDAR-based digital terrain model based on scale-space theory. Optics and Lasers in Engineering, vol. 48(3), 2010, pp. 316–319. https://doi.org/10.1016/j.optlaseng.2009.11.003.
  20. Ali T., Mehrabian A.: A novel computational paradigm for creating a Triangular Irregular Network (TIN) from LiDAR data. Nonlinear Analysis: Theory, Methods & Applications, vol. 71(12), 2009, pp. e624–e629. https://doi.org/10.1016/j.na.2008.11.081.
  21. Linyu G., Xiaoping L., Yingcheng L., Pei L., Xiaofeng S., Huijie L.: Application of breakline and manual additional points in TIN modeling. [in:] International Conference on Geo-Spatial Solutions for Emergency Management and the 50th Anniversary of the Chinese Academy of Surveying and Mapping, 14–16 September 2009, Beijing, China, vol. XXXVIII-7/C4, 2009, pp. 346–351. https://www.isprs.org/proceedings/xxxviii/7-c4/346_gsem2009.pdf [access: 12.01.2024].
  22. Hu G., Wang Ch., Li S., Dai W., Xiong L., Tang G., Strobl J.: Using vertices of a triangular irregular network to calculate slope and aspect. International Journal of Geographical Information Science, vol. 36(2), 2022, pp. 382–404. https://doi.org/10.1080/13658816.2021.1933493.
  23. Mingwei Z., Wang J.: A new method of feature line integration for construction of DEM in discontinuous topographic terrain. Environmental Earth Sciences, vol. 81, 2022, 387. https://doi.org/10.1007/s12665-022-10527-1.
  24. Zhou Q., Chen Yu.: Generalization of DEM for terrain analysis using a compound method. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66(2), 2011, pp. 38–45. https://doi.org/10.1016/j.isprsjprs.2010.08.005.
  25. Liu X., Che W., Wang Ch.: Research on a correction method to existing grid-based DEM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37(B1), 2008, pp. 1165–1170.
  26. Bogaert P., Delincé J., Kay S.: Assessing the error of polygonal area measurements: A general formulation with applications to agriculture. Measurement Science and Technology, vol. 16(5), 2005, 1170. https://doi.org/10.1088/0957-0233/16/5/017.
  27. Bykat A.: Automatic generation of triangular grid: I – subdivision of a general polygon into convex subregions. II – triangulation of convex polygons. The International Journal for Numerical Methods in Engineering, vol. 10(6), 1976, pp. 1329-1342. https://doi.org/10.1002/nme.1620100612.
  28. Conde López E., Salete Casino E., Escribano J.F., Ureña A.V.: Application of finite element method to create a digital elevation model. Mathematics, vol. 11(6), 2023, 1522. https://doi.org/10.3390/math11061522.
  29. Bathe K.-J.: Finite Element Method. [in:] Wah B.W. (ed.), Wiley Encyclopedia of Computer Science and Engineering, John Wiley & Sons, 2008. https://doi.org/10.1002/9780470050118.ecse159.
  30. Suchocki C., Wasilewski A.: Ustalenie optymalnej siatki GRID dla numerycznego modelu klifu zbudowanego z danych pozyskanych z naziemnego skaningu laserowego. Prace Naukowe Instytutu Gornictwa Politechniki Wrocławskiej. Konferencje, vol. 129(54), 2009, pp. 111–118.
  31. Kienzle S.: The effect of grid cell size on major terrain derivatives. https://proceedings.esri.com/library/userconf/proc04/docs/pap1606.pdf [access: 20.06.2024].
  32. de Aguiar L.A., Guimaraes M.B., Monteiro D.K., da Costa P.D.O.: Development of a computational routine in Fortran language for analysis of plane and space trusses using the finite element method. [in:] CILAMCE 2022: Proceedings of the XLIII Ibero-Latin-American Congress on Computational Methods in Engineering, 21–25 November 2022, ABMEC Foz do Iguaçu – PR – Brazil, Brazil. http://surl.li/tkxsn [access: 30.04.2024].
  33. Massey P.: Admissible subspaces and the subspace iteration method. BIT Numerical Mathematics, vol. 64, 2024, 12. https://doi.org/10.1007/s10543-024-01012-1.
  34. Mayer H.: Object extraction for digital photogrammetric workstations. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 35(B2), 2004, pp. 165–173.
  35. Ferreira Z.: Global and local processes influencing altimetric error patterns in digital elevation models (DEM): An approach on vertical accuracy assessment and spatial aspects of DEM error. 2024. https://doi.org/10.13140/RG.2.2.14037.84964.
  36. Kim J., Lee S., Seo J., Lee d.e., Choi H.: The integration of earthwork design review and planning using UAV-based point cloud and BIM. Applied Sciences, vol. 11, 2021, 3435. https://doi.org/10.3390/app11083435.
  37. Szypuła B.: DEM from topographic maps – as good as DEM from LiDAR? [in:] Alvioli M., Marchesini I., Melelli L., Guth P. (eds.), Proceedings of the Geomorphometry 2020 Conference, CNR Edizioni, Perugia, Italy, 2020, pp. 119–123. https://doi.org/10.30437/GEOMORPHOMETRY2020_34.
  38. Burke W., Morgan S., Namonje-Kapembwa T., Muyanga M., Mason N.: Beyond the “Inverse Relationship”: Area mismeasurement may affect actual productivity, not just how we understand it. Agricultural Economics, vol. 54(4), 2023, pp. 557–569. https://doi.org/10.1111/agec.12775.
  39. Holden S., Fisher M.: Can area measurement error explain the inverse farm size productivity relationship? Centre for Land Tenure Studies Working Paper, no. 12/13, Norwegian University of Life Sciences (NMBU), Centre for Land Tenure Studies (CLTS), 2013. https://www.econstor.eu/handle/10419/242719 [access: 16.03.2024].
  40. Chen C., Li Y.: An orthogonal least-square-based method for DEM generalization. International Journal of Geographical Information Science, vol. 27(1), 2012, pp. 154–167. https://doi.org/10.1080/13658816.2012.674136.
  41. Garnero G., Godone D.: Comparisons between different interpolation techniques. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XL-5/W3, 2013, pp. 139–144. https://doi.org/10.5194/isprsarchives-XL-5-W3-139-2013.
  42. de Kok T., van Kreveld M., Löffler M.: Generating realistic terrains with higherorder Delaunay triangulations. Computational Geometry, vol. 36(1), 2007, pp. 52–65. https://doi.org/10.1016/j.comgeo.2005.09.005.
  43. Gudmundsson J., Haverkort H.J., van Kreveld M.: Constrained higher order Delaunay triangulations. Computational Geometry, vol. 30(3), 2005, pp. 271–277. https://doi.org/10.1016/j.comgeo.2004.11.001.
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