Creation of the digital elevation model with the use of unmanned aerial vehicle

The necessary sequence of stages has been developed and the unmanned technology for creating a digital elevation model by the example of the land use of Novosibirsk region has been implemented. The technology consists of a set of stages: reconnaissance of the terrain, fi xing reference signs, satellite measurements, aerial photography fl ights, processing the results of aerial photography and the construction of digital elevation model. The technological process was signifi cantly affected by unfavorable weather conditions - low clouds, gusty wind, high air humidity. Remote sensing study with the use of unmanned aerial vehicle of the Supercam S 250 F type made it possible to create a large-scale orthophotoplan and a digital elevation model on the farm territory (M 1 : 1000). For photogrammetric processing of digital data obtained on the farm, a two-stage method of satellite determination was used. The essence of this method was to obtain a large number of satellite measurements in a static mode and further statistical processing. For statistical processing of satellite measurements, information was used on the coordinate location of two base ground stations of the Novosibirsk Region satellite network - Kochenevo and Novosibirsk. Remoteness of support points from the ground satellite station of Novosibirsk was at a distance of over 90 km. As a result of equalization calculations, the obtained average square displacement errors of the planned and high-altitude position of the support points in various test sites were under 0.02 m in the plan, and under 0.03 m by height. In the process of photogrammetric processing of the results of aerial photography with the use of unmanned aerial vehicle, the tasks of transferring the position of points on a digital image in the pixel coordinate system into the coordinate system of the area, building digital irregular (TIN, Triangulated Irregular Network) and regular (DEM, Digital Elevation Model) surface models, and based on them, textured terrain models (TTM, Textured Terrain Model) and orthophotoplans, were solved.

unmanned technologies, unmanned aerial vehicle, orthophotoplan, digital elevation model, satellite measurements

1. Tanasienko A.A. Spetsifi ka erozii pochv v Sibiri: monografi ya. [Specifi city of soil erosion in Siberia] Novosibirsk: Izdatelstvo SO RAN [Novosibirsk: Publishing House of the Siberian Branch of the Russian Academy of Sciences], 2003, 175 p. (In Russian).

2. Reinhard J.R. Degradatsiya pochv ekosistem yuga Zapadnoy Sibiri. [Soil degradation of ecosystems of the south of Western Siberia]. Omsk, Lodz (Poland), 2009, 634 p. (In Russian).

3. Hengl T., Evans I.S. Mathematical and digital models of the land surface. Developments in soil science, 2009, vol. 33, pp. 31–63. DOI: 10.1016 / s0166-2481 (08) 00002-0.

4. James L.A., Hodgson M.E., Ghoshal S., Latiolais M.M. Geomorphic change detection using historic geography and DEM differencing: The temporal dimension of geospatial analysis. Geomorphology, 2012, vol. 137, no. 1, pp. 181–198. DOI: 10.1016 / j.geomorph. 2010.10.039

5. Florinsky I. Digital terrain analysis in soil science and geology. Academic Press, 2016, 486 p.

6. Lastochkin A.N. Sistemno-morfologicheskoye osnovaniye nauk o Zemle (geotopologiya. strukturnaya geografi ya i obshchaya teoriya geosistem). [The system-morphological basis of earth sciences (geotopology, structural geography and the general theory of geosystems)] SPb: Izdatelstvo SPbGU [St. Petersburg: St. Petersburg State University Publ.], 2002, 762 p. (In Russian).

7. Shary P.A. Geomorfometriya v naukakh o zemle i ekologii. obzor metodov i prilozheniy [Geomorphometry in earth sciences and ecology, review of methods and applications]. Izvestiya Samarskogo nauchnogo tsentra RAN [Proceedings of Samara Scientifi c Center of the Russian Academy of Sciences]. 2006, vol. 8, no. 2, pp. 459–473. (In Russian).

8. Grohmann C.H. Morphometric analysis in geographic information systems: applications of free software GRASS and R. Computers & Geosciences, 2004, vol. 30, no. 9–10, pp. 10551067. DOI: 10.1016/j.cageo 2004.08.002.

9. Kalichkin V.K., Pavlova A.I. Agronomicheskiye geoinformatsionnyye sistemy: monografi ya [Agronomic geographic information systems]. Novosibirsk: Izdatelstvo SFNTSA RAN [Novo sibirsk: SFSCA RAS Publ.], 2018, 347 p. (In Russian).

10. Tadono T., Ishida H., Oda F., Naito S., Minakawa K., Iwamoto H. Precise Global DEM Generation By ALOS PRISM. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, vol. II–4, pp.71– 76. URL: https://www.aw3d.jp/wp/wp-content/themes/AW3DEnglish/technology /doc/ pdf/technology_03.pdf

11. Takaku J., Tadono T., Tsutsui K., Ichikawa M. Validation of AW3D Global DSM Generated from ALOS PRISM. ISPRS Annals of the Photogrammetry. Remote Sensing and Spatial Information Sciences, 2016, vol. III–4, pp. 2531. URL: https://www.aw3d.jp/wp/wp-content/themes/AW3DJapanese/technology/doc/ pdf/technology_01.pdf

12. Tahar K.N. Multi rotor UAV at different altitudes for slope mapping studies. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 2015, Vol. 40, no. 1, pp. 9–16. DOI: 10.5194/isprsarchives-XL-1-W4-9-2015.

13. Krsak B., Blistan P., Paulikova A., Puskarova P., Kovanic L., Palkova J., Zeliznakova V. Use of low-cost UAV photogrammetry to analyze the accuracy of a digital elevation model in a case study. Measurement, 2016, vol. 91, pp. 276–287. DOI: 10.1016/j.measurement.2016.05.028.

14. Rinaudo F., Chiabrando F., Lingua A., Spanт A. Archaeological site monitoring: UAV photogrammetry can be an answer. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. (XXII ISPRS Congress), 2012, vol. 39, no. B5, pp. 583–588. DOI: 10.5194/isprsarchives-XXXIX-B5-5832012.

15. Mesas-Carrascosa F.J., Rumbao I.C., Berrocal J.A.B., Porras A.G.-F. Positional quality assessment of orthophotos obtained from sensors onboard multi-rotor UAV platforms. Sensors, 2014, vol. 14, no. 12, pp. 22394–22407. DOI: 10.3390/s141222394.

16. Wang L., Liu H. An effi cient method for identifying and fi lling surface depressions in digital elevation models for hydrologic analysis and modelling. International Journal of Geographical Information Science, 2006, vol. 20, no. 2., pp.193-213. DOI: 10.1080/13658810500433453.