Using aerial survey materials in order to determine the rock and altitude components of the characteristics of forest plantations when conducting surveys at the facilities of Rosneft

UDK: 528.4:622.276
DOI: 10.24887/0028-2448-2022-9-111-115
Key words: photogrammetry, aerial LiDAR survey, UAVs, forest inventory, canopy height model
Authors: N.N. Filin (Rosneft – NTC LLC, RF, Krasnodar), A.N. Pogorodniy (Rosneft – NTC LLC, RF, Krasnodar), S.A. Arbuzov (Siberian State University of Geosystems and Technologies, RF, Novosibirsk), N.N. Berdnikov (Rosneft Oil Company, RF, Moscow)

The article presents the study of an object-oriented approach use in the classification of the species composition of a forest stand based on multispectral aerial photography using DJI P4 Multispectral equipment, as well as checking the reliability and accuracy of determining the fixation of heights of woody vegetation using aerial photography and airborne laser scanning and obtaining data for determining taxation indicators, as an integral part of forest management and forest inventory work, in order to develop a methodology for estimating cutting areas based on airborne laser scanning and digital aerial photography, developed in the interests of Rosneft.

As a part of the study authors checked the possibility of using an object-oriented approach on DJI P4 Multispectral data in order to classify forest elements by species composition, as well as identify trees and determine their heights. The comparison is made of height marks of two point clouds obtained as a result of laser reflections and photogrammetric processing of aerial photographs. The possibility of using a photogrammetric point cloud to determine the heights of woody vegetation using aerial photography methods is evaluated. The tree vertices were searched for by the point cloud of laser reflections, as well as by the photogrammetric point cloud. An OBIA classification was performed using statistical data of spectral channels in the red (R), green (G), blue (B), near infrared (NIR) and red edge (Red Edge) ranges with a check of the possibility of extracting information about the species composition of the forest stand. The verification of the reliability and determination of the accuracy of fixing the heights of woody vegetation was carried out using aerial photography and airborne laser scanning.

Conclusions are drawn about the possibility of using OBIA based on multispectral data for the classification of tree species, a photogrammetric cloud of points in order to determine tree heights. A general conclusion is given on the feasibility of developing and applying new methods for obtaining data for determining forest inventory indicators during forest inventory at the facilities of Rosneft Oil Company.



1. Pogorodniy A.N., Filin N.N., Shumeyko S.A. et al., The unmanned aerial vehicles usage experience on tasks of forest inventory and topography (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2021, no. 9, pp. 90–94, DOI:

2. Shumeyko S.A., Filin N.N., The use of non-professional unmanned aerial vehicle system for the tasks of engineering geodesy and mapping oil and gas fields territory (In Russ.), Neftyanoe khozyaystvo = Oil Industry, 2019, no. 10, pp. 42–45, DOI:

3. Lopez J.P.A., Object-based methods for mapping and monitoring of urban trees with multitemporal image analysis, Ph.D. dissertation, University of Twente, 2012.

4. Rollan T.A.M. et al., Combining watershed transformation and local maxima approach in developing a tree detection and counting methodology using object-based image analysis, Proceedings of South East Asian Survey Congress 2015, 2015.

5. Tiede D., Hoffmann Ch., Process oriented object-based algorithms for single tree detection using laser scanning, EARSeL-Proceedings of the Workshop on 3D Remote Sensing in Forestry, 14th-15th Feb 2006, Vienna, pp. 151-156.

6. De Luca G., Silva J.M.N., Cerasoli S. et al., Object-based land cover classification of cork oak woodlands using UAV imagery and Orfeo ToolBox, Remote Sens., 2019, no. 11 (10), 1238 r., DOI: 10.3390/rs11101238

7. Adhikari A., Kumar M., Agrawal Sh, et al.,  An integrated object and machine learning approach for tree canopy extraction from UAV datasets, Journal of the Indian Society of Remote Sensing, 2021, V. 49, pp. 471–478, DOI: 10.1007/s12524-020-01240-2

8. Qingwang Liu; Shiming Li; Xin Tian et al., Dominant trees analysis using UAV LiDAR and photogrammetry, Proceedings of  IEEE International Geoscience and Remote Sensing Symposium 2020, pp. 4649–4652, DOI: 10.1109/IGARSS39084.2020.9323664

9. St‐Onge B., Vega C., Fournier R.A. et al., Mapping canopy height using a combination of digital photogrammetry and lidar, International Journal of Remote Sensing, 2008, V. 29, no. 11,  DOI:10.1080/01431160701469040

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