Natalya V. Ivanova, PhD, Researcher of the Institute of Mathematical Problems of Biology RAS – the Branch of the Keldysh Institute of Applied Mathematics of RAS (142290, Russia, Moscow Region, Pushchino, Professor Vitkevich Street, 1); iD ORCID:; e-mail:
Maxim P. Shashkov, Researcher of the Institute of Physicochemical and Biological Problems in Soil Sciences of RAS (142290, Russia, Moscow Region, Pushchino, Institutskaya Street, 2); iD ORCID:; e-mail:
Vladimir N. Shanin, PhD, Senior Researcher of the Institute of Physicochemical and Biological Problems in Soil Sciences of RAS (142290, Russia, Moscow Region, Pushchino, Institutskaya Street, 2); iD ORCID:; e-mail:

Reference to article

Ivanova N.V., Shashkov M.P., Shanin V.N. 2021. Study of pine forest stand structure in the Priosko-Terrasny State Nature Biosphere Reserve (Russia) based on aerial photography by quadrocopter. Nature Conservation Research 6(4): 1–14.

Section Research articles

In this paper, we investigated three pine (Pinus sylvestris) forest plots (each of 50 × 50 m), different by age and composition, located in the Prioksko-Terrasny State Nature Biosphere Reserve (Moscow Region, Russia). This study was aimed to evaluate the forest stand attributes based on the photogrammetric point clouds and canopy height models (CHM). For aerial photography, we used the unmanned aerial vehicle (UAV) quadrocopter DJI Phantom 4. At the first step, we used Agisoft Metashape software for the building of dense photogrammetric point clouds and orthophotoplans. Then we used the lidR package in the R environment for processing of dense point clouds. We used a cloth simulation filter for classification of ground points, spatial interpolation algorithm tin for creating a normalised dataset, and the algorithm lmf (local maximum filter) for individual tree detection and tree height assessment. For accuracy assessment, we collected field-based data, and calculated recall (r), precision (p), and F-score (F). Finally, we calculated CHMs (30 cm/pixel) derived from dense point clouds using the pit-free algorithm. To address the value of UAV data for delineating tree crowns, we compared the outputs of CHM data using two common algorithms (watershed and region-growing), and the result of manual orthophotoplans vectorisation. We obtained a high accuracy of individual tree detection. The algorithm found 46.7% to 87.5% of trees accounted on the sample plots by the field-based surveys. The recall (r) value varied from 0.5 to 0.9. The value of p varied from 0.9 to 1.0. The F-score, considering both factors (p and r), varied from 0.7 to 0.9. The highest accuracy was obtained in the site with a single-layer stand, where large trees with well-distinct tree crowns dominated. Spatial heterogeneity of tree stands reduces the accuracy of tree detection. We also found that tree heights estimated on the dense clouds were well matched with tree heights measured in the field. This dependency was described by the linear regression of y = 0.99x, R2 = 0.99. With both the watershed and region-growing algorithms, the total crown area estimation often exceeded the results of orthophotoplans manual vectorisation, where differences reached 25.1%. Differences between two delineation algorithms varied from 0.2% to 19.7% for the same sites. More accurate results were obtained for plots with lesser density of tree stands. Overall, our results have shown the potential of using photogrammetric point clouds for estimating tree attributes (heights and density) in single-layer pine stands. Widely used tree crown segmentation algorithms do not provide reliable estimates of the crown projection area, and more accurate results could be obtained after further improvement of the technique.


Agisoft Metashape, digital models of tree canopy heights, lidR, photogrammetry point clouds, UAV

Artice information

Received: 13.03.2021. Revised: 28.05.2021. Accepted: 02.07.2021.

The full text of the article

Agisoft LLC. 2019. Agisoft Metashape (Version 1.5). Software. Available from:
Aleshko R.A., Alekseeva, A.A., Shoshina K.V., Bogdanov A.P., Guriev A.T. 2017. Development of the methodology to update the information on a forest area using satellite imagery and small UAVs. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 14(5): 87–99. DOI: 10.21046/2070-7401-2017-14-5-87-99 [In Russian]
Alonzo M., Bookhagen B., Roberts D.A. 2014. Urban tree species mapping using hyperspectral and lidar data fusion. Remote Sensing of Environment 148: 70–83. DOI: 10.1016/j.rse.2014.03.018
Alonzo M., Andersen H.E., Morton D.C., Cook B.D. 2018. Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion. Forests 9(3): 119. DOI: 10.3390/f9030119
Anderson K., Gaston K.J. 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment 11(3): 138–146. DOI: 10.1890/120150
Arkhipov V.Yu., Murashev I.A., Buyvolov Yu. A. 2020. Birds of the Prioksko-Terrasnyi biosphere Reserve (the annotated species lists). Moscow: KMK Scientific Press Ltd. 80 p. (Flora and fauna of state nature reserves. Vol. 135]. [In Russian]
Bennett G., Hardy A., Bunting P., Morgan P., Fricker A. 2020. A Transferable and Effective Method for Monitoring Continuous Cover Forestry at the Individual Tree Level Using UAVs. Remote Sensing 12(13): 2115. DOI: 10.3390/rs12132115
Birdal A.C., Avdan U., Türk T. 2017. Estimating tree heights with images from an unmanned aerial vehicle. Geomatics, Natural Hazards and Risk 8(2): 1144–1156. DOI: 10.1080/19475705.2017.1300608
Bogdanov A.P., Aleshko R.A., Ilintsev A.S. 2019. Relationship between tree crown diameter and various taxation indicators in the North-taiga forest area. Forest Science Issues 2(4): 1–10. DOI: 10.31509/2658-607x-2019-2-4-1-10 [In Russian]
Chen Q., Baldocchi D., Gong P., Kelly M. 2006. Isolating individual trees in a savanna woodland using small footprint lidar data. Photogrammetric Engineering and Remote Sensing 72(8): 923–932. DOI: 10.14358/PERS.72.8.923
Dalponte M., Coomes D.A. 2016. Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods in Ecology and Evolution 7(10): 1236–1245. DOI: 10.1111/2041-210X.12575
Dandois J., Ellis E.C. 2013. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sensing of Environment 136: 259–276. DOI: 10.1016/j.rse.2013.04.005
Denisov S.A., Domrachev A.A., Elsukov A.S. 2016. Quadrocopter practical application for forest regeneration monitoring. Vestnik of Volga State University of Technology. Series: Forest. Ecology. Nature Management 4(32): 34–46. DOI: 10.15350/2306-2827.2016.4.34 [In Russian]
Domnina E.A., Timonov A.S., Kantor G.Ya., Kislitsyna, A.P. Savinykh V.P. 2017. Experience of detailed mapping of floodplain meadow vegetation. Theoretical and Applied Ecology 1: 42–49. DOI: 10.21046/2070-7401-2020-17-1-150-163 [In Russian]
Ershov D.V., Gavrilyuk E.A., Belova E.I., Nikitina A.D. 2020. Determination of the species structure of a forest area using orthophotoimages from unmanned aerial vehicles. In: Actual Problems of Modern Forestry. Simferopol: ARIAL. P. 141–152. [In Russian]
Eysn L., Hollaus M., Lindberg E., Berger F., Monnet J.M., Dalponte M., Kobal M., Pellegrini M., Lingua E., Mongus D., Pfeifer P. 2015. A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the alpine space. Forests 6(5): 1721–1747. DOI: 10.3390/f6051721
Goutte C., Gaussier E. 2005. A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Proceedings of the European Conference on Information Retrieval. Berlin/Heidelberg: Springer. P. 345–359.
Hansen E.H., Gobakken T., Bollandsås O.M., Zahabu E., Næsset E. 2015. Modeling aboveground biomass in dense tropical submontane rainforest using airborne laser scanner data. Remote Sensing 7(1): 788–807. DOI: 10.3390/rs70100788
Hudak A.T., Haren A.T., Crookston N.L., Liebermann R.J., Ohmann J.L. 2014. Imputing forest structure attributes from stand inventory and remotely sensed data in western Oregon, USA. Forest Science 60(2): 253–269. DOI: 10.5849/forsci.12-101
Ivanova N.V., Shashkov M.P., Shanin V.N., Grabarnik P.Ya. 2020. Quality Assessment of Automatical Tree Detection Based on Aerial Photography Using a Quadcopter. In: Mathematical Biology and Bioinformatics. Pushchino: IMPB RAS. Article: e36. DOI: 10.17537/icmbb20.31 [In Russian]
Khosravipour A., Skidmore A.K., Skidmore M., Wang T., Hussin Y. 2014. Generating Pit-free Canopy Height Models from Airborne Lidar. Photogrammetric Engineering and Remote Sensing 80(9): 863–872. DOI: 10.14358/PERS.80.9.863
Koch B., Heyder U., Weinacker H. 2006. Detection of individual tree crowns in airborne lidar data. Photogrammetric Engineering and Remote Sensing 72(4): 357–363. DOI: 10.14358/PERS.72.4.357
Kolarik N.E., Gaughan A.E., Stevens F.R., Pricope N.G., Woodward K., Cassidy L., Salerno J., Hartter J. 2020. A multi-plot assessment of vegetation structure using a micro-unmanned aerial system (UAS) in a semi-arid savanna environment. ISPRS Journal of Photogrammetry and Remote Sensing 164: 84–96. DOI: 10.1016/j.isprsjprs.2020.04.011
Kovyazin V.F., Vinogradov K.P., Kitcenko A.A., Vasilyeva E.A. 2020. Airborne laser scanning for clarification of the valuation indicators of forest stands. Russian Forestry Journal 6: 42–54. DOI: 10.37482/0536-1036-2020-6-42-54 [In Russian]
Krisanski S., Taskhiri M.S., Turner P. 2020. Enhancing Methods for Under-Canopy Unmanned Aircraft System Based Photogrammetry in Complex Forests for Tree Diameter Measurement. Remote Sensing 12(10): 1652. DOI: 10.3390/rs12101652
Li W., Guo Q., Jakubowski M.K., Kelly M. 2012. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering & Remote Sensing 78(1): 75–84. DOI: 10.14358/PERS.78.1.75
Medvedev A.A., Telnova N.O., Kudikov A.V. 2019. Highly detailed remote sensing monitoring of tree overgrowth on abandoned agricultural lands. Forest Science Issues 3: 1–12. DOI: 10.31509/2658-607x-2019-2-3-1-12 [In Russian]
Medvedev A.A., Telnova N.O., Kudikov A.V., Alekseenko N.A. 2020. Use of photogrammetric point clouds for the analysis and mapping of structural variables in sparse northern boreal forests. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 17(1): 150–163. DOI: 10.21046/2070-7401-2020-17-1-150-163 [In Russian]
Messinger M., Gregory P., Asner G.P., Silman M. 2016. Rapid assessment of Amazon forest structure and biomass using small unmanned aerial systems. Remote Sensing 8(8): 615. DOI: 10.3390/rs8080615
Miller E., Dandois J.P., Detto M., Hall J.S. 2017. Drones as a Tool for Monoculture Plantation Assessment in the Steepland Tropics. Forests 8(5): 168. DOI: 10.3390/f8050168
Mohan M., Silva C.A., Klauberg C., Jat P., Catts G., Cardil A., Hudak A.T., Dia M. 2017. Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests 8(9): 340. DOI: 10.3390/f8090340
Nunes M.H., Ewers R.M., Turner E.C., Comes D.A. 2017. Mapping Aboveground Carbon in Oil Palm Plantations Using LiDAR: A Comparison of Tree-Centric versus Area-Based Approaches. Forests 9(8): 816. DOI: 10.3390/rs9080816
Otero V., Van De Kerchove R., Satyanarayana B., Martínez-Espinosa C., Fisol M.A.B., Ibrahim M.R.B., Sulong I., Mohd-Lokman H., Lucas R., Dahdouh-Guebas F. 2018. Managing mangrove forests from the sky: Forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia. Forest Ecology and Management 411: 35–45. DOI: 10.1016/j.foreco.2017.12.049
Pajares G. 2015. Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs). Photogrammetric Engineering and Remote Sensing 81(4): 281–330. DOI: 10.14358/PERS.81.4.281
Panagiotidis D., Abdollahnejad A., Surový P., Chiteculo V. 2017. Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing 38(8–10): 2392–2410. DOI: 10.1080/01431161.2016.1264028
Picos J., Bastos G., Míguez D., Alonso L., Armesto J. 2020. Individual Tree Detection in a Eucalyptus Plantation Using Unmanned Aerial Vehicle (UAV)-LiDAR. Remote Sensing 12(5): 885. DOI: 10.3390/rs12050885
Popescu S., Wynne R. 2004. Seeing the Trees in the Forest: Using Lidar and Multispectral Data Fusion with Local Filtering and Variable Window Size for Estimating Tree Height. Photogrammetric Engineering and Remote Sensing 70(5): 589–604. DOI: 10.14358/PERS.70.5.589
Puliti S., Ørka H.O., Gobakken T., Næsset E. 2015. Inventory of small forest areas using an unmanned aerial system. Remote Sensing 7(8): 9632–9654. DOI: 10.3390/rs70809632
QGIS Development Team. 2019. QGIS Geographic Information System. Open Source Geospatial Foundation Project. Available from:
R Core Team. 2020. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Available from:
Roussel J.R., Auty D., De Boissieu F., Meador A.S., Jean-François B. 2020. Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. Package 'lidR'. Version 2.2.2. Available from:
Sannikov P.Yu., Andreev D.N., Buzmakov S.A. 2018. Identification and analysis of deadwood using an unmanned aerial vehicle. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa 15(3): 103–113. DOI: 10.21046/2070-7401-2018-15-3-103-113 [In Russian]
Shashkov M., Ivanova N., Shanin V., Grabarnik P. 2019. Ground Surveys Versus UAV Photography: The Comparison of Two Tree Crown Mapping Techniques. In: I. Bychkov, V. Voronin (Eds.): Information Technologies in the Research of Biodiversity. Cham: Springer. P. 48–56. DOI: 10.1007/978-3-030-11720-7_8
Silva C.A., Hudak A.T., Vierling L.A., Loudermilk E.L., O'Brien J.J., Hiers J.K., Jack S.B., Gonzalez-Benecke C., Lee H., Falkowski M.J., Khosravipour A. 2016. Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data. Canadian Journal of Remote Sensing 42(5): 554–573. DOI: 10.1080/07038992.2016.1196582
Sokolova M., Japkowicz N., Szpakowicz S. 2008. Beyond accuracy, F-score and ROC: A family of discriminant measures for performance evaluation. Proceedings of the Australasian Joint Conference on Artificial Intelligence. Berlin/Heidelberg: Springer. P. 1015–1021.
Zarco-Tejada P.J., Diaz-Varela R., Angileri V., Loudjani P. 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy 55: 89–99. DOI: 10.1016/j.eja.2014.01.004
Zaugolnova L.B. (Ed.). 2000. Assessment and conservation of forest biodiversity in the reserves of European Russia. Moscow: Nauchnyy Mir. 196 p. [In Russian]
Zhang J., Hu J., Lian J., Fan Z., Ouyang X., Ye W. 2016. Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring. Biological Conservation 198: 60–69. DOI: 10.1016/j.biocon.2016.03.027
Zhang W., Qi J., Wan P., Wang H., Xie D., Wang X., Yan G. 2016. An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote Sensing 8(6): 501. DOI: 10.3390/rs8060501