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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 6, pp. 93-107

Biometric parameter determination of pine stands based on WorldView-3 imagery and UAV survey

S.V. Knyazeva 1 , A.D. Nikitina 1 , E.A. Gavrilyuk 1 , E.V. Tikhonova 1 , N.V. Koroleva 1 
1 Center for Forest Ecology and Productivity RAS, Moscow, Russia
Accepted: 27.10.2022
DOI: 10.21046/2070-7401-2022-19-6-93-107
The article presents the results of a study of the possibilities to model the main characteristics of stands (average values of height, age and diameter of tree trunks, as well as the density (number) of trees per 1 ha) based on the results of thematic processing of images from the highly detailed optical satellite system WorldView-3 and data obtained during aerial photography from an unmanned aerial vehicle (UAV), by the example of pine forests of the Curonian Spit National Park (Kaliningrad region). The best results of modeling by both random forests (RF) and multiple linear regression (LR) were obtained for the parameter average trunk diameter: the coefficient of determination R 2 ranges from 0.54 (for WorldView-3 data) to 0.88 (for UAV data), root mean square error (RMSE) ranges from 4.6 to 3 cm, respectively. The possibilities to determine the average age of stands are significantly worse: R 2 from 0.47 to 0.65 with RMSE from 18.5 to 24 years. The average height parameter is characterized by fairly good indicators of the relationship with the visual properties of satellite images (R 2 = 0.53, RMSE = 2.9 m), but the best modeling results are obtained using a digital terrain model (RMSE less than 2 m). For the parameter average tree density, the best result was achieved by the LR method (R 2 = 0.69 and RMSE = 600 pcs/ha). In general, it can be stated that an increase in spatial resolution when using UAV data (<10 cm) instead of ultra-high resolution satellite data (<1 m) gives the greatest performance gain for regression models of the average barrel diameter parameter. When modeling, on the basis of WorldView-3 satellite data, the decoding characteristics of the tree canopy determined by orthophotoplanes from UAVs, we obtained fairly moderate results: R 2 in the range from 0.54 (for the closeness of the tree canopy) to 0.68 (for the average crown area).
Keywords: biometric parameters, pine stand, satellite data, orthophotoplanes, regression models, detecting characteristics of the tree canopy
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References:

  1. Alekseev A. S., Nikiforov A. A., Mikhailova A. A., Vagizov M. R., New method for tree stands growing stock determination using high resolution images done by unmanned aerial vehicle, Aerokosmicheskie metody i geoinformatsionnye tekhnologii v lesovedenii, lesnom khozyaistve i ekologii (Aerospace methods and GIS technologies in forestry, forest management and ecology), Proc. 6th All-Russia Conf., Moscow, 20–22 Apr. 2016, Moscow: CEPF RAS, 2016, pp. 80–84 (in Russian).
  2. Aleshko R. A., Alekseeva A. A., Shoshina K. V., Bogdanov A. P., Guriev A. T., 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, 2017, Vol. 14, No. 5, pp. 87–99 (in Russian), DOI: 10.21046/2070-7401-2017-14-5-87-99.
  3. Bogdanov A. P., Ilintsev A. S., Aleshko R. A., Relationship between tree crown diameter and various taxation indicators in the north-taiga forest area, Forest Science Issues, 2019, Vol. 2(4), pp. 1–10 (in Russian), DOI: 10.31509/2658-607x-2019-2-4-1-10.
  4. Zhirin V. M., Knyazeva S. V., Eidlina S. P., Estimation of linkages between biometric indexes of forests and pattern of canopy spaces on super-high resolution satellite images, Lesovedenie, 2018, No. 3, pp. 163–177 (in Russian), DOI: 10.7868/S0024114818030014.
  5. Ivanova N. V., Shashkov M. P., Shanin V. N., Study of pine forest stand structure in the Priosko-Terrasny State Nature Biosphere Reserve (Russia) based on aerial photography by quadrocopter, Nature Conservation Research, 2021, Vol. 6(4), pp. 1–14 (in Russian), https://dx.doi.org/10.24189/ncr.2021.042.
  6. Lebkov V. F., Kaplina N. F., Structure of natural pine stands by tree crown extent, Aktual’nye problemy lesnogo kompleksa, 2006, No. 13, pp. 65–69 (in Russian).
  7. Lopatin E., Karjalainen T., Development of the low-cost high precision forest inventory and planning technology based on hyperspectral multi-angular data from unmanned aerial vehicles and long-term forest sector modelling, Aerokosmicheskie metody i geoinformatsionnye tekhnologii v lesovedenii, lesnom khozyaistve i ekologii (Aerospace methods and GIS-technologies in forestry, forest management and ecology), Proc. 6th All-Russia Conf., Moscow, 20–22 Apr. 2016, Moscow: CEPF RAS, 2016, pp. 38–39 (in Russian).
  8. Medvedev A. A., Telnova N. O., Kudikov A. V., Alekseenko N. A., 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, 2020, Vol. 17, No. 1, pp. 150–163 (in Russian), DOI: 10.21046/2070-7401-2020-17-1-150-163.
  9. Nikitina A. D., Using UAV’s digital terrain model to determine characteristics of a pine forest, Sbornik materialov uchastnikov XVII Bol’shogo geograficheskogo festivalya, posvyashchennogo 195-letiyu rossiiskogo krugosvetnogo puteshestviya F. P. Litke (Materials of the participants of the XVII Great Geographical Festival dedicated to the 195th anniversary of the Russian round-the-world trip of F. P. Litke), Saint Petersburg: Svoe izd., 2021, pp. 503–506 (in Russian).
  10. Nikolaev A. I., New methods in the assessment of qualitative and quantitative characteristics of forest stands, Trudy Sankt-Peterburgskogo nauchno-issledovatel’skogo instituta lesnogo khozyaistva (Proc. Saint Petersburg Forestry Research Institute), 2016, No. 1, pp. 79–85 (in Russian), DOI: 10.21178/2079-6080.2016.8.79.
  11. Ardila J., Bijker W., Tolpekin V., Stein A., Context-sensitive extraction of tree crown objects in urban areas using VHR satellite images, Intern. J. Applied Earth Observation and Geoinformation, 2012, Vol. 15, pp. 57–69, https://doi.org/10.1016/j.jag.2011.06.005.
  12. Bagaram M., Giuliarelli D., Chirici G., Giannetti F., Barbati A., UAV remote sensing for biodiversity monitoring: Are forest canopy gaps good covariates? Remote Sensing, 2018, Vol. 10, No. 9, Art. No. 1397, https://doi.org/10.3390/rs10091397.
  13. Belmonte A., Sankey T., Seyednasrollah B., Biederman J. A., Quantifying snow cover and persistence in a post restoration environment: UAV-derived forest structure data and forest gap radiation modeling, American Geophysical Union, Fall Meeting, 2019, Vol. 2019, Abstr. No. EP11C-2146.
  14. Breiman L., Random forests, Machine Learning, 2001, Vol. 45, No. 1, pp. 5–32, https://doi.org/10.1023/A:1010933404324.
  15. Brosofske K. D., Froese R. E., Falkowski M. J., Banskota A., A review of Methods for Mapping and Prediction of Inventory Attributes for Operational Forest Management, Forest Science, 2014, Vol. 60, Issue 4, pp. 733–756, https://doi.org/10.5849/forsci.12-134.
  16. Brovkina O., Cienciala E., Surový P., Janata P., Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands, Geo-spatial information science, 2018, Vol. 21, No. 1, pp. 12–20, https://doi.org/10.1080/10095020.2017.1416994.
  17. Cao J., Leng W., Liu K., Liu L., He Z., Zhu Y., Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models, Remote Sensing, 2018, Vol. 10, No. 1, Art. No. 89, https://doi.org/10.3390/rs10010089.
  18. Carlson J., Radiomics: ‘Radiomic’ Image Processing Toolbox, R Package version 0.1.3, 2018, https://CRAN.R-project.org/package=radiomics.
  19. Chen G., Hay G. J., St-Onge B., A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: A case study in Quebec, Canada, Intern. J. Applied Earth Observation and Geoinformation, 2012, Vol. 15, pp. 28–37, https://doi.org/10.1016/j.jag.2011.05.010.
  20. Cheng G., Han J. A., Survey on Object Detection in Optical Remote Sensing Images, ISPRS J. Photogrammetry and Remote Sensing, 2016, Vol. 117, pp. 11–28, https://doi.org/10.48550/arXiv.1603.06201.
  21. Ferreira M. P., Wagner F. H., Aragão L. E. O., Shimabukuro Y. E., Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis, ISPRS J. Photogrammetry and Remote Sensing, 2019, Vol. 149, pp. 119–131, https://doi.org/10.1016/j.isprsjprs.2019.01.019.
  22. Franklin S. E., Ahmed O. S., Williams G., Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle, Photogrammetric Engineering and Remote Sensing, 2017, Vol. 83, No. 7, pp. 501–507, https://doi.org/10.14358/PERS.83.7.501.
  23. Fritz A., Kattenborn T., Koch B., UAV-based photogrammetric point clouds —Tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds, Intern. Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences, 2013, Vol. 40, pp. 141–146, http://dx.doi.org/10.5194/isprsarchives-XL-1-W2-141-2013.
  24. Getzin S., Nuske R., Wiegand K., Using unmanned aerial vehicles (UAV) to quantify spatial gap patterns in forests, Remote Sensing, 2014, Vol. 6(8), pp. 6988–7004, http://dx.doi.org/10.3390/rs6086988.
  25. Gini R., Passoni D., Pinto L., Sona G., Use of unmanned aerial systems for multispectral survey and tree classification: A test in a park area of northern Italy, European J. Remote Sensing, 2014, Vol. 47, No. 1, pp. 251–269, https://doi.org/10.5721/EuJRS20144716.
  26. Goodbody T., Coops N., Hermosilla T., Tompalski P., Crawford P., Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems, Intern. J. Remote Sensing, 2018, Vol. 39, No. 15–16, pp. 5246–5264, https://doi.org/10.1080/01431161.2017.1402387.
  27. Groot A., Cortini F., Wulder M., Crown-fibre attribute relationships for enhanced forest inventory: Progress and prospects, The Forestry Chronicle, 2015, Vol. 91, pp. 266–279, DOI: 10.5558/tfc2015-048.
  28. Guyon I., Weston J., Barnhill S., Vapnik V ., Gene selection for cancer classification using support vector machines, Machine Learning, 2002, Vol. 46(1–3), pp. 389–422, https://doi.org/10.1023/A:1012487302797.
  29. Immitzer M., Atzberger C., Koukal T ., Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data, Remote Sensing, 2012, Vol. 4, pp. 2661–2693, https://doi.org/10.3390/rs4092661.
  30. Kampen M., Lederbauer S., Mund J., Immitzer M ., UAV-Based Multispectral Data for Tree Species Classification and Tree Vitality Analysis, Dreiländertagung der DGPF, der OVG und der SGPF in Wien, Österreich, 2019, Vol. 28, pp. 623–639.
  31. Lehmann E., Caccetta P., Lowell K., Mitchell A., Zheng-Shu Zhou, Held A., Milne T., Tapley I., SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system, Remote Sensing of Environment, 2015, Vol. 156, pp. 335–348, https://doi.org/10.1016/j.rse.2014.09.034.
  32. Liaw A., Wiener M., Classification and Regression by RandomForest, R News, 2002, Vol. 2(3), pp. 18–22.
  33. Michez A., Piégay H., Lisein J., Claessens H., Lejeune P., Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system, Environmental Monitoring and Assessment, 2016, Vol. 188, No. 3, p. 146, https://link.springer.com/article/10.1007/s10661-015-4996-2.
  34. Näsi R., Honkavaara E., Lyytikäinen-Saarenmaa P., Blomqvist M., Litkey P., Hakala T., Viljanen N., Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level, Remote Sensing, 2015, Vol. 7, No. 11, pp. 15467–15493, https://doi.org/10.3390/rs71115467.
  35. Palace M., Keller  M., Asner G., Hagen S., Braswell B., Amazon forest structure from IKONOS satellite data and the automated characterization of forest canopy properties, Biotropica, 2008, Vol. 40, No. 2, pp. 141–150, https://doi.org/10.1111/j.1744-7429.2007.00353.x.
  36. Pandey S., Chand N., Nandy S., Muminov A., Sharma A., Ghosh S., Srinet R ., High-Resolution Mapping of Forest Carbon Stock Using Object-Based Image Analysis (OBIA) Technique, J. Indian Society of Remote Sensing, 2020, Vol. 48, pp. 865–875, http://dx.doi.org/10.1007/s12524-020-01121-8.
  37. Parmar C., Rios Velazquez E., Leijenaar R., Jermoumi M., Carvalho S., Mak R. H., Mitra S., Shankar B. U., Kikinis R., Haibe-Kains B., Lambin Ph., Aerts H. J., Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation, PLoS ONE, 2014, Vol. 9, No. 7, pp. 102–107, https://doi.org/10.1371/journal.pone.0102107.
  38. Puliti S., Solberg S., Granhus A., Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration, Remote Sensing, 2019, Vol. 11, No. 3, Art. No. 233, https://doi.org/10.3390/rs11030233.
  39. Rikimaru A., Roy P. S., Miyatake S., Tropical forest cover density mapping, Tropical Ecology, 2002, Vol. 43, No. 1, pp. 39–47.
  40. Shashkov M., Ivanova N., Shanin V., Grabarnik P., Ground Surveys Versus UAV Photography: The Comparison of Two Tree Crown Mapping Techniques, Information Technologies in the Research of Biodiversity, Cham: Springer, 2019, pp. 48–56, https://doi.org/10.1007/978-3-030-11720-7_8.
  41. Siegel A. F., Robust Regression Using Repeated Medians, Biometrika, 1982, Vol. 69, No. 1, pp. 242–244, https://doi.org/10.1093/biomet/69.1.242.
  42. Sudhakar S., Vijayakumar V., Kumar C., Priya V., Ravi L., Subramaniyaswamy V ., Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires, Computer Communications, 2020, Vol. 149, pp. 1–16, https://doi.org/10.1016/j.comcom.2019.10.007.
  43. Tuominen S., Näsi R., Honkavaara E., Balazs A., Hakala T., Viljanen N., Reinikainen J., Tree species recognition in species rich area using UAV-borne hyperspectral imagery and stereo-photogrammetric point cloud, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2017, Vol. XLII-3/W3, pp. 185–194, DOI: 10.5194/isprs-archives-XLII-3-W3-185-2017.
  44. Waser L., Küchler M., Jütte K., Stampfer T., Evaluating the Potential of WorldView-2 Data to Classify Tree Species and Different Levels of Ash Mortality, Remote Sensing, 2014, Vol. 6, pp. 4515–4545, DOI: 10.3390/rs6054515.
  45. White J., Coops N., Wulder M., Vastaranta M., Hilker T., Tompalski P., Remote sensing technologies for enhancing forest inventories: A review, Canadian J. Remote Sensing, 2016, Vol. 42, No. 5, pp. 619–641, https://doi.org/10.1080/07038992.2016.1207484.
  46. Yilmaz V., Güngör O., Estimating crown diameters in urban forests with Unmanned Aerial System-based photogrammetric point clouds, Intern. J. Remote Sensing, 2019, Vol. 40, No. 2, pp. 468–505, https://doi.org/10.1080/01431161.2018.1562255.