Comparison of Methodological Approaches to Measuring Trees’ Heights and Crown Projections Based on the Laser Scanning Data from an Unmanned Aerial Vehicle in the middle taiga forests

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

Boreal forests are the most important reservoirs of terrestrial carbon. Their wood makes up to 30—35 % of their overall carbon stock, making their inventory a task of national importance. The world practice of recent decades is scaling the forest inventory data using unmanned aerial vehicles (UAVs), which are an effective and inexpensive means of collecting information at the level of individual biogeocoenoses. The variety of survey and processing methods requires finding optimal approaches to wood stock inventory using UAVs. The article is dedicated to comparing two methods (Matlab — ML and TerraScan — TS) of estimating the number, height and projective cover of tree crowns by processing point clouds of laser reflections of different densities (100 and 2800 points / m2; ML100, ML2800 and TS100, TS2800). Lidar survey was carried out on three 50 × 50 metres sample plots. SP 1 — forest crossed by a stream; SP 2 — forest on a flat surface; SP 3 — forest bordering an oligotrophic swamp. The number of trees identified using ML100 / ML2800 for SPs 1, 2 and 3 was: 55 / 66, 67 / 87 and 174 / 220; while using TS100 and TS2800 resulted in following numbers: 66 / 95, 85 / 156 and 82 /166 respectively. The average tree height reaches 10—24 m, with little difference between the two processing methods at each site. The total projective crown cover for SPs 1, 2 and 3 was 2569, 2494 and 2059 m2 in ML; 2091, 2424 and 1506 m2 in TS. During remote assessment, only the first storey trees’ number could be estimated with a sufficient degree of completeness: for this task, the ML100 method was sufficient. However, for a more comprehensive inventory of trees, including lower storeys, it is preferable to use TS2800, under the condition of an adequate selection of the minimum tree height parameter’s value.

Texto integral

Acesso é fechado

Sobre autores

D. Ilyasov

Yugra University

Autor responsável pela correspondência
Email: d_ilyasov@ugrasu.ru
Rússia, Chekhova st., 16, Khanty-Mansiysk, 628012

A. Alekseev

Isaev Centre for Forest Ecology and Productivity of the RAS

Email: poxpox@mail.ru
Rússia, Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997

Bibliografia

  1. Anderson K., Gaston K.J., Lightweight unmanned aerial vehicles will revolutionize spatial ecology, Frontiers in Ecology and the Environment, 2013, Vol.11, No. 3, pp. 138—146.
  2. Bartalev S.A., Egorov V.A., Zharko V.O., Lupyan E.A., Plotnikov D.E., Khvostikov S.A., Shabanov N.V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Satellite mapping of vegetation cover of Russia), Moscow: Izd-vo IKI RAN, 2016, 208 p.
  3. Bolota Zapadnoi Sibiri, ikh stroenie i gidrologicheskii rezhim, (Bogs of Western Siberia, their structure and hydrological regime), Leningrad: Gidrometeoizdat, 1976, 448 p.
  4. Brede B., Calders K., Lau A., Raumonen P., Bartholomeus H.M., Herold M., Kooistra L., Non-destructive tree volume estimation through quantitative structure modelling: Comparing UAV laser scanning with terrestrial LIDAR, Remote Sensing of Environment, 2019, Vol. 233, Article 111355.
  5. Brede B., Terryn L., Barbie N., Bartholomeus H.M., Bartolo R., Calders K., Derroire G., Krishna Moorthy S.M., Lau A., Levick S.R., Raumonen P., Verbeeck H., Wang D., Whiteside T., van der Zee J., Herold M., Non-destructive estimation of individual tree biomass: Allometric models, terrestrial and UAV laser scanning, Remote Sensing of Environment, 2022, Vol. 280, Article 113180.
  6. Cao L., Liu H., Fu X., Zhang Z., Shen X., Ruan H., Comparison of UAV LiDAR and digital aerial photogrammetry point clouds for estimating forest structural attributes in subtropical planted forests, Forests. 2019, Vol. 10, No. 2, Article 145.
  7. Chen Q., Baldocchi D., Gong P., Kelly M., Isolating individual trees in a savanna woodland using small footprint lidar data, Photogrammetric Engineering and Remote Sensing, 2006, Vol. 72, No. 8, pp. 923—932.
  8. Dixon R.K., Solomon A.M., Brown S., Houghton R.A., Trexier M.C., Wisniewski J., Carbon pools and flux of global forest ecosystems, Science, 1994, Vol. 263, No. 5144, pp. 185—190.
  9. Extract Forest Metrics and Individual Tree Attributes from Aerial Lidar Data, available at: https://www.mathworks.com/help/lidar/ug/extraction-of-forest-metrics-and-individual-tree-attributes.html.
  10. Ganz S., Käber Y., Adler P. Measuring tree height with remote sensing — A comparison of photogrammetric and LiDAR data with different field measurements, Forests, 2019, Vol. 10, No. 8, Article № 694.
  11. Gyawali A., Aalto M., Peuhkurinen J., Villikka M., Ranta T. Comparison of individual tree height estimated from LiDAR and digital aerial photogrammetry in young forests, Sustainability, 2022, Vol. 14, No. 7, Article 3720.
  12. Hyyppä E., Yu X., Kaartinen H., Hakala T., Kukko A., Vastaranta M., Hyyppä J. Comparison of backpack, handheld, under-canopy UAV, and above-canopy UAV laser scanning for field reference data collection in boreal forests, Remote Sensing, 2020, Vol. 12, No. 20, Article 3327.
  13. Ilyasov D.V., Kaverin A.A., Zhernov S.N., Glagolev M.V., Niyazova A.V., Kupriianova I.V., Filippov I.V., Terentieva I.E., Sabrekov A.F., Lapshina E.D., Estimation of tree cover height on oligotrophic bog based on UAV lidar surveying, Environmental Dynamics and Global Climate Change, 2023, Vol. 14, No. 4, pp. 237—248.
  14. IPCC, 2023: Climate Change 2023: Synthesis Report, Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Core Writing Team, IPCC, Geneva, Switzerland. 184 p. doi: 10.59327/IPCC/AR6-9789291691647
  15. Kupriianova I.V., Kaverin A.A., Filippov I.V., Ilyasov D.V., Lapshina E.D., Logunova E.V., Kulyabin M.F., The main physical and geographical characteristics of the Mukhrino field station area and its surroundings, Environmental Dynamics and Global Climate Change, 2022, Vol. 13, No. 4, pp. 215—252.
  16. Kuželka K., Slavík M., Surový P., Very high density point clouds from UAV laser scanning for automatic tree stem detection and direct diameter measurement, Remote Sensing, 2020, Vol. 12, No. 8, Article 1236.
  17. Lister A.J., Andersen H., Frescino T., Gatziolis D., Healey S., Heath L.S., Liknes G.C., McRoberts R., Moisen G.G., Nelson M., Riemann R., Schleeweis K., Schroeder T.A., Westfall J., Wilson B.T., Use of remote sensing data to improve the efficiency of national forest inventories: A case study from the United States national forest inventory, Forests, 2020, Vol. 11, No. 12, Article 1364.
  18. Mielcarek M., Stereńczak K., Khosravipour A., Testing and evaluating different LiDAR-derived canopy height model generation methods for tree height estimation, International Journal of Applied Earth Observation and Geoinformation, 2018, Vol. 71, pp. 132—143.
  19. Pinaevskaya E.A., Rost raznykh form sosny obyknovennoi (Pinus sylvestris L.) na bolotnykh pochvakh severnoi taigi (Growth of different forms of Pinus sylvestris L. on marshy soils of the norhern taiga), Vestnik Krasnoyarskogo gosudarstvennogo agrarnogo universiteta, 2016, No. 11, pp. 163—170.
  20. Prikaz Federal’nogo agentstva lesnogo khozyaistva ot 6 maya 2022 g. (Order of the Federal Forestry Agency of May 6, 2022 No. 556), available at: https://rosleshoz.gov.ru/doc/пр_№ 556_2022.05.06 (January 14, 2024).
  21. Puliti S., Breidenbach J., Schumacher J., Hauglin M., Klingenberg T.F., Astrup R., Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat, Remote Sensing of Environment, 2021, Vol. 265, Article 112644.
  22. Reese H., Nilsson M., Sandström P., Olsson H., Applications using estimates of forest parameters derived from satellite and forest inventory data, Computers and Electronics in Agriculture, 2002, Vol. 37, No. 3, pp. 37—55.
  23. Rogers S.R., Manning I., Livingstone W., Comparing the spatial accuracy of digital surface models from four unoccupied aerial systems: Photogrammetry versus LiDAR, Remote Sensing, 2020, Vol. 12, No. 17, Article 2806.
  24. Schepaschenko D.G., Shvidenko A.Z., Mukhortova L.V., Vedrova E.F., The pool of organic carbon in the soils of Russia, Eurasian Soil Science, 2013, Vol. 46, No. 2, pp. 107—116.
  25. Sibona E., Vitali A., Meloni F., Caffo L., Dotta A., Lingua E., Motta R., Garbarino M. Direct measurement of tree height provides different results on the assessment of LiDAR accuracy, Forests, 2016, Vol. 8, No. 1, Article 7.
  26. Sochilova E.N., Surkov N.V., Ershov D.V., Khamedov V.A., Otsenka zapasov fitomassy lesnykh porod po sputnikovym izobrazheniyam vysokogo prostranstvennogo razresheniya (na primere lesov Khanty-Mansiiskogo AO) (Assessment of biomass of forest species using satellite images of high spatial resolution (on the example of the forest of Khanty-Mansi autonomous okrug)), Voprosy lesnoi nauki, 2018, Vol. 1, No. 1, pp. 1‒23.
  27. Sun W., Liu X., Review on carbon storage estimation of forest ecosystem and applications in China, Forest Ecosystems, 2020, Vol. 7, No. 1, pp. 1—14.
  28. Tyurin A.V., Taksatsiya lesa (Forest estimation), Moscow: Gosudarstvennoe lesotekhnicheskoe izd-vo, 1938, 300 p.
  29. Ukaz Prezidenta Rossiiskoi Federatsii ot 4 noyabrya 2020 g., No. 666 “O sokrashchenii vybrosov parnikovykh gazov” (Decree of the President of the Russian Federation of November 4, 2020, No. 666 “On the reduction of greenhouse gas emissions”), available at: http://www.kremlin.ru/acts/bank/45990 (August 13, 2024).
  30. Wallace L., Lucieer A., Watson C., Turner D., Development of a UAV—LiDAR system with application to forest inventory, Remote Sensing, 2012, Vol. 4, No. 6, pp. 1519—1543.
  31. Wang D., Wan B., Liu J., Su Y., Guo Q., Qiu P., Wu X., Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV—LiDAR data and Sentinel-2 imagery, International Journal of Applied Earth Observation and Geoinformation, 2020, Vol. 85, Article 101986.
  32. Zamolodchikov D.G., Grabovskii V.I., Chestnykh O.V., Dinamika balansa ugleroda v lesakh federal’nykh okrugov Rossiiskoi Federatsii (Dynamic pattern of carbon balance in the forests of federal districts of the Russian Federation), Voprosy lesnoi nauki, 2018, Vol. 1, No. 1, pp. 1—24.
  33. Zamolodchikov D.G., Grabovskii V.I., Kraev G.N., A twenty year retrospective on the forest carbon dynamics in Russia, Contemporary Problems of Ecology, 2011, Vol. 4, No. 7, pp. 706—715
  34. Zhao K., Suarez J.C., Garcia M., Hu T., Wang C., Londo A., Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux, Remote Sensing of Environment, 2018, Vol. 204, pp. 883—897.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar
2. Fig. 1. The area of the study and the location of the laying of test areas on the site of the Mukhrino swamp complex.

Baixar (645KB)
Metadados
3. Fig. 2. Tree tops identified in Matlab (ML, red) and TerraScan (TS, green) based on a cloud of PLO with a density of 100 points/m2 on the plots (from top to bottom): PP 1 (mixed forest on rough terrain), PP 2 (forest on a flat surface) and PP 3 (the forest on the border with the forested swamp). On the left — against the background of a canopy scheme; on the right — against the background of a digital terrain model. The crosses show trees that match with an accuracy of 1 m (between ML and TS), the numbers next to them show the difference in their heights in meters.

Baixar (1MB)
Metadados
4. Fig. 3. Tree crown projections identified in TerraScan (TS, left) and Matlab (ML, right) based on an APHID cloud with a density of 100 points/m2 in the following areas: PP 1 (mixed forest on rough terrain), PP 2 (forest on a flat surface) and PP 3 (forest on the border with a forested swamp). The figures show the area of crown projections, m.

Baixar (1MB)
Metadados
5. 4. Probability density of tree heights (blue, left) and crown areas (green, right). The calculation based on TerraScan is shown in dark, while Matlab is shown in light.

Baixar (336KB)
Metadados

Declaração de direitos autorais © Russian Academy of Sciences, 2025