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Ciências Agrárias
Publicado: 2023-07-26

Estimating tree volume based on crown mapping by UAV pictures in the Amazon Forest

Universidade Federal do Paraná
Universidade Federal do Paraná
Universidade Federal do Paraná
Universidade Federal do Paraná
Universidade Federal do Paraná
Servicios Ecosistémicos y Cambio Climático
Embrapa Acre
Embrapa Acre
Instituto Militar de Engenharia
Universidade Federal do Paraná
Universidade Federal do Paraná
RGB pictures, diameter class,orthomosaic, tropical forest, UAV

Resumo

The use of remote sensing images obtained by unmanned aerial vehicle (UAV) systems enables measuring the morphometry of the tree canopy to estimate the volume stock in the Amazon Forest. In this study, we used RGB images from a low-cost UAV to map tree species and extract volumetric stock estimates in an Amazonian Forest. Individual tree crowns (ITC) were outlined in the UAV images and identified to the species level using forest inventory data. The average diameter and crown area of the trees were measured to estimate the volume, basal area and DBH per diameter class for 260 ha of tropical forest. The RMSE volume adjustment for the separate field inventory dataset was 19.31% with an R2 of 0.967. The UAV system images has the potential to map tree species and estimate tree biometry in the Amazon Forest, providing valuable insights for forest management and conservation.

Referências

  1. DE ALMEIDA PAPA, D.; DE ALMEIDA, D.R.A.; SILVA, C.A.; FIGUEIREDO, E.O.; STARK, S.C.; VALBUENA, R.; RODRIGUEZ, L.C.E.; D’ OLIVEIRA, M.V.N. Evaluating tropical forest classification and field sampling stratification from lidar to reduce effort and enable landscape monitoring. Forest Ecology and Management n. 457, 117634, 2020. 10.1016/j.foreco.2019.117634.
  2. ASSMANN, E. The principles of forest yield: studies in the organic production, structure, increment and yield of forest stands. Pergamon Press, 1970.
  3. COOPS, N.C.; TOMPALSKI, P.; GOODBODY, T.R.H.; ACHIM, A.; MULVERHILL, C.; (2022). Framework for near real-time forest inventory using multi source remote sensing data. Forestry: An International Journal of Forest Research URL: https://doi.org/10.1093%2Fforestry% 2Fcpac015, doi:10.1093/forestry/cpac015.
  4. CORTE, A.P.D.; DA CUNHA NETO, E.M.; REX, F.E.; SOUZA, D.; BEHLING, A.; MOHAN, M.; SANQUETTA, M.N.I.; SILVA, C.A.; KLAUBERG, C.; SANQUETTA, C.R.; VERAS, H.F.P.; DE ALMEIDA, D.R.A.; PRATA, G.; ZAMBRANO, A.M.A.; TRAUTENMÜLLER, J.W.; DE MORAES, A.; KARASINSKI, M.A.; BROADBENT, E.N. High-density uas-lidar in an integrated crop-livestock forest system: Sampling forest inventory or forest inventory based on individual tree detection (itd). Drones n 6, 2022. doi:10. 3390/drones6020048.
  5. CORTE, A.P.D.; SOUZA, D.V.; REX, F.E.; SANQUETTA, C.R.; MOHAN, M.; SILVA, C.A.; ZAMBRANO, A.M.A.; PRATA, G.; DE ALMEIDA, D.R.A.; TRAUTENMÜLLER, J.W.; KLAUBERG, C.; DE MORAES, A.; SANQUETTA, M.N.; WILKINSON, B.; BROADBENT, E.N. Forest inventory with high268 density UAV-lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture, n 179, 105815, 2020. doi: 10.1016/j.compag.2020.105815.
  6. DA COSTA, M.B.T.; SILVA, C.A.; BROADBENT, E.N.; LEITE, R.V.; MOHAN, M.; LIESENBERG, V.; STODDART, J.; DO AMARAL, C.H.; DE ALMEIDA, D.R.A.; DA SILVA, A.L.; GOYA, L.R.R.Y.; CORDEIRO, V.A.; REX, F.; HIRSCH, A.; MARCATTI, G.E.; CARDIL, A.; DE MENDONÇA, B.A.F.; HAMAMURA, C.; CORTE, A.P.D.; MATRICARDI, E.A.T.; HUDAK, A.T.; ZAMBRANO, A.M.A.; VALBUENA, R.; DE FARIA, B.L.; JUNIOR, C.H.S.; ARAGAO, L.; FERREIRA, M.E.; LIANG, J.; DE PÁDUA CHAVES E CARVALHO, S.; KLAUBERG, C. Beyond trees: Mapping total aboveground biomass density in the brazilian savanna using high-density UAV-lidar data. Forest Ecology and Management, n 491, 119155, 2021. doi: 10.1016/j.foreco.2021.119155.
  7. DA CUNHA NETO, E.M.; REX, F.E.; VERAS, H.F.P.; MOURA, M.M.; SANQUETTA, C.R.; KÄFER, P.S.; SANQUETTA, M.N.I.; ZAMBRANO, A.M.A.; BROADBENT, E.N.; CORTE, A.P.D. Using high density uas-lidar for deriving tree height of araucaria angustifolia in an urban atlantic rain forest. Urban Forestry Urban Greening n 63, p. 127–197, 2021. doi: 10.1016/j.ufug.2021.127197.
  8. DALLA CORTE, A.P.; REX, F.E.; ALMEIDA, D.R.A.D.; SANQUETTA, C.R.; SILVA, C.A.; MOURA, M.M.; WILKINSON, B.; ZAMBRANO, A.M.A.; CUNHA NETO, E.M.D.; VERAS, H.F.P.; MORAES, A.D.; KLAUBERG, C.; MOHAN, M.; CARDIL, A.; BROADBENT, E.N. Measuring individual tree diameter and height using gatoreye high-density uav-lidar in an integrated crop-livestock-forest system. Remote Sensing, n. 12, 2020. doi: 10.3390/rs12050863.
  9. D’OLIVEIRA, M.V.N.; BROADBENT, E.N.; OLIVEIRA, L.C.; ALMEIDA, D.R.A.; PAPA, D.A.; FERREIRA, M.E.; ZAMBRANO, A.M.A.; SILVA, C.A.; AVINO, F.S.; PRATA, G.A.; MELLO, R.A.; FIGUEIREDO, E.O.; JORGE, L.A.D.C.; JUNIOR, L.; ALBUQUERQUE, R.W.; BRANCALION, P.H.S.; WILKINSON, B.; OLIVEIRA-DA COSTA, M. Aboveground biomass estimation in amazonian tropical forests: a comparison of aircraft- and gatoreye uas-borne lidar data in the chico mendes extractive reserve in acre, brazil. Remote Sensing, n. 12, 2020. doi: 10.3390/rs12111754.
  10. FANKHAUSER, K.; STRIGUL, N.; GATZIOLIS, D. Augmentation of traditional forest inventory and airborne laser scanning with unmanned aerial systems and photogrammetry for forest monitoring. Remote Sensing, n. 10, 1562, 2018a. doi: 10.3390/rs10101562.
  11. FANKHAUSER, K.; STRIGUL, N.; GATZIOLIS, D. Augmentation of traditional forest inventory and airborne laser scanning with unmanned aerial systems and photogrammetry for forest monitoring. Remote Sensing, n. 10, 1562, 2018b. doi: 10.3390/rs10101562.
  12. FERREIRA, M.P.; DE ALMEIDA, D.R.A.; DE ALMEIDA PAPA, D.; MINERVINO, J.B.S.; VERAS, H.F.P.; FORMIGHIERI, A.; SANTOS, C.A.N.; FERREIRA, M.A.D.; FIGUEIREDO, E.O.; FERREIRA, E.J.L. Individual tree detection and species classification of amazonian palms using UAS images and deep learning. Forest Ecology and Management, n. 475, 118397, 2020. doi: 10.1016/j.foreco.2020.118397.
  13. FIGUEIREDO, E.O.; D’OLIVEIRA, M.V.N.; FEARNSIDE, P.M.; DE ALMEIDA PAPA, D. Models to estimate volume of individual trees by morphometry of crowns obtained with lidar. Cerne, n. 20, p. 621–628, 2014. doi: 10.1590/01047760201420041693.
  14. GUERRA-HERNÁNDEZ, J.; TOMÉ, M.; GONZÁLEZ-FERREIRO, E. Cartografía de variables dasométricas en bosques mediterráneos mediante análisis de los umbrales de altura e inventario a nivel de masa con datos lidar de baja resolución. Revista de Teledeteccion, n. 2016, p. 103–117, 2016. doi: 10.4995/raet.2016.3980.
  15. HUANG, H.; LI, X.; CHEN, C. Individual tree crown detection and delineation from very-high-resolution uav images based on bias field and marker-controlled watershed segmentation algorithms. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, n. 11, p. 2253–2262, 2018. doi: 10.1109/JSTARS.2018.2830410.
  16. IIZUKA, K.; YONEHARA, T.; ITOH, M.; KOSUGI, Y. Estimating tree height and diameter at breast height (dbh) from digital surface models and orthophotos obtained with an unmanned aerial system for a japanese cypress (chamaecyparis obtusa) forest. Remote Sensing, n. 10, 2018. doi: 10.3390/rs10010013.
  17. KATTENBORN, T.; LEITLOFF, J.; SCHIEFER, F.; HINZ, S. Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J. Photogramm. Remote Sens., n. 173, p. 24–49, 2021. doi: 10.1016/j.isprsjprs.2020.12.010.
  18. KEENAN, R.J.; REAMS, G.A.; ACHARD, F.; DE FREITAS, J.V.; GRAINGER, A.; LINDQUIST, E. Dynamics of global forest area: Results from the FAO global forest resources assessment. Forest Ecology and Management, n. 352, p. 9–20, 2015. doi: 10.1016/j.foreco.2015.06.014.
  19. LATIFI, H. Remote sensing-assisted temperate forest inventories: a complement or an alternative? 2020. URL: http://rgdoi.net/10.13140/RG.2.2.30602.29121, doi:10.13140/RG.2.2.30602.29121.
  20. LATIFI, H.; HEURICH, M. Multi-scale remote sensing-assisted forest inventory: A glimpse of the state-of-the-art and future prospects. Remote Sensing, n. 11, 1260, 2019. doi: 10.3390/rs11111260.
  21. LIN, Y.; JIANG, M.; YAO, Y.; ZHANG, L.; LIN, J. Use of uav oblique imaging for the detection of individual trees in residential environments. Urban Forestry Urban Greening, n. 14, p. 404–412, 2015. doi: 10.1016/j.ufug.2015.03.003.
  22. MEYER, H.A. Structure, growth, and drain in balanced uneven-aged forests. Journal of Forestry, n. 2, p. 85–92, 1952.
  23. MORALES, G.; KEMPER, G.; SEVILLANO, G.; ARTEAGA, D.; ORTEGA, I.; TELLES, J. Automatic segmentation of Mauritia flexuosa in unmanned aerial vehicle (UAS) imagery using deep learning. Forests, n. 9, 2018. doi: 10.3390/f9120736.
  24. MOURA, M.M.; OLIVEIRA, L.E.S.; SANQUETTA, C.R.; BASTOS, A.; MOHAN, M.; DALLA CORTE, A.P. Towards amazon forest restoration: Automatic detection of species from uav imagery. Remote Sensing, n. 13, 2021. doi: 10.3390/ rs13132627.
  25. R CORE TEAM. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria, 2021. URL: https://www.R-project.org/.
  26. RAMOS, A.M.; DOS SANTOS, L.A.R.; FORTES, L.T.G. Normais climatológicas do Brasil, 1961-1990. Instituto Nacional de Meteorologia-INMET, Ministério da Agricultura, 2009.
  27. TOMPALSKI, P.; COOPS, N.C.; WHITE, J.C.; GOODBODY, T.R.; HENNIGAR, C.R.; WULDER, M.A.; SOCHA, J.; WOODS, M.E. Estimating changes in forest attributes and enhancing growth projections: a review of existing approaches and future directions using airborne 3d point cloud data. Current Forestry Reports, n. 7, p. 1–24, 2021. doi: 10.1007/s40725-021-00135-w.
  28. TORRES, D.L.; FEITOSA, R.Q.; HAPP, P.N.; ROSA, L.E.C.L.; JUNIOR, J.M.; MARTINS, J.; BRESSAN, P.O.; GONÇALVES, W.N.; LIESENBERG, V. Applying fully convolutional architectures for semantic segmentation of a single tree species in urban environment on high resolution UAV optical imagery. Sensors, n. 20, 563, 2020. doi: 10.3390/s20020563.
  29. TORRES-SÁNCHEZ, J.; LÓPEZ-GRANADOS, F.; SERRANO, N.; ARQUERO, O.; PEÑA, J.M. High throughput 3-d monitoring of agricultural-tree plantations with unmanned aerial vehicle (uav) technology. PLoS ONE, n. 10, 2015. doi: 10.1371/journal.pone.0130479.
  30. TORRESAN, C.; CAROTENUTO, F.; CHIAVETTA, U.; MIGLIETTA, F.; ZALDEI, A.; GIOLI, B. Individual tree crown segmentation in two-layered dense mixed forests from uav lidar data. Drones, n. 4, 10, 2020.
  31. TUDORAN, G.M. Using mathematical models based on unmanned aerial vehicle optical imagery to estimate tree and stand characteristics. Bulletin of the Transilvania University of Braşov 15, 2022. URL: https://doi.org/10.31926/but.ens.2022.15.64.1.5,doi:10.31926/ but.ens.2022.15.64.1.5.
  32. TUDORAN, G.M.; DOBRE, A.C.; CICS, A, A.; PASCU, I.S. Development of mathematical models for the estimation of dendrometric variables based on unmanned aerial vehicle optical data: A romanian case study. Forests, n. 12, 2021. doi: 10.3390/f12020200.
  33. VELOSO, H.P.; RANGEL-FILHO, A.L.R.; LIMA, J.C.A. Classificação da vegetação brasileira, adaptada a um sistema universal. IBGE, 1991.
  34. VERAS, H.F.P.; FERREIRA, M.P.; DA CUNHA NETO, E.M.; FIGUEIREDO, E.O.; CORTE, A.P.D.; SANQUETTA, C.R. Fusing multi-season UAS images with convolutional neural networks to map tree species in amazonian forests. Ecological Informatics, n. 71, 101815, 2022. doi: 10.1016/j.ecoinf.2022.101815.
  35. YAN, W.; GUAN, H.; CAO, L.; YU, Y.; GAO, S.; LU, J. An automated hierarchical approach for three-dimensional segmentation of single trees using uav lidar data. Remote Sensing, n. 10, 2018. doi: 10.3390/ rs10121999.

Como Citar

Veras, H. F. P., Cunha Neto, E. M. da, Brasil, I. D. S., Madi, J. P. S., Araujo, E. C. G., Camaño, J. D. Z. ., … Sanquetta, C. R. . (2023). Estimating tree volume based on crown mapping by UAV pictures in the Amazon Forest. Scientific Electronic Archives, 16(7). https://doi.org/10.36560/16720231742