Estimación del área basimétrica en masas maduras de Pinus sylvestris en base a una única medición del escáner láser terrestre (TLS)

  1. Molina-Valero, Juan Alberto 1
  2. Ginzo Villamayor, María José 2
  3. Novo Pérez, Manuel Antonio 3
  4. Álvarez-González, Juan Gabriel 1
  5. Pérez-Cruzado, César 1
  1. 1 Unidad de Gestión Ambiental y Forestal Sostenible (UXAFORES), Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior de Ingeniería, Universidade de Santiago de Compostela
  2. 2 Departamento de Estadística, Análisis Matemático y Optimización, Universidad de Santiago de Compostela
  3. 3 Instituto Tecnológico de Matemática Industrial (ITMATI)
Show affiliations +
Journal:
Cuadernos de la Sociedad Española de Ciencias Forestales

ISSN: 1575-2410,  2386-8368

Year of publication: 2019

Issue: 45

Pages: 97-116

Type: Article

DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Cuadernos de la Sociedad Española de Ciencias Forestales

Abstract

Terrestrial Laser Scanning (TLS) with LiDAR devices has emerged as a new technique of high potential value for implementation in forest inventories (FI). In this study we developed an algorithm to produce stand basal area metrics (G). The research was performed in 40 plots established in mature Pinus sylvestris stands covering the area of distribution and range of site qualities for this species in Spain. The proposed algorithm was obtained in four main steps: (1) normalization of point clouds obtained by TLS; (2) identification of individual trees; (3) reduction of the point cloud density; and (4) determination of Gmetrics. The G estimated in plots of 7 m of radius yielded the best results, with a Pearson correlation coefficient value of 0.86. This enabled us to produce a linear regression model with values of 0.75 for R2adj and 10.66 m2 for RMSE across all plots. Examination of the linear regression model by site yielded higher values of R2adj and RMSE, of respectively 0.82 and 8.57 m2. Although the results indicate that TLS is a good tool forestimating G in mature P. sylvestris stands, further research covering all stages of development is required for comparison of G values estimated in stands with different structures.

Bibliographic References

  • Arias-Rodil, M., Diéguez-Aranda, U., Álvarez-González, J., Pérez-Cruzado, C., Castedo-Dorado, F., González-Ferreiro, E., 2018. Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data. Ann. For. Sci. 75(2), 36. https://doi.org/10.1007/s13595-018-0712-z
  • Brede, B., Lau, A., Bartholomeus, H. M., Kooistra, L., 2017. Comparing RIEGL RiCOPTER UAV LiDAR Derived Canopy Height and DBH with Terrestrial LiDAR. Sens. 17(10), 2371. https://doi.org/10.3390/s17102371
  • Cabo, C., Ordóñez, C., López-Sánchez, C.A., Armesto, J., 2018. Automatic dendrometry: Tree detection, tree height and diameter estimation using terrestrial laser scanning. Int. J. Appl. Earth Obs. Geoinformation. 69, 164-174. https://doi.org/10.1016/j.jag.2018.01.011
  • Clutter, J.L., Fortson, J.C., Piennard, L.V., Brister, G.H., Bailey, R.L., 1983. Timber management: a quantitative approach. John Wiley & Sons, Inc., New York.
  • Corona, P., Di Biase, R.M., Fattorini, L., D'Amati, M., 2019. A Monte Carlo appraisal of tree abundance and stand basal area estimation in forest inventories based on terrestrial laser scanning. Can. J. Forest Res. 49(1), 41-52. https://doi.org/10.1139/cjfr-2017-0462
  • Ducey, M.J., Astrup, R., 2013. Adjusting for nondetection in forest inventories derived from terrestrial laser scanning. Can. J. Remote Sens. 39(5), 410-425
  • Ester, M., Kriegel, H.P., Sander, J., Xu, X., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd. 96(34), 226-231
  • Gobakken, T., Næsset, E., 2004. Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scand. J. For. Res. 19(6), 529-542. https://doi.org/10.1080/02827580410019454
  • Guerra-Hernández, J., Tomé, M., González-Ferreiro, E., 2016. Cartografia 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. Rev. Teledetec. 46, 103-117. https://doi.org/10.4995/raet.2016.3980
  • Holmgren, J., 2004. Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning. Scand. J. For. Res. 19(6), 543-553. https://doi.org/10.1080/02827580410019472
  • Hopkinson, C., Chasmer, L., Young-Pow, C., Treitz, P., 2004. Assessing forest metrics with a ground-based scanning lidar. Can. J. Remote Sens. 34(3), 573-583. https://doi.org/10.1139/x03-225
  • Liang, X., Hyyppä, J., Kaartinen, H., Lehtomäki, M., Pyörälä, J., Pfeifer, N., Holopainen, M., Brolly, G., Francesco, P., Hackenberg, J., Huang, H., Jo, H., Katoh, M., Liu, L., Mokroš, M., Morel, J., Olofsson, K., Poveda-Lopez, J., Trochta, J., Wang, D., Wang, Y., Wang, J., Xi, Z., Yang, B., Zheng, G., Kankare, V., Luoma, V., Yu, X., Chen, L., Vastaranta, M., Saarinen, N., Wang, Y., 2018. International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS J. Photogramm. Remote Sens. 144, 137-179. https://doi.org/10.1016/j.isprsjprs.2018.06.021
  • Liang, X., Kankare, V., Hyyppä, J., Wang, Y., Kukko, A., Haggrén, H., Yu, X., Kaartinen, H., Jaakkola, A., Guan, F., Holopainen, M., Vastaranta, M., 2016. Terrestrial laser scanning in forest inventories. ISPRS J. Photogramm. Remote Sens. 115, 63-77. https://doi.org/10.1016/j.isprsjprs.2016.01.006
  • Lovell, J.L., Jupp, D.L.B., Newnham, G.J., Culvenor, D.S., 2011. Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint. ISPRS J. Photogramm. Remote Sens. 66(1), 46-55. https://doi.org/10.1016/j.isprsjprs.2010.08.006
  • Moskal, L.M., Zheng, G., 2012. Retrieving Forest Inventory Variables with Terrestrial Laser Scanning (TLS) in Urban Heterogeneous Forest. Remote Sens. 4(1), 1-20. https://doi.org/10.3390/rs4010001
  • Newnham, G.J., Armston, J.D., Calders, K., Disney, M.I., Lovell, J.L., Schaaf, C.B., Strahler, A.H., Danson, F.M., 2015. Terrestrial Laser Scanning for Plot-Scale Forest Measurement. Curr. Forestry Rep. 1(4), 239-251. https://doi.org/10.1007/s40725-015-0025-5
  • Olofsson, K., Holmgren, J., Olsson, H., 2014. Tree Stem and Height Measurements using Terrestrial Laser Scanning and the RANSAC Algorithm. Remote Sens. 6(5), 4323-4344. https://doi.org/10.3390/rs6054323
  • R Core Team, 2019. R: a language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing
  • Roussel, J., Auty, D., De Boissieu, F., Meador, A., 2019. Airborne LiDAR Data Manipulation and Visualization for ForestryApplications. R package version 2.1.2.
  • Strahler, A.H., Jupp, D.L., Woodcock, C.E., Schaaf, C.B., Yao, T., Zhao, F., Yang, X., Lovell, J., Culvenor, D., Newnham, G., Ni-Miester, W., Boykin-Morris, W., 2008. Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna®). Can. J. Remote Sens. 34(sup2), S426-S440. https://doi.org/10.5589/m08-046
  • Torralba, J., Ruiz, L.A., Carbonell-Rivera, J.P., Crespo-Peremarch, P., 2019. Análisis de posiciones y densidades TLS (Terrestrial Laser Scanning) para optimizar la estimación de parámetros forestales. In: Ruiz, L.A., Javier Estornell, Abel Calle and Juan Carlos Antuña-Sánchez, (eds.), Teledetección: hacia una visión global del cambio climático. 443-446
  • Van Leeuwen, M., Nieuwenhuis, M., 2010. Retrieval of forest structural parameters using LiDAR remote sensing. Eur. J. Forest Res. 129(4), 749-770. https://doi.org/10.1007/s10342-010-0381-4
  • Yao, T., Yang, X., Zhao, F., Wang, Z., Zhang, Q., Jupp, D., Lovell, J., Culvenor, D., Newnham, G., Ni-Meister, W., Schaaf, C., Woodcock, C., Wang, J., Li, X., Strahler, A., 2011. Measuring forest structure and biomass in New England forest stands using Echidna ground-based lidar. Remote Sens. Environ. 115(11), 2965-2974. https://doi.org/10.1016/j.rse.2010.03.019
  • 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 Sens. 8(6), 501. https://doi.org/10.3390/rs8060501
Data source: Dialnet