Caracterización del crecimiento y producción de rodales forestales a partir de datos PNOA-LiDAR multitemporales en la provincia de Lugo

  1. Salgado, L. 1
  2. Colina, A. 1
  3. Docampo, M.L. 2
  4. López Sánchez, C.A. 3
  1. 1 Instituto de Recursos Naturales y Ordenación del Territorio, Campus de Mieres, Universidad de Oviedo
  2. 2 Departamento de Ingeniería Agroforestal, Escola Politécnica Superior de Enxeñaría, Campus de Lugo, Universidade de Santiago de Compostela
  3. 3 Departamento de Biología de Organismos y Sistemas, Escuela Politécnica de Mieres, Campus de Mieres, Universidad de Oviedo
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Revista:
Cuadernos de la Sociedad Española de Ciencias Forestales

ISSN: 1575-2410,  2386-8368

Ano de publicación: 2020

Número: 46

Páxinas: 231-244

Tipo: Artigo

DOI: 10.31167/CSECFV5I45.19907 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Outras publicacións en: Cuadernos de la Sociedad Española de Ciencias Forestales

Resumo

In this study, data from the 4th National Forest Inventory (IFN-4) and from the 1st and 2nd National Airborne Laser Exploration Survey (ALS) are used to develop predictive performance models and direct estimates for the three main commercial forest species (Eucalyptus globulus, Pinus pinasterand Pinus radiata) grown in the province of Lugo. The integration of both types of data required prior harmonization due to the differences in the time of data acquisition and the difficulties to accurately geolocate the IFN-4 plots. The harmonized data of E. globulus, P.pinaster and P.radiata have been used to develop predictive models to determine the total volume with cortex (VCC) at a point t different from the capture of the PNOA-LiDAR data. The annual increase in volume with cortex (IAVC) was calculated by means of the relationship between the variables of the IFN4 plots, with the metrics derived from the PNOA-LiDAR data, by the Random Forest (RF) linear regression method and several techniques of machine learning. These data are also used to obtain VCC values directly using multi-temporal PNOA-LiDAR data, the latter being captured at the projection time t of the other method.

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Fonte de datos: Dialnet