Bosque (Valdivia)  vol.38 no.1; Abstract: S0717-92002017000100003
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Bosque (Valdivia)
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Abstract

MARTIN-GARCIA, Saray, DIEGUEZ-ARANDA, Ulises, ALVAREZ GONZALEZ, Juan Gabriel et al. Estimation of timber stocks of Pinus radiata stands at the provincial scale using low resolution LiDAR data. Bosque (Valdivia), 2017, vol.38, no.1, p.17-28. ISSN 0717-9200.

In this study, the stand volume (V, m3 ha-1) of Pinus radiata plantations of the province of Lugo (NW of Spain) was estimated using LiDAR-derived metrics from the low-density countrywide LiDAR dataset provided by the Spanish National Aerial Photography Program. The estimate was based on a oriented field inventory design to improve the fit of the model and on a inferential method based on the variability of the model parameter estimation and the variability of the LiDAR variable in the target population, which allowed to estimate the population mean and the standard error of V. A total of 25 permanent field inventory plots -that represent adequately the existing range of ages, stand densities and sites of the species in the province- were measured in order to fit the empirical relationship between field-measured V and LiDAR-derived metrics, using linear and (multiplicative) power function models. Regression analysis indicated a strong relationship between V and the 70 percentile of the LiDAR normalized height distribution (h70) using a power function model (R2 = 0.899; RMSE = 63.4 m3 ha-1). The selected model was applied to all timber and high-pole development stands of P. radiata with coverage of this species superior to the 60 % according to the Spanish National Forest Map. Finally, a spatial-explicit map that describes V was produced. In 2009, the estimated mean population for V was 166.3 m3 ha-1 with a relative sampling error of 13.9 %. This methodology will allow generating thematic maps every six years (temporal resolution scheduled for the PNOA LiDAR flights).

Keywords: Airborne Laser Scanning (ALS); Forest Inventory (FI); Remote Sensing (RS); estimators; regression models.

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