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Biogeosciences and Forestry Biogeosciences and Forestry Large scale semi-automatic detection of forest roads from low density LiDAR data on steep terrain in Northern Spain Covadonga Prendes (1) , Sandra Buján (2) , Celestino Ordoñez (3) , Elena Canga (1) While forest roads are important to forest managers in terms of facilitating the exploitation of wood and timber, their role is far more multifunctional. They permit access to emergency services in the case of forest fires as well as acting as fire breaks, enhance biodiversity, and provide access to the public to enjoy recreational activities. Detailed maps of forest roads are an essential tool for better and more timely forest management and automatic/semi-automatic tools allow not only the creation of forest road databases, but also enable these to be updated. In Spain, LiDAR data for the entire national territory is freely available, and the capture of higher density data is planned in the next few years. As such, the development of a forest road detection methodology based on LiDAR data would allow maps of all forest roads to be developed and regularly updated. The general objective of this work was to establish a low density LiDAR data-based methodology for the semi-automatic detection of the centerline of forest roads on steep terrain with various types of canopy cover. Intensity and slope images were generated using the currently available LiDAR data of the study area (0.5 points m-2). Two image classification approaches were evaluated: pixel-based and object-oriented classification (OBIA). The LiDAR-derived centerlines obtained with the two approaches were compared with the real centerlines which had previously been digitized in the field. The road width, type of surface and type of vegetation cover were also recorded. The effectiveness of the two approaches was evaluated through three quality indicators: correctness, completeness and quality. In addition, the accuracy of the LiDAR-derived centerlines was also evaluated by combining GIS analysis and statistical methods. The pixel-based approach obtained higher values than OBIA for two of the three quality measures (correctness: 93% compared to 90%; and quality: 60% compared to 56%) as well as in terms of positional accuracy (± 5.5 m vs. ± 6.8 for OBIA). The results obtained in this study demonstrate that producing road maps is among the most valuable and easily attainable products of LiDAR data analysis.

International audience• Context Despite the economic importance of Castanea sativa Mill. in northwest Spain, studies of its growth and yield are practically non-existent. • Aims A compatible system formed by a taper function, a total volume equation, and a merchantable volume equation was developed for chestnut (C. sativa Mill.) coppice stands in northwest Spain. • Methods Data from 203 destructively sampled trees were used for the adjustment. Outliers were removed with a non-parametric local adjustment, providing a final data set of measurements taken from 3,188 sections which was used to test five taper models (compatible and non-compatible). A second-order continuous autoregressive error structure was used to model the error term and account for autocorrelation. Presence of multicollinearity was evaluated with the condition number. Comparison of the models was carried out using overall goodness-of-fit statistics and graphical analysis. • Results Results show that the models developed by Fang et al. in For Sci 46: 1–12, 2000 and Kozak in For Chron 80, N 4: 507–515, 2004 were superior to other equations in predicting diameter for chestnut coppice stands. • Conclusion The compatible volume system developed by Fang et al. in For Sci 46: 1–12, 2000 was finally selected as it provided the best compromise between describing stem pro-file and also estimating merchantable height, merchantable volume, and total volume and therefore provides the first specific tool for more effective management of chestnut cop-pice stands

Aim of the study: The aim of this study was to develop a model for above-ground biomass estimation for Pinus radiata D. Don in Asturias.Area of study: Asturias (NE of Spain). Material and methods: Different models were fitted for the different above-ground components and weighted regression was used to correct heteroscedasticity. Finally, all the models were refitted simultaneously by use of Nonlinear Seemingly Unrelated Regressions (NSUR) to ensure the additivity of biomass equations.Research highlights: A system of four biomass equations (wood, bark, crown and total biomass) was develop, such that the sum of the estimations of the three biomass components is equal to the estimate of total biomass. Total and stem biomass equations explained more than 92% of observed variability, while crown and bark biomass equations explained 77% and 89% respectively.