Wildlife-related accidents are a serious problem in different countries and describing their temporal pattern allows for the development of measures to mitigate them. We described the temporal pattern of wild ungulate-related accidents occurring between January 2006 and December 2010 in the Autonomous Region of Galicia, northwest of Spain. We examined the temporal distribution of the accidents according to months, phenological and hunting seasons, days of the week and time of the day. From the 6,255 wild ungulate-related traffic accidents analysed, 36.5% were related to roe deer Capreolus capreolus and 62.8% were related to wild boar Sus scrofa. The monthly distribution of accidents was not random but follows a characteristic pattern for each species. Roe deer-related accidents have their maximum in April and May, coinciding with the breeding season, followed by July, coinciding with the rut. Wild boarrelated accidents have their maximum between October and January, coinciding with the hunting season but also with months with the longest nights. Both roe deer-and wild boar-related accidents showed an increase at weekends, specially on Sundays. During the hunting season, the wild boar-related accidents showed a marked peak on the same day. This weekly pattern was explained by drivers' behaviour and by hunting. For roe deer, peaks of accidents oc-

The present study addresses the tree counting of a Eucalyptus plantation, the most widely planted hardwood in the world. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) was used for the estimation of Eucalyptus trees. LiDAR-based estimation of Eucalyptus is a challenge due to the irregular shape and multiple trunks. To overcome this difficulty, the layer of the point cloud containing the stems was automatically classified and extracted according to the height thresholds, and those points were horizontally projected. Two different procedures were applied on these points. One is based on creating a buffer around each single point and combining the overlapping resulting polygons. The other one consists of a two-dimensional raster calculated from a kernel density estimation with an axis-aligned bivariate quartic kernel. Results were assessed against the manual interpretation of the LiDAR point cloud. Both methods yielded a detection rate (DR) of 103.7% and 113.6%, respectively. Results of the application of the local maxima filter to the canopy height model (CHM) intensely depends on the algorithm and the CHM pixel size. Additionally, the height of each tree was calculated from the CHM. Estimates of tree height produced from the CHM was sensitive to spatial resolution. A resolution of 2.0 m produced a R2 and a root mean square error (RMSE) of 0.99 m and 0.34 m, respectively. A finer resolution of 0.5 m produced a more accurate height estimation, with a R2 and a RMSE of 0.99 and 0.44 m, respectively. The quality of the results is a step toward precision forestry in eucalypt plantations.

Agradecimientos a: María Loureiro agradece la financiación recibida a través del programa ERANET-BIODIVERSA, proyecto "FIREMAN", número EUI2008-03685.Recibido en mayo de 2011. Aceptado en enero de 2012.REsumEn: El objetivo de este trabajo es estudiar la relevancia de los múltiples factores socio-económi-cos, agrarios y ambientales en la ocurrencia de los incendios forestales en Galicia. Los modelos econométri-cos presentados analizan el número de incendios ocurridos así como el número de hectáreas quemadas, en función de los múltiples factores indicados. A raíz de los resultados obtenidos, se concluye que determinadas políticas públicas preventivas diseñadas hacia una reorientación el sector agro-ganadero, el cuidado de la pirámide poblacional y un mejor aprovechamiento de los usos del suelo, pueden ayudar a disminuir los incendios forestales en Galicia de forma significativa.PALABRAs CLAVEs: Relación causa-efecto, factores socio-económicos, climatología, regresión.Clasificación JEL: Q23, Q51. The causality of wildfires in GaliciaABsTRACT: The goal of this research is to analyze the importance of the main factors contributing to the occurrence of wildfires in Galicia. The econometric models are specified taking into account as a dependent variable the number of fires and affected area, while these depend on a number of explanatory variables, including climatic and socio-economic characteristics. Based on the obtained results, we conclude that public policies should be oriented to re-structuring the agro-livestock sector, considering the evolution of the population pyramid, and new land uses. Such policies can help to reduce wildfires in Galicia.

This paper describes a methodology using LiDAR point clouds with an ultra-high resolution in the characterization of forest fuels for further wildfire prevention and management. Biomass management strips were defined in three case studies using a particular Spanish framework. The data were acquired through a UAV platform. The proposed methodology allows for the detection, measurement and characterization of individual trees, as well as the analysis of shrubs. The individual tree segmentation process employed a canopy height model, and shrub cover LiDAR-derived models were used to characterize the vegetation in the strips. This way, the verification of the geometric legal restrictions was performed automatically and objectively using decision trees and GIS tools. As a result, priority areas, where wildfire prevention efforts should be concentrated in order to control wildfires, can be identified.

Over the last several decades, thanks to improvements in and the diversification of open-access satellite imagery, land cover mapping techniques have evolved significantly. Notable changes in these techniques involve the automation of different steps, yielding promising results in terms of accuracy, class detection and efficiency. The most successful methodologies that have arisen rely on the use of multi-temporal data. Several different approaches have proven successful. In this study, one of the most recently developed methodologies is tested in the region of Galicia (in Northwestern Spain), with the aim of filling gaps in the mapping needs of the Galician forestry sector. The methodology mainly consists of performing a supervised classification of individual images from a selected time series and then combining them through aggregation using decision criteria. Several of the steps of the methodology can be addressed in multiple ways: pixel resolution selection, classification model building and aggregation methods. The effectiveness of these three tasks as well as some others are tested and evaluated and the most accurate and efficient parameters for the case study area are highlighted. The final land cover map that is obtained for Galicia has high accuracy metrics (an overall accuracy of 91.6%), which is in line with previous studies that have followed this methodology in other regions. This study has led to the development of an efficient open-access solution to support the mapping needs of the forestry sector.