Influence of road noise disturbances on black vulture nesting area: Case of "Cabeza de Hierro" state in Spanish Mediterranean Central Mountain

The real and potential road influence on soundscape is considered a relevant management aspect that can assess the negative effects of massive visitants on such sensitive species -that have been living for centuries in the area-. As a first approach to the study of human disturbances sound impact, acoustic engineering tools allow us to model noise pollution caused by the main road that crosses the state ?Cabeza de Hierro? (M-604). For these preliminary results we use the French method XPS 31-133, recommend at EU level. Noise emission levels in black vulture nesting area are analyzed to understand the influence of human activities on rural areas and road management on biodiversity conservation. This approach develops a useful tool to make compatible the public enjoyment of forest services such as recreation or landscape scenary, the conservation of biodiversity as well as a suitable social and economic activity level ?timber and firewood harvesting, industry?- at the region.

Safety and health in forest harvesting operations. Diagnosis and preventive actions. A review

Aim of study: to review the present state of the art in relation to the main labour risks and the most relevant results of recent studies evaluating the safety and health conditions of the forest harvesting work and better ways to reduce accidents. Area of study: It focuses mainly on developed Countries, where the general concern about work risks prevention, together with the complex idiosyncrasy of forest work in forest harvesting operations, has led to a growing interest from the forest scientific and technical community. Material and Methods: The main bibliographic and Internet references have been identified using common reference analysis tools. Their conclusions and recommendations have been comprehensively summarized. Main results: Collection of the principal references and their most important conclusions relating to the main accident risk factors, their causes and consequences, the means used towards their prevention, both instrumental as well as in the aspects of training and business management, besides the influence of the growing mechanization of logging operations on those risks. Research highlights: Accident risk is higher in forest harvesting than in most other work sectors, and the main risk factors such as experience, age, seasonality, training, protective equipment, mechanization degree, etc. have been identified and studied. The paper summarizes some relevant results, one of the principal being that the proper entrepreneurial risk management is a key factor leading to the success in minimizing labour risks..

Comparison of Airborne Laser Scanning Methods for Estimating Forest Structure Indicators Based on Lorenz Curves

The purpose of this study was to compare a number of state-of-the-art methods in airborne laser scan- ning (ALS) remote sensing with regards to their capacity to describe tree size inequality and other indi- cators related to forest structure. The indicators chosen were based on the analysis of the Lorenz curve: Gini coefficient ( GC ), Lorenz asymmetry ( LA ), the proportions of basal area ( BALM ) and stem density ( NSLM ) stocked above the mean quadratic diameter. Each method belonged to one of these estimation strategies: (A) estimating indicators directly; (B) estimating the whole Lorenz curve; or (C) estimating a complete tree list. Across these strategies, the most popular statistical methods for area-based approach (ABA) were used: regression, random forest (RF), and nearest neighbour imputation. The latter included distance metrics based on either RF (NN–RF) or most similar neighbour (MSN). In the case of tree list esti- mation, methods based on individual tree detection (ITD) and semi-ITD, both combined with MSN impu- tation, were also studied. The most accurate method was direct estimation by best subset regression, which obtained the lowest cross-validated coefficients of variation of their root mean squared error CV(RMSE) for most indicators: GC (16.80%), LA (8.76%), BALM (8.80%) and NSLM (14.60%). Similar figures [CV(RMSE) 16.09%, 10.49%, 10.93% and 14.07%, respectively] were obtained by MSN imputation of tree lists by ABA, a method that also showed a number of additional advantages, such as better distributing the residual variance along the predictive range. In light of our results, ITD approaches may be clearly inferior to ABA with regards to describing the structural properties related to tree size inequality in for- ested areas.