ABSTRACT
Wind power is one of the fastest growing renewable energy sectors and plays a focal role in the transition to a fossil fuel free society in Europe. Technological developments have enabled the construction of turbines within forested areas, which has raised concerns regarding the audio-visual impact on these landscapes. However, there is a paucity of research with regard to the role that forests may play in mitigating the negative impacts of wind farms. In this study, we created a simplified model for noise attenuation based on the ISO 9613-2 and Nord2000 noise models and a visibility model which both relates the audio-visual effect to forest stand structure and applied them in the GIS environment. Our findings suggest that forests can act as effective noise barriers, with the sound attenuation level dependent on the distance that sound travels through the forest, as well as the size and density of the trees. However, in the case of a high elevation sound source (such as wind turbines), the forest begins to act as a noise shield from a distance of between 500 and 1500 m, depending on the height of the forest and the land topography. While current noise models do not consider the impact of tree species, our visibility model accounts for tree size, density and species, as well as understorey and thinning. Our results indicate that spruce trees provide a better visual constraint whereas visibility distances within mature Calluna-type pine forests tend to be more extensive. Both models include variables that can be adjusted by forest management, thereby allowing integration with forest planning software. Overall, this study presents indicative methods for the evaluation of potential forest landscape shields, a concept that could have broad applications, including Landscape Value Trading.
Subject(s)
Energy-Generating Resources , Wind , Forests , Trees , NoiseABSTRACT
Avian species persistence in a forest patch is strongly related to the degree of isolation and size of a forest patch and the vegetation structure within a patch and its matrix are important predictors of bird habitat suitability. A combination of space-borne optical (Landsat), ALOS-PALSAR (radar), and airborne Light Detection and Ranging (LiDAR) data was used for assessing variation in forest structure across forest patches that had undergone different levels of forest degradation in a logged forest-agricultural landscape in Southern Laos. The efficacy of different remote sensing (RS) data sources in distinguishing forest patches that had different seizes, configurations, and vegetation structure was examined. These data were found to be sensitive to the varying levels of degradation of the different patch categories. Additionally, the role of local scale forest structure variables (characterized using the different RS data and patch area) and landscape variables (characterized by distance from different forest patches) in influencing habitat preferences of International Union for Conservation of Nature (IUCN) Red listed birds found in the study area was examined. A machine learning algorithm, MaxEnt, was used in conjunction with these data and field collected geographical locations of the avian species to identify the factors influencing habitat preference of the different bird species and their suitable habitats. Results show that distance from different forest patches played a more important role in influencing habitat suitability for the different avian species than local scale factors related to vegetation structure and health. In addition to distance from forest patches, LiDAR-derived forest structure and Landsat-derived spectral variables were important determinants of avian habitat preference. The models derived using MaxEnt were used to create an overall habitat suitability map (HSM) which mapped the most suitable habitat patches for sustaining all the avian species. This work also provides insight that retention of forest patches, including degraded and isolated forest patches in addition to large contiguous forest patches, can facilitate bird species retention within tropical agricultural landscapes. It also demonstrates the effective use of RS data in distinguishing between forests that have undergone varying levels of degradation and identifying the habitat preferences of different bird species. Practical conservation management planning endeavors can use such data for both landscape scale monitoring and habitat mapping.
