Cost-efficiency analysis of multiple ecosystem services across forest management regimes.

Forest management is at the crossroads of economic, environmental, and social goals, often requiring strategic trade-offs. As global demands surge, it's vital to employ management strategies fostering multifunctional landscapes, enabling ecosystem integrity while procuring resources. Historically, the boreal forest in Fennoscandia has been intensively managed for timber, causing environmental shifts and conflicts with biodiversity conservation and climate mitigation policies. Application of current management practices while increasing harvests are a threat to both biodiversity and carbon stocks. To explore this issue, we quantify the cost-efficiency of two forest management regimes: rotation forestry (RF) and continuous cover forestry (CCF), considering specific forest attributes like soil type (mineral and peat soils), site type (fertility classes) and tree stand age, which have been underexplored in previous research. We simulated 45,559 forest stands for 100 years in Northern boreal forests of Finland. We proposed two straightforward cost-efficiency indices (CEI) to evaluate the performance of these management regimes, specifically focusing on their impact on economic output, biodiversity conservation (measured as a biodiversity index for six forest vertebrates, including five bird species and one mammal) and carbon stock. Our findings suggest that continuous cover forestry holds the potential to deliver more cost-efficient ecosystem services and maintain greater biodiversity compared to rotation forestry approaches. Continuous cover forestry, however, is not optimal for all at management units, which calls for alternative management options depending on the stand characteristics. The cost-efficiency indices performance of rotation forestry and continuous cover forestry depend on the characteristics of the initial stand which is largely determined by the previous management of the stand. Our results contribute to guiding forest management towards enhanced sustainability and ecological balance. The great variation in stand characteristics suggest a need for diverse management strategies to create multifunctional landscapes. Our proposed cost-efficiency indices could serve as practical tools for decision-making.

Detecting Spatial Patterns of Peatland Greenhouse Gas Sinks and Sources with Geospatial Environmental and Remote Sensing Data.

Peatlands play a key role in the circulation of the main greenhouse gases (GHG) - methane (CH4), carbon dioxide (CO2), and nitrous oxide (N2O). Therefore, detecting the spatial pattern of GHG sinks and sources in peatlands is pivotal for guiding effective climate change mitigation in the land use sector. While geospatial environmental data, which provide detailed spatial information on ecosystems and land use, offer valuable insights into GHG sinks and sources, the potential of directly using remote sensing data from satellites remains largely unexplored. We predicted the spatial distribution of three major GHGs (CH4, CO2, and N2O) sinks and sources across Finland. Utilizing 143 field measurements, we compared the predictive capacity of three different data sets with MaxEnt machine-learning modeling: (1) geospatial environmental data including climate, topography and habitat variables, (2) remote sensing data (Sentinel-1 and Sentinel-2), and (3) a combination of both. The combined dataset yielded the highest accuracy with an average test area under the receiver operating characteristic curve (AUC) of 0.845 and AUC stability of 0.928. A slightly lower accuracy was achieved using only geospatial environmental data (test AUC 0.810, stability AUC 0.924). In contrast, using only remote sensing data resulted in reduced predictive accuracy (test AUC 0.763, stability AUC 0.927). Our results suggest that (1) reliable estimates of GHG sinks and sources cannot be produced with remote sensing data only and (2) integrating multiple data sources is recommended to achieve accurate and realistic predictions of GHG spatial patterns.

Forest landscape shield models for assessing audio-visual disturbances of wind turbines.

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.

Cost-efficient management of peatland to enhance biodiversity in Finland.

Peatlands provide a variety of ecosystem services besides being important ecosystems for biodiversity. Sustainable peatland management requires that its impacts are identified, and all management is allocated in a cost-efficient manner. In this study, we assessed how peatland management influences the habitat suitability of red-listed plant species and the financial performance of management measured as net present value (NPV). The study was done in three landscapes in Finland. We considered four peatland management scenarios i.e., no management activity (NOMANAGE), hydrological restoration (REST), wood harvesting for bioenergy (BIOENERGY), and timber production (TIMBER). The NPVs of different management scenarios were compared to the habitat suitability of red-listed peatland plant species. A cost-impact analysis was used, with TIMBER as a baseline scenario, to find out which alternative scenario would be the most cost-efficient in contributing to habitat suitability. Our study shows that potential habitat areas were significantly different between the scenarios. REST provided the largest potential habitat areas, followed by BIOENERGY, NOMANAGE, and TIMBER. TIMBER provided the best financial performance when low interest rates were used. REST and BIOENERGY were more cost-efficient in enhancing potential habitat areas than NOMANAGE. REST would improve suitable habitats and provide financial benefits when a higher interest rate was used. In conclusion, even a win-win condition could be achieved in some cases (33%), in which higher NPV was achieved simultaneously with improved potential habitat areas. The study provides information for alleviating the economic barriers of restoration and targeting land use and management options cost-efficiently.

A Comparison of Three Airborne Laser Scanner Types for Species Identification of Individual Trees.

Species identification is a critical factor for obtaining accurate forest inventories. This paper compares the same method of tree species identification (at the individual crown level) across three different types of airborne laser scanning systems (ALS): two linear lidar systems (monospectral and multispectral) and one single-photon lidar (SPL) system to ascertain whether current individual tree crown (ITC) species classification methods are applicable across all sensors. SPL is a new type of sensor that promises comparable point densities from higher flight altitudes, thereby increasing lidar coverage. Initial results indicate that the methods are indeed applicable across all of the three sensor types with broadly similar overall accuracies (Hardwood/Softwood, 83-90%; 12 species, 46-54%; 4 species, 68-79%), with SPL being slightly lower in all cases. The additional intensity features that are provided by multispectral ALS appear to be more beneficial to overall accuracy than the higher point density of SPL. We also demonstrate the potential contribution of lidar time-series data in improving classification accuracy (Hardwood/Softwood, 91%; 12 species, 58%; 4 species, 84%). Possible causes for lower SPL accuracy are (a) differences in the nature of the intensity features and (b) differences in first and second return distributions between the two linear systems and SPL. We also show that segmentation (and field-identified training crowns deriving from segmentation) that is performed on an initial dataset can be used on subsequent datasets with similar overall accuracy. To our knowledge, this is the first study to compare these three types of ALS systems for species identification at the individual tree level.