• Rural Digital Europe
• Open Access close
• Academy of Finland close
• 15. Life on land close

Given the high percentage of private forest ownership in Finland, family forest owners have an important role in mitigating climate change. The study aims to explore Finnish family forest owners' perceptions on climate change and their opinions on increasing carbon storage in their forests through new kinds of management activities and policy instruments. The data consists of thematic face-to-face interviews among Helsinki metropolitan area forest owners (n = 15). These city-dwellers were expected to be more aware of and more interested in climate change mitigation than forest owners at large. Forests as carbon fluxes appear to be a familiar concept to most of the forest owners, but carbon storage in their own forests was a new idea. Four types concerning forest owners' view on storing carbon in their forests could be identified. The Pioneer utilizes forestland versatilely and has already adopted practices to mitigate climate change. The Potential is concerned about climate change, but this is not seen in forest practices applied. The Resistant is generally aware of climate change but sees a fundamental contradiction between carbon storing and wood production. The Indifferent Owner believes that climate change is taking place but does not acknowledge a relation between climate change and the owner's forests. Peer reviewed

Abstract Sisal (Agave sisalana) is a climate-resilient crop grown on large-scale farms in semi-arid areas. However, no studies have investigated soil greenhouse gas (GHGs: CO2, N2O and CH4) fluxes from these plantations and how they relate to other land cover types. We examined GHG fluxes (Fs) in a sisal chronosequence at Teita Sisal Estate in southern Kenya. The effects of stand age on Fs were examined using static GHG chambers and gas chromatography for a period of one year in seven stands: young stands aged 1–3 years, mature stands aged 7–8 years, and old stands aged 13–14 years. Adjacent bushland served as a control site representing the surrounding land use type. Mean CO₂ fluxes were highest in the oldest stand (56 ± 3 mg C m-2 h-1) and lowest in the 8-year old stand (38 ± 3 mg C m-2 h-1), which we attribute to difference in root respiration between the stand. All stands had 13–28% higher CO₂ fluxes than bushland (32 ± 3 mg C m-2 h-1). CO2 fluxes in the wet season were about 70% higher than dry season across all sites. They were influenced by soil water content (WS) and vegetation phenology. Mean N2O fluxes were very low (

There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500-900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3-61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between -14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits. Peer reviewed

Abstract Forest resource management has undergone profound changes in the last decades, including a tendency to apply participatory approach that seeks to involve local communities. However, the success of the participatory approach tends to remain dependent on the historical and societal context in question. To understand how the participation of forest communities has been changing as a result of the enforcement or non-enforcement of forest management practices, we carried out a study in Cameroon's Korup National Park, with villages within and outside the park that continue to utilise the resources in the park. The empirical research included focus group discussions and semi-structured interviews in three communities and key informant interviews with selected community members, government officials, and non-governmental organisations involved in the park's management. Research findings show that although the forest management system has changed in various policy documents, over the past years from a top-down to participatory approach, a centralised state system is still operational in the national park, with participatory approaches used merely as a legitimizing tool. We show how the existing horizontal power relations (such as gender roles within the communities) and vertical power relations (such as government-community dimensions) simultaneously impact the outcomes of participatory approaches on the ground. Finally, our case shows how the existing governance structures continue to reproduce inequalities and exclusions that originated from the colonial times and through path dependency still influence livelihoods and day-to-day survival of people in the communities.

Boreal forests have been intensively managed, influencing the spatiotemporal occurrence of dead wood, and leading to changes in saproxylic species assemblages. Some particular traits, such as habitat specialization, can be expected to make species sensitive to alterations in the amount, dynamics and heterogeneity of dead-wood habitats. We compared the saproxylic beetle assemblages of 320 dead standing Scots pines within 52 pine forest stands in three regions in Finland and Russia with contrasting forest-use history. We used the joint species distribution model of Hierarchical Modelling of Species Communities (HMSC) to analyze how the beetle communities respond to alteration of forest structure. We applied scenario simulations to examine relationships between selected species traits and environmental gradients. Our results show that tree-level characteristics were the most important variables shaping the community composition in dead standing pines, but that also the characteristics of the forest stand as well as the larger-scale landscape context affected assemblage composition. Most importantly, managed forest stands and managed forest landscapes had lost species that are specialized in their resource use. The generalist species thriving in managed forest stands and managed forest landscapes were able to utilize dead wood of small diameter and man-made dead wood types, such as cut stumps and logging residues. We conclude that forest management not only reduces the amount of resources for saproxylic beetles locally, but has also landscape-level effects so that the most vulnerable species with specialized resource use and short-lived substrates can be lost also from remnant natural forest patches embedded in managed landscapes. Peer reviewed

Resin is a first-line defense in pine trees, but important questions regarding its temporal and spatial variation remain unsolved. Resin pressure varies according to water potential in dry conditions, but in moist conditions, it follows temperature dynamics for a yet unknown reason. Relations between resin composition, resin pressure, and shoot monoterpene emissions are also unquantified. To gain mechanistic understanding on the resin dynamics in a boreal forest, we measured temperature and water potential dependency of Scots pine resin pressure. We attempted to quantify the temperature dependency of resin pressure in terms of three contributions: (1) saturation vapor pressure, (2) thermal expansion, and (3) N2, O2, and CO2 solubility. We also analyzed monoterpene composition in the resin and the shoot emissions of 16 pines with gas chromatography mass spectrometry to study their interrelations. We show that in moist conditions, resin pressure is driven by temperature at a diurnal scale, but also affected by soil water potential at a day-to-day scale. Diurnal temperature dependency was explained by thermal expansion of resin and changes in bubble volume due to changes in gas solubility in resin with temperature. Resin pressures correlated also with total monoterpene and α-pinene content in resin and with total monoterpene and Δ3-carene and terpinolene emissions from shoots. Resin is a first-line defense in pine trees, but important questions regarding its temporal and spatial variation remain unsolved. Resin pressure varies according to water potential in dry conditions, but in moist conditions, it follows temperature dynamics for a yet unknown reason. Relations between resin composition, resin pressure, and shoot monoterpene emissions are also unquantified. To gain mechanistic understanding on the resin dynamics in a boreal forest, we measured temperature and water potential dependency of Scots pine resin pressure. We attempted to quantify the temperature dependency of resin pressure in terms of three contributions: 1) saturation vapor pressure, 2) thermal expansion, and 3) N2, O2, and CO2 solubility. We also analyzed monoterpene composition in the resin and the shoot emissions of 16 pines with gas chromatography mass spectrometry to study their interrelations. We show that in moist conditions, resin pressure is driven by temperature at a diurnal scale, but also affected by soil water potential at a day-to-day scale. Diurnal temperature dependency was explained by thermal expansion of resin and changes in bubble volume due to changes in gas solubility in resin with temperature. Resin pressures correlated also with total monoterpene and α-pinene content in resin and with total monoterpene and ∆3-carene and terpinolene emissions from shoots. Peer reviewed

Airborne laser scanning (ALS) is considered as the most accurate remote sensing data for the predictive modelling of AGB. However, tropical landscapes experiencing land use changes are typically heterogeneous mosaics of various land cover types with high tree species richness and trees outside forests, making them challenging environments even for ALS. Therefore, combining ALS data with other remote sensing data, or stratification by land cover type could be particularly beneficial in terms of modelling accuracy in such landscapes. Our objective was to test if spectral-temporal metrics from the Landsat time series (LTS), simultaneously acquired hyperspectral (HS) data, or stratification to the forest and non-forest classes improves accuracy of the AGB modelling across an Afromontane landscape in Kenya. The combination of ALS and HS data improved the cross-validated RMSE from 51.5 Mg ha−1 (42.7%) to 47.7 Mg ha−1 (39.5%) in comparison to the use of ALS data only. Furthermore, the combination of ALS data with LTS and HS data improved accuracies of the models for the forest and non-forest classes, and the overall best results were achieved when using ALS and HS data with stratification (RMSE 40.0 Mg ha−1, 33.1%). We conclude that ALS data alone provides robust models for AGB mapping across tropical mosaic landscapes, even without stratification. However, ALS and HS data together, and additional forest classification for stratification, can improve modelling accuracy considerably in similar, tree species rich areas. Peer reviewed

Abstract It has been argued that even centimeter-level resolution is needed for mapping vegetation patterns in spatially heterogeneous landscapes such as northern peatlands. However, there are few systematic tests for determining what kind of spatial resolution and data combinations are needed and what the differences in mapping accuracy are when different datasets are omitted or included. We conducted 78 different object-based supervised random forest classifications on a patterned fen and its surroundings in Kaamanen, northern Finland, using remotely sensed optical imagery, topography, and vegetation height datasets from different platforms (unmanned aerial vehicle (UAV), aerial, satellite) with spatial resolution ranging from 5 cm to 3 m. We compared differences in classification performance when we altered (1) classification and segmentation input data and features calculated from the data, or (2) the segmentation scale. We constructed training data with the help of transect-based field sampling and UAV imagery and tested classification accuracy using 412 field-surveyed vegetation plots. The most accurate classifications (75.7% overall accuracy) were obtained when we segmented a 5 cm resolution UAV image with a small segmentation scale and calculated features from all datasets. Classification accuracy was 2.2 percentage points (pp) lower with the most accurate aerial image (50 cm resolution) based classification, and 7.6 pp and 11.9 pp lower with the most accurate WorldView-2 (2 m resolution) and PlanetScope (3 m resolution) satellite image based classifications respectively. Classification accuracies were low (46.7–56.0%) when we used only spectral data from one dataset. The inclusion of gray-level co-occurrence matrix textural features increased classification accuracy by 0.4–12.1 pp and inclusion of multiple datasets by 8.2–25.0 pp. Segmentation scale had a minor effect on classification accuracy (2.5–7.3 pp difference between the finest and coarsest segmentation scale); however, both too small and large segmentation scale might lead to suboptimal classification. The differences in land cover type areal coverage were relatively small between classifications with multiple datasets, but if classifications included features from only one dataset, the differences were larger. We conclude that multiple different optical, topographical, and vegetation height datasets should be used when mapping vegetation in spatially heterogeneous landscapes, and that sub-meter resolution data (e.g. UAV or aerial) are necessary for the most accurate maps. Although UAV data is not essentially needed for classification, it is useful for training dataset construction and especially helpful in areas lacking other sub-meter resolution data.