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The assessments of future climate risks are common; however, usually, they focus on climate projections without considering social changes. We project heat risks for Finland to evaluate (1) what kind of differences there are in heat vulnerability projections with different scenarios and scales, and (2) how the use of socio-economic scenarios influences heat risk assessments. We project a vulnerability index with seven indicators downscaled to the postal code area scale for 2050. Three different scenario sets for vulnerability are tested: one with five global Shared Socioeconomic Pathways (SSPs) scenarios; the second with three European SSPs (EUSSPs) with data at the sub-national scale (NUTS2); and the last with the EUSSPs but aggregated data at the national scale. We construct projections of heat risk utilizing climatic heat hazard data for three different Representative Concentration Pathways (RCPs) and vulnerability and exposure data for five global SSPs up to 2100. In the vulnerability projections, each scenario in each dataset shows a decrease in vulnerability compared to current values, and the differences between the three scenario sets are small. There are evident differences both in the spatial patterns and in the temporal trends when comparing the risk projections with constant vulnerability to the projections with dynamic vulnerability. Heat hazard increases notably in RCP4.5 and RCP8.5, but a decrease of vulnerability especially in SSP1 and SSP5 alleviates risks. We show that projections of vulnerability have a considerable impact on future heat-related risk and emphasize that future risk assessments should include the combination of long-term climatic and socio-economic projections.
Armand Landreau; Sirkku Juhola; Alexandra Jurgilevich; Aleksi Räsänen. Combining socio-economic and climate projections to assess heat risk. Climatic Change 2021, 167, 1 -20.
AMA StyleArmand Landreau, Sirkku Juhola, Alexandra Jurgilevich, Aleksi Räsänen. Combining socio-economic and climate projections to assess heat risk. Climatic Change. 2021; 167 (1-2):1-20.
Chicago/Turabian StyleArmand Landreau; Sirkku Juhola; Alexandra Jurgilevich; Aleksi Räsänen. 2021. "Combining socio-economic and climate projections to assess heat risk." Climatic Change 167, no. 1-2: 1-20.
David Olefeldt; Mikael Hovemyr; McKenzie A. Kuhn; David Bastviken; Theodore J. Bohn; John Connolly; Patrick Crill; Eugénie S. Euskirchen; Sarah A. Finkelstein; Hélène Genet; Guido Grosse; Lorna I. Harris; Liam Heffernan; Manuel Helbig; Gustaf Hugelius; Ryan Hutchins; Sari Juutinen; Mark J. Lara; Avni Malhotra; Kristen Manies; A. David McGuire; Susan M. Natali; Jonathan A. O'Donnell; Frans-Jan W. Parmentier; Aleksi Räsänen; Christina Schädel; Oliver Sonnentag; Maria Strack; Suzanne Tank; Claire Treat; Ruth K. Varner; Tarmo Virtanen; Rebecca K. Warren; Jennifer D. Watts. Supplementary material to "The Boreal-Arctic Wetland and Lake Dataset (BAWLD)". 2021, 1 .
AMA StyleDavid Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, David Bastviken, Theodore J. Bohn, John Connolly, Patrick Crill, Eugénie S. Euskirchen, Sarah A. Finkelstein, Hélène Genet, Guido Grosse, Lorna I. Harris, Liam Heffernan, Manuel Helbig, Gustaf Hugelius, Ryan Hutchins, Sari Juutinen, Mark J. Lara, Avni Malhotra, Kristen Manies, A. David McGuire, Susan M. Natali, Jonathan A. O'Donnell, Frans-Jan W. Parmentier, Aleksi Räsänen, Christina Schädel, Oliver Sonnentag, Maria Strack, Suzanne Tank, Claire Treat, Ruth K. Varner, Tarmo Virtanen, Rebecca K. Warren, Jennifer D. Watts. Supplementary material to "The Boreal-Arctic Wetland and Lake Dataset (BAWLD)". . 2021; ():1.
Chicago/Turabian StyleDavid Olefeldt; Mikael Hovemyr; McKenzie A. Kuhn; David Bastviken; Theodore J. Bohn; John Connolly; Patrick Crill; Eugénie S. Euskirchen; Sarah A. Finkelstein; Hélène Genet; Guido Grosse; Lorna I. Harris; Liam Heffernan; Manuel Helbig; Gustaf Hugelius; Ryan Hutchins; Sari Juutinen; Mark J. Lara; Avni Malhotra; Kristen Manies; A. David McGuire; Susan M. Natali; Jonathan A. O'Donnell; Frans-Jan W. Parmentier; Aleksi Räsänen; Christina Schädel; Oliver Sonnentag; Maria Strack; Suzanne Tank; Claire Treat; Ruth K. Varner; Tarmo Virtanen; Rebecca K. Warren; Jennifer D. Watts. 2021. "Supplementary material to "The Boreal-Arctic Wetland and Lake Dataset (BAWLD)"." , no. : 1.
Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal-Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5° grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ~28 % each of the total wetland area, while the highest methane emitting marsh and tundra wetland classes occupied 5 and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low methane-emitting large lakes (> 10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 and 4 %, respectively. Small (< 0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area, but contributed disproportionally to the overall spatial uncertainty of lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain) of which 8 % was associated with high-methane emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that will have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake and river extents, and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern Boreal and Arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data is freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).
David Olefeldt; Mikael Hovemyr; McKenzie A. Kuhn; David Bastviken; Theodore J. Bohn; John Connolly; Patrick Crill; Eugénie S. Euskirchen; Sarah A. Finkelstein; Hélène Genet; Guido Grosse; Lorna I. Harris; Liam Heffernan; Manuel Helbig; Gustaf Hugelius; Ryan Hutchins; Sari Juutinen; Mark J. Lara; Avni Malhotra; Kristen Manies; A. David McGuire; Susan M. Natali; Jonathan A. O'Donnell; Frans-Jan W. Parmentier; Aleksi Räsänen; Christina Schädel; Oliver Sonnentag; Maria Strack; Suzanne Tank; Claire Treat; Ruth K. Varner; Tarmo Virtanen; Rebecca K. Warren; Jennifer D. Watts. The Boreal-Arctic Wetland and Lake Dataset (BAWLD). 2021, 2021, 1 -40.
AMA StyleDavid Olefeldt, Mikael Hovemyr, McKenzie A. Kuhn, David Bastviken, Theodore J. Bohn, John Connolly, Patrick Crill, Eugénie S. Euskirchen, Sarah A. Finkelstein, Hélène Genet, Guido Grosse, Lorna I. Harris, Liam Heffernan, Manuel Helbig, Gustaf Hugelius, Ryan Hutchins, Sari Juutinen, Mark J. Lara, Avni Malhotra, Kristen Manies, A. David McGuire, Susan M. Natali, Jonathan A. O'Donnell, Frans-Jan W. Parmentier, Aleksi Räsänen, Christina Schädel, Oliver Sonnentag, Maria Strack, Suzanne Tank, Claire Treat, Ruth K. Varner, Tarmo Virtanen, Rebecca K. Warren, Jennifer D. Watts. The Boreal-Arctic Wetland and Lake Dataset (BAWLD). . 2021; 2021 ():1-40.
Chicago/Turabian StyleDavid Olefeldt; Mikael Hovemyr; McKenzie A. Kuhn; David Bastviken; Theodore J. Bohn; John Connolly; Patrick Crill; Eugénie S. Euskirchen; Sarah A. Finkelstein; Hélène Genet; Guido Grosse; Lorna I. Harris; Liam Heffernan; Manuel Helbig; Gustaf Hugelius; Ryan Hutchins; Sari Juutinen; Mark J. Lara; Avni Malhotra; Kristen Manies; A. David McGuire; Susan M. Natali; Jonathan A. O'Donnell; Frans-Jan W. Parmentier; Aleksi Räsänen; Christina Schädel; Oliver Sonnentag; Maria Strack; Suzanne Tank; Claire Treat; Ruth K. Varner; Tarmo Virtanen; Rebecca K. Warren; Jennifer D. Watts. 2021. "The Boreal-Arctic Wetland and Lake Dataset (BAWLD)." 2021, no. : 1-40.
Cross-scale interactions affect resilience in a wide array of social systems such as flood risk management, but it has been argued that studies of such interactions remain limited. Based on qualitative interviews, quantitative surveys, and policy document analysis, I employed the panarchy framework in an analysis of temporal changes and cross-scale interactions in flood risk management at the local and regional scale in Rovaniemi, in Finnish Lapland. The results revealed that administrative co-operation in flood preparedness has functioned well in Rovaniemi in recent decades and few changes have been made to it. Nevertheless, flood defense measures have been the subject of a persistent and dynamic conflict, which has been locked in a polarized phase. Among local residents' approaches to flood risk management, there have been few changes in preparedness, although administrative actors have emphasized communication and self-preparedness in recent years. I discuss how the cross-scale mismatches have contributed to hinder the flood risk management, sharpen the conflict over flood defense measures, and keep the local residents’ level of preparedness low.
Aleksi Räsänen. Cross-scale interactions in flood risk management: A case study from Rovaniemi, Finland. International Journal of Disaster Risk Reduction 2021, 57, 102185 .
AMA StyleAleksi Räsänen. Cross-scale interactions in flood risk management: A case study from Rovaniemi, Finland. International Journal of Disaster Risk Reduction. 2021; 57 ():102185.
Chicago/Turabian StyleAleksi Räsänen. 2021. "Cross-scale interactions in flood risk management: A case study from Rovaniemi, Finland." International Journal of Disaster Risk Reduction 57, no. : 102185.
Aboveground vegetation biomass in northern treeless landscapes – peatlands and Arctic tundra – has been modelled with spectral information derived from optical remote sensing in several studies. However, synthesized overviews of biomass patterns across circumpolar sites have been limited. Based on data from eight study sites in Europe, Siberia and Canada, we ask (1) how biomass is divided between plant functional types (PFTs) and (2) how well biomass patterns can be detected with widely available, moderate spatial resolution (3–10 m) satellite imagery and topographic data. We explain biomass patterns using random forest regressions with the predictors being spectral bands and indices calculated from multi-temporal Sentinel-2 and PlanetScope imagery and topographic information calculated from ArcticDEM data. Our results indicate that there are notable differences in vegetation composition between northern landscapes with mosses, graminoids and deciduous shrubs being the most dominant PFTs. Remote sensing data detects biomass patterns, but regression performance varies between sites (explained variance 36–70%, normalized root mean square error 9–19%). There is also variability between sites whether Sentinel-2 or PlanetScope data is more suitable to detect biomass patterns and which the most important predictors are. Topographic information has a minor or negligible importance in most of the sites. Our results suggest that there is no easily generalizable relationship between satellite-derived vegetation greenness and biomass.
Aleksi Räsänen; Julia Wagner; Gustaf Hugelius; Tarmo Virtanen. Aboveground biomass patterns across treeless northern landscapes. International Journal of Remote Sensing 2021, 42, 4532 -4557.
AMA StyleAleksi Räsänen, Julia Wagner, Gustaf Hugelius, Tarmo Virtanen. Aboveground biomass patterns across treeless northern landscapes. International Journal of Remote Sensing. 2021; 42 (12):4532-4557.
Chicago/Turabian StyleAleksi Räsänen; Julia Wagner; Gustaf Hugelius; Tarmo Virtanen. 2021. "Aboveground biomass patterns across treeless northern landscapes." International Journal of Remote Sensing 42, no. 12: 4532-4557.
Context Spatial patterns of CH4 fluxes can be modeled with remotely sensed data representing land cover, soil moisture and topography. Spatially extensive CH4 flux measurements conducted with portable analyzers have not been previously upscaled with remote sensing. Objectives How well can the CH4 fluxes be predicted with plot-based vegetation measures and remote sensing? How does the predictive skill of the model change when using different combinations of predictor variables? Methods We measured CH4 fluxes in 279 plots in a 12.4 km2 peatland-forest-mosaic landscape in Pallas area, northern Finland in July 2019. We compared 20 different CH4 flux maps produced with vegetation field data and remote sensing data including Sentinel-1, Sentinel-2 and digital terrain model (DTM). Results The landscape acted as a net source of CH4 (253–502 µg m−2 h−1) and the proportion of source areas varied considerably between maps (12–50%). The amount of explained variance was high in CH4 regressions (59–76%, nRMSE 8–10%). Regressions including remote sensing predictors had better performance than regressions with plot-based vegetation predictors. The most important remote sensing predictors included VH-polarized Sentinel-1 features together with topographic wetness index and other DTM features. Spatial patterns were most accurately predicted when the landscape was divided into sinks and sources with remote sensing-based classifications, and the fluxes were modeled for sinks and sources separately. Conclusions CH4 fluxes can be predicted accurately with multi-source remote sensing in northern boreal peatland landscapes. High spatial resolution remote sensing-based maps constrain uncertainties related to CH4 fluxes and their spatial patterns.
Aleksi Räsänen; Terhikki Manninen; Mika Korkiakoski; Annalea Lohila; Tarmo Virtanen. Predicting catchment-scale methane fluxes with multi-source remote sensing. Landscape Ecology 2021, 1 -19.
AMA StyleAleksi Räsänen, Terhikki Manninen, Mika Korkiakoski, Annalea Lohila, Tarmo Virtanen. Predicting catchment-scale methane fluxes with multi-source remote sensing. Landscape Ecology. 2021; ():1-19.
Chicago/Turabian StyleAleksi Räsänen; Terhikki Manninen; Mika Korkiakoski; Annalea Lohila; Tarmo Virtanen. 2021. "Predicting catchment-scale methane fluxes with multi-source remote sensing." Landscape Ecology , no. : 1-19.
Early warning systems (EWSs) have been developed to trigger timely action to disasters, yet persistent humanitarian crises resulting from hazards such as drought indicate that these systems need improvements. We focus our research on the county of Turkana in Kenya, where drought repeatedly results in humanitarian crises, especially with regard to food insecurity. Focusing on the key elements of the Kenyan EWS, we ask two questions: firstly, what indicators, especially meteorological drought indicators, are used in the national biannual assessments conducted by the Kenyan National Drought Management Authority and monthly drought bulletins for Turkana? Secondly, are there differences in the methodology used for analysis of meteorological indicators in the different documents? Firstly, by utilizing a food systems framework, we conduct qualitative content analysis of the use of indicators in the documents; secondly, we analyze rainfall data and its use. The EWS relies primarily on food availability indicators, with less focus for food access and utilization. The biannual assessments and the country bulletins use different sets of rainfall data and different methodologies for establishing the climate normal, leading to discrepancies in the output of the EWS. We recommend further steps to be taken towards standardization of methodologies and cooperation between various institutions to ensure streamlining of approaches.
Sofie Sandström; Sirkku Juhola; Aleksi Räsänen. Fluctuating Rainfall, Persistent Food Crisis—Use of Rainfall Data in the Kenyan Drought Early Warning System. Atmosphere 2020, 11, 1328 .
AMA StyleSofie Sandström, Sirkku Juhola, Aleksi Räsänen. Fluctuating Rainfall, Persistent Food Crisis—Use of Rainfall Data in the Kenyan Drought Early Warning System. Atmosphere. 2020; 11 (12):1328.
Chicago/Turabian StyleSofie Sandström; Sirkku Juhola; Aleksi Räsänen. 2020. "Fluctuating Rainfall, Persistent Food Crisis—Use of Rainfall Data in the Kenyan Drought Early Warning System." Atmosphere 11, no. 12: 1328.
Wetlands, including peatlands, supply crucial ecosystem services such as water purification, carbon sequestration and regulation of hydrological and biogeochemical cycles. Peatlands are especially important as carbon sinks and stores because of the incomplete decomposition of vegetation within the peat. Good knowledge of individual wetlands exists locally, but information on how different wetland systems interact with their surroundings is lacking. In this study, the ability to use a depression-based digital elevation model (DEM) method to inventory wetlands in northern landscapes and assess their hydrological connectivity was investigated. The method consisted of three steps: (1) identification and mapping of wetlands, (2) identification of threshold values of minimum wetland size and depth, and (3) delineation of a defined coherent area of multiple wetlands with hydrological connectivity, called wetlandscape. The results showed that 64% of identified wetlands corresponded with an existing wetland map in the study area, but only 10% of the wetlands in the existing map were identified, with the F1 score being 17%. Therefore, the methodology cannot independently map wetlands and future research should be conducted in which additional data sources and mapping techniques are integrated. However, wetland connectivity could be mapped with the depression-based DEM methodology by utilising information on upstream and downstream wetland depressions, catchment boundaries and drainage flow paths. Knowledge about wetland connectivity is crucial for understanding how physical, biological and chemical materials are transported and distributed in the landscape, and thus also for resilience, management and protection of wetlandscapes.
Emelie Stengård; Aleksi Räsänen; Carla Sofia Santos Ferreira; Zahra Kalantari. Inventory and Connectivity Assessment of Wetlands in Northern Landscapes with a Depression-Based DEM Method. Water 2020, 12, 3355 .
AMA StyleEmelie Stengård, Aleksi Räsänen, Carla Sofia Santos Ferreira, Zahra Kalantari. Inventory and Connectivity Assessment of Wetlands in Northern Landscapes with a Depression-Based DEM Method. Water. 2020; 12 (12):3355.
Chicago/Turabian StyleEmelie Stengård; Aleksi Räsänen; Carla Sofia Santos Ferreira; Zahra Kalantari. 2020. "Inventory and Connectivity Assessment of Wetlands in Northern Landscapes with a Depression-Based DEM Method." Water 12, no. 12: 3355.
Aleksi Räsänen; Sari Juutinen; Margaret Kalacska; Mika Aurela; Pauli Heikkinen; Kari Mäenpää; Aleksi Rimali; Tarmo Virtanen. Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing. GIScience & Remote Sensing 2020, 57, 943 -964.
AMA StyleAleksi Räsänen, Sari Juutinen, Margaret Kalacska, Mika Aurela, Pauli Heikkinen, Kari Mäenpää, Aleksi Rimali, Tarmo Virtanen. Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing. GIScience & Remote Sensing. 2020; 57 (7):943-964.
Chicago/Turabian StyleAleksi Räsänen; Sari Juutinen; Margaret Kalacska; Mika Aurela; Pauli Heikkinen; Kari Mäenpää; Aleksi Rimali; Tarmo Virtanen. 2020. "Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing." GIScience & Remote Sensing 57, no. 7: 943-964.
Despite a notable increase in the literature on community resilience, the notion of ‘community’ remains underproblematised. This is evident within flood risk management (FRM) literature, in which the understanding and roles of communities may be acknowledged but seldom discussed in any detail. The purpose of the article is to demonstrate how community networks are configured by different actors, whose roles and responsibilities span spatial scales within the context of FRM. Accordingly, the authors analyse findings from semi-structured interviews, policy documents, and household surveys from two flood prone areas in Finnish Lapland. The analysis reveals that the ways in which authorities, civil society, and informal actors take on multiple roles are intertwined and form different types of networks. By implication, the configuration of community is fuzzy, elusive and situated, and not confined to a fixed spatiality. The authors discuss the implications of the complex nature of community for FRM specifically, and for community resilience more broadly. They conclude that an analysis of different actors across scales contributes to an understanding of the configuration of community, including community resilience, and how the meaning of community takes shape according to the differing aims of FRM in combination with differing geographical settings.
Aleksi Räsänen; Vera Kauppinen; Sirkku Juhola; Gunhild Setten; Haakon Lein. Configurations of community in flood risk management. Norsk Geografisk Tidsskrift - Norwegian Journal of Geography 2020, 74, 165 -180.
AMA StyleAleksi Räsänen, Vera Kauppinen, Sirkku Juhola, Gunhild Setten, Haakon Lein. Configurations of community in flood risk management. Norsk Geografisk Tidsskrift - Norwegian Journal of Geography. 2020; 74 (3):165-180.
Chicago/Turabian StyleAleksi Räsänen; Vera Kauppinen; Sirkku Juhola; Gunhild Setten; Haakon Lein. 2020. "Configurations of community in flood risk management." Norsk Geografisk Tidsskrift - Norwegian Journal of Geography 74, no. 3: 165-180.
Community resilience is often assessed in disaster risk management (DRM) research and it has been argued that it should be strengthened for more robust DRM. However, the term community is seldom precisely defined and it can be understood in many ways. We argue that it is crucial to explore the concept of community within the context of DRM in more detail. We identify three dominating views of conceptualizing community (place-based community, interaction-based community, community of practice and interest), and discuss the relevance of these conceptualizations. We base this discussion on quantitative and qualitative empirical and policy document data regarding flood and storm risk management in Finland, wildfire risk management in Norway and volcanic risk management Iceland. According to our results, all three conceptualizations of community are visible but in differing situations. Our results emphasize the strong role of public sector in DRM in the studied countries. In disaster preparedness and response, a professionalized community of practice and interest appear to be the most prominent within all three countries. The interaction-based community of informal social networks is of less relevance, although its role is more visible in disaster response and recovery. The place-based (local) community is visible in some of the policy documents, but otherwise its role is rather limited. Finally, we argue that the measured resilience of a community depends on how the community is conceptualized and operationalized, and that the measures to strengthen resilience of a particular community should be different depending on what the focal community is.
Aleksi Räsänen; Haakon Lein; Deanne Bird; Gunhild Setten. Conceptualizing community in disaster risk management. International Journal of Disaster Risk Reduction 2020, 45, 101485 .
AMA StyleAleksi Räsänen, Haakon Lein, Deanne Bird, Gunhild Setten. Conceptualizing community in disaster risk management. International Journal of Disaster Risk Reduction. 2020; 45 ():101485.
Chicago/Turabian StyleAleksi Räsänen; Haakon Lein; Deanne Bird; Gunhild Setten. 2020. "Conceptualizing community in disaster risk management." International Journal of Disaster Risk Reduction 45, no. : 101485.
Within northern peatlands, landscape elements such as vegetation and topography are spatially heterogenic from ultra‐high (centimeter level) to coarse scale. In addition to within‐site spatial heterogeneity, there is evident between‐site heterogeneity, but there is a lack of studies assessing whether different combinations of remotely sensed features and mapping approaches are needed in different types of landscapes. We evaluated the value of different mapping methods and remote sensing datasets and analyzed the kinds of differences present in vegetation patterns and their mappability between three northern boreal peatland landscapes in northern Finland. We utilized field‐inventoried vegetation plots together with spectral, textural, topography and vegetation height remote sensing data from 0.02‐ to 3‐m pixel size. Remote sensing data included true‐color unmanned aerial vehicle images, aerial images with four spectral bands, aerial lidar data and multiple PlanetScope satellite images. We used random forest regressions for tracking plant functional type (PFT) coverage, non‐metric multidimensional scaling ordination axes and fuzzy k‐medoid plant community clusters. PFT regressions had variable performance for different study sites (R2 −0.03 to 0.69). Spatial patterns of some spectrally or structurally distinctive PFTs could be predicted relatively well. The first ordination axis represented wetness gradient and was well predicted using remotely sensed data (R2 0.64 to 0.82), but the other three axes had a less straightforward explanation and lower mapping performance (R2 −0.09 to 0.53). Plant community clusters were predicted most accurately in the sites with clear string‐flark topography but less accurately in the flatter site (R2 0.16–0.82). The most important remote sensing features differed between dependent variables and study sites: different topographic, spectral and textural features; and coarse‐scale and fine‐scale datasets were the most important in different tasks. We suggest that multiple different mapping approaches should be tested and several remote sensing datasets used when maps of vegetation are produced.
Aleksi Räsänen; Mika Aurela; Sari Juutinen; Timo Kumpula; Annalea Lohila; Timo Penttilä; Tarmo Virtanen. Detecting northern peatland vegetation patterns at ultra‐high spatial resolution. Remote Sensing in Ecology and Conservation 2019, 6, 457 -471.
AMA StyleAleksi Räsänen, Mika Aurela, Sari Juutinen, Timo Kumpula, Annalea Lohila, Timo Penttilä, Tarmo Virtanen. Detecting northern peatland vegetation patterns at ultra‐high spatial resolution. Remote Sensing in Ecology and Conservation. 2019; 6 (4):457-471.
Chicago/Turabian StyleAleksi Räsänen; Mika Aurela; Sari Juutinen; Timo Kumpula; Annalea Lohila; Timo Penttilä; Tarmo Virtanen. 2019. "Detecting northern peatland vegetation patterns at ultra‐high spatial resolution." Remote Sensing in Ecology and Conservation 6, no. 4: 457-471.
Aleksi Räsänen; Sari Juutinen; Eeva‐Stiina Tuittila; Mika Aurela; Tarmo Virtanen. Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation. Journal of Vegetation Science 2019, 30, 1016 -1026.
AMA StyleAleksi Räsänen, Sari Juutinen, Eeva‐Stiina Tuittila, Mika Aurela, Tarmo Virtanen. Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation. Journal of Vegetation Science. 2019; 30 (5):1016-1026.
Chicago/Turabian StyleAleksi Räsänen; Sari Juutinen; Eeva‐Stiina Tuittila; Mika Aurela; Tarmo Virtanen. 2019. "Comparing ultra‐high spatial resolution remote‐sensing methods in mapping peatland vegetation." Journal of Vegetation Science 30, no. 5: 1016-1026.
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.
Aleksi Räsänen; Tarmo Virtanen. Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes. Remote Sensing of Environment 2019, 230, 111207 .
AMA StyleAleksi Räsänen, Tarmo Virtanen. Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes. Remote Sensing of Environment. 2019; 230 ():111207.
Chicago/Turabian StyleAleksi Räsänen; Tarmo Virtanen. 2019. "Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes." Remote Sensing of Environment 230, no. : 111207.
Climate change is likely to increase the risks related to heat waves in urban areas. We map spatial pattern of heat wave vulnerability and risk in the Helsinki metropolitan area in southern Finland. First, we assess differences that zoning, i.e., differences in spatial units of analysis, and weighting, i.e., weights given to indicators when constructing the index, cause in map production. Second, we evaluate how maps of consensus and certainty could pave the way for visualizing and assessing uncertainties in risk and vulnerability indices. For vulnerability, we use socioeconomic data using 5 different zoning options and 11 different weighting options. For risk, we add two extra layers to vulnerability maps: hazard map showing the spatial pattern of heat based on Landsat satellite images and exposure map showing the spatial pattern of population. We found that when different zoning options are used, the spatial pattern of vulnerability may differ dramatically. In risk maps, the differences between zoning options are smaller. Contrary to previous literature, differences in indicator weighting alter the final maps slightly. The consensus and certainty maps show their potential, e.g., in pointing out areas which may have both high risk/vulnerability and high certainty for risk/vulnerability. Finally, we discuss other possibilities in tackling the uncertainties in mapping and propose new avenues for research.
Aleksi Räsänen; Kimmo Heikkinen; Noora Piila; Sirkku Juhola. Zoning and weighting in urban heat island vulnerability and risk mapping in Helsinki, Finland. Regional Environmental Change 2019, 19, 1481 -1493.
AMA StyleAleksi Räsänen, Kimmo Heikkinen, Noora Piila, Sirkku Juhola. Zoning and weighting in urban heat island vulnerability and risk mapping in Helsinki, Finland. Regional Environmental Change. 2019; 19 (5):1481-1493.
Chicago/Turabian StyleAleksi Räsänen; Kimmo Heikkinen; Noora Piila; Sirkku Juhola. 2019. "Zoning and weighting in urban heat island vulnerability and risk mapping in Helsinki, Finland." Regional Environmental Change 19, no. 5: 1481-1493.
The non-uniform spatial integration, an inherent feature of the eddy covariance (EC) method, creates a challenge for flux data interpretation in a heterogeneous environment, where the contribution of different land cover types varies with flow conditions, potentially resulting in biased estimates in comparison to the areally averaged fluxes and land cover attributes. We modelled flux footprints and characterized the spatial scale of our EC measurements in Tiksi, a tundra site in northern Siberia. We used leaf area index (LAI) and land cover class (LCC) data, derived from very-high-spatial-resolution satellite imagery and field surveys, and quantified the sensor location bias. We found that methane (CH4) fluxes varied strongly with wind direction (−0.09 to 0.59 µgCH4m-2s-1 on average) during summer 2014, reflecting the distribution of different LCCs. Other environmental factors had only a minor effect on short-term flux variations but influenced the seasonal trend. Using footprint weights of grouped LCCs as explanatory variables for the measured CH4 flux, we developed a multiple regression model to estimate LCC group-specific fluxes. This model showed that wet fen and graminoid tundra patches in locations with topography-enhanced wetness acted as strong sources (1.0 µgCH4m-2s-1 during the peak emission period), while mineral soils were significant sinks (−0.13 µgCH4m-2s-1). To assess the representativeness of measurements, we upscaled the LCC group-specific fluxes to different spatial scales. Despite the landscape heterogeneity and rather poor representativeness of EC data with respect to the areally averaged LAI and coverage of some LCCs, the mean flux was close to the CH4 balance upscaled to an area of 6.3 km2, with a location bias of 14 %. We recommend that EC site descriptions in a heterogeneous environment should be complemented with footprint-weighted high-resolution data on vegetation and other site characteristics.
Juha-Pekka Tuovinen; Mika Aurela; Juha Hatakka; Aleksi Räsänen; Tarmo Virtanen; Juha Mikola; Viktor Ivakhov; Vladimir Kondratyev; Tuomas Laurila. Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness. Biogeosciences 2019, 16, 255 -274.
AMA StyleJuha-Pekka Tuovinen, Mika Aurela, Juha Hatakka, Aleksi Räsänen, Tarmo Virtanen, Juha Mikola, Viktor Ivakhov, Vladimir Kondratyev, Tuomas Laurila. Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness. Biogeosciences. 2019; 16 (2):255-274.
Chicago/Turabian StyleJuha-Pekka Tuovinen; Mika Aurela; Juha Hatakka; Aleksi Räsänen; Tarmo Virtanen; Juha Mikola; Viktor Ivakhov; Vladimir Kondratyev; Tuomas Laurila. 2019. "Interpreting eddy covariance data from heterogeneous Siberian tundra: land-cover-specific methane fluxes and spatial representativeness." Biogeosciences 16, no. 2: 255-274.
Arctic areas have experienced greening and changes in permafrost caused by climate change during recent decades. However, there has been a lack of automated methods in mapping changes in fine-scale patterns of permafrost landscapes. We mapped areal coverage of bare peat areas and changes in them in a peat plateau located in north-western Russia between 2007 and 2015. We utilized QuickBird and WorldView-3 satellite image data in an object-based setting. We compared four different one-class classifiers (one-class support vector machine, binary support vector machine, random forest, rotation forest) both in a fully supervised binary setting and with positive and unlabelled training data. There was notable variation in classification performance. The bare peat area F-score varied between 0.77 and 0.96 when evaluated by cross-validated training data and between 0.22 and 0.57 when evaluated by independent test data. Overall, random forest performed the most robustly but all classifiers performed well in some classifications. During the 8 year period, there was a 21%–26% decrease in the bare peat areal coverage. We conclude that (1) tested classifiers can be used in one-class settings and (2) there is a need to develop methods for tracking changes in single land cover types.
Aleksi Räsänen; Vladimir Elsakov; Tarmo Virtanen. Usability of one-class classification in mapping and detecting changes in bare peat surfaces in the tundra. International Journal of Remote Sensing 2019, 40, 4083 -4103.
AMA StyleAleksi Räsänen, Vladimir Elsakov, Tarmo Virtanen. Usability of one-class classification in mapping and detecting changes in bare peat surfaces in the tundra. International Journal of Remote Sensing. 2019; 40 (11):4083-4103.
Chicago/Turabian StyleAleksi Räsänen; Vladimir Elsakov; Tarmo Virtanen. 2019. "Usability of one-class classification in mapping and detecting changes in bare peat surfaces in the tundra." International Journal of Remote Sensing 40, no. 11: 4083-4103.
This chapter discusses the emergence of national adaptation policy in the developed world. It presents findings from empirical studies that have examined the development of national-level strategies and focuses on understanding the process of institutionalization and implementation of national adaptation, particularly in the context of vertical governance. Studies so far have shown that local authorities need support and guidance from the national level, and national adaptation strategies and climate change-related legislation can be key in adaptation action. While the drivers and enablers of adaptation policy implementation and institutionalization are both internal and external, the barriers are mainly internal. The chapter also discusses the content of national strategies and how they also need to take into account the indirect impacts of climate change, and addresses the emerging issue of monitoring and evaluation of national adaptation. Finally, in-depth comparative case studies are needed to further elaborate on the processes of institutionalization of national-level adaptation and to understand the links between institutionalisation and implementation progress.
Alexandra Jurgilevich; Fanny Groundstroem; Johannes Klein; Aleksi Räsänen; Sirkku Juhola. The emergence and institutionalization of nation adaptation strategies. Research Handbook on Climate Change Adaptation Policy 2019, 212 -227.
AMA StyleAlexandra Jurgilevich, Fanny Groundstroem, Johannes Klein, Aleksi Räsänen, Sirkku Juhola. The emergence and institutionalization of nation adaptation strategies. Research Handbook on Climate Change Adaptation Policy. 2019; ():212-227.
Chicago/Turabian StyleAlexandra Jurgilevich; Fanny Groundstroem; Johannes Klein; Aleksi Räsänen; Sirkku Juhola. 2019. "The emergence and institutionalization of nation adaptation strategies." Research Handbook on Climate Change Adaptation Policy , no. : 212-227.
Aleksi Räsänen; Sari Juutinen; Mika Aurela; Tarmo Virtanen. Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling. International Journal of Remote Sensing 2018, 40, 1175 -1199.
AMA StyleAleksi Räsänen, Sari Juutinen, Mika Aurela, Tarmo Virtanen. Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling. International Journal of Remote Sensing. 2018; 40 (3):1175-1199.
Chicago/Turabian StyleAleksi Räsänen; Sari Juutinen; Mika Aurela; Tarmo Virtanen. 2018. "Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling." International Journal of Remote Sensing 40, no. 3: 1175-1199.
To tackle problems related to water quantity and quality, transformations in water management systems have become of increasing interest. Transformative capacity can be defined as the ability first to adapt to changes, and if needed, to carry out fundamental changes in a specific system. Using a framework of ten components of transformative capacity and an analysis of earlier historical research, policy documents and data gathered in a stakeholder scenario workshop, we examine the relationship between past and future transformations and transformative capacity in river basin management in the River Vantaa basin, located in southern Finland. In the past, River Vantaa was heavily polluted by municipal wastewater. The water quality has gradually improved but is still not considered good. The most successful changes have been concentrated on point source pollution, such as municipal wastewater, and they have mostly been driven by public administration and municipal coordination. In the future, more effort should be put on diffuse pollution, especially agricultural loading, and this requires changes in societal values and new forms of governance. We show how the past transformations have partly been driven by transformative capacity, but some transformations have enabled changes in the components of transformative capacity, indicating the interconnectedness of the different components. Furthermore, the interplay between transformations and transformative capacity occurs across spatial and temporal scales. We discuss how transformations take time, how transformative capacity evolves over longer time-spans, and how capacity and trajectories in local and wider scales are in a continuous interaction.
Aleksi Räsänen; Paula Schönach; Alexandra Jurgilevich; Milja Heikkinen; Sirkku Juhola. Role of Transformative Capacity in River Basin Management Transformations. Water Resources Management 2018, 33, 303 -317.
AMA StyleAleksi Räsänen, Paula Schönach, Alexandra Jurgilevich, Milja Heikkinen, Sirkku Juhola. Role of Transformative Capacity in River Basin Management Transformations. Water Resources Management. 2018; 33 (1):303-317.
Chicago/Turabian StyleAleksi Räsänen; Paula Schönach; Alexandra Jurgilevich; Milja Heikkinen; Sirkku Juhola. 2018. "Role of Transformative Capacity in River Basin Management Transformations." Water Resources Management 33, no. 1: 303-317.