Advancing the study of driving forces of landscape change

Over the past 25 years, the study of driving forces of landscape change has developed into a central theme in land change science by contributing to theory development, promoting the analysis of causation of change and gaining insights into how landscape development could be steered into a societally more desirable direction. Based on this progress, we designate important research avenues, reviewing critical challenges forming the base for advancing the study of driving forces of landscape change and addressing the question on how the study of driving forces can contribute to system transformative research. For each of the research avenues, we describe the current dominant approach and provide some specific ways of advancing both the conceptualization and the research methods. Together, advancing on these research avenues will promote a more social-ecological systems perspective to the study of driving forces of landscape change.ISSN:1747-4248ISSN:1747-423

Forest disturbance map for the Carpathian ecoregion, 1984-2012

Detailed knowledge of forest cover dynamics is crucial for many applications from resource management to ecosystem service assessments. Landsat data provides the necessary spatial, temporal and spectral detail to map and analyze forest cover and forest change processes. With the opening of the Landsat archive, new opportunities arise to monitor forest dynamics on regional to continental scales. In this study we analyzed changes in forest types, forest disturbances, and forest recovery for the Carpathian ecoregion in Eastern Europe. We generated a series of image composites at five year intervals between 1985 and 2010 and utilized a hybrid analysis strategy consisting of radiometric change classification, post-classification comparison and continuous index- and segment-based post-disturbance recovery assessment. For validation of the disturbance map we used a point-based accuracy assessment, and assessed the accuracy of our forest type maps using forest inventory data and statistically sampled ground truth data for 2010. Our Carpathian-wide disturbance map achieved an overall accuracy of 86% and the forest type maps up to 73% accuracy. While our results suggested a small net forest increase in the Carpathians, almost 20% of the forests experienced stand-replacing disturbances over the past 25 years. Forest recovery seemed to only partly counterbalance the widespread natural disturbances and clear-cutting activities. Disturbances were most widespread during the late 1980s and early 1990s, but some areas also exhibited extensive forest disturbances after 2000, especially in the Polish, Czech and Romanian Carpathians. Considerable shifts in forest composition occurred in the Carpathians, with disturbances increasingly affecting coniferous forests, and a relative decrease in coniferous and mixed forests. Both aspects are likely connected to an increased vulnerability of spruce plantations to pests and pathogens in the Carpathians. Overall, our results exemplify the highly dynamic nature of forest cover during times of socio-economic and institutional change, and highlight the value of the Landsat archive for monitoring these dynamics

Forest disturbances, forest recovery, and changes in forest types across the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites

Detailed knowledge of forest cover dynamics is crucial for many applications from resource management to ecosystem service assessments. Landsat data provides the necessary spatial, temporal and spectral detail to map and analyze forest cover and forest change processes. With the opening of the Landsat archive, new opportunities arise to monitor forest dynamics on regional to continental scales. In this study we analyzed changes in forest types, forest disturbances, and forest recovery for the Carpathian ecoregion in Eastern Europe. We generated a series of image composites at five year intervals between 1985 and 2010 and utilized a hybrid analysis strategy consisting of radiometric change classification, post-classification comparison and continuous index- and segment-based post-disturbance recovery assessment. For validation of the disturbance map we used a point-based accuracy assessment, and assessed the accuracy of our forest type maps using forest inventory data and statistically sampled ground truth data for 2010. Our Carpathian-wide disturbance map achieved an overall accuracy of 86% and the forest type maps up to 73% accuracy. While our results suggested a small net forest increase in the Carpathians, almost 20% of the forests experienced stand-replacing disturbances over the past 25 years. Forest recovery seemed to only partly counterbalance the widespread natural disturbances and clear-cutting activities. Disturbances were most widespread during the late 1980s and early 1990s, but some areas also exhibited extensive forest disturbances after 2000, especially in the Polish, Czech and Romanian Carpathians. Considerable shifts in forest composition occurred in the Carpathians, with disturbances increasingly affecting coniferous forests, and a relative decrease in coniferous and mixed forests. Both aspects are likely connected to an increased vulnerability of spruce plantations to pests and pathogens in the Carpathians. Overall, our results exemplify the highly dynamic nature of forest cover during times of socio-economic and institutional change, and highlight the value of the Landsat archive for monitoring these dynamics

Processes and driving forces in changing cultural landscapes across Europe

Context Cultural landscapes evolve over time. However, the rate and direction of change might not be in line with societal needs and more information on the forces driving these changes are therefore needed. Objectives Filling the gap between single case studies and meta-analyses, we present a comparative study of landscape changes and their driving forces based in six regions across Europe conducted using a consistent method. Methods A LULC analysis based on historical and contemporary maps from the nineteenth and twentieth century was combined with oral history interviews to learn more about perceived landscape changes, and remembered driving forces. Land cover and landscape changes were analysed regarding change, conversions and processes. For all case study areas, narratives on mapped land cover change, perceived landscape changes and driving forces were compiled. Results Despite a very high diversity in extent, direction and rates of change, a few dominant processes and widespread factors driving the changes could be identified in the six case study areas, i.e. access and infrastructure, political shifts, labor market, technological innovations, and for the more recent period climate change. Conclusions Grasping peoples’ perception supplements the analyses of mapped land use and land cover changes and allows to address perceived landscape changes. The list of driving forces determined to be most relevant shows clear limits in predictability: Whereas changes triggered by infrastructural developments might be comparatively easy to model, political developments cannot be foreseen but might, nevertheless, leave major marks in the landscape

SERONTO: a Socio-Ecological Research and Observation oNTOlogy.

SERONTO is an ontology developed within ALTER-Net, a Long Term Biodiversity, Ecosystem, and Awareness Research Network funded by the European Union. ALTER-Net addresses major biodiversity issues at a European scale. Within this framework SERONTO has been developed to solve the problem of integrating and managing data stored and collected at different locations within the European Union. SERONTO is a product of a group of people with diverse scientific backgrounds. The ontology is a formal description of the concepts and relationships for the most important aspects of biodiversity data derived from monitoring, experiments and investigations. SERONTO is an ontology that enables seamless presentation of data from different origins in a similar conceptual manner. With SERONTO, meta-analysis, data mining, and data presentation should be possible across datasets collected for different purposes. SERONTO consists of a core ontology and a separate unit and dimensions ontology. The core ontology is designed to be the basis for domain specific ontologies (e.g. species, geography, water, vegetation), which extend the concepts and relationships of the core for their specific needs and requirements. The concepts of the core are derived from scientific principles and lean heavily on statistical methodology. Important considerations in designing SERONTO were 1. Repeatability: The ontology should be capable of holding enough meta-data that another person can repeat the experiment or observation at another place and time. It is not obligatory, however, to provide all information for all datasets; for instance, some information may be missing for old datasets. 2. Transparency: It must be possible to record and retrieve meta-data describing what actually happened. SERONTO includes concepts of things going wrong and documenting data collection under less than ideal conditions. If data and meta-data are available in this way, it will be clear what assumptions must be made to combine data and correctly interpret analyses. Important concepts in the SERONTO core are: 1. Investigation item – the research object or experimental unit; 2. Parameters – the measurement, classification and treatment of the investigation item; 3. Value sets – placeholders for time series and other complex data; 4. Reference lists – nominal values, such as species lists; 5. Methods – used for each parameter, including units, scale, and dimensions; 6. Sampling structure – the origin of the research object or population, and the way it was chosen; 7. Groupings of objects, such as experimental blocks, on which observer, time or other aspects are assigned or related to; 8. Additional information, such as actors (observer, observer groups and institutions), project information, etc., can be attached to several different concepts. Each subsequent analysis has to make assumptions. The assumptions of any particular analysis can be found in the deviation between how the data were obtained and the requirements of the analytical method. The presentation will go deeper into the design considerations and the core concepts. Explanations of the concepts, their interrelationships, and their use in subsequent analysis will be given along with examples from different domains