KELK 2003 - landschapsmodule; kennismodel voor de bepaling van effecten van ruimtegebruiksveranderingen op de landschapskwaliteit

KELK (een methode van het Natuurplanbureau) modelleert geen processen, maar gebruikt expertkennis (vuistregels) en metamodellen. Klek is bedoeld voor quick scan toepassingen op het terrein van bestemming en inrichting van de groene ruimte. Daarbij kunnen de kwalificaties meegegeven worden "herkenbaarheid verleden" "belevingswaarde" en "recreatieve gebruikswaarde

Linking Bayesian Belief Networks and GIS to assess the ecosystem integrity in the Brazlian Amazon

Deforestation and climate change heavily impact the ecosystem of the Amazon rainforest threatening its resilience and the sustainability of many human activities. Land protection may prevent ecosystems and their services to deteriorate from the pressures of agricultural expansion, population growth and wood harvesting. In the Brazilian Amazon land protection occurs in several forms such as environmental conservation, setting biodiversity priority areas and the delineation of indigenous lands. Still, the effects are not clear as understanding of the ecosystems is incomplete and responses to human actions are highly uncertain. Bayesian Belief Networks (BBN) are models that probabilistically represent correlative and causal relationships among variables. BBNs have been successfully applied to natural resource management to address environmental management problems and to assess the impact of alternative management measures. By training the probabilistic relationships using field data, Remote Sensing data and GIS data the BBN can provide information on the ecosystems: the ecosystem integrity and their likely response to climate change or alternative management actions. An increasing number of studies train and apply BBNs with evidence originating from GIS data; a cumbersome and error prone soft-linking method requiring manual conversion of data files between the BBN and GIS software systems. This paper presents the full integration of a BBN software system within an existing GIS based Discussion Support System (DSS) illustrated by the case of the ecosystem integrity of the Brazilian amazon. The full integration speeds up the processing and thereby allows doing multiple runs within a short period of time such as a stakeholder workshop. Each consecutive run is based upon insights from a previous one. Furthermore, the DSS provides the management of different options, visualize spatial summaries and trade-offs between different impact indicators and see regional differences

QUICKScan: a pragmatic approach to decision support

Decision Support Tools (DST) are a key instrument for preparing legislative proposals and policy initiatives. They provide insight about options, conflicts, synergies and trade-offs between issues, sectors and regions at multiple scales. DST range from integrated systems modelling to value-based knowledge systems resulting from expert groups. The results of the expert groups do not provide regional differentiation making it difficult to obtain useful insights for policy making. The ‘black-box’ complex tools are found not transparent by the decision makers that seek to understand the modelling behind the results to be able to cope with the scientific uncertainty and changing policy context. In addition, the policy questions need to be answered in a short period to fit the time horizon of policy making, e.g. a couple of months, which is possible with expert groups, but the complex models are often not ready to deal with this urgency. The QUICKScan tool aims at filling the gap in the pallet of available tools by defining a methodology -supported by modelling software to visualize quantitative and value-based modelling in the decision process. The tool enables the creation of alternative storylines for policy questions by the stakeholders, and translates these in-situ into a model by combining tacit expert knowledge with available spatial explicit monitoring- and statistical-data. QUICKScan builds on concepts from Participatory Modelling and Participatory GIS and uses visualisation and interpretation tools which are essential to support the exploration of options allowing and facilitating the discussion and interaction on the definition of alternatives, analysing their consequences, determining trade-offs and synergies and compare the consequences of alternatives. The QUICKScan tool is designed to calculate fast, and therefor perform multiple iterations of a modelling exercise during a workshop. The results of each iteration feed the discussion among stakeholders and policy makers creating input for a next iteration

Data integration technologies to support integrated modelling

Over the recent years the scientific activities of our organisation in large research projects show a shifting priority from model integration to the integration of data itself. Our work in several large projects on integrated modelling for impact assessment studies has clearly shown the importance of data availability for integrated modelling, but of no less importance is the integration, or alignment, of the required input data itself. Moving from the fairly technical model integration in OpenMI and OpenMI related projects, and moving towards basic semantic integration in the SEAMLESS and SENSOR projects, our focus is now shifting towards researching and applying techniques such as Semantic Web technologies to improve data discoverability, its integration, and in the future on reasoning about the constructed integrated knowledge. This paper will present an overview of the on-going work in our European 7th Framework Programme (FP7) project TREES4FUTURE, focussing on automated harvesting of forestry related data sets and enriching its meta data for search ability; the FP7 LIAISE Network of Excellence on linking impact assessment instruments such as models and data to sustainability expertise; and the FP7 research project SEMAGROW on developing visions on processing and querying large RDF triple-stores of integrated agricultural data. In the end we aim at bringing the results of all these projects together to achieve a next step in integrated modelling and to present ways to use Natural Language Processing based methods to help providing meta data