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Environmental sensing data provide crucial information for environment-related decision-making. Formal data are provided by official environmental institutes. Beyond those, however, there is a growing body of so-called informal sensing data, which are contributed by citizens using low-cost sensors. How good are these informal data, and how might they be applied, next to formal environmental sensing data? Could both types of sensing data be gainfully integrated? This paper presents the results of an online survey investigating perceptions within citizen science communities, environmental institutes and their networks of formal and informal environmental sensing data. The results show that citizens and experts had different views of formal and informal environmental sensing data, particularly on measurement frequency and the data information provision power. However, there was agreement, too, for example, on the accuracy of formal environmental sensing data. Furthermore, both agreed that the integration of formal and informal environmental sensing data offered potential for improvements on several aspects, particularly spatial coverage, data quantity and measurement frequency. Interestingly, the accuracy of informal environmental sensing data was largely unknown to both experts and citizens. This suggests the need for further investigation of informal environmental sensing data and the potential for its effective integration with formal environmental sensing data, if hurdles like standardisation can be overcome., (Copyright © 2017 Elsevier B.V. All rights reserved.)

By 2050, 75 % of the world's population will live in cities and the occurrence of heat wave events might have doubled. Mapping the climate and land use change impact for urban heat events should set the agenda for adaptation planning at the local scale. Literature on urban heat mapping does not reveal a clear indicator to visualise the urban heat impacts that includes consequences of land use and climate changes for planning purposes. This paper introduces a stepwise approach to develop a single complex indicator to map the urban heat impact for local climate adaptation planning processes. Information on climatic drivers and land use characteristics are combined and projected for future land use and climate change impacts. Next, several visualisation techniques are developed to investigate which techniques are most effective to visualise complex information with multiple variables in one visualisation. A usability test is performed to investigate how indicator and map meet the information and communication needs of policy makers. Our findings reveal that it is important to add information on future impacts to set the agenda for adaptation planning at the local scale. Applying cartographic techniques in a map series presentation has proven to be effective to map complex information in a single image and fulfil most of the identified information needs. Based on our finding, we introduce the information enrichment chain as a promising approach to support local adaptation planning.

Finding land use strategies that merge land-based climate change mitigation measures and adaptation strategies is still an open issue in climate discourse. This article explores synergies and trade-offs between REDD+, a scheme that focuses mainly on mitigation through forest conservation, with "Climate Smart Agriculture", an approach that emphasizes adaptive agriculture. We introduce a framework for ex-ante assessment of the impact of land management policies and interventions and for quantifying their impacts on land-based mitigation and adaptation goals. The framework includes a companion modelling (ComMod) process informed by interviews with policymakers, local experts and local farmers. The ComMod process consists of a Role-Playing Game with local farmers and an Agent Based Model. The game provided a participatory means to develop policy and climate change scenarios. These scenarios were then used as inputs to the Agent Based Model, a spatially explicit model to simulate landscape dynamics and the associated carbon emissions over decades. We applied the framework using as case study a community in central Vietnam, characterized by deforestation for subsistence agriculture and cultivation of acacias as a cash crop. The main findings show that the framework is useful in guiding consideration of local stakeholders' goals, needs and constraints. Additionally the framework provided beneficial information to policymakers, pointing to ways that policies might be re-designed to make them better tailored to local circumstances and therefore more effective in addressing synergistically climate change mitigation and adaptation objectives., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

In case of an environmental accident, initially available data are often insufficient for properly managing the situation. In this paper, new sensor observations are iteratively added to an initial sample by maximising the global expected value of information of the points for decision making. This is equivalent to minimizing the aggregated expected misclassification costs over the study area. The method considers measurement error and different costs for class omissions and false class commissions. Constraints imposed by a mobile sensor web are accounted for using cost distances to decide which sensor should move to the next sample location. The method is demonstrated using synthetic examples of static and dynamic phenomena. This allowed computation of the true misclassification costs and comparison with other sampling approaches. The probability of local contamination levels being above a given critical threshold were computed by indicator kriging. In the case of multiple sensors being relocated simultaneously, a genetic algorithm was used to find sets of suitable new measurement locations. Otherwise, all grid nodes were searched exhaustively, which is computationally demanding. In terms of true misclassification costs, the method outperformed random sampling and sampling based on minimisation of the kriging variance.

In the last decade policy makers have increasingly recognized the need to include people's perceptions in methods for describing landscape quality. At the same time, a third wave of Geographic Information Systems (GIS) has become available that make it technically possible to model landscape quality in a realistic manner. However, as there is often a mismatch between science and policy, it remains unclear to what extent perception-based models developed by scientists can be useful to policy makers. The aim of the present study was to evaluate the usefulness to policy making of a GIS-based procedure for describing perceived landscape openness. To this end, a workshop was organized which was attended by eight Dutch policy makers who acted as representatives of their province (region). The Group Decision Room (GDR) technique was used to elicit the policy makers' evaluations of the procedure in an anonymous and reliable manner. The procedure was presented to the policy makers using cases from their own province, which they assessed using a mixture of qualitative and quantitative methods. The results show that policy makers rated the procedure as being highly relevant to policy making, scientifically credible, usable by policy makers and feasible to implement in the policy making process. They especially appreciated the flexibility and transparency of the procedure. The policy makers concluded that the procedure would be of most value for monitoring landscape changes and for analysing impacts on landscape openness in land use scenario studies. However, they requested guidelines for proper implementation of the various options in the procedure. In general, the current study shows that explicit and transparent evaluation of the usefulness of GIS-based tools can aid integration at the science-policy interface and help to ensure that both scientists and policy makers are informed of interrelated options and requirements., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Sensor technology, which benefits from high temporal measuring resolution, real-time data transfer and high spatial resolution of sensor data that shows in-field variations, has the potential to provide added value for crop production. The present paper explores how sensors and sensor networks have been utilised in the crop production process and what their added-value and the main bottlenecks are from the perspective of users. The focus is on sensor based applications and on requirements that users pose for them. Literature and two use cases were reviewed and applications were classified according to the crop production process: sensing of growth conditions, fertilising, irrigation, plant protection, harvesting and fleet control. The potential of sensor technology was widely acknowledged along the crop production chain. Users of the sensors require easy-to-use and reliable applications that are actionable in crop production at reasonable costs. The challenges are to develop sensor technology, data interoperability and management tools as well as data and measurement services in a way that requirements can be met, and potential benefits and added value can be realized in the farms in terms of higher yields, improved quality of yields, decreased input costs and production risks, and less work time and load.

In rural regions, land use changes (LUC) are often the result of the decision-making of individual farmers. To influence this decision-making, compulsory and voluntary mechanisms are implemented. However, farmers' decision-making is a heterogeneous process that depends on their ability and willingness to take certain decisions. Discrepancies between farmers' ability and willingness and the design of voluntary mechanisms occur frequently. This makes it necessary to understand how farmers' participation in these mechanisms can affect LUC. The aim of this paper was to demonstrate an agent-based approach to analyse and explore how voluntary mechanisms can influence LUC processes in rural regions. This approach was applied to a rural region in Australia, where clearing of native vegetation has occurred for agricultural development. Historical land cover data and semi-structured interviews were used to parameterise an agent-based model. Factors that influence farmers' ability and willingness to participate in these mechanisms were identified. Three scenarios were simulated with the model to explore how the implementation of different voluntary mechanisms can affect the landscape structure of the region. This paper identifies how the diversity of farmers' decision-making can influence the landscape structure in the region. The advantages and limitations of an agent-based approach in relation to LUC research and policy are discussed., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

The relation between human population growth and land use change is much debated. Here we present a case study from Papua New Guinea where the population has increased from 2.3 million in 1975 to 5.2 million in 2000. Since 85% of the population relies on subsistence agriculture, population growth affects agricultural land use. We assessed land use change in the Morobe province (33,933 km2) using topographic maps of 1975 and Landsat TM images of 1990 and 2000. Between 1975 and 2000, agricultural land use increased by 58% and population grew by 99%. Most new agricultural land was taken from primary forest and the forest area decreased from 9.8 ha person(-1) in 1975 to 4.4 ha person(-1) in 2000. Total population change and total land use change were strongly correlated. Most of the agricultural land use change occurred on Inceptisols in areas with high rainfall (>2500 mm year(-1)) on moderate to very steep slopes (10-56%). Agricultural land use changes in logged-over areas were in the vicinity of populated places (villages), and in close proximity to road access. There was considerable variation between the districts but districts with higher population growth also had larger increases in agricultural areas. It is concluded that in the absence of improved farming systems the current trend of increased agriculture with rapid population growth is likely to continue.

Risk analysis (RA) has been proposed as a means of assessing fitness for use of spatial data but is only rarely adopted. The proposal is that better decisions can be made by accounting for risks due to errors in spatial data. Why is RA so rarely adopted? Most geographical information science (GISc) literature stresses educational and technical constraints. In this article we propose, based on decision theory, a number of hypotheses for why the user would be more or less willing to spend resources on RA. The hypotheses were tested with a questionnaire, which showed that the willingness to spend resources on RA depends on the presence of feedback mechanisms in the decision-making process, on how much is at stake, and to a minor extent on how well the decision-making process can be modeled.

Multi-agent Systems (MAS) offer a conceptual approach to include multi-actor decision making into models of land use change. The main goal is to explore the use of MAS to simulate spatial scenarios based on modelling multi-actor decision-making within a spatial planning process. We demonstrate MAS that consists of agents representing organizations and interest groups involved in an urban allocation problem during a land use planning process. The multi-actor based decision-making is modelled by generating beliefs and preferences of actors about the location of and relation between spatial objects. This allows each agent to confront these beliefs and preferences with it's own desires and with that of other agents. The MAS loosely resembles belief, desire and intentions architecture. Based on a case study for a hypothetical land use planning situation in a study area in the Netherlands we discuss the potential and limitations of the MAS to build models that enable spatial planners to include the 'actor factor' in their analysis and design of spatial scenarios. In addition, our experiments revealed the need for further research on the representation of spatial objects and reasoning, learning and communication about allocation problems using MAS.

Precision farming aims to optimize the use of soil resources and external inputs on a site-specific basis. Base ingredients for research in the field of precision farming are spatial data, including a characterization of the spatial variability, and simulation models for the characterization of the processes that take place. Geographical information systems (GIS) are systems for the storage, analysis and presentation of spatial data. A combination of GIS and simulation models is highly relevant for precision farming. Currently only static one- or two-dimensional simulation models can be fully supported by commercial GIS systems. Within precision agriculture an engineering component can be also distinguished, in which the research findings are translated into operational systems for use at farm level. GIS can support this engineering activity by providing a good platform for storage of base data, simple modelling, presentation of results, development of a user interface and, in combination with a global positioning system, controlling the navigation of farm vehicles. On the basis of GIS a decision support system could be developed for operational application of precision agriculture at farm level.