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There is an implementation gap between government plans and land-use changes at the local level in the Vietnamese Mekong Delta. This stands in the way of the sustainable development of the delta, especially in the face of environmental degradation, climate change, and water-use conflicts.To narrow the gap between plans and practice, the government needs a better understanding of what drives land-use decisions at the farm level. Our research developed and applied a multi-scale framework to identify the principal drivers of land-use changes at the farm level in the Vietnamese Mekong Delta over the past 40 years. We conducted semi-structured interviews with 31 farmers in the highly flooded zone, then used transcript analysis to quantify the influence of the drivers mentioned by farmers. We found drastic shifts in land uses, predominantly towards rice intensification. Among the 43 change drivers the farmers mentioned, those operating at the regional scale were particularly influential, including the activities of local authorities, neighborhood effects, and the development of water management infrastructure. Market factors have become more prominent in the last two decades, motivating farmers to shift from double to triple rice or to gradually diversify into vegetables.However, agricultural diversification remains limited by the agro-hydrological context, which favors triple rice cropping, as well as household scale factors such as natural and physical assets of the farm,household capital, and labor capacity. The local community also played a key role in land-use change,though with a double-edged effect, both delaying implementation of central government policy and forcing farmers to follow the majority’s decision.

The development of a European Spatial Data Infrastructure (SDI) officially started with the entry into force of the INSPIRE Directive in 2007. INSPIRE’s implementation phase should be completed by the European Union (EU) and its member states at the end of 2021: a pivotal point to evaluate INSPIRE’s current governance and explore future scenarios. First, INSPIRE’s governing system is evaluated through an online survey by its involved stakeholders. Second, these results are applied in an agent-based model to explore potential governance scenarios and strategies. The results show that strong aspects of INSPIRE’s governing system are the supported vision and its formal structures, such as standards, technology and roles and responsibilities. Weak aspects are the access to resources, especially budget and time resources, and data use. The agent-based simulations show that INSPIRE is probably more constrained by its budget resources than its current dominant hierarchical interaction mix, although a combination of adaptive governance and continuous budget proved the most sustainable governance scenario.

In order to facilitate and coordinate spatial data sharing and exchange, many organisations have developed spatial data infrastructures (SDIs). SDI governance plays a pivotal role in the development and evolution of an SDI, but as SDIs are complex adaptive systems, governing is a challenge. This research therefore proposes a complexity perspective to SDI governance by exploring the use of agent-based modelling to simulate and examine SDI governance interactions. In this agent-based simulation, we examine interactions between SDI stakeholders, data availability and the effects of different governance styles (hierarchical, network and laissez-faire governance) and budget policies. The simulation shows that it is possible to mimic SDI governance dynamics through agent-based modelling. By running different scenarios, it appears that a network approach is more successful compared to a hierarchical or laissez-faire approach. Expert validation shows that overall the results of the simulation are credible and insightful, although improvements can be made to make the model more realistic. With agent-based modelling, SDI governance becomes more tangible and visible, which facilitates discussion and understanding. Agent-based modelling therefore appears to be a helpful new approach in a better understanding of the complexities and dynamics of SDI governance

Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data. To assure consistency, multiple images are used, interpreters are trained, sites are interpreted by several individuals, or the procedure includes a review. But little is known about important factors influencing the quality of visually interpreted data. We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers. Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project. Four cycles of visual interpretation were conducted, each was followed by review and feedback. Each interpreted site element was labelled according to dominant land cover type. We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal, training, and environmental categories. Variable importance was assessed using random forest regression. Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts, while the environmental variables complexity and image availability had least impact. Feedback loops reduced updating and hence improved consistency of the interpretations. Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data.