An experiment in >1000 river and riparian sites found spatial patterns and controls of carbon processing at the global scale.

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a b s t r a c t a r t i c l e i n f oThe strengthening of spatial database infrastructures, further promoted by the INSPIRE Directive adopted in 2007, has led to an increased use of spatial data in planning and decision-making. Given that land-use plans are intrinsically spatial, such evidence and approaches can significantly benefit plan-making. A spatial framework could especially support the specific Strategic Environmental Assessment (SEA) aspects of the plan-making process. Spatial tools such as Geographic Information Systems (GIS) are particularly well-placed to support the environmental integration sought in SEA by providing evidence through the spatial assessment of multiple environmental datasets. Moreover, GIS bring the opportunity to augment conventional assessment techniques (e.g. matrix-based assessments) by acting as visual mediators of spatial knowledge and by providing an effective tool for the spatial and temporal analysis of environmental impacts. This paper presents a GIS-based approach to SEA (GISEA), and analyses the above premise by evaluating the barriers, limitations, opportunities and benefits of its implementation. The GISEA approach has been applied to seven development plans of differing scales in the Republic of Ireland. The results of the case studies revealed that current issues in SEA (e.g. restricted time-frames and institutional arrangements) condition the implementation of a GIS-based approach. Moreover, GIS expertise, data accessibility and quality remain limiting factors to an effective GIS application in SEA. However, the results also confirmed that GIS have the potential to increase the objectivity and accuracy of the assessment, enhance both the understanding of environmental and planning considerations and the delivery of information, and, therefore, help to improve the effectiveness of SEA practice.

Provisions for citizen involvement in the assessment of potential environmental effects of certain plans, programmes and projects are present in current legislation. An international survey revealed that public participation is common practice in European and some other countries worldwide. However, a number of issues are observed to affect public involvement in EIA/SEA processes and expert opinion differs when evaluating the effectiveness of existing participative methods.Results suggest that technology-aided methods can improve traditional participation processes. In particular, GIS has the potential to increase community knowledge and enhance involvement by communicating information more effectively. Variable accessibility to technology and data quality remain issues. Combining technology with more conventional ways of gathering, evaluating and presenting data are seen as offering a solution to the need to promote the integration of public perceptions in environmental assessment procedures. Recommendations to improve current public participation methods and measures for making GIS available to the general public are provided.

Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.