The development of world oceans & coasts and concepts of sustainability

The current phase of technological development and expansion in the world economy is leading to greater human activity and development offshore. Some have described this as the colonisation of the oceans, one phase in the wider history of world industrialisation. This study empirically tests notions of 'industrialisation' and 'colonisation' in the oceans for the first time. It finds that human activity in the oceans has increased by multiple factors in the most recent long term wave of economic development. The methods include the combined use of Raster and R! to overcome methodological challenges to analyse large spatial datasets which map the footprint of human activity. In response to increasing human activity in the oceans, nations and supranational institutions are developing new governance regimes. These regimes are characterised by policy integration and a commitment to sustainability. Sustainable development is a central tenet of most national and international policies for the world's oceans. An analysis of sustainable development terminology within coastal and ocean policy is provided for seven major maritime governance regimes: Australia, Canada, New Zealand, EU, South Africa, UK and the US. The results show that sustainability is highly differentiated in the context of 'the blue planet' (oceans and coasts). The diverse interpretations of sustainability present an impasse to measuring progress in the field. Therefore the paper concludes by offering a framework for explanation and interpretation of sustainable development, by linking it to foundational assumptions held by systems of thought or philosophical traditions. © 2013 Elsevier Ltd

The development of world oceans & coasts and concepts of sustainability

The current phase of technological development and expansion in the world economy is leading to greater human activity and development offshore. Some have described this as the colonisation of the oceans, one phase in the wider history of world industrialisation. This study empirically tests notions of 'industrialisation' and 'colonisation' in the oceans for the first time. It finds that human activity in the oceans has increased by multiple factors in the most recent long term wave of economic development. The methods include the combined use of Raster and R! to overcome methodological challenges to analyse large spatial datasets which map the footprint of human activity. In response to increasing human activity in the oceans, nations and supranational institutions are developing new governance regimes. These regimes are characterised by policy integration and a commitment to sustainability. Sustainable development is a central tenet of most national and international policies for the world's oceans. An analysis of sustainable development terminology within coastal and ocean policy is provided for seven major maritime governance regimes: Australia, Canada, New Zealand, EU, South Africa, UK and the US. The results show that sustainability is highly differentiated in the context of 'the blue planet' (oceans and coasts). The diverse interpretations of sustainability present an impasse to measuring progress in the field. Therefore the paper concludes by offering a framework for explanation and interpretation of sustainable development, by linking it to foundational assumptions held by systems of thought or philosophical traditions. © 2013 Elsevier Ltd

Community detection in spatial networks: Inferring land use from a planar graph of land cover objects

This paper applies three algorithms for detecting communities within networks. It applies them to a network of land cover objects, identified in an OBIA, in order to identify areas of homogenous land use. Previous research on land cover to land use transformations has identified the need for rules and knowledge to merge land cover objects. This research shows that Walktrap, Spinglass and Fastgreedy algorithms are able to identify land use communities but with different spatial properties. Community detection algorithms, arising from graph theory and networks science, offer methods for merging sub-objects based on the properties of the network. The use of an explicitly geographical network also identifies some limitations to network partitioning methods such as Spinglass that introduce a degree of randomness in their search for community structure. The results show such algorithms may not be suitable for analysing geographic networks whose structure reflects topological relationships between objects. The discussion identifies a number of areas for further work, including the evaluation of different null statistical models for determining the modularity of geographic networks. The findings of this research also have implications for the many activities that are considering social networks, which increasingly have a geographical component

Ecosystem classifications based on summer and winter conditions

Ecosystem classifications map an area into relatively homogenous units for environmental research, monitoring, and management. However, their effectiveness is rarely tested. Here, three classifications are (1) defined and characterized for Canada along summertime productivity (moderate-resolution imaging spectrometer fraction of absorbed photosynthetically active radiation) and wintertime snow conditions (special sensor microwave/imager snow water equivalent), independently and in combination, and (2) comparatively evaluated to determine the ability of each classification to represent the spatial and environmental patterns of alternative schemes, including the Canadian ecozone framework. All classifications depicted similar patterns across Canada, but detailed class distributions differed. Class spatial characteristics varied with environmental conditions within classifications, but were comparable between classifications. There was moderate correspondence between classifications. The strongest association was between productivity classes and ecozones. The classification along both productivity and snow balanced these two sets of variables, yielding intermediate levels of association in all pairwise comparisons. Despite relatively low spatial agreement between classifications, they successfully captured patterns of the environmental conditions underlying alternate schemes (e.g.; snow classes explained variation in productivity and vice versa). The performance of ecosystem classifications and the relevance of their input variables depend on the environmental patterns and processes used for applications and evaluation. Productivity or snow regimes, as constructed here, may be desirable when summarizing patterns controlled by summer- or wintertime conditions, respectively, or of climate change responses. General purpose ecosystem classifications should include both sets of drivers. Classifications should be carefully, quantitatively, and comparatively evaluated relative to a particular application prior to their implementation as monitoring and assessment frameworks

Community detection in spatial networks: Inferring land use from a planar graph of land cover objects

This paper applies three algorithms for detecting communities within networks. It applies them to a network of land cover objects, identified in an OBIA, in order to identify areas of homogenous land use. Previous research on land cover to land use transformations has identified the need for rules and knowledge to merge land cover objects. This research shows that Walktrap, Spinglass and Fastgreedy algorithms are able to identify land use communities but with different spatial properties. Community detection algorithms, arising from graph theory and networks science, offer methods for merging sub-objects based on the properties of the network. The use of an explicitly geographical network also identifies some limitations to network partitioning methods such as Spinglass that introduce a degree of randomness in their search for community structure. The results show such algorithms may not be suitable for analysing geographic networks whose structure reflects topological relationships between objects. The discussion identifies a number of areas for further work, including the evaluation of different null statistical models for determining the modularity of geographic networks. The findings of this research also have implications for the many activities that are considering social networks, which increasingly have a geographical component