Forest monitoring and social media - Complementary data sources for ecosystem surveillance?

Forest monitoring captures human impacts and other biotic and abiotic influences on forests and is a prerequisite for the sustainable use and protection of forest ecosystems. Forest inventories for example are a key tool to plan sustainable harvesting, whereas Forest Observational Studies provide the empirical basis for an improved understanding and long-term evaluation of forest ecosystem dynamics. To that end detailed data is collected at stand level, often integrated in larger forest observational networks, which feeds into forest ecosystem models. Forests exist however in a constantly changing societal context and the direct or indirect impact of human activity has become a crucial driver on all types of ecosystems. The Millenium Ecosystem Assessment underlines the linkage between social and ecological systems, highlighting the centrality of ecosystem services to human well-being and the requirement for ecosystem monitoring in the "anthropocene" to provide a holistic view of ecosystems as social-ecological systems. Framing information about the social context of a forest ecosystem, gaining the expertise and providing resources to collect this type of information is usually outside the scope of data collection for forest inventories and monitoring. Studies in other domains faced a similar challenge and turned to data mining informal online information sources to supplement traditional monitoring and data collection strategies. This paper explores how forest monitoring approaches especially Forest Observational Studies with their long-term and large-scale focus may be complemented by social media mining. We outline (a) how social media mining methods from other domains could be applied to forest monitoring, (b) discuss identification of stakeholders, events and demands on forest ecosystems as examples of social contextual information that could be obtained via this route and (c) explain how this information could be automatically mined from social media, online news and other similar online information sources. The proposed approach is discussed on the basis of examples from a broad set of other domains. (c) 2013 Elsevier B.V. All rights reserved.