Cumulative effects assessment (CEA) is typically a part of most project-based EIA frameworks and applications and refers to the consideration of the accumulation of human-induced changes on the environment over space and time (Noble, 2010). CEA accounts for additive effects of several development projects, including past, present and future projects, as well as impact interactions over time, and secondary or indirect effects. Examples include time lags, cross boundary, fragmentation, and compounding effects from multiple sources or pathways (Blaser et al., 2004).
Geographic Information systems (GIS) are systems of computer hardware and software for storing, transforming, managing, analyzing and displaying spatial information (Treweek, 1999). The use of GIS in EIA involves determining the location of human and environmental variables and understanding the relationships between them. GIS allows environmental information to be added and updated over time and space making it dynamic and ideal for evaluating planning options for development (Atkinson and Canter, 2011).
As GIS is becoming increasingly functional and popular, its use for environmental resource analysis has increased three-fold in the last three decades (Li et al., 2011). Since CEA usually deals with complex multifaceted systems, the ability of GIS to store, manipulate, analyze, and display sets of geographical data makes it well-suited to this task (Warner and Diab, 2002). Furthermore, GIS is conducive to the typically larger geographic scale of CEA studies which require regional analysis. Useful applications for CEA include the ability to establish baseline conditions and study boundaries for regional assessment, measuring change over time, identifying locations that are impacted by multiple actions and ones most heavily affected, forecasting future conditions, and calculating additive effects (Blaser, 2004).
GIS is particularly useful for the assessment of cumulative ecological effects because it facilitates the mapping and modelling of ecological impacts conveyed over large geographical scales using remotely sensed data (Treweek, 1999). By quantifying the spatial attributes of habitat distribution and organization, ecologists can describe declines or recoveries of habitat types in a study area and recognize when thresholds of habitat loss and fragmentation are exceeded, thereby demonstrating resource vulnerability (Treweek, 1999; Atkinson and Canter, 2011).
In spite of the many positive aspects, there are some limitations of using GIS for impact assessment. In addition to the typical disadvantages of high time, cost, and skill requirements, it can be difficult to address indirect effects (Blaser et al., 2004) and the magnitude of cumulative effects from multiple past, present, and future actions. Other potential problems may arise from data errors resulting from entering data at different scales, compatibility issues between different data forms and systems, and a lack of quality assurance and control on data sets used (Atkinson and Canter, 2011). Despite these potential setbacks, GIS shows much promise and will surely become increasingly valuable and even essential for cumulative effects assessment.
An example of a GIS-based model for assessing cumulative effects in Canada is a predictive modeling approach focusing on cultural and historical sites in the tar sands region of Alberta (Clarke and Lowell, 2002). Nine layers of environmental and human variables were combined to identify zones for potential cumulative effects on these sites based on existing and approved mining development projects. Another example comes from Popplewell et al. (2003) who developed a GIS-based model founded on landscape metrics derived from a satellite image classification of landcover to quantify the structure of grizzly bear habitats within bear management units in west-central Alberta. A combination of effects caused by human and natural disturbances was used to analyze differences in bear habitat.
Atkinson, SF and LW Canter 2011. Assessing the cumulative effects of projects using geographic information systems. Environmental Impact Assessment Review, 31, 457-464.
Blaser, B, Liu, H, McDermott, D, Nuszdorfer, F, Phan, NT, Vanchindorj, U, Johnson, L and J Wyckoff 2004. GIS-Based Cumulative Effects Assessment. Colorado Department of Transportation Research Branch. University of Colorado, Denver, 39p.
Clarke, G and S Lowell 2002. Historical resources cumulative effects management through predictive modeling. In Kennedy, AJ (ed). Cumulative Environmental Effects Management: Tools and Approaches. Alberta Society of Professional Biologists, p. 279–95.
Li, R, Bettinger, P, Danskin, S and R Hayashi 2005. A historical perspective on the use of GIS and remote sensing in natural resource management, as viewed through papers published in North American Forestry Journals from 1976 to 2005. Cartographica, 42, 165–79.
Noble, BF 2010. Introduction to Environmental Impact Assessment: A Guide to Principles and Practice. Don Mills, Canada: Oxford University Press.
Poppelwell, C, Franklin, SE, Stenhouse, G and M Hall-Beyer 2003. Using landscape structure to classify grizzly bear density in Alberta Yellowhead Ecosystem bear management units. Ursus, 14(1), 27-34.
Treweek, J. 1999. Ecological Impact Assessment. Oxford, UK: Blackwell Science Ltd.
Warner, LL and RD Diab 2002. Use of geographic information systems in an environmental impact assessment of an overhead power line. Impact Assessment Project Appraisal, 20, 39–47.