Applications in GIS for Conservation Planning

The use of geographic information systems is valuable for conservation of species and their habitat due to several inherent spatial factors related to conservation such as forest cover, species distribution and land cover change.  There are many factors to consider when delineating protected areas and conserving species and this requires expertise from diverse backgrounds as well as opinions from local communities [1]. GIS simplifies the process of integrating expert and stakeholder opinion by using means such as multi-criteria decision making and pairwise comparison to assign different weights to factors of relevance. The relative importance of various criteria can be weighted to ensure the optimal space is selected for the focus of conservation efforts [2].  Figure A shows an example of a map produced using expert pairwise comparison to delineate the highest quality forests for the expansion of protected areas by the Finnish Forest and Park Service.

fgi1

A map of Southern Finland showing the priority rank for the highest quality forest for new protected areas. Exisiting protected areas are shown in black.
Source: Lehtomaki et al., 2009.

In addition to integrating opinions, GIS also offers integrative technology for conservation planning for developers. A good example is the Smart Infrastructure Planner (SIP) toolkit developed by the WWF. This application is open source and can be downloaded as a GIS toolbox that can assess the impacts of a development on a particular species or habitat type. Inputs include items such as work site, roads, towns, land cover, prey distribution and water sources. These items are all integrated into one analysis (See fig. B below).

Figure B.

Image

Habitat suitability is one of the Modules of SIP. The above fields are taken into consideration when generating a habitat suitability map (See also Fig. C).
Source: WWF, 2012.

All factors can be weighted based on their influence in determining the presence of a species. Next, any of the five modules of SIP can be run which include habitat suitability which is shown above (see also fig C), habitat suitability change (fig. D), landscape statistics, patch statistics and patch distance. The output maps can be shown through different time steps to show how developments will impacts habitat over time in response to various factors. This application also provides the user with mitigation recommendations based on the type of infrastructure being proposed and its location in the landscape [3].

Figure C.

An example of an output map from the habitat suitability module. Run this analysis twice to get current habitat suitability and predicted future suitability with consideration of new developments. Source: WWF, 2012.

Figure D.

Image

An example of the output from the habitat suitability change analysis. Run this module with the outputs from the habitat suitability module to show areas of change over a given time period. Source: WWF, 2012.

The tool described above provides a comprehensive and organized way to visualize impacts to habitat from development. It has the potential to be very useful for cumulative impact assessment, as it has the ability to integrate the entire landscape and include all projects and infrastructure. However, when using SIP as well as pairwise comparison and multi-criteria analysis for conservation, stakeholders and experts must be careful to consider all aspects of the landscape in question. Often, “charismatic mega-fauna” such as primates, large cats or other large mammals are the focus of conservation efforts because they draw more attention and are more visible to the public [4]. If more well known species end up the sole focus of multi-criteria analysis and/or SIP analysis, less popular species, such as insects or plants, may suffer. If these tools are used with proper consideration of ecosystem components, they can be very useful for impact prediction.

References

[1] Mitchell, N, and Schaab, G 2008 Developing a disturbance index for five East African forests using GIS to analyse historical forest use as an important driver of current land use/cover African Journal of Ecology46(4), 572-584

[2] Lehtomäki, J, Tomppo, E, Kuokkanen, P, Hanski, I, and Moilanen, A 2009 Applying spatial conservation prioritization software and high-resolution GIS data to a national-scale study in forest conservation. Forest ecology and management258(11), 2439-2449

[3] WWF. (2012) The Smart Infrastructure Planner Toolkit User Guide. Retrieved from http://worldwildlife.org/publications/smart-infrastructure-planner-beta

[4] Leader-Williams, N and Dublin, H T 2000 Charismatic megafauna as flagship species. Priorities for the conservation of mammalian diversity: has the panda had its day, 53-81

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Post-Disaster Impact Assessment: A Toolbox for Saving Lives in a Changing World

Natural disasters such as earthquakes, tsunamis, hurricanes and floods affect millions of people every year. Climate change will likely add to the natural disasters which already occur around the world. The global climate is warming, causing increases in tropical storms; desert areas are becoming drier, contributing to increases in droughts and food shortages; and land-glaciers are melting, leading to an increase in avalanches and landslides [1][4]. In addition to these climate-driven disasters, the Earth is continuously undergoing geologic changes which result in volcanic eruptions, landslides and earthquakes. With growing populations near hazard-prone areas, post-disaster impact assessment is going to be an important toolbox for rebuilding in safer areas and saving lives. 

Original Data from the EM-DAT International Disaster Database, Center for Research on the Epidemiology of Disasters, University of Louvain (www.emdat.be/)

Figure 1: A steady increase in climate-related natural disasters is apparent from the blue bar plot in this graph (Source [3])

 On March 11, 2011, Eastern Japan was hit by a magnitude-9.0 earthquake and subsequent 40.5 meter-high tsunami which killed over 19,000 people and destroyed 835 000 homes [5][6]. Japan is a developed county known for having countermeasures and evacuation plans for tsunami disasters [5]. They utilize both hard (i.e. breakwaters and sea walls) and soft (i.e. awareness and education) mitigation measures to ensure the minimize loss of life [5].

Source: CloudFront.net

Figure 2: Shows Japan and many other countries’ vulnerability due to the “Ring of Fire”, an extremely active zone of crustal plate boundaries (Source: CloudFront.net)

Impact assessment for damages was used to determine the performance of buildings materials and to record which locations were most vulnerable to flooding and sea level rise. Post-disaster on-the-ground fieldwork can be coupled with GIS tools to assess damages and to plan for the future. GIS technologies were used after the 2011 tsunami to show how well the hard mitigation measures performed. Types of GIS data that can be used are: elevation maps to map vulnerable areas and to locate shelters away from flood-prone land, land use to map vulnerability of structures and towns, and road networks to map access routes to affected areas [2]. For example, vulnerability assessment was done by mapping housing damage in vulnerable inundation areas and assessing “fragility curves” for different types of construction materials [5]. Disaster Science and Engineering experts Suppasri et al. (2013) were able to visually inform Japanese authorities of the best construction materials to be used in future development projects.

Examples of different damage levels for the same tsunami inundation depth. Figure from [4], page 1005

Figure 3: Examples of different damage levels for the same tsunami inundation depth (Source [5], p.1005)

Mapping disaster damage coupled with on-the-ground studies such as photographs and written accounts are essential for informing policy for future improvements to counter-disaster management strategies.  As shown above, this can also be said for types of construction materials used for building homes to increase resilience against tsunamis. Furthermore, GIS applications could be used to map historical inundation areas to plan new prevention plans, choose evacuation areas, and to visually inform residents of their location’s vulnerability to natural disasters such as tsunamis [5]. Scenario analysis mapping is an essential tool to be used to inform policy in choosing relocation areas away from vulnerable coastlines, as well as away from other potential natural disaster areas (e.g. landslides and floods).

On a warming planet where natural hazards have the potential to augment, and where societies will continue to be subject to various geologic hazards, post-disaster impact assessors will be needed to contribute to reconstruction efforts and to inform future disaster-planning in vulnerable areas.

Works Cited

[1] Bury, J.T. et al. (2011). Glacier recession and human vulnerability in the Yanamarey watershed of the Cordillera Blanca, Peru. Climatic Change , 105, 179-206.

[2] Latif, S., Islam, R., Khan, M. I., & Ahmed, S. I. (2011). OpenStreetMap for the Disaster Management in Bangladesh. IEEE Conference on Open Systems, (pp. 429-433). Langkawi, Malaysia.

[3] Leaning, J., & Guha-Sapir, D. (2013). Natural Disasters, Armed Conflict, and Public Health. New England Journal of Medicine, 369(19), 1836-1842.

[4] Malone, E. L., & Engel, N. L. (2011). Evaluating regional vulnerability to climate change: purposes and methods. WIREs Climate Change , 2, 462-474.

[5] Suppasri, A., Shuto, N., Imamura, F., Koshimura, S., Mas, E., & Yalciner, A. C. (2013). Lessons Learned from the 2011 Great East Japan Tsunami: Performance of Tsunami Countermeasures, Coastal Buildings, and Tsunami Evacuation in Japan. Pure and Applied Geophysics , 170, 993-1018.

[6] Utani, A., Mizumoto, T., & Okumura, T. (2011). How geeks responded to a catastrophic disaster of a high-tech country: rapid development of counter-disaster systems for the great east Japan earthquake of March 2011. Proceedings of the Special Workshop on Internet and Disasters, (pp. 1-8). Tokyo, Japan.

Using GIS to Synthesize the Watershed Approach into EIA

As planners and decision makers being to recognize the spatial significance of managing resources at the watershed scale, the use of Geographic Information Systems (GIS) as a main researching and teaching tool will only grow in importance. An early definition of GIS by Burrough (1986) deems it ‘a powerful set of tools for collecting, storing and retrieving at will, transforming and displaying spatial data from the real world.’ Figure 1 shows a very basic cross-sectional model of a unit watershed with different land use types.

New_watershed_Andrea_Gauthier_b

Figure 1. Different land uses across a cross-sectional model of a watershed. (Source: http://conservation-ontario.on.ca/resources/graphics/)

Development projects that require EIA, such as highways, factories and mines tend to impact the landscape by increasing impermeable surface cover. These impacts become compounded when there are multiple developments within the same watershed. The negative impacts of increasing impervious surface cover are noted by Barnes et. al (2002)

“The growth and spread of impervious surfaces within urbanizing watersheds pose significant threats to the quality of natural and built environments. These threats include increased stormwater runoff, reduced water quality, higher maximum summer temperatures, degraded and destroyed aquatic and terrestrial habitats, and the diminished aesthetic appeal of streams and landscapes.”

Since development impacts have cumulative effects, watershed conservation and management needs to be built into regional frameworks developed through Strategic Environmental Assessment (SEA). Regional SEA is the most appropriate framework within which to address cumulative effects when the primary goal is to influence the nature and pace of conservation and development in support of regional sustainability (Gunn and Noble, 2009). Using GIS software to better understand how these cumulative effects impact the integrity of watersheds is where I see the EIA process taking a major leap forward.

Utilization of GIS applications allows professionals to store, analyze and manipulate large amounts of spatial data on one interface. In today’s world it is not only professionals that have access to watershed data; web-based hydrological models such as Model My Watershed (http://www.wikiwatershed.org/model.html) are making watershed data available to all sectors so we can collectively conceptualize how our decisions affect the world around us in terms of both space and time. Model My Watershed has developed three user-friendly applications that are free to the public and help us visualize how different land use patterns accumulate together to affect streams, rivers, lakes and entire watersheds.

Companies such as SRK Consulting have already begun utilizing GIS software to assist in the EIA process. http://www.srk.com/en/newsletter/application-gis-eia-process Throughout the years of utilizing GIS applications for EIA the team at SRK has already realized some of the benefits:

“Potential risk factors may be identified upfront and presented to the client to assess the viability of proceeding with the project. This approach reduces timeframes and usually presents the client with a cost savings.”

It is the responsibility of those conducing each EIA to understand the current state of development within the watershed they are dealing with, and to understand how proposed projects will affect the integrity of entire watersheds. Anthropogenic stress on watersheds is accelerating along with human development; making GIS applications an integral tool when making more informed decisions and monitoring the impacts our development has on these vital hydrological units around the world.

References:

Barnes, K. B., J. M. Morgan III, and M. C. Roberge., 2002. Impervious surfaces and the    quality             of natural and built environments. Baltimore, Md.: Department of             Geography and          Environmental Planning, Towson University. 28 p

Burrough, P.A., 1986. Principles of Geographic Information Systems for Land          Resources       Assessment Clarendon Press, Oxford. pp. 193. sis.agr.ca/cansis/references/1986

Gunn, J., Noble, B., 2009. Integrating Cumulative Effects in Regional Strategic Environmental       Assessment Frameworks: Lessons from Practice. Journal of Environmental Assessment            Policy and Management, 11:03

Noble, Bram., 2008. Introduction to Environmental Impact Assessment: A Guide to             Principles and Practice, Second Edition. Toronto: Oxford University Press

SRK Consulting., 2013. Application of GIS in the EIA Process.        http://www.srk.com/en/newsletter/application-gis-eia-process

Mapping Cumulative Effects: GIS for CEA

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.

References

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.