Home Articles Spatial Data Infrastructures: The future of Wetland Rapid Assessment Models in Developing...

Spatial Data Infrastructures: The future of Wetland Rapid Assessment Models in Developing Countries

Moses Musinguzi
PhD Student(Mr.)
Uppsala University, Dept of Earth Sciences,
Email: [email protected]

Gerhard Bax
Associate Professor
Dept. of Earth Sciences
Email: [email protected]

Sandy Tickodri-Togboa
Associate Professor
Makerere University
Email: [email protected]

This paper highlights opportunities for improved wetland assessment and the need for Spatial Data Infrastructures (SDI) as supporting frameworks.
For a long time, the society view of wetlands as wastelands led to their continued conversion for agricultural use or for industrial development. Limited knowledge on the benefits of wetlands and their associated functions and values resulted into wetland reclamation in many countries, and the impact of their loss is being realized in different forms. Wetland functions are natural processes that occur in wetlands with or without the knowledge of humankind while values are benefits that society attaches to wetlands. With increased knowledge and appreciation of wetland benefits over the last few decades, wetland assessment and management are becoming very serious concerns for governments and the international community. The initial attempt to protect wetlands has been an “either or” scenario (either you are in a wetland and therefore no permit or you are on a dry land and you get a permit). This approach is slowly giving way to the concept of “wise use” as adopted in Uganda or “No net loss in functions and values” as applied in the US. In both policies, wetland resources are used without impacting on the functions that they perform and the values attached to them. To support such policies, impact assessments are required to ensure that developments carried out in wetlands do not impact negatively on the functions and values from the wetlands.

Wetland assessment includes wetland-related data gathering, data analysis and the presentation of resulting information to decision makers. It provides information upon which decisions on different management options and mitigation measures for specific wetlands or sections of wetlands are made.
Researchers and scientists in the field of wetland assessment have for a long time been involved in developing models and techniques for rapidly assessing wetland functions and values. While developing the techniques, the guiding principle has been to provide a solution that is cheap and affordable to evaluate a number of functions for many wetlands while ensuring that the activity is accomplished in a short time.
Techniques for rapid assessment of wetland have improved over time from those that evaluated a single site for a single function or problem, to more complicated models that combine variables and parameters to assign functional indices to various wetlands in a watershed. The latest generation of models incorporate wetland measurements and landscape characteristics to evaluate the capacity of wetlands to perform a number of functions. These models recognize the role of biotic and abiotic factors for contributing to the capacity of a wetland to perform its functions.
Because of the enormous data involved, the models utilize the capabilities of Geographical Information Systems (GIS) to integrate, analyze and display multi-thematic data. The Hydro-geomorphic techniques (HGM) are one of those latest techniques that require collection of field data about wetland characteristics and analyzing it with existing spatial data. This study applied SWAMP, one of the HGM based models for the wetlands in Lake Kyoga basin of Uganda, with a view of identifying the various issues for its applicability for wetlands in Uganda. SWAMP is a GIS based model developed by NOAA Coastal Services Centre for assessing functional capacities for wetlands in the Ashepoo-Combahee-Edisto (ACE) river basin of South Carolina, but can be used in other areas as long as there is local knowledge of wetland systems. The model required input of the following datasets: Soils, Landuse, Landover, wetland boundaries, Hydrography, roads and watershed boundaries. In addition a digital elevation model and administrative boundaries were found to be desirable.
While attempting to test the models, it was identified that lack of infrastructures to access existing data presents more challenges to wetland assessment than lack of assessment tools. Apart from institutional bottlenecks, the technical issues included variations in data formats, semantics, variations in scale and scarcity. The effort and time taken to locate a spatial dataset, determine its quality and fitness for use, resolve issues of semantics and finally acquire it, is so enormous that its defeats the whole purpose of wetland assessment models. As scientists in developed countries continue to improve on the existing models for wetland assessment, their counterparts in developing countries need to first and foremost, address the issue of data accessibility and interoperability since it is key to wetland assessment using GIS as a platform. With a lot of donor support for data capture of environmental data and mainly in the Sub-Saharan African region, the major challenge in the next few years will shift from availability of data to issues of accessibility, semantics and interoperability. Development of relevant and coordinated Spatial Data Infrastructures at various levels is therefore seen as the future for the implementation of wetland assessment models. The SDI will provide a basis for spatial data discovery, evaluation, accessibility and application, and hence shorten the complexities for wetland assessment.