The focus on developing data instead of the analytical environment for using data in the decision-making process partly results from limitations on data accessibility. However, there are also organizational barriers caused by a focus on applied analysis rather than basic research. Research investments have the potential to advance understanding of relationships that tie data together. For example the relationship between soil texture, slope, rain intensity, and other factors that determine soil erosion is embodied in the Universal Soil Loss Equation, which itself must be locally calibrated. This equation is a powerful analytical tool, and was an outcome of basic research. It remains to be tested and adapted for use in many parts of Africa.

Deploying a spatial decision-support system requires field research, case studies, and pilot projects. Development assistance investments targeted to research-based programs would promote a shift from descriptive to process-based analysis in spatial decision-support systems. There are some examples where these long-term investments can work. USAID’s Cooperative Research Support Program (Chapter 3) recognizes the importance of research to development assistance programs and has promoted collaborative research between U.S. land grant universities and African organizations, mostly in agriculture and natural resource management. With the increasing availability of geographic data and decision-support tools, there is an opportunity for these programs to emphasize the spatial aspects of the research.

This section draws from lessons learned in examples from the previous sections and discusses approaches that could increase integration of decision-support systems into development policy-making and natural resources management.

The contribution of decision-support systems to policy dialog depends on geospatial capacity (Box 7-3 and Chapter 8) (e.g., good data and equipment and trained staff) and communication among policy-makers, scientists, GIS experts, and civil society (Bassolé, et al., 2001). These interactions are “most effective within a fully supportive geo-information policy environment at the national level” (Bassolé et al., 2001).

In addition to geospatial capacity, demand will spur the development and use of decision-support systems. In the committee’s opinion, the agricultural and natural resource management sectors are a likely primary source of this demand, as these sectors are the main users of geographic data and tools. The livelihoods of the majority of Africans depend on agriculture and natural resources, and pressing problems within these sectors include soil infertility and erosion, pollution from farm chemicals, pressure from grazing, and competition for resources. Addressing these problems demands better data and better ways of analyzing the relationship between human activities and changes on the land surface. Hence, decision support in the area of land cover (Chapter 6) will be one of the more fruitful application areas of geographic data and tools.

A further rationale for focusing on land cover is that it is basic information for many applications. Land use and cover change is at the nexus of a range of issues including habitat fragmentation, biodiversity, food and agriculture, water quality, urbanization and settlement, and human health and disease. Land cover is also readily obtained from satellites.

International activities could accelerate the use of decision-support systems for land-cover applications in Africa. For example, as the U.S. Geographic Information for Sustainable Development Alliance (GISD, 2002) initiatives are implemented, the need will arise to identify data for understanding land transformation processes. In addition, U.N. initiatives, notably the Global Land Cover Network and the GOFC/GOLD programs (Chapter 6), emphasize routine observations and analysis of land cover and change.

Strategies to improve or create these data sets are needed now, and these strategies should build on existing initiatives and networks. An effective land-cover decision-support system for Africa would include:

• Development of standardized land cover and environmental classification systems. Classification systems are central to the use of the product layers in a spatial data infrastructure. Classification systems are defined and formalized but are not rigid. FAO’s Africover Land Cover Classification System (Chapter 6) is an emerging standard in Africa.

• Development of a system for land-cover baseline and change detection across spatial scales. This component would include a baseline map and compilations of change maps from repeated observations over time. These maps can be constructed using multi-resolution remotely sensed data (e.g., AVHRR, MODIS, SPOT VEGETATION, ASTER [Annex Box 6-4], Landsat). Satellite observations alone cannot explain socio-economic and political factors that are among the causes of land-cover and environmental change, nor can they always identify trends or dynamics at the scale needed by decision-makers. Hence, remotely sensed data should be coupled with multi-scale geographically referenced economic and social data.

• Identification of “hot spots” of change. Because resources are scarce, and there are limited opportunities for decision-makers to make comprehensive evaluations, an approach that identifies areas of rapid change, high risk, or other critical occurrences should be developed. The routine identification of such hot spots will guide decision-makers to critical locations and times for the most efficient use of resources (e.g., Figure 6-5).

• Analysis and modeling of the relationship of land-cover change to proximate causes. Identification of

Front Matter (R1-R14)

Executive Summary (1-10)

1 Introduction (11-17)

2 Agenda 21 Implementation: Progress, Challenges, and the Role of Geographic Information (18-28)

3 Geographic Information Activities in Africa (29-36)

4 Facilitating the Use of Geographic Data: Spatial Data and Telecommunications Infrastructures (37-51)

5 Geographic Data for Sustainable Development I: Framework Data (52-78)

6 Geographic Data for Sustainable Development II: Other Thematic Data (79-103)

7 GIS-Based Decision Support Systems in Africa (104-113)

8 Building Capacity to Apply Geographic Information to Sustainable Development in Africa (114-127)

9 Lessons Learned and Recommendations (128-136)

Appendix A: Biographical Sketches of Committee Members (137-141)

Appendix B: Oral and Written Contributors (142-145)

Appendix C: FGDC Statement (146-147)

Appendix D: Acronyms (148-152)

Appendix E: Glossary (153-155)