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Abstract

Land use change in developing countries is of great interest to policymakers and researchers from many backgrounds. Concerns about consequences of deforestation for global climate change and biodiversity have received the most publicity, but loss of wetlands, declining land productivity, and watershed management are also problems facing developing countries. In developing countries, analysis is especially constrained by lack of data. This paper reviews modeling approaches for data-constrained environments that involve methods such as neural nets and dynamic programming and research results that link individual household survey data with satellite images using geographic positioning systems.

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