Modelling Deforestation and Land‐Use Change: Sparse Data Environments

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Abstract
Land‐use change in developing countries is of great interest to policy‐makers and researchers with diverse interests. 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. Analyses of these problems are especially constrained by lack of data. This article reviews modelling approaches for data‐constrained environments that involve discrete choice methods including neural nets and dynamic programming, and research results that link individual household survey data with satellite images using geographic positioning systems.