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Modelling Deforestation and Land‐Use Change: Sparse Data Environments
Author(s) -
Pinto Alessandro De,
Nelson Gerald C.
Publication year - 2007
Publication title -
journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.157
H-Index - 61
eISSN - 1477-9552
pISSN - 0021-857X
DOI - 10.1111/j.1477-9552.2007.00119.x
Subject(s) - deforestation (computer science) , land use , land use, land use change and forestry , productivity , climate change , environmental resource management , wetland , watershed , natural resource economics , environmental planning , geography , computer science , environmental science , economics , economic growth , ecology , machine learning , biology , programming language
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.