Modelling Deforestation and Land‐Use Change: Sparse Data Environments
综述了在数据稀缺环境下模拟森林砍伐与土地利用变化的建模方法，包括神经网络和动态规划等离散选择方法，并介绍了将家庭调查数据与卫星图像结合的研究成果，对关注发展中国家环境问题的政策制定者和研究者有参考价值。
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.