Nonlinear regression models to identify functional forms of deforestation in East Asia

Identification of the factors involved in deforestation could lead to a comprehensive understanding of deforestation on a broad scale, as well as prediction capability. In this paper, regression models with two explanatory variableshuman population and relief energy, i.e., the difference between the maximum and minimum altitudes in a sampled areawere verified as to whether they could elucidate aspects of deforestation. The functional forms of the nonlinear regression models were estimated by step functions analyzed with the use of high-precision Japanese data. Candidate smooth regression models were then derived from the obtained sigmoidal shapes by the step functions. Models with spatially dependent errors were also developed. Akaike's information criterion was used to evaluate the models on four data sets for the East Asia region. From the evaluation, we selected the best three models that systematically showed the best relative appropriateness to the real data.