  Deforestation, as one of the challenging environmental problems in the world,
has been recorded the most serious threat to environmental diversity and one of
the main components of land-use change. In this paper, we investigate spatial
distribution of deforestation using artificial neural networks and satellite
imagery. Modeling deforestation can be conducted considering various factors in
determining the relationship between deforestation and environmental and
socioeconomic factors. Therefore, in order to ascertain this relationship, the
proximity to roads and habitats, fragmentation of the forest, height from sea
level, slope, and soil type. In this research, we modeled land cover changes
(forests) to predict deforestation using an artificial neural network due to
its significant potential for the development of nonlinear complex models. The
procedure involves image registration and error correction, image
classification, preparing deforestation maps, determining layers, and designing
a multi-layer neural network to predict deforestation. The satellite images for
this study are of a region in Hong Kong which are captured from 2012 to 2016.
The results of the study demonstrate that neural networks approach for
predicting deforestation can be utilized and its outcomes show the areas that
destroyed during the research period.
