Importance of Input Classification to Graph Automata Simulations of Forest Cover Change in the Peruvian Amazon

Abstract

The forests of the Amazon arguably represent the single-most reported upon ecosystem globally, with the “Arc of Deforestation” having captured scientific and popular attention. Critiques establishing deforestation as a myth present evidence of an incomplete if not erroneous assessment of forest processes as interacting within larger institutional and climatic systems. As remote assessments of deforestation or reforestation may be strongly dependent upon the seasonality of input images, this work brackets the potential range of forest-change findings by running graph automata simulations while varying forest cover inputs. Results confirm that model results are quite sensitive to input amounts of forest cover as small as those detected even in one intra-annual cycle previously. These findings are interpreted in light of the seasonality of previous work throughout the Amazon and suggest that the overestimation of deforestation may be systematically underestimating reforestation processes at work in the Amazon.