Importance of Input Classification to Graph Automata Simulations of Forest Cover Change in the Peruvian Amazon
In an area of Peru difficult for remote sensing imaging of deforestation and regeneration, the authors evaluate landcover and detect changes in landuse using novel data simulation techniques.
Research goals & Methods
The authors aim to compensate for remote assessments of deforestation or reforestation that may be strongly dependent on the seasonality of input images. To do this, they ran graph automata simulations while varying forest cover inputs to model land cover change.
conclusions & Takeaways
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.
Importance of Input Classification to Graph Automata Simulations of Forest Cover Change in the Peruvian Amazon. In: Landscape Series. Landscape Series. Springer Netherlands; 2009:205–225. doi:10.1007/978-1-4020-9656-3_9..
- Department of Geography and the Environment, The University of Texas at Austin
- A. Moffett Pritzker School of Medicine, The University of Chicago