Spatial modeling of deforestation processes in the Central Peruvian Amazon

Abstract

This study examines the relation between primary forest loss and landscape characteristics in the Ucayali region, Peru. Seven variables (rivers, elevation, annual precipitation, soil suitability for agriculture, population density, paved roads, and unpaved roads), were identified as potential deforestation drivers. The variables were converted into spatially explicit layers of continuous data and divided into a 9km2 grid. A multiple regression analysis was conducted to determine variable significance. Distance to paved and unpaved roads were strongly associated with deforestation, followed by distance to rivers, annual precipitation and elevation. All significant variables were negatively correlated with deforestation. Variables excluded from the model were population density and soil suitability for agriculture, suggesting that the influence of population density on forest clearing across the study area was not significant, and that deforestation activities were undertaken regardless whether soils are suitable for agriculture or not. Based on the linear regression analysis, the significant variables were selected and added to the Land Change Modeler in order to project primary forest coverage by 2025. The modeling results predict extensive deforestation along the Aguaytia River and at the forest/non-forest interface along the paved highway. The rate of primary forest removal is expected to increase from 4783hay−1 (for the 2007–2014 period) to 5086hay−1 (for the 2015–2025 period). A preliminary survey questionnaire conducted to explore deforestation intentions by farmers in the region, partly confirmed the overall deforestation trends as projected by the model.