Image-based logistic regression parameters of deforestation in Rondonia, Brazil

Achieves an improved understanding of those factors associated with deforestation in the tropics. We developed a logistic regression model of deforestation based on features detectable on Landsat imagery. The model was developed for a study area in Central Rondoˆnia, Brazil using images covering a 20-year time period. The model incorporates five main parameters in predicting the amount of deforestation on individual settlement lots. These include (1) proximity of a lot to nearest paved road (PPR); (2) proximity of a lot to nearest secondary road; (3) distance to nearest forest/cleared edge (PFC); (4) distance to a market center; (5) distance to a major urban center. PPR and PFC were the strongest indicators of deforestation. In Rondoˆnia, deforestation strongly follows the pattern of roads creating a fishbone network. Deforestation increases at the edges of forest clearings as agriculture becomes unproductive over time. Several other parameters including agriculture and pasture suitability indices and soil ranking, using ancillary GIS data were tested using logistic regression but accounted for little variability in the model. The model is compared to actual Landsat classifications and field verification of deforestation in cross validation scheme over the 20-year time period