Between 2004 and 2007, more than 50,000 kilometers (31,000 miles) of roads were built in the Brazilian Amazon, contributing to untold deforestation and habitat loss, according to a new study which is the first to measure the number of roads built in a rainforest ecosystem over an extended period of time.
Writing in the journal Regional Environmental Change, researchers from Imperial College London also pointed out that the way Amazon road networks develop is still poorly understood and that similar studies in the future could could provide more accurate predictions of where road building might occur.
They contend a better understanding of the development of road networks will help combat future deforestation.
"Even though roads often occupy less than 2 percent of a country's land surface, they may have an ecological impact on an area up to ten times as large," the researchers said in a statement. "These indirect effects can include changes in air and soil temperature and moisture, as well as restrictions on the movement of animals."
Rob Ewers, a life scientist at Imperial College and a study co-author, said knowing where the roads are and the speed at which they are built is a key to predicting deforestation.
"A number of models currently exist which rely on this knowledge, but there are no good studies of how quickly roads get built and where they go when they are built," he said.
"An understanding of road networks is the big missing gap in our ability to predict the future of this region."
Over the three-year period, the researchers used existing road maps and satellite data to track the evolution of road networks in the Brazilian Amazon, finding that regions with high economic growth were also where the roads spread the fastest. Regions with newer settlements also saw much road building, though the researchers added that once an extensive network of roads was in place the growth slowed.
The researchers said they hope that their findings will improve our understanding of how land use is changing around the world, and help predict deforestation more accurately.