By Scott Miller
New insights into the impact forests have on surface temperature will provide a valuable tool in efforts to mitigate climate change, according to a new research paper co-authored by Clemson University scientist Thomas O’Halloran.
For the first time, scientists have created a global map measuring the cooling effect forests have by regulating the exchange of water and energy between the Earth’s surface and the atmosphere. In many locations, this cooling effect works in concert with forests’ absorption of carbon dioxide. By coupling information from satellites with local data from sensors mounted to research towers extending high above tree canopies, O’Halloran and his collaborators throughout the world have given a much more complete, diagnostic view of the roles forests play in regulating climate.
Their findings have important implications for how and where different types of land cover can be used to mitigate climate change with forest protection programs and data-driven land-use policies. Results of their study were recently published in the journal Nature Climate Change.
By Darryl Fears
Over several decades in the past century, city populations swelled as Americans moved away from rural forests. Now the forests are moving farther away from Americans.
A new study of satellite images taken over 10 years starting in 1990 shows the rural forest canopy disappearing. Forest space disappeared from the United States in such big chunks that the average distance from any point in the nation to a forest increased by 14 percent, about a third of a mile.
While that’s no big deal to a human driving a car with a pine-scented tree dangling from the rearview mirror, it is to a bird hoping to rest or find food on epic seasonal flights across the globe, according to the study published Wednesday in the journal PLOS One.
But forests aren’t just for the birds. They improve the quality of life for fauna and flora, from bears to flowers. Altering forests can change the dynamics of ecosystems and can potentially “affect water chemistry, soil erosion, carbon sequestration patterns, local climate, biodiversity distribution and human quality of life,” a statement announcing the report said.
New England forests provide numerous benefits to the region’s residents, but are undergoing rapid development. We used boosted regression tree analysis (BRT) to assess geographic predictors of forest loss to development between 2001 and 2011. BRT combines classification and regression trees with machine learning to generate non-parametric statistical models that can capture non-linear relationships. Based on National Land Cover Database (NLCD) maps of land cover change, we assessed the importance of the biophysical and social variables selected for full region coverage and minimal collinearity in predicting forest loss to development, specifically: elevation, slope, distance to roads, density of highways, distance to built land, distance to cities, population density, change in population density, relative change in population density, population per housing unit, median income, state, land ownership categories and county classification as recreation or retirement counties. The resulting models explained 6.9% of the variation for 2001–2011, 4.5% for 2001–2006 and 1.8% for 2006–2011, fairly high values given the complexity of factors predicting land development and the high resolution of the spatial datasets (30-m pixels). The two most important variables in the BRT were “population density” and “distance to road”, which together made up 55.5% of the variation for 2001–2011, 49.4% for 2001–2006 and 42.9% for 2006–2011. The lower predictive power for 2006–2011 may reflect reduced development due to the “Great Recession”. From our models, we generated high-resolution probability surfaces, which can provide a key input for simulation models of forest and land cover change.