In 2017, the Georgia Department of Transportation began a new tree removal program, clear-cutting trees on all highway agency property across the state (Hurt, 2018). Far from an isolated case, large-scale tree removal is somewhat common along roadsides across the country. Since 2015, for example, public outcry against tree clearing along highways has been documented in parts of California, Connecticut, Florida, Maine, Pennsylvania, and South Carolina. These actions are often classified as maintenance projects, which typically have no requirements for environmental review or public input. As justification for roadside tree clearing, DOTs often cite safety and the potential for run-off-the-road crashes with trees. AASHTO encourages a roadside kept free of fixed objects such as trees, called the clear zone, in its Roadside Design Guide, though the dimension is not meant to be uniform (AASHTO, 2012). While this idea of a clear zone has been maintained as a rule for over 50 years, its relationship to road safety is still disputed (Wolf & Bratton, 2006; NCHRP, 2000).
Many states do not track vegetation management practices along the roadside, and thus the impacts of highway agency tree removal are unknown (FHWA & Volpe, 2010). Without knowledge of the spatial extent of clearing, highway agencies cannot assess the environmental trade-offs of their actions. In this study, I address the research question:
What are the impacts of large-scale roadside tree removal by highway agencies in states that have increased this practice in recent years?
Using the USDA Farm Service Agency’s National Agriculture Imagery Program (NAIP) one-meter imagery and machine learning image analysis in ArcGIS, I analyze the before and after tree canopy cover along roadsides in Georgia. High-resolution satellite imagery is necessary for capturing change in the narrow dimensions of roadsides. NAIP data captures leaf-on vegetation conditions every two to three years. After classifying the images, I reclassify into a binary tree canopy raster and compare the before/after target years, with a resulting tree canopy change raster. My goal is twofold: 1) to quantify the acreage of cleared land and 2) to produce a map depicting the spatial distribution of cleared roadsides.
Results are intended for both applied and theoretical contexts. Understanding the spatial distribution of tree removal is the first step necessary to assess impacts and evaluate trade-offs. Impacts may include removal of sequestered carbon, soil loss, increased erosion, increased sedimentation to nearby streams, habitat loss, and degradation of the visual environment. Findings can be utilized by both state highway agencies and the Federal Highway Administration.
For theory, results can illustrate how application of a single engineering practice, devolved from federal guidance and applied as a uniform rule instead of a contextual guideline, can result in drastic changes to the roadside landscape.