Ground-Truthing Forest Change Detection Algorithms in Working Forests of the US Northeast

Abstract The need for reliable landscape-scale monitoring of forest disturbance has grown with increased policy and regulatory attention to promoting the climate benefits of forests. Change detection algorithms based on satellite imagery can address this need but are largely untested for the forest types and disturbance regimes of the US Northeast, including management practices common in northern hardwoods and mixed hardwood-conifer forests. This study ground-truthed the “off-the-shelf” outputs of three satellite-based change detection algorithms using detailed harvest records and maps covering 43,000 ha of working forests in northeastern New York.