This was presented by Madeleine Desrochers as a part of a series of contributed talks from the 2022 FEMC Annual Conference. To learn more about the conference, visit: https://www.uvm.edu/femc/cooperative/conference/2022. Remote monitoring tools are a powerful way to track changes to forests at a landscape scale, and the interest and need for these tools are increasing rapidly. A growing number of policies and regulations aimed at addressing climate change are putting more pressure on forest landowners to conserve their land and increase the storage of carbon in their forests. Forest carbon markets, along with other long-standing sustainable forestry certification programs require extensive monitoring and verification. Additionally, as consequence to our changing climate, forest disturbance regimes are expected to shift - with more severe weather events and escalating invasive insect outbreaks. Remote monitoring tools can be used to address these challenges and to provide actionable information on how and where disturbance is taking place - both to understand its effects on forest ecosystem structure, functions, and services and to inform stewardship actions in response. However, while there are many disturbance detection algorithms available, they are largely untested for the disturbance regimes, forest types, and management practices of the northern forest region. Our recent study validated the outputs of three common satellite-based disturbance detection algorithms using detailed harvest records from 43,000 ha of working forest land in northeastern New York. The tested algorithms performed best in detecting clearcuts, but performed much worse and poorly overall in detecting the partial harvest prescriptions (e.g., shelterwoods, thinnings) that are far more common in the northern forest region. Of the three algorithms tested, the Landtrendr algorithm consistently outperformed the others at detecting partial harvests and estimating harvest intensity, but there is still substantial room for improvement. Overall, we suggest that disturbance detection algorithms need further training and tuning to be used for accurate monitoring of harvest-related activities in working forests of the US Northeast.