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Restoration based forest management has increased significantly over the last decade across North America. Retention harvesting shows promise for restoring and maintaining forest structural and compositional diversity and also increasing resilience and adaptive capacity. This includes deliberate retention of large living trees, snags and downed woody debris (DWD). However, lack of consistent monitoring limits our understanding of the effectiveness of these strategies and our ability to adapt management accordingly. We investigate the use of readily available Landsat sensor data to remotely estimate and map DWD and basal area (BA) following retention harvesting in northeastern Minnesota, USA. We used multi-temporal winter Landsat throughout a single season to calibrate models for DWD (R2: 0.54, RMSE = 19.02 mha), total BA (R2: 0.55, RMSE = 1.85 mha), hardwood BA (R2: 0.67 RMSE =1.23 mha), and conifer BA (R2: 0.52 mha, RMSE = 0.94 mha). This novel approach uses winter imagery with varying snow accumulation to estimate and map residual forest structures. In addition to practical treatment monitoring, this research provides a valuable tracking tool from which we may deepen our long-term understanding of wildlife responses to DWD, fire and carbon dynamics, and forest nutrient cycling.

Quantifying residual forest structures following retention harvesting in northeast Minnesota using Landsat sensor data