UC–Change: a classification-based time series change detection technique for improved forest disturbance mapping using multi-sensor imagery

• Unsupervised Classification to Change (UC–Change) is a new change detection approach. • UC–Change can use data with varying spectral resolution and no atmospheric correction. • Outperformed other common methods in an area of diverse forested terrain. • 85.2 % of 1974–2018 cutblock pixels detected in a 100 km × 100 km area in British Columbia. • Provides powerful new options for improved multi-sensor time-series image analysis. Unsupervised Classification to Change (UC–Change) is a versatile technique that detects forest disturbances in satellite images by analyzing changes in the spatial distribution of spectral classes over time. This approach can fully utilize the spectral resolution of individual sensors without requiring atmospheric correction or radiometric normalization. Resulting multisensor capabilities set UC–Change apart from established time-series change detection methods, such as Continuous Change Detection and Classification (CCDC), LandTrendr, Composite2Change (C2C), and Global Forest Change (GFC). With the growing number of Earth observation satellites, the ability to utilize diverse datasets is increasingly important for extracting information relevant to sustainable natural resource management. The algorithm’s effectiveness is demonstrated using a dataset containing 275 Landsat and Sentinel–2 images acquired over a forested area in British Columbia, Canada, from 1972 to 2020. The 100 km × 100 km study site has been actively harvested in recent decades and experienced many wildfires and a mountain pine beetle (MPB) outbreak. The spatio-temporal accuracy of clearcut and fire-scar detection was assessed using the Vegetation Resources Inventory (VRI) and National Burned Area Composite (NBAC) products, respectively, and compared against the C2C 1985 – 2020, CCDC 2002 – 2019, and GFC 2001 – 2022 maps available online. Overall, the UC–Change algorithm detected 85.2 % of the reference VRI 1974 – 2018 cutblock pixels at a temporal resolution of ± 1 year (90.3 % at ± 3 years). It detected 86.0 % of 1985 – 2018 VRI pixels, outperforming C2C (58.8 %). For the period 2002 – 2018, UC–Change mapped 87.1 % of the reference cutblock pixels, exceeding C2C (54.5 %), CCDC (74.0 %), and GFC (70.4 %). UC–Change, C2C, CCDC, and GFC detected 71.0 %, 54.6 %, 37.4 %, and 67.2 % of 2006 – 2018 reference forest fire pixels, respectively. UC–Change provided improved forest harvest and fire-scar detection in areas heavily affected by the MPB outbreak and forests characterized by low canopy cover. It represents a new, fundamentally different approach to time-series analysis, suitable for independent use or in concert with existing methods.