Continuous monitoring and sub-annual change detection in high-latitude forests using Harmonized Landsat Sentinel-2 data

Spatially and temporally precise monitoring of forest resources is becoming increasingly vital in rapidly changing environmental and economic conditions. However, recent developments in moderate resolution satellite imagery have led to spectrally consistent and temporally dense datasets capable of capturing both seasonal variation and long-term trajectories. Previous studies have demonstrated the ability of continuous monitoring algorithms to detect transitional changes such as timber harvesting at sub-annual scales. However, less is known about the ability to use these algorithms in areas such as high-latitude forests which have large data gaps over winter due to prolonged snow cover. This study investigated the use of a time series of 7-day Harmonized Landsat Sentinel-2 (HLS) composites along with the Bayesian Estimator of Abrupt Change, Seasonality, and Trend (BEAST) algo-rithm for change detection and monitoring in two managed forest areas in Canada. On average, both sites had no data periods (over winter) lasting similar to 200 days. Despite this, BEAST successfully detected change, with overall accuracies of 88.7% and 97.3% for both sites when compared to an independent validation dataset derived from visual interpretation of PlanetScope images. A simulation of change detection in near-real time revealed that approximately 75% of changes were detected within 7 and 11 valid images for each site. Furthermore, moni-toring the trend slope of the spectral index showed that areas with a new declining slope were typically about twice as likely to occur in photointerpreted polygons of non-stand replacing disturbance, indicating that such a technique could be used as an early warning signal of fine-scale changes such as those due to insect defoliation or drought. Overall, this study demonstrated the efficacy of a continuous change detection and monitoring framework, which could be used to provide insights at previously unavailable temporal and spatial scales, thereby allowing forest managers the ability to quickly react to changes in uncertain future conditions.