Object-based continuous monitoring of land disturbances from dense Landsat time series

Abstract

An accurate mapping of land disturbances with regard to their timing and locations is the prerequisite for the success of downstream disturbance characterization. This study introduces and tests a novel approach that integrates a spatial perspective into the dense Landsat time series analysis, called “Object-Based COntinuous monitoring of Land Disturbance” (OB-COLD). The new algorithm is based on the recognition that the pixels under effects of the same disturbance event often present similar spectral change concurrently in a short time; such pixels experiencing concurrent change could be grouped as an analytic spatial unit, namely “change object”. OB-COLD first generates a change-magnitude snapshot every 60 days by applying per-pixel time series analysis to measure spectral change history. Then, an object-based change analysis is applied for each time-stamped snapshot: two levels of change objects are generated through over-segmentation and region merging; the changing area is determined by examining three object-level properties derived at different scales: the average change magnitude, the pre-change cover type, and the object size. Lastly, OB-COLD reconstructs model coefficients for each temporal segment based on the temporal breaks indicated by new snapshot-based change maps. We tested the new algorithm using 3000 randomly selected reference sample plots and eight Landsat ARD tiles across the continental United States. The accuracy assessment suggests that OB-COLD achieved 76.9% producer's accuracy, which is significantly higher than the per-pixel time series algorithm (i.e., COLD) (16.3 percentage increase) while keeping a comparable user's accuracies (58.7% vs. 57.8%). The quantitative and qualitative evaluations both suggest that OB-COLD could significantly reduce omission errors, particularly for stress disturbances. Most commission errors (73%) are attributed to agricultural practices and climate variability. The proposed algorithm is computationally scalable to large-scale spatiotemporal datasets with advanced cyberinfrastructure resources, holding great potential as the base detection algorithm for the next generation of land disturbance products.