An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites

- We present an approach to produce annual proxy image composites with Landsat series.
- Composites cover large areas and are free clouds and shadows with no data gaps.
- Change metrics are generated and integrated with large area annual composites.
- The protocol supports regional and national level mapping of land cover and changes.

Mapping and monitoring land cover and land cover change remains a top priority for land managers. Uniquely, remote sensing offers the capacity to acquire information in a systematic (spatially, temporally, and categorically) and synoptic fashion that is appealing from monitoring and reporting perspectives. The opening of the Landsat archive and new processing and analysis opportunities enable the characterization of large areas and the generation of dynamic, transparent, systematic, repeatable, and spatially exhaustive information products. Best Available Pixel (BAP) approaches enable the production of periodic image composites free of haze, clouds, or shadows over large areas. In this paper we demonstrate an integrated protocol to produce spatially exhaustive annual BAP image composites that are seasonally constrained and free of atmospheric perturbations. These annual BAP composites for the years inclusive of 1998 to 2010 provide for generation of a suite of change metrics for the period 2000 to 2010 using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM +) data. The study area is the > 375,000 km2 forested area of Saskatchewan, Canada. We evaluate the robustness of the protocol by comparing in-filled (or proxy) values with a true reference set for surface reflectance. An initial change detection pass is used to aid in the allocation of proxy values (for missing and anomalous values) and to allocate change events to the correct year, with a second pass to characterize key change points and related time series trends (e.g., change year, post-change slopes, among others). In so doing, a multi-temporal data cube (via a series of annual proxy composites) and a set of change metrics are generated. Approximately 35% of the pixels in our study area required proxy values, either as a result of missing data or our noise detection approach. Overall, our results indicate strong agreement between the assigned proxy values and the reference data (R = 0.71-0.91, RMSE = 0.008-0.025). Agreement was stronger for pixel series with no change events (R = 0.73-0.92, RMSE = 0.007-0.024), relative to pixel series with change events (R = 0.63-0.87, RMSE = 0.010-0.029). The generated change metrics, derived via temporal and spatial analysis of the annual BAP composites, were an important precursor to the generation of valid proxy values, and - importantly - provide valuable information for the further assessment and understanding of land cover and land cover change. Our results indicate that the demonstrated protocol provides a reliable approach to generate proxy image composites containing no data gaps, along with a suite of informative change metrics that provide a comprehensive characterization of land cover changes (including disturbance and recovery) allowing for an improved understanding of landscape dynamics. The protocol is efficient and may be applied over large areas to support regional and national mapping and monitoring activities.