Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2
Reiche, Johannes; Hamunyela, Eliakim; Verbesselt, Jan; Hoekman, Dirk; Herold, Martin
Combining observations from multiple optical and synthetic aperture radar (SAR) satellites can provide temporally dense and regular information at medium resolution scale, independently of weather, season, and location. This has the potential to improve near real-time deforestation monitoring in dry tropical regions, where traditional optical only monitoring systems typically suffer from limited data availability due to persistent cloud cover. In this context, the recently launched Sentinel-1 satellites promise unprecedented potential, because for the first time dense and regular SAR observations are free and openly available. We demonstrate multi-sensor near real-time deforestation detection in tropical dry forests, through the combination of Sentinel-1 C-band SAR time series with ALSO-2 PALSAR-2 L-band SAR, and Landsat-7/ETM+ and 8/OLI. We used spatial normalisation to reduce the dry forest seasonality in the optical and SAR time series, and combined them within a probabilistic approach to detect deforestation in near real-time. Our results for a dry tropical forest site in Bolivia, showed that, as a result of high observation availability of Sentinel-1, deforestation events were detected more timely with Sentinel-1 than compared to Landsat and ALOS-2 PALSAR-2. The spatial and temporal accuracies of the multi-sensor approach were higher than the single-sensor results. We improved the precision of the reference data derived from the multi-sensor satellite time series, which enabled a more robust estimation of the temporal accuracy. We quantified how the near real-time deforestation detection is associated with a trade-off between the confidence in detection and the temporal accuracy. We showed that the trade-off affects the choice on how to use the near-real time data for different applications such as fast alerting with high temporal accuracy but lower confidence versus accurate detection at lower temporal detail. When aiming for a high confidence in change area estimates for example, deforestation was detected with a user's accuracy of 88%, a producer's accuracy of 89% (low area bias), and a mean time lag of 31 days using all sensors. This is on average 7 days earlier than when using only Sentinel-1 observations, and six weeks earlier than when relying only on Landsat observations. We showed that confident near real-time deforestation alerts can be provided with a mean time lag of 22 days, but these are associated with a higher commission error. With more dense time series data expected from the Sentinel-1 and -2 sensors for the upcoming decade, spatial and temporal detection accuracy of multi-sensor deforestation monitoring in the tropics will improve further.