Consolidated Sentinel-1 SAR-based index provides robust annual forest loss assessment in tropical forests with semi-permanent cloud cover

Tropical moist forests are one of the richest ecosystems on earth and provide multiple ecosystem services, but are also supporting many human activities and thus undergo continuous threats from logging, fire, shifting cultivation, road development or urban expansion. From this perspective, continuous monitoring and mapping those ecosystems keeps emphasize the importance of their preservation for the 21^st century.

Satellite remote sensing has proven to be essential to assess the deforestation in wide and remote areas at regional scale. Nevertheless, annual-based assessments using optical images tend to show inconsistency in tropical regions where the semi-persistent cloud coverage prevents steady periodic cloud-free acquisitions. The 8-year-old+ C-band SAR archive from Sentinel-1 provides now robust material to produce consistent yearly-based forest loss assessment.

Previous studies showed that a sudden decrease of the backscattered signal in an intact tropical forest canopy indicates a forest loss. However, SAR-based forest loss detection is sensitive to commission errors due to the speckle, showing their limitations as the thresholding of significant land cover change may include stable targets. Moreover, recent near-real-time detection systems focus on mapping forest loss alerts from a temporal early-warning perspective but are less reliable in providing accurate quantification of the degraded surfaces.

In this study, we introduce an annual index called MinB-Q10, computed pixel-based with a statistical probabilistic approach that combines VV and VH features. The result is represented as a chi-squared distribution based on Euclidean distance. The index is designed to highlight statistical deviation from stable forest distribution. The study has been developed in Democratic Republic of the Congo. It was calibrated in study area surrounding Yangambi (2.000 km²) and validated in a disconnected study area located around Kisangani (12.000 km²). The probabilistic approach and statistical considerations are developed to design an early-warning system as a second step, where the annual index would consolidate spatial assessment.

The preliminary results indicate a marked reduction of the commission errors compared with standard thresholding methods. Object-based accuracy assessment from optical independent imagery (in progress) enables to identify and distinguish the proportion of lost surface from the geometric accuracy of the detections. Moreover, combining pixel-counting with statistical estimates of the false detection rate generates unbiased prediction of the regional forest loss.