Monitoring forest regrowth using SAR images: The Cusum approach

Small-scale shifting cultivation is the main cause of disturbance in African tropical forests. As a consequence, being able to monitor and precisely quantify deforestation and secondary forest regrowth remains a challenge compared to large scale deforestation processes observed in South American and South-East Asian forests. Remote sensing data has been widely used to identify spatio-temporal variability in forest regrowth. However current approaches primarily rely on optical imagery, which is known to be subject to multiple limitations (e.g.cloud cover) in tropical area . The Synthetic Aperture Radar (SAR) is a promising way for overcoming these limitations. In this study, we developed an approach based on SAR signal (Sentinel-1 and PALSAR-2) to monitor forest regrowth. Our approach is based on a recent change detection technique relying on the cumulated sum of the signal anomalies (CuSum algorithm) that has been developed for detecting deforestation. Here, we show that this method is also able to monitor, not only forest regrowth, but also various land use dynamics and land use changes. Our approach was tested on a small area, east of Kisangani in the Democratic Republic of the Congo. We quantified the number of changes that could be attributed to increased vegetation, for which we compared plots occupied by different vegetations and transition types: 'Agroforestry', 'Cropland' and ' Forest Regrowth'. We showed that each vegetation type can be defined by very specific signal change. These preliminary results suggest that the CuSum method applied on SAR data is promising for monitoring land-use dynamics at a small spatial scale, and specifically for identifying secondary forest regrowth.