Mapping small-sized logging disturbances in tropical forests using Sentinel-1 time series and an extensive ground truth dataset

Audrey Mercier^1,2,3*Julie Betbeder³Frédéric Mortier^3,4Nicolas Barbier²Pierre Ploton^2,5Guillaume Cornu³Pierre Couteron²

• ¹Aalto University, Otakaari, Finland
• ²Botanique et Modelisation de l'Architecture des Plantes et des Vegetations, Montpellier, France
• ³CIRAD Unite de recherche Forets et Societes, Montpellier, France
• ⁴Georgetown University, Washington, United States
• ⁵Universite de Yaounde I, Yaoundé, Cameroon

Deforestation and forest degradation are the main threats to biodiversity and carbon stocks in tropical forests. Advances in optical and SAR satellite sensors have enabled the development of real-time monitoring of deforestation on a global scale. SAR is particularly appealing in tropical areas due to its insensitivity to cloud cover. However, the automatic detection of small disturbed areas (such as individual tree felling gaps) remains a major challenge. Thanks to a unique dataset consisting of 23,759 locations of individual tree felling gaps and multi-date drone lidar acquisitions, we evaluated the potential of Sentinel-1 dense time series for monitoring small-sized forest disturbances substantially smaller than 0.1 ha on both FSC-certified and artisanal logging sites in the Congo Basin. We designed a new method for forest monitoring using the fused-lasso technique optimized to detect abrupt changes of at least 0.02 ha in Sentinel-1 time series using the fused-lasso technique (Fused-Lasso Change Detection, FLCD). We assessed our new method along with the Cumulative Sum (CuSum) that also proved promising for detecting small impacts, referring for the first time to precise disturbance dates over large areas. Both approaches reached similar rates of confirmed felling gaps that were similarly increasing with gap size, and similar rates of unconfirmed detected gaps. The FLCD method estimates the dates of tree felling more accurately in FSC-certified areas (-2 days difference for FLCD and -19 for CuSum on average). The effective resolution of the S-1 images limits detection for the smallest gaps, yet the approach can help detect and monitor degradation fronts. Fused lasso regression is relevant for modeling the temporal trajectories of the radar signal, which will allow taking advantage of both the increasing availability of UAV-borne data and the lengthening of the S-1 image series.