Article | Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

Article

Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

Details

Citation

Ruiz-Ramos J, Marino A, Boardman C & Suarez J (2020) Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries. Remote Sensing, 12 (18), Art. No.: 3061. https://doi.org/10.3390/rs12183061

Abstract
Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum–spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user’s accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user’s accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4 ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories.

Keywords
Sentinel-1; SAR; change detection; deforestation; forest degradation; forest mapping

Journal
Remote Sensing: Volume 12, Issue 18