Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

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.

GEDI Elevation Accuracy Assessment: A Case Study of Southwest Spain

“…Several SAR satellites can be used for forest monitoring [30], such as SENTINEL 1 [31], ALOS PALSAR [32], ICESat GLAS [11], ENVISAT ASAR and RADARSAT [33] and one of the most used, TanDEM-X [34]. Since September 2010, a second satellite TanDEM-X (TDX) was added to TerraSAR-X (TSX) for the acquisition of a global digital elevation model (DEM) from bistatic X-band interferometric SAR acquisitions.…”

Long-Time Interval Satellite Image Analysis on Forest-Cover Changes and Disturbances around Protected Area, Zeya State Nature Reserve, in the Russian Far East

“…Other satellite sensors, such as, the Sentinel-1 and Sentinel-2 data, could help us generate images with high temporal resolution for differentiating small agricultural areas and grassland [52]. The synthetic aperture radar (SAR) have been widely used to detect clearcutting in the tropical forest [53], however, our study did not involve such a method. We will also apply SAR-based change detection approach to monitor clearcutting evidences in our study area in the future.…”

Improving Forest Baseline Maps in Tropical Wetlands Using GEDI-Based Forest Height Information and Sentinel-1

“…Other global, wall-to-wall forest height products exist [21][22][23], although these have a much coarser spatial resolution (500 m-1 km) and use input LiDAR data from before 2010. Combining a forest height product with radar data could improve the forest mapping accuracy and remove Landsat artifacts and cloud gaps from the resulting map [24]. As described by Joshi et al [24], most of the shortcomings of optical data-from which the GEDI forest height product is partly derived [19]-and radar data are complementary and, therefore, show great combination potential.…”

“…Combining a forest height product with radar data could improve the forest mapping accuracy and remove Landsat artifacts and cloud gaps from the resulting map [24]. As described by Joshi et al [24], most of the shortcomings of optical data-from which the GEDI forest height product is partly derived [19]-and radar data are complementary and, therefore, show great combination potential. Sentinel-1 C-band radar data exhibit great potential with high spatial and temporal resolution and free access at a global scale [25].…”