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.

Over the past decades, the great technological advances made in airborne and space sensors have led to a significant improvement in remote sensing methods and techniques used for studying worldwide natural ecosystem disturbances. More commonly, optical sensors are chosen for detecting landscapes transformation, however, in addition to the problematics associated to this technology due the requirement of certain technical and environmental conditions (sunlight, no cloud-coverage), these systems may offer a significant lower performance when studying the post-disturbance evolution. In response to this challenge, this research aims to highlight the capabilities of Synthetic Aperture Radar - SAR satellite sensors for investigating the environmental evolution of forest areas affected by Mediterranean fire events. The use of a multitemporal analysis of ESA-Sentinel 1 SAR and Landsat 7 & Sentinel 2 optical satellite image series allowed us to explore the post-fire natural evolution of the areas affected by the Doñana national park forest fire occurred in June-July 2017 in two different ways: 1. Monitoring radar image intensity changes to evaluate environmental disturbances within the affected areas; 2. Comparing the response of both optical and radar satellite systems to the extraction of long-term environmental information. The results obtained from both the backscatter signal analysis and the systems comparison illustrate the SAR technology's efficiency in detecting and studying temporal changes in the natural conditions of the areas affected by fire events.

This study addresses the escalating challenges posed by forest drought and wildfires, emphasizing the critical need to monitor forest conditions to mitigate associated risks. While traditional optical sensors have proven valuable for vegetation surface water (VSW) assessment, their limitations in regions with persistent cloud cover prompt an exploration of the alternatives. The study advocates the efficacy of Synthetic Aperture Radar (SAR) systems, known for their cloud-penetrating capabilities and sensitivity to changes in dielectric properties. Leveraging Sentinel-1 C-band dual polarization SAR data, the research investigates the impact of Vegetation Surface Water (VSW) on backscatter coefficients in a temperate coniferous forest through the application of generalized linear models. Despite the challenges posed by precipitation and canopy characteristics, the study unveils detectable modulation in backscatter, particularly in VH polarization, indicating the potential of SAR-based methods in forest monitoring. The occurrence of rain on the day of Sentinel-1 image acquisition, and therefore the presence of VSW, triggers an increase of 0.35 dB in VV backscatter, and an increase of 0.45 dB in VH backscatter. The findings underscore the importance of considering surface water content in radar backscatter analyses for accurate biomass estimations and change detection, suggesting avenues for future research and potential correction mechanisms.

Wetlands are among the most productive natural ecosystems in the world, generally being important biodiversity hotspots. However, the complex nature of these landscapes together with the fragile and dynamic relationships among the organisms inhabiting these regions, make wetland ecosystems especially vulnerable to environmental disturbance, such as climate change. Thus, developing new automated systems which allow the continuous monitoring and mapping of wetland dynamics is crucial for informing decision-making and preserving their natural health. Synthetic Aperture Radar (SAR) sensors deployed on satellite platforms such as SENTINEL-1 are increasingly recognized as essential for wetland monitoring. The high sensitivity of SAR sensors to environmental variation makes them particularly suitable for investigating the hydrological processes occurring within these ecosystems.The main objective of this paper is to propose a rapid polarimetric SAR (PolSAR) change detection tool for monitoring and mapping the flood dynamics and environmental condition of the North Rupununi region, Guyana. By making use of dense Sentinel-1 timeseries data and the Google Earth Engine (GEE) platform, we were able to map temporal open water and temporal flooded vegetation areas in a continuous and near-real time basis. The outcomes derived from this study significantly contributed to identify the hydrological mechanisms of the region of study while providing essential and valuable information for rapid response and environmental impact assessment.