Analyses of Time Series InSAR Signatures for Land Cover Classification: Case Studies over Dense Forestry Areas with L-Band SAR Images

Reducing model uncertainty of climate change impacts on high latitude carbon assimilation

The Arctic-Boreal Region (ABR) has a large impact on global vegetation-atmosphere interactions and is experiencing markedly greater warming than the rest of the planet, a trend that is projected to continue with anticipated future emissions of CO₂ . The ABR is a significant source of uncertainty in estimates of carbon uptake in terrestrial biosphere models such that reducing this uncertainty is critical for more accurately estimating global carbon cycling and understanding the response of the region to global change. Process representation and parameterization associated with gross primary productivity (GPP) drives a large amount of this model uncertainty, particularly within the next 50 years, where the response of existing vegetation to climate change will dominate estimates of GPP for the region. Here we review our current understanding and model representation of GPP in northern latitudes, focusing on vegetation composition, phenology, and physiology, and consider how climate change alters these three components. We highlight challenges in the ABR for predicting GPP, but also focus on the unique opportunities for advancing knowledge and model representation, particularly through the combination of remote sensing and traditional boots-on-the-ground science.

Differential SAR Interferometry Using Sentinel-1 Imagery-Limitations in Monitoring Fast Moving Landslides: The Case Study of Cyprus

Cyprus, being located on the Mediterranean fault zone, exhibits a unique geodynamic regime since its tectonic evolution is driven by the interaction of the Eurasian and African plate. Besides its seismological interest, many active landslides and slope instabilities in areas of steep topography occur in Cyprus, having substantial impact on the built environment, by posing an imminent threat for entire settlements and critical infrastructure. Moreover, extreme meteorological events occur rarely, like severe rainfall and thunderstorms, that combined with the geological properties in some areas and the seismically stressed ground, can lead to landslides, causing severe damages to critical infrastructure. In the present study, the DInSAR methodology is applied for the detection of two individual landslide events that were triggered by heavy rainfall in Limassol and Paphos Districts in February 2019. Six co-event interferometric Synthetic Aperture Radar (SAR) pairs were used to produce displacement maps in vertical and east-west directions to study the resulting slope deformations. The above are carried out using Sentinel-1 imagery that are freely provided under the Copernicus umbrella. The limitations that arise from the speed and complexity of the deformations under study and the adverse residing meteorological conditions that caused these phenomena are investigated, as found in literature. Indeed, the sparse vegetation at the slopes affected by the landslides, the residing meteorological conditions, the heavy rainfall that triggered the two landslides, and the temporal phase aliasing effect due to the speed of the ground deformation were found to be the main limitations for the application of DInSAR methodology, resulting in the underestimation of the ground deformation that occurred.

Small Scale Landslide Detection Using Sentinel-1 Interferometric SAR Coherence

Infrastructure is operational under normal circumstances and is designed to cope with common natural disruptions such as rainfall and snow. Natural hazards can lead to severe problems at the areas where such phenomena occur, but also at neighboring regions as they can make parts of a road network virtually impassable. Landslides are one of the most devastating natural hazards worldwide, triggered by various factors that can be monitored via ground-based and/or satellite-based techniques. Cyprus is in an area of high susceptibility to such phenomena. Currently, extensive field campaigns including geotechnical drilling investigations and geophysical excavations are conducted to monitor land movements, and, at the same time, determine the geological suitability of areas. Active satellite remote sensors, namely Synthetic Aperture Radar (SAR), have been widely used for detecting and monitoring landslides and other ground deformation phenomena using Earth Observation based techniques. This paper aims to demonstrate how the use of Copernicus open-access and freely distributed datasets along with the exploitation of the open-source processing software SNAP (Sentinel’s Application Platform), provided by the European Space Agency, can be used for landslide detection, as in the case study near Pissouri, where a landslide was triggered by heavy rainfall on 15 February 2019, which caused a major disturbance to everyday commuters since the motorway connecting the cities of Limassol and Paphos remained closed for more than a month. The Coherent Change Detection (CCD) methodology was applied successfully by detecting the phenomenon under study accurately, using two indicators (the coherence difference and the normalized coherence difference). Receiver Operating Characteristic (ROC) analysis was carried out to measure their performance with the coherence difference having an overall accuracy of 93% and the normalized coherence difference having an overall accuracy of 94.8% for detecting the landslide and non-landslide areas. The probability of landslide detection was 63.2% in the case of the coherence difference and increased to 73.7% for the normalized coherence difference, whereas the probability of false alarm for both indicators was approximately 1%.

Performance Analysis of Open Source Time Series InSAR Methods for Deformation Monitoring over a Broader Mining Region

Time Series Interferometric Synthetic Aperture Radar (TSInSAR) methods have been widely and successfully applied for spatiotemporal ground deformation monitoring. The main groups of methodological approaches are often referred to as Persistent Scatterer (PS), Small Baseline (SB), and hybrid approaches that incorporate PS and SB concepts. While TSInSAR techniques have long been able to provide accurate deformation rates for various applications, their corresponding performance in complex environments such as mining areas has to be investigated. This study focuses on comparing the performance of three open source TSInSAR toolboxes (Stamps, Giant, Mintpy) over an extended region that includes an active opencast coal mine. We present the deformation results of each TSInSAR method on a Sentinel-1 dataset of 125 acquisitions spanning around 2.5 years over the Ptolemaida-Florina coal mine site that is characterized by several environmental and surface deformation conditions. First, a cross-comparison analysis is presented over different land cover classes. The study shows that all TSInSAR methods are capable for generating similar ground deformation results when the area has stable ground scattering conditions and the dataset sufficient temporal sampling. The most controversial results between TSInSAR approaches were found in land cover classes that include medium to high vegetation. An external comparative analysis between the different results from TSInSAR methods and leveling measurements is also performed. Stamps approach presented the best agreement with the in-situ deformation rates. The Giant approach yielded the best cumulative deformation results due to our a priori knowledge of temporal behavior of deformation in the vicinity of the leveling locations. Finally, we discuss the main pros and cons of each TSInSAR approach and we highlight the importance of comparison analysis that can provide insights and can lead to better interpretation of the results.