Abstract
Since 2014, Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data have become an important source in the field of displacement detection thanks to regular acquisitions and 7.5 years of temporal coverage at global level. Despite the increasing number of publications on the role of S1 in landslide detection, there is still a need for research to further clarify the capabilities of the sensor and the applicable image analysis techniques. Previous studies have successfully exploited high-resolution ALOS-PALSAR image-based intensity and coherence analysis at the 2018 Hokkaido landslides. Nevertheless, they expressed a clear need to analyse the capabilities of other sensors (such as S1). This raises the question: Do we need SAR imagery with higher spatial resolution (such as ALOS-PALSAR) or are freely available S1 imagery also suitable for rapid landslide detection? The S1 images could provide suitable material for a comparative analysis and could answer the aforementioned question. Therefore, 17 ascending and 19 descending S1 images were analysed to test S1 accuracy on landslide detection. Multitemporal analyses of both intensity and coherence were performed along with coherence differences, multitemporal features (MTF) and MTF differences of coherence images. In addition, the spatial analysis of the classification results was also evaluated to highlight the potential of S1 coherence analysis. S1 was found to have limitations at the site, as single coherence differences provided low-quality results. However, the results were significantly improved by calculating the MTF on coherence and almost reached the success rate of the ALOS-PALSAR-based coherence analysis, even though the improvement of the results with intensity was not possible. Half of the false positives were identified in the 30–45-m buffer zone of the agreement, underlining that the spatial resolution of the S1 is not appropriate for accurate landslide detection. Only an approximation of the landslide-affected area can be given with considerable overestimation. Due to the inclusion of post-event images, the sensor is not perfectly applicable for rapid detection purposes here.
Highlights
Introduction distributed under the terms andDue to intense population growth, urbanisation, economic development and climate change, landslides have become the most hazardous surface processes, causing economic and human losses worldwide [1–6]
Continuous changes in intensity values were observed over the whole site in both Continuous changes in intensity values were observed over the whole site in both polarisations and geometries (Figure 8)
According to the review of the literature and our experiences related to this specific topic, we summarize the most relevant aspects of Sentinel-1 and ALOS-PALSAR satellites (Table 4) regarding landslide detection purposes
Summary
Introduction distributed under the terms andDue to intense population growth, urbanisation, economic development and climate change, landslides have become the most hazardous surface processes, causing economic and human losses worldwide [1–6]. Most common ground-based techniques, such as field geodesy, including total station or levelling-based monitoring networks [11,12], inclinometer and extensometer surveys [13], GPS (Global Positioning System) surveys [14], geomorphological mapping [15] and TLS (Terrestrial Laser Scanning) [16] and GB-InSAR (Ground-based Synthetic Aperture Radar Interferometry) [17], require more effort for onsite measurements [18,19] and provide high-resolution, detailed data, but only for a limited area [20–22]. Remote sensing techniques, such as orthophoto interpretation, photogrammetry, LIDAR [23], InSAR (Synthetic Aperture Radar Interferometry), DInSAR
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