The increasing availability of very high resolution (VHR) images regularly acquired over urban areas opens new attractive opportunities for monitoring human settlements at the level of individual buildings. This paper presents a novel approach to building change detection in multitemporal VHR synthetic aperture radar (SAR) images. The proposed approach is based on two concepts: 1) the extraction of information on changes associated with increase and decrease of backscattering at the optimal building scale and 2) the exploitation of the expected backscattering properties of buildings to detect either new or fully demolished buildings. Each detected change is associated with a grade of reliability. The approach is validated on the following: 1) COSMO-SkyMed multitemporal spotlight images acquired in 2009 on the city of L'Aquila (Italy) before and after the earthquake that hit the region and 2) TerraSAR-X multitemporal spotlight images acquired on the urban area of the city of Trento (Italy). Results demonstrate that the proposed approach allows an accurate identification of new and demolished buildings while presents a low false-alarm rate and a high reliability.

Knowing the timing and the evolution of the snow melting process is very important, since it allows the prediction of (i) the snowmelt onset, (ii) the snow gliding and wet-snow avalanches, (iii) the release of snow contaminants, and (iv) the runoff onset. The snowmelt can be monitored by jointly measuring snowpack parameters such as the snow water equivalent (SWE) or the amount of free liquid water content (LWC). However, continuous measurements of SWE and LWC are rare and difficult to obtain. On the other hand, active microwave sensors such as the synthetic aperture radar (SAR) mounted on board satellites are highly sensitive to LWC of the snowpack and can provide spatially distributed information with a high resolution. Moreover, with the introduction of Sentinel-1, SAR images are regularly acquired every 6 d over several places in the world. In this paper we analyze the correlation between the multitemporal SAR backscattering and the snowmelt dynamics. We compared Sentinel-1 backscattering with snow properties derived from in situ observations and process-based snow modeling simulations for five alpine test sites in Italy, Germany and Switzerland considering 2 hydrological years. We found that the multitemporal SAR measurements allow the identification of the three melting phases that characterize the melting process, i.e., moistening, ripening and runoff. In particular, we found that the C-band SAR backscattering decreases as soon as the snow starts containing water and that the backscattering increases as soon as SWE starts decreasing, which corresponds to the release of meltwater from the snowpack. We discuss the possible reasons of this increase, which are not directly correlated to the SWE decrease but to the different snow conditions, which change the backscattering mechanisms. Finally, we show a spatially distributed application of the identification of the runoff onset from SAR images for a mountain catchment, i.e., the Zugspitze catchment in Germany. Results allow us to better understand the spatial and temporal evolution of melting dynamics in mountain regions. The presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.

The availability of very high resolution (VHR) synthetic aperture radar (SAR) images, which can be acquired by satellites over the same geographical area with short repetition interval, makes the development of effective unsupervised change detection (CD) techniques very important. This paper proposes a hierarchical approach to CD in VHR SAR images for addressing surveillance applications, where VHR data are acquired with high temporal resolution (e.g., one image every few days). The proposed approach is based on two concepts: exploitation of a multiscale technique for a preliminary detection of areas containing changes in backscattering at different scales (hot spots) and explicit modeling of the semantic meaning of changes by using both the intrinsic SAR image properties (e.g., acquisition geometry and scattering mechanisms) and the available prior information. In order to illustrate the effectiveness of the proposed approach, a problem of freight traffic surveillance is addressed considering two data sets. Each of them is made up of a pair of multitemporal VHR SAR images acquired by the COSMO-SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) constellation in spotlight mode. Each data set defines a complex CD problem due to both the presence of a variety of changes on the ground and the complexity of object backscattering. Experimental results point out the effectiveness of the proposed approach.

Rock glaciers are widespread periglacial landforms in mountain regions like the European Alps. Depending on their ice content, they are characterized by slow downslope displacement due to permafrost creep. These landforms are usually mapped within inventories, but understand their activity is a very difficult task, which is frequently accomplished using geomorphological field evidences, direct measurements, or remote sensing approaches. In this work, a powerful method to analyze the rock glaciers’ activity was developed exploiting the synthetic aperture radar (SAR) satellite data. In detail, the interferometric coherence estimated from Sentinel-1 data was used as key indicator of displacement, developing an unsupervised classification method to distinguish moving (i.e., characterized by detectable displacement) from no-moving (i.e., without detectable displacement) rock glaciers. The original application of interferometric coherence, estimated here using the rock glacier outlines as boundaries instead of regular kernel windows, allows describing the activity of rock glaciers at a regional-scale. The method was developed and tested over a large mountainous area located in the Eastern European Alps (South Tyrol and western part of Trentino, Italy) and takes into account all the factors that may limit the effectiveness of the coherence in describing the rock glaciers’ activity. The activity status of more than 1600 rock glaciers was classified by our method, identifying more than 290 rock glaciers as moving. The method was validated using an independent set of rock glaciers whose activity is well-known, obtaining an accuracy of 88%. Our method is replicable over any large mountainous area where rock glaciers are already mapped and makes it possible to compensate for the drawbacks of time-consuming and subjective analysis based on geomorphological evidences or other SAR approaches.

The kinematic acceleration of rock glaciers observed in recent decades shows that the behavior of these landforms is related to climate change. Velocity variations on yearly to seasonal time scales are frequently investigated, but velocity changes measured on shorter time scales (i.e., on hourly resolutions) are as yet poorly investigated. We used a ground based synthetic aperture radar to investigate, on an hourly time scale, the displacement of a rock glacier located in Val Senales (European Alps, northern Italy). We observed velocity fluctuations occurring at a very regular pace, characterized by phases of sharp acceleration (up to 0.9 mm/hr) lasting 4–11 hr followed by long phases of stagnation lasting 13–20 hr. This study describes an unprecedented observation of an hourly velocity rhythm of an active rock glacier and opens up new perspectives in the analysis and interpretation of rock glacier kinematics.

Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.