The mean shift algorithm has been shown to perform well in optical image segmentation. However, the conventional mean shift algorithm performs poorly if it is directly used with Synthetic Aperture Radar (SAR) images due to the large dynamic range and strong speckle noise. Recently, the Generalized Mean Shift (GMS) algorithm with an adaptive variable asymmetric bandwidth has been proposed for Polarimetric SAR (PolSAR) image filtering. In this paper, the GMS algorithm is further developed for PolSAR image segmentation. A new merging predicate that is defined in the joint spatial-range domain is derived based on the GMS algorithm. A pre-sorting strategy and a post-processing step are also introduced into the GMS segmentation algorithm. The proposed algorithm can be directly used for PolSAR image superpixel segmentation without any pre-processing steps. Experiments using Airborne SAR (AirSAR) and Experimental SAR (ESAR) L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm. The parameter settings, stability, quality, and efficiency of the GMS algorithm are also discussed at the end of this paper.

The Karakoram has had an overall slight positive glacier mass balance since the end of 20th century, which is anomalous given that most other regions in High Mountain Asia have had negative changes. A large number of advancing, retreating, and surging glaciers are heterogeneously mixed in the Karakoram increasing the difficulties and inaccuracies to identify glacier surges. We found two adjacent glaciers in the eastern Karakoram behaving differently from 1995 to 2019: one was surging and the other was advancing. In order to figure out the differences existing between them and the potential controls on surges in this region, we collected satellite images from Landsat series, ASTER, and Google Earth, along with two sets of digital elevation model. Utilizing visual interpretation, feature tracking of optical images, and differencing between digital elevation models, three major differences were observed: (1) the evolution profiles of the terminus positions occupied different change patterns; (2) the surging glacier experienced a dramatic fluctuation in the surface velocities during and after the event, while the advancing glacier flowed in a stable mode; and (3) surface elevation of the surging glacier decreased in the reservoir and increased in the receiving zone. However, the advancing glacier only had an obvious elevation increase over its terminus part. These differences can be regarded as standards for surge identification in mountain ranges. After combining the differences with regional meteorological conditions, we suggested that changes of thermal and hydrological conditions could play a role in the surge occurrence, in addition, geomorphological characteristics and increasing warming climate might also be part of it. This research strongly contributes to the literatures of glacial motion and glacier mass change in the eastern Karakoram through remote sensing.

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.

Land surface deformation in metropolitan areas, which can cause varying degrees of hazard to both human lives and to properties, has been documented for decades in cities worldwide. Xuzhou, is one of the most important energy and industrial bases in eastern China, and has experienced significant land subsidence due to both excessive extraction of karst underground water and exploitation of mineral resources in recent decades. Furthermore, Xuzhou has recently undergone rapid urbanization in terms of urban expansion and underground construction, which could induce additional pressure on the urban land surface. However, most previous research on land surface deformation in the Xuzhou urban areas has been conducted based on traditional ground-based deformation monitoring techniques with sparse measurements. Little is known about the regional spatiotemporal behavior of land surface displacement in Xuzhou. In this study, a detailed interferometric synthetic aperture radar (InSAR) time series analysis was performed to characterize the spatial pattern and temporal evolution of land surface deformation in central areas of Xuzhou during 2015–2018. A method based on principal component analysis was adopted to correct artifacts in the InSAR signal. Results showed the correction strategy markedly reduced the discrepancy between global navigation satellite systems and InSAR measurements. Noticeable land subsidence (−5 to −41 mm/yr) was revealed widely within the Xuzhou urban areas, particularly along subway lines under construction, newly developed districts, and in old coal goafs. Remarkable consistent land uplift (up to +25 mm/yr) was found to have significantly affected two long narrow areas within the old goafs since 2015. The possible principal influencing factors contributing to the land surface displacements such as subway tunneling, building construction, mining, underground water levels and geological conditions are then discussed.

Underground coal fires have become a worldwide disaster, which brings serious environmental pollution and massive energy waste. Xinjiang is one of the regions that is seriously affected by the underground coal fires. After years of extinguishing, the underground coal fire areas in Xinjiang have not been significantly reduced yet. To extinguish underground coal fires, it is critical to identify and monitor them. Recently, remote sensing technologies have been showing great potential in coal fires’ identification and monitoring. The thermal infrared technology is usually used to detect thermal anomalies in coal fire areas, and the Differential Synthetic Aperture Radar Interferometry (DInSAR) technology for the detection of coal fires related to ground subsidence. However, non-coal fire thermal anomalies caused by ground objects with low specific heat capacity, and surface subsidence caused by mining and crustal activities have seriously affected the detection accuracy of coal fire areas. To improve coal fires’ detection accuracy by using remote sensing technologies, this study firstly obtains temperature, normalized difference vegetation index (NDVI), and subsidence information based on Landsat8 and Sentinel-1 data, respectively. Then, a multi-source information strength and weakness constraint method (SWCM) is proposed for coal fire identification and analysis. The results show that the proposed SWCM has the highest coal fire identification accuracy among the employed methods. Moreover, it can significantly reduce the commission and omission error caused by non-coal fire-related thermal anomalies and subsidence. Specifically, the commission error is reduced by 70.4% on average, and the omission error is reduced by 30.6%. Based on the results, the spatio-temporal change characteristics of the coal fire areas have been obtained. In addition, it is found that there is a significant negative correlation between the time-series temperature and the subsidence rate of the coal fire areas (R² reaches 0.82), which indicates the feasibility of using both temperature and subsidence to identify and monitor underground coal fires.