Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%.

The advantage of implementing the Water Cloud Model (WCM) is in being able to express complex scattering characteristics in a vegetated area with simple bulk vegetation descriptors. However, there has been a lack of understanding or consensus about the optimal set of vegetation descriptors. In this paper, the original and improved expressions of WCM are evaluated and the optimal vegetation descriptors are presented by examining the relationship between WCM vegetation parameters and the theoretical scattering model predictions. In addition, the condition-specific regression relationship between bulk vegetation descriptors and theoretical scattering and attenuation coefficients, expressed by the $A$ and $B$ parameters in the WCM, is analyzed in relation to the shape, size, and orientation distribution of the scatterer. Furthermore, the influence of radar observation conditions on the parameterization of the WCM is presented. The results show that the particle moisture content and the vegetation water content can be the optimal vegetation descriptors, denoted by the $V_1$ and $V_2$ variables in the WCM, respectively.

In the diverse applications of polarimetric Synthetic Aperture Radar (SAR) systems, it is a crucial to conduct polarimetric calibration, which aims to remove the radar system distortion effects prior to utilizing polarimetric SAR observations. The objective of this study is to evaluate the performance of different polarimetric calibration methods. Two widely used methods, the Van Zyl and Quegan methods, and one recently proposed method, such as the Villa method, have been selected among various calibration methods in literature. The selected methods have basic differences in their assumptions that are applied to the polarimetric system model. In order to evaluate the calibration performances under different system parameters and ground characteristics, comparative analysis of the calibration results were conducted on synthetic polarimetric SAR data and ALOS PALSAR quad-pol mode data. Based on the experimental results, the advantages and limitations of different methods were clarified, and a simple hybrid calibration method is presented to further improve the polarimetric calibration performance.

Recently, as global climate change and local disturbances such as wildfires continue, long- and short-term changes in the high-latitude vegetation systems have been observed in various studies. Although remote sensing technology using optical satellites has been widely used in understanding vegetation dynamics in high-latitude areas, there has been limited understanding of various landscape changes at different spatiotemporal scales, their mutual relationships, and overall long-term landscape changes. The objective of this study is to devise a change monitoring strategy that can effectively observe landscape changes at different spatiotemporal scales in the boreal ecosystems from temporally sparse time series remote sensing data. We presented a new post-classification-based change analysis scheme and applied it to time series Landsat data for the central Yakutian study area. Spectral variability between time series data has been a major problem in the analysis of changes that make it difficult to distinguish long- and short-term land cover changes from seasonal growth activities. To address this issue effectively, two ideas in the time series classification, such as the stepwise classification and the lateral stacking strategies were implemented in the classification process. The proposed classification results showed consistently higher overall accuracies of more than 90% obtained in all classes throughout the study period. The temporal classification results revealed the distinct spatial and temporal patterns of the land cover changes in central Yakutia. The spatiotemporal distribution of the short-term class illustrated that the ecosystem disturbance caused by fire could be affected by local thermal and hydrological conditions of the active layer as well as climatic conditions. On the other hand, the long-term class changes revealed land cover trajectories that could not be explained by monotonic increase or decrease. To characterize the long-term land cover change patterns, we applied a piecewise linear model with two line segments to areal class changes. During the former half of the study period, which corresponds to the 2000s, the areal expansion of lakes on the eastern Lena River terrace was the dominant feature of the land cover change. On the other hand, the land cover changes in the latter half of the study period, which corresponds to the 2010s, exhibited that lake area decreased, particularly in the thermokarst lowlands close to the Lena and Aldan rivers. In this area, significant forest decline can also be identified during the 2010s.

Due to large temperature variations between the summer and winter seasons the active layer of permafrost undergoes repetitive thawing and freezing, and the increase of global temperature has accelerated permafrost degradation related to surface deformation seasonally and annually. Repeated freezing and thawing causes frost heave and thaw settlement, which results in displacement in the activity layer of permafrost. This surface displacement is also associated with ground ice and soil moisture content, and these factors in permafrost region could be observed through timely-dense SAR data. In particular, since the revisit time of Sentinel-1 (C-band) is relatively dense, timeseries SAR interferometry could be useful tools for monitoring and mapping subsurface soil properties over such a wide area. In this study, since the degree of freezing and thawing is very different spatially and temporally, we propose the method to indirectly estimate the ground ice content of the freezing period and the moisture content of the thawing period by quantifying the displacement using timeseries InSAR measurements in the Lena-river floodplain, Siberia.

Wildfires on permafrost covered with the boreal forest can influence vegetation composition, surface soil moisture, and the active layer. Since wildfires on permafrost occur extensively in unpredictable areas, remote sensing is a useful tool for monitoring burn severity and ecosystem changes. Optical spectral indices such as the differenced normalized burn ratio (dNBR) and normalized difference vegetation index (NDVI) were traditionally used to detect burn severity and vegetation regrowth. However, since optical imagery is significantly affected by cloud cover and weather conditions, there is a limitation in acquiring temporally dense images. Synthetic Aperture Radar (SAR) can obtain images regardless of day/night or weather conditions, so it is possible to densely observe the area of interest spatiotemporally. In addition, SAR images, unlike optical images, can acquire information on the active layer of the permafrost in the winter season. This study aimed to analyze winter season time-series SAR backscattering coefficient change with burn severity in south Northwest Territories, Canada using optical and SAR data. The study area, south Northwest Territories, belongs to the discontinuous permafrost zone and consisted of the taiga. Burn severity and vegetation regrowth were estimated by dNBR and NDVI using optical imagery. To increase the temporal resolution, Landsat-8 OLI and Sentinel-2 MSI were acquired through the cloud-based Google Earth Engine (GEE) in the summer season. C-band dual-polarization Sentinel-1 and X-band single-polarization TerraSAR-X were obtained to understand the multi-frequency backscattering coefficient to fire-induced changes. The changes pattern of the SAR backscattering coefficient varies according to the burn severity, especially in the winter season, not affected by vegetation and soil moisture. It can be seen that the wildfires affected the changes in the scattering mechanism in permafrost on the boreal forests. These results represent that C-band and X-band SAR images have the potential to monitor the changes of the active layer with burn severity.