The glacier snowline can be used as an indicator of a glacier’s equilibrium line, which is a pivotal parameter for studying the effect of climate change on glaciers. However, the relationship between snowline altitudes (SLAs) and climatic regime, as well as the comparison between different glacier types, has received less attention. Using Google Earth Engine, we first developed an automated algorithm that employs the Otsu thresholding method to distinguish snow-covered areas from clean ice on a near-infrared band of Landsat imagery available from 1995 to 2016 and further to delineate glacier SLAs in the three regions of the eastern Tibetan Plateau (TP). The three study regions, Sepu Kangri (maritime), Bu’Gyai Kangri (continental), and western Qiajajima (continental), in the eastern TP have different climate regimes and are on a latitudinal transect from south to north. We then investigated the impacts of climatic factors on the SLA and its variability over the period studied. The results over the eastern TP indicate that (1) the SLA increased by 94, 55, and 49 m from south to north during the 22-year period, with the SLA variations of maritime glaciers being the most pronounced; (2) the southern maritime glaciers were mainly affected by precipitation, whereas the northern continental glaciers were influenced by temperature. Owing to the difference in primary climatic factors affecting snowlines, continental glaciers were found to have higher SLAs on the south slope, whereas maritime glaciers had higher SLAs on the north slope.
In the society of PolSAR image segmentation, change detection and classification, the classical Wishart distribution has
been used for a long time, but it especially suit to low-resolution SAR image, because in traditional sensors, only a small
number of scatterers are present in each resolution cell. With the improving of SAR systems these years, the classical
statistical models can therefore be reconsidered for high resolution and polarimetric information contained in the images
acquired by these advanced systems. In this study, SAR image segmentation algorithm based on level-set method, added
with distance regularized level-set evolution (DRLSE) is performed using Envisat/ASAR single-polarization data and
Radarsat-2 polarimetric images, respectively. KummerU heterogeneous clutter model is used in the later to overcome the
homogeneous hypothesis at high resolution cell. An enhanced distance regularized level-set evolution (DRLSE-E) is also
applied in the later, to ensure accurate computation and stable level-set evolution. Finally, change detection based on
four polarimetric Radarsat-2 time series images is carried out at Genhe area of Inner Mongolia Autonomous Region,
NorthEastern of China, where a heavy flood disaster occurred during the summer of 2013, result shows the recommend
segmentation method can detect the change of watershed effectively.
In the past two decades, the object characteristics at L- and C-band backscattering coefficient have been widely studied,
but little attention was paid to S-band, due to the main spaceborne SAR sensors are designed at L- and C-band by now.
HJ-1C is planned to be launched at the end of 2009, on which an S-Band SAR is loaded. For the applications of S-band
SAR, it is necessary to obtain backscattering coefficient and analysis object characteristics at this band. In this paper, S-band
relation models are preliminarily analysed for several typical ground objects, using measurements obtained by the
scatterometer system of University of Electronic Science and Technology of China (UESTC). Result shows the S-band
scatterometer system is instable in some cases, but object characteristics can be revealed qualitative. Therefore, some
improvements shall be done to advance scatterometer's performance and field-work methods when more measurements
were taken. For the quantitative analysis of S-band measurements, observations from SMEX02 PALS instrument are
used to compare with the theorical simulation from water-cloud model, the result shows good.
The advances in polarimetric synthetic aperture radar (SAR) interferometry techniques provide a promising way to extract sub-canopy surface parameters using processed SAR images. In this paper, we evaluate the fully maximum likelihood decomposition model of polarimetric SAR interferometry for sub-canopy soil moisture estimation. We further propose a methodology for sub-canopy soil estimation using repeat pass space-borne SIR-C (Shuttle Imaging Radar C) L-band polarimetric SAR interferometric data. The comparison of the inversion results with the field measurements and the climate data of Hotan region from 1951 to 2006 suggests good inversion potential of the proposed method.
More than 69,000 people died following the magnitude 8.0 Wenchuan earthquake of May 12, 2008. Bad weather hampered relief efforts, and in some cases rescuers had to trek into the disaster area on foot and search for trapped survivors by hand as roads were blocked by debris. Due to travel difficulties, spatial information needs to be extracted in the disaster area by remote sensing techniques. The main problem focused on in this paper is how to use the all-weather and all-day/night capability of Synthetic Aperture Radar (SAR) to extract primary seismic disaster information after the earthquake. Using air- and space-borne SAR images with different bands, polarizations and incidence angles, including multi-polarization X-band air-borne data, C-band polarimetric Radarsat-2, X-band TerraSAR-X with high resolution, multi-polarization X-band COSMO-SkyMed and L-band multi-polarization ALOS-PalSAR space-borne data, we perform image characteristics analysis of landslides. Obvious differences can be recognized between old and new landsides in SAR images with different bands. Multi-polarization SAR can play an important role in landslide discrimination. Two SAR images at different incidence angles do not provide much more information if the difference between the two angles is small. Landslide recognition accuracy strongly depends on the direction of view, especially for large incidence angles, in which case the characteristic difference for landslide recognition is great. There are different polarization responses between a landslide and its surroundings that can be used to recognize the landslide. Interferometric SAR images, on the other hand, do not provide good recognition capability due to temporal decorrelation and resolution. Meanwhile, information extraction of barrier lakes using different resolution and incidence angle SAR images is analyzed in this paper; small incidence angles and high resolutions improve the object recognition and information extraction of barrier lakes.
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