基于遥感的祁连山东部冷龙岭冰川变化研究

利用1970年的地形图,1995、1999、2002和2009年的Landsat\|TM/ETM遥感影像,通过遥感图像处理和GIS技术对祁连山冷龙岭地区的冰川变化进行研究。结果表明：研究区的冰川面积1990年以来变化显著,1995年冰川面积比1970年减少了13.79%,1999年冰川面积比1995年减少了21.96%,2002年冰川面积比1999年减少了10.54%,2009年冰川面积比2002年减少了6.35%。选取研究区不同规模的18条典型冰川进行面积长度的变化分析,总体情况与前面一致。     

中天山穆什科托夫冰川跃动特征及控制机理分析

天山地区孕育着大量的跃动型冰川,目前该地区冰川跃动过程及跃动控制机制尚不明确。利用Landsat、Sentinel-1A、TerraSAR-X/TanDEM-X等多源遥感数据获得了中天山穆什科托夫冰川跃动前后的表面特征、流速和高程变化。结果表明：(1)该冰川主干表面流速从2017年夏末开始增加,在冬季流速达到最大峰值,约为4.4 m d^-1,2018年夏末急剧减小;(2)2000—2012年冰川积蓄区平均增厚9.23±4.62 m,跃动前锋形成,而冰舌部分是以减薄为主;2012—2014年冰舌部分持续减薄,中上游仍以积累为主,增厚约1.23±0.91 m;2014—2018年冰川积蓄区出现明显减薄,最大减薄42.6±1.82 m,接收区高程显著增加,最高隆起75.6±1.82 m。根据冰川表面流速及高程变化特征,确认2017—2018年为该冰川跃动活跃期;结合冰川流动定律,认为穆什科托夫冰川跃动主要受冰下水文控制。根据现有的资料及数据,推断该冰川跃动间隔约为60 a。     

高分辨率SAR影像提取冰川面积与冰面河

冰川面积变化是冰川积累与消融的直接体现，与气候变化密切相关。遥感的方法可以为冰川的轮廓及面积监测提供可靠手段，但常用的光学遥感容易受到冰川区多变气象条件的影响。合成孔径雷达（SAR）不受天气影响，尤其是高分辨率SAR影像能够提供冰川表面丰富的细节特征，更好地监测冰川变化。应用相位一致性方法和快速行进法相结合的方法提取冰川轮廓和表面纹理。依据提取的冰川轮廓计算的冰川面积误差在5%以下，表明该方法能够准确地提取冰川面积。同时，在高分辨率SAR图像上，利用提取的冰川表面纹理信息可以有效监测到光学图像上难以识别的冰面河，而冰面河与冰川中长期消融密切相关，提取的冰面河信息将为冰川监测提供一种新的视角。     

基于深度学习的中国第二次冰川编目半自动化更新

中国第二次冰川编目的部分数据用第一次冰川编目替代，这些数据集中分布在藏东南地区。该地区地形陡峭、气候恶劣，常年多云层覆盖，无法获取有效的光学影像，缺乏系统性的冰川调查。针对传统阈值分割方法受噪声影响大、标准Unet计算量大导致运行缓慢等问题，对Unet模型进行压缩，通过修改样本尺寸、卷积核数量和优化器等模型参数，提升模型训练效率以及冰川提取精度。利用冰川的极化特性和地形特征，选用45景ENVISAT ASAR影像和NASA DEM，基于Unet及其压缩网络进行深度学习，参考光学影像和其它辅助数据对误分和漏分的冰川逐个进行人工目视判读，完成了未更新编目的冰川边界提取及修正，并对属性进行了更新。结果表明：基于SAR影像和地形特征的深度学习可以有效识别云层覆盖区域的冰川。在第二次冰川编目未完成的地区，共有冰川8 374条，总面积5 622.65±303.58 km2，误差占总冰川面积的5.4%，整体呈退缩状态，冰川碎片化现象居多。该数据集更新了中国第二次冰川编目中的替代数据，可为探讨藏东南冰川变化和物质平衡等相关研究提供可靠的数据支撑。     

基于Landsat数据的郭扎错北面冰川近20年来面积动态变化遥感研究

冰川能够敏感地反映区域环境变化,是研究全球变化的重要因素之一。昆仑山地区冰川集中,是研究冰川动态变化的理想区域。根据郭扎错北面1991~2009年Landsat TM与ETM+遥感影像,研究了该地区冰川近20 a来的变化情况。结果发现,该地区冰川变化显著,并得出以下结论：① 郭扎错北面冰川面积在1991~2009年间具有先增加后减少的波动规律;② 该区域内存在东部冰川比西部变化量大和变化率快的差异性;③ 该地区中峰冰川在2001~2004年间面积大幅增加,可能与2001年11月14日发生在昆仑山口以西的8.1级强烈地震有关;④ 该研究区内冰川面积变化主要受年均温度和年累积降水量的综合影响。     

基于高分遥感数据和深度学习的石冰川自动提取研究

石冰川是以冰岩混合物为基础形成的一类具有舌状堆积纹理的冰缘地貌,了解其分布和变化对于寒区环境研究具有重要价值,遥感技术的发展为石冰川的识别提供了有效的手段。针对石冰川发育地的偏远和调查的困难,以及其光谱特征的微弱性,提出了一种基于深度学习的石冰川识别方法,以ResNet作为训练网络,得到石冰川的图像分类模型,以国产高分一号遥感影像作为实验数据,在念青唐古拉山西段展开了应用,共识别出石冰川96条。验证结果表明：该方法具有较高的识别精度（98.72%的总体精度、89.48%的生产精度和81.77 %的用户精度）,证明该方法能够有效地识别石冰川,并为在大区域开展石冰川的调查和分析提供了基础。     

高分辨率SAR影像提取冰川面积与冰面河

冰川面积变化是冰川积累与消融的直接体现,与气候变化密切相关。遥感的方法可以为冰川的轮廓及面积监测提供可靠手段,但常用的光学遥感容易受到冰川区多变气象条件的影响。合成孔径雷达（SAR）不受天气影响,尤其是高分辨率SAR影像能够提供冰川表面丰富的细节特征,更好地监测冰川变化。应用相位一致性方法和快速行进法相结合的方法提取冰川轮廓和表面纹理。依据提取的冰川轮廓计算的冰川面积误差在5%以下,表明该方法能够准确地提取冰川面积。同时,在高分辨率SAR图像上,利用提取的冰川表面纹理信息可以有效监测到光学图像上难以识别的冰面河,而冰面河与冰川中长期消融密切相关,提取的冰面河信息将为冰川监测提供一种新的视角。     

南极1：20万大剖面卫星影像数字制图

本文论述了在缺少地面控制点的情况下，进行南极卫星影像数字制图的方法和技术。利用ＴＭ和ＭＳＳ卫星影像数据，制作了从中山站至南极点大剖面考察区内的１：２０万卫星影像图。     

基于时序像素跟踪算法的南伊内里切克冰川运动提取与特征分析

选用Sentinel-1A卫星TOPS模式下获取的8景升轨SAR数据，基于小基线集像素跟踪时序分析技术（Small BAseline Subset Pixel Tracking technique， SBAS-PT），获取了南伊内里切克冰川2018年1月至2018年12月期间不同时段的表面流速分布及其时空变化特征。研究结果表明：2018年南伊内里切克冰川1月到3月整体运动速率较小，从4月起速率明显增加，7月到8月达到最高，9月份起运动速率开始放缓，10月到12月期间冰川表面运动速率较小，全年的平均表面流速约为30cm·d^-1。总体而言，南伊内里切克冰川中上游区域流速明显高于冰川下游，冰川下游冰川物质消融减薄和补给量减少以及表碛物增多等因素致使冰川末端区域逐渐趋于稳定。     

基于SAR数据的山地冰川表面运动速度分析

合成孔径雷达(SAR)因其可全天时、全天候工作且不受云、雨的影响而成为遥感应用的前沿领域。SAR干涉测量(Inteferometry)利用SAR数据的相位信息可获得大地表面厘米级的形变而成为冰川表面流速监测广泛使用的手段;SAR图像相关方法(SRFT)能克服干涉测量方法因失相干严重而难以产生清晰的干涉条纹以及可见光图像质量由于云遮、雪盖限制的不足而成为目前山地冰川表面流速遥感监测的首选方式。为深入探讨SAR图像相关方法的适用性,以天山科契卡尔巴西冰川为研究区域,分析使用不同时间基线的ALOS PALSAR数据与ENVISat ASAR C-band 的图像相关方法估计冰川的表面流速,并使用实地测杆的DGPS(Differential GPS)测量流速进行对比验证,发现在冰川表碛覆盖区域使用图像相关方法测量的值与实测值有很好的一致性,而在裸露冰区域或坡度较大区域,误差比较大。比较长时间基线的SAR数据对特征识别的结果发现：时间基线为1 a的冬季获取的数据对估计值与实测值在表碛覆盖区域比较一致,这可能是由于前后两次获取图像时天气或地面状况比较接近。比较ALOS PALSAR 数据与ENVISat ASAR数据发现：波长较长的L-band(23.5 cm) 比C-band (5.7 cm)SAR数据更适合山地冰川的表面流速估计;另外在运用SAR数据特征匹配方法时也可能是极化方式的差异使得ALOS PALSAR (HH极化)数据比ENVISat ASAR(VV极化)数据更适合冰川研究。     

Semi-automated feature-tracking of East Antarctic sea ice from Envisat ASAR imagery

While feature tracking of sea ice using cross-correlation methods on pairs of satellite Synthetic Aperture Radar (SAR) images has been extensively carried out in the Arctic, this is not the case in the Antarctic. This is due to the dynamic nature of Antarctic pack ice, its microwave signature, the tendency for SAR swath paths to be poorly aligned with the often narrow sea ice zone around the continent and inadequate satellite sampling. A semi-automated system, known as IPADS (IMCORR [IMageCORRelation] Processing, Analysis and Display System), has been developed to map fast ice and pack ice in Antarctica using multiple pairs of SAR images. The software processing pipeline uses overlapping image pairs which are geocoded and roughly registered using only data contained in the image headers. Next, fast ice maps are rapidly generated using zero motion features located within ocean regions. This also provides precise image registration. Finally, the same image pairs are re-examined for pack ice motion in a slower off-line batch process. The pack and fast ice are identified using a cluster-based search method which compares both location and motion information. Each image pair generates a NetCDF file which adds to a growing database of Antarctic sea ice motion and ice roughness. Five image-pair examples are presented to illustrate the methods used as well as their strengths and limitations. Substantial pack ice motion can often be detected in the marginal ice zone on SAR images only a few days apart.     

Some active and passive microwave signatures of Antarctic sea ice from mid-winter to spring 1991

Antarctic sea ice is often covered by a deep snow layer which acts as an emitter and a scatterer to microwave radiation leading to possible misinterpretations of ice signatures, particularly at high frequencies. The algorithms for ice identification, based on the observations of the Special Sensor Microwave Imager, at 19GHz (vertical and horizontal polarizations) and 37Ghz (vertical polarization), have proven to be inefficient for distinguishing new and old ice over the Antarctic Ocean. At an equivalent resolution and analysed on a weekly basis, complementary information can be obtained from active microwave measurements provided, at 5·3GHz (vertical polarization), by the Active Microwave Instrument, the scatterometer of ERS–1. Based on data obtained from the end of August to the end of November 1991, during the austral winter and spring radar backscatter is analysed as a function of the incidence angle. At low incidence angles, the derivative of the backscatter is closely related to the water concentration as derived from passive radiometry, whereas, at high incidence angles, the backscatter is mainly due to ice, as the water contribution is strongly reduced. During the whole period, stable features are apparent on the images obtained from the backscattering coefficients at 50°. On those images, higher values characterize the marginal ice zone, the polynya areas and the advected ice within the Ross Sea. At high incidence angles, the strong signatures of deformed/ rough ice depart significantly from the information classically extracted from the radiometers, the brightness temperatures as well as the derived products, polarization, spectral gradient ratios and concentration. It is therefore possible to classify the Antarctic ice cover into geographical clusters where the active microwave signatures can be attributed. to a peculiar ice type. Though those clusters are not totally identified, their stability and the coherence of their patterns show that they are related to geophysical structures. Four backscatter curves, simulating distinct behaviours over the Antarctic region, are proposed for sea water, marginal ice, first-year ice of the inner part of the pack and multi-year ice.     

Changes in Antarctic coastline between 1997 and 2016 using RADARSAT and MODIS data

ABSTRACT

Studies have already indicated more disintegrations and calving of ice margins and a significant negative ice balance at certain parts of Antarctica in recent times. Changes in extent of ice shelves as well as glacier fronts are indeed good signatures of climate change. This paper essentially discusses the changes in Antarctic ice margins between 1997 and 2016. Change-detection technique has been employed using the Radar Satellite (RADARSAT) mosaic of 1997 and the mosaic of Antarctic ice margins generated using MODerate resolution Imaging Satellite (MODIS) images of 2016, with the help of Earth Resources Development Assessment system (ERDAS) Imagine software. Hotspots, where significant changes occurred, have been identified. Most part of the Antarctic coast between 75° E and 45° W, covering all West Antarctica and about half of East Antarctica, has shown definite retreat during this period. Major retreats are observed over the both sides of Ross ice shelf, Ronne ice shelf, Thwaites glacier, and Mertz glacier. Major advancements are observed over Filchner ice shelf, Amery ice shelf, middle portion of Ross ice shelf, and Stancomwills glacier tongue. Over the East Antarctica, areas of advancement and retreat are nearly same. But, over the West Antarctica, the area of retreat is significantly higher than area of advancement.     

GRANTISM: An ExcelTM model for Greenland and Antarctic ice-sheet response to climate changes

Over the last decades, the response of large ice sheets on Earth, such as the Greenland and Antarctic ice sheets, to changes in climate has been successfully simulated with large-scale numerical ice-sheet models. Since these models are highly sophisticated, they are only applicable on the scientific level as they demand a large amount of CPU time. Based on similar physics, a computationally fast flowline model of the Greenland and Antarctic ice sheet is presented here, primarily designed for educational purposes. Using an over-implicit numerical scheme, the model runs fast and behaves in a similar way to changes in background temperature forcing as major ice-sheet models do. A user-friendly interface and the implementation within a common spreadsheet program (Excel^TM) make the model suitable for the classroom.     

Special colour enhancement for three channels having similar radiances

A special enhancement algorithm is derived to colour separate ground-cover classes whose recorded wavelength distributions are similar but have significantly differing intensities. The special enhancement function is derived from considering the spectrum locus of a CIE 1931 (x,y) chromaticity diagram. By analogy with the relationship between the chromaticity co-ordinates and the channel radiance levels a function is derived that stretches the radiances to maximize the colour differences between such ground-cover classes. This Sinsusoidal Squeeze algorithm was applied to enhance the colour differentiation of Antarctic ice types as recorded by LANDSAT. A comparison between this special enhancement and the conventional colour composite results for Antarctic ice pack is presented. Field checks conducted during the 1980-81 Antarctic summer field season confirm our ability to delineate up to seven different types of ice by this technique. Additional applications of this special algorithm are believed to be forest class differentiation and bathymetric feature delineation.     

Grove Mountains meteorite recovery and relevant data distribution service

Meteorites are extremely valuable in providing clues about the origin, evolution, and composition of the Sun, the Moon, the Earth, other planets, and asteroids. Since the first discovery of a meteorite in Antarctica, more and more meteorite concentrations on bare ice stranding sites were discovered. Antarctica is identified as a prolific source of extraterrestrial materials. The Grove Mountains area, covered by ice, snow, and nunataks, is located in the Antarctic inland area. It is about 380 km away from the Chinese Zhongshan Antarctic Research Station in East Antarctica. Since 1998, 11,452 meteorites have been collected from the Grove Mountains by the Chinese National Antarctic Research Expedition (CHINARE). It is confirmed that the Grove Mountains area is a productive search area for meteorites in Antarctica. More and more meteorite recoveries led to the recognition that unique mechanisms relating to meteorite concentrations exist in Antarctica. Besides meteorite field collections, the extraction of blue ice based on satellite images, meteorite concentration mechanisms, and meteorite data distribution service are discussed in this paper. Wide distribution of blue ice indicates the enrichment of meteorites. Based on the different spectrum characteristics and coherence of snow, blue ice, and bare rocks, blue ice areas are extracted from optical images and coherence maps. According to meteorite field collections and optical images, moraines are also identified as meteorite concentration sites in the Grove Mountains area. The meteorite concentration theories should be further analyzed by taking into account ice-flow dynamics, mountains' blocking effect, katabatic wind and ice ablation, and others. Moreover, in order to strengthen the visualization and network sharing of the valuable meteorite data, desktop software based on ArcObjects and web software based on ArcIMS are developed within this study. The desktop software also enables further analysis of the meteorite concentration mechanisms in the Grove Mountains.     

Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing

Polar ice masses and sheets are sensitive indicators of climate change. Small-scale surface roughness significantly impacts the microwave emission of the sea ice/snow surface; however, published results of surface roughness measurements of sea ice are rare. Knowing the refractive index is important to discriminate between objects. In this study, the small-scale roughness and refractive index over sea ice are estimated with AMSR-E observations and a unique method. Consequently, the small-scale surface roughness of 0.25 cm to 0.5 cm at AMSR-E 6.9 GHz shows reasonable agreement with the results of known observations, ranging from 0.2 cm to 0.6 cm for the sea ice in the Antarctic and Arctic regions. The refractive indexes are retrieved from 1.6 to 1.8 for winter, from 1.2 to 1.4 for summer in the Arctic and the Antarctic, which are similar to those of the sea ice and results from previous studies. This research shows the physical characteristics of the sea ice edges and melting process. Accordingly, this investigation provides an effective procedure for retrieving the small-scale roughness and refractive index of sea ice and snow. Another advantage of this study is the ability to distinguish sea ice from the sea surface by their relative small-scale roughness.     

Subglacial water presence classification from polar radar data

Ground and airborne radar depth-sounding of the Greenland and Antarctic ice sheets have been used for many years to remotely determine characteristics such as ice thickness, subglacial topography, and mass balance of large bodies of ice. Ice coring efforts have supported these radar data to provide ground truth for validation of the state (wet or frozen) of the interface between the bottom of the ice sheet and the underlying bedrock. Subglacial state governs the friction, flow speed, transport of material, and overall change of the ice sheet. In this paper, we utilize machine learning and classifier combination to model water presence from airborne polar radar data acquired on Greenland in 1999 and 2007. The underlying method results in radar independence, allowing model transfer from 1999 to 2007 radar data to produce water presence maps of the Greenland ice sheet with differing radars. We focus on how to construct a successful set of classifiers capable of high classification accuracy. Utilizing multiple machine learning algorithms is shown to be successful for this classification problem, achieving 86% classification accuracy in the best case. Several heuristics are presented for constructing teams of multiple classifiers for predicting subglacial water presence. The presented methodology could also be applied to radar data acquired over the Antarctic ice sheet.     

Transect method for Antarctic cloud property retrieval using AVHRR data

For studies of Antarctic climate change, the Advanced Very High Resolution Radiometer (AVHRR) offers a time series spanning more than two decades, with numerous overpasses per day from converging polar orbits, and with radiometrically calibrated thermal infrared channels. However, over the Antarctic Plateau, standard multispectral application of AVHRR data for cloud optical property retrieval with individual pixels is problematic due to poor scene contrasts and measurement uncertainties. We present a method that takes advantage of rapid changes in radiances at well-defined cloud boundaries. We examine a transect of AVHRR-measured radiances in the three thermal infrared channels across a boundary between cloudy and cloud-free parts of the image. Using scatter diagrams, made from the data along this transect, of the brightness temperature differences between channels 3 and 4, and channels 4 and 5, it is possible to fit families of radiative transfer solutions to the data to estimate cloud effective temperature, thermodynamic phase, and effective particle radius. The major approximation with this method is that along such a transect, cloud water path has considerable spatial variability, while effective radius, phase, and cloud temperature have much less variability. To illustrate this method, two AVHRR images centred about the South Pole are analysed. The two images are chosen based on their differing contrasts in brightness temperature between clear and cloud-filled pixels, to demonstrate that our method can work with varying cloud top heights. In one image the data are consistent with radiative transfer simulations using ice cloud. In the other, the data are inconsistent with ice cloud and are well simulated with supercooled liquid water cloud at 241.5 K. This method therefore has potential for climatological investigation of the radiatively important phase transition in the extremely cold and pristine Antarctic environment.     

Oceansat-MSMR imaging of the Antarctic and the Southern Polar Ocean

This paper highlights preliminary results from the analysis of the first year's data from the multi-channel scanning microwave radiometer (MSMR) onboard India's Oceansat-1 satellite, over the climatologically and geologically important Antarctic and Southern Ocean regions. Weekly averaged microwave brightness temperature images formed using the MSMR data clearly demarcated open water and sea ice regions. Different levels of ice concentration also showed up as different brightness temperature values. Signatures of several large and prominent continental features like the Trans-Antarctic Mountain ranges, Gamburtsev sub-glacial mountain, Wilkes and Aurora sub-glacial basins and the Ross Ice Shelf were seen in these images. Some quasi-circular coastal 'eddy' like features were also observed in sea ice regions off the eastern Antarctic coast. These studies reveal the potential of Oceansat-MSMR for long term monitoring of the polar regions of the Earth.