SAR监测冰川变化研究进展
  <sup>*</sup>

doi:10.11867/j.issn.1001-8166.2014.09.0985

遥感现已成为大范围冰川制图与监测的必备手段。合成孔径雷达(SAR)全天候的成像能力,对地表湿度、粗糙度的高度敏感性,以及对地物一定的穿透能力,使其在冰川制图与监测中展现出巨大的潜力。SAR对冰川的制图与监测理论方法还在不断完善,目前正处于快速发展阶段,是冰川遥感研究的一个热点。针对国内外近年来研究进展,对SAR通过散射差异识别雪冰带、监测雪冰界线与冰川物质平衡之间的关联、干涉法与相关算法监测运动速度、干涉生成DEM监测冰川厚度变化等方面的新成果进行论述,阐明了SAR冰川探测的优势和局限性,并对未来若干极具潜力的SAR冰川探测理论和方法进行了展望。

关键词: 合成孔径雷达, 冰川, 遥感

Remote sensing is now widely used in glacier classification and monitoring. The synthetic aperture radar is hardly influenced by weather conditions and sensitive to ground wetness and roughness. Meanwhile, it has the penetration ability to ground objects. So it is promising in glacier mapping and monitoring. Until now SAR application in glacier is limited in theory and experiments, which is a hot topic in remote sensing of glacier research. This paper reviews the application of SAR in glacier zones recognition using the scattering property difference, flow velocity generation using InSAR and feature tracking, and glacier thickness monitoring using DInSAR generated DEM. The merits and limitations of SAR for glacier monitoring are investigated, and the promising theory and techniques for SAR to detect glaciers are introduced.

Key words: Synthetic aperture radar, Glacier, Remote sensing.

中图分类号:

P343.6

图1 科其喀尔冰川区全极化SAR表碛识别结果 ^{[

47
]} a1. ALOS/PALSAR数据;a2.仅用a1数据HV通道识别表碛;a3.通过a1数据极化分解与后向散射系数联合开展冰川区分类;b1.RADARSAT-2数据;b2.仅用b1中的HV通道识别表碛;b3.综合b1图像的后向散射系数与极化分解方法进行分类结果

Fig.1 Recognition of the debris on the Keqikaer Glacier ^{[

47
]} a1. ALOS/PALSAR data; a2. Detection of the debris using HV in a1; a3. Detailed classification by combining target decomposition and backscatter coefficient; b1. RADARSAT-2 data; b2. Detection of the debris using HV in b1; b3. Detailed classification of the glacier by combining target decomposition and backscatter coefficient

图2 C波段SAR探测大陆型冰川冰带及雪线变化模型 ^{[

54
]} 蓝色和黄色表示SAR图像能分辨的区域,数值是各个区域的阈值

Fig.2 The glacier zones detected by C-band SAR ^{[

54
]} Blue and yellow represent the detectable zones on SAR images, and the values are the thresholds

表1. 干涉测量与特征跟踪方法获取冰川运动速度对比

Table 1. Comparison of the InSAR and Feature tracking methods.

方法名称	共同点	像对时间间隔	冰川运动状况	变化提取精度
干涉测量	都采用同轨且基线较近的SAR像对	几天至几十天	运动较慢	cm甚至mm
特征跟踪	都采用同轨且基线较近的SAR像对	数月至数十月	运动较快	配准精度×像素尺寸

表1. 干涉测量与特征跟踪方法获取冰川运动速度对比

Table 1. Comparison of the InSAR and Feature tracking methods.

方法名称	共同点	像对时间间隔	冰川运动状况	变化提取精度
干涉测量	都采用同轨且基线较近的SAR像对	几天至几十天	运动较慢	cm甚至mm
特征跟踪	都采用同轨且基线较近的SAR像对	数月至数十月	运动较快	配准精度×像素尺寸

图3 干涉方法与相关方法获取的慕士塔格冰川冬季运动速度对比 a1,b1,c1分别为2009年,2010年和2011年DInSAR的结果;a2,b2,c2分别为对应的相关算法结果^{[

78
]};原文中的年平均速度是在假设冰川全年具有与冬季相同的运动速度基础上获得的

Fig.3 Winter flow velocity detected by DInSAR on the Dongkemadi Glacier a1,b1,c1 are acquired in 2009, 2010 and 2011 with DInSAR; a2,b2,c2 are acquired with feature tracking^{[

78
]};The glaciers were supposed to flow in the same velocity in a year in the original paper

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摘要

Glacier Change Monitoring Using SAR: An Overview