ASAR数据与ETM＋图像融合探测干旱区浅层地下水的应用

介绍了ETM＋图像与ASAR数据在探测浅层地下水方面的优缺点,并通过对内蒙古阿拉善左旗地区遥感资料的处理,研究了光谱图像与雷达数据融合后在探测浅层地下水方面的应用效果。研究表明该方法虽不能精确地探测到浅层地下水的埋深与范围,但对掌握宏观区域上浅表层地下水的分布较之单一数据源具有更快速、更直观的效果。     

基于卫星雷达数据的鄱阳湖湿地水面检测与淹露分析

以鄱阳湖自然保护区湿地为例,对多时相、多极化的合成孔径雷达(ENVISAT ASAR)数据作主成分分析,得到主成分分量影像,其中次要分量波段包含大部分的局部变异信息,使用主成分分量波段和log算子提取了ASAR影像中湿地淹没变化信息.结果表明,鄱阳湖自然保护区湿地对外湖水位变化有滞后效应,但总体上,其淹没变化仍显著地受到鄱阳湖外湖洪水的影响.与分类后检测法、分类前变化探测法等方法相比,所采用的变化检测方法可得到更为理想的ASAR影像湿地淹没变化信息提取的精度.使用同极化/交叉极化组合模式的ASAR数据对水面变化检测的精度明显优于同极化波段(HH/VV)组合的数据.     

Monitoring the effect of restoration measures in Indonesian peatlands by radar satellite imagery

In the context of the ongoing climate change discussions the importance of peatlands as carbon stores is increasingly recognised in the public. Drainage, deforestation and peat fires are the main reasons for the release of huge amounts of carbon from peatlands. Successful restoration of degraded tropical peatlands is of high interest due to their huge carbon store and sequestration potential. The blocking of drainage canals by dam building has become one of the most important measures to restore the hydrology and the ecological function of the peat domes. This study investigates the capability of using multitemporal radar remote sensing imagery for monitoring the hydrological effects of these measures. The study area is the former Mega Rice Project area in Central Kalimantan, Indonesia, where peat drainage and forest degradation is especially intense. Restoration measures started in July 2004 by building 30 large dams until June 2008. We applied change detection analysis with more than 80 ENVISAT ASAR and ALOS PALSAR images, acquired between 2004 and 2009. Radar signal increases of up to 1.36 dB show that high frequency multitemporal radar satellite imagery can be used to detect an increase in peat soil moisture after dam construction, especially in deforested areas with a high density of dams. Furthermore, a strong correlation between cross-polarised radar backscatter coefficients and groundwater levels above -50 cm was found. Monitoring peatland rewetting and quantifying groundwater level variations is important information for vegetation re-establishment, fire hazard warning and making carbon emission mitigation tradable under the voluntary carbon market or REDD (Reducing Emissions from Deforestation and Degradation) mechanism.     

Coastal Flood Inundation Monitoring with Satellite C‐band and L‐band Synthetic Aperture Radar Data

Satellite Synthetic Aperture Radar (SAR) was evaluated as a method to operationally monitor the occurrence and distribution of storm‐ and tidal‐related flooding of spatially extensive coastal marshes within the north‐central Gulf of Mexico. Maps representing the occurrence of marsh surface inundation were created from available Advanced Land Observation Satellite (ALOS) Phased Array type L‐Band SAR (PALSAR) (L‐band) (21 scenes with HH polarizations in Wide Beam [100 m]) data and Environmental Satellite (ENVISAT) Advanced SAR (ASAR) (C‐band) data (24 scenes with VV and HH polarizations in Wide Swath [150 m]) during 2006‐2009 covering 500 km of the Louisiana coastal zone. Mapping was primarily based on a decrease in backscatter between reference and target scenes, and as an extension of previous studies, the flood inundation mapping performance was assessed by the degree of correspondence between inundation mapping and inland water levels. Both PALSAR‐ and ASAR‐based mapping at times were based on suboptimal reference scenes; however, ASAR performance seemed more sensitive to reference‐scene quality and other types of scene variability. Related to water depth, PALSAR and ASAR mapping accuracies tended to be lower when water depths were shallow and increased as water levels decreased below or increased above the ground surface, but this pattern was more pronounced with ASAR. Overall, PALSAR‐based inundation accuracies averaged 84% (n = 160), while ASAR‐based mapping accuracies averaged 62% (n = 245).     

ENVISAT卫星先进合成孔径雷达数据水体提取研究——改进的最大类间方差阈值法

星载雷达遥感是目前洪涝灾害水情监测的重要技术手段之一,而欧空局ENVISAT卫星上搭载的先进合成孔径雷达ASAR是目前功能最为强大的星载雷达系统。依据微波遥感影像中水体后向散射系数相对较低的特征,将图像分割中的常用算法——阈值法应用到ENVISAT ASAR数据水体提取中。进行了洞庭湖地区2007年枯水期和洪水期两景ENVISAT/ASAR APP-1P影像的实例研究,结果表明,综合考虑类间和类内方差两个因素的改进的最大类间方差法较之双峰法和最大类间方差法,其确定的最优阈值水体提取精度最高。另外,该方法相对简单、容易实现,可极大地提高当前ASAR数据计算机水体识别的自动化水平,进而推动ASAR数据于阴雨或多云天气条件下在洪涝灾害水情监测中的应用。