基于ALOS极化干涉SAR数据的DEM提取方法研究

极化干涉理论的发展以及全极化卫星的发射,为极化干涉的研究应用提供了广阔的发展空间,开展极化干涉SAR提取DEM的研究对于解决单极化干涉SAR在植被覆盖区DEM提取精度较低的问题具有重要意义。基于山东泰安地区的一对ALOS PALSAR全极化干涉数据,利用相干性最优方法和最大化相位中心分离的方法获取极化干涉信息,并通过对优化后干涉信息的滤波、解缠、基线精确估计等处理来提取DEM,最后将相干优化的结果与传统HH、HV、VV单极化干涉结果进行对比分析。结果表明:优化方法较单极化干涉方法可以有效降低干涉图的噪声,减少相位解缠的残差点,提高解缠相位的质量,并且不同优化方法之间还存在一定差异,数值半径方法获取的DEM精度要好于其他两种优化方法;通过等值线-Goldstein二级干涉SAR滤波方法可进一步提高单极化和优化干涉图的质量,降低残差点,提高相位质量,在水体覆盖的区域通过滤波窗口的设置可以很好地改善DEM的精度。     

Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use

In the context of reducing emissions from deforestation and forest degradation (REDD) and the international effort to reduce anthropogenic greenhouse gas emissions, a reliable assessment of aboveground forest biomass is a major requirement. Especially in tropical forests which store huge amounts of carbon, a precise quantification of aboveground biomass is of high relevance for REDD activities. This study investigates the potential of X- and L-band SAR data to estimate aboveground biomass (AGB) in intact and degraded tropical forests in Central Kalimantan, Borneo, Indonesia. Based on forest inventory data, aboveground biomass was first estimated using LiDAR data. These results were then used to calibrate SAR backscatter images and to upscale the biomass estimates across large areas and ecosystems. This upscaling approach not only provided aboveground biomass estimates over the whole biomass range from woody regrowth to mature pristine forest but also revealed a spatial variation due to varying growth condition within specific forest types. Single and combined frequencies, as well as mono- and multi-temporal TerraSAR-X and ALOS PALSAR biomass estimation models were analyzed for the development of accurate biomass estimations. Regarding the single frequency analysis overall ALOS PALSAR backscatter is more sensitive to AGB than TerraSAR-X, especially in the higher biomass range (> 100 t/ha). However, ALOS PALSAR results were less accurate in low biomass ranges due to a higher variance. The multi-temporal L- and X-band combined model achieved the best result and was therefore tested for its temporal and spatial transferability. The achieved accuracy for this model using nearly 400 independent validation points was r² = 0.53 with an RMSE of 79 t/ha. The model is valid up to 307 t/ha with an accuracy requirement of 50 t/ha and up to 614 t/ha with an accuracy requirement of 100 t/ha in flat terrain. The results demonstrate that direct biomass measurements based on the synergistic use of L- and X-band SAR can provide large-scale AGB estimations for tropical forests. In the context of REDD monitoring the results can be used for the assessment of the spatial distribution of the biomass, also indicating trends in high biomass ranges and the characterization of the spatial patterns in different forest types.     

Mapping arctic landfast ice extent using L-band synthetic aperture radar interferometry

In recent years methods have been developed to extract the seaward landfast ice edge from series of remote sensing images, with most of them relying on incoherent change detection in optical, infrared, or radar amplitude imagery. While such approaches provide valuable results, some still lack the required level of robustness and all lack the ability to fully automate the detection and mapping of landfast ice over large areas and long time spans. This paper introduces an alternative approach to mapping landfast ice extent that is based on coherent processing of interferometric L-band Synthetic Aperture Radar (SAR) data. The approach is based on a combined interpretation of interferometric phase pattern and interferometric coherence images to extract the extent and stability of landfast ice. Due to the low complexity of the base imagery used for landfast ice extraction, significant improvements in automation and reduction of required manual interactions by operators can be achieved. A performance analysis shows that L-band interferometric SAR (InSAR) data enable the mapping of landfast ice with high robustness and accuracy for a wide range of environmental conditions.     

遥感影像融合方法在ALOS影像水体信息提取中的应用研究

为了应用ALOS遥感影像调查浙江省德清县水域分布,首先采用小波变换、GS变换和高通滤波三种影像融合方法,分别对已配准的ALOS多光谱与全色波段影像进行融合;然后采用监督分类方法分别对融合前后影像提取水体信息,通过比较这三种融合方法在水体信息提取的效果,给出了适于水体信息提取的影像融合方法。     

基于多层分割的面向对象遥感影像分类方法研究

利用ALOS数据,在Definiens Developer 7软件中用分形网络演化法(FNEA)进行多级分割,获取影像对象。综合运用对象的光谱、空间特征和不同层对象之间的关系,提取了湖北省洪湖市试验区土地覆盖与土地利用信息。最后,用一种基于单层分割的面向对象分类方法和基于像素的最大似然法与这种基于多级分割的面向对象分类方法进行了对比分析。结果表明,基于多级分割的面向对象分类方法,不仅克服了基于像素的最大似然法出现的“椒盐”现象,在分类精度上较这两种分类方法也有大幅度的提高。     

Correlation Analysis between L-band SAR and ForestStem Volume in Northeast China

Because Synthetic Aperture Radar(SAR)can penetrate into forest canopy and interact with the primary stem volume contents of the trees (trunk and branch),SAR data are widely used for forest stem volume estimation.This paper investigated the correlation between SAR data and forest stem volume in Xunke,Heilongjiang using the stand-wise forest inventory data in 2003 and ALOS PALSAR data for five dates in 2007.The influences of season and polarizations on the relationship between stem volume and SAR data were studied by analyzing the scatterplots;that was followed by interpretation of the mechanisms primarily based on a forest radar backscattering model-water cloud model.The results showed that the relationship between HV polarization backscatter and stem volume is better than HH polarization,and SAR data in summer dry conditions are more correlated to stem volume than the data acquired in other conditions.The interferometric coherence with 46-day temporal baseline is negatively correlated to the stem volume.The correlation coefficients from winter coherence are higher than those from summer coherence and backscatter.The study results suggest using the interferometric coherence in winter as the best choice for forest stem volume estimation with L-band SAR data.     

Change Detection of Forest Coverage in Sichuan GiantPanda Sanctuaries Using PALSAR Time Series Data

Time series features of SAR backscattering in the period of 2007 to 20011 were used to monitor the change of forest coverage in Wolong and Caopo Nature Reserve in Sichuan Prov.China.Firstly，PALSAR data were separated into 4 groups；then，a decision tree approach was derived using the J48 algorithm for the forest fine mapping，resulting in the overall classification accuracy(OA)and kappa coefficient(KC)equaling to 94.31% and 0.89，respectably.After that，four time\|series maps of forest coverage in study area were obtained in the observation period of 2007\|2011.Change detection demonstrated that the proportion of the forest coverage was reduced from 56.66% to 48.11%(that is 8.55% declining)caused by the Wenchuan Earthquake.However，the ecological system was recovering since the second year of the event，such as the forest coverage increased up to 48.52%(equals to a recovered square of 9.19 km² that is amounting to 13.24%of the damaged area).This study implied that Wenchuan Earthquake could be a negative impact on the deforestation of the study area；nonetheless the ecological system is recovering nowadays jointly contributed by the combination of natural and anthropogenic impactors.     

乌兰布和沙漠高分辨率遥感影像梭梭林解译方法探讨

利用ALOS、SPOT5高分辨率遥感影像,结合野外调查,完成了乌兰布和沙漠研究区遥感影像信息的目视解译及植被制图,并结合GPS野外样点进行精度评价,解译精度为93.3%,梭梭林面积为165 410.62 hm²,占乌兰布和沙漠地区总土地面积的17.27%。最后,对乌兰布和沙漠遥感影像梭梭林解译的方法进行了探讨。     

埃塞俄比亚西部岩浆熔离型铁矿遥感找矿模型

为解决在地质资料匮乏的情况下发挥多源遥感数据优势开展地质找矿研究这一科学问题,以非洲埃塞俄比亚西部研究区为例,选取ASTER及ALOS PALSAR单极化雷达数据为数据源,针对研究区重点类型铁矿--岩浆熔离型铁矿建立了其遥感找矿模型,提取与成矿作用相关的蚀变、构造、岩体信息。研究基于ASTER遥感数据,通过比值、主成分方法进行蚀变信息提取研究,并基于ASTER及ALOS PALSAR单极化雷达融合数据进行控矿构造及赋矿岩体信息提取研究。最终通过综合分析,基于ArcGIS平台圈定了3处遥感找矿靶区。研究结果与1∶25万地球化学数据对比,具有很高一致性,表明遥感找矿模型能够快速、准确地进行找矿靶区预测。     

Comparison of L-band and X-band differential interferometric synthetic aperture radar for mine subsidence monitoring in central Utah

Differential interferometric synthetic aperture radar(DIn SAR), a satellite-based remote sensing technique, has potential application for measuring mine subsidence on a regional scale with high spatial and temporal resolutions. However, the characteristics of synthetic aperture radar(SAR) data and the effectiveness of DIn SAR for subsidence monitoring depend on the radar band(wavelength). This study evaluates the effectiveness of DIn SAR for monitoring subsidence due to longwall mining in central Utah using L-band(24 cm wavelength) SAR data from the advanced land observing satellite(ALOS)and X-band(3 cm wavelength) SAR data from the Terra SAR-X mission. In the Wasatch Plateau region of central Utah, which is characterized by steep terrain and variable ground cover conditions, areas affected by longwall mine subsidence are identifiable using both L-band and X-band DIn SAR.Generally, using L-band data, subsidence magnitudes are measurable. Compared to X-band, L-band data are less affected by signal saturation due to large deformation gradients and by temporal decorrelation due to changes in the surface conditions over time. The L-band data tend to be stable over relatively long periods(months). Short wavelength X-band data are strongly affected by signal saturation and temporal decorrelation, but regions of subsidence are typically identifiable over short periods(days). Additionally,though subsidence magnitudes are difficult to precisely measure in the central Utah region using X-band data, they can often be reasonably estimated.