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.     

Monitoring on subsidence due to repeated excavation with DInSAR technology

DInSAR technology was used to monitor subsidence caused by underground coal mining activities in mountainous area, with multi source SAR data, including 8 EnviSAT C-band and 4 ALOS L-band, and 4 programmed TerraSAR-X dataset. The results revealed that 2-pass DInSAR technique sometimes failed to retrieve the mining-caused subsidence due to spatial and/or temporal de-correlation. We also noticed that there existed residual topographic phase after the compensation with SRTM DEM, which could almost overwhelm the subsidence information when the perpendicular baseline was relatively large. Based on the mining materials, analysis was made on the shape of subsidence area. For the well geo-coded results from TerraSAR-X, confirmed by GPS surveying results of corner reflectors, we tried to extract the advance distance of influence besides the subsidence area. Due to the big deformation gradient over stopingfaces, the X-band SAR data could not capture the maximum value subsidence revealed by GPS survey in our preliminary results, the same as C-band EnviSAT data. This will turn to be our research subject in the next few months.     

基于改进型GIW算法的TerraSAR-X图像斑点噪声抑制

针对TerraSAR-X图像中的斑点噪声抑制问题,分析了噪声的基本数学模型,并考察了经典处理方法,进而结合Kuan滤波的基本思想,给出了一种改进型梯度倒数加权(GIW)滤波算法,利用邻域像素和中心像素的灰度梯度倒数及其邻域统计信息构造滤波加权模板,然后通过卷积操作实现斑点噪声的抑制。对TerraSAR-X图像处理的结果表明该算法原理简单,实现方便,可有效抑制图像中的斑点噪声,同时具有优良的边缘细节保持能力,非常适合边缘提取、目标检测等后续处理。     

基于MRF模型和形态学运算的SAR图像分割

针对目标监测分析中的SAR图像分割问题,构造了一种基于马尔可夫随机场(MRF)模型和形态学运算的处理方法.首先利用SAR图像邻域空间上的马尔可夫性以及像素灰度的高斯分布模型,以较少的迭代次数实现了SAR图像的初分割;然后通过形态学运算进行处理,抑制干扰性分割,同时填充目标区域内部空洞,改善分割效果.实验结果显示,该方法...     

基于TerraSAR-X数据监测矿区地表沉陷

采用TerraSAR-X数据,运用GAMMA软件进行D-INSAR二轨法数据处理,获得某矿区工作面地表下沉数据,绘制出相应的下沉盆地,对比实测数据,研究得出:矿区D-INSAR监测下沉值曲线与水准实测下沉值曲线在走势上一致,得到了两者差值和点位距离地表最大下沉点距离之间的变化规律;尝试性的给出了改正公式;改正效果满足地表形变监测的需要。     

利用TerraSAR-X影像监测矿区沉降试验研究

阐述合成孔径雷达差分干涉测量(D-InSAR)技术原理和方法,采用二轨法获取西山矿区地表形变信息。通过ARCGIS软件显示形变图,并提取角反射器处形变值,与GPS实测结果一致。验证了高分辨率TerraSAR-X影像监测矿区地表沉降的可行性。     

时序DInSAR在重复采动地表沉陷监测中的应用

基于TerraSAR-X高分辨率SAR数据,利用时序DInSAR技术监测矿区开采沉陷,探讨其在开采沉陷监测中的定量化应用。结合矿区工作面回采进度资料,对时序DInSAR结果进行分析,提取不同开采阶段的超前影响角、边界角、起动距等参数,以认识和描述上覆岩层存在重复采动时的地表沉陷规律。利用在工作面上方角反射器位置上同步获取的GPS观测结果对DInSAR技术进行验证,结果表明X波段SAR数据可以准确监测微小形变,从而保证了开采沉陷影响范围监测及角量参数提取的可靠性。     

星载SAR传感器外场定标实验研究—以TerraSAR-X卫星为例

传感器的定标精度是衡量SAR卫星系统性能和数据质量的重要标志。以TerraSAR\|X卫星为例,系统设计了外场定标实验流程,围绕高精度获取参考目标外场实际几何坐标和散射截面积的目标,对角反射器的设计与制作、布设方案、状态参数计算和坐标精密测量等问题进行了研究。基于同步获取的SAR数据和外业数据,进行了TerraSAR\|X几何和辐射定标参数的精度验证。验证结果证明了实验总体设计和关键问题解决的有效性,对于其他星载SAR定标工作具有借鉴意义。
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Settlement detection and impervious surface estimation in the Mekong Delta using optical and SAR remote sensing data

Information about the extent of impervious surface and its rate of development is a valuable indicator of urban growth and environmental quality and thus relevant for a wide range of research related to urban ecosystems. Using SPOT-5 data from 2005 to 2009, impervious surface was estimated at a subpixel level for the area of Can Tho province in the Mekong Delta, based on a Support Vector Regression model. Training data comprised a set of SPOT-5 reflectance values each associated with an individual value of subpixel imperviousness as their respective labels. The latter were obtained on the basis of a land cover map, which in turn was derived from a pansharpened QB subset by means of an object-oriented image classification approach. In addition, by varying different sets of training data in the model building process the spectral interrelationships between the urban land cover classes (water, bare soil, vegetation, and impervious surface) and their effect on the estimation of subpixel imperviousness could be examined. In order to exclude irrelevant areas (natural/undeveloped land) from the impervious surface estimation process, single-polarised TerraSAR-X data were used to delineate settlement areas by an object-oriented image classification approach. Furthermore, a change detection method was applied for the respective time period in order to test the suitability of the approach for the automated detection of structural developments within the urban topography. Settlement areas were correctly identified with overall accuracies between 81% and 94%, whilst the comparison of the modelled impervious estimates to the training values gave an absolute mean error below 15%. The results prove the suitability of the approach for an area-wide but selective mapping and monitoring of impervious surface cover within settlement areas only.     

基于时间序列TerraSAR-X影像的线状地物沉降监测试验研究

新一代高分辨雷达卫星尤其是TerraSAR-X的发射运行,给时序InSAR分析技术带来新的挑战,得益于其高分辨率特点使得特定目标的短期内微小形变监测成为可能。本文利用2009年3月～2010年1月间27景3m分辨率的TerraSAR-X数据,基于改进的时间序列InSAR技术获得京津城际高铁武清段和北辰段沿线区域地表时序形变特征,并对形变规律及成因进行分析。