基于相干点目标的多基线D-InSAR技术与地表形变监测

失相干（Decorrelation）与大气波动是影响重复轨差分干涉测量（D-InSAR）进行地表形变信息提取的主要因素。相干性降低使得干涉纹图在空间上表现为不连续,难以完成相位解缠。重复观测时大气波动引起的相位延迟在空间域上的不均一分布则降低了D-InSAR提取形变信息,特别是空间范围覆盖较大的形变场的精度。介绍了一种基于相干目标的多基线D-InSAR数据处理算法。该算法根据少量SAR数据构成多基线干涉纹图集,分别利用点目标检测算法和相干系数均值作为相干目标提取的测度;利用相位回归分析模型对干涉相位进行时间域迭代处理,从干涉相位中提取线性形变速率和DEM误差改正,通过迭代处理补偿高程误差,解算线性地表形变速率。该算法提高了D-InSAR形变监测的时间采样率,能准确获取每个观测时刻的形变累积量。以沧州地区2004—2005年的SAR数据为例,获取了该地区地表沉降线性速率及其演变状况。     

基于差分干涉SAR的煤田火区地表形变监测

煤田火区地表形态变化是煤火监测和分析的可用指标之一。由于矿区地表严重的去相干噪声,使得星载合成孔径雷达干涉测量技术应用于煤田火区地表形变检测比较困难。结合煤田火区地表形变特点,利用L波段的ALOS PALSAR数据进行差分干涉处理,利用干涉条纹频率精确估计基线,并用自适应滤波方法降低去相干噪声的影响。在去除平地相位和参考地形相位后,获取煤田火区的地表形变。研究表明:地表形变与煤火燃烧具有一定的相关关系,通过地表形变分析有助于对地下煤火燃烧情况的判断;利用差分干涉SAR技术检测煤田火区地表形变,进而对煤火进行监测和分析是可行的。     

高分辨率雷达卫星COSMO-SkyMed干涉测量生成DEM的实验研究

介绍了COSMO-SkyMed对地观测系统,分析了高分辨率SAR传感器和Tandem观测星座与相干性之间的关系,并利用间隔1 d的COSMO-SkyMed Tandem数据生成了祁连山区的DEM。提出通过对无地形变化的差分干涉相位进行滤波来提取大气和轨道误差引起的测量偏差,探讨了实验干涉数据去相干的因素,并将生成的DEM与ASTER GDEM进行了对比。     

利用矩阵值域的极化干涉SAR相干性的研究

极化SAR干涉测量 (PolInSAR)的相干性是反演植被参数的重要信息来源,极化空间中不同极化状态对应的相干性分布与复极化相干矩阵的值域相关。本文讨论了不同结构的复极化相干矩阵值域的特点,分析了不同极化状态下干涉相干性与复极化相干矩阵的值域的关系。利用模拟数据和真实全极化SAR数据分析了滑动窗口大小和不同散射体对复极化干涉矩阵值域的影响,以及复极化干涉矩阵的结构对极化干涉SAR相干性分布空间的影响,有助于更加准确地获取极化干涉SAR最优极化基和估计最优相干性。     

基于ALOS/PALSAR雷达干涉数据的中国西部山谷冰川冰流运动规律研究

中国西部山谷冰川相对于南极和格陵兰的冰川来说,一般规模较小,冰川周围地形变化大,冰流运动复杂,容易导致冰川表面的完全失相干,从而很难提取出山谷冰川的冰流速度.为了削弱常规干涉测量中时间和空间失相干问题对山谷冰川冰面运动速度提取的影响,采用2007-12-10和2008-01-25的一对时间间隔46 d的L波段ALOS/PALSAR雷达数据,利用两轨法差分干涉测量成功的获取了青藏高原冬克玛底冰川表面的冰流运动速度,并将所得结果与该地区利用1996年C波段的ERS tandem数据获取的结果进行了对比验证.研究表明对于中国西部无人区的山谷冰川来说,雷达差分干涉测量技术是获取其冰流运动规律的有效手段,另外,相同时间间隔的L波段SAR数据比C波段SAR数据在提取冰流运动信息方面更具优势.     

基于ALOS PALSAR数据进行雷达差分干涉提取玉树地震同震地表形变场

本文针对2010年4月14日玉树地震引起的地表形变,使用日本ALOS卫星PALSAR L波段雷达影像数据,应用两轨雷达差分干涉(DInSAR)处理得到了以玉树为中心11 000km2范围内的同震形变场,空间分辨率为8m,并在此基础上对玉树地震的震源机制和发震机理进行了分析。研究结果表明L波段雷达数据适合在地形起伏较大的地区进行DInSAR形变探测。该同震形变场信息可为玉树地震的同震形变反演提供参考数据。该研究进一步证实DInSAR技术在大规模地表形变探测和地学研究领域具有广阔的应用前景。     

一种监测东营油田地表形变的方法

针对传统永久散射方法和相干目标法干涉图相位解缠的不足,该文提出了一种改进的多主影像相干目标小基线干涉方法。采用融合永久散射方法和相干目标法的优点,较好地平衡了失相干因素的去相干性与干涉目标点之间的矛盾,利用较小的数据集实现地表形变反演;基于14景COSMO-SKYMED雷达影像,采用多主影像相干目标小基线干涉方法提取地表形变信息。基于水准数据验证后的实验结果表明:所提方法不仅可以准确快速地监测出地表沉降区,而且监测精度较高,研究结果可以应用于城市形变监测、地震、滑坡等领域。     

多频率InSAR提取沼泽湿地DEM精度对比分析

选取3种波长的干涉SAR数据对提取沼泽湿地区域的DEM，并随机从1：10 000地形图中选取111个点数据进行精度验证，最后对比分析了沼泽湿地植被对于不同SAR波长的干涉相干性差异。结果表明：L-band ALOS-1 PALSAR精细模式的HH单视复数数据与1：10 000地形图数据吻合度较好，76.58%的高程值差异在3 m以内，其相干系数比C-band Sentinel-1A IW模式的VV单视复数数据和X-band TerraSAR HH单视复数数据要高；更适合利用雷达干涉测量技术提取沼泽湿地的DEM；不同湿地植被类型的相干系数有较大差异，岛状林和灌草结合的湿地植被分布区相干系数值较大，而浅水沼泽植被区和深水沼泽植被区相对较低。     

L波段差分干涉SAR卫星基础形变产品分类

L波段差分干涉SAR卫星(陆探一号,LT-1)是我国第1组以干涉为核心任务的L频段全极化民用SAR卫星星座,LT-1由1型2星组成,利用差分形变测量技术完成指定区域的形变监测任务。本文综合研究了我国形变监测需求以及卫星观测能力,提出了基础形变产品的3个层次。第1层次为形变场产品,是使用同一地区两景检校的单视复数(single look complex, SLC)影像进行差分干涉生产的。第2层次为形变速率场产品,是使用同一地区多景检校的SLC影像,通过对其形变场产品进行加权堆叠生产的。第3层次为形变时序产品,是使用同一地区多景检校产品,通过时序建模生产的。本文以覆盖山西省大同市云冈区的Sentinel-1数据为例,对形变产品进行了研究分析,并对产品的特性和结果进行了初步说明。本文提出的形变产品体系能够为产品的业务化生产提供参考。     

IPTA方法在地面沉降监测中的应用

干涉点目标分析(IPTA)技术是一种新兴的In SAR形变测量技术。它克服了常规雷达差分干涉测量(DIn SAR)受失相干因素和大气延迟影响较大的问题。介绍了IPTA方法及处理流程,并对参数迭代估计、基线精化等关键问题进行了阐述。对加利福尼亚州安大略市的39幅ENVISAT ASAR数据进行IPTA处理,提取了2005—2010年间的地面沉降信息并进行了形变分析。结果表明,该方法在大范围地面沉降监测中应用效果良好。     

顾及地形因素的S-RVOG模型和PD相干最优算法联合反演植被高度

针对极化干涉SAR植被高度反演中RVOG模型未考虑地形影响,且三阶段算法受到地面相位估计误差和纯体相干性估计误差影响,提出了一种植被高度反演思路,采用考虑地形因素的S-RVOG模型作为反演模型校正地形影响,同时引入PD相干最优算法用于改善三阶段算法中直线拟合地表相位估计和纯体相干性估计精度。为验证算法的有效性,首先采用欧空局提供的PolSARpro软件模拟了不同地形坡度水平的PolInSAR数据进行仿真试验,然后采用德国宇航局提供的E-SAR机载全极化SAR数据进行真实植被场景测试,并进行了定性和定量分析。结果表明,本文方法对于不同坡度水平数据,均能有效改善传统RVOG反演模型中地形影响和三阶段算法自身误差影响,反演精度更高。     

Multi-frequency satellite radar imaging of cultural heritage: the case studies of the Yumen Frontier Pass and Niya ruins in the Western Regions of the Silk Road Corridor

Synthetic Aperture Radar (SAR) remote sensing is increasingly favoured in archaeological applications. However, the effectiveness of this technology for archaeological prospection has so far not been fully assessed. In this study, an integrated single-date and multi-temporal SAR data-processing chain was proposed to sharpen archaeological signs and hence their detection and monitoring. In total, 14 scenes of X-band Cosmo-SkyMed, C-band Sentinel-1 and L-band PALSAR data covering the Western Regions of the Silk Road Corridor in China were employed for two important archaeological sites including the Yumen Frontier Pass with emerging archaeological traces and Niya ruins with subsurface remains. The results pointed out that single-date satellite radar data were useful for the identification of subsurface traces buried under desert in the landscape-scale, whereas for the identification of emerging monuments, Sentinel-1 was limited by its lower spatial resolution compared to TerraSAR and PALSAR data. Multi-date products, such as interferometric coherence, the averaged radar signatures and RGB multi-temporal composites, were effective to sharpen archaeological traces as well as for change detection in Yumen Frontier Pass. This study presents a pilot assessment of satellite SAR data for the analysis and monitoring of archaeological features in the predominantly arid-sandy environmental characteristic of investigated sites.     

Classification of dual- and single polarized SAR images by incorporating visual features

Fully and partially polarimetric SAR data in combination with textural features have been used extensively for terrain classification. However, there is another type of visual feature that has so far been neglected from polarimetric SAR classification: Color. It is a common practice to visualize polarimetric SAR data by color coding methods and thus it is possible to extract powerful color features from such pseudo color images so as to gather additional crucial information for an improved terrain classification. In this paper, we investigate the application of several individual visual features over different pseudo color generated images along with the traditional SAR and texture features for a novel supervised classification application of dual- and single-polarized SAR data. We then draw the focus on evaluating the effects of the applied pseudo coloring methods on the classification performance. An extensive set of experiments show that individual visual features or their combination with traditional SAR features introduce a new level of discrimination and provide noteworthy improvement of classification accuracies within the application of land use and land cover classification for dual- and single-pol image data.     

Detection of Slope Instability in Hong Kong Based on Multi-baseline Differential SAR Interferometry Using ALOS PALSAR Data

The monitoring of slope instability requires detailed observations of mass movements, which generally cannot be obtained by geodetic methods or global positioning systems (GPS). Differential synthetic aperture radar (SAR) interferometry has proven to be an effective way of measuring land deformation with millimeter accuracy over wide areas. Using data from the newly launched L-band ALOS PALSAR interferometer and the multi-baseline differential SAR interferometry technique, slope instability in Hong Kong was analyzed by means of measured surface displacement along look vectors. Owing to its enhanced vegetation penetration, less temporal decorrelation enabled the L-band data to improve spaceborne radar sensor land-surface deformation measurements. The results were validated by ENVISAT ASAR-derived outcomes and other ground survey data.     

InSAR影像干涉相关性的遥感应用研究

合成孔径雷达干涉测量(InSAR)是雷达微波遥感技术的一个新研究领域.以往InSAR遥感应用研究主要集中在通过利用雷达回波信号所携带的相位信息来获取地表高程信息方面.随着InSAR研究的不断深入,干涉相位相关性中所包含的丰富信息越来越受到人们的重视,其应用领域也在不断扩大.本文所提出的基于合成孔径雷达影像干涉相位相关性信息的SAR彩色图像合成方法就是在这一新兴领域上的一个研究尝试.它能将不同类型的地物以不同的颜色直观地显示在图像上,弥补了SAR图像在遥感应用上的不足.该方法非常适合林业资源监测等方面的遥感应用研究.     

Modeling and mapping aboveground biomass of the restored mangroves using ALOS-2 PALSAR-2 in East Kalimantan,Indonesia

Accurate estimation of forest aboveground biomass (AGB) using remote sensing is a requisite for monitoring, reporting and verification (MRV) system of the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation. However, attaining high accuracy remains a great challenge in the diverse tropical forests. Among available technologies, l-band Synthetic Aperture Radar (SAR) estimates AGB with reasonably high accuracy in the terrestrial tropical forests. Nevertheless, the accuracy is relatively low in the mangrove forests. In this context, the study was carried out to model and map AGB using backscatter coefficients of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array l-band SAR-2 (PALSAR-2) in part of the restored mangrove forest at Mahakam Delta, Indonesia. PALSAR-2 data was acquired with image scene observation during the peak low tide on 30 July 2018 from Japan Aerospace Exploration Agency. The forest parameters namely tree height and diameter at breast height were measured from 71 field plots in September-October 2018. The parameters were used in mangrove allometry to calculate the field AGB. Finally, HV polarized backscatter coefficients of PALSAR-2 were used to model AGB using linear regression. The model demonstrated a comparatively high performance using three distinct methods viz. independent validation (R² of 0.89 and RMSE of 23.16 tons ha⁻¹), random k-fold cross validation (R² of 0.89 and RMSE of 24.59 tons ha⁻¹) and leave location out cross validation (LLO CV) (R² of 0.88 and RMSE of 24.05 tons ha⁻¹). The high accuracy of the LLO CV indicates no spatial overfitting in the model. Thus, the model based on LLO CV was used to map AGB in the study area. This is the first study that successfully obtains high accuracy in modeling AGB in the mangrove forest. Therefore, it offers a significant contribution to the MRV mechanism for monitoring mangrove forests in the tropics and sub-tropics.     

Assessing integration of intensity,polarimetric scattering,interferometric coherence and spatial texture metrics in PALSAR-derived land cover classification

Synthetic aperture radar (SAR) is an important alternative to optical remote sensing due to its ability to acquire data regardless of weather conditions and day/night cycle. The Phased Array type L-band SAR (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) provided new opportunities for vegetation and land cover mapping. Most previous studies employing PALSAR investigated the use of one or two feature types (e.g. intensity, coherence); however, little effort has been devoted to assessing the simultaneous integration of multiple types of features. In this study, we bridged this gap by evaluating the potential of using numerous metrics expressing four feature types: intensity, polarimetric scattering, interferometric coherence and spatial texture. Our case study was conducted in Central New York State, USA using multitemporal PALSAR imagery from 2010. The land cover classification implemented an ensemble learning algorithm, namely random forest. Accuracies of each classified map produced from different combinations of features were assessed on a pixel-by-pixel basis using validation data obtained from a stratified random sample. Among the different combinations of feature types evaluated, intensity was the most indispensable because intensity was included in all of the highest accuracy scenarios. However, relative to using only intensity metrics, combining all four feature types increased overall accuracy by 7%. Producer’s and user’s accuracies of the four vegetation classes improved considerably for the best performing combination of features when compared to classifications using only a single feature type.     

Time-series maps of aboveground biomass in dipterocarps forests of Malaysia from PALSAR and PALSAR-2 polarimetric data

Background

Malaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO₂ emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO₂ emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO₂ emissions (and removal) in the dipterocarps forests of Peninsular Malaysia.

Results

This study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year^?1 and subsequently caused a carbon loss of approximately 9 million Mg C year^?1, which is equal to emissions of 33 million Mg CO₂ year^?1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha^?1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%.

Conclusion

The PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+?implementation in Malaysia.

     

Evaluation of image fusion methods using PALSAR,RADARSAT-1 and SPOT images for land use/ land cover classification

This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.