ALOS-2 PALSAR-2的干涉相干性分析--以黄河上游地区为例

作为目前仅有的L波段星载SAR系统,PALSAR-2在国土资源调查和地质灾害监测方面有着广泛和独特的应用潜力。自其发射运行以来,它的数据质量成为用户们广泛关注的焦点。对于涉及雷达干涉测量的应用来说,干涉相干性是SAR数据最重要的质量评价指标之一。本文选取了覆盖黄河上游山区的PALSAR-2与PALSAR各一对影像,开展了干涉处理实验分析。本文提出采用相干分解技术,可以抑制几何去相干的影响,并从地物类型和地形坡度两个方面,分析比较它们的相干性分布差异,同时,给出了黄河上游地区的差分干涉初步结果。实验结果表明,在地形起伏较大的地区,相似观测模式获取的PALSAR-2数据的相干性通常优于PALSAR数据,干涉性能有显著的提升。同时,PALSAR-2差分干涉图在拉西瓦水电站果卜岸坡坡体上探测到了清晰的形变条纹,在滑坡体形变监测方面展现出巨大的应用潜力。

结合Landsat 8与PALSAR-2影像的龙南县针叶林蓄积量遥感估测研究

林分蓄积量估测是林业遥感的重要研究领域,由于云雾天气和光谱饱和现象等因素限制了光学遥感影像估测林分蓄积量的精度。合成孔径雷达(SAR)具有穿透性强、受云雾影响小等特点,弥补了光学遥感的不足。以江西省龙南县的针叶林为研究对象,结合Landsat 8与PALSAR-2双极化SAR影像数据,在遥感数据预处理基础上,提取了光谱信息、植被指数、纹理信息和后向散射系数等共245个遥感因子。基于Pearson相关系数法和多元逐步回归法,筛选出65个遥感因子参与林分蓄积量估测。以林分郁闭度作为分层因子,分别采用线性、KNN、支持向量机(SVM)、多重感知机(MLP)和随机森林(RF)5种模型估测林分蓄积量,并对估测结果进行精度检验。实验结果表明:1)相比单独使用Landsat 8的光谱和纹理信息,基于郁闭度分级并融合PALSAR-2的后向散射信息明显提高了蓄积量的反演精度;2)对于低郁闭度林分,线性模型精度最高(rRMSE=21.16%),中郁闭度林分,多重感知机模型估测效果最好(rRMSE=30.61%),高郁闭度林分,多重感知机模型估测效果最好(rRMSE=27.53%)。在结合PALSAR-2的后向散射系数的基础上,郁闭度分层能有效改善中高蓄积量区域的反演精度。

基于轨道参数修正的PALSAR-2影像正射校正技术

对PALSAR-2影像进行正射校正来评估新一代L波段的传感器的应用潜力有重要的意义。校正过程中的轨道参数误差会影响最终的校正精度。基于此,给出一种基于轨道参数修正和RD模型简化解算的PALSAR-2影像校正方法,利用模拟SAR与真实SAR影像的配准,修正轨道参数,再利用修正后的轨道参数与RD模型简化解算,完成校正工作。将该方法同时应用于PALSAR-2和PALSAR影像,并与没有经过轨道参数修正的方法进行比较,结果表明该方法可操作性强,相比于没有经过轨道参数修正的方法有较高的精度,且新一代L波段传感器影像的校正精度更高,这也进一步证实了新一代L波段传感器有更强的性能指标,应用潜力更大。

Evaluating the Extraction Approaches of Flood Extended Area by Using ALOS-2/PALSAR-2 Images as a Rapid Response to Flood Disaster

Flash floods are recurrent events around the Japan region almost every year. Torrential rain occurred around Kanto and Tohoku area due to typhoon No. 18 in September 2015. Overflowing of the Kinugawa River led to river bank collapse. Thus, the flood extended into Joso City, Ibaraki Prefecture, Japan. ALOS-2/PALSAR-2 was the fastest satellite to record this flood disaster area. A quick method to extract the flood inundation area by utilizing the ALOS-2/ PALSAR-2 image as a rapid response to the flood disaster is required. This study evaluated three methods to extract the flood immediately after the flood occurring. This study compared the extraction approaches of flooded area by unsupervised classification, supervised classification and binary/threshold of backscattering value of flood. The results show that unsupervised classification and supervised classification are overestimated. This study recommends the binarization of the backscattering value to extract the extended flood area. This method is a straight forward approach and generates a similar distribution with the field survey by using the aerial photo with high accuracy (94% of kappa coefficient). We utilized slope map which derived from DEM data to eliminate the overestimated area due to shadowing effect in SAR images.

Seasonal Effects of Backscattering Intensity of ALOS-2 PALSAR-2 (L-Band) on Retrieval Forest Biomass in the Tropics

This research has used the L-band radar from ALOS-2 PALSAR-2 and field work data for evaluation of seasonal effects of backscattering intensity on retrieval forest biomass in the tropics. The effects of seasonality and HH, and HV polarizations of the SAR data on the biomass were analyzed. The dry season HV polarization could explain 61% of the biomass in this study region. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies were not very sensitive to the in situ biomass. Strong dependence of the biomass estimates with season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. This research expects that the results obtained in this research will contribute to monitoring of the quantity and quality of forest biomass in Vietnam and other tropical countries.

Time series analysis of L-band PALSAR-2 images in Istanbul and Kocaeli,Turkey

ABSTRACT Conducting long measurements of infrastructure deformation is a critical engineering task.Conventional methods are both timeconsuming and expensive,limiting their use for large-scale applica-tions.The synergy of synthetic aperture radar(SAR)and geographic information systems(GIS)offers a complementary approach.This study focuses on the feasibility of using time series analysis of L-band PALSAR-2 images to discover land displacements in Istanbul and Kocaeli,significant industrial and residential areas in Turkey.PALSAR-2 phase and intensity information were analyzed.For phase analysis,14 L-band images from 2014 to 2021 were taken into account.Small baseline subset(SBAS)analysis was performed using 44 pairs,and results of the velocity,coherence and back-scattering values are presented.Coherence of all pairs and their correlations were calculated.Principal Component Analysis(PCA)reduced the dimension of coherence pairs,enhancing feature extraction and the final geocoded velocity map revealed a fastest subsidence rate of−58 mm/yr and a mean subsidence of−20 mm/yr.These findings were confirmed through mean vertical velocity from Sentinel-1 datasets and field observations.The results showed that immature land subsidence in the mentioned areas are growing slowly,which can be taken as a serious risk in future.

Precise Three-Dimensional Deformation Retrieval in Large and Complex Deformation Areas via Integration of Offset-Based Unwrapping and Improved Multiple-Aperture SAR Interferometry:Application to the 2016 Kumamoto Earthquake

Conventional synthetic aperture radar(SAR)interferometry(InSAR)has been successfully used to precisely measure surface deformation in the line-of-sight(LOS)direction,while multiple-aperture SAR interferometry(MAI)has provided precise surface deformation in the along-track(AT)direction.Integration of the InSAR and MAI methods enables precise measurement of the two-dimensional(2D)deformation from an interferometric pair;recently,the integration of ascending and descending pairs has allowed the observation of precise three-dimensional(3D)deformation.Precise 3D deformation measurement has been applied to better understand geological events such as earthquakes and volcanic eruptions.The surface deformation related to the 2016 Kumamoto earthquake was large and complex near the fault line;hence,precise 3D deformation retrieval had not yet been attempted.The objectives of this study were to①perform a feasibility test of precise 3D deformation retrieval in large and complex deformation areas through the integration of offset-based unwrapped and improved multiple-aperture SAR interferograms and②observe the 3D deformation field related to the 2016 Kumamoto earthquake,even near the fault lines.Two ascending pairs and one descending the Advanced Land Observing Satellite-2(ALOS-2)Phased Array-type L-band Synthetic Aperture Radar-2(PALSAR-2)pair were used for the 3D deformation retrieval.Eleven in situ Global Positioning System(GPS)measurements were used to validate the 3D deformation measurement accuracy.The achieved accuracy was approximately 2.96,3.75,and 2.86 cm in the east,north,and up directions,respectively.The results show the feasibility of precise 3D deformation measured through the integration of the improved methods,even in a case of large and complex deformation.

联合ALOS-2和Landsat 8的绿洲土壤水分反演模型研究

机器学习和多源数据融合是土壤水分反演研究的热点方向,但对L波段合成孔径雷达(synthetic aperture radar,SAR)数据的研究较少。以额济纳绿洲为研究区,利用ALOS-2 PALSAR-2和Landsat 8影像提取雷达和光学特征参数,通过参数重要性评分进行特征筛选,采用随机森林方法建立基于雷达、光学以及雷达-光学特征参数协同的土壤水分反演模型,对比模型精度,反演绿洲土壤水分。结果表明,与C波段相比,L波段SAR数据对干旱荒漠绿洲区土壤水分含量敏感性更高;雷达特征参数中重要性较高的为表面散射和体散射分量,二面角散射和螺旋体散射分量相对偏低;光学特征参数中植被供水指数重要性最高,增强型植被指数重要性最低。雷达特征参数方案最优模型决定系数R^(2)、均方根误差(root mean square error,RMSE)分别为0.67、2.16%,光学特征参数方案模型精度普遍较低且精度相当,R^(2)、RMSE分别为0.5、2.47%;雷达-光学参数协同反演的最优模型R^(2)、RMSE分别为0.72、1.99%,相比单一数据源,R^(2)分别提升7.46%、38.4%,RMSE分别降低8.54%、22.6%。研究证明,基于多源数据融合的随机森林模型在干旱荒漠绿洲区具有较高的预测精度和良好的适用性。

Sentinel-1数据在西南山区水库变形斜坡InSAR监测中的适用性评价:以溪洛渡水库为例

Sentinel卫星凭借其超高的辐射分辨率、稳定的轨道系统、较大的覆盖能力、较短的重返时间、可免费下载的数据,在斜坡灾害识别监测方向上有广泛的应用。自1963年意大利瓦伊昂特大滑坡发生以来,岸坡地质灾害一直是峡谷区水库关注的主要问题之一。以金沙江上游溪洛渡水库区为例,结合PALSAR-2、TerraSAR-X数据,评价Sentinel-1 SAR数据在西南山区水库变形斜坡InSAR监测中的适用性,以理论结合实际结果分析Sentinel-1数据是否可以在一定条件下替代其他商业数据,为今后相关行业应用提供参考。结果显示:Sentinel-1数据在研究区可解译的变形斜坡约200处,类型有滑坡、危岩体和塌岸;经现场核查,Sentinel-1数据解译的最小变形斜坡投影面积约为2400 m^(2),约35 m(长)×77 m(宽)大小,共16个变形像元聚集。高山峡谷区叠掩、阴影现象严重,通过对雷达常用观测模式下的SAR数据的比较,在SAR数据交集区域,有效观测面积为Sentinel-1升轨70.3%,Sentinel-1降轨68.9%,PALSAR-2升轨70.4%,PALSAR-2降轨67.6%,TerraSAR-X降轨52.5%,在不考虑分辨率的情况下,在库区Sentinel-1数据与其他两种SAR数据观测能力相比持平或更优秀。6月至11月初是溪洛渡水库的水位上升期,周边植被发育较好,造成数据相干性较差,2017年后Sentinel-1A(1B)双星拍摄获取的SAR数据量增加,高频观测可使相干性提高,利用2017年后该卫星数据可有效识别水库蓄—排水周期内的区域性变形斜坡发育变化情况。当长时间缺失SAR数据时,会造成最近一对SAR数据间的某些像元测量的变形超过其InSAR最大量程,解缠时丢失相位周期。Sentinel-1数据由于连续性较好,监测斜坡的变形趋势较为连续,因此更适合连续小变形的趋势识别。

基于PS-InSAR和offset tracking技术的金沙江白格滑坡形变监测

2018-10-11西藏江达县波罗乡白格村附近发生山体滑坡,导致金沙江断流并形成堰塞湖。收集高分二号、高分三号卫星数据,分析滑坡形成的堰塞湖对上游村镇的影响,并基于Sentinel-1以及ALOS-2 PALSAR-2数据,分别应用PS-InSAR和offset tracking技术获取滑坡体在滑坡发生前的运动特征。结果表明,白格滑坡在灾害发生前已有明显的滑移,根据PS-InSAR技术结果,2017~2018年白格滑坡边缘处存在年平均视线向滑动速率超过2.5 cm/a的PS点,且在白格滑坡周边发现2处潜在滑坡;根据offset tracking结果,白格滑坡在2015-07~2018-07期间的3个时间段呈现出滑动不断加速的特征,滑坡累计滑动量超过30 m。应用InSAR技术可在滑坡灾害发生前识别出活动性滑坡,为滑坡监测预警提供重要依据。

Forest height mapping using inventory and multi-source satellite data over Hunan Province in southern China

Background:Accurate mapping of forest canopy heights at a fine spatial resolution over large geographical areas is challenging.It is essential for the estimation of forest aboveground biomass and the evaluation of forest ecosystems.Yet current regional to national scale forest height maps were mainly produced at coarse-scale.Such maps lack spatial details for decision-making at local scales.Recent advances in remote sensing provide great opportunities to fill this gap.Method:In this study,we evaluated the utility of multi-source satellite data for mapping forest heights over Hunan Province in China.A total of 523 plot data collected from 2017 to 2018 were utilized for calibration and validation of forest height models.Specifically,the relationships between three types of in-situ measured tree heights(maximum-,averaged-,and basal area-weighted-tree heights)and plot-level remote sensing metrics(multispectral,radar,and topo variables from Landsat,Sentinel-1/PALSAR-2,and SRTM)were analyzed.Three types of models(multilinear regression,random forest,and support vector regression)were evaluated.Feature variables were selected by two types of variable selection approaches(stepwise regression and random forest).Model parameters and model performances for different models were tuned and evaluated via a 10-fold cross-validation approach.Then,tuned models were applied to generate wall-to-wall forest height maps for Hunan Province.Results:The best estimation of plot-level tree heights(R2 ranged from 0.47 to 0.52,RMSE ranged from 3.8 to 5.3 m,and rRMSE ranged from 28%to 31%)was achieved using the random forest model.A comparison with existing forest height maps showed similar estimates of mean height,however,the ranges varied under different definitions of forest and types of tree height.Conclusions:Primary results indicate that there are small biases in estimated heights at the province scale.This study provides a framework toward establishing regional to national scale maps of vertical forest structure.

Improvement of Bare Soil Semi-Empirical Radar Backscattering Models (Oh and Dubois) with SAR Multi-Spectral Satellite Data (X-, C- and L-Bands)

The objective of this study is to improve the performance of semi-empirical radar backscatter models, which are mainly used in microwave remote sensing (Oh 1992, Oh 2004 and Dubois). The study is based on satellite and ground data collected on bare soil surfaces during the Multispectral Crop Monitoring experimental campaign of the CESBIO laboratory in 2010 over an agricultural region in southwestern France. The dataset covers a wide range of soil (viewing top soil moisture, surface roughness and texture) and satellite (at different frequencies: X-, C- and L-bands, and different incidence angles: 24.3° to 53.3°) configurations. The proposed methodology consists in identifying and correcting the residues of the models, depending on the surface properties (roughness, moisture, texture) and/or sensor characteristics (frequency, incidence angle). Finally, one model has been retained for each frequency domain. Results show that the enhancements of the models significantly increase the simulation performances. The coefficient of correlation increases of 23% in mean and the simulation errors (RMSE) are reduced to below 2 dB (at the X and C-bands) and to 1 dB at the L-band, compared to the initial models. At the X- and C-bands, the best performances of the modified models are provided by Dubois, whereas Oh 2004 is more suitable for the L-band (r is equal to 0.69, 0.65 and 0.85). Moreover, the modified models of Oh 1992 and 2004 and Dubois, developed in this study, offer a wider domain of validity than the initial formalism and increase the capabilities of retrieving the backscattering signal in view of applications of such approaches to stronglycontrasted agricultural surface states.

基于PALSAR雷达数据的于田绿洲土壤盐渍化反演

土壤盐渍化是当今土地退化和荒漠化的主要形式之一,不仅严重制约农业和经济的发展,并且对生态环境和人类生存造成威胁。本研究以新疆于田绿洲为研究区,利用四极化PALSAR(Phased Array type L-band Synthetic Aperture Radar)数据后向散射系数,土壤含水量,土壤pH值以及土壤盐分实测值,采用多元线性回归模型、地理加权回归模型和BP神经网络模型,以土壤含盐量作为因变量建立了定量反演模型。从土壤盐分反演结果图可以看出,反演结果与地面实地考察结果基本一致。经过模型验证得知,3层BPANN模型的均方根误差RMSE=0.99,平均相对误差MRE=0.31,模型性能指数RPD=5.34,其模型预测能力优于前2种传统模型。本文建立的神经网络模型无需考虑复杂的介电常数,在一定程度上能够满足土壤盐渍化监测的需要,促进PALSAR数据在土壤盐渍化监测中的应用。

星载LiDAR产品辅助的InSAR森林高度反演

针对星载重轨InSAR森林高度反演受时间去相干制约与模型解算辅助数据难以获取的问题。考虑ALOS-2 PALSAR-2干涉数据特点,采用一种顾及时间去相干影响的半经验散射模型,利用最新发布的星载激光雷达ICESat-2 ATL08高程产品中的植被高度数据作为辅助数据,并结合主成分分析思想(PCA)对ALOS-2 PALSAR-2相干幅度信息与树高的关联模型进行参数解算。实验结果表明,在ICESat-2树高数据辅助条件下,通过散射模型可以较好抑制时间去相干的影响,进而反演出可靠的模型参数及森林高度(RMSE约为3 m)。本研究验证了联合星载重轨干涉SAR与星载LiDAR数据实现大范围、大尺度森林高度反演的可行性。

西域都护府/且末古城数字地望考与长波段雷达次地表考古初探

西汉时期的西域都护府治所乌磊城与"西域三十六国"之一的且末城,在历史文献中曾多次出现。然而由于尚未发现合乎描述的古城或者证明古城身份的证据,目前的考古界对于这两座古城的具体位置未能确定。在总结前人研究成果的基础之上,首先利用《汉书·西域传》中所记载的古城间道里数,结合数字地图空间分析以及自然地理环境与路网关系,对两个未知古城嫌疑热点区域进行定位。其次,利用合成孔径雷达(Synthetic Aperture Radar,SAR)对干旱地表的穿透性,定量评估了L波段PALSAR-2卫星(Phased Array Type L-band Synthetic Aperture Radar)在古城热点区域的穿透深度。结果表明:在位于阳霞绿洲东北缘的西域都护府疑似区,PALSAR-2穿透能力有限,次地表古城直接探测并不可行,需通过对周边古河道、古道路以及古烽燧等的探测及空间分析确定其位置;然而在位于今且末县城北约100km的且末古城疑似区,PALSAR-2穿透性较好,具备直接探测次地表古城的潜力。

江苏全省地面沉降InSAR监测

针对江苏省是长三角地区地面沉降比较严重的地区,InSAR技术作为最有效的地表形变监测手段,曾被用来获取江苏局地的地面沉降信息,但用于全省域监测尚无先例的现状。该文介绍了利用时间序列InSAR技术开展江苏全省地面沉降监测的方法和成果,提出了适用于区域级和国家级合成孔径雷达干涉测量(InSAR)形变监测的技术方法,给出了基于ALOS PALSAR影像和RADARSAT-2影像的江苏全省2007-2011年及2012-2015年2个时段的地面沉降监测成果,利用江苏省CORS站数据,对InSAR获取的地面沉降速率进行了精度评价。结果表明,2个时段的InSAR监测结果的精度分别为3.8(mm·a^-1)和4.0(mm·a^-1)。最后对2个时段的全省地面沉降的时空分布、动态变化情况进行了分析,并对InSAR监测成果的应用前景进行了展望。