空间微波遥感研究与应用—丛书述评

《空间微波遥感研究与应用丛书》共10部学术专著已由科学出版社2019～2020年内出版.该丛书包括了我国科学工作者近年来在星载微波主动遥感的合成孔径雷达技术、被动遥感的气象和海洋卫星的地球遥感技术等领域的部分研究成果.合成孔径雷达领域包括了:星载高分辨率宽幅SAR、SAR图像信息解译应用软件、SAR图像智能解译、空天目...     

Applications of Radarsat‐1 synthetic aperture radar imagery to assess hurricane‐related flooding of coastal Louisiana

The Louisiana coast is subjected to hurricane impacts including flooding of human settlements, river channels and coastal marshes, and salt water intrusion. Information on the extent of flooding is often required quickly for emergency relief, repairs of infrastructure, and production of flood risk maps. This study investigates the feasibility of using Radarsat‐1 SAR imagery to detect flooded areas in coastal Louisiana after Hurricane Lili, October 2002. Arithmetic differencing and multi‐temporal enhancement techniques were employed to detect flooding and to investigate relationships between backscatter and water level changes. Strong positive correlations (R ² = 0.7–0.94) were observed between water level and SAR backscatter within marsh areas proximate to Atchafalaya Bay. Although variations in elevation and vegetation type did influence and complicate the radar signature at individual sites, multi‐date differences in backscatter largely reflected the patterns of flooding within large marsh areas. Preliminary analyses show that SAR imagery was not useful in mapping urban flooding in New Orleans after Hurricane Katrina's landfall on 29 August 2005.     

Flood boundary delineation from Synthetic Aperture Radar imagery using a statistical active contour model

Flood extent maps derived from remotely sensed data can provide distributed validation data for hydraulic models of fluvial flow, and can be used for flood relief management and to develop spatially accurate hazard maps. A statistical active contour model is used to delineate a flood from the first European Remote Sensing satellite Synthetic Aperture Radar (ERS-1 SAR) imagery as a region of homogeneous speckle statistics. The segmentation uses both local tone and texture measures and is capable of accurate feature boundary representation. The results are assessed by comparison with simultaneous aerial photography, the SAR segmentation scheme classifying 75% by area of the shoreline region correctly. Seventy per cent of the shoreline coincides with the ground data to within 20 m. The main error is due to unflooded vegetation giving similar radar returns to open water.     

基于MOS-1b/MESSR的洪灾遥感监测

以湖南洞庭湖为研究区，MOS-1b/MESSR影像数据作为遥感信息源，应用ERDAS IMAGINE处理系统，对枯水期和洪水期两个不同时相的各波段影像数据进行组合运算、比值变换等处理，以及影像、光谱、直方图的对比分析。基于处理与分析结果，利用分类技术，建立了水体分类模型（B1+B2）/(B3+B4)>t，可快速准确地提取水体信息；同时，还提取和建立了水深系数模型WDI=B1/B2，得出了基于非线性回归分析的水体深度探测模型。依据洪水信息提取模型，对枯水期与洪水期的分类专题图进行叠加分析，得到了洞庭湖区洪水期的洪水分布图与洪水淹没信息，可为防洪救灾决策提供重要的科学依据。     

Uncertainty analysis of flash‐flood system using remote sensing and a geographic information system based on GcIUH in the Yeongdeok Basin,Korea

The current paper focuses on minimizing flood damage in the Yeongdeok basin of South Korea by establishing a flood prediction model based on a geographic information system (GIS), remote sensing and geomorphoclimatic instantaneous unit hydrograph (GcIUH) techniques. The GIS database for flash flood prediction was created using data from digital elevation models (DEMs), soil maps and Landsat satellite imagery. Flood prediction was based on the peak discharge calculated at the sub‐basin scale using hydrogeomorphologic techniques and the threshold runoff value. Using the developed flash flood prediction model, rainfall conditions with the potential to cause flooding were determined based on the cumulative rainfall for 20 min, considering rainfall duration, peak discharge and flooding in the Yeongdeok basin.     

基于仿真数据的HJ-1C卫星SAR成像处理方法

合成孔径雷达(SAR,synthetic aperture radar)是对地遥感观测的重要技术手段.作为民用星载SAR的第一颗卫星,HJ-1C卫星即将发射,因此文中针对其成像处理中涉及的关键技术开展了研究工作,包括精确的斜视等效距离模型下的扩展ECS(extended chirp scaling)算法的补偿因子、三次相位误差补偿因子形式的分析,快速的回波数据模拟算法的建模和实现,以及精确的Doppler参数计算方法以及对扫描模式成像算法中拼接环节的算法改进.通过理论研究分析和仿真验证,证明基于等效斜视模型的扩展chirp scaling算法是适应条带、扫描成像模式的精确算法.     

Mapping of flood dynamics and spatial distribution of vegetation in the Amazon floodplain using multitemporal SAR data

This paper presents the use of time series of SAR images to map the flood temporal dynamics and the spatial distribution of vegetation over a large Amazonian floodplain. The region under study (3500 km²) presents a diversity of landscape units with open lakes, bogs, large meadows, savannahs, alluvial forests and terra firma forest, covered by 21 images acquired by J-ERS between 1993 and 1997. Ground data include in situ observations of vegetation structure and flood extent as well as water level records. Image analysis demonstrates that temporal variations of the radar backscatter can be used to monitor efficiently the flood extent regardless of the landscape units. Also, analysis of the backscatter temporal variation greatly reduces the confusion between smooth surfaces (e.g. open water bodies, bare soils) inherent to L-band backscatter. The mapping method is based on decision rules over two decision variables: 1) the mean backscatter coefficient computed over the whole time series; 2) the total change computed using an “Absolute Change” estimator. The first variable provides classification into rough vegetation types while the second variable yields a direct estimate of the intensity of change that is related to flood dynamics. The classifier is first applied to the whole time series to map the maximum and minimum flood extent by defining 3 flood conditions: never flooded (NF); occasionally flooded (OF); permanently flooded (PF). It also furnishes the broad land cover type: open water/bare soils/low vegetation/forest. The accuracy of the flood extent mapping shows a kappa value of 0.82. Then, the classifier is run iteratively on the OF pixels to monitor flood stages during which the occasionally flooded areas get submerged. The mapping accuracy is assessed on one intermediate flood stage, showing a precision in excess of 90%. The importance of the time sampling for flood mapping is discussed along with the influence of SAR backscatter accuracy and the number of images. Then general guidelines for floodplain mapping are presented. By combining water level reports with maps of different flood stages the flooding pattern can be retrieved along with the vegetation succession processes. It is shown that the spatial distribution of vegetation communities is governed by flood stress and can be modelled as a function of the mean annual exposure to floods.     

Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies

In case of a seismic event, a fast and draft damage map of the hit urban areas can be very useful, in particular when the epicentre of the earthquake is located in remote regions, or the main communication systems are damaged. Our aim is to analyse the capability of remote sensing techniques for damage detection in urban areas and to explore the combined use of radar (SAR) and optical satellite data. Two case studies have been proposed: Izmit (1999; Turkey) and Bam (2003; Iran). Both areas have been affected by strong earthquakes causing heavy and extended damage in the urban settlements close to the epicentre. Different procedures for damage assessment have been successfully tested, either to perform a pixel by pixel classification or to assess damage within homogeneous extended areas. We have compared change detection capabilities of different features extracted from optical and radar data, and analysed the potential of combining measurements at different frequency ranges. Regarding the Izmit case, SAR features alone have reached 70% of correct classification of damaged areas and 5 m panchromatic optical images have given 82%; the fusion of SAR and optical data raised up to 89% of correct pixel‐to‐pixel classification. The same procedures applied to the Bam test case achieved about 61% of correct classification from SAR alone, 70% from optical data, while data fusion reached 76%. The results of the correlation between satellite remote sensing and ground surveys data have been presented by comparing remotely change detection features averaged within homogeneous blocks of buildings with ground survey data.     

COSMO雷达数据在多云多雾地区土地变化监测研究

近年来新型成像雷达遥感(极化、干涉)及数据处理技术的发展,SAR遥感影像上获得的地表信息越来越多,如何利用雷达信息探测土地变化成为研究的新课题。但是雷达影像不同于光学影像,目前雷达数据解译仍存在着一些困难。本文主要针对多云多雾地区雷达数据土地变化监测,以四川成都地区COSMO数据为例,利用雷达相干影像,后向散射强度,强度比值影像,提出一种新的雷达处理手段,减少了雷达数据土地变化监测的工作量,提高工作效率。     

Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery

This article presents for the first time the combination of dual-polarimetric C-band Sentinel-1 synthetic aperture radar (SAR) data and quad-polarimetric L-band ALOS-2/PALSAR-2 imagery for mapping of flooded areas with a special focus on flooded vegetation. L-band SAR data is well suited for mapping of flooded vegetation, while C-band enables an accurate extraction open water areas. Polarimetric decomposition-based unsupervised Wishart classification is combined with object-based post-classification refinement and the integration of spatial contextual information and global auxiliary data. In eight different scenarios, focusing on single datasets or fusion of classification results of several ones, respectively, different polarimetric decomposition and classification principles, including the entropy/anisotropy/alpha and the Freeman–Durden–Wishart classification, were investigated. The helix scattering component of the Yamaguchi decomposition, derived from ALOS-2 imagery, showed high suitability to refine the Sentinel-1-based detection of flooded vegetation. A test site at the Evros River (Greek/Turkish border region) was chosen, which was affected by a flooding event that occurred in spring 2015. The validation was based on high spatial resolution optical WorldView-2 imagery acquired with short temporal delay to the SAR data.     

Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas

Remote sensing in the optical band is a well-established tool for monitoring changes in forested areas, although it can suffer from limitations, especially where frequent cloud cover occurs. The increased availability of space-borne radar imagery offers additional means for assessing the state of forests and monitoring their dynamics. In this study, the potential of multi-temporal space-borne SAR data for monitoring vegetation recovery over burned areas next to the Mediterranean coast is investigated. In particular, the study considers a set of ERS-SAR images, C-band and VV polarization, taken over the Castel Fusano pinewood, located near Rome, Italy, devastated in summer 2000 by a fire that burned about 350 ha of the wood. Starting from the analysis of the information contained in the variations, both in burnt and unburnt areas, of the inter annual multitemporal backscattering signatures, the study presents two different approaches, one more qualitative, the other one more quantitative, for the retrieval of the biomass re-growth after the fire. In the quantitative case, the inversion procedure computes the biomass re-growth rate by means of simulations carried out with the Tor Vergata scattering model. The obtained results are satisfactory as they are in agreement with simultaneous analysis based on optical data and in-situ measurement campaigns.     

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.     

基于COSMOＧSkyMed和SPOTＧ５的城镇洪水淹没信息快速提取研究

洪水是我国最为频繁的自然灾害之一,如何快速准确地获取洪水淹没范围在救灾减灾工作中具有重要意义.目前,卫星遥感技术已广泛应用于洪水信息提取的研究中.不同的遥感数据源在洪水信息提取中各有利弊,综合研究雷达影像和可见光影像的优缺点,建立了基于多源遥感数据的洪水淹没信息快速提取模型.首先,利用灾中第一时间获取的COSMOＧSkyMed雷达影像,采用面向对象的方法提取出洪灾发生时的水域空间信息;其次,利用灾前SPOTＧ５高分辨率光学影像,采用多光谱影像波段运算和决策树分类的思想提取出常态下的水域空间信息;最后,对灾中雷达影像COSMOＧSkyMed提取的水体和灾前光学影像SPOTＧ５提取的水体进行空间差值运算,得到洪水淹没范围信息,并利用洪水当天拍摄的无人机遥感影像对结果进行精度评价.将该模型应用于２０１３年浙江余姚水灾,监测结果表明:在洪水发生后,能够快速获取淹没范围空间信息,并且提取精度达到９３．７％,为洪灾的防治以及抗洪抢险救灾工作提供强有力的技术支撑和基础数据信息.     

Synergistic use of optical and InSAR data for urban impervious surface mapping: a case study in Hong Kong

A wide range of urban ecosystem studies, including urban hydrology, urban climate, land use planning and watershed resource management, require accurate and up‐to‐date geospatial data of urban impervious surfaces. In this study, the potential of the synergistic use of optical and InSAR data in urban impervious surface mapping at the sub‐pixel level was investigated. A case study in Hong Kong was conducted for this purpose by applying a classification and regression tree (CART) algorithm to SPOT 5 multispectral imagery and ERS‐2 SAR data. Validated by reference data derived from high‐resolution colour‐infrared (CIR) aerial photographs, our results show that the addition of InSAR feature information can improve the estimation of impervious surface percentage (ISP) in comparison with using SPOT imagery alone. The improvement is especially notable in separating urban impervious surface from the vacant land/bare ground, which has been a difficult task in ISP modelling with optical remote sensing data. In addition, the results demonstrate the potential to map urban impervious surface by using InSAR data alone. This allows frequent monitoring of world's cities located in cloud‐prone and rainy areas.     

Multi-sensor remote sensing of eddy-induced upwelling in the southern coastal region of Sicily

We identify upwelling signatures caused by eddy interactions with bottom topography on the downstream of the Malta Plateau, using space-borne remote sensing Advanced Very High Resolution Radiometer (AVHRR) imagery, ERS-1 Synthetic Aperture Radar (SAR) imagery, Topex/Poseidon/ERS-2 (TPE) altimeter maps, and Orbview2/SeaWiFS imagery. The cyclonically rotating eddy contributes to upwelling, reducing the sea surface temperature (SST) by about 1-2°C inside the eddy with respect to the ambient ocean. Ocean colour imagery shows increases in pigment concentrations (0.5 mg m^-3) associated with the eddy. In TPE altimeter maps, the eddy is manifested as a 40-50 km wide 'bowl-shaped' structure, with negative sea height anomalies ranging between 6 and 10 cm. The eddy-type motion is further evidenced in SAR imagery by the appearance of striations forming along the boundaries of the eddy as well as reduced backscatter at its centre. The study demonstrates the utility of sensor fusion and identifies a set of generic indicators for upwelling identification and tracking using multiple sensors.     

单极化合成孔径雷达影像在土地利用分类中的潜力分析

从我国土地利用调查应用出发,为了解决我国多云多雨地区土地利用分类及遥感动态监测问题,以面向对象影像分割、分类软件--Definiens Developer作为处理平台,对中分辨率星载合成孔径雷达（SAR）（以ENVISAT ASAR和Radarsat-1为例）、高分辨率星载SAR（以TerraSAR-X为例）进行分类处理,分析了单极化星载中、高分辨率星载SAR在土地利用分类中的能力,并对该模式星载SAR在土地利用分类中的影像特征和可解析程度进行了小结。     

Identification of central Kenyan Rift Valley Fever virus vector habitats with landsat TM and evaluation of their flooding status with airborne imaging radar

Rift Valley Fever (RVF) is a mosquito-borne virus that affects livestock and humans in Africa. Landsat Thematic Mapper (TM) data are shown to be effective in identifying dambos, intermittently flooded areas that are potential mosquito breeding sites, in an area north of Nairobi, Kenya. Positive results were obtained from a limited test of flood detection in dambos with airborne high resolution L, C, and X band multipolarization synthetic aperture radar (SAR) imagery. L and C bands were effective in detecting flooded dambos, but LHH was by far the best channel for discrimination (p < 0.01) between flooded and nonflooded sites in both sedge and short grass environments. This study demonstrates the feasibility of a combined passive and active remote sensing program for monitoring the location and condition of RVF vector habitats, thus making future control of the disease more promising.     

Submarine groundwater discharge in tidal flats revealed by space-borne synthetic aperture radar

Most space-borne sensors cannot detect subsurface features. Groundwater is a typical subsurface feature, and its discharge to coastal ocean waters plays an important role in transporting terrestrial chemical constituents and providing habitats for various species of fauna and flora. This is the first paper to report observational evidence for submarine groundwater discharge (SGD) in tidal flats using space-borne synthetic aperture radar (SAR). Tidal flats are composed of high-moisture-saturated sediments and water puddles. These shallow water puddles were imaged effectively by using SAR systems. The presence of water puddles is usually indicated by low radar backscatter in SAR images due to specular reflections on the water surface. This effect was proved by comparing radar backscattering coefficients obtained from two space-borne SAR systems, TerraSAR-X and RADARSAT-2, with those obtained from two theoretical scattering models, IEM and Oh model. We observed relatively large, widely distributed water puddles in belt shape along the upper parts of the tidal flat, which were confirmed to be related to the discharge of groundwater. The results of this research suggest that SAR can be a powerful tool for observing and determining the areal distributions of possible groundwater discharge in large tidal flats, which is normally difficult to detect with traditional measurement tools or survey techniques for groundwater discharge. We firmly believe that this technique can reduce significantly the efforts of field work to confirm SGD in tidal flats.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.