面向双站SAR系统的长波PolInSAR目标自适应分解北大核心CSCD

双站SAR系统无时间去相干的特性,结合长波的强穿透能力,在估计植被结构参数上应用前景极大,借助极化干涉SAR分解技术研究双站SAR系统下的植被区散射过程,对揭示信号与地物的交互过程,构建植被结构参数反演模型具有重要意义。考虑模型适用性和双站SAR系统存在的不可忽略的去相干,将极化干涉矩阵表达为极化方位角扩展的广义表面散射矩阵、广义二次散射矩阵和Neumann自适应体散射矩阵与其对应相干成分乘积的和的形式,基于残差最小二乘准则,使用非线性最小二乘优化技术同时求解所有模型参数。使用BioSAR 2008项目的 L波段全极化机载数据对方法进行测试,获取了实验区不同散射机制的相干成分、相位分布和能量信息,结合机载激光雷达数据进行了分析。结果表明:分解方法对植被区不同散射机制区分良好,有效抑制了体散射功率高估;植被区表面散射在垂直向上的分布与植被高度和穿透程度存在联系,体散射相位中心高度与机载激光雷达植被高接近且趋势一致;有效估计了散射机制的相干性。     

SAR数据湿地植被生物量反演方法研究进展

针对未来的碳释放量有可能成为气候环境不确定性的最大来源,而生物量的精确制图可以大大减小碳排放和吸收中的不确定性,能更好地理解其在全球变化中的重要地位。基于SAR数据在湿地植被生物量反演制图中的有效性及重要性,该文从极化分解理论、微波植被散射模型、生物量反演方法等方面对现有的湿地植被生物量反演方法进行综述,探讨SAR数据在湿地植被生物量反演中的应用潜力。     

基于SAR与Landsat TM的小区域稻田的识别研究——以南京市江宁区为例

以南京市江宁区为研究区域,根据区域特征、作物物候期和水稻的生长特点,采用分层分类的方法提取稻田分布信息。通过比较多时相SAR数据、TM和多时相SAR融合与TM和单时相SAR融合数据识别水稻的精度和提取的水稻种植面积,分析了不同数据对区域多云雨,不同种植方式、面积小且分布破碎的水稻稻田的识别程度,并根据野外实地走访调查分析了主要影响因素。结果表明:多时相SAR数据、TM和多时相SAR数据的水稻识别精度都高于72%,高于TM和单时相SAR融合数据的结果;前两者提取的水稻种植面积和稻田分布接近,主要影响因素是地物分布、不同种植方式水稻物候期和水稻稻田面积小且分布破碎。     

基于ENVISat ASAR影像的湿地植被冠层下淹水范围提取以扎龙保护区为例

以扎龙自然保护区湿地为例,结合ENVISat ASAR多极化(HH/HV)雷达影像与传统的光学影像Landsat TM (band1~5,7),分析雷达影像后向散射系数与Landsat TM影像不同波段反射率在淹水植被、非淹水植被、明水面和裸土不同地表覆被类型的差异。选择训练样本,采用分类回归树(Classification and Regression Tree,CART)模型,分别对两种影像进行分类,可视化表达湿地植被淹水范围空间分布情况。基于实测的植被冠层下淹水范围与非淹水范围样本点对两种数据源的分类结果进行精度验证。结果表明:HH/HV极化影像中,植被覆盖下水体的后向散射系数与其他地表覆被类型有明显区别,分类结果总精度为79.49%,Kappa系数为0.70,湿地植被淹水范围提取精度较高。而TM影像分类结果中,由于部分地区植被覆盖水体,淹水植被分类误差较高。将雷达影像引入沼泽湿地研究,提高了植被淹水范围提取效果,为有效分析湿地生态水文过程提供基础,对湿地水资源合理利用及生物多样性保护具有重要意义。     

全极化SAR数据土地覆盖分类精度分析

鉴于全极化SAR数据进行土地覆盖分类时不同特征组合会对分类结果带来巨大的影响,该文以美国国家土地覆盖数据为参考分析全极化SAR数据不同特征组合的土地覆盖分类精度。文中以分类精度为准则选取适用的分类方法,对比分析了不同分解组合和波段组合对分类结果的影响,同时给出同一时期成像的TM数据分类结果做比较。结果表明,SAR数据通过有效的分解组合能提高总体分类精度。同时,SAR数据不同分解特征之间有信息冗余和信息互补的关系,波段组合分类时需考虑其对分类结果的影响。波段组合分类得到了最高的总体分类精度71.6%和Kappa系数0.6,表明全极化SAR数据土地覆盖分类,尤其对于一些如"有林湿地"等待定类别,可以达到很好的分类质量。     

面向土地利用类型识别的高分辨率SAR数据复合技术研究

2007年以来,相继发射了3颗1m/3m高分辨率SAR卫星,极大地丰富了土地利用动态遥感监测数据源。SAR图像增强是土地利用动态遥感监测必要的预处理步骤。本文针对直接基于SAR数据进行土地利用类型识别中存在的问题,提出了SAR与光学图像、多时相SAR图像、多极化SAR图像合成和单极化SAR图像彩色合成等4种影像复合方法,分析评价了SAR与光学图像融合的应用效果,研究结果可为高分辨率极化SAR数据业务化应用提供技术参考。     

极化散射与SAR 遥感信息获取

合成孔径雷达(SAR ) 对地观测与成像技术是近20 年来空间微波遥感技术最重要的进展。JPL 的SIR-C SAR 与加拿大Radarsat SAR-2 等星载或机载SAR 的全极化散射测量提出了自然地表全极化散射信息获取与处理的关键性科学问题。充分理解自然地表极化散射特性, 进而发展自然地表特征信息的分类、识别和反演算法是SAR 遥感应用的关键问题。近年来, 对于极化SAR 遥感已有广泛的研究。自然地表全极化散射的数值建模与M ueller 矩阵模拟解、相干矩阵及其特征值分析、信息熵等都有了研究与应用。但是, 如何将SAR 图像相干矩阵特征值和信息熵的全极化散射测量与自然地表特征参数直接关联, 并由此发展地表的分类、识别与参数反演等信息获取与处理还有待于大量的研究。本文将论述我们在SAR 全极化散射理论与应用的若干研究进展。第一个问题是如何将SAR 图像相干矩阵特征值和信息熵与同极化、交叉极化后向散射系数的测量直接关联, 与M ueller 矩阵解一起研究地表的分类与识别。第二个问题是如何有全极化散射测量反演地面数字程(DEM )。第三个问题是如何利用多时相SAR 遥感识别、获取与评估地面特征时间上的变化。     

水面船只SAR探测的极化方式研究

利用SIR-C SAR的C和L波段全极化数据,分析水面船只的极化散射特性和船只与背景海面雷达后向散射的信噪比特性,研究水面船只SAR探测的最优极化方式。结果显示,二面角散射是水面船只SAR成像的主要机理。线性极化中,HV极化具有最大的船只与背景海面雷达后向散射信噪比。与线性极化相比,圆极化的雷达后向散射信噪比更优。C波段和L波段的水面船只的极化散射特性存在较大的差异,L波段的信噪比大于C波段的信噪比。水面船只的雷达后向散射特性表明,L波段的圆极化是水面船只探测的最优极化方式。     

基于灰色系统理论的植被物理参数与极化分解参数的关联分析

结合灰色系统理论,基于3期（2013、2015和2016年）鄱阳湖湿地植被物理参数数据和RADARSAT-2极化SAR影像数据,分别建立植被生物量、极化分解分量与鄱阳湖植被物理参数的关系模型,并分析不同植被物理参数对生物量积累的贡献和对极化分解分量的影响。结果表明：在植被生长旺盛初期阶段到旺盛稳定阶段,对植被生物量积累贡献较大的主要是植株参数和下垫面参数,对极化分解分量影响较大的主要是下垫面参数和茎秆参数,并根据各阶段较大关联度数据合理地分析和确定了野外采样参数。     

极化SAR图像Pauli-Wishart非监督分类算法

针对全极化SAR影像经典非监督分类方法中H/α初始划分适应性有限及武断僵硬的问题,结合极化总功率提出一种结合Pauli分解与Wishart距离的极化SAR影像非监督分类方法。利用极化总功率Span对数据进行基于散射强度的初始划分;结合初分类结果与Pauli分解得到的HH,HV,VV 3个波段进行迭代分类;基于Wishart距离进行聚类得到分类结果。实验采用NASA-JPL实验室的2组L波段全极化SAR数据验证了基于Pauli基迭代改进分类方法的有效性,分类结果与传统的H/α-Wishart分类方法对比,分类精度和合理性都有提高。     

Analysis of multi-frequency and multi-polarization SAR data for wetland mapping in Hamoun-e-Hirmand wetland

ABSTRACT

The complex, dynamic and narrow boundaries between vegetation types make wetland mapping challenging. Hereafter the case study of the Hamoun-e-Hirmand wetland is considered by analysing eight Synthetic Aperture Radar (SAR) Images acquired in dry and wet periods with three wavelengths (X-band ~ 3 cm, C-band ~ 6 cm, and L-band ~ 25 cm), three polarizations (HH, VV and VH), and four incidence angles (22°, 30°, 34° and 53°). Then, the Support Vector Machine (SVM) classification method was applied to classify TerraSAR-X, Sentinel-1, and ALOS-PALSAR images. The final wetland land cover map was created by combining the classification results obtained from each sensor. In the case in question, results show that TerraSAR-X (X-band, HH-53°) and Sentinel-1 data (C-band, VV-34°) were useful for determining the flooded vegetation area in the wet period. This is crucial for the conservation of water bird habitats since flooded vegetation is an ideal environment for the nesting and feeding of water birds. PALSAR data (L-band in both HH and VH polarizations, 30°) were capable of separating the classes of vegetation density in the wetland. In the dry period, Sentinel-1 (VV and VH, 34°) and TerraSAR-X (HH, 22° and 53°) had higher potential in land cover mapping than PALSAR (HH and VH, 30°). Based on these results, Sentinel-1 in VV and VH provides the highest ability to discriminate between dry and green plants. TerraSAR-X is better for separating meadow and bare land. The results obtained in this paper can reduce the ambiguity in selecting satellite data for wetland studies. The results can also be used to produce more accurate data from satellite images and to facilitate wetland investigation, conservation and restoration.     

ALOS PALSAR数据在漳江口红树林提取中的应用

红树林是国际上生物多样性保护和湿地生态保护的重要对象,及时获取红树林面积变化是加强红树林保护的迫切需求。以福建漳江口红树林国家级自然保护区为研究区域,对2007年多时相的ALOS PALSAR数据进行处理,分析红树林与各典型地物L波段HH、HV极化的后向散射值的时间变化特征及去极化特征。红树林在L波段HH和HV极化的后向散射随时间变化不明显,与其它林地较为相似,但两者去极化能力差异明显。相对于时相信息,极化信息对于提取红树林的作用更为重要。基于面向对象分类方法,提出应用HH、HV和HV与HH比值进行红树林提取的方法,取得较好的分类结果。     

基于植被指数的若尔盖高原湿地光合有效辐射吸收比例估算研究

植被光合有效辐射吸收比例（FPAR）是湿地生态系统碳收支和气候变化的关键参量,直接反映湿地植被生长发育状况。基于植被指数的经验统计方法简单高效,被广泛运用于草原、森林及作物等植被FPAR的模拟,却较少用于湿地,缺乏不同植被指数对湿地FPAR估算适应性的系统研究。研究对比了14种常见的植被指数,选出最优植被指数用于反演若尔盖高原湿地生长季FPAR。结果表明：常见的植被指数中,MSAVI指数动态考虑了土壤信息,能较好地适应湿地植被FPAR的估算,误差和R²均优于其他植被指数。若尔盖高原湿地生长季FPAR取值在0.22—0.80之间,整体分布较为均匀,泥炭湿地、湿草甸及沼泽湿地平均FPAR分别为0.46、0.63和0.58;生长季期间若尔盖高原不同类型湿地FPAR随时间呈现先增加后降低趋势。     

基于GF-1遥感数据的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算

随着我国遥感技术迅速的发展,国产系列卫星数据越来越多的应用到各个行业中.在湿地遥感监测方面,湿地生物量和碳储量的遥感估算研究是研究人员非常关注的研究问题,我国自主研制的高分(GF)系列卫星为湿地生态系统的资源监测提供新的途径和方法.提出了基于GF-1卫星的若尔盖高寒沼泽湿地地上生物量与土壤有机碳密度估算方法,通过选取G...     

Long-term vegetation changes in four types of wetland in China and USA between 2000 and 2011: observations from MODIS

Wetland vegetation assessment is a critical component of evaluating wetland protective policies and providing useful information for global climate change research. In this study, four types of wetland are chosen to evaluate their long-term changes and potential linkage to anthropogenic (e. g., agricultural activities) and natural influences (e. g., hydrometeorological effects). These wetland types include the Yancheng wetland (coastal wetland), the Chongming wetland (river wetland), the Hongze Lake wetland (lake wetland) and the Everglades wetland (marsh wetland) in the contrasting environments of China and USA. The vegetation coverages of these wetlands were evaluated from 2000 to 2011 by using over 2,200 Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m resolution images. Four vegetation indices (VIs) were compared to evaluate their effectiveness in assessing relative changes: the Normalized Difference Vegetation Index (NDVI), the Floating Algae Index (FAI), the Enhanced Vegetation Index (EVI), and the Visible Atmospherically Resistance Index (VARI). FAI performance was relatively insensitive in terms of both the statistical error and the aerosol effects compared with other VIs and was thus chosen to study the long-term vegetation changes. The results showed that 1) agricultural influence appeared to be relatively minimal compared with hydrometeorological effects in the Everglades wetland and in the core partitions of the Yancheng, Chongming, and Hongze Lake wetlands over the 12-year period, and 2) the entire partitions of the Yancheng, Chongming and Hongze Lake wetlands showed noticeable influences from agricultural activities in addition to natural variability. The cost-effective method that we demonstrate in this study may be extended to other wetlands near the same geographical location and to other satellite platforms.     

Coastal wetland vegetation classification with a Landsat Thematic Mapper image

Coastal wetland vegetation classification with remotely sensed data has attracted increased attention but remains a challenge. This paper explored a hybrid approach on a Landsat Thematic Mapper (TM) image for classifying coastal wetland vegetation classes. Linear spectral mixture analysis was used to unmix the TM image into four fraction images, which were used for classifying major land covers with a thresholding technique. The spectral signatures of each land cover were extracted separately and then classified into clusters with the unsupervised classification method. Expert rules were finally used to modify the classified image. This research indicates that the hybrid approach employing sub-pixel information, an analyst's knowledge and characteristics of coastal wetland vegetation distribution shows promise in successfully distinguishing coastal vegetation classes, which are difficult to separate with a maximum likelihood classifier (MLC). The hybrid method provides significantly better classification results than MLC.     

A multitemporal multiple density slice method for wetland mapping across the state of Queensland,Australia

The Australian and Queensland Governments are developing comprehensive wetland maps at a scale of 1:100 000 for the state of Queensland, Australia. Spectral classifications for water features were developed using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery acquired over a 16‐year period. A multiple density slice/supervised classification method, the Standing Water Body (SWB) method, was developed to separate the main spectral and land cover elements of wetlands (vegetation, water and shadow cast by vegetation and topographic relief) and used rules to combine spectral classes to provide multitemporal (MT) information on wetland extent and water inundation regimes for features of at least 0.25 ha. Accuracy assessment in four trial areas compared the SWB method to the Normalized Difference Water Index (NDWI). The assessments of classified features were scale adjusted to maximum class‐area proportions to enable statistical comparison and to account for the large area of non‐wetland in the four trial areas. The average overall accuracy for wetland classification was 95.9% for the SWB method and 95.3% for the NDWI. The average unadjusted KHAT statistic for the wetland classification was 0.84 and 0.90 for the SWB and NDWI, respectively. The scale‐adjusted KHAT statistic was much lower for both methods, averaging 0.45 for the SWB and 0.39 for the NDWI, mainly due to large omission errors. A method for the implementation of the SWB method for systematic and repeatable mapping of wetland areas is presented. The study recommends enhancement of the SWB classification through the inclusion of the NDWI classification and ancillary data such as vegetation mapping and drainage networks.     

基于面向对象多端元混解模型的植被覆盖度反演及其时空分布研究北大核心CSCD

近年来湿地的植被退化一直是全球关注的问题,对湿地植被覆盖度进行反演并研究其时空分布特征显得尤为重要。而为了解决植被反演中存在的混合像元问题,提出了基于面向对象的多端元光谱混合分析方法。以扎龙湿地保护区为研究对象,中高分辨率Landsat影像为数据源,从时间尺度和植被覆盖度等级变化层面,研究湿地植被时空变化特征。面向对象多端元混解模型,有效地减少了计算量和混合像元的端元变化,且反演值与检验值相关性较高,均方根误差较小,优于传统多端元混解模型方法,提高了植被覆盖度反演精度。扎龙湿地多年植被覆盖度整体呈现退化趋势,2001～2017年的平均变化速率高于1985～2000年,对于提高全球气候变化情景下植被转移预测精度具有重要理论意义。     

Changes in the wetland vegetation growth patterns in large lakes on the Yangtze Plain

In this study, the vegetation emergence times (VET), an important phenological characteristics, were obtained for 25 large lakes on the Yangtze Plain between 2001 and 2014. This was carried out by extracting the normalized difference vegetation index (NDVI) time series from the moderate-resolution imaging spectroradiometer (MODIS) data using a decision tree method. This is the first comprehensive documentation of the changes in temporal and spatial distribution in wetland vegetation phenology for large lakes on the Yangtze Plain. The results showed that considerable changes in the VET occurred in 25 wetland ecosystems in the Yangtze Plain. Specifically, 76% of the lakes showed delayed trends in the VET, and 32% of them were statistically significant (p < 0.05). In contrast, 24% of the lakes displayed advanced trends in the VET and 17% of them were statistically significant (p < 0.05) over the past 14 years. An analysis of the driving factors of VET revealed that a VET change was more sensitive to temperature and sunshine duration than to precipitation for most of the lakes. The temperature in 1–2 months before VET had great effect on the vegetation growth, while such a pattern was not evident for sunshine duration for 5 months before VET. Furthermore, the amounts of chemical fertilizers used in nearby farmlands have also played an important role in the vegetation growth for some of the lakes. This record of change in vegetation phenology provides critical information for wetland ecosystem monitoring in the Yangtze Plain.     

基于多极化星载SAR数据的水稻/旱田识别-以江苏省海安县为例 

合成孔径雷达(SAR)数据对于南方多云多雨天气的地表农作物类型的探测具有独特的优势。以江苏省海安县为例,基于多极化SAR数据,包括双极化ALOS PALSAR以及全极化Radarsat\|2数据,采用面向对象的方法,针对当地水稻/旱田进行识别。针对双极化SAR数据,利用了其强度信息进行分类识别;而基于全极化数据,除强度信息外,还利用了其SAR信号统计分布概率进行分类规则建立。结果表明:L波段的ALOS PALSAR在识别旱地的桑树方面具有很大的优势,而基于两种分类方法的C波段Radarsat\|2数据识别水稻的精度分别为85%和75%,略低于ALOS PALSAR的识别结果(87.5%)。