基于植被供水指数的旱区土壤湿度反演方法研究

植被供水指数(VSWI)是进行干旱研究的有效指标,是进行区域土壤湿度反演的重要方法。利用MODIS数据,提取归一化植被指数(NDVI)、修正的土壤调整植被指数(MSAVI)、增强型植被指数(EVI)和地表温度(Ts)等参数,建立植被供水指数、基于MSAVI的植被供水指数(VSWI-M)、基于EVI的植被供水指数(VSWI-E),并对比三种指数反演土壤湿度的效果;在此基础上,建立分区域、基于NDVI阈值的混合植被供水指数(MVSWI)模型,利用20 cm土壤墒情实测数据对模型进行检验,RE,RMSE误差结果显示,MVSWI模型具有较好的精度,可以用来估算土壤湿度。     

归一化阴影植被指数NSVI的构建及其应用效果

以ALOS AVNIR-2、CBERS-02B CCD、HJ1A-CCD2、Landsat 7 ETM四幅中分辨率遥感影像为试验数据,分析明亮区植被、阴影区植被与水体区的光谱特征与差异,基于近红外波段与归一化植被指数NDVI,构建归一化阴影植被指数NSVI,并评价其光谱差异增强及分类效果.结果表明,NSVI大幅扩大了明亮区植被、阴影区植被、水体区间的光谱相对差异,降低光谱混淆概率;利用NSVI阈值法对四幅试验影像进行分类,总精度均大于97%,总Kappa在0.96以上,且阴影区植被的检测精度均在94%以上,总Kappa系数亦高于0.96.该指数利用地物在近红外波段的辐射差异,解决NDVI只能部分削弱地形影响的问题,扩大地物间的光谱差异,从而提升地物尤其是阴影检测的有效性,且不存在NDVI“易饱和”问题,可为遥感影像阴影去除提供一种新的解决方案.     

归一化阴影植被指数NSVI的构建及其应用效果（英文）

以ALOS AVNIR-2、CBERS-02B CCD、HJ1A-CCD2、Landsat 7 ETM四幅中分辨率遥感影像为试验数据,分析明亮区植被、阴影区植被与水体区的光谱特征与差异,基于近红外波段与归一化植被指数NDVI,构建归一化阴影植被指数NSVI,并评价其光谱差异增强及分类效果.结果表明,NSVI大幅扩大了明亮区植被、阴影区植被、水体区间的光谱相对差异,降低光谱混淆概率;利用NSVI阈值法对四幅试验影像进行分类,总精度均大于97%,总Kappa在0.96以上,且阴影区植被的检测精度均在94%以上,总Kappa系数亦高于0.96.该指数利用地物在近红外波段的辐射差异,解决NDVI只能部分削弱地形影响的问题,扩大地物间的光谱差异,从而提升地物尤其是阴影检测的有效性,且不存在NDVI"易饱和"问题,可为遥感影像阴影去除提供一种新的解决方案.     

基于小波变换与支持向量机回归的冬小麦叶面积指数估算

叶面积指数(LAI)是作物长势诊断及产量预测的重要参数。通过对冬小麦采样点的高光谱曲线进行连续小波变换(CWT),然后利用小波系数与LAI 建立支持向量机回归(SVR)模型,实现冬小麦不同生育时期的叶面积指数估算。通过对所研究方法与选取的植被指数、偏最小二乘(PLS)回归等5种方法的反演结果进行统计分析。结果表明:利用连续小波变换确定的LAI 的敏感波段为680、739、802、895 nm,对应尺度分别为8、4、9 和8,对应小波系数的LAI 回归确定系数(R2)明显高于冠层反射率的回归确定系数;利用小波系数与LAI 建立的SVR 模型的反演精度最高,模型实测值与预测值的检验精度(R2)为0.86,均方根误差(RMSE)为0.43;而常用植被指数(归一化植被指数,NDVI;比值植被指数,RVI)建立的估测模型对冬小麦多个生育时期LAI 反演精度最低(R2 0.76,RMSE0.56)。因此利用连续小波变换进行数据预处理,能更好地筛选出对叶面积指数敏感的信息,LAI 回归方法比较结果表明,SVR 比PLS 更适合于LAI 的估测,通过将CWT 与SVR 结合(CWT-SVR)能实现不同生育时期冬小麦叶面积指数的遥感估算。     

耦合辐射传输模型的植被含水量遥感改进监测

基于耦合的叶片与冠层辐射传输模型,研究并使用了改进的植被指数提取植被含水量,以实现植被水分含量的遥感监测.使用PROSAIL模拟的高光谱数据,首先对NDVI、WI、NDWI、MSI、CSI及NDII等已有的植被指数提取含水量进行可行性分析比较.结果显示NDVI不能估算出植被含水量,CSI估算能力也不理想,其余四个指数WI,NDWI,NDII,MSI估算植被含水量时相关系数平方在0.76左右;之后对NDWI提取植被含水量进行了敏感因子分析,结果表明NDWI与MCARI具有较好的线性关系,从而得出改进的植被含水量估算方法.该方法可以较好地去除LAI的干扰,估算的相关系数平方提高为0.97,增加了27%,估算残差由初始的0.0156降低为0.00535,减少了65%.     

基于MODIS温度植被角度指数的农作物估产模型研究

利用MODIS数据,以河北石家庄和邢台地区冬小麦产量估算为例,探讨了综合植被指数与陆表温度的温度植被角度指数在农作物估产中的应用研究.首先,根据冬小麦物候历,计算了冬小麦抽穗期四种参量指数:归一化植被指数(NDVI)、增强型植被指数(EVI)、温度植被角度指数(TVA)和增强型温度植被角度指数(ETVA);其次,将实测的冬小麦产量数据与NDVI、EVI、VTA和EVTA数据进行回归分析,建立模型.结果表明,实测产量数据与这四种指数均具有很好的线性回归关系,相关系数R2均在0.60以上(分别为0.61、0.65、0.68、0.74),其中基于TVA和ETVA的估产模型要好于NDVI和EVI模型.由此可见,综合了MODIS光学反射和辐射信息的TVA/ETVA,能有效应用于实践估产中,并提高预测的准确性.     

基于Sentinel数据的滇池湖滨湿地地上生物量反演

为了评估基于Sentine 1/2影像数据反演滇池湖滨带湿地森林地上生物量(AGB)的效果和能力,以Sentinel-1 A/B(SAR)和Sentinel-2 A/B(多光谱)卫星图像为数据源,获取SAR双极化后向散射系数、多光谱波段、植被指数和林冠生物物理变量等因子,利用线性回归和机器学习算法,建立了多个滇池湖滨湿地生物量反演模型。所有模型与滇池湖滨湿地样地地上生物量的相关性为0.619~0.84,均方根误差(RMSE)范围为40.14~59.7 t/ha,其中基于SAR的模型反演精确度最低;在多光谱波段中,红色和红边(波段4,5和7)与生物量有很好的相关性;叶面积指数(LAI)模型是生物量反演的最佳变量组合(r=0.84,RMSE=40.14);基于Sentine 1/2影像数据反演滇池湖滨带湿地地上生物量具有可行性。     

基于分形理论与GEOEYE-1影像的NDVI连续空间尺度转换模型构建及应用

尺度效应是定量遥感领域一个重要而基础的科学问题。尺度效应的解决方法为尺度转换。传统的尺度转换方法面临涉及的空间分辨率离散、有限及传感器间成像参数归一化的干扰等问题。在提出并实践基于分形理论构建归一化差分植被指数（Normalized Difference Vegetation Index, NDVI）连续空间尺度转换模型（continuous spatial scaling model, CSSM）的基础上,本文基于GEOEYE-1多光谱影像、以NDVI为研究对象继续就分形方法在高空间分辨率基础图影像中的适用性进行研究,并就分形模型构建最合理尺度层级的确定进行细致探讨。实验获得如下结论：（1）基于分形理论的NDVI连续空间尺度转换模型构建方法适用于高空间分辨率遥感影像;（2）在给定的条件下,NDVI尺度转换分形模型构建时最合理尺度层级存在且可计算。论文的研究使得分形方法所适用的反演量类型及基础图空间分辨率范围皆有大的扩展。     

森林冠层氮含量遥感估算

使用高光谱数据估算叶片与冠层尺度的森林氮含量.首先采用基于高斯误差函数的BP神经网络Erf-BP建立叶片尺度氮含量的遥感估算模型;其次根据几何光学模型原理,推导冠层光谱与叶片光谱的尺度转化函数,将Hy-perion影像的冠层光谱转换到叶片尺度并反演叶片尺度的氮含量;最后,利用森林结构参数LAI得到研究区域冠层尺度氮含量.结果表明,隐含层包含8个神经元的Erf-BP模型最优,检验精度为76.8597%;利用尺度转化函数估算670 nm和865 nm冠层光谱与实测光谱决定系数为0.5203和0.4117;反演叶片尺度氮含量与实测数据的决定系数为0.7019;该方法为高精度快速估算叶片和冠层尺度森林氮含量提供参考.     

地理信息系统支持下Spot/vegetation NDVI影像的大尺度神经网络分类

以内蒙古地区Spot/vegetation归一化植被指数（NDVI）影像为基本信息源,综合应用地理信息系统（GIS）技术进行了大尺度神经网络分类实验研究.建立多年份高分辨影像数据库,通过GIS软件集成与遥感影像目视解译方法,在全区范围选取了“纯净”样本数据,并辅助应用DTM数据和影像化多年气像观测数据,完成土地覆盖类型的BP人工神经网络分类.结果表明,GIS技术支持下,大面积区域尺度上spot/vegetation NDVI影像的BP神经网络分类可达到较高的分类精度.     

Multivariate Gaussian MRF for multispectral scene segmentation andanomaly detection

Gaussian Markov random field texture models and multivariate parametric clustering algorithms have been applied extensively for segmentation, restoration, and anomaly detection of single-band and multispectral imagery, respectively. The present work extends and combines these previous efforts to demonstrate joint spatial-spectral modeling of multispectral imagery, a multivariate (vector observations) GMRF texture model is employed. Algorithms for parameter estimation and image segmentation are discussed, and a new anomaly detection technique is developed. The model is applied to imagery from the Daedalus sensor. Image segmentation results from test images are discussed and compared to spectral clustering results. The test images are collages, with known texture boundaries constructed from larger data cubes. Anomaly detection results for two Daedalus images are also presented, assessed using receiver operating characteristic (ROC) performance curves, and compared to spectral clustering models. It is demonstrated that even the simplest first-order isotropic texture models provide significant improvement in image segmentation and anomaly detection over pure spectral clustering for the data sets examined. The sensitivity of anomaly detection performance to the choice of parameter estimation method and to the number of texture segments is examined for one example data set     

Classifying land development in high-resolution Satellite imagery using hybrid structural-multispectral features

It is well known that combining spatial and spectral information can improve land use classification from satellite imagery. Human activity on the ground, such as construction, induces changes in both the photometric structure of the image and in its spectral content owing to, primarily, changes in vegetation density and surface materials. This paper introduces a novel approach to combine spatial (more precisely, structural) information extracted from (1-m resolution) panchromatic Ikonos imagery with the multispectral response (4-m resolution) available from the same sensor. Of the prior work combining spatial and spectral information, none has extracted structural features as we do, and none has combined these information sources as early in the process. The classifier we describe here, discriminating urban and rural regions, is a front-end component of a fairly complete satellite image analysis system that identifies suburban residential areas and extracts their street networks and single-family houses. We extract structural information in the form of photometric straight lines and their spatial arrangement over relatively small neighborhoods. To capture the multispectral information, we turn to the well-known normalized difference vegetation index (NDVI) and an improved linearized version of our own development (details of the structural analysis and the theoretical development of the linearized NDVI appear elsewhere). This paper addresses the novel combination of these types of features (hybrids) by using the structural features, straight line support regions based on gradient orientation, as cue regions for multispectral analysis. We test the hybrid features in a range of parametric and nonparametric classifiers. We also implement and test a probabilistic relaxation algorithm followed by the maximum a priori decision rule. We report extensive results that indicate significant improvements in classification accuracy using the hybrid features.     

Estimation of subpixel vegetation density of natural regions usingsatellite multispectral imagery

A procedure is presented for estimating the subpixel fractional canopy density of natural or undisturbed semivegetated regions on a pixel-by-pixel basis using one satellite multispectral image and a physical modeling approach. The method involves applying a model of the bulk, nondimensional plant geometry combined with a simple model of canopy reflectance and transmittance to the red and near-infrared reflectance space of the atmospherically corrected satellite image. Shadow effects are parameterized assuming Poisson-distributed and geometrically similar plant canopies. The method is applied to the estimation of fractional cover and leaf area index, using Landsat thematic mapper imagery, of two physiologically different plant communities. The first is the Landes Forest, a coniferous region in south central France, during the June 1986 HAPEX-Mobilhy Experiment. The second is the semiarid Walnut Gulch basin of southeast Arizona that contains predominantly shrubs and grasses, during the June 1990 MONSOON Experiment. The procedure offers a physically based alternative to empirical vegetation indices for estimating regionally variable canopy densities of natural, homogeneous systems with little or no ground truth     

Designing optimal spectral indexes for remote sensing applications

Satellite remote sensing data constitute a significant potential source of information on our environment, provided they can be adequately interpreted. Vegetation indexes, a subset of the class of spectral indexes, represent one of the most commonly used approaches to analyze data in the optical domain. An optimal spectral index is very sensitive to the desired information (e.g. the amount of vegetation), and as insensitive as possible to perturbing factors (such as soil color changes or atmospheric effects). Since both the desired signal and the perturbing factors vary spectrally, and since the instruments themselves only provide data for particular spectral bands, optimal indexes should be designed for specific applications and particular instruments. This paper describes a rational approach to the design of an optimal index to estimate vegetation properties on the basis of the red and near-infrared reflectances of the AVHRR instrument, taking into account the perturbing effects of soil brightness changes, atmospheric absorption and scattering. The rationale behind the Global Environment Monitoring index (GEMI) is explained, and this index is proposed as an alternative to the Normalized Difference Vegetation Index (NDVI) for global applications. The techniques described here are generally applicable to any multispectral sensor and application     

Atmospheric correction of Landsat ETM+ land surface imagery. II. Validation and applications

For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on atmospheric correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.     

多源卫星遥感数据监测巢湖蓝藻水华爆发研究

湖泊蓝藻水华的精准动态监测,可为水利及环保部门评价污染水体的防治效果、优化和调整防治政策提供依据。论文以巢湖为研究对象,利用Landsat TM/OLI、HJ-1B CCD/IRS和NPP-VⅡRS三种不同空间分辨率的影像数据,通过归一化水体指数（Normalized Difference Water Index,NDWI）实现巢湖水域范围提取,利用归一化植被指数（Normalized Difference Vegetation Index,NDVI）和浮游藻类指数（Floating Algae Index,FAI）提取2010~2014年共22景巢湖蓝藻的爆发区域。进一步的,对NDVI和FAI两种方法计算的蓝藻爆发区域进行对比分析,评价Landsat、HJ-1B以及VⅡRS三种影像数据对巢湖蓝藻水华空间和时间的监测效果及适用性,进而结合气象因素分析不同气象因子对蓝藻水华爆发的影响。研究结果表明:（1）相比NDVI指数,FAI指数（Landsat和HJ-1B数据为主,VⅡRS数据辅助）能降低薄云对蓝藻水华提取效果的影响,可提高蓝藻水华爆发区域、程度的识别能力;（2）气象因子中气温和日照时长加重了蓝藻水华爆发的严重程度,降水则对蓝藻水华的爆发起到一定的抑制作用。综上所述,论文引入VⅡRS卫星影像研究巢湖蓝藻水华爆发,利用FAI指数降低薄云对蓝藻水华爆发面积提取精度的影响,取得的研究结果可为基于多源卫星遥感数据的巢湖蓝藻水华动态监测系统开发提供重要的方法支持,有利于推进卫星遥感技术在安徽省河长制和湖长制中发挥重要作用。     

联合NDRI特征和空间相关性的机载MS-LiDAR数据分类

对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此,提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index,NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云,获取兼具空间位置及其多光谱信息的单一点云数据;然后利用空间邻域增长下的地面滤波算法分离地面和非地面点;接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数,并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类;最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性,实验结果表明：算法的平均总体精度和Kappa系数分别可达90.17%和...     

A nonlinear multispectral statistical CLEAN-based precipitationparameter-retrieval algorithm

An iterative algorithm incorporating CLEAN deconvolution concepts for precipitation parameter retrieval using passive microwave imagery is presented. The CLEAN algorithm was originally designed to deconvolve single-channel radio astronomy images. In order to use CLEAN to retrieve precipitation parameters from multispectral passive-microwave imagery, extensions of the algorithm to accommodate nonlinear, multispectral, and statistical data mere designed and implemented. The primary advantage of the nonlinear multispectral statistical (NMS) CLEAN retrieval algorithm relative to existing algorithms is the use of high-resolution (high-frequency) imagery to guide the retrievals of precipitation parameters from lower resolution (Low-frequency) imagery. The NMS-CLEAN retrieval algorithm was used to estimate rain rate (RR) and integrated ice content (IIC) using simulated imagery of oceanic convection as would be observed from six channels of the proposed Advanced Microwave-Scanning Radiometer. Both the accuracy and structural detail of the retrieved rain rate were improved relative to the retrievals from a single-step, nonlinear, statistical algorithm. Reduced error and improved spatial resolution of a more minor magnitude was also seen in the integrated ice-content retrievals. This study also showed that spatially-simple storm structures resulted in better performance of the NMS-CLEAN retrieval algorithm     

基于Landsat8 影像的蓝藻水华提取方法对比研究

对湖泊蓝藻水华实现精准的遥感监测, 可为湖泊的污染防控和治理提供科学依据。基于 Landsat8 多光谱数 据, 以巢湖为研究对象, 利用归一化植被指数 (NDVI) 和浮游藻类指数 (FAI) 分别对蓝藻水华进行提取, 并将提取的蓝 藻水华强度划分为三级, 进一步对这两种提取方法进行对比, 分析了巢湖的蓝藻水华分布。研究结果表明: (1) 针对 NDVI 和 FAI 两种不同提取方法确定的阈值及分级阈值可以有效地提取巢湖的蓝藻水华; (2) 相对于 NDVI 方法, FAI 方法可以降低云对提取精度的影响; (3) 巢湖西半湖及沿岸水体富营养化一直比较严重, 东半湖的富营养化也日趋严 重。研究确定的蓝藻水华提取阈值及分级阈值不仅为巢湖的蓝藻水华污染治理和预警提供方法支持, 还为其它湖泊 蓝藻水华的遥感监测提供参考依据。     

Relief-F筛选波段的小麦白粉病早期诊断研究

为了准确监测小麦白粉病染病早期病情,给喷药防治提供技术指导,论文将染病初期的小麦叶片作为研究对象。首先,利用高光谱图像数据,通过图像特征分割出叶片区域和病斑区域,定量计算病情严重度;其次引入Relief-F算法提取染病早期最敏感波段和波段差,计算出白粉病病害指数PMDI （Powdery mildew disease index）;并通过分析病情指数DI （Disease index）与11种植被指数（含PMDI指数）的相关性及线性模型,得出PMDI模型有最高的决定系数（R2=0.839 9）和最低的均方根误差（RMSE=4.522 0）,效果优于其他病害植被指数的结果（其中,Normalized Difference Vegetation Index,NDVI的模型决定系数最高,R2=0.777 1,RMSE=5.336 4）;最后,选择PMDI和NDVI植被指数分别构建小麦白粉病染病早期病情严重度的支持向量回归模型。结果表明:经敏感波段筛选构建的PMDI指数的预测结果更好,预测模型的R2=0.886 3,RMSE=3.553 2,可以实现小麦白粉病早期无损诊断,这为指导作物病害喷药防治提供重要的技术支撑。