引入路网和建筑物信息的DMSP/OLS数据去饱和方法

由于OLS (operational linescan system)传感器的缺陷,DMSP/OLS数据中的城市中心灯光值存在饱和性。提出了一种基于灯光贡献的综合指数去饱和方法,将路网和建筑物引入到去饱和模型中,并将增强型植被指数(enhanced vegetation index,EVI)作为辅助修正数据对夜间灯光数据去饱和。将该去饱和结果与基于EVI修正的灯光指数(EVI adjusted nighttime light index,EANTLI)从城区内部的地物区分能力、与辐射定标数据的拟合程度、对用电量的估算能力3方面进行比较。结果表明,综合指数在城市内部的细节刻画方面具有明显优势,地物区分能力较高;综合指数与辐射定标数据的整体拟合程度更高,且抽取穿过城区的单行数据拟合其R2最高可达0.928,相比EANTLI可提高0.1;与地区用电量拟合程度同样高于EANTLI,R2可达到0.901。综上,引入路网和建筑物的综合指数能够更好地解决数据饱和问题,且具有更高的可靠性。     

自适应加权Savitzky-Golay滤波重构MODIS植被指数时间序列

针对多数植被指数序列重构方法不能有效去除连续云噪声的问题,该文提出了一种改进的自适应加权Savitzky-Golay滤波算法(IAW-SG)。以湖南省2001—2017年MOD13Q1植被指数产品归一化植被指数(NDVI)为数据源,通过解析质量控制参数,设置并在迭代过程中更新相应权重,自动调整滤波窗口大小以得到拟合结果,最终拟合曲线较为光滑,云噪声被有效消除;同时,该方法还被运用到MOD09Q1地表反射率产品得到的时间分辨率更高的NDVI序列产品,也可以得到较好拟合的效果。实验结果表明:该文算法拟合NDVI曲线能够较好地反映不同类型植被正常生长情况、年际变化规律等信息,同时还能最大限度保留原始序列有效值,可为生态环境监测提供高质量基础数据。该文算法通过植被指数产品质量控制参数实现权重和滤波窗口大小动态调整,具有较强的去噪能力,同时具有较强的数据保真性,能最大限度减少拟合误差。     

改进的RFM自适应谱修正迭代算法

针对RFM求解时存在的法矩阵病态和计算效率问题,提出一种改进的自适应谱修正迭代算法.首先基于法矩阵对称正定特性,采用分解法求解方程,避免了矩阵求逆过程;其次将岭参数作为初始谱修正因子,根据相邻两次迭代残差比值逐步调整谱修正因子,在迭代过程中平衡了法矩阵病态改善效果和迭代速度的关系.利用多组"天绘一号"01、"天绘一号"02星影像数据验证该算法的有效性.实验结果表明,该算法能够有效地克服法矩阵病态影响,生成高精度RFM参数,相比谱修正迭代法拟合时间缩短23.37％.     

基于垂直距离最小二乘拟合的双曲线沉降模型

引入临时坐标系,采用高斯-牛顿迭代算法,在双曲线基于垂直距离最小二乘拟合算法的基础上增加一个角度约束条件和两个平移约束条件,对沉降数据进行双曲线几何拟合而非代数拟合,提出了一种基于垂直距离最小二乘拟合的双曲线沉降模型的曲线参数估计算法。算例表明,改进算法改善了传统算法的拟合精度。     

南方典型植被遥感丰度信息提取

针对遥感影像在南方丘陵地区典型植被丰度信息提取中存在的大量混合像元问题，为进一步提高线性解混精度，通过计算像元EVI值，构建了Landsat 8时间序列影像南方典型植被（端元）和混合像元的EVI时间序列曲线，分析了不同生育期内各种地物类型的植被指数变化曲线，发现不同地物在植被指数时间序列中具有各自独立的波动规律。选取多个端元及其EVI时间序列曲线，利用光谱匹配方法对匹配EVI时间序列曲线和多个端元进行了匹配，达到利用不同端元组合进行光谱解混的目的。试验结果表明，与传统方法相比，阔叶林解混精度有明显提高，针叶林解混精度也有所提高。该研究成果可以为南方丘陵地区植被环境的研究提供有力支撑。     

利用时间序列傅立叶分析重构无云NDVI图像

利用基于傅立叶变换的HANTS算法，对中国地区（不包括南海诸岛）AVHRR NDVI时间序列数据进行简化和压缩，将植被的动态变 化情况通过NDVI在时间和空间上量化，实现了时间序列图像中云和错误信息的检测、去除和替代。利用HANTS算法提取时间序列的傅立 叶分量（幅值分量、频率分量），并由这些分量得出NDVI时间序列拟合曲线，依照曲线进行时间上的插值，从而重构无缝的时间序列图 像。     

基于法方程病态的谱修正迭代算法的探讨

本文通过分析法方程病态问题产生的原因,结合谱修正迭代算法原理,探讨法方程病态对参数估值的影响,对谱修正迭代的改进算法的适用范围进行扩展,从理论上证明修正因子r取大于零的实数时,迭代改进算法的逆矩阵二范数值随迭代次数的增加而趋近于零,从而得到平差参数接近真值的估值。经过分析发现,谱修正迭代改进算法的迭代速度主要取决于修正因子r和迭代初值的取值。本文从理论和实例证明了迭代速度与修正因子取值保持线性的变化规律,同时用实例证明了不同初值对迭代速度的影响。     

对渐进三角网点云数据滤波算法的改进

针对渐进三角网滤波算法（PTD）进行拓普康LiDAR点云数据处理过程中易将地物点误判为地面点的缺陷，本文提出两种改进方法。一种是采用局部坡度拟合法对PDT算法进行改进，将点云数据按高程值与拟合坡面法求解的拟合高程值的差由小到大进行排序，将为地面点可能性更大的点优先判定，从而获取更加精确的TIN；另一种是引入薄板样条曲线（TPS）插值法，对PTD算法进行改进，将PTD中候选点判断参数改为TPS法中的弯曲能量增长值，从而减少误判。结果表明，使用以上两种改进算法，综合考虑第1类误差和第2类误差影响，在大部分地形特征下比传统PTD算法表现更优，对低矮植被、桥、斜坡等特殊地物的滤波效果更佳。     

基于RANSAC算法的稳健点云平面拟合方法

针对点云平面拟合中存在粗差及异常值等问题,对结合特征值法的随机抽样一致性(random sample consensus,RANSAC)平面拟合算法进行了改进。该方法以RANSAC算法为基础,结合特征值法,利用点到平面模型距离的标准偏差来自动选取阈值t,通过阈值t检测并剔除异常数据点,达到获得理想平面拟合参数的目的。用改进的算法和传统的特征值法分别对点云数据进行处理,结果表明,改进的算法适用于存在误差和异常值的点云数据拟合,能稳定地获得较好的平面参数估值,具有较强的鲁棒性。     

一种改进的最小二乘平面拟合算法

针对点云平面拟合中存在离群点和噪声点等问题,从概率分析的角度提出了一种改进的最小二乘平面拟合算法。该算法基于概率统计思想,采用中位数法筛选最佳初始平面模型,并利用迭代最小二乘法剔除离群点,逐步优化模型,从而获取最佳平面。运用不同的迭代方法对仿真数据和实测数据进行平面拟合,实验结果表明:当点云存在高离群率和大离群幅时,相比于其他迭代方法,本文算法仍可以准确地拟合出最佳平面。     

Land cover classification of the North China Plain using MODIS_EVI time series

Moderate Resolution Imaging Spectroradiometer (MODIS) data have played an important role in global environmental and resource research. However, its low spatial resolution has been an impediment to researchers pursuing more accurate classification results. In this research, the high temporal resolution of MODIS was employed to improve the accuracy of land cover classification of the North China Plain using MODIS_EVI time series from 2003. Harmonic Analysis of Time Series (HANTS) was performed on the MODIS_EVI image time series to reduce cloud and other noise effects. The improved MODIS_EVI time series was then classified into 100 clusters by the Iterative Self-Organizing Data Analysis Technique (ISODATA). To distinguish ambiguous land cover classes, a decision tree was built on five phenological features derived from EVI profiles, Land Surface Temperature (LST) and topographic slope. The overall accuracy of the final land cover map was 75.5%, indicating the promise of using MODIS EVI time series and decision trees for broad area land cover classification.     

区域作物生长过程的遥感提取方法

提出利用时序NDVI数据提取作物生长过程方法。遥感数据在采集过程中受云、大气因子的影响 ,以及混合像元问题 ,造成时序植被指数值变得没有规律 ,对比性不强。采用基于最小二次方拟合的谐函数分析方法 ,依据作物轮作规律和生长周期性特征 ,用主要频率的正弦、余弦谐函数重建时序图像 ,去除了影像中云污染的影响。以中国的旱地为例 ,考虑到像元内旱地对NDVI值的贡献率 ,计算区域内旱地像元加权平均值来反映其作物生长过程。同时与区域所有像元的平均值、旱地平均值等统计方法的结果进行对比分析 ,表明区域内旱地的加权平均值能够削弱旱地比例和地域间的差异 ,突出耕地上作物的生长过程特征。通过与地面实测数据分析 ,平滑前后的作物生长过程与叶面积指数相关性增加 5 %— 11% ,采用区域加权平均的方法得到的作物生长过程 ,比旱地平均和NDVI平均的结果与叶面积指数的相关性增加 14 %— 17%。     

端元可变非线性混合像元分解模型

遥感影像中混合像元普遍存在。端元固定的情况下对混合像元进行分解,很难高精度地识别影像地物。本文基于支持向量机,提出了端元可变的非线性混合像元分解模型。首先,通过构建多个支持向量机获取每个像元的优化端元集,在优化端元集的基础上运用支持向量机与两两配对方法相结合的算法获取像元组分。试验结果表明,本文提出的方法效果优于传统的多端元光谱分解法。     

A spatial–temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images

The mixed pixel problem affects the extraction of land cover information from remotely sensed images. Super-resolution mapping (SRM) can produce land cover maps with a finer spatial resolution than the remotely sensed images, and reduce the mixed pixel problem to some extent. Traditional SRMs solely adopt a single coarse-resolution image as input. Uncertainty always exists in resultant fine-resolution land cover maps, due to the lack of information about detailed land cover spatial patterns. The development of remote sensing technology has enabled the storage of a great amount of fine spatial resolution remotely sensed images. These data can provide fine-resolution land cover spatial information and are promising in reducing the SRM uncertainty. This paper presents a spatial–temporal Hopfield neural network (STHNN) based SRM, by employing both a current coarse-resolution image and a previous fine-resolution land cover map as input. STHNN considers the spatial information, as well as the temporal information of sub-pixel pairs by distinguishing the unchanged, decreased and increased land cover fractions in each coarse-resolution pixel, and uses different rules in labeling these sub-pixels. The proposed STHNN method was tested using synthetic images with different class fraction errors and real Landsat images, by comparing with pixel-based classification method and several popular SRM methods including pixel-swapping algorithm, Hopfield neural network based method and sub-pixel land cover change mapping method. Results show that STHNN outperforms pixel-based classification method, pixel-swapping algorithm and Hopfield neural network based model in most cases. The weight parameters of different STHNN spatial constraints, temporal constraints and fraction constraint have important functions in the STHNN performance. The heterogeneity degree of the previous map and the fraction images errors affect the STHNN accuracy, and can be served as guidances of selecting the optimal STHNN weight parameters.     

运用动态谐波回归模型对MODIS叶面积指数时间序列产品的分析与预测

运用动态谐波回归模型(Dynamic Harmonic Regression,DHR)对MODIS的长时间序列的LAI产品进行分析,可以从中分离出LAI随时间变化的多年趋势、季节变化及残差等主要成分,通过建立的模型实现LAI年间变化的短时预测。本文将所述DHR模型分析方法试用于遥感数据产品随时间变化的信息提取,对LAI年间变化的预测结果证明该方法用于遥感像元尺度LAI产品的时间序列分析与预测的效果良好。     

变端元混合像元分解冬小麦种植面积测量方法

针对线性混合像元分解(Linear Spectral Unmixing,LSU)在端元(Endmember)个数不变情况下常会出现端元分解过剩现象导致分解结果精度不高的问题,以地物分布的聚集性特征为基础,提出了基于格网的变端元线性混合像元分解(Dynamic Endmember LSU,DELSU)方法.以冬小麦为研究...     

基于MERIS影像空间、光谱及时间维信息提取亚像元级的土地覆盖组分

及时获取有效的土地覆盖信息是地球系统模拟的基础。因此,中等空间分辨率传感器如MODIS或MERIS空前的通道设置与观测能力,使其具有快速更新土地覆盖图的能力。本文说明了如何结合MERIS的空间维（像元大小为300m）、光谱维（可见光与近红外范围内15个通道）和时间维（重返周期2—3d）,用于获取不同区域土地覆被组分的亚像元级组成权重。利用4月、7月和8月三期MERIS FR1b级数据得到荷兰主要土地覆被类型的组成权重。单一时相和多时相的数据都使用单个像元最优化的端元数进行线性光谱分解。利用一种形态偏离指数得到MERIS的空间维并用于端元的选择。应用荷兰土地利用数据库（LGN5）25m分辨率的栅格数据作为本文的参考数据。基于这种数据的高分辨率,因此可以从像元和亚像元的水平同时评价的分类精度。结果显示,结合4月和7月的影像可以获得最优的分类结果,精度约为58%。总的说来,亚像元和像元级的分类精度相似。通过几种组分类别和日期的光谱融合表明,物候状况对于数据获取时相最佳结合的选择以及正确识别土地覆盖类型的重要性。     

Spatio-temporal pattern analysis of land use/cover change trajectories in Xihe watershed

Human-induced land use/cover change has been considered to be one of the most important parts of global environmental changes. In loess hilly and gully regions, to prevent soil loss and achieve better ecological environments, soil conservation measures have been taken during the past decades. The main objective of this study is to quantify the spatio-temporal variability of land use/cover change spatial patterns and make preliminary estimation of the role of human activity in the environmental change in Xihe watershed, Gansu Province, China. To achieve this objective, the methodology was developed in two different aspects, that is, (1) analysis of change patterns by binary image of change trajectories overlaid with different natural geographic factors, in which Relative Change Intensity (RCI) metric was established and used to make comparisons, and (2) analysis based on pattern metrics of main trajectories in the study area. Multi-source and multi-temporal Remote Sensing (RS) images (including Landsat ETM+ (30 June 2001), SPOT imagery (21 November 2003 and 5 May 2008) and CBERS02 CCD (5 June 2006)) were used due to the constraints of the availability of remotely sensed data. First, they were used to extract land use/cover types of each time node by object-oriented classification method. Classification results were then utilized in the trajectory analysis of land use/cover changes through the given four time nodes. Trajectories at every pixel were acquired to trace the history of land use/cover change for every location in the study area. Landscape metrics of trajectories were then analyzed to detect the change characteristics in time and space through the given time series. Analysis showed that most land use/cover changes were caused by human activities, most of which, under the direction of local government, had mainly led to virtuous change on the ecological environments. While, on the contrary, about one quarter of human-induced changes were vicious ones. Analysis through overlaying binary image of change trajectories with natural factors can efficiently show the spatio-temporal distribution characteristics of land use/cover change patterns. It is found that in the study area RCI of land use/cover changes is related to the distance to the river line. And there is a certain correlation between RCI and slope grades. However, no obvious correlation exists between RCI and aspect grades.     

A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America

Abstract

It is widely accepted that natural resources should only be sustainably exploited and utilized to effectively preserve our planet for future generations. To better manage the natural resources, and to better understand the closely linked Earth systems, the concept of Digital Earth has been strongly promoted since US Vice President Al Gore's speech in 1998. One core element of Digital Earth is the use and integration of remote sensing data. Only satellite imagery can cover the entire globe repeatedly at a sufficient high-spatial resolution to map changes in land cover and land use, but also to detect more subtle changes related for instance to climate change. To uncover global change effects on vegetation activity and phenology, it is important to establish high quality time series characterizing the past situation against which the current state can be compared. With the present study we describe a time series of vegetation activity at 10-daily time steps between 1998 and 2008 covering large parts of South America at 1 km spatial resolution. Particular emphasis was put on noise removal. Only carefully filtered time series of vegetation indices can be used as a benchmark and for studying vegetation dynamics at a continental scale. Without temporal smoothing, subtle spatio-temporal patterns in vegetation composition, density and phenology would be hidden by atmospheric noise and undetected clouds. Such noise is immanent in data that have undergone solely a maximum value compositing. Within the present study, the Whittaker smoother (WS) was applied to a SPOT VGT time series. The WS balances the fidelity to the observations with the roughness of the smoothed curve. The algorithm is extremely fast, gives continuous control over smoothness with only one parameter, and interpolates automatically. The filtering efficiently removed the negatively biased noise present in the original data, while preserving the overall shape of the curves showing vegetation growth and development. Geostatistical variogram analysis revealed a significantly increased signal-to-noise ratio compared to the raw data. Analysis of the data also revealed spatially consistent key phenological markers. Extracted seasonality parameters followed a clear meridional trend. Compared to the unfiltered data, the filtered time series increased the separability of various land cover classes. It is thus expected that the data set holds great potential for environmental and vegetation related studies within the frame of Digital Earth.