基于FY-3号气象卫星的中国典型积雪覆盖区的时空动态研究

基于风云三号(FY-3)气象卫星的积雪产品数据(snow cover),提出了基于经验模态分解的趋势提取方法,探讨了2010—2019年中国积雪时空动态变化趋势。结果表明:1)中国积雪覆盖频率(Snow Cover Frequency,SCF)具有显著的季节性特征,呈现先增加后减少的特点,每年2月、3月SCF达到当年最大;东北的SCF呈显著下降的年际变化趋势,其他地区变化不大。2)中国整体的积雪覆盖率(Snow Cover Rate,SCR)下降了约1.2％,内蒙古和西藏的SCR下降了约1.5％,其他地区的SCR变化不显著;主要积雪覆盖区的SCR均在2016年发生由增加到减少的转变。     

基于改进的EMD的运城市持续极端气温的初步分析

采用每日最高（最低）气温的历史同期序列的分位数作为该日的极端阈值,运用改进的经验模态分解（empirical mode decomposition,EMD）方法对山西省运城市1971—2005年每年发生的持续（3 d及3 d以上）极端气温频数进行了初步分析。结果表明,1971—2005年运城市每年的持续极端高温频数（frequency of sustained extreme-high temperature,FSEHT）和持续极端低温频数（frequen-cy of sustained extreme-lowtemperature,FSELT）序列均存在线性下降趋势;在0.05的水平下,运城市的FSEHT序列与该地区的年平均气温序列的线性相关性不显著,而该地区的FSELT序列与年平均气温序列的负线性相关关系是显著的;此外,这两个频数序列分别存在不同的周期振荡。从主要周期看,运城市的FSEHT和FSELT序列与厄尔尼诺现象的周期相一致。     

DT-CWT结合MRF的遥感图像变化检测

为了解决多尺度遥感图像变化检测在降噪时丢失大量高频信息及单一像素孤立性的问题,提出了一种双树复小波变换DT-CWT(Dual-tree Complex Wavelet Transform)和马尔可夫随机场MRF(Markov Random Field)相结合的非监督遥感图像变化检测算法,首先采用DT-CWT对差异图像进行多尺度分解,并根据MRF模型分割算法提取高频区域的变化特征,然后进行相应层的高、低频重构,再对重构后的各层建立MRF模型并根据贝叶斯最大后验概率准则MAP(Maximum A Posterior)进行最终分割,最后对各层分割结果进行求交融合,得到最终的变化检测结果掩膜图。对比实验结果表明,该方法在去除杂点和噪声的同时能够较好地保留高频信息,并且边缘检测更加平滑,具有较高的变化检测精度和很好的鲁棒性。     

地理加权回归模型的多重共线性诊断方法

地理加权回归是常用的空间分析方法,已广泛应用于各个领域,但利用此方法进行回归分析前,往往忽略了对设计矩阵进行局部多重共线性的诊断,从而导致对模型的估计不准确。因此,本文在引入了全局模型的多重共线性诊断方法的基础上,对这些方法进行了改进,改进后构建了加权方差膨胀因子法和加权条件指标方法——分解比法,用于诊断地理加权回归模型设计矩阵的多重共线性问题。实验结果表明,多重共线性不存在于全局模型,而可能存在于局部模型中,构建的两种方法能够有效地诊断地理加权回归模型的多重共线性问题,且加权条件指标方法——分解比法比加权方差膨胀因子法在诊断多重共线性问题上更有优势。     

An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data

Large footprint waveform LiDAR sensors have been widely used for numerous airborne studies. Ground peak identification in a large footprint waveform is a significant bottleneck in exploring full usage of the waveform datasets. In the current study, an accurate and computationally efficient algorithm was developed for ground peak identification, called Filtering and Clustering Algorithm (FICA). The method was evaluated on Land, Vegetation, and Ice Sensor (LVIS) waveform datasets acquired over Central NY. FICA incorporates a set of multi-scale second derivative filters and a k-means clustering algorithm in order to avoid detecting false ground peaks. FICA was tested in five different land cover types (deciduous trees, coniferous trees, shrub, grass and developed area) and showed more accurate results when compared to existing algorithms. More specifically, compared with Gaussian decomposition, the RMSE ground peak identification by FICA was 2.82 m (5.29 m for GD) in deciduous plots, 3.25 m (4.57 m for GD) in coniferous plots, 2.63 m (2.83 m for GD) in shrub plots, 0.82 m (0.93 m for GD) in grass plots, and 0.70 m (0.51 m for GD) in plots of developed areas. FICA performance was also relatively consistent under various slope and canopy coverage (CC) conditions. In addition, FICA showed better computational efficiency compared to existing methods. FICA’s major computational and accuracy advantage is a result of the adopted multi-scale signal processing procedures that concentrate on local portions of the signal as opposed to the Gaussian decomposition that uses a curve-fitting strategy applied in the entire signal. The FICA algorithm is a good candidate for large-scale implementation on future space-borne waveform LiDAR sensors.     

Abrupt spatiotemporal land and water changes and their potential drivers in Poyang Lake, 2000–2012

Driven by various natural and anthropogenic factors, Poyang Lake, the largest freshwater lake in China, has experienced significant land use/cover changes in the past few decades. The aim of this study is to investigate the spatial–temporal patterns of abrupt changes and detect their potential drivers in Poyang Lake, using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) 16-day maximum value composite vegetation indices between 2000 and 2012. The breaks for additive seasonal and trend (BFAST) method was applied to the smoothed time-series normalized difference vegetation index (NDVI), to detect the timing and magnitude of abrupt changes in the trend component. Large part of Poyang Lake (98.9% for trend component) has experienced abrupt changes in the past 13 years, and the change patterns, including the distributions in timing and magnitudes of major abrupt trend changes between water bodies and land areas were clearly differentiated. Most water bodies had abrupt increasing NDVI changes between 2010 and 2011, caused by the sequential severe flooding and drought in the two years. In contrast, large parts of the surrounding land areas had abrupt decreasing NDVI changes. Large decreasing changes occurred around 2003 at the city of Nanchang, which were driven by urbanization. These results revealed spatial–temporal land cover changing patterns and potential drivers in the wetland ecosystem of Poyang Lake.     

基于PolInSAR三分量分解的建筑物高度向信息提取方法

传统的基于InSAR提取建筑物高程的方法难以区分同一分辨单元内不同散射体的高度信息.文中提出一种基于PolInSAR Freeman三分量分解的城市建筑物高度向信息提取方法,可有效获取同一分辨单元内不同散射体的散射中心.首先对极化干涉互协方差矩阵进行Freeman三分量分解,分离不同散射机制对应的相位中心,然后根据相位信息重建建筑物三维模型.利用覆盖美国加州Richmond地区的两景高分辨率TerraSAR-X极化SAR数据进行实验,结果表明,PolInSAR三分量分解能够分离同一散射单元内不同散射体的相位中心,从而有效提取出建筑物高度向信息及其三维模型.     

InSAR干涉图的矢量分离式小波滤波

提出了一种新的InSAR干涉图滤波方法,并提出采用滤波前后干涉相位的均方差来进行滤波精度评价的策略。对不同类型的多幅干涉图进行了滤波实验,结果表明,本文滤波方法效果较好,精度较高。     

针对倾斜建筑物的四分量分解方法

经典的Freeman-Durden三分量分解模型存在着体散射能量过度估计的现象,这种现象易导致倾斜建筑物(即建筑物的排列方向与雷达飞行方向不平行)被错误解译为森林.虽然存在多种分解方法来避免倾斜建筑物的错误解译,但从非反射对称散射的角度来应对体散射能量过度估计目前研究仍较少.因此,提出一种新的四分量分解方法,可有效抑制...     

相对定向中的临界配置分析

相对定向的解算需要利用足够数量的同名像点。针对同名像点对应的空间点分布处于临界配置时相对定向不能获得唯一解的问题,该文对摄影测量和计算机视觉领域内常用的相对定向算法进行了研究。分析了它们的联系和区别,利用物方形式的共面条件方程推导了临界配置中危险表面的通用方程,分析了直线和平面配置下相对定向的退化情况,并利用奇异值分解推导了不同相对定向算法的临界配置条件。