基于遥感数据的哈尔滨土地利用/覆被变化研究

以哈尔滨市为例,基于Landsat数据,利用遥感技术和GIS技术对2000和2015年的遥感影像数据进行解译分类,通过对分类结果的统计分析,定量研究哈尔滨市15年间的土地利用变化过程,结果显示:哈尔滨市土地综合利用程度较高,区域内耕地面积减少;相反,城市建成区面积大幅增加,区域内绿地面积逐年减少,应注意生态环境维护保持。     

2000～2015年会理县地表覆盖变化监测

以LandSat多时相影像为数据源,采用基于规则的面向对象分类方法,监测会理县2000～ 2015年地表覆盖变化.结合1∶50 000 DEM数据,分析了会理县地表覆盖构成与转移、变化动态度及潜在退耕地情况.监测分析结论能为会理县加强空间管制、促进土地资源集约利用、指导生态文明建设等提供决策信息支撑.     

L1范数与IQR统计量组合的GNSS坐标序列粗差探测算法

GNSS坐标时间序列中不可避免地含有粗差,未剔除的粗差将会导致参数估计有偏。因此,粗差探测与剔除是GNSS坐标序列分析中一项重要的数据预处理工作。针对GNSS坐标时间序列特点,提出了一种将L1范数(L1-norm)估计与四分位距统计量IQR(interquartile range)组合的移动开窗粗差探测算法,称之为L1_Mod IQR。该方法的主要思想是,首先利用L1范数估计得到较"真实"的残差,然后再对残差采用IQR统计量进行粗差探测。将L1_Mod IQR法与"3σ"法、基于最小二乘的τ检验法等粗差探测算法进行了模拟计算与对比,验证了该算法的有效性。进一步采用L1_Mod IQR算法对中国区域10个IGS站的高程时间序列进行了分析,结果表明中国区域IGS站高程序列的粗差剔除率最小为0.1%,最大为2.6%。并且以WUHN站为例与SOPAC提供的结果进行了对比,结果表明SOPAC提供的"Clean"数据仍含有大量的粗差,而L1_Mod IQR算法能够有效地剔除粗差。     

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.     

新疆哈密地区近54a日照时数的均一性分析及其影响因素

以新疆哈密地区1961-2014年6个气象观测站地面气象观测资料为依据,利用线性趋势分析、相关性分析、M-K突变检验分析等方法,对哈密地区近54a四季及年平均日照时数的变化趋势、异常年份、突变检验及影响因子进行分析,得出以下结论：近54a哈密地区春季与年平均日照时数均呈上升趋势,而夏、秋、冬季变化比较平稳;年日照时数异常偏少出现在1998年,但从未出现过异常偏多年;年日照时数在1973年和2008年发生两次突变,减少、增加趋势不显著;年、春季日照时数增加与低云量的减少关系密切。     

ZQZ TF型风传感器冻结故障自动检测装置设计

近年来我国大部分地区在冬、春两季多遭受冻雨灾害,自动气象站的风向风速传感器容易因冻结而无法正常工作。针对冻结故障的实时检测问题,本文设计了一种基于ZQZ-TF型风传感器的故障自动检测装置。该装置通过检测风传感器的工作电压和工作电流,实现对传感器实时状态的监控。同时,结合具体的故障诊断算法,能够快速判断风传感器是否被冻结。系统的核心硬件电路、软件算法可分别集成在自动气象站风向风速数据采集电路和嵌入式软件中,具有结构紧凑、操作简单的特点。经验证,该装置能够实时检测风传感器冻结故障,且工作稳定,检测精度高。     

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R² = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.     

利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识

青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。     

利用GNSS干涉信号振荡幅度反演土壤湿度

根据干涉效应和GNSS接收机信噪比估计方法,推导了利用GNSS干涉信号幅度进行土壤湿度反演的模型,建模过程考虑了天线增益、土壤介电常数和噪声的影响。提出了使用AMPD算法从含有噪声的归一化干涉功率曲线中提取干涉峰值与谷值,进而反演了土壤介电常数与土壤湿度的方法,并对其进行了仿真。结果表明,利用提取出来的干涉谷值进行反演性能比峰值好,相对稳定准确的卫星仰角范围为5°~25°,湿度大于0.06cm~3/cm~3时反演结果更为准确,标准差在0.01cm~3/cm~3左右波动。     

纹理特征向量与最大化熵法相结合的SAR影像非监督变化检测

合成孔径雷达(SAR)影像具有明显的斑点噪声,在变化检测中,一般需要考虑空间邻域信息。本文结合SAR影像丰富的纹理信息,提出一种考虑空间邻域信息的高分辨率SAR影像非监督变化检测方法,用基于灰度共生矩阵(GLCM)的32维纹理特征向量构造差异影像。通过最大化熵法自动选取阈值,对精度指标随窗口大小的变化进行回归分析,得到适合于变化检测的窗口为11×11。试验表明,本文方法优于马尔科夫随机场法,可以减小斑点噪声的影响,有效提高高分辨率SAR影像变化检测的精度。