利用中文微博数据的地理情感特征挖掘

情感是一类存在于空间和时间中的地理知识,但其获取存在难度。位置社交网络为情感的度量提供了优质数据源,基于2016年9月19日—25日和2016年10月1日—7日(国庆假期)两周发布的345万条带有地理位置的新浪微博数据,通过清洗、分词以及基于词典的情感度量方法,计算了每条微博的情感倾向,通过情感聚合探测了城市微博情感的空间分布特征,并在格网尺度上分析了微博情感在时空域中的热/冷点模式及趋势。研究表明:微博整体情感倾向以积极为主,个体情感的空间分布具有随机性,城市情感的空间分布极其不均匀,表现为局部地区情感高值/低值聚集区与低-高值/高-低值异常区伴随出现的分片分布特征,且日常时期这种局部差异性更为显著;以时空立方体为格网单元,发现中国东西部地区呈现对立的情感模式,东部以情感热点模式为主,且两个时期表现为不同的时空热点类型,西部则以情感冷点模式突出,且相比于日常时期,国庆假期的冷点模式存在加强趋势。研究结果揭示了微博情感在地理时空域分布的多样性和差异性,为分析中国居民幸福感、指导社会经济发展规划等提供辅助支持。     

基于BP网络与空间统计分析的山东人口空间分布模式预测研究

运用BP网络对山东省17地市未来人口总量进行预测,在预测数据的基础上运用空间自相关方法对未来人口的空间分布模式进行分析。研究表明:2005-2010年山东省人口密度的空间分布模式总体呈现"西南-东北"模式,存在着空间集聚现象;2006-2010年17地市局部空间关联类型基本没有发生变化,西部和南部8个地区存在着明显的"高-高"集聚;北部5个地区存在着明显的"低-低"集聚;淄博和青岛存在着两个"高-低"关联的孤立点。     

亚热带森林参数的机载激光雷达估测

通过应用机载激光雷达数据,在分析云南省中部的78块样地的基础上提出2个预测森林不同生物特性的统计模型(加权平均高度的预测模型和生物量的预测模型),并讨论了预测结果及其精确性。从激光雷达数据中提取了2组变量(树冠高度变量组和植被密度变量组)作为自变量,采用逐步回归方法进行自变量选择。结果表明,激光雷达数据与森林的平均树高和地上各部分生物量有很强的相关性。对于3种不同森林类型(针叶林,阔叶林和混交林),平均树高估测均能达到比较高的精度;生物量的估测结果是针叶林优于阔叶林,混交林的生物量与激光雷达数据则没有明显相关性。最后,对回归分析的结果和影响预测精度的因素进行讨论,认为预测结果的精度可能与森林类型、激光雷达采样时间和采样密度以及坐标误差等因素有关。     

MIMU信号下的变形预测

为了能够对自然界中的变形进行有效预测,提出了一种惯性测量参数的多维时间序列分段模式的变形预测方法。首先采集MIMU测量单元信号,建立MIMU时间序列的AR模型,采用Kalman滤波进行信号处理;然后把处理过的数据分为不同数据段,设第一段数据为基准段,把后采集的数据段与第一段数据进行相似度量,计算相似度,预测变化规律;最后采集不同车辆通过大桥时的MIMU信号,处理数据结果能够反映大桥变化规律,说明变形预测方法是有效的。     

基于DEM地形信息量的景观格局预测——以珠海市为例

针对传统景观格局分析方法的数据局限性,该文提出了一种基于数字高程模型地形信息量的景观格局预测分析方法。结合信息熵理论,利用数字高程模型计算地形信息量,研究不同分级地形信息量下景观格局特征的变化特征,以及不同景观类型在不同地形信息量等级的分布特征,对具有相似地形特征的区域建立利用地形信息量对其景观格局特征进行预测分析的方法。该文选择珠海市地形特征相似的两个区域进行了试验分析,对珠海市斗门区建立基于数字高程模型地形信息量的景观格局预测方法,预测在相似地形特征下南坪镇的景观格局特征,并以南坪镇真实的景观特征进行验证。实验结果显示,基于数字高程模型地形信息量的邻域景观预测分析方法不仅能正确预测分析不同地形信息量下的景观分析指数,同时也能正确预测不同景观类型的空间分布特征。     

遥感信息处理不确定性的可视化表达

如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验.     

一种基于参考数据的志愿者地理信息质量评价方法

志愿者地理信息数据大都依靠志愿者上传,数据质量未知,阻碍了志愿者地理信息的广泛应用,因此是志愿者地理信息首要解决的问题。本文分析了影响志愿者地理信息质量的相关因素和基于参考数据评价方法的相关研究成果,在此基础上提出了一种基于参考数据的志愿者地理信息质量评价方法,以最常见的数据完整性和数据精度作为质量评价的质量元素,详细论述了这两种质量元素的内涵。针对数据完整性评价,提出了与匹配相结合的几何数据完整性度量方法和基于属性项饱和度的属性数据完整性度量方法;针对几何数据精度评价,提出了基于变缓冲区的度量方法。然后给出了该评价方法的一般流程。最后,以深圳市Open Street Map数据为例,选取最新导航数据作为参考数据进行试验验证。实验结果表明,深圳市Open Street Map数据点要素完整性较差,但是线要素完整性与精度都非常高,可以作为基础地理信息的更新数据源。     

北斗导航系统电文播发方式研究

基于各电文信息的重要程度和需要的重复周期是不同的,以及高播发速率和低播发速率有各自的优势这两个认识,提出了一种多速率播发方式。在一个信号上可以根据信息内容、区域使用环境等因素改变播发速率,达到解决使用灵敏度与首次定位时间、信息容量间矛盾的目的。给出了此种播发方式的播发方法、接收方法和性能分析。     

高维数据非参数密度估计的低维流形代表点法

非参数核方法由于采用统一的度量标准,在大数据中利用高维样本数据学习时容易遭遇维数灾难问题。挖掘高维空间中的低维几何特性,有助于揭示数据分布的流形结构,进而利用有限样本的高维数据在低维子空间逼近数据的真实分布。基于此,提出一种新的高维数据密度非参数估计的低维流形代表点法,通过从高维空间中挖掘数据分布的几何结构来估计密度。首先,通过寻找局部区域内能够代表流形结构主方向的点,计算局部协方差矩阵,描述局部的数据分布;然后,考虑流形结构中附近数据点不同的影响,根据每个样本数据点对密度的贡献进行加权。与传统的核密度估计方法和流形核密度方法进行了对比实验,结果表明,该方法能够快速稳健地进行密度估计,反映数据的真实分布。     

单样本对卷积神经网络遥感图像时空融合

遥感图像时空融合是一种生成兼具高时空分辨率的合成遥感数据的技术。近年来,产生了一些基于卷积神经网络的时空融合方法。这些方法效果良好,但需要较多的图像样本对训练模型,限制了它们的应用。针对此问题,本文提出了一种单样本对卷积神经网络时空融合方法（SS-CNN）。该方法以高空间分辨率图像的波段平均图像提供的空间信息激励卷积神经网络建立高、低空间分辨率图像间的超分关系,进而利用该超分关系映射求解目标高空间分辨率图像。在实验中使用两个模拟数据集和一个真实数据集对该方法进行了测试,并与两种常用的时空融合方法做了比较。实验结果表明,SS-CNN在单样本对训练的情况下,可以较好地预测地物的物候变化和类型的变化,且在异质性高、地块破碎的区域表现良好。其不足之处在于会在地物边界上会造成轻微的模糊,将来需针对此问题做进一步改进。     

Learning to combine multiple string similarity metrics for effective toponym matching

Several tasks related to geographical information retrieval and to the geographical information sciences involve toponym matching, that is, the problem of matching place names that share a common referent. In this article, we present the results of a wide-ranging evaluation on the performance of different string similarity metrics over the toponym matching task. We also report on experiments involving the usage of supervised machine learning for combining multiple similarity metrics, which has the natural advantage of avoiding the manual tuning of similarity thresholds. Experiments with a very large dataset show that the performance differences for the individual similarity metrics are relatively small, and that carefully tuning the similarity threshold is important for achieving good results. The methods based on supervised machine learning, particularly when considering ensembles of decision trees, can achieve good results on this task, significantly outperforming the individual similarity metrics.     

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.     

接收机自主完好性监测的算法分析

RAIM技术在接收机端实现卫星完好性监测,因此,监测算法的原理、正确性和灵敏度值得分析与研究。文中给出了RAIM故障检测和识别的数学模型,同时给出了两者的完好性保证算法。对告警门限值、误警率、漏检率之间的关系进行了较为全面的分析,分析了故障检测和故障识别功能与误警率、漏检率之间的关系。分析结果表明:可视卫星数相同时,误警概率越低,FD和FI的精度越低;误警概率相同时,可视卫星数越多,FD的灵敏度越高,FI的灵敏度越低;RAIM可用性一定时,同时满足低漏检率和低误警率是不可能的。     

Fusion of airborne LiDAR and multispectral sensors reveals synergic capabilities in forest structure characterization

Forest stand structure is an important concept for ecology and planning in sustainable forest management. In this article, we consider that the incorporation of complementary multispectral information from optical sensors to Light Detection and Ranging (LiDAR) may be advantageous, especially through data fusion by back-projecting the LiDAR points onto the multispectral image. A multivariate data set of both LiDAR and multispectral metrics was related with a multivariate data set of stand structural variables measured in a Scots pine forest through canonical correlation analysis (CCA). Four statistically significant pairs of canonical variables were found, which explained 83.0% accumulated variance. The first pair of canonical variables related indicators of stand development, i.e. height and volume, with LiDAR height metrics. CCA also found attributes describing stand density to be related to LiDAR and spectral variables determining canopy coverage. Other canonical variables pertained to Lorenz curve-derived attributes, which are measures of within-stand tree size variability and heterogeneity, able to discriminate even-sized from uneven-sized stands. The most relevant result was to find that metrics derived from the multispectral sensor showed significant explanatory potential for the prediction of these structural attributes. Therefore, we concluded that metrics derived from the optical sensor have potential for complementing the information from the LiDAR sensor in describing structural properties of forest stands. We recommend the use of back-projecting for jointly exploiting the synergies of both sensors using similar types of metrics as they are customary in forestry applications of LiDAR.     

利用InSAR技术观测台湾花莲地震断层滑动与运动机理分析

基于Sentinel-1卫星升降轨SAR数据,采用D-InSAR技术提取了2018年台湾花莲县Mw6.4地震的同震形变场。结果表明,2018年花莲地震造成的最大地表形变量为38.2 cm,以隆升为主,断层上下盘最大相对位移为50 cm。利用InSAR观测得到的升降轨地表形变数据,分别构建2018年与2021年台湾花莲两次地震的断层三维滑动分布模型。结果表明,2018年花莲Mw6.4地震主震断层为靠近米伦断层的西倾隐伏断层,断层最大滑动量为1.8 m,以左旋走滑为主兼具少量逆冲分量,断层破裂传播至米伦断层西侧,影响了苓顶断层和米伦断层的地震活动性。2021年花莲Mw6.0地震发生在苓顶断层北段,断层最大滑动量为0.38 m,断层滑动以左旋走滑为主,两次地震事件均具有高倾角滑动特征。综合两次地震静态库仑应力的重新分布和M-T图发现,2018年花莲地震对2021年地震起触发作用,应力沿断层从高纬度向邻近低纬度传输累积,花莲地区及近海海域短周期内地震活动性仍强烈,主要表现为小震频发、中强震孕育周期短等特点。     

Predicting the abundance of clays and quartz in oil sands using hyperspectral measurements

Clay minerals play a crucial role in the processability of oil sands ores and in the management of tailings. An increase in fine content generally leads to a decrease in both bitumen recovery performance and tailings settling rate. It is thus important to identify clay types and their abundance in oil sands ores and tailings. This study made use of oil sands samples characterized for quantitative mineralogy by x-ray diffraction, to gain an understanding of changes in the reflectance spectra of oil sands. The sample suite included bitumen-removed oil sands ore samples and their different fine size fractions. Spectral metrics applicable to the prediction of quartz and clay contents in oil sands were then derived with a focus on metrics correlating with sample content in total 2:1 clays (total of illite and illite-smectite) and kaolinite. Metrics in the shortwave infrared (SWIR) and longwave infrared (LWIR) were found to correlate with mineral contents. The best predictions of clays and quartz were achieved using LWIR metrics (R² > 0.89). Results also demonstrated the applicability of LWIR metrics in the prediction of kaolinite and total 2:1 clays.     

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.     

Sentinel-1 observations of the 2016 Menyuan earthquake: A buried reverse event linked to the left-lateral Haiyuan fault

Knowledge on the interaction of active structures is essential to understand mechanics of continental deformation and estimate the earthquake potential in complex tectonic settings. Here we use Sentinel-1A radar imagery to investigate coseismic deformation associated with the 2016 Menyuan (Qinghai) earthquake, which occurred in the vicinity of the left-lateral Haiyuan fault. The ascending and descending interferograms indicate thrust-dominated slip, with the maximum line-of-sight displacements of 58 and 68 mm, respectively. The InSAR observations fit well with the uniform-slip dislocation models except for a larger slip-to-width ratio than that predicted by the empirical scaling law. We suggest that geometric complexities near the Leng Long Ling restraining bend confine rupture propagation, resulting in high slip occurred within a small area and much higher stress drop than global estimates. Although InSAR observations cannot distinguish the primary plane, we prefer the west-dipping solution considering aftershocks distribution and the general tectonic context. Both InSAR modelling and aftershock locations indicate that the rupture plane linked to the Haiyuan fault at 10 km depth, a typical seismogenic depth in Tibet. We suggest that the earthquake more likely occurred on a secondary branch at a restraining bend of the Haiyuan fault, even though we cannot completely rule out the possibility of it being on a splay of the North Qilian Shan thrusts.     

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data

Background

LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m^?2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m.

Results

The results show that LiDAR pulse density of 5 pulses m^?2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m^?2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system.

Conclusion

LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m^?2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.

     

升降轨道ASAR雷达干涉揭示的巴姆地震（Mw6．5）3D同震形变场

通过对覆盖同一地区的升降轨道ASAR雷达数据进行两路差分干涉（D-INSAR）处理，得到了2003年12月26日伊朗巴姆（Bam）地震（Mw6．5）在两种成像几何状态下的视线向（LOS）同震形变场。利用Okada弹性形变模型对形变场进行正演计算，得到地震断层的几何参数及形变的南北向分量。对得到的干涉条纹图进行相位解绕处理，综合分析两种不同的雷达成像几何关系，并利用模型估计得到的南北向分量，计算了该地震造成的3D同震形变场，结果显示了典型的单断层右旋走滑活动特征。研究表明，合并升降轨道雷达数据，能够为形变模型提供有效约束，用一条简化的断层模型就可以解释升降两条轨能的干涉形变场，更为复杂的双断层模型是不必要的。