Estimating zero-plane displacement height and aerodynamic roughness length using synthesis of LiDAR and SPOT-5 data

In this study, a combination of low and high density airborne LiDAR and satellite SPOT-5 HRG data were used in conjunction with ground measurements of forest structure to parameterize four models for zero-plane displacement height d(m) and aerodynamic roughness length z_0m(m), over cool-temperate forests in Heihe River basin, an arid region of Northwest China. For the whole study area, forest structural parameters including tree height (Ht) (m), first branch height (FBH) (m), crown width (CW) (m) and stand density (SD)(trees ha^− 1) were derived by stepwise multiple linear regressions of ground-based forest measurements and height quantiles and fractional canopy cover (f_c) derived from the low density LiDAR data. The high density LiDAR data, which covered a much smaller area than the low density LiDAR data, were used to relate SPOT-5's reflectance to the effective plant area index (PAIe) of the forest. This was done by linear spectrum decomposition and Li-Strahler geometric-optical models. The result of the SPOT-5 spectrum decomposition was applied to the whole area to calculate PAIe (and leaf area index LAI). Then, four roughness models were applied to the study area with these vegetation data derived from the LiDAR and SPOT-5 as input. For validation, measurements at an eddy covariance site in the study area were used. Finally, the four models were compared by plotting histograms of the accumulative distribution of modeled d and z_0m in the study area. The results showed that the model using by frontal area index (FAI) produced best d estimate, and the model using both LAI and FAI generated the best z_0m. Furthermore, all models performed much better when the representative tree height was Lorey's mean height instead of using an arithmetic mean.     

Applying tree-based ensemble algorithms to the classification of ecological zones using multi-temporal multi-source remote-sensing data

The decision tree method has grown fast in the past two decades and its performance in classification is promising. The tree-based ensemble algorithms have been used to improve the performance of an individual tree. In this study, we compared four basic ensemble methods, that is, bagging tree, random forest, AdaBoost tree and AdaBoost random tree in terms of the tree size, ensemble size, band selection (BS), random feature selection, classification accuracy and efficiency in ecological zone classification in Clark County, Nevada, through multi-temporal multi-source remote-sensing data. Furthermore, two BS schemes based on feature importance of the bagging tree and AdaBoost tree were also considered and compared. We conclude that random forest or AdaBoost random tree can achieve accuracies at least as high as bagging tree or AdaBoost tree with higher efficiency; and although bagging tree and random forest can be more efficient, AdaBoost tree and AdaBoost random tree can provide a significantly higher accuracy. All ensemble methods provided significantly higher accuracies than the single decision tree. Finally, our results showed that the classification accuracy could increase dramatically by combining multi-temporal and multi-source data set.     

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.     

Mapping the aerodynamic roughness length of desert surfaces from the POLDER/ADEOS bi-directional reflectance product

Surface roughness is a key parameter for computing the emissions and for simulating the atmospheric cycle of mineral dust. However its assessment on the basis of field measurements from source areas scattered round the globe requires much effort. Here we investigate the retrieval of the aerodynamic roughness length of arid areas using surface bi-directional reflectance products derived from passive multi-directional measurements in the solar spectrum of the POlarization and Directionality of the Earth's Reflectances (POLDER) sensor. The so-called protrusion coefficient (PC) of the surface derived from the POLDER bi-directional reflectance distribution function (BRDF) is well suited to estimate surface roughness. From an appropriate selection of POLDER data, a composite PC dataset has been established over the Sahara and the Arabian Peninsula. We have investigated the relationship between aerodynamic roughness length and PC, and have derived a statistically significant empirical relationship between these two parameters. This relationship is applied to the POLDER-derived PC to map the aerodynamic roughness length of arid areas in northern Africa and the Arabian Peninsula at the spatial resolution of POLDER (～1/16°). When degrading these data for global models, we show that the information is essentially preserved at coarser resolutions up to ¼°. This map of roughness length derived from the POLDER instrument, and a corresponding map derived from a geomorphologic classification, have been tested by comparing the predicted dust event frequencies obtained using them to dust indices (IDDI) derived from Meteosat IR observations over the Sahara desert. The agreement using the POLDER derived roughness length is at least as good as using the map of roughness length derived from the geomorphologic approach. Our results show promising new prospects for regional and global scale simulations of mineral dust emissions from arid regions.     

基于多源数据的白洋淀水域变化分析

以白洋淀水域为研究区域,针对白洋淀水域上游2个河道水文站的年平均流量、水位及水域遥感监测面积等数据,进行Mann-Kendall趋势、突变的检验,以及水位-面积拟合,并对结果进行分析,以期从多源数据中挖掘白洋淀水域的变化特征。分析结果表明:紫荆关站与阜平站的年平均流量呈显著下降趋势,且这种下降趋势在1990年后更加显著;白洋淀的水面面积经过急速下降—持续较低—缓慢上升的发展阶段,整体呈不显著下降状态,并且按先中部东部,再南部北部,最后西部的空间顺序增长;由于白洋淀上游河流流量不足,引水济淀仍然是保持白洋淀正常生态环境的有效途径;白洋淀春季、秋季、冬季水位-面积正相关关系明显,夏季较差,春、秋、冬三季水位-面积整体正相关且拟合关系较好。     

A study of spatial and temporal dynamics of total ozone over Southwest China with multi-source remote-sensing data

This article describes a study of the spatial and temporal dynamics of total ozone over Southwest China using satellite-retrieved total ozone products from 1996 to 2008 and a ground-based Dobson spectrophotometer. The findings indicated that the value of total ozone (265.7 Dobson unit (DU)) over Southwest China is lower than the value (273.7 DU) over the adjacent region at the same latitude by about 8 DU, and is about 13.8 DU lower than the global average at the same latitude (279.5 DU), and that there is a distinctly low-value area due to the higher elevation. The relationship of total ozone and the elevation presents a negative correlation, the terrain being the main factor to affect this condition. In the long term, the variation of total ozone exhibits a slightly increasing trend from 1996 over this region. Total ozone presents an obvious seasonal change, with the largest value appearing in springtime and the smallest appearing in wintertime. The difference between the regional seasonal mean value of total ozone in springtime and wintertime is about 28 DU, although the difference between the maximum and minimum monthly total ozone throughout a year is up to 50 DU. There is a positive correlation between the variation of total ozone and relative humidity. Relative humidity may be an important factor impacting on the pattern of seasonal change of total ozone.     

基于STM32的多源数据采集板卡设计

以STM32F429芯片为控制核心,实现了多源数据采集板卡的设计、制作、调试及驱动软件的编写。板卡具有2路独立CAN总线、 2路独立RS485、 4路RS232、 8路输入和8路输出可配置GPIO端口,且板载GPS/BD模块。软件功能包括所有接口驱动与GPS/BD模块位置地理信息提取。经测试表明,全负荷工作时,所有数据通道均处于良好的工作状态,各端口能够按约定波特率以最大数据更新速率同步收发数据。此外,板卡各接口模块均进行电器隔离,数据传输稳定可靠,满足设备长时间加电工作的要求。     

基于多源出行数据的居民行为模式分析方法

基于对智能交通卡数据的挖掘与分析能够为城市交通建设和城市管理提供有力支持,但现有研究数据大都仅包含公交或地铁这两方面数据,且主要关注群体性宏观出行规律。针对这一问题,以某城市交通卡数据为例,该数据包含着城市居民日常出行公交、地铁、出租车等多源数据,首先提出行程链的概念对居民出行行为建模,在此基础上给出不同维度的周期性出行特征;然后提出一种基于最长公共子序列的空间周期性特征提取方法,并对城市居民出行规律进行聚类分析;最后通过规则定义5个评价指标对该方法的有效性进行初步验证。结果表明引入该方法的聚类算法对聚类结果有6.8%的效果提升,有利于发现居民的行为模式。     

基于多源遥感数据融合的土壤水分反演研究

土壤含水量作为地表的重要参量之一,对地球能量循环、水循环、碳循环及生态环境都有十分重要的意义。以南京市金川河流域为研究区,融合哨兵 2 号 L2A 数据和 Landsat 8 遥感数据 2 种数据源,分别采用偏最小二乘法(PLSR)、最小二乘-支持向量机(LS-SVM)、反向传播神经网络(BPNN)和随机森林(RF)4 种建模方法,建立遥感数据与土壤含水量之间的关系,并进行模型的验证与评价。结果表明：1)土壤含水量与哨兵 2 号和 Landsat 8 各波段反射率均呈负相关关系,和海岸带监测波段(波长为 430~450 nm)和近红外波段(波长为 2 100~2 300 nm)相关性最佳;2)融合后的遥感数据相较于单一遥感数据源,预测土壤含水量的能力更佳, 最优模型 R2 达 0.996,均方根误差仅为 0.003 g/g;3)4 种建模方法中,建模效果从好到差依次为 PLSR,RF, LS-SVM,BPNN。融合哨兵 2 号 L2A 和 Landsat 8 数据,结合 PLSR 建模方法可进行土壤含水量的精准反演, 相较于现有研究反演精度大大提升,对研究该地区地表与地下水循环和生态环境治理有一定参考价值。     

基于DBSCAN算法和多源数据的缺失公交到站数据修补

针对缺失公交到站信息修补方法考虑因素较少、准确度低、鲁棒性差的现状,提出了基于DBSCAN算法和多源数据的缺失公交到站数据修补方法。该方法使用公交全球定位系统（GPS）、公交集成电路卡（IC）等多源数据进行缺失到站信息的修补。对于缺失的到站名称、到站经纬度数据,用已有完整到站数据和静态线路信息关联分析进行修补。对于缺失的到站时刻数据,则按以下步骤进行修补：首先,对每一个缺失数据站点与其最近的未缺失数据站点,将这两站点间历史完整到站数据的行程时间和班次时序进行基于DBSCAN算法的聚类;其次,判断研究班次的两个相邻的数据完整的班次所属簇是否为同一个簇,若为同一个簇则不作改变,否则将两个簇合并;最后,将簇中点对应最大行程时间作为缺失行程时间判断是否有乘客在该站点上车刷卡,若有则由乘客开始刷卡时刻推算到站时刻,若无则将簇中点对应最大、最小行程时间的均值作为缺失行程时间推算到站时刻。以厦门市公交到站数据为例,在缺失到站名称、经纬度修补中,基于GPS数据聚类的方法、基于极大概率估计的方法和所提方法皆可进行100%的修补;在缺失到站时刻修补中,所提方法的平均相对误差比两种对比方法分别低0.0301%和0.0004%,相关系数比对比方法分别高0.005和0.0075。实验结果表明,所提算法在缺失公交到站数据修补中能有效提高修补的准确度,降低缺失站点个数变化对于准确度的影响。     

Mapping urban land cover based on spatial-spectral classification of hyperspectral remote-sensing data

In this article, an innovative classification framework for hyperspectral image data, based on both spectral and spatial information, is proposed. The main objective of this method is to improve the accuracy and efficiency of high-resolution land-cover mapping in urban areas. The spatial information is obtained by an enhanced marker-based minimum spanning forest (MMSF) algorithm. A pixel-based support vector machine (SVM) algorithm is first used to classify the hyperspectral image data, then the enhanced MMSF algorithm is applied in order to increase the accuracy of less accurately classified land-cover types. The enhanced MMSF algorithm is used as a binary classifier. These two classes are the low-accuracy class and remaining classes. Finally, the SVM algorithm is trained for classes with acceptable accuracy. In the proposed approach, namely MSF-SVM, the markers are extracted from the classification maps obtained by both SVM and watershed segmentation algorithms, and are then used to build the MSF. Three benchmark hyperspectral data sets are used for the assessment: Berlin, Washington DC Mall, and Quebec City. Experimental results demonstrate the superiority of the proposed approach compared with SVM and the original MMSF algorithms. It achieves approximately 5, 6, and 7% higher rates in kappa coefficients of agreement in comparison with the original MMSF algorithm for the Berlin, Washington DC Mall, and Quebec City data sets, respectively.     

Large scale data mining based on data partitioning

Dealing with very large databases is one of the defining challenges in data mining research and development. Some databases are simply too large (e.g., with terabytes of data) to be processed at one time. For efficiency and space reasons, partitioning them into subsets for processing is necessary. However, since the number of itemsets in each partitioned data subset can be a combinatorial amount and each of them may be a large itemset in the original database, data mining results from these subsets can be very large in size. Therefore, the key to data partitioning is how to aggregate the results from these subsets. It is not realistic to keep all results from each subset, because the rules from one subset need to be verified for usefulness in other subsets. This article presents a model of aggregating association rules from different data subsets by weighting. In particular, the aggregation efficiency is enhanced by rule selection.     

Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data

Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R² = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R² = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions.     

基于负载均衡的多源流数据实时存储系统

物联网感知流数据多以时序数据为主,具有数据量大、连续到达、多来源等特点。现有的基于HBase的交通流数据存储系统在数据写入并发量大时,仍然存在存储效率低与系统可用性不高的问题。针对该问题,设计并实现了基于负载均衡的多源流数据实时存储系统。该系统将数据代理扩展为集群架构,提出了一种基于负载均衡的任务调度算法,实现了任务与数据代理之间的按序匹配,使数据代理集群负载均衡地处理任务,实现数据并行存储到HBase数据库中。实验对比结果表明:该系统使各数据代理的数据分配比例维持在0.3~0.4,同时以约1.5倍于单数据代理的速度将数据写入HBase数据库。     

A modified model of surface temperature inversion based on Landsat 8 remote-sensing data and measured data

ABSTRACT

This article verified the error between inversed land surface temperature (LST) and measured LST and developed the modified model based on Landsat 8 remote-sensing data. First, a single-channel algorithm was used to invert the surface temperature using four Landsat 8 remote-sensing images and the LST of the 98 measured points were obtained meantime. Then, the modified model between inversed LST and measured LST was developed based on LST for the 74 measured points. Finally, the developed models were used to modify the inversion temperatures at other 24 measured points, and the mean absolute error (MAE) and mean square error (MSE) between the measured temperature and the inversed temperature before and after the modification were compared to verify the validity of the model. The results showed that the MAE and the MSE of temperature for the 24 measured points used for verification reduced by 0.26 and 0.20 K, respectively, after modification. The development of the modified model can provide an important reference for using Landsat 8 remote-sensing image to invert surface temperature in other regions.     

基于多源异构数据融合的网络安全态势评估体系

针对基于单点网络数据很难准确地检测网络恶意活动且无法有效地分析网络状况的问题,本文通过引入多源异构数据融合策略,借鉴层次化网络分析思想,构建出包含流量探测模块、属性提炼模块、决策引擎模块、多源融合模块、态势评估模块等五大模块的网络安全态势评估体系。评估体系以BP神经网络为决策引擎分析各数据源的数据,使用指数加权D-S证据理论融合各决策引擎的输出结果,并基于层次化网络威胁评估方法评估网络威胁状况。实验结果表明:不同探测器探测到的数据对于识别不同类型攻击的优势不同;多源融合技术进一步将识别攻击类型的准确率提升到88.7%;层次化网络威胁评估方法能够有效地评估网络威胁状况。     

基于激光雷达多源数据融合的路障检测系统设计

车辆行驶模式的改变会导致道路障碍观测盲区的出现,如何在满足车辆运行安全的条件下,对道路障碍进行快速检测已经成为了一项亟待解决的问题;基于此引入激光雷达多源数据融合理论,设计路障检测系统;利用多源以太网接口电路,提供激光图像传感器、路障特征提取分类器、TVPS150PBS图像解码器所需的应用电子量,再借助可编程逻辑器件,对待融合多源数据进行整合,完成路障检测系统硬件设计;规划雷达工作模式的识别环境,利用多源异构传感器设备,对检测角点特征进行提取处理,整合上述所有数据信息,完成雷达脉冲组序列的建模处理,实现基于激光雷达多源数据融合路障检测系统的应用;实验结果表明,激光雷达多源数据融合原理支持下路障检测系统的PF指标最大数值与最小数值的差值为60 MHz;PR指标的差值为4800 MHz,PW指标的差值为700 MHz;以上指标表明所设计系统的观测盲区更小,可在满足车辆运行安全的条件下,实现对道路障碍的快速检测.     

基于信息资源聚合的农民专业合作社信息系统

针对农民专业合作社信息获取与信息发布难、成本高的问题,提出基于XM L Schema的农民专业合作社信息聚合元数据表示规范,设计数据传输流程和信息定制与智能推送机制,建设两层部署体系的农民专业合作社信息系统。系统信息服务中心与农业搜索引擎（AgSoso）相结合,为远端用户系统提供最新的信息资源;系统客户端实现合作社生产经营信息的本地化管理,并向信息服务中心同步最新的农产品等信息。应用结果表明,该方案投入成本、维护成本低,资源利用效率高,具有较高的实用应用价值,为农民专业合作社提供了一套基于多来源数据聚合服务的技术解决方案。     

Retrieval of forest canopy attributes based on a geometric-optical model using airborne LiDAR and optical remote-sensing data

In the retrieval of forest canopy attributes using a geometric-optical model, the spectral scene reflectance of each component should be known as prior knowledge. Generally, these reflectances were acquired by a foregone survey using an analytical spectral device. This article purposed to retrieve the forest structure parameters using light detection and ranging (LiDAR) data, and used a linear spectrum decomposition model to determine the reflectances of the spectral scene components, which are regarded as prior knowledge in the retrieval of forest canopy cover and effective plant area index (PAI_e) using a simplified Li–Strahler geometric-optical model based on a Satellites Pour l'Observation de la Terre 5 (SPOT-5) high-resolution geometry (HRG) image. The airborne LiDAR data are first used to retrieve the forest structure parameters and then the proportion of the SPOT pixel not covered by crown or shadow K_g of each pixel in the sample was calculated, which was used to extract the reflectances of the spectral scene components by a linear spectrum decomposition model. Finally, the forest canopy cover and PAI_e are retrieved by the geometric-optical model. As the acquired time of SPOT-5 image and measured data has a discrepancy of about 2 months, the retrieved result of forest canopy cover needs a further validation. The relatively high value of R ² between the retrieval result of PAI_e and the measurements indicates the efficiency of our methods.     

基于GML的多源异构空间数据集成系统的设计

多源异构的空间数据成为空间信息共享的瓶颈,在网络环境下如何实现多源异构空间数据的集成,成为当前GIS(地理信息系统)发展的一个热点和难点问题.提出了一个基于GML(地理标记语言)的多源异构空间数据集成模型,解决了多源异构空间数据向GML文档的转换,GML与空间数据库服务器的交互以及GML向SVG(可缩放矢量图形)的转换,并使用WebService技术,在Microsoft.NET平台上设计实现了多源异构空间数据集成系统,从而实现了基于GML的空间数据集成和基于SVG的空间数据可视化.