网络三维影像地图的栅格数据组织方法研究

网络三维影像地图在全球的推广改变了地理空间信息的原有服务模式,促进了地理空间信息的共享和服务水平的提升,成为当前地理空间信息服务领域研究的热点。栅格数据作为影像地图系统的基础数据,它的高效组织是系统成功的关键。本文将对网络三维影像地图系统的栅格数据组织过程中的关键问题进行分析,结合实际提出一套解决方案。基于本文的方案组织"神州遨游"系统的栅格数据,取得了较好的效果,对同类软件的开发具有参考意义。     

Estimating regional deformation from a combination of space and terrestrial geodetic data

We discuss an approach for efficiently combining different types of geodetic data to estimate time-dependent motions of stations in a region of active deformation. The primary observations are analyzed separately to produce loosely constrained estimates of station positions and coordinate system parameters which are then combined with appropriate constraints to estimate velocities and coseismic displacements. We define noninteger degrees of freedom to handle the case of finite constraints and stochastic perturbation of parameters and develop statistical tests for determining compatibility between different data sets. With these developments, we show an example of combining space and terrestrial geodetic data to obtain the deformation field in southern California. Received: 23 January 1997 / Accepted: 30 September 1997     

DEM数据辅助的山脊线和山谷线提取方法的研究

从数字化地形资料中自动提取山脊线和山谷线的技术在测绘、工程设计等方面有着重要的意义。本文对现有的山脊线和山谷线自动提取的方法进行深入研究后 ,指出充分利用数据中所含有的地形信息是正确提取山脊线和山谷线的有效途径 ,那种试图仅依靠二维等高线形态分析的方法很难得到理想的结果。依照该思想本文设计出了一种基于DEM数据辅助的山脊线和山谷线提取方法。该方法先利用等高线数据建立区域地形概略DEM ,然后借助三维地形分析的技术辅助进行山脊线和山谷线的提取。文后通过实验验证了该方法的有效性 ,它所提取的山脊线和山谷线与实际地形相符合。     

电子地图海量专题要素麻点图技术研究

针对JavaScript成为目前Web电子地图开发的主流技术,而浏览器能承载的数据量和JavaScript的渲染能力有限的问题,该文通过对基于栅格图片的模式和基于插件的矢量数据模式的对比分析后,基于矢量瓦片技术、空间搜索技术和实时渲染技术等关键技术,提出了对海量专题要素进行服务端与客户端协同处理、实时分级渲染的麻点图技术方案,实现了海量专题要素的实时渲染和即时交互,并应用在"天地图·陕西"电子地图系统中。     

FY-2C云分类资料估算日照百分率

日照百分率是地球辐射研究、太阳能资源评估等工作中使用的一个重要指标量。为探讨FY-2C静止气象卫星资料估算日照百分率的可能性,以江西省为例,采用了FY-2C静止气象卫星2007年整年白天逐小时云检测资料和同期江西省87个国家气象站逐小时日照时数资料,建立了北京时间8:00-17:00逐时多元线性回归和权重系数两种估算模型,估算得到了覆盖江西省范围的5 km空间分辨率的逐时日照百分率格点资料。另外,用反距离加权(IDW)和克里金法(Kriging)模型对全省8:00-17:00逐时的日照百分率进行5 km分辨率空间插值,以进行多种方法对比分析。将各模型的估算结果与未参与模型构建的17站实测资料进行对比分析后发现,基于FY-2C云检测资料的两种估算模型的平均绝对误差(MAE)和相对误差均比IDW和Kriging的小50%以上,且其均方根误差(RMSIE)均明显小于后两者,误差离散性亦更小。基于FY-2C云检测资料的日照百分率的两个估算模型中多元回归模型的平均绝对误差(MAE)为3.40%,平均相对误差为8.62%,略小于权重系数模型的3.47%和8.75%,两者的均方根误差(RMSIE)则相差无几,误差空间分布也较为一致;各时次两种模型的估算值多大于实测值,仅17:00的估算结果小于实测值,8:00和17:00的MAE、RMSIE、相对误差均明显大于其他时次。对试验结果进行综合分析得出结论:基于FY-2C云检测资料的两种日照百分率估算模型均明显优于传统的IDW和Kriging方法;就2007年资料而言,多元回归模型优于权重系数模型。     

冠层光能利用率的叶绿素荧光光谱探测

设计了玉米生长期日变化试验,同步获取玉米冠层光谱和通量观测数据,探究从植被发射荧光光谱角度实现植被光能利用率可靠反演的可能性。运用涡度相关法获取群体生态系统净生产力(NEP),通过呼吸作用拟合得到冠层总初级生产力(GPP);在此基础上结合吸收光合有效辐射(APAR)获取冠层光能利用率(LUE);同时,利用叶绿素荧光光谱分离算法,提取了光合作用叶绿素荧光绝对强度和相对强度。结论表明,植被发射荧光光谱与光合有效辐射(PAR)显著正相关,760nm波段荧光与PAR的复相关系数R2在0.99以上;叶绿素荧光绝对强度与NEP和GPP显著正相关,荧光和NEP对环境日变化具有类似的响应特征;688nm和760nm植被发射的叶绿素荧光相对强度与LUEGPP存在可靠负相关关系,即叶绿素荧光强度越大,光能利用率越低。同时,通过比较几种植被指数与各种光合参量的相关性表明,叶绿素荧光能够更好的跟踪植被光合状态的变化,更适宜于植被光能利用率的探测。     

基于簇模型的矢量地图数据的高效压缩方法

This paper proposes a new method for the compression of vector data map. Three key steps are encompassed in the proposed method, namely, the simplification of vector data map via the elimination of vertices, the compression of removed vertices based on a clustering model, and the decoding of the compressed vector data map. The proposed compression method was implemented and applied to compress vector data map to investigate its performance in terms of the compression ratio and distortions of geometric shapes. The results show that the proposed method provides a feasible and efficient solution for the compression of vector data map and is able to achieve a promising ratio of compression and maintain the main shape characteristics of the spatial objects within the compressed vector data map. Supported by the National 863 Program of China (No. 2007AAl2Z241), the Program for New Century Excellent Talents in University (No. NCET-07-0643), the National Natural Science Foundation of China (No. 40571134, No. 40871185), the National 973 Program of China (No. 108085).     

Point grid map: a new type of thematic map for statistical data associated with geographic points

ABSTRACT

Social, economic, and environmental statistical data associated with geographic points are currently globally available in large amounts. When conventional thematic maps, such as proportional symbol maps or point diagram maps, are used to represent these data, the maps appear cluttered if the point data volumes are relatively large or cover a relatively dense region. To overcome these limitations, we propose a new type of thematic map for statistical data associated with geographic points: the point grid map. In a point grid map, an input point data set is transformed into a grid in which each point is represented by a square grid cell of equal size while preserving the relative position of each point, which leads to a clear and uncluttered appearance, and the grid cells can be shaded or patterned with symbols or diagrams according to the attributes of the points. We present an algorithm to construct a point grid map and test it with several simulated and real data sets. Furthermore, we present some variants of the point grid map.     

交通图出版前的数据质量控制研究

数据质量控制一直以来都是地理信息系统(GIS)的一个关键环节,随着GIS在地图出版领域的应用,地图出版前的数据质量控制也成为了目前需要迫切解决的问题。本文研究了交通图编制的基本要求以及编制规范,通过GIS数据和交通图出图数据的比较,总结了在交通图出版前有可能存在的数据质量问题,并提出了相应的控制措施。     

Estimating chlorophyll content of crops from hyperspectral data using a normalized area over reflectance curve (NAOC)

The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm², similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.     

地图数据网络分发中数据加密算法的研究

本文详细论述了网络分发中数据加密算法的基本概念、算法分类和各自的特点,重点分析了对称加密算法中的DES、AES和非对称加密算法中的RSA加密算法,在综合比较几种加密算法的基础上,结合实际应用,提出了一种新的加密算法-散列组合加密算法,新算法既满足了数据加密安全性的要求,也满足了加密效率的要求。     

多种算法融合的高鲁棒性矢量地图数据水印技术研究

本文在总结当前各种矢量数据水印算法的优缺点基础上,提出了一种通过融合多种水印算法构造一套具有高鲁棒性、强度可控的矢量地图数据数字水印解决方案.经过大量试验表明该方案从实现上具有简洁、方便、可控的优点,同时数字水印具有较强抗破坏、抗干扰能力,具有较强的应用推广价值.     

Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping

Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.     

Utilization of Landsat data to quantify land-use and land-cover changes related to oil and gas activities in West-Central Alberta from 2005 to 2013

Land use and land cover (LULC) change detection associated with oil and gas activities plays an important role in effective sustainable management practices, compliance monitoring, and reclamation assessment. In this study, a mapping methodology is presented for quantifying pre- and post-disturbance LULC types with annual Landsat Best-Available-Pixel multispectral data from 2005 to 2013. Annual LULC and land disturbance maps were produced for one of the major conventional oil and gas production areas in West-Central Alberta with an accuracy of 78% and 87%, respectively. The highest rate of vegetation loss (178 km²/year) was observed in coniferous forest compared to broadleaf forest, mixed forest, and native vegetation. Integration of ancillary oil and gas geospatial data with annual land disturbances indicated that less than 20% of the total land disturbances were attributable to oil and gas activities. In 2013, approximately 44% of oil and gas disturbances from 2005 to 2013 showed evidence of vegetation recovery. In the future, geospatial data related to wildfire, logging activities, insect defoliation, and other natural and anthropogenic factors can be integrated to quantify other causes of land disturbances.     

Sentinel-2数据的冬小麦地上干生物量估算及评价

作物生物量快速精确的监测对于农业资源的合理利用与农田的精准管理具有重要意义。近年来,遥感技术因其独特的优势已被广泛用于作物生物量的估算中。本文主要针对不同宽波段植被指数在冬小麦生物量(文中的生物量均是指地上干生物量)估算方面的表现进行探索。首先利用欧洲空间局最新的Sentinel-2A卫星数据提取出17种常见的植被指数,之后分别构建其与相应时期内采集的冬小麦地上生物量间的最优估算模型,通过分析两者间的相关性与敏感性,获取适宜进行生物量估算的指数。最后,绘制了研究区的生物量空间分布图。结果表明,所选的植被指数均与生物量显著相关。其中,红边叶绿素指数(CI_(re))与生物量的估算精度最高(决定性系数R~2为0.83;均方根误差RMSE为180.29 g·m~(–2))。虽然相关性较高,但部分指数,如归一化差值植被指数(NDVI)等在生物量较高时会出现饱和现象,从而导致生物量的低估。而加入红边波段的指数不仅能够延缓指数的饱和趋势,而且能够提高反演精度。此外,通过敏感性分析发现,归一化差值指数和比值指数分别在作物生长的早期和中后期对生物量的变化保持较高的敏感性。由于红边比值指数(SR_(re))和MERIS叶绿素敏感指数(MTCI)在冬小麦全生长季内一直对生物量的变化保持高灵敏性,二者是生物量估算中最为稳定的指数。     

机载激光雷达森林垂直结构剖面参数的沿海平原人工林林分特征反演

中国是世界上人工林面积最大的国家,实时、定量、精确地获取人工林林分特征对于人工林资源监测、管理以及全球碳循环具有重要意义。以北亚热带沿海平原人工林为研究对象,借助机载激光雷达LiDAR(Light Detection And Ranging)点云数据并结合地面实测的55个样地来反演人工林林分特征。首先,构建冠层高度分布剖面CHD(Canopy Height Distribution)和枝叶剖面FP(Foliage Profile);然后,通过Weibull函数分别对CHD和FP进行拟合并提取Weibull参数作为特征变量(第1组);同时,还直接基于点云提取了LiDAR高度变量HRM(HeightRelated Metrics)和冠层密度变量DRM(Density-Related Metrics)(第2组);最后,结合地面实测数据和两组特征变量构建了多元回归模型用于预测各林分特征(即林分密度、平均胸径、胸高断面积、Lorey’s树高、蓄积量和地上生物量)。结果表明:(1)与只使用基于点云的特征变量(即第2组)相比,结合点云特征变量(第2组)和冠层垂直结构剖面特征变量(第1组)的各林分特征预测精度均有所提升(ΔAdjusted R2=0—0.13,ΔrRMSE=0.08—3.65%);(2)对各林分特征预测的结果中,Lorey’s树高(Adjusted R2=0.85, rRMSE=7.66%)和蓄积量(Adjusted R2=0.84,rRMSE=14.27%)的预测精度最高,地上生物量(Adjusted R2=0.78, rRMSE=14.15%)、胸高断面积(Adjusted R2=0.73, rRMSE=14.70%)和平均胸径(Adjusted R2=0.64, rRMSE=15.05%)次之,林分密度(Adjusted R2=0.58,rRMSE=26.16%)的预测精度最低;(3)Weibull函数较准确地反映了亚热带人工林垂直冠层结构,可以有效提高林分特征反演精度。     

Comparison of upscaling cropland and non-cropland map using uncertainty weighted majority rule-based and the majority rule-based aggregation methods

Aggregation method is seriously impacted by the landscape characteristics, which has been emphasized due to proportional errors. This research proposed an uncertainty weighted majority rule-based aggregation method (UWMRB) to upscale the cropland/non-cropland map. The Cropland Data Layer for 2016 at 30m resolution, with its corresponding confidence level data, were collected to conduct the experiment using UWMRB and majority rule-based aggregation method. Proportional errors of crop/non-crop were used to assess the accuracy of the two methods. Ordinal logistic regression was used to obtain the probability of an error occurring to predict the uncertainty of both methods. The results show that UWMRB can achieve the lower proportional errors with lower uncertainty. Also, it can reduce the influence of complexity and fragmentation of landscape on aggregation performance. Additionally, the examination of UWMRB provides an important view of application of uncertainty information for upscaling land cover maps in an efficient way.     

图形、图像融合利用的集成学习智能化简方法及其在岛屿岸线化简中的应用

为充分利用已有化简成果及其蕴含的化简知识,本文集成几种机器学习算法提出图形、图像融合利用的智能化简方法,实现顶点取舍决策的学习和优化。首先,分别利用全连接神经网络和卷积神经网络设计、构建基于图形的顶点取舍模型和基于图像的顶点取舍模型,通过样本训练各模型拟合从图形特征到顶点取舍和从栅格图像到顶点取舍的映射;然后,基于线性加权、朴素贝叶斯、支持向量机、人工神经网络构建多种融合决策模型,实现基于图形和基于图像的顶点取舍的融合利用;最后,通过试验用例对所有模型进行测试。试验结果表明：基于图形和基于图像的顶点取舍模型在一定程度上学习、掌握了化简算子,融合利用后还能进一步提高化简准确性、实现优势互补。     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

Estimating crustal deformations from a combination of baseline measurements and geophysical models

The estimation of crustal deformations from repeated baseline measurements is a singular problem in the absence of prior information. One often applied solution is a free adjustment in which the singular normal matrix is augmented with a set of inner constraints. These constraints impose no net translation nor rotation for the estimated deformations X which may not be physically meaningful for a particular problem. The introduction of an available geophysical model from which an expected deformation vector \(\bar X\) and its covariance matrix \(\sum _{\bar X} \) can be computed will direct X to a physically more meaningful solution. Three possible estimators are investigated for estimating deformations from a combination of baseline measurements and geophysical models.