沿海湿地植被遥感分类研究

沿海湿地植被遥感分类是遥感在土地利用变化研究领域的一项重要内容,本文以江苏盐城沿海湿地核心区为研究区域,探讨了利用遥感技术进行湿地植被分类的方法和技术流程。在此基础上,以Landsat-5TM图像为遥感数据源,以盐城丹顶鹤湿地保护区样点数据为辅助数据,分别用非监督分类法、监督分类法与基于LBV变换的分类法,对研究区进行分类,并对各种分类方法的结果进行精度分析。结果表明,非监督分类的结果最差,特别是对芦苇,其错分误差达到51．65％。监督分类法的精度虽然高于非监督分类,但其制图精度只有66．56％。而基于LBV变换分类法能够有效解决关于米草和芦苇的错分现象,它对其他地物的分类精度也很高,基本上都在90％以上。总体来说,LBV变换法已经达到了核心区分类的精度要求。     

基于分形理论的面积精度估算

分形理论是研究非线性系统的重要工具。文中首先讨论栅格数据中面积精度估算的一般公式,然后基于分形理论进行了分析与研究３,选用了等面积的圆,正方形,复杂多边形和ｋｏｃｈ分形雪花图案四种图形进行了计算分析比较,得出了一些有益的结论。     

On the accuracy assessment of celestial reference frame realizations

The scatter of the celestial pole offset (CPO) time-series obtained from very long baseline interferometry (VLBI) observations is used as a measure of the accuracy of celestial reference frame (CRF) realizations. Several scatter indices (SI), including some proposed for the first time, are investigated. The first SI is based on residual analysis of CPO series with respect to a free core nutation (FCN) model. The second group of SIs includes Allan deviation and its extensions, which allow the treatment of unequally weighted and multidimensional observations. Application of these criteria to several radio source catalogues (RSCs) showed their ability to perform a preliminary assessment of the quality of each RSC. The 2D Allan deviation estimate seems to be the most sensitive measure. The proposed extensions of Allan deviation, weighted and multidimensional, can also be used for the statistical analysis of other time-series.     

近景摄影测量监测网的建立及其数据处理

介绍为采用近景摄影测量的方法进行储气罐容积测定与形变监测,而建立的监测网的精度要求,布网方法,观测与数据处理等问题。     

生态环境遥感分类数据矢量化方法与应用

遥感分类数据矢量化建库是生态环境遥感制图及其应用分析过程中主要的处理内容和基础工作。以福建省海岸带生态环境遥感分类数据为例 ,在分析遥感图像处理系统对遥感分类和专题信息提取后处理中矢量化能力不足和结果存在严重缺陷的基础上 ,综合应用遥感和地理信息基础软件平台 ,提供的系列数据管理、分析和数据转换功能模块 ,开展基于生态环境遥感影像分类数据的矢量化建库技术探讨 ,提出一个生态环境遥感分类数据矢量化建库的通用技术方法和流程 ,并对矢量化结果做了一个简要的评价和分析 ,以满足大面积生态环境遥感调查、分析和制图的实际需要。     

中国大陆科学钻探孔区的地震波速模型

大陆钻探区地震资料的解释正确与否取决于对该区确定地震波速模型, 地震波速模型的确定是反射地震处理极为困难的问题.中国大陆科学钻探提供了难得的深孔岩性、构造与地球物理测井资料,可以对结晶岩区地震资料时深转换的精度与地震波速模型的确定进行检验.本文介绍了科学钻探前根据地表资料建立波速模型的方法及钻探后由钻孔资料建立波速模型的方法.由地表资料建立的波速模型其精度分布是不均匀的,其波速反演的方差将随深度成正比地放大,在有钻孔时应尽量根据钻孔资料加以修正.由地球物理测井和岩芯的波速测试结果建立的波速模型结予波速估计值均匀的约束,说明由钻孔资料取得的波速模型可以有效地降低在深部的误差,提高地震反射与岩性变化和波阻抗合成的地震道之间的相关性.为检验波速模型,由测井资料与波速模型作出的合成地震道与反射地震剖面对比时还必须考虑到井孔的实际轨迹,否则对比相关性可能明显减小.     

Influence of neighbourhood information on ‘Local Climate Zone’ mapping in heterogeneous cities

Local climate zone (LCZ) mapping is an emerging field in urban climate research. LCZs potentially provide an objective framework to assess urban form and function worldwide. The scheme is currently being used to globally map LCZs as a part of the World Urban Database and Access Portal Tools (WUDAPT) initiative. So far, most of the LCZ maps lack proper quantitative assessment, challenging the generic character of the WUDAPT workflow. Using the standard method introduced by the WUDAPT community difficulties arose concerning the built zones due to high levels of heterogeneity. To overcome this problem a contextual classifier is adopted in the mapping process. This paper quantitatively assesses the influence of neighbourhood information on the LCZ mapping result of three cities in Belgium: Antwerp, Brussels and Ghent. Overall accuracies for the maps were respectively 85.7 ± 0.5, 79.6 ± 0.9, 90.2 ± 0.4%. The approach presented here results in overall accuracies of 93.6 ± 0.2, 92.6 ± 0.3 and 95.6 ± 0.3% for Antwerp, Brussels and Ghent. The results thus indicate a positive influence of neighbourhood information for all study areas with an increase in overall accuracies of 7.9, 13.0 and 5.4%. This paper reaches two main conclusions. Firstly, evidence was introduced on the relevance of a quantitative accuracy assessment in LCZ mapping, showing that the accuracies reported in previous papers are not easily achieved. Secondly, the method presented in this paper proves to be highly effective in Belgian cities, and given its open character shows promise for application in other heterogeneous cities worldwide.     

双频激光干涉仪在测距仪精度自动检测中的应用

目前用于工业测量的全站仪、测距仪的自动化程度和精度越来越高,一般是在厂房、车间等室内条件下,用于设备的高精度安装和检测等工作,与野外测量条件相比较,大气条件能得到较好的控制。在室内条件下,此类仪器的测距精度常常要高于厂家给出的测距标称精度指标值,往往能达到亚毫米级的水平。目前测距仪的精度检测通常是在野外基线上进行,影响测量精度的因素除大气条件外,还有对中误差、棱镜误差等的影响,因此不能客观地反映工业测量全站仪、测距仪的实际使用精度。本文作者提出了一种用双频激光干涉仪进行工业测量用全站仪、测距仪的自动化检测方法,给出了相应的误差评定公式,得到较好的结果。     

Using high resolution QuickBird imagery for crop identification and area estimation

Imagery from recently launched high spatial resolution satellite sensors offers new opportunities for crop assessment and monitoring. A 2.8-m multispectral QuickBird image covering an intensively cropped area in south Texas was evaluated for crop identification and area estimation. Three reduced-resolution images with pixel sizes of 11.2 m, 19.6 m, and 30.8 m were also generated from the original image to simulate coarser resolution imagery from other satellite systems. Supervised classification techniques were used to classify the original image and the three aggregated images into five crop classes (grain sorghum, cotton, citrus, sugarcane, and melons) and five non-crop cover types (mixed herbaceous species, mixed brush, water bodies, wet areas, and dry soil/roads). The five non-crop classes in the 10-category classification maps were then merged as one class. The classification maps were filtered to remove the small inclusions of other classes within the dominant class. For accuracy assessment of the classification maps, crop fields were ground verified and field boundaries were digitized from the original image to determine reference field areas for the five crops. Overall accuracy for the unfiltered 2.8-m, 11.2-m, 19.6-m, and 30.8-m classification maps were 71.4, 76.9, 77.1, and 78.0%, respectively, while overall accuracy for the respective filtered classification maps were 83.6, 82.3, 79.8, and 78.5%. Although increase in pixel size improved overall accuracy for the unfiltered classification maps, the filtered 2.8-m classification map provided the best overall accuracy. Percentage area estimates based on the filtered 2.8-m classification map (34.3, 16.4, 2.3, 2.2, 8.0, and 36.8% for grain sorghum, cotton, citrus, sugarcane, melons, and non-crop, respectively) agreed well with estimates from the digitized polygon map (35.0, 17.9, 2.4, 2.1, 8.0, and 34.6% for the respective categories). These results indicate that QuickBird imagery can be a useful data source for identifying crop types and estimating crop areas.