基于关系数据库的邻接域处理

本文就跨图幅的地物(邻接域)的管理问题作了一些有益的探讨，为了达到有效管理这个目的，本文采用Oracle公司提供的空间数据插件Spatial将空间数据和属性数据存入Oracle数据库中，这样不仅可以通过标准SQL语句可下裁任意区域的数据，而且数据库还会自动维护数据的完整性。因此，用空间数据库的管理方式使邻接域问题就不存在了。     

Geostatistical approaches to refinement of digital elevation data

Data refinement refers to the processes by which a dataset’s resolution, in particular, the spatial one, is refined, and is thus synonymous to spatial downscaling. Spatial resolution indicates measurement scale and can be seen as an index for regular data support. As a type of change of scale, data refinement is useful for many scenarios where spatial scales of existing data, desired analyses, or specific applications need to be made commensurate and refined. As spatial data are related to certain data support, they can be conceived of as support-specific realizations of random fields, suggesting that multivariate geostatistics should be explored for refining datasets from their coarser-resolution versions to the finer-resolution ones. In this paper, geostatistical methods for downscaling are described, and were implemented using GTOPO30 data and sampled Shuttle Radar Topography Mission data at a site in northwest China, with the latter’s majority grid cells used as surrogate reference data. It was found that proper structural modeling is important for achieving increased accuracy in data refinement; here, structural modeling can be done through proper decomposition of elevation fields into trends and residuals and thereafter. It was confirmed that effects of semantic differences on data refinement can be reduced through properly estimating and incorporating biases in local means.     

Multi‐scale approach to mining significant spatial co‐location patterns

Spatial co‐location pattern mining aims to discover a collection of Boolean spatial features, which are frequently located in close geographic proximity to each other. Existing methods for identifying spatial co‐location patterns usually require users to specify two thresholds, i.e. the prevalence threshold for measuring the prevalence of candidate co‐location patterns and distance threshold to search the spatial co‐location patterns. However, these two thresholds are difficult to determine in practice, and improper thresholds may lead to the misidentification of useful patterns and the incorrect reporting of meaningless patterns. The multi‐scale approach proposed in this study overcomes this limitation. Initially, the prevalence of candidate co‐location patterns is measured statistically by using a significance test, and a non‐parametric model is developed to construct the null distribution of features with the consideration of spatial auto‐correlation. Next, the spatial co‐location patterns are explored at multi‐scales instead of single scale (or distance threshold) discovery. The validity of the co‐location patterns is evaluated based on the concept of lifetime. Experiments on both synthetic and ecological datasets show that spatial co‐location patterns are discovered correctly and completely by using the proposed method; on the other hand, the subjectivity in discovery of spatial co‐location patterns is reduced significantly.     

高光谱影像数据预处理技术评价

高光谱影像数据预处理是构建高效的影像数据库以及实现地理目标成分信息反演与地理目标识别的基础。本文从反射率演算、光谱微分技术以及高光谱影像数据的降维处理等三个方面对高光谱影像数据预处理进行了研究。实现了基于高光谱影像的反射率反演算法，针对高光谱遥感数据影像分辨率比较低的现状，提出了直方图均衡化、色调平衡、对比度调整等影像处理算法，并给出了实验结果。     

基于MODIS数据的雪面温度遥感反演

On the basis of simplification of the Planck function in a low temperature range, this paper revises the practical split-window algorithm and presents a method for retrieving snow surface temperature （Ts） based on MODIS data in the middle-latitude region. The application of this method in Qinghai Lake region reveals that it is feasible for the retrieval of Ts. Results of correlation analysis indicate that there was strong negative relationship between Ts and altitude. By analyzing three typical areas in which land cover was relatively homogenous, this paper discusses the relationship between Ts and normalized difference snow index （NDSI） and then presents a new concept named ＂NDSI-Ts space＂.     

Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat (Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha⁻¹ lower than the RMSE (233.35 kg ha⁻¹) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.     

倒Y形竖井联系测量

介绍在小直径竖井联系测量中取消联系三角形,改用单根钢丝定点,全站仪引测高程的施测方法。     

方向数据插值方法研究

方向数据是矢量数据的标量表示,通常标量数据的插值方法不适用于方向数据。本文根据方向数据的特征,把其分解成沿坐标轴的分量,通过对分量数据的插值形成表面,最后合成反算出方向数据,给出了一种对于方向数据的行之有效的插值方法;并用实验进行了验证,对其插值结果和精度进行了统计和分析,取得了较为理想的内插结果。     

线性体图像自动矢量化算法研究

依据线性体图像的特点,提出了一种最大长度原则下基于端点提取的自动跟踪矢量化算法。该算法通过对节点及其信息进行提取快速抓住线性体的总体拓扑结构,并以节点信息为指导对线性体进行矢量跟踪和直线提取解决了复杂的线性体交叉问题。算法具有精度高、抗噪性好和适应性强的特点。     

瑞安市基础控制网改造与原有控制网精度分析

通过瑞安市基础控制网的改造实践,对各地控制网改造的方法进行了探讨,并对原有测绘成果转换到新坐标基准中进行了分析、探讨.