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RTK配合全站仪在数字化测图中的应用

本文主要叙述了RTK配合全站仪进行数字化测图的可行性,介绍了RTK的系统组成、工作原理,并对RTK配合全站仪联合测图的作业流程进行了一系列论述;指出了RTK与全站仪联合进行数字化测图是一种高速度、高效率的新方法.     

索佳SET×130R系列全站仪的维修

介绍了索佳SET×130R系列全站仪的常规检查和调整,维修程序,为技术人员在解决索佳全站仪常见故障和几种指标参数的校正提供参考.     

Summer weather characteristics on the Grove Mountain of East Antarctica

The summer weather characteristics of the Grove Mountain, East Antarctica, are presented based on the data obtained by Chinese National Antarctic Expedition (CHINARE) in January 1999. The result shows that the pattern of daily variation of temperature and the prevailing wind direction in Grove is similar to that of Zhongshan Station. However, the daily range of temperature and strong wind frequency are much higher than those of Zhongshan Station. The change of wind direction is close to the weather system that impacted the Grove Mountain. The warm and wet air from northern parts often causes the precipitation. The clear weather appears when controlled by eastern winds in January.     

传统村落景观基因信息链与自动识别模型构建——以陕西省为例

传统村落作为活的文化遗产,承载了大量历史记忆,是地域文化景观基因识别与模型构建研究的重要切入点。以陕西省71个国家级传统村落为例,基于景观基因理论建立陕西省传统村落景观基因识别体系,识别出陕西省传统村落景观基因特征;运用类型学原理和N级编码理论对景观基因进行编码,构建陕西省传统村落景观基因信息链并生成基因谱系;借鉴生物学中“胞-链-形”DNA碱基序列模型,提取出环境基因、建筑基因、农耕文化基因和宗族文化基因四个公共基因作为景观基因元（胞）,以村内道路系统作为基因链,构建传统村落景观基因DNA模型与自动识别模型,以此对传统村落的区位、类型、特征和文化基因进行自动识别。为传统村落景观基因信息有效传承与存储,以及乡村建设动态发展提供理论借鉴。     

DOT2008A海面地形模型在我国邻近海域的精度评估

文中首先介绍了DOT2008A模型建立的原理和方法,利用我国48个长期验潮站19 a连续观测数据验证DOT2008A模型的精度,其均方差为0.118 m,残差的标准偏差为0.086 m;联合多源测高卫星建立的海面地形模型数据验证DOT2008A的精度,均方差为0.106 m,残差的标准偏差为0.099 5 m,且其残差分布图基本符合正态分布。从实验结果来看,DOT2008A模型在我国周边海域精度较高,其建立方法较为合理,转化应用价值较高;同时与多源测高卫星建立的海面地形比较结果来看,验证了自主建立的海面地形模型的正确性,对建立我国高精度海面地形模型具有重要的借鉴意义。     

利用半参数模型估计确定验潮组网的深度基准面

深度基准面的确定是进行海洋测量的基础,准确确定深度基准面是进行海洋测量的有效前提。提出了一种将CORS技术与验潮组网技术进行结合的新传递深度基准面的方法,实现长、短期验潮站同步验潮实现深度基准面的增强传递;提出了采用半参数模型理论来求取潮差比的计算方法;具体阐述了验潮组网通过间接平差求取短期验潮站的深度基准面的计算方法,减少了验潮测量的人力投入,计算潮差比时考虑了系统误差的影响,计算短期验潮站时考虑了观测误差对深度基准面传递的影响。     

测量实习全站仪单人考核方法研究

测量实习一般采用分组方式展开实习工作,但为了保证每个人都能够掌握各个测量角色所应完成任务,要求实习过程中变换角色,但是具体实习实施过程中由于场地分散等原因无法进行全程监管,无法保证每个同学全掌握相关仪器的使用。文中以全站仪单人考核为例,在测量实习开始先进行全站仪碎部测量单人考核,从而保证每个人熟练掌握全站仪,能够顺利完成整个碎部测量实习工作,而在实习结束时进行全站仪点位放样单人考核,提高学生全站仪使用能力。     

Long-term soil moisture content estimation using satellite and climate data in agricultural area of Mongolia

The purpose of this study is to estimate long-term SMC and find its relation with soil moisture (SM) of climate station in different depths and NDVI for the growing season. The study area is located in agricultural regions in the North of Mongolia. The Pearson’s correlation methodology was used in this study. We used MODIS and SPOT satellite data and 14 years data for precipitation, temperature and SMC of 38 climate stations. The estimated SMC from this methodology were compared with SM from climate data and NDVI. The estimated SMC was compared with SM of climate stations at a 10-cm depth (r² = 0.58) and at a 50-cm depth (r² = 0.38), respectively. From the analysis, it can be seen that the previous month’s SMC affects vegetation growth of the following month, especially from May to August. The methodology can be an advantageous indicator for taking further environmental analysis in the region.     

River centerline extraction using the multiple direction integration algorithm for mixed and pure water pixels

The river centerline is a basic hydrological characteristic. Most prior studies have used remote sensing data to extract the river centerline from the open water region in a pure water pixel region. Extracting this type of river is relatively easy. However, extracting the centerline of a micro-river, which is mainly composed of mixed water pixels, is challenging. This paper presents a novel method, called the Multiple Direction Integration Algorithm (MDIA), to extract the river centerline using an image-enhancing method combined with river morphology. MDIA can be applied to regions mainly composed of pure water pixels, as well as to regions consisting of mixed water pixels in the index image. The method first calculates the normalized difference vegetation index (NDVI) and enhances the river linear structure using a Hessian matrix. Second, a small window is constructed as a circular structural element. In the window region, the local threshold is automatically obtained using water-oriented clustering segmentation and prior river knowledge to judge the pixel type. After completing the river centerline extraction in the current window, the next detecting window is generated to continue judgment. The structural element automatically executes river centerline judgment until the entire river centerline is extracted. The Landsat 8 images of six regions with different geomorphologies were chosen to analyze the method’s performance. The test sites include high mountain region, low mountain region, plains region with farmland and a residential region. The experimental results show that the optimal threshold of the processing results ranged from 0.2 to 0.3. In this range, the user’s accuracy is 0.813 to 0.997, and the producer’s accuracy is 0.981 to 1. The MDIA effectively and correctly extracts the river network in mixed-pixel regions. The presented method provides an effective algorithm for river centerline extraction that can be used to expand and update river datasets and provide reliable river centerline data for relevant hydrology studies.