点亮一盏灯 照亮一大片——江苏省金坛市开展土地管理示范村建设的经验

史有"江东福地"之称的江苏省金坛市,地处长江三角洲、苏锡常对外开放区.为了实现全市经济社会又好又快发展,市政府提出,既要富民强市,又要绿水青山;既要扩大开放、加快发展,又要十分珍惜、合理利用土地资源,实现可持续发展.     

我国测绘保障服务成绩斐然

我国测绘部门坚持围绕中心,服务大局、积极主动提供测绘保障和服务,取得可喜成绩,测绘事业正朝着又好又快的方向稳步推进。     

“四不像”和南海子

北京有个“四不像”,那还是儿时去动物园的模糊记忆,只记得初见它的模样,感觉无比新奇高兴得又蹦又跳。     

点亮一盏灯照亮一大片——江苏省金坛市开展土地管理示范村建设的经验

史有“江东福地“之称的江苏省金坛市,地处长江三角洲、苏锡常对外开放区.为了实现全市经济社会又好又快发展,市政府提出,既要富民强市,又要绿水青山;既要扩大开放、加快发展,又要十分珍惜、合理利用土地资源,实现可持续发展.……     

基于航测内外业一体化拓展

地形图成图工序诸多,各工序之间即有区别又有联系,即有分工又要合作,即要相互了解又要相互理解。对已实现的航测内外业一体化作业进一步加以拓展,由外业作业人员直接完成内业工作,省去许多中间环节,工作巧妙安排,人员巧妙利用。将为我们数字产品的生产提高效率,更省时省力的作业方法有待尝试。     

在改革创新中促进和保障科学发展——浅谈用科学发展观统领国土资源工作

在我国全面建设小康社会的今天,要使有限的国土资源持续保障我国经济社会又好又快发展,国土资源系统必须以科学发展观为统领,进一步解放思想,改革创新,加快构建保障科学发展新机制。     

江苏省滨海县国土资源局 坚持集约节约用地 推进体制机制创新

近年来,江苏省滨海县国土资源局以科学发展观为统领,以“保护资源、保障发展、维护权益、服务社会”为总体要求,集约节约用地,推进体制机制创新,促进全县经济社会又好又快发展。     

“四不象”和南海子

<正>北京有个"四不象",那还是儿时去动物园的模糊记忆,只记得初见它的模样,感觉无比新奇高兴得又蹦又跳。"四不象"听起来很神秘,其实就是我国独     

红叶,穿越时空的爱恋

加拿大东部在霜降以后本来天气已经转凉了,有几天夜间的最低温度都到了零度以下。但是像每年秋天一样,十月初气温又回升了。近一个星期的时间里天天阳光灿烂,暖烘烘的像又回到了夏天。人们把刚穿上没几天的毛衣又脱下来换回了T恤衫。大家都在说：印第安夏天来了,赶紧再享受一下入冬前的最好时光吧。     

GPS大地高高差代替水准高差的探讨

一、前言 在工程项目对高程精度要求高、水准测量又十分困难的情况下,在局部区域内,利用GPS大地高高差代替水准高差进行等级水准平差,既能达到等级水准测量精度,又能节约成本,从而提高工作效率。     

阿克苏河-塔里木河水系水质污染遥感监测与评价

 采用Landsat TM数据分析了阿克苏河—塔里木河断面水质污染状况,通过波段的DN值和常规监测数据建立能反映水质状况的 污染物监测模型。结果发现,将2000年常规监测数据代入模型后,与遥感数据的结果基本吻合| 重建阿克苏河—塔里木河的连续水体 污染变化曲线,得出污染物浓度随着远离上游而增加。     

地图符号及其本质的研究

本文探讨了制约地图符号设计的内在的基本规律,提出了地图符号的约定性原则和等价性原理。把作为地图符号的物质时象同传输过程中的地图符号区别开来,分析了地图符号的价值及价格,并对内涵地图符号和外延地图符号及其相互关系进行了研究,最后提出了新的地图符号分类原理。     

大地重力学的新进展

介绍了当前利用卫星探测地球重力场的技术及其实践,也就是目前采用的高轨卫星追踪低轨卫星技术(hl-SST)、低轨卫星追踪低轨卫星技术(ll-SST)、卫星重力梯度测定技术(SSG),及其相应的正在运行的CHAMP、GRACE、GOCE卫星和考虑发射的GRACEFollow-On卫星。运行的三颗卫星所提供的地球重力场信息不论在精度和分辨率方面都是大地重力学的一个重大进展。介绍了地形数据在构建地球重力场模型中的作用和美国SRTM对全球地形数据的测量。最后介绍了EGM2008与我国已有大陆重力值的比较,平均差值约为11mgal,与我国大陆现有的高程异常值的比较,平均差值为27cm左右。     

Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India)

The area around Sataun in the Sirmur district of Himachal Pradesh, India (falling between the rivers Giri and Tons; both tributaries of the Yamuna River) was studied for landslide vulnerability on behalf of the inhabitants. The study was made using extensive remote sensing data (satellite and airborne). It is well supported by field evidence, demographic and infrastructural details and aided by Geographic Information System (GIS) based techniques. Field observations testify that slope, aspect, geology, tectonic planes, drainage, and land use all influence landslides in the region. These parameters were taken into consideration using the statistical approach of landslide hazard zonation. Using the census data of 1991, vulnerability of the populace to the landslide hazard was accessed. As most of the infrastructure in the region is concentrated around population centres, population data alone was used for vulnerability studies.     

Behavior-based aggregation of land categories for temporal change analysis

Comparison between two time points of the same categorical variable for the same study extent can reveal changes among categories over time, such as transitions among land categories. If many categories exist, then analysis can be difficult to interpret. Category aggregation is the procedure that combines two or more categories to create a single broader category. Aggregation can simplify interpretation, and can also influence the sizes and types of changes. Some classifications have an a priori hierarchy to facilitate aggregation, but an a priori aggregation might make researchers blind to important category dynamics. We created an algorithm to aggregate categories in a sequence of steps based on the categories’ behaviors in terms of gross losses and gross gains. The behavior-based algorithm aggregates net gaining categories with net gaining categories and aggregates net losing categories with net losing categories, but never aggregates a net gaining category with a net losing category. The behavior-based algorithm at each step in the sequence maintains net change and maximizes swap change. We present a case study where data from 2001 and 2006 for 64 land categories indicate change on 17% of the study extent. The behavior-based algorithm produces a set of 10 categories that maintains nearly the original amount of change. In contrast, an a priori aggregation produces 10 categories while reducing the change to 9%. We offer a free computer program to perform the behavior-based aggregation.     

城市数字高程模型和数字正射影像建库系统架构

数字高程模型和数字正射影像是城市基础地理信息的核心载体.两者数据都是连续的地表模型数据.本论文旨在研究建立一个以这两种数据为主体的数据库集成管理系统,并能够提供基本的分析和应用功能,为城市的规划、建设、管理和社会各行业提供完善、优质和高效的地理空间数据服务.论文以宁波市两者数据的建库为例.首先介绍一些有关背景资料;然后详细研究了该系统的设计情况,包括设计的原则、技术路线,数据组织和功能规划;最后进行总结,分析了该系统架构设计的优缺点.     

关于社区详图编制与设计的探讨

地图是人们生活中不可或缺的一种工具,是国防建设的重要支持。专题图作为地图的一个大类,是以集中表现某种主题内容的地图。如今,根据不同的需要,专题图表现形式繁多。社区详图作为以社区为地图表现范围,以与社区相关信息为主题的专题图首次出现在杭州。本文从地图的分类出发,通过杭州市社区详图的编制等情况,进一步与大家共同探讨社区详图的编制与设计,以及社区详图在日常的生活中所发挥的作用及领域。     

利用DPS与RS技术生成DOM复合产品的试验研究

DPS具有几何精度高的特点，而RS具有信息量大、现势性强的优点，本文的目的是将两者的优点融合起来，形成DPS与RS的复合产品。研究了融合的理论基础，设计了融合的工艺流程，经过试验得出了令人满意的结果。     

Multiple-entity based classification of airborne laser scanning data in urban areas

There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information.     

Automatic representation and reconstruction of DBM from LiDAR data using Recursive Minimum Bounding Rectangle

Three-dimensional building models are important for various applications, such as disaster management and urban planning. The development of laser scanning sensor technologies has resulted in many different approaches for efficient building model generation using LiDAR data. Despite this effort, generation of these models lacks economical and reliable techniques that fully exploit the advantage of LiDAR data. Therefore, this research aims to develop a framework for fully-automated building model generation by integrating data-driven and model-driven methods using LiDAR datasets.The building model generation starts by employing LiDAR data for building detection and approximate boundary determination. The generated building boundaries are then integrated into a model-based processing strategy because LiDAR derived planes show irregular boundaries due to the nature of LiDAR point acquisition. The focus of the research is generating models for the buildings with right-angled-corners, which can be described with a collection of rectangles under the assumption that the majority of the buildings in urban areas belong to this category. Therefore, by applying the Minimum Bounding Rectangle (MBR) algorithm recursively, the LiDAR boundaries are decomposed into sets of rectangles for further processing. At the same time, the quality of the MBRs is examined to verify that the buildings, from which the boundaries are generated, are buildings with right-angled-corners. The parameters that define the model primitives are adjusted through a model-based boundary fitting procedure using LiDAR boundaries. The level of details in the final Digital Building Model is based on the number of recursions during the MBR processing, which in turn are determined by the LiDAR point density. The model-based boundary fitting improves the quality of the generated boundaries and as seen in experimental results, the quality depends on the average LiDAR point spacing. This research thus develops an approach which not only automates the building model generation, but also achieves the best accuracy of the model while utilizing only LiDAR data.