摘要
森林小班是准确标示到图上的基本区划单位,是森林资源二类调查统计和经营管理的基本单位。在森林地图采集与处理过程中,由于数据来源、采集方式、采集批次及林地类型的不同,采用分层的方式存储数据。而在实际制图过程中,往往需要对森林小班进行跨图层统一编号。传统森林小班编号工作人工参与度高,存在主观性强、效率低,易出现错误、漏号、重号等现象。为提高森林小班编号的效率和准确率,基于ArcGIS Model Builder设计了可嵌入在ArcGIS Desktop运行的符合ESRI空间建模标准的通用模型,实现了在不破坏原图层属性和几何结构情况下跨图层森林小班自动编号功能,解决了南北延伸较长和面积相差较大的小班编号在视觉上的跳跃问题、岛洞型小班的编号重复问题。通过案例数据测试,效果良好,有效提高了工作质量和效率,避免了森林小班编号过程中产生错误、漏号、重号现象,即准确、快捷、高效,还节省大量时间和人力投入,随着数据量的增加,效率提升更明显,有着良好的推广意义。
Forest sub-compartments are the basic divisional units which are accurately marked on the map and are the basic units of Class II forest resources investigation, statistics, operation and management. In the process of forest map acquisition and processing, due to different data sources, acquisition methods, acquisition batches and types of forest land, the data are stored by layers. However, in an actual process of drawing, it is usually necessary to number the forest sub-compartments across layers in a unified way. Traditionally,forest sub-compartments are mostly manually numbered, so the numbering is rather subjective and inefficient. Meanwhile, number errors, omissions and repetitions occur frequently. To improve the numbering efficiency and accuracy, this paper, based on ArcGIS Model Builder, designs a general model which can be embedded in ArcGIS desktop and conforms to ESRI spatial modeling standard. It realizes the automatic numbering function of forest sub-compartments across layers without destroying the attributes and geometric structure of original layers. It solves the problem of longer north-south extension and larger area difference of sub-compartment numbering in visual jump and duplicate numbering of island-cave sub-compartments simultaneously. Through the case data test, the effect is good and the work quality and efficiency are effectively improved. by testing data of the case, it is found to be fairly effective for improving work and efficiency. It avoids errors, number omissions and repetitions in numbering forest classes. In other words, it is accurate, convenient and efficient. In addition, it significantly reduces time consumption and human resources. The greater the amount of data it processes, the higher the efficiency is. Therefore, it is of great significance for being promoted.
作者
曹明兰
李亚东
樊昌晋
高绍伟
CAO Minglan;LI Yadong;FAN Changjin;GAO Shaowei(Beijing Polytechnic College,Beijing 100042,China;Beijing Key Laboratory of Precision Forestry of Beijing Forestry University,Beijing 100083,China;Forestry Investigation and Design Team of Shanxi Sanggan River Poplar High Yield Forest Experimental Bureau,Datong 037000,Shanxi,China)
出处
《中南林业科技大学学报》
CAS
CSCD
北大核心
2019年第9期16-20,共5页
Journal of Central South University of Forestry & Technology
基金
北京市教育委员会科研计划一般项目(KM201810853001)
作者简介
曹明兰,副教授,博士;E-mail:nm_cml@163.com.
