地理学报 ›› 2020, Vol. 75 ›› Issue (3): 620-630.doi: 10.11821/dlxb202003013

• 土地利用与生态系统服务 • 上一篇    下一篇

南北过渡带1∶5万植被类型图遥感制图案例研究

姚永慧1, 张俊瑶1,2, 索南东主1,2   

  1. 1. 中国科学院地理科学与资源研究所 资源与环境信息系统国家重点实验室,北京 100101
    2. 中国科学院大学,北京100049
  • 收稿日期:2018-12-17 修回日期:2019-12-19 出版日期:2020-03-25 发布日期:2020-05-25
  • 作者简介:姚永慧(1975-), 女, 湖北安陆人, 博士, 副研究员, 研究方向地理时空数据分析。E-mail: yaoyh@lreis.ac.cn
  • 基金资助:
    国家自然科学基金项目(41871350);国家自然科学基金项目(41571099);科技基础资源调查项目(2017FY100900)

Compilation of 1∶50000 vegetation type map with remote sensing images based on mountain altitudinal belts of Taibai Mountain in the north-south transitional zone of China

YAO Yonghui1, ZHANG Junyao1,2, SUONAN Dongzhu1,2   

  1. 1. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-12-17 Revised:2019-12-19 Online:2020-03-25 Published:2020-05-25
  • Supported by:
    National Natural Science Foundation of China(41871350);National Natural Science Foundation of China(41571099);Scientific and Technological Basic Resources Survey Project(2017FY100900)

摘要:

编制南北过渡带地区1∶25万和典型山地1∶5万植被类型图是南北过渡带综合科学考察的主要任务之一。以往植被类型图的编制都是采用大量的地面调查来完成的,地面调查方法虽然精确,但费时费力,并且由于自然条件的限制,地面调查往往只能覆盖较小的范围。遥感数据因为其全覆盖的优势,可以很好地弥补样方调查的局限性,但目前大范围的植被类型遥感信息提取尤其是自动提取方面仍然存在一定的困难和瓶颈。本文以1∶5万太白山植被类型图的编制为例,利用多源多时相的高分辨率遥感数据,结合地面调查数据、以往的各种比例尺的植被类型图数据和森林资源调查数据等,探讨并研究基于山地垂直带谱的中大比例尺植被类型图的遥感提取方法和制图方法。研究结果表明:① 山地植被垂直带谱可以有效地支持1∶5万山区植被类型图的遥感制图。利用太白山植被垂直带谱和1∶1万数字表面模型数据(DSM)可以生成具有垂直带谱信息的地形约束因子;将地形约束因子与多源多时相高分辨率遥感数据、地面调查数据、以往的小比例尺植被类型图数据等相结合,可以有效提取各级植被类型,从而实现中大比例尺植被类型图的编制。② 典型山地1∶5万植被类型图的遥感制图基本流程为植被型组解译→植被群系组、群系、亚群系解译→植被型、植被亚型分类,采取自上而下和自下而上相结合的分类方法来分类。本文的研究成果可以为中大比例尺植被类型图的编制提供示范和科学依据。

关键词: 植被类型图, 高分遥感, 太白山, 植被垂直带谱, 遥感解译

Abstract:

The compilation of 1:250000 vegetation type map in the north-south transitional zone and 1:50000 vegetation type maps in typical mountainous areas is one of the main tasks of integrated scientific investigation of the north-south transitional zone of China. In the past, vegetation type maps were compiled by a large number of ground field surveys. Although the field survey method is accurate, it is not only time-consuming, but also only covers a small area due to the limitations of physical environment conditions. Remote sensing data can make up for the limitation of field survey because of its full coverage. However, there are still some difficulties and bottlenecks in the extraction of remote sensing information of vegetation types, especially in the automatic extraction. As an example of the compilation of 1:50000 vegetation type map, this paper explores and studies the remote sensing extraction and mapping methods of vegetation type with medium and large scales based on mountain altitudinal belts of Taibai Mountain, using multi-temporal high resolution remote sensing data, ground survey data, previous vegetation type map and forest survey data. The results show that: (1) mountain altitudinal belts can effectively support remote sensing classification and mapping of 1:50000 vegetation type map in mountain areas. Terrain constraint factors with mountain altitudinal belt information can be generated by mountain altitudinal belts, 1:10000 Digital Surface Model (DSM) data of Taibai Mountain. In combination of the terrain constraint factors with multi-temporal and high-resolution remote sensing data, ground survey data and previous small-scale vegetation type map data, the vegetation types at all levels can be extracted effectively. (2) The basic remote sensing interpretation and mapping process for typical mountains is interpretation of vegetation type-group→interpretation of vegetation group & subgroup→interpretation and classification of vegetation type & subtype, which is a combination method of top-down method and bottom-up method, not the top-down or the bottom-up classification according to the level of mapping units. The results of this study provide a demonstration and scientific basis for the compilation of large and medium scale vegetation type maps.

Key words: vegetation type map, high resolution remote sensing data, mountain altitudinal belts, remote sensing interpretation, Taibai Mountain