大理大学学报 ›› 2019, Vol. 4 ›› Issue (12): 62-68.

• 生命科学 • 上一篇    下一篇

基于Google Earth的山区森林覆盖率快速估算方法#br# ——以云南省为例

  

  1. (1.大理大学东喜玛拉雅研究院,云南大理671003;2.西南林业大学生物多样性保护与利用学院,
    昆明650224;3.云南省中国三江并流区域生物多样性协同创新中心,云南大理671003)
  • 收稿日期:2019-02-07 出版日期:2019-12-15 发布日期:2019-12-15
  • 通讯作者: 任国鹏,副研究员,博士,E-mail:Rengp@eastern-himalaya.cn.
  • 作者简介:高颖,硕士研究生,主要从事野生动植物保护与利用研究.
  • 基金资助:
    云南省应用基础研究计划资助项目(2015FB157);国家自然科学基金资助项目(31560599)

A Cost-Effective Approach to Estimating Forest Coverage over Mountainous Regions Based on#br# Google Earth: A Case Study in Yunnan

  1. (1. Institute of Eastern-Himalaya Biodiversity Research, Dali University, Dali, Yunnan 671003, China; 2. College of Biodiversity
    Conservation and Utilization, Southwest Forestry University, Kunming 650224, China; 3. Collaborative Innovation
    Center for the Biodiversity in the Three Parallel Rivers of China, Dali, Yunnan 671003, China)
  • Received:2019-02-07 Online:2019-12-15 Published:2019-12-15

摘要: 及时准确地估算森林覆盖,对森林管理成效评价等方面有重要的作用。然而,我国的森林主要分布于地形复杂、交通
不便的山区,现有的森林覆盖估算方法,或费时费力,或成本较高。Google Earth为估算森林覆盖率提供了一种有效的工具。
应用系统集群采样原理布设样点,以Google Earth高分辨率卫星影像目视解译为主要手段,提出一种森林覆盖率快速估算方
法,并将之应用于云南省。基于Google Earth 对42 880个样方进行解译,结果表明,2013—2015年,云南省森林覆盖率为51.3%
(95% CI:50.8%~51.8%),其中,密林覆盖率为27.3%(95% CI:26.8%~27.8%)。通过不同的样本量下1 000次的放回抽样分析,
森林覆盖率的估算精度与样本量的关系是幂函数关系。当样方量达到20 000时,估算的精度可高于1%。基于Google Earth目
视解译方法的森林资源估算调查,具有很强的优越性,可快速准确且高效地估算山区森林覆盖率。

关键词: Google Earth, 遥感, 目视解译, 森林覆盖率, 抽样

Abstract: It is important to estimate forest coverage for forest management and conservation in a certain region. However, forests in
China are mainly distributed in remote and mountainous regions. It is costly to investigate forest coverage due to the complex
topography. In this study, we developed a cost-effective approach to estimating forest coverage based on free high-resolution images in
Google Earth and applied this approach in Yunnan Province, which is a mountainous region. According to the interpretation of 42 880
sample plots based on Google Earth, from 2013 to 2015, forest coverage rate is 51.3%(95% CI:50.8%-51.8%)in Yunnan, and dense
forest coverage rate is 27.3%(95% CI:26.8%-27.8%). Through the analysis of 1 000 times of return samplings under different sample
sizes, the relationship between the estimation accuracy of forest coverage and the sample size is a power function . The precision of
estimated forest cover rate showed a power law dependence on sample size. If the sample size is higher than 20 000, the precision
would be improved to 1%. As the results indicate, it is feasible and cost-efficient to measure forest coverage rate in mountainous region
based on visual interpretation of sample plots on Google Earth.

Key words: Google Earth, remote sensing, visual interpretation, forest coverage, sampling