基于回归决策树和ASTER卫星影像的城市森林研究

黄初冬; 邵芸; 李静; 柳晶辉; 陈洁琼

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基于回归决策树和ASTER卫星影像的城市森林研究

黄初冬 , 邵芸 , 李静 , 柳晶辉 , 陈洁琼

文章导航 > 浙江农林大学学报 > 2008 > 25(2): 240-244

黄初冬, 邵芸, 李静, 柳晶辉, 陈洁琼. 基于回归决策树和ASTER卫星影像的城市森林研究[J]. 浙江农林大学学报, 2008, 25(2): 240-244.

引用本文:

黄初冬, 邵芸, 李静, 柳晶辉, 陈洁琼. 基于回归决策树和ASTER卫星影像的城市森林研究[J]. 浙江农林大学学报, 2008, 25(2): 240-244.

HUANG Chu-dong, SHAO Yun, LI Jing, LIU Jing-hui, CHEN Jie-qiong. Urban forest research based on regression tree techniques and ASTER imagery[J]. Journal of Zhejiang A&F University, 2008, 25(2): 240-244.

Citation:

HUANG Chu-dong, SHAO Yun, LI Jing, LIU Jing-hui, CHEN Jie-qiong. Urban forest research based on regression tree techniques and ASTER imagery[J]. Journal of Zhejiang A&F University, 2008, 25(2): 240-244.

基于回归决策树和ASTER卫星影像的城市森林研究

黄初冬^1,2,
邵芸¹,
李静³,
柳晶辉^1,2,
陈洁琼⁴

1.
中国科学院遥感应用研究所, 北京 100101;
2.
中国科学院研究生院, 北京 100049;
3.
浙江中医药大学外国语学院, 浙江杭州 310053;
4.
浙江科技学院信息与电子工程学院, 浙江杭州 310023

基金项目:

“十五”国家科技攻关计划项目(2002BA90407);中国科学院奥运科技资助项目(KACX1-10)

详细信息

作者简介: 黄初冬,博士研究生,从事遥感在城市生态环境中的应用研究。E-mail:cdhuang@tsinghua.org.cn

中图分类号: S757

Urban forest research based on regression tree techniques and ASTER imagery

1.
Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China;
2.
Graduate University, Chinese Academy of Sciences, Beijing 100049, China;
3.
College of Foreign Studies, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, China;
4.
School of Information and Electronic Engineering, Zhejiang Science and Technology University, Hangzhou 310023, Zhejiang, China

摘要: 城市森林已成为城市生态环境研究和城市规划的热点问题之一。介绍了一种运用遥感影像分析城市森林的方法。分析过程分为两大内容:①运用回归决策树模型和ASTER影像分析城市森林丰度,即根据分类训练区内ASTER影像的光谱与实际森林丰度之间的对应关系,建立回归决策树模型,然后用该模型计算整个研究区域内各像元的森林丰度。②运用CITYgreen模型分析该地区城市森林生态功能,即运用CITYgreen模型,估算研究区域内城市森林的覆盖率、碳储量和清除空气污染物的能力,以及在清除空气污染物方面的生态价值。以北京市通州新城为研究区域,根据估算得出,该地区森林覆盖面积约为11.62 km2,碳储量约为12.36 万t,清除污染物总量为267.1 t·a-1。讨论了该方法的优缺点以及需要改进的方面。
- 森林经理学 /
- 城市森林 /
- 回归决策树 /
- 通州新城 /
- 城市森林丰度
Abstract: Urban forest was of great interest to a variety of scientific and urban planning applications. This paper presented the process and results of analysis using remote sensing imagery in this area. There were mainly two key steps in the analysis: the first was to analyze urban forest abundance utilizing regression tree techniques and advanced spaceborne thermal emission and reflection radiometer(ASTER) images; the second was to analyze the ecological benefits of urban forest using CITYgreen model. The analysis of urban forest abundance was accomplished implementing classification models,which were based on relationships between urban forest abundance and the spectrum on ASTER imagery,and were generated using regression tree techniques. Subsequently,the calculation of forest coverage,carbon storage and the amount of air pollutants removal about urban forest was carried out introducing CITYgreen model. According to the analysis,the total area in Tongzhou District was about 11.62 km2; the forest coverage was about 7.6%; the carbon storage and the amount of air pollutants removal amounted to 123 589 t and 267.1 t·a-1 respectively. At the end,the advantages and disadvantages,as well as some of the conceivable improvements of this strategy were discussed.
- forest management /
- urban forest /
- regression tree /
- Tongzhou District /
- urban forest abundance

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链接本文:
https://zlxb.zafu.edu.cn/article/id/2884

https://zlxb.zafu.edu.cn/article/zjnldxxb/2008/2/240

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基于回归决策树和ASTER卫星影像的城市森林研究

1. 中国科学院遥感应用研究所, 北京 100101;

2. 中国科学院研究生院, 北京 100049;

3. 浙江中医药大学外国语学院, 浙江杭州 310053;

4. 浙江科技学院信息与电子工程学院, 浙江杭州 310023

基金项目:

“十五”国家科技攻关计划项目(2002BA90407);中国科学院奥运科技资助项目(KACX1-10)

作者简介:
黄初冬,博士研究生,从事遥感在城市生态环境中的应用研究。E-mail:cdhuang@tsinghua.org.cn

收稿日期: 2007-03-27
修回日期: 2007-10-31
刊出日期: 2008-04-10

中图分类号: S757

关键词:

摘要: 城市森林已成为城市生态环境研究和城市规划的热点问题之一。介绍了一种运用遥感影像分析城市森林的方法。分析过程分为两大内容:①运用回归决策树模型和ASTER影像分析城市森林丰度,即根据分类训练区内ASTER影像的光谱与实际森林丰度之间的对应关系,建立回归决策树模型,然后用该模型计算整个研究区域内各像元的森林丰度。②运用CITYgreen模型分析该地区城市森林生态功能,即运用CITYgreen模型,估算研究区域内城市森林的覆盖率、碳储量和清除空气污染物的能力,以及在清除空气污染物方面的生态价值。以北京市通州新城为研究区域,根据估算得出,该地区森林覆盖面积约为11.62 km2,碳储量约为12.36 万t,清除污染物总量为267.1 t·a-1。讨论了该方法的优缺点以及需要改进的方面。

English Abstract

Urban forest research based on regression tree techniques and ASTER imagery

1. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China;

2. Graduate University, Chinese Academy of Sciences, Beijing 100049, China;

3. College of Foreign Studies, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang, China;

4. School of Information and Electronic Engineering, Zhejiang Science and Technology University, Hangzhou 310023, Zhejiang, China

Received Date: 2007-03-27
Rev Recd Date: 2007-10-31
Publish Date: 2008-04-10

Keywords:

Abstract: Urban forest was of great interest to a variety of scientific and urban planning applications. This paper presented the process and results of analysis using remote sensing imagery in this area. There were mainly two key steps in the analysis: the first was to analyze urban forest abundance utilizing regression tree techniques and advanced spaceborne thermal emission and reflection radiometer(ASTER) images; the second was to analyze the ecological benefits of urban forest using CITYgreen model. The analysis of urban forest abundance was accomplished implementing classification models,which were based on relationships between urban forest abundance and the spectrum on ASTER imagery,and were generated using regression tree techniques. Subsequently,the calculation of forest coverage,carbon storage and the amount of air pollutants removal about urban forest was carried out introducing CITYgreen model. According to the analysis,the total area in Tongzhou District was about 11.62 km2; the forest coverage was about 7.6%; the carbon storage and the amount of air pollutants removal amounted to 123 589 t and 267.1 t·a-1 respectively. At the end,the advantages and disadvantages,as well as some of the conceivable improvements of this strategy were discussed.

黄初冬, 邵芸, 李静, 柳晶辉, 陈洁琼. 基于回归决策树和ASTER卫星影像的城市森林研究[J]. 浙江农林大学学报, 2008, 25(2): 240-244.

引用本文:

黄初冬, 邵芸, 李静, 柳晶辉, 陈洁琼. 基于回归决策树和ASTER卫星影像的城市森林研究[J]. 浙江农林大学学报, 2008, 25(2): 240-244.