Journal of Mountain Science

, Volume 8, Issue 3, pp 414–426 | Cite as

Evaluation on the two filling functions for the recovery of forest information in mountainous shadows on Landsat ETM + Image

  • Amir reza Shahtahmassebi
  • Ke Wang
  • Zhangquan Shen
  • Jinsong Deng
  • Wenjuan Zhu
  • Ning Han
  • Fengfang Lin
  • Nathan Moore
Article
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Accepted:

Abstract

In general, topographic shadow may reduce performance of forest mapping over mountainous regions in remotely sensed images. In this paper, information in shadow was synthesized by using two filling techniques, namely, roifill and imfill, in order to improve the accuracy of forest mapping over mountainous regions. These two methods were applied to Landsat Enhanced Thematic Mapper (ETM +) multispectral image from Dong Yang County, Zhejiang Province, China. The performance of these methods was compared with two conventional techniques, including cosine correction and multisource classification. The results showed that by applying filling approaches, average overall accuracy of classification was improved by 14 percent. However, through conventional methods this value increased only by 9 percent. The results also revealed that estimated forest area on the basis of shadow- corrected images by ‘roifill’ technique was much closer to the survey data compared to traditional algorithms. Apart from this finding, our finding indicated that topographic shadow was an accentuated problem in medium resolution images such as Landsat ETM+ over mountainous regions.

Keywords

Shadow Imfill Roifill Landsat ETM+ 
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Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Amir reza Shahtahmassebi
    • 1
    • 2
  • Ke Wang
    • 1
    • 2
  • Zhangquan Shen
    • 1
    • 2
  • Jinsong Deng
    • 1
    • 2
  • Wenjuan Zhu
    • 1
    • 2
  • Ning Han
    • 1
    • 2
  • Fengfang Lin
    • 1
    • 2
  • Nathan Moore
    • 1
    • 2
    • 3
  1. 1.Institute of Agriculture Remote Sensing and Information TechnologyZhejiang UniversityHangzhouChina
  2. 2.Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang ProvinceHangzhouChina
  3. 3.Department of GeographyMichigan State UniversityEast LansingUSA

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