An automatic feature extraction method of satellite multispectral images for interpreting deforestation effects in soil degradation

Irene Erlyn Wina Rachmawan, Yasushi Kiyoki, Shiori Sasaki

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Deforestation is still a major nature phenomenon in our society. For assessing deforestation effect, satellites remote sensing provides a fundamental data for observation. While new remote-sensing technologies are able to represent high-resolution forest mapping, the application is still limited only for detecting and mapping the deforestation area. In this paper, we proposed a new method for automatically extract features of Satellite Multispectral images for interpreting deforestation effect in the context of soil degradation. We proposed an idea to interpret reflected "substances (material)" of bare soil in deforested area in spectrum domain into human language. The objectives of this paper are to (1) recognize the deforestation activity automatically. (2) Identify deforestation causes and examines the deforestation effect based on deforestation causes. (3) Scrutinize deforestation effects on soil degradation. (4) Representing nature knowledge of deforestation effect in human language using semantic computing, to bring the clear, comprehensible knowledge even for people who are not familiar with forestry. As for the experimental study, Riau Tropical Forest has been selected as the study area, where the multispectral data was acquired by using Landsat 8 Satellite between 2013 and 2014; Where forest fire and logging activities are reported and detected.

Original languageEnglish
Pages (from-to)676-687
Number of pages12
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume7
Issue number2
DOIs
Publication statusPublished - 2017

    Fingerprint

Keywords

  • Deforestation
  • Degradation
  • Feature extraction
  • Remote-sensing
  • Semantic analysis
  • Soil

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Computer Science(all)
  • Engineering(all)

Cite this