A new approach to semantic computing with interval matrix decomposition for interpreting deforestation phenomenon

Irene Erlyn Wina Rachmawan, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Deforestation is a major problem in ecosystem degradation and one of the main sources of carbon emission to the atmosphere. The use of multi-temporal satellite remote sensing has proven to be effective means to monitor forest conditions on global scale. The effectiveness of the utilization of remotely sensed images will depend on the analysis model and parameter selection procedure to provide information that meets the requirements of deforestation monitoring. Here we demonstrate the ability of semantic computing for analyzing satellite images that applied to interpreting tropical deforestation. The typical semantic computing works for interpreting numerical or textual data. It still remains challenging for utilizing remote sensing images for environmental monitoring in semantic computing since the data is presented in interval value. Therefore, we proposed interval matrix decomposition for automatically generate semantic projection that address uncertain value in environmental parameters. In this study, independent dimensions of Landsat Thematic Mapper imagery (Landsat-8) and The Phased Array Type L-band SAR-2 (PALSAR-2) were combined to create an integrated interpretation of environmental condition for a study area in the deforestation zone of global tropical forest. We proposed essential semantic interval-dimensions derived from heterogonous satellite images, combination of L-Band SAR and optical, namely: HV gamma-naught, red, green, blue, NIR, SWIR channel; and combinational features: Soil temperature, soil moisture, temporal change density, temporal change velocity, shape and texture. Afterward, semantic computing is employed as analysis model to explain significant knowledge of deforestation activity. The experimental result shows that integrated independent dimensions from both the optical and SAR domains, has potential for presenting for aspect-based deforestation assessment and to enable the design of robust forest monitoring systems.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXX
EditorsTatiana Endrjukaite, Hannu Jaakkola, Alexander Dudko, Yasushi Kiyoki, Bernhard Thalheim, Naofumi Yoshida
PublisherIOS Press
Pages353-368
Number of pages16
ISBN (Electronic)9781614999324
DOIs
Publication statusPublished - 2019 Jan 1

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume312
ISSN (Print)0922-6389

Keywords

  • Computing
  • Deforestation
  • Dimensional database
  • Multispectral image
  • PALSAR
  • Retrieval
  • Semantic
  • soil degradation

ASJC Scopus subject areas

  • Artificial Intelligence

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    Rachmawan, I. E. W., & Kiyoki, Y. (2019). A new approach to semantic computing with interval matrix decomposition for interpreting deforestation phenomenon. In T. Endrjukaite, H. Jaakkola, A. Dudko, Y. Kiyoki, B. Thalheim, & N. Yoshida (Eds.), Information Modelling and Knowledge Bases XXX (pp. 353-368). (Frontiers in Artificial Intelligence and Applications; Vol. 312). IOS Press. https://doi.org/10.3233/978-1-61499-933-1-353