TY - GEN
T1 - A semantic multispectral images analysis retrieval method for interpreting deforestation effects in soil degradation
AU - Rachmawan, Irene Erlyn Wina
AU - Kiyoki, Yasushi
N1 - Funding Information:
This work is supported in part by MEXT Grant-in-Aid for the Program for Leading Graduate School, “Global Environmental System Larders (GESL)” and Multimedia Database Laboratory (MDBL), Graduate School of Media and Governance, Keio University. We thank the anonymous reviewers for their valuable comments and suggestions
Publisher Copyright:
© 2018 The authors and IOS Press. All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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 retrieve the information contained on Satellite Multispectral images in order to 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 by performing calculation for semantic retrieval, to bring the clear comprehensible knowledge even for people who are not familiar with forestry. Semantic retrieval formed by understanding queries and showing queries result based on semantic calculation. As for 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.
AB - 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 retrieve the information contained on Satellite Multispectral images in order to 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 by performing calculation for semantic retrieval, to bring the clear comprehensible knowledge even for people who are not familiar with forestry. Semantic retrieval formed by understanding queries and showing queries result based on semantic calculation. As for 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.
KW - Computing
KW - Deforestation
KW - Dimensional database
KW - Multispectral image
KW - Retrieval
KW - Semantic
KW - Soil degradation
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U2 - 10.3233/978-1-61499-834-1-90
DO - 10.3233/978-1-61499-834-1-90
M3 - Conference contribution
AN - SCOPUS:85063385322
T3 - Frontiers in Artificial Intelligence and Applications
SP - 90
EP - 109
BT - Information Modelling and Knowledge Bases XXIX
A2 - Chawakitchareon, Petchporn
A2 - Hansuebsai, Aran
A2 - Jaakkola, Hannu
A2 - Kiyoki, Yasushi
A2 - Yoshida, Naofumi
A2 - Koopipat, Chawan
A2 - Sornlertlamvanich, Virach
A2 - Thalheim, Bernhard
PB - IOS Press
T2 - 27th International Conference on Information Modelling and Knowledge Bases, EJC 2017
Y2 - 5 June 2017 through 9 June 2017
ER -