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Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology: Study Case of Central Yakutia (Russia)

Topics: Ecological and Environmental Management; Image Processing and Pattern Recognition; Land Management; Remote Sensing in Climate Change Studies; Spatial Analysis and Integration; Spatial Information and Society; Spatial Modeling and Reasoning

Authors: Sébastien Gadal 1 ; Moisei Zakharov 1 ; Jūratė Kamičaitytė 2 and Yuri Danilov 3

Affiliations: 1 Aix-Marseille Univ., CNRS, ESPACE UMR 7300, Univ., Nice Sophia Antipolis, Avignon Univ., 13545 Aix-en-Provence, France, North-Eastern Federal University, 670000 Yakutsk, Republic of Sakha, Russian Federation ; 2 Kaunas University of Technology, Kaunas, Lithuania ; 3 North-Eastern Federal University, 670000 Yakutsk, Republic of Sakha, Russian Federation

Keyword(s): Geographic Ontology, Image Analysis, Knowledge Database, Image Processing, Alas Landscape, Remote Sensing.

Abstract: Approaches of geographic ontologies can help to overcome the problems of ambiguity and uncertainty of remote sensing data analysis for modeling the landscapes as a multidimensional geographic object of research. Image analysis based on the geographic ontologies allows to recognize the elementary characteristics of the alas landscapes and their complexity. The methodology developed includes three levels of geographic object recognition: (1) the landscape land cover classification using Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) classifiers; (2) the object-based image analysis (OBIA) used for the identification of alas landscape objects according to their morphologic structures using the Decision Tree Learning algorithm; (3) alas landscape’s identification and categorization integrating vegetation objects, territorial organizations, and human cognitive knowledge reflected on the geo-linguistic object-oriented database made in Central Yakutia. The result gives an ontol ogy-based alas landscape model as a system of geographic objects (forests, grasslands, arable lands, termokarst lakes, rural areas, farms, repartition of built-up areas, etc.) developed under conditions of permafrost and with a high sensitivity to the climate change and its local variabilities. The proposed approach provides a multidimensional reliable recognition of alas landscape objects by remote sensing images analysis integrating human semantic knowledge model of Central Yakutia in the subarctic Siberia. This model requires to conduct a multitemporal dynamic analysis for the sustainability assessment and land management. (More)

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Paper citation in several formats:
Gadal, S.; Zakharov, M.; Kamičaitytė, J. and Danilov, Y. (2020). Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology: Study Case of Central Yakutia (Russia). In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM; ISBN 978-989-758-425-1; ISSN 2184-500X, SciTePress, pages 112-118. DOI: 10.5220/0009569101120118

@conference{gistam20,
author={Sébastien Gadal. and Moisei Zakharov. and Jūratė Kamičaitytė. and Yuri Danilov.},
title={Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology: Study Case of Central Yakutia (Russia)},
booktitle={Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM},
year={2020},
pages={112-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009569101120118},
isbn={978-989-758-425-1},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM
TI - Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology: Study Case of Central Yakutia (Russia)
SN - 978-989-758-425-1
IS - 2184-500X
AU - Gadal, S.
AU - Zakharov, M.
AU - Kamičaitytė, J.
AU - Danilov, Y.
PY - 2020
SP - 112
EP - 118
DO - 10.5220/0009569101120118
PB - SciTePress