Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia)
International audience 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 recog...
Published in: | Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management |
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Main Authors: | , , , |
Other Authors: | , , , , , , , , , , , , , , , |
Format: | Other/Unknown Material |
Language: | English |
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HAL CCSD
2020
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Online Access: | https://doi.org/10.5220/0009569101120118 https://hal-amu.archives-ouvertes.fr/hal-02554659/file/GISTAM_2020_59_CR.pdf https://hal-amu.archives-ouvertes.fr/hal-02554659 |
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English |
topic |
Russia Artificial intelligence methods Arctic Remote Sensing Alas Landscape Image Processing Knowledge Database Image Analysis Geographic Ontology geo demo |
spellingShingle |
Russia Artificial intelligence methods Arctic Remote Sensing Alas Landscape Image Processing Knowledge Database Image Analysis Geographic Ontology geo demo GADAL, Sébastien Zakharov, Moisei Kamičaitytė, Jūratė Danilov, Yuri Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) |
topic_facet |
Russia Artificial intelligence methods Arctic Remote Sensing Alas Landscape Image Processing Knowledge Database Image Analysis Geographic Ontology geo demo |
description |
International audience 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 Ya-kutia. The result gives an ontology-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. |
author2 |
North-Eastern Federal University Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (. - 2019) (UNS) COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU) Aix Marseille Université (AMU) Kaunas University of Technology (KTU) Polar Urban Centers PUR CNRS PEPS INEE RICOCHET (A la Recherche de l’Intégration des Connaissances dans l’Observation des CHangements Environnementaux : mise en œuvre d’une recherche-aTelier en Sibérie Orientale (Khamagatta)) Polytechnic Institute of Setúbal/IPS Knowledge Systems Institute ATHENA Research & Innovation Information Technologies Cédric Grueau Rober Laurini Lemonia Ragia IEEE Geoscience and Remote Sensing Society ACM SIGSPATIAL |
format |
Other/Unknown Material |
author |
GADAL, Sébastien Zakharov, Moisei Kamičaitytė, Jūratė Danilov, Yuri |
author_facet |
GADAL, Sébastien Zakharov, Moisei Kamičaitytė, Jūratė Danilov, Yuri |
author_sort |
GADAL, Sébastien |
title |
Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) |
title_short |
Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) |
title_full |
Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) |
title_fullStr |
Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) |
title_full_unstemmed |
Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) |
title_sort |
alas landscape modeling by remote sensing image analysis and geographic ontology. study case of central yakutia (russia) |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://doi.org/10.5220/0009569101120118 https://hal-amu.archives-ouvertes.fr/hal-02554659/file/GISTAM_2020_59_CR.pdf https://hal-amu.archives-ouvertes.fr/hal-02554659 |
op_coverage |
Online Streaming, Portugal |
geographic |
Arctic |
geographic_facet |
Arctic |
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Arctic Climate change permafrost Subarctic Yakutia Siberia termokarst |
genre_facet |
Arctic Climate change permafrost Subarctic Yakutia Siberia termokarst |
op_source |
Hyper Article en Ligne - Sciences de l'Homme et de la Société 6th International Conference on Geographic Information Sytems Theory, Applications and Management 6th International Conference on Geographic Information Sytems Theory, Applications and Management, Polytechnic Institute of Setúbal/IPS; Knowledge Systems Institute; ATHENA Research & Innovation Information Technologies, May 2020, Online Streaming, Portugal. pp.112-118, ⟨10.5220/0009569101120118⟩ |
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hal-02554659 doi:10.5220/0009569101120118 10670/1.mfx57m https://hal-amu.archives-ouvertes.fr/hal-02554659/file/GISTAM_2020_59_CR.pdf https://hal-amu.archives-ouvertes.fr/hal-02554659 |
op_doi |
https://doi.org/10.5220/0009569101120118 |
container_title |
Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management |
container_start_page |
112 |
op_container_end_page |
118 |
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1766341630945656832 |
spelling |
fttriple:oai:gotriple.eu:10670/1.mfx57m 2023-05-15T15:10:39+02:00 Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) GADAL, Sébastien Zakharov, Moisei Kamičaitytė, Jūratė Danilov, Yuri North-Eastern Federal University Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE) Université Côte d'Azur (UCA)-Avignon Université (AU)-Université Nice Sophia Antipolis (. - 2019) (UNS) COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU) Aix Marseille Université (AMU) Kaunas University of Technology (KTU) Polar Urban Centers PUR CNRS PEPS INEE RICOCHET (A la Recherche de l’Intégration des Connaissances dans l’Observation des CHangements Environnementaux : mise en œuvre d’une recherche-aTelier en Sibérie Orientale (Khamagatta)) Polytechnic Institute of Setúbal/IPS Knowledge Systems Institute ATHENA Research & Innovation Information Technologies Cédric Grueau Rober Laurini Lemonia Ragia IEEE Geoscience and Remote Sensing Society ACM SIGSPATIAL Online Streaming, Portugal 2020-05-07 https://doi.org/10.5220/0009569101120118 https://hal-amu.archives-ouvertes.fr/hal-02554659/file/GISTAM_2020_59_CR.pdf https://hal-amu.archives-ouvertes.fr/hal-02554659 en eng HAL CCSD SCITEPRESS hal-02554659 doi:10.5220/0009569101120118 10670/1.mfx57m https://hal-amu.archives-ouvertes.fr/hal-02554659/file/GISTAM_2020_59_CR.pdf https://hal-amu.archives-ouvertes.fr/hal-02554659 Hyper Article en Ligne - Sciences de l'Homme et de la Société 6th International Conference on Geographic Information Sytems Theory, Applications and Management 6th International Conference on Geographic Information Sytems Theory, Applications and Management, Polytechnic Institute of Setúbal/IPS; Knowledge Systems Institute; ATHENA Research & Innovation Information Technologies, May 2020, Online Streaming, Portugal. pp.112-118, ⟨10.5220/0009569101120118⟩ Russia Artificial intelligence methods Arctic Remote Sensing Alas Landscape Image Processing Knowledge Database Image Analysis Geographic Ontology geo demo Conference Output https://vocabularies.coar-repositories.org/resource_types/c_c94f/ 2020 fttriple https://doi.org/10.5220/0009569101120118 2023-01-22T18:43:15Z International audience 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 Ya-kutia. The result gives an ontology-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. Other/Unknown Material Arctic Climate change permafrost Subarctic Yakutia Siberia termokarst Unknown Arctic Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management 112 118 |