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Affiliation

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

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1 Papers

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

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

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

Digital Object Identifier: 10.5220/0009569101120118

Proceedings of GISTAM 2020, ISBN: 978-989-758-425-1

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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 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.