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

• Published in: Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management
• Main Authors: Gadal, Sébastien, Zakharov, Moisei, Kamičaitytė, Jūratė, Danilov, Yuri
• Other Authors: North-Eastern Federal University, Études des Structures, des Processus d’Adaptation et des Changements de l’Espace (ESPACE), Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Avignon Université (AU)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA), 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)), IEEE Geoscience and Remote Sensing Society, ACM SIGSPATIAL, ANR-14-CE22-0015,ECN FRANCE,Vers des moteurs propres et efficaces: contribution de la FRANCE au réseau ECN(2014), ANR-15-CE22-0006,PUR,Pôles URbains(2015)
• Format: Conference Object
• Language: English
• Published: CCSD 2020
• Subjects: Geographic Ontology; Image Analysis; Knowledge Database; Image Processing; Alas Landscape; Remote Sensing; Arctic; Russia; Artificial intelligence methods; [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]; [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation; [SDE.ES]Environmental Sciences/Environment and Society; [SHS.GEO]Humanities and Social Sciences/Geography; [SHS.STAT]Humanities and Social Sciences/Methods and statistics; Climate change; permafrost; Subarctic; Yakutia; Siberia; termokarst
• Online Access: https://amu.hal.science/hal-02554659; https://amu.hal.science/hal-02554659v1/document; https://amu.hal.science/hal-02554659v1/file/GISTAM_2020_59_CR.pdf; https://doi.org/10.5220/0009569101120118

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.