Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia) - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

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

Résumé

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.
Fichier principal
Vignette du fichier
GISTAM_2020_59_CR.pdf (406.44 Ko) Télécharger le fichier
Proceeding-Gadal-Zakharov-Kamicaityte-Danilov.pdf (2.41 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Commentaire : Can be deleted. A news version was just published. Sébastien Gadal
Loading...

Dates et versions

hal-02554659 , version 1 (26-04-2020)

Identifiants

Citer

Sébastien Gadal, Moisei Zakharov, Jūratė Kamičaitytė, Yuri Danilov. Alas Landscape Modeling by Remote Sensing Image Analysis and Geographic Ontology. Study case of Central Yakutia (Russia). 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⟩. ⟨hal-02554659⟩
262 Consultations
202 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More