Challenges of the Geospatial Environmental Monitoring and Modelling of Possible Geographic Change Scenarios (Example of the Aral Sea)

International audience Remote sensing became one of the major geospatial approaches for monitoring, measuring, and analysing changes of the territories and environment. Progress in optic, image processing and in GIS permits measuring, modelling and mapping of the joint socio-environmental impacts (p...

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Bibliographic Details
Main Author: Gadal, Sébastien
Other Authors: 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), The Interparliamentary Assembly of Member Nations of the Commonwealth of Independent States (IPA CIS) - The Federation Council of the Federal Assembly of the Russian Federation - Ministry of Natural Resources and Environment of the Russian Federation, RSF Land Ontology: Semantics, Semiotics, and Geographic Modeling
Format: Conference Object
Language:English
Published: HAL CCSD 2017
Subjects:
Online Access:https://amu.hal.science/hal-01816601
Description
Summary:International audience Remote sensing became one of the major geospatial approaches for monitoring, measuring, and analysing changes of the territories and environment. Progress in optic, image processing and in GIS permits measuring, modelling and mapping of the joint socio-environmental impacts (pollution, biological, anthropogenic, health, environmental, etc.) of climate change, societies, and environment. The Arctic, the Aral Sea are some outstanding examples. The generalization of Earth observation, optical and radar systems, and the free access to geospatial data (Landsat, Spot, and Sentinel) contributed significantly to the description, analysis and modelling of changes and impacts on the territories and environment. Progress of methods and techniques of analysis of the environmental impacts, climate changes, and societal transformation by remote sensing contributes significantly not only to the understanding of changes and their socio-environmental consequences but also to the awareness of the population. The generalization of daily observation systems (Formosat) or over short periods (SPOT, Sentinel) coupled with field measurements, and integrated into GIS, allows continuous monitoring of areas that are subjects to high environmental constraints. It also allows to generate retrospective simulation models using from 30- to 40-year-old image archive (Landsat, SPOT). The fusion of remote sensing models with learning machine modelling allows to simulate the possible scenarios of evolution of the environment and its geographical space.