Novel Satellite-Based Methodologies for Multi-Sensor and Multi-Scale Environmental Monitoring to Preserve Natural Capital

Global warming, as the biggest manifestation of climate change, has changed the distribution of water in the hydrological cycle by increasing the evapotranspiration rate resulting in anthropogenic and natural hazards adversely affecting modern and past human properties and heritage in different parts of the world. The comprehension of environmental issues is critical for ensuring our existence on Earth and environmental sustainability. Environmental modeling can be described as a simplified form of a real system that enhances our knowledge of how a system operates. Such models represent the functioning of various processes of the environment, such as processes related to the atmosphere, hydrology, land surface, and vegetation. The environmental models can be applied on a wide range of spatiotemporal scales (i.e. from local to global and from daily to decadal levels); and they can employ various types of models (e.g. process-driven, empirical or data-driven, deterministic, stochastic, etc.). Satellite remote sensing and Earth Observation techniques can be utilized as a powerful tool for flood mapping and monitoring. By increasing the number of satellites orbiting around the Earth, the spatial and temporal coverage of environmental phenomenon on the planet has in-creased. However, handling such a massive amount of data was a challenge for researchers in terms of data curation and pre-processing as well as required computational power. The advent of cloud computing platforms has eliminated such steps and created a great opportunity for rapid response to environmental crises. The purpose of this study was to gather state-of-the-art remote sensing and/or earth observation techniques and to further the knowledge concerned with any aspect of the use of remote sensing and/or big data in the field of geospatial analysis. In order to achieve the goals of this study, some of the water-related climate-change phenomena were studied via different mathematical, statistical, geomorphological and physical models using different satellite and in-situ data on different centralized and decentralized computational platforms. The structure of this study was divided into three chapters with their own materials, methodologies and results including: (1) flood monitoring; (2) soil water balance modeling; and (3) vegetation monitoring. The results of this part of the study can be summarize in: 1) presenting innovative procedures for fast and semi-automatic flood mapping and monitoring based on geomorphic methods, change detection techniques and remote sensing data; 2) modeling soil moisture and water balance components in the root zone layer using in-situ, drone and satellite data; incorporating downscaling techniques; 3) combining statistical methods with the remote sensing data for detecting inner anomalies in the vegetation covers such as pest emergence; 4) stablishing and disseminating the use of cloud computation platforms such as Google Earth Engine in order to eliminate the unnecessary steps for data curation and pre-processing as well as required computational power to handle the massive amount of RS data. As a conclusion, this study resulted in provision of useful information and methodologies for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.