Earth’s ecosystems are extremely valuable to humanity, playing a key role ecologically, economically, and socially. Wildfires constitute a significant threat to the environment, especially in vulnerable ecosystems, such as those that are commonly found in the Mediterranean. Due to their strong impact on the environment, they provide a crucial factor in managing ecosystems behavior, causing dramatic modifications to land surface processes dynamics leading to land degradation. The soil erosion phenomenon downgrades soil quality in ecosystems and reduces land productivity. Thus, it is imperative to implement advanced erosion prediction models to assess fire effects on soil characteristics. This study focuses on examining the wildfire case that burned 30 km² in Malesina of Central Greece in 2014. The added value of remote sensing today, such as the high accuracy of satellite data, has contributed to visualizing the burned area concerning the severity of the event. Additional data from local weather stations were used to quantify soil loss on a seasonal basis using RUSLE modeling before and after the wildfire. Results of this study revealed that there is a remarkable variety of high soil loss values, especially in winter periods. More particularly, there was a 30% soil loss rise one year after the wildfire, while five years after the event, an almost double reduction was observed. In specific areas with high soil erosion values, infrastructure works were carried out validating the applied methodology. The approach adopted in this study underlines the significance of using remote sensing and geoinformation techniques to assess the post-fire effects of identifying vulnerable areas based on soil erosion parameters on a local scale.

The environmental effects of wildfires are a hot issue in current research. This study examines the effects of the 2021 wildfires in the Attica region in Greece based on Earth observation and GIS-based techniques for the development of a web app that includes the derived knowledge. The effects of wildfires were estimated with the use of Sentinel-2 satellite imagery concerning burned area extent and burn severity using a NBR-based method. In addition, the erosion risk was modeled on a pre-fire and post-fire basis with the RUSLE. This study highlights the importance of assessing the effects of wildfires with a holistic approach to produce useful knowledge tools in post-fire impact assessment and restoration.

River deltas are considered among the most diverse ecosystems with significant environmental and agricultural importance. These landscapes are vulnerable to any human activity or natural process that can disturb the fragile balance between water and land, thus causing morphological changes in the delta fronts. Earth observation (EO), with its capability to provide systematic, inter-temporal and cost-effective data provides a promising potential in monitoring the dynamic changes of river deltas. Recent advances in geoinformation technologies have allowed the development of cloud-based platforms for EO processing such as Google Earth Engine (GEE). It offers unique advantages such as rapid processing of a large amount of data in a cost and time-efficient manner. This study aims to assess the added value of GEE in monitoring the coastal surface area of river deltas based on the full Landsat archive (TM, ETM+, OLI, L9) and a machine learning (ML) technique. As a case study two river deltas, Axios & Aliakmonas, were selected located in northern Greece. Those are two of the largest rivers of the country, with Axios being also the second largest in the Balkans. Their joint river deltas create a fertile valley with great environmental and agricultural importance, which has also exhibited very strong dynamics in terms of its morphological characteristics over the last decades. In order to gain a better insight into the coastal dynamics of the studied region, Landsat multi-spectral data covering the period 1984 - present time was integrated into GEE and a Machine Learning (ML) classification approach was developed in the cloud-based environment. The two rivers delta dynamics were also mapped independently using photo interpretation serving as our reference dataset to map the river delta dynamics, in accordance to other studies. All the geospatial data analysis of the extracted morphological features of the river deltas was conducted in a geographical information system (GIS) environment. Our results evidenced the unique advantages of cloud platforms such as GEE, towards the operationalization of the investigated approaches for coastal morphological changes such as those found in the studied river deltas. Unique characteristics of the proposed herein methodology consist of the exploitation of the cloud-based platform GEE together with the advanced ML image processing algorithm and the full utilization of the Landsat images available today. The proposed approach also can be fully-automated and is transferable to other similar areas and can prove valuable help in understanding the spatiotemporal changes in coastal surface area over large areas.KEYWORDS: Google Earth Engine, Landsat, Machine Learning, Earth Observation, river delta

Climate change has resulted to an increase in the occurrence and frequency of natural disasters worldwide. An increased concern today is wildfire incidents, which constitute one of the greatest problems due to the ecological, economical and social impacts. Thus, it is very important to obtain accurately and robustly information on burned area cartography. Recent advances in the field of geoinformation have allowed the development of cloud- based platforms for EO data processing such as Google Earth Engine (GEE). The latter allows rapid processing of large amount of data in an efficient way, saving costs and time since there is also no need to locally download and process the EO datasets in specialized software packages committing also own computing resources. In the present study, a GEE-based approach that exploits machine learning (ML) techniques is developed with the purpose of automating the mapping of burnt areas from ESA’s Sentinel-2 imagery. To demonstrate the developed scheme, as a case study is used one of the largest wildfire events occurred in the summer of 2021 in the outskirts of Athens, Greece. A Sentinel-2 image, obtained from GEE immediately after the fire event, was combined with ML classifiers for the purpose of mapping the burnt area at the fire-affected site. Accuracy  assessment was conducted on the basis of both the error matrix approach and the Copernicus Rapid Mapping operational product specific to this fire event. All the geospatial analysis was conducted in a GIS environment. Our results evidenced the ability of the synergistic use of Sentinel-2 imagery with ML to map accurately and robustly the burnt area in the studied region. This information can provide valuable help towards prioritization of activities relevant to the rehabilitation of the fire-affected areas and post fire management activities. Last but not least, this study provides further evidence of the unique advantages of GEE towards a potential an automation of burnt area delineation over large scales.KEYWORDS: GEE, Machine Learning, Sentinel-2, Burnt area mapping, Copernicus