Use and assessment of remote sensing for the safety of maritime shipping

Αντικείμενο της εργασίας είναι η εφαρμογή της δορυφορικής τηλεπισκόπησης για τον υπολογισμό και την εκτίμηση φυσικών παραμέτρων συνδεόμενων με κινδύνους για τη ναυτιλία. Ειδικότερα, μέσω της χρήσης δορυφορικών εικόνων σε διάφορες φασματικές περιοχές, θα εξαχθούν οι κατάλληλες παράμετροι, ώστε να μελετηθεί η κίνηση των θαλασσίων ρευμάτων, η μεταβλητότητα στην παγοκάλυψη σε διαύλους ναυσιπλοΐας, ο εντοπισμός πετρελαιοκηλίδων και η παρουσία επικίνδυνων φορτίων. Στο πλαίσιο της εργασίας, θα διαμορφωθεί η εργαλειοθήκη που θα συμβάλει στην ασφάλεια της ναυσιπλοΐας και θα αξιολογηθεί η εφαρμοστικότητά της και η δυνατότητα επιχειρησιακής χρήσης, βάσει των διαθέσιμων δεδομένων και μελλοντικών δορυφορικών αποστολών.The scope of this work is the implementation of satellite remote sensing for the calculation and estimation of physical parameters associated with risk for maritime shipping. In particular, through the use of satellite imagery in different spectral regions and the exploitation of the advantages of passive and active remote sensing, the appropriate parameters will be extracted, in order to study the wind speed and direction, the variability of sea ice coverage in marine channels, oil spillages and the presence of dangerous cargoes. As part of the work, Sentinel Application Platform (SNAP) and QGIS will be configured, which based on satellites observations, will contribute to marine navigation. Finally, the thesis will evaluate the applicability of the toolbox to business function depending on the available satellite data and the future satellite missions

Deep Learning Based Burnt Area Mapping Using Sentinel 1 for the Santa Cruz Mountains Lightning Complex (CZU) and Creek Fires 2020

The study presented here builds on previous synthetic aperture radar (SAR) burnt area estimation models and presents the first U-Net (a convolutional network architecture for fast and precise segmentation of images) combined with ResNet50 (Residual Networks used as a backbone for many computer vision tasks) encoder architecture used with SAR, Digital Elevation Model, and land cover data for burnt area mapping in near-real time. The Santa Cruz Mountains Lightning Complex (CZU) was one of the most destructive fires in state history. The results showed a maximum burnt area segmentation F1-Score of 0.671 in the CZU, which outperforms current models estimating burnt area with SAR data for the specific event studied models in the literature, with an F1-Score of 0.667. The framework presented here has the potential to be applied on a near real-time basis, which could allow land monitoring as the frequency of data capture improves

GFM Product User Manual

This Product User Manual (PUM) is the reference document for all end-users and stakeholders of the new Global Food Monitoring (GFM) product of the Copernicus Emergency Management Service (CEMS). The PUM provides all of the basic information to enable the proper and effective use of the GFM product and associated data output layers. This manual includes a description of the functions and capabilities of the GFM product, its applications and alternative modes of operation, and step-by-step guidance on the procedures for accessing and using the GFM product

Mapping Natural Forest Remnants with Multi-Source and Multi-Temporal Remote Sensing Data for More Informed Management of Global Biodiversity Hotspots

Global terrestrial biodiversity hotspots (GBH) represent areas featuring exceptional concentrations of endemism and habitat loss in the world. Unfortunately, geospatial data of natural habitats of the GBHs are often outdated, imprecise, and coarse, and need updating for improved management and protection actions. Recent developments in satellite image availability, combined with enhanced machine learning algorithms and computing capacity, enable cost-efficient updating of geospatial information of these already severely fragmented habitats. This study aimed to develop a more accurate method for mapping closed canopy evergreen natural forest (CCEF) of the Eastern Arc Mountains (EAM) ecoregion in Tanzania and Kenya, and to update the knowledge on its spatial extent, level of fragmentation, and conservation status. We tested 1023 model possibilities stemming from a combination of Sentinel-1 (S1) and Sentinel-2 (S2) satellite imagery, spatial texture of S1 and S2, seasonality derived from Landsat-8 time series, and topographic information, using random forest modelling approach. We compared the best CCEF model with existing spatial forest products from the EAM through independent accuracy assessment. Finally, the CCEF model was used to estimate the fragmentation and conservation coverage of the EAM. The CCEF model has moderate accuracy measured in True Skill Statistic (0.57), and it clearly outperforms other similar products from the region. Based on this model, there are about 296,000 ha of Eastern Arc Forests (EAF) left. Furthermore, acknowledging small forest fragments (1-10 ha) implies that the EAFs are more fragmented than previously considered. Currently, the official protection of EAFs is disproportionally targeting well-studied mountain blocks, while less known areas and small fragments are underrepresented in the protected area network. Thus, the generated CCEF model should be used to design updates and more informed and detailed conservation allocation plans to balance this situation. The results highlight that spatial texture of S2, seasonality, and topography are the most important variables describing the EAFs, while spatial texture of S1 increases the model performance slightly. All in all, our work demonstrates that recent developments in Earth observation allows significant enhancements in mapping, which should be utilized in areas with outstanding biodiversity values for better forest and conservation planning.Peer reviewe

ELULC-10, a 10 m European land use and land cover map using Sentinel and landsat data in Google Earth Engine

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.Peer ReviewedPostprint (published version

Continuous Monitoring of Environmental Disturbances by Cumulative Sums of Dense SAR Satellite Timeseries

Climate change together with growing socio-economic pressures are leading to a significant increase in alterations to natural ecosystems. The alteration of natural cycles and dynamics through the direct destruction or continuous degradation, are threatening the conservation of these natural spaces on a global scale. Satellite remote sensing is a suitable solution for large-scale monitoring and evaluation of natural landscapes under threat, as it provides a consistent source of information for both historical and updated environmental studies. However, most current remote sensing-based environmental monitoring tools still present certain limitations which hinder access to continuous and real-time information. The design and development of new methods and approaches to environmental remote sensing is required to mitigate the current environmental degradation trends. This thesis analyses the current challenges associated with environmental monitoring to focus on the development of new change detection methods applied to the study of environmental disturbances in highly dynamic natural ecosystems. By exploiting the frequent monitoring capabilities of Synthetic Aperture Radar (SAR) dense timeseries, this research introduces new approaches based on Cumulative Sum (CuSum) strategies for continuous and near-real-time investigation. These approaches have been applied to monitor permanent and cyclical disturbances in highly threatened forest and wetland ecosystems. The main scientific contribution of this thesis is the introduction of three novel SAR-based change detection approaches capable of exploiting dense satellite imagery time series for continuous and near-real-time monitoring. The outcome of this research provides environmental managers with a fully operational alternative tool capable of rapid and continuous monitoring of environmental dynamics

Estimating Above-Ground Biomass in Finnish Forests Using Remote Sensing Data

Above-ground biomass (AGB) estimation is an important tool for predicting carbon flux and the effects of global warming. This study describes a novel application of remote-sensing based AGB estimation in the hemi-boreal vegetation zone of Finland, using Sentinel-1, Sentinel-2, ALOS-2 PALSAR-2, and the Multi-Source National Forest Inventory by Natural Resources Institute Finland as sources of data. A novel method of extracting data from the features of the surrounding observations is proposed, and the method’s effectiveness was evaluated. The findings showed that the method showed promising results, with the model trained using the extracted features achieving the highest evaluation scores in the study. In addition, the viability of using free and highly available satellite datasets for AGB estimation in the hemi-boreal Finland was analyzed, with the results suggesting that the free Synthetic Aperture Radar (SAR) based products had a low performance. The features extracted from the optical data of Sentinel-2 produced well-performing models, although the accuracy might still be too low to be feasible