Contribution of Sentinel-2 data for applications in vegetation monitoring

With the entry into operation of the Sentinel-2 mission in June 2015, a new land monitoring costellation of twin satellites has been added to Copernicus project from ESA and new insights have been derived through the combination of Sentinel-2 data with other optical/multispectral data, and with other data from satellites belonging to the same Copernicus project. To this end, the objective of this paper has been to present new added-value tools first through the integration of different satellite platforms: data from NASA Landsat-8 and ESA Sentinel-1 have been used and combined, and furthermore through the comparison of satellite data all from the same Copernicus project: data from Sentinel-1 and Sentinel-2 have been jointly processed and compared. Although data from optical/multispectral sensors, as those of Landsat-8 and Sentinel-2, and data from SAR on board of Sentinel-1, are very different, their combination provides useful and interesting results. The integration and combination of these data can find useful application in many fields from oceans to waterways, from land surfaces to fossil deposits, from vegetation to forest areas. In this works authors have focused their interest in green areas and vegetation monitoring applications, by choosing as case of interest the Royal Palace of Caserta and its gardens. The idea has started from the increasing interest in monitoring the cultural heritage monuments and in particular the surrounding vegetation with the green areas and the parks inside. Satellite images can put into evidence boundaries modifications, the vegetation state, their possible degradation, and other phenomena such as changes in the territories due both to natural and to anthropogenic causes. Data combination from different sources as above specified gives a good number of indexes very useful to analyze the vegetation state and its health in a very deep way. Many of these indexes have been calculated and discussed for investigation

Monitoring of critical infrastructures by micro-motion estimation : the Mosul dam destabilization

In this paper, authors propose a new procedure to provide a tool for monitoring critical infrastructures. Particularly, through the analysis of COSMO-SkyMed satellite data, a detailed and updated survey is provided, for monitoring the accelerating destabilization process of the Mosul dam, that represents the largest hydraulic facility of Iraq and is located on the Tigris river. The destructive potential of the wave that would be generated, in the event of the dam destruction, could have serious consequences. If the concern for human lives comes first, the concern for cultural heritage protection is not negligible, since several archaeological sites are located around the Mosul dam. The proposed procedure is an in-depth modal assessment based on the micro-motion estimation, through a Doppler sub-apertures tracking and a Multi-Chromatic Analysis (MCA). The method is based initially on the Persistent Scatterers Interferometry (PSI) that is also discussed for completeness and validation. The modal analysis has detected the presence of several areas of resonance that could mean the presence of cracks, and the results have shown that the dam is still in a strong destabilization. Moreover, the dam appears to be divided into two parts: the northern part is accelerating rapidly while the southern part is decelerating and a main crack in this north-south junction is found. The estimated velocities through the PS-InSAR technique show a good agreement with the GNSS in-situ measurements, resulting in a very high correlation coefficient and showing how the proposed procedure works efficiently

A Machine Learning Approach to Long-Term Drought Prediction using Normalized Difference Indices Computed on a Spatiotemporal Dataset

Climate change and increases in drought conditions affect the lives of many and are closely tied to global agricultural output and livestock production. This research presents a novel approach utilizing machine learning frameworks for drought prediction around water basins. Our method focuses on the next-frame prediction of the Normalized Difference Drought Index (NDDI) by leveraging the recently developed SEN2DWATER database. We propose and compare two prediction methods for estimating NDDI values over a specific land area. Our work makes possible proactive measures that can ensure adequate water access for drought-affected communities and sustainable agriculture practices by implementing a proof-of-concept of short and long-term drought prediction of changes in water resources.Comment: 4 pages, 3 figures, 1 table, IEEE IGARSS 2023 Conferenc

Integration of Sentinel-1 and Sentinel-2 data for Earth surface classification using Machine Learning algorithms implemented on Google Earth Engine

In this study, Synthetic Aperture Radar (SAR) and optical data are both considered for Earth surface classification. Specifically, the integration of Sentinel-1 (S-1) and Sentinel-2 (S-2) data is carried out through supervised Machine Learning (ML) algorithms implemented on the Google Earth Engine (GEE) platform for the classification of a particular region of interest. Achieved results demonstrate how in this case radar and optical remote detection provide complementary information, benefiting surface cover classification and generally leading to increased mapping accuracy. In addition, this paper works in the direction of proving the emerging role of GEE as an effective cloud-based tool for handling large amounts of satellite data.Comment: 4 pages, 7 figures, IEEE InGARSS conferenc

Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms

The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific machine learning (ML) algorithms. As a case study, Cairo City has been identified, in Egypt country, as one of the five most populous megacities of the last decade in the world. Classification and change detection analysis of the region of interest (ROI) have been carried out from July 2013 to July 2021. Results demonstrate the validity of the proposed method in identifying changed and unchanged urban areas over the selected period. Furthermore, this work aims to evidence the growing significance of GEE as an efficient cloud-based solution for managing large quantities of satellite data.Comment: 4 pages, 6 figures, 2023 InGARSS Conferenc

Classification Schemes for the Radar Reference Window: Design and Comparisons

In this paper, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems by resorting to suitable approximations of the model order selection rules due to the intractable mathematics associated with the maximum likelihood estimation of some parameters. Remarkably, the classification results provided by the proposed architectures represent an advanced clutter map since, besides the estimation of the clutter parameters, they contain a clustering of the range bins in terms of homogeneous subsets. In fact, such information can drive the conventional detectors towards more reliable estimates of the clutter covariance matrix according to the position of the cells under test. The performance analysis confirms that the conceived architectures represent a viable means to recognize the scenario wherein the radar is operating at least for the considered simulation parameters.Comment: Accepted by IEEE Transactions on Aerospace and Electronic System

Urban Sprawl and COVID-19 Impact Analysis by Integrating Deep Learning with Google Earth Engine

Timely information on land use, vegetation coverage, and air and water quality, are crucial for monitoring and managing territories, especially for areas in which there is dynamic urban expansion. However, getting accessible, accurate, and reliable information is not an easy task, since the significant increase in remote sensing data volume poses challenges for the timely processing and analysis of the resulting massive data volume. From this perspective, classical methods for urban monitoring present some limitations and more innovative technologies, such as artificial-intelligence-based algorithms, must be exploited, together with performing cloud platforms and ad hoc pre-processing steps. To this end, this paper presents an approach to the use of cloud-enabled deep-learning technology for urban sprawl detection and monitoring, through the fusion of optical and synthetic aperture radar data, by integrating the Google Earth Engine cloud platform with deep-learning techniques through the use of the open-source TensorFlow library. The model, based on a U-Net architecture, was applied to evaluate urban changes in Phoenix, the second fastest-growing metropolitan area in the United States. The available ancillary information on newly built areas showed good agreement with the produced change detection maps. Moreover, the results were temporally related to the appearance of the SARS-CoV-2 (commonly known as COVID-19) pandemic, showing a decrease in urban expansion during the event. The proposed solution may be employed for the efficient management of dynamic urban areas, providing a decision support system to help policy makers in the measurement of changes in territories and to monitor their impact on phenomena related to urbanization growth and density. The reference data were manually derived by the authors over an area of approximately 216 km2, referring to 2019, based on the visual interpretation of high resolution images, and are openly available