24 research outputs found
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
Estimation of Ground NO2 Measurements from Sentinel-5P Tropospheric Data through Categorical Boosting
Atmospheric pollution has been largely considered by the scientific community
as a primary threat to human health and ecosystems, above all for its impact on
climate change. Therefore, its containment and reduction are gaining interest
and commitment from institutions and researchers, although the solutions are
not immediate. It becomes of primary importance to identify the distribution of
air pollutants and evaluate their concentration levels in order to activate the
right countermeasures. Among other tools, satellite-based measurements have
been used for monitoring and obtaining information on air pollutants, and over
the years their performance has increased in terms of both resolution and data
reliability. This study aims to analyze the NO2 pollution in the Emilia Romagna
Region (Northern Italy) during 2019, with the help of satellite retrievals from
the {\nobreak Sentinel\nobreak-5P} mission of the European Copernicus Programme
and ground-based measurements, obtained from the ARPA site (Regional Agency for
the Protection of the Environment). The final goal is the estimation of ground
NO2 measurements when only satellite data are available. For this task, we used
a Machine Learning (ML) model, Categorical Boosting, which was demonstrated to
work quite well and allowed us to achieve a Root-Mean-Square Error (RMSE) of
0.0242 over the 43 stations utilized to get the Ground Truth values. This
procedure, applicable to other areas of Italy and the world and on longer
timelines, represents the starting point to understand which other actions must
be taken to improve its final performance
Perspectives on the structural health monitoring of bridges by synthetic aperture radar
Large infrastructures need continuous maintenance because of materials degradation due to atmospheric agents and their persistent use. This problem makes it imperative to carry out persistent monitoring of infrastructure health conditions in order to guarantee maximum safety at all times. The main issue of early warning infrastructure fault detection is that expensive in-situ distributed monitoring sensor networks have to be installed. On the contrary, the use of satellite data has made it possible to use immediate and low-cost techniques in recent years. In this regard, the potential of spaceborne Synthetic Aperture Radar for the monitoring of critical infrastructures is demonstrated in geographically extended areas, even in the presence of clouds, and in really tough weather. A complete procedure for damage early-warning detection is designed, by using micro-motion (m-m) estimation of critical sites, based on modal proprieties analysis. Particularly, m-m is processed to extract modal features such as natural frequencies and mode shapes generated by vibrations of large infrastructures. Several study cases are here considered and the “Morandi” Bridge (Polcevera Viaduct) in Genoa (Italy) is analyzed in depth highlighting abnormal vibration modes during the period before the bridge collapsed
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