costeLAB, the Italian thematic platform for coastal and marine downstream applications of institutional and research users in the context of Copernicus data exploitation

2021 ◽  
Author(s):  
Deodato Tapete ◽  
Laura Candela ◽  
Alessandro Coletta ◽  
Maria Girolamo Daraio ◽  
Rocchina Guarini ◽  
...  
Keyword(s):  
Marine Ecosystem ◽  
Satellite Image ◽  
European Space Agency ◽  
National Research Council ◽  
Hyperspectral Data ◽  
Sensor Data ◽  
Temperature Cycle ◽  
User Needs ◽  
Synthetic Aperture Radar Data ◽  
Space Agency

<p>Coastal and marine environmental management is of vital importance in Italy.  Currently there is a growing interest in facilitating user uptake of satellite technologies and Copernicus ecosystem resources, also at non-technical local and regional governmental authorities, and a thematic working table dedicated to “Coastal” issues has been set up in the context of the Italian Copernicus User Forum (Geraldini et al., 2021).</p><p>The Italian Space Agency (ASI) has promoted the development of the thematic platform costeLAB as a tool dedicated to monitoring, management and study of coastal areas (sea and land). costeLAB hosts cutting edge tools for satellite image processing and geospatial integration with in-situ data, so as to allow an efficient access to archive data and facilitate direct engagement of users interested in deriving information according to their requirements. costeLAB is built in the framework of Progetto Premiale “Rischi Naturali Indotti dalle Attività Umana - COSTE”, n. 2017-I-E.0, funded by the Italian Ministry of University and Research (MUR), coordinated by ASI and developed by e-GEOS and Planetek Italia, with the National Research Council of Italy (CNR), Meteorological Environmental Earth Observation (MEEO) and Geophysical Applications Processing (G.A.P.) s.r.l. as subcontractors.</p><p>Operating in systematic and on-demand modes, costeLAB provides users with validated algorithms and advanced data management resources to analyse multi-mission and multi-sensor data – particularly Copernicus Sentinels and ASI’s COSMO-SkyMed Synthetic Aperture Radar data – and to generate products based on user-selected input parameters, without the need for large data volume transfers. costeLAB aligns with the concept of the European Space Agency’s Thematic Exploitation Platforms, and represents a mean to exploit the Italian Sentinel Collaborative Ground Segment equipped with Sentinel-1/2/3 data archives and programmable computing resources. The platform aims to support downstream applications from a wider user community including the Civil Protection, environmental protection agencies and regulators, coastal scientists, academics, practitioners, and the general public.</p><p>costeLAB offers a portfolio of about 30 products among which: coastline, defence works, coastal habitat maps, flooding, hydrocarbon beaching, chlorophyll, wave and wind fields. These products can be generated as “state”, “change”, “damage”, “hazard” or “exposition” maps according to the operational scenarios “baseline knowledge”, “ordinary monitoring”, “extraordinary monitoring” and “post-event”.</p><p>We show some of the platform products and how they address specific user needs towards downstream applications, in support to national policies and directives. Examples include products of “Marine Ecosystem” (i.e. “sea level” and “day sea surface temperature cycle”). Thanks to ad hoc Copernicus Marine Environment Monitoring Service (CMEMS) data integration function implemented in costeLAB, these products are generated from pre-processed input data made available in near real time through CMEMS.</p><p>costeLAB is also equipped with the “Virtual Laboratory” module, purposely designed as a collaborative environment allowing users (in particular, researchers and analysts) to access “Software as a Service” resources to test proprietary or shared processors, exploit costeLAB computing resources, generate and integrate products, publish results. An example of collaborative research including experiments with ASI’s PRISMA hyperspectral data is presented.</p><p> </p><p>Geraldini et al. (2021) User Needs Analysis for the Definition of Operational Coastal Services. Water 13(1):92.</p>

scholarly journals On the Use of Tri-Stereo Pleiades Images for the Morphometric Measurement of Dolines in the Basaltic Plateau of Azrou (Middle Atlas, Morocco)

2021 ◽  
Vol 13 (20) ◽  
pp. 4087
Author(s):  
Maria Teresa Melis ◽  
Luca Pisani ◽  
Jo De Waele
Keyword(s):  
Remote Sensing ◽  
Satellite Image ◽  
European Space Agency ◽  
Morphometric Study ◽  
Stereo Images ◽  
Space Agency ◽  
Middle Atlas ◽  
Mapping Techniques ◽  
Remote Sensing Techniques ◽  
Basaltic Plateau

Hundreds of large and deep collapse dolines dot the surface of the Quaternary basaltic plateau of Azrou, in the Middle Atlas of Morocco. In the absence of detailed topographic maps, the morphometric study of such a large number of features requires the use of remote sensing techniques. We present the processing, extraction, and validation of depth measurements of 89 dolines using tri-stereo Pleiades images acquired in 2018–2019 (the European Space Agency (ESA) © CNES 2018, distributed by Airbus DS). Satellite image-derived DEMs were field-verified using traditional mapping techniques, which showed a very good agreement between field and remote sensing measures. The high resolution of these tri-stereo images allowed to automatically generate accurate morphometric datasets not only regarding the planimetric parameters of the dolines (diameters, contours, orientation of long axes), but also for what concerns their depth and altimetric profiles. Our study demonstrates the potential of using these types of images on rugged morphologies and for the measurement of steep depressions, where traditional remote sensing techniques may be hindered by shadow zones and blind portions. Tri-stereo images might also be suitable for the measurement of deep and steep depressions (skylights and collapses) on Martian and Lunar lava flows, suitable targets for future planetary cave exploration.

scholarly journals Global maps of Forel-Ule index, hue angle and Secchi disk depth derived from twenty-one years of monthly ESA-OC-CCI data

2020 ◽  
Author(s):  
Jaime Pitarch ◽  
Marco Bellacicco ◽  
Salvatore Marullo ◽  
Hendrik J. van der Woerd
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
European Space Agency ◽  
Sensor Data ◽  
Secchi Disk ◽  
Secchi Disk Depth ◽  
The North ◽  
Space Agency ◽  
Free Data ◽  
Hue Angle

Abstract. We document the development and public release of a new dataset (1997–2018), consisting of global maps of the Forel-Ule index, hue angle and Secchi disk depth. Source data comes from the European Space Agency (ESA) Ocean Colour (OC) Climate Change Initiative (CCI), which is providing merged multi-sensor data from the mid-resolution sensors in operation at a specific time from 1997 to the present day. Multi-sensor satellite datasets are advantageous tools for ecological studies because they increase the probabilities of cloud-free data over a given region, as data from multiple satellites whose overpass times differ by a few hours are combined. Moreover, data merging from heritage and present satellites can expand the duration of the time series indefinitely, which allows the calculation of significant trends. Additionally, data are remapped consistently and analysis-ready for scientists. Also, the products described in this article have the exclusive advantage of being linkable to in-situ historic observations and thus enabling the construction of very long time series. Monthly data are presented at a spatial resolution of ~4 km at the equator and are available at PANGAEA, https://doi.org/10.1594/PANGAEA.904266 (Pitarch et al., 2019a). Two smaller and easier to handle test datasets have been produced from the former: a global dataset at 1 degree spatial resolution and another one for the North Atlantic at 0.25 degree resolution.

scholarly journals Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 641 ◽  
Author(s):  
Joel Segarra ◽  
Maria Luisa Buchaillot ◽  
Jose Luis Araus ◽  
Shawn C. Kefauver
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Satellite Image ◽  
European Space Agency ◽  
Stress Monitoring ◽  
Biotic Stresses ◽  
Space Agency ◽  
Wide Range ◽  
Technical Features ◽  
Sentinel 2

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.

scholarly journals Quantification of oil lost from tanker vessel using space borne radar datasets - Case study of Haldia port oil spill, July 2018.

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
S.J Prasad ◽  
T.M. Balakrishnan Nair
Keyword(s):  
Synthetic Aperture Radar ◽  
Oil Spill ◽  
European Space Agency ◽  
Fuel Oil ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Space Agency ◽  
Sar Data ◽  
Spilled Oil ◽  
Aperture Radar

Abstract 686884 Determining the spilled volume of the marine oil pollutant is an essential requisite for the oil spill modellers and the responders. Generally, the mass of the spilled pollutant is computed from the total quantity and the remaining quantity of the storage tank of the distressed vessel. A method to estimate the quantity of the spilled oil pollutant using the space -borne synthetic aperture radar dataset is elaborated here. The synthetic aperture radar data, its ability to penetrate cloud cover, irrespective of weather conditions, has been widely used to detect the signature of spilt oil. SAR data available from European Space Agency and Canadian Space Agency were used to detect the oil spills as they are proved to be appropriate for oil spill detection. Minor oil spill occured off Haldia Port, off Kolkata from SSL tanker vessel on 14 July 2018. The geographical location of the distressed vessel is 88.775 ′E, 21.441 ′N. The zone of the vessel distress was monitored for oil slicks. The acquisition plan of the Radar satellite Sentinel -1A was obtained from European Space Agency. As per that, the pass of the Sentinel -1A was available on 15 July 2018 and 17 July 2018 for the region of study. The Synthetic Aperture Radar (SAR) datasets were obtained from Sentinel -1A as per their availability. Those datasets were processed using Sentinel Application Platform (SNAP) tool box. The SAR data is subjected to terrain correction, which automatically reprojects the radar scene. The next stage is performing radiometric calibration, which converts the amplitude into intensity values. The radar reflectance values are converted to Sigma0 intensity values in Sentinel tool box. This Sigma0 values were wrote in netcdf format for identifying the oil slicks. The pixels of lesser intensity values are identified and are interpreted for oil slicks. The zone of the oil slicks in the radar scene are considered as irregular polygons. The area of those polygons were computed. Later the volume of the spilled oil is computed using the thickness of the spilled oil pollutant. Finally the mass of the pollutant is computed. It was collectively estimated from the SAR datasets, that, 33 Tons of Fuel oil was lost from SSL vessel that sank off Haldia Port. This paper elaborates in detail about the method of processing SAR dataset and estimating the quantity of oil lost from the vessel using SAR datasets.

scholarly journals Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach

2021 ◽  
Vol 13 (2) ◽  
pp. 300
Author(s):  
Kashyap Raiyani ◽  
Teresa Gonçalves ◽  
Luís Rato ◽  
Pedro Salgueiro ◽  
José R. Marques da Silva
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Satellite Image ◽  
European Space Agency ◽  
Scene Classification ◽  
Continuous Increase ◽  
Main Research ◽  
Space Agency ◽  
High Resolution Satellite Imagery ◽  
Sentinel 2

Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of 0.84, a considerable improvement when compared to the Sen2Cor corresponding performance of 0.59. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package.

scholarly journals PENGENALAN CITRA SATELIT SENTINEL-2 UNTUK PEMETAAN KELAUTAN

OSEANA ◽  
2017 ◽  
Vol 42 (3) ◽  
pp. 40-55
Author(s):  
Nadya Oktaviani ◽  
Hollanda A Kusuma
Keyword(s):  
Spatial Resolution ◽  
Satellite Image ◽  
Atmospheric Correction ◽  
European Space Agency ◽  
Image Data ◽  
Processing Parameters ◽  
Cloud Detection ◽  
Space Agency ◽  
Thin Cloud ◽  
Sentinel 2

RECOGNITION AND UTILIZATION OF SATELLITE IMAGE SENTINEL-2 FOR MARINE MAPPING. Sentinel-2 is a satellite launched by a collaboration between The European Commission and the European Space Agency in the Global Monitoring for Environment and Security (GMES) program. The satellite has a mission to scan the Earth’s surface simultaneously at an angle of 180 each satellite with a 5-day temporal resolution with the same appearance on the equator and has a spatial resolution of 10 m, 20 m, and 60 m. There are 13 multispectral channels including VNIR and SWIR. Four channels with 10 m spatial resolution adapt with SPOT 4/5 and user’s comply requirements for land cover classification. Six channels with 20 m spatial resolution becomes a requirement for other Level 2 processing parameters. Channels with 60 m spatial resolution are specified for atmospheric correction and cloud filtering (443 nm for aerosols, 940 nm for moisture, and 1375 for thin cloud detection). Based on these specifications, Sentinel-2 can be an alternative for users to obtain image data with spatial, temporal, radiometric, and spectral resolution is better than SPOT and Landsat. Sentinel-2 can be downloaded for free and easy by the general public. The existence of image by Sentinel-2 is expected to be used optimally, especially for remote sensing analysis in marine field.

Step-by-Step Processing of Sentinel-1 data for Estimation of Rice Area.

2019 ◽  
Author(s):  
Awais Karamat ◽  
Muhammad Nawaz ◽  
Ali Imam Mirza ◽  
Muhammad Rahat Jamil ◽  
Ali Asghar ◽  
...  
Keyword(s):  
Satellite Image ◽  
European Space Agency ◽  
Kappa Statistics ◽  
Random Forest Classification ◽  
Rice Farmers ◽  
Space Agency ◽  
Forest Classification ◽  
Rice Area ◽  
Local Farmers ◽  
Strong Agreement

Rice has become an essential part of four pillars of food security, especially in Asia, where it is produced over large spatial extents and also consumed widely. About 89 % of the global rice production is targeted and achieved from Asian countries. We downloaded Sentinel-1 datasets from official website of European Space Agency (ESA) for identification of rice patterns in the study site. The data was selected in Ground Range Detection (GRD) format and applied the toolbox in Sentinel Application Platform (SNAP) for further processing. We applied the orbit file for geometric and radiometric corrections, LEE filter for removal of spackles, resampling to convert 20*20m2 to 10*10m2 pixel size and finally the Random Forest Classification (RFC) to classify the satellite image. The classification results of Sentinel image for the year 2018, show that the total area of the study site was 360021 ha, including 144991 ha as rice area, 130598 as other vegetation, 19339 ha as water body and the built-up area was estimated as 5693 ha. Kappa statistics resulted the overall accuracy of 85% which is in strong agreement to ground reality. We observed that the rice area was increased from 140403 ha in 2017 to 144991 ha in 2018. The main reason of this increase in rice area was observed as the preference of local farmers to grow rice in comparison to other crops because the local government was offering high subsidy to rice farmers. Moreover, district Nankana-Sahib produces rice of expert quality which is famous throughout the world therefore, it is considered as cash crop.

An Image Reconnaissance: Agricultural Patterns and Related Environmental Impacts Viewed From Space

2018 ◽  
Author(s):  
Richard W. Hazlett ◽  
Joshua Peck
Keyword(s):  
Comprehensive Evaluation ◽  
Human Impacts ◽  
Rapid Development ◽  
Satellite Image ◽  
European Space Agency ◽  
Agricultural Practices ◽  
Human Interaction ◽  
Buffer Zones ◽  
Space Agency ◽  
Wilderness Areas

Satellite reconnaissance of the Earth’s surface provides critical information about the state of human interaction with the natural environment. The strongest impact is agricultural, reflecting land-use approaches to food production extending back to the dawn of civilization. To variable degrees, depending upon location, regional field patterns result from traditional farming practices, surveying methods, regional histories, policies, political agendas, environmental circumstances, and economic welfare. Satellite imaging in photographic true or false color is an important means of evaluating the nature and implications of agricultural practices and their impacts on the surrounding world. Important platforms with publicly accessible links to satellite image sets include those of the European Space Agency, U.S. National Aeronautics and Space Administration, the Centre D’etudes Spatiales, Airbus, and various other governmental programs. Reprocessing of data worldwide in scope by commercial concerns including Digital Globe, Terrametrics, and GoogleEarth in the 21st century enable ready examination of most of the Earth’s surface in great detail and natural colors. The potential for monitoring and improving understanding of agriculture and its role in the Earth system is considerable thanks to these new ways of viewing the planet. Space reconnaissance starkly reveals the consequences of unique land surveys for the rapid development of agriculture and political control in wilderness areas, including the U.S. Public Land Survey and Tierras Bajas systems. Traditional approaches toward agriculture are clearly shown in ribbon farms, English enclosures and medieval field systems, and terracing in many parts of the world. Irrigation works, some thousands of years old, may be seen in floodplains and dryland areas, notably the Maghreb and the deep Sahara, where center-pivot fields have recently appeared in areas once considered too dry to cultivate. Approaches for controlling erosion, including buffer zones, shelter belts, strip and contour farming, can be easily identified. Also evident are features related to field erosion and soil alteration that have advanced to crisis stage, such as badland development and widespread salinization. Pollution related to farm runoff, and the piecemeal (if not rapid) loss of farmlands due to urbanization can be examined in ways favoring more comprehensive evaluation of human impacts on the planetary surface. Developments in space technologies and observational platforms will continue indefinitely, promising ever-increasing capacity to understand how humans relate to the environment.

scholarly journals Plant functional type classification for Earth System Models: results from the European Space Agency's Land Cover Climate Change Initiative

2015 ◽  
Vol 8 (1) ◽  
pp. 429-462 ◽  
Author(s):  
B. Poulter ◽  
N. MacBean ◽  
A. Hartley ◽  
I. Khlystova ◽  
O. Arino ◽  
...  
Keyword(s):  
Land Cover ◽  
European Space Agency ◽  
User Needs ◽  
Earth System ◽  
Functional Type ◽  
System Models ◽  
Global Land Cover ◽  
Space Agency ◽  
Global Land ◽  
Earth System Models

Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land-cover datasets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as one of thirteen Essential Climate Variables targeted for research development. The LC_CCI was implemented in three phases, first responding to a survey of user needs, then developing a global, moderate resolution, land-cover dataset for three time periods, or epochs, 2000, 2005, and 2010, and the last phase resulting in a user-tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFT). The translation was performed as part of consultative process among map producers and users and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three-earth system modeling teams shows significant differences between the LC_CCI PFT dataset and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as Phase 2 of the European Space Agency CCI program continues.

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