Data-fusion of satellite and ground sensors for river hydro-morphodynamics monitoring

2020 ◽  
Author(s):  
Maria Nicolina Papa ◽  
Michael Nones ◽  
Carmela Cavallo ◽  
Massimiliano Gargiulo ◽  
Giuseppe Ruello
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Satellite Images ◽  
Sampling Rate ◽  
Ecological Status ◽  
Multispectral Images ◽  
Sar Images ◽  
Shoreline Changes ◽  
Fluvial Morphology ◽  
Sentinel 2

<p>Changes in fluvial morphology, such as the migration of channels and sandbars, are driven by many factors e.g. water, woody debris and sediment discharges, vegetation and management practice. Nowadays, increased anthropic pressure and climate change are accelerating the natural morphologic dynamics. Therefore, the monitoring of river changes and the assessment of future trends are necessary for the identification of the optimal management practices, aiming at the improvement of river ecological status and the mitigation of hydraulic risk. Satellite data can provide an effective and cost-effective tool for the monitoring of river morphology and its temporal evolution.</p><p>The main idea of this work is to understand which remote sensed data, and particularly which space and time resolutions, are more adapt for the observation of sandbars evolution in relatively large rivers. To this purpose, multispectral and Synthetic Aperture Radar (SAR) archive data, with different spatial resolution, were used. Preference was given to satellite data freely available. Moreover, the observations extracted by the satellite data were compared with ground data recorded by a fixed camera.</p><p>The study case is a sandy bar (area about 0.4 km<sup>2 </sup>and maximum width about 350 m) in a lowland reach of the Po River (Italy), characterized by frequent and relevant morphological changes. The bar shoreline changes were captured by a fixed video camera, installed on a bridge and operating for almost two years (July 2017 - November 2018). To this purpose, we used: Sentinel-2 multispectral images with a spatial resolution of 10 m, Sentinel-1 SAR images with a resolution of 5 x 20 m and CosmoSkyMed SAR images with a resolution of 5 m. It is worth noting that the Sentinel data of the Copernicus Programme are freely available while the CosmoSkyMed data of the Italian Space Agency (ASI) are freely distributed for scientific purpose after the successful participation to an open call. In order to validate the results provided by Sentinel and CosmoSkyMed data, we used very high resolution multispectral images (about 50 cm).</p><p>Multispectral images are easily interpreted, but are affected by the presence of cloud cover. For instance, in this analysis, the expendable multispectral images were equal to about 50% of the total archive. On the other hand, the SAR images provide information also in the presence of clouds and at night-time, but they have the drawback of more complex processing and interpretation. The shorelines extracted from the satellite images were compared with those extracted from photographic images, taken on the same day of the satellite acquisition. Other comparisons were made between different satellite images acquired with a temporal mismatch of maximum two days.</p><p>The results of the comparisons showed that the Sentinel-1 and Sentinel-2 data were both adequate for the shoreline changes observation. Due to the higher resolution, the CosmoSkyMed data provided better results. SAR data and multispectral data allowed for automatic extraction of the bar shoreline, with different degree of processing burden. The fusion of data from different satellites gave the opportunity of highly increase the sampling rate.</p>

SentiLake - Sentinel-2 satellite data-based service for monitoring of Latvian lakes

2020 ◽  
Author(s):  
Dainis Jakovels ◽  
Agris Brauns ◽  
Jevgenijs Filipovs ◽  
Tuuli Soomets
Keyword(s):  
Water Quality ◽  
Satellite Data ◽  
Ecological Status ◽  
Quality Parameters ◽  
Water Bodies ◽  
Water Quality Parameters ◽  
Chl A ◽  
Regular Monitoring ◽  
Sentinel 2

<p>Lakes and water reservoirs are important ecosystems providing such services as drinking water, recreation, support for biodiversity as well as regulation of carbon cycling and climate. There are about 117 million lakes worldwide and a high need for regular monitoring of their water quality. European Union Water Framework Directive (WFD) stipulates that member states shall establish a programme for monitoring the ecological status of all water bodies larger than 50 ha, in order to ensure future quality and quantity of inland waters. But only a fraction of lakes is included in in-situ monitoring networks due to limited resources. In Latvia, there are 2256 lakes larger than 1 ha covering 1.5% of Latvian territory, and approximately 300 lakes are larger than 50 ha, but only 180 are included in Inland water monitoring program, in addition, most of them are monitored once in three to six years. Besides, local municipalities are responsible for the management of lakes, and they are also interested in the assessment of ecological status and regular monitoring of these valuable assets. </p><p>Satellite data is a feasible way to monitor lakes over a large region with reasonable frequency and support the WFD status assessment process. There are several satellite-based sensors (eg. MERIS, MODIS, OLCI) available specially designed for monitoring of water quality parameters, however, they are limited only to use for large water bodies due to a coarse spatial resolution (250...1000 m/pix). Sentinel-2 MSI is a space-borne instrument providing 10...20 m/pix multispectral data on a regular basis (every 5 days at the equator and 2..3 days in Latvia), thus making it attractive for monitoring of inland water bodies, especially the small ones (<1 km<sup>2</sup>). </p><p>Development of Sentinel-2 satellite data-based service (SentiLake) for monitoring of Latvian lakes is being implemented within the ESA PECS for Latvia program. The pilot territory covers two regions in Latvia and includes more than 100 lakes larger than 50 ha. Automated workflow for selecting and processing of available Sentinel-2 data scenes for extracting of water quality parameters (chlorophyll-a and TSM concentrations) for each target water body has been developed. Latvia is a northern country with a frequently cloudy sky, therefore, optical remote sensing is challenging in or region. However, our results show that 1...4 low cloud cover Sentinel-2 data acquisitions per month could be expected due to high revisit frequency of Sentinel-2 satellites. Combination of C2X and C2RCC processors was chosen for the assessment of chl-a concentration showing the satisfactory performance - R<sup>2</sup> = 0,82 and RMSE = 21,2 µg/l. Chl-a assessment result is further converted and presented as a lake quality class. It is expected that SentiLake will provide supplementary data to limited in situ data for filling gaps and retrospective studies, as well as a visual tool for communication with the target audience.</p>

scholarly journals Using satellite images to assess the state of arable fields on the example of the East Kazakhstan region

2021 ◽  
Vol 82 (4) ◽  
pp. 179-186
Author(s):  
A. S. Tlebaldinova ◽  
◽  
Ye. V. Ponkina ◽  
M. Ye. Mansurova ◽  
S. Sh. Ixanov ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Satellite Data ◽  
Satellite Images ◽  
Vegetation Index ◽  
The State ◽  
Experimental Station ◽  
Arable Fields ◽  
East Kazakhstan ◽  
Expert Analysis ◽  
Sentinel 2

This article proposes a methodology for assessing the state of arable fields based on the use of Sentinel 2 satellite data. The essence of this methodology is cluster analysis of NDVI vegetation index profiles for a number of years, as well as expert analysis of the obtained results. The proposed method for assessing the state of arable fields has been tested on the example of arable lands of East Kazakhstan Agricultural Experimental Station LLP. This method can be used to optimize crop placement.

scholarly journals Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires

2019 ◽  
Vol 11 (6) ◽  
pp. 622 ◽  
Author(s):  
Federico Filipponi
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Satellite Data ◽  
High Spatial Resolution ◽  
National Level ◽  
Burned Area ◽  
Commission Error ◽  
Burned Areas ◽  
Sentinel 2

Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. The high availability of free medium-high spatial resolution optical satellite data, offered by the Copernicus Programme, has enabled the development of more detailed post-fire mapping. This research study deals with the exploitation of Sentinel-2 time series to map burned areas, taking advantages from the high revisit frequency and improved spatial and spectral resolution of the MSI optical sensor. A novel procedure is here presented to produce medium-high spatial resolution burned area mapping using dense Sentinel-2 time series with no a priori knowledge about wildfire occurrence or burned areas spatial distribution. The proposed methodology is founded on a threshold-based classification based on empirical observations that discovers wildfire fingerprints on vegetation cover by means of an abrupt change detection procedure. Effectiveness of the procedure in mapping medium-high spatial resolution burned areas at the national level was demonstrated for a case study on the 2017 Italy wildfires. Thematic maps generated under the Copernicus Emergency Management Service were used as reference products to assess the accuracy of the results. Multitemporal series of three different spectral indices, describing wildfire disturbance, were used to identify burned areas and compared to identify their performances in terms of spectral separability. Result showed a total burned area for the Italian country in the year 2017 of around 1400 km2, with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. Results demonstrate how the proposed procedure allows for the medium-high resolution mapping of burned areas, offering a benchmark for the development of new operational downstreaming services at the national level based on Copernicus data for the systematic monitoring of wildfires.

scholarly journals BUILDING DETECTION FROM SAR IMAGES USING UNET DEEP LEARNING METHOD

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 215-218
Author(s):  
R. A. Emek ◽  
N. Demir
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Optical Image ◽  
Building Detection ◽  
Multispectral Images ◽  
Learning Method ◽  
Sar Images ◽  
Optical Images ◽  
Image Patches ◽  
Sentinel 2

Abstract. SAR images are different from the optical images in terms of image properties with the values of scattering instead of reflectance. This makes SAR images difficult to apply the traditional object detection methodologies. In recent years, deep learning models are frequently used in segmentation and object detection purposes. In this study, we have investigated the potential of U-Net models for building detection from SAR and optical image fusion. The datasets used are Sentinel 1 SAR and Sentinel-2 multispectral images, provided from ‘SpaceNet 6 Multi Sensor All-Weather Mapping’ challenge. These images cover an area of 120 km2 in Rotterdam, the Netherlands. As training datasets 20 pieces of 900 by 900 pixel sized HV polarized and optical image patches have been used together. The calculated loss value is 0.4 and the accuracy is 81%.

scholarly journals A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3232
Author(s):  
Nicola Genzano ◽  
Nicola Pergola ◽  
Francesco Marchese
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Global Scale ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

scholarly journals Aircraft Detection above Clouds by Sentinel-2 MSI Parallax

2021 ◽  
Vol 13 (15) ◽  
pp. 3016
Author(s):  
Peder Heiselberg ◽  
Henning Heiselberg
Keyword(s):  
Time Delay ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multispectral Images ◽  
English Channel ◽  
Challenging Problem ◽  
Almost All ◽  
Aircraft Detection ◽  
Sentinel 2

Detection of aircrafts in satellite images is a challenging problem when the background is strongly reflective clouds with varying transparency. We develop a fast and effective detection algorithm that can find almost all aircrafts above and between clouds in Sentinel-2 multispectral images. It exploits the time delay of a few seconds between the recorded multispectral images such that a moving aircraft is observed at different positions due to parallax effects. The aircraft speed, heading and altitude are also calculated accurately. Analysing images over the English Channel during fall 2020, we obtain a detection accuracy of 80%, where the most of the remaining were covered by clouds. We also analyse images in the 1.38 μm water absorption band, where only 61% of the aircrafts are detected.

scholarly journals Assessment of landscape fires in 2020 in Russia

2021 ◽  
Vol 4 (2) ◽  
pp. 1-17 ◽  
Author(s):  
I. V. Glushkov ◽  
◽  
V. V. Lupachik ◽  
I. V. Zhuravleva ◽  
A. Yu. Yaroshenko ◽  
...  
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Prescribed Burning ◽  
Public Domain ◽  
The Public ◽  
Burned Areas ◽  
Anthropogenic Vegetation ◽  
Main Factors ◽  
Sentinel 2

Landscape fires are one of the main factors of anthropogenic vegetation and soil transformation, as well as anthropogenic climate change. Despite this, until now there have been no detailed estimates of the scale of all types of landscape fires throughout Russia for a full calendar year. This work was aimed at providing such an assessment for landscape fires in 2020, as well as creating a publicly available map of these fires. The fires were mapped by experts and specially trained volunteers on the basis of Sentinel-2 MSI medium spatial resolution satellite images (20 m/pixel) for two periods — from January 1 to May 15, and from May 16 to December 31 inclusive. The total area of identified landscape fires was 25.84 million hectares. The division of the identified fires into presumably controlled (prescribed burning) and uncontrolled (actually fires) was not carried out within the framework of this work. However, the burned areas, corresponding to the area criterion established for single prescribed burnings (up to 10 hectares), account for only 0.96% of the total area of detected fires. The final map of landscape fires, compiled as a result of this work, is available in the public domain at https://maps.greenpeace.org/maps/gpru/fires_2020.

scholarly journals Detection of Land Subsidence due to Excessive Groundwater Use Varying with Different Land Cover Types in Quetta valley, Pakistan Using ESA-Sentinel Satellite Data

2017 ◽  
Author(s):  
Waqas Ahmad ◽  
Minha Choi ◽  
Soohyun Kim ◽  
Dongkyun Kim
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Land Subsidence ◽  
European Space Agency ◽  
Multispectral Images ◽  
Sar Images ◽  
Entire Study ◽  
Space Agency ◽  
Quetta Valley ◽  
Entire Study Area

Abstract. In this study, we show that land subsidence in Quetta valley, Pakistan is caused by exploitation of groundwater based on the analysis of European Space Agency (ESA) Sentinel satellite data. For this, we performed interferometry analysis using twenty nine Sentinel-1 SAR images to obtain twenty eight interferograms of land subsidence for the period between 16 Oct 2014 and 06 Oct 2016. Then, the land subsidence was compared with the land cover of the study area that was derived from Sentienl-2 multispectral images. The results reveal that, for the period of two years, the entire study area experienced highly uneven land subsidence with its magnitude ranging between 10 mm to 280 mm. The spatial pattern of land subsidence showed a high correlation with that of land covers. While urban and cultivated area with high groundwater extraction showed great amount of land subsidence, barren and seasonally cultivated area did not show as much land subsidence.

scholarly journals Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

2019 ◽  
Vol 11 (2) ◽  
pp. 122 ◽  
Author(s):  
Zhongbin Li ◽  
David Roy ◽  
Hankui Zhang ◽  
Eric Vermote ◽  
Haiyan Huang
Keyword(s):  
Aerosol Optical Depth ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Urban Environments ◽  
Landsat 8 ◽  
Urban Aerosol ◽  
Medium Resolution ◽  
Aerosol Monitoring ◽  
Sentinel 2A ◽  
Sentinel 2

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

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