scholarly journals METHODS FOR IDENTIFICATION AND VISUALIZATION OF MUNICIPAL WASTE DISPOSAL SITES USING AEROSPACE DATA

2021 ◽  
Vol 2021 (2/2021) ◽  
pp. 56-60
Author(s):  
Adlin Dancheva
Keyword(s):  
Waste Disposal ◽  
Land Surface ◽  
European Space Agency ◽  
Radar Data ◽  
Great Depth ◽  
Municipal Waste ◽  
Household Waste ◽  
Disposal Site ◽  
Landsat 8 ◽  
Space Agency

With the increase of temperatures in the summer, the danger of self-ignition of landfills for household waste increases, because of the biochemical processes that take place inside them. The most recent example is the municipal landfill for non-hazardous waste near the town of Dupnitsa. The self-ignition started on July 23, 2021 and continued to smolder for almost a week, which led to a great danger of burning the area around it or poisoning the population. It is believed that one of the main reasons is the great depth of the accumulated waste. There is an urgent need to do quality control of most of the landfills for municipal waste in our country. Some of them are located next to major river arteries or international roads. Non-compliance with the requirements for maintenance, management and operation hides a serious potential for an ecological catastrophe. It is essential that stricter measures are taken and that these landfills are monitored. The aim of the present work is to reveal the possibilities and potential of aerospace data and to show different methods for processing, interpretation, and visualization. They can easily identify, map, and survey a waste disposal site. Optical images of the multispectral instrument (MSI) of the Sentinel 2 platform and radar (SAR) data from the Sentinel 1 platform of the Copernicus program of the European Space Agency were used. Thermal bands from the Landsat 5 - 7 (ETM) and Landsat 8 (OLI/TIRS) sensors of the Landsat program were used to calculate the land surface temperature. Satellite images have been orthogonized, and composite images between optical and radar data have been created for better visualization.

A Spatial and Temporal Continuum Remotely Sensed Soil Moisture Dataset of the Tibet Plateau From 2002 to 2015

2020 ◽  
Author(s):  
Yaokui Cui ◽  
Chao Zeng ◽  
Jie Zhou ◽  
Xi Chen
Keyword(s):  
Soil Moisture ◽  
Tibetan Plateau ◽  
Land Surface ◽  
European Space Agency ◽  
Remotely Sensed ◽  
General Regression Neural Network ◽  
Tibet Plateau ◽  
The Tibetan Plateau ◽  
Space Agency ◽  
Spatio Temporal

<p><strong>Abstract</strong>:</p><p>Surface soil moisture plays an important role in the exchange of water and energy between the land surface and the atmosphere, and critical to climate change study. The Tibetan Plateau (TP), known as “The third pole of the world” and “Asia’s water towers”, exerts huge influences on and sensitive to global climates. Long time series of and spatio-temporal continuum soil moisture is helpful to understand the role of TP in this situation. In this study, a dataset of 14-year (2002–2015) Spatio-temporal continuum remotely sensed soil moisture of the TP at 0.25° resolution is obtained, combining MODIS optical products and ESA (European Space Agency) ECV (Essential Climate Variable) combined soil moisture products based on General Regression Neural Network (GRNN). The validation of the dataset shows that the soil moisture is well reconstructed with R<sup>2</sup> larger than 0.65, and RMSE less than 0.08 cm<sup>3</sup> cm<sup>-3</sup> and Bias less than 0.07 cm<sup>3</sup> cm<sup>-3 </sup>at 0.25° and 1° spatial scale, compared with the in-situ measurements in the central of TP. And then, spatial and temporal characteristics and trend of SM over TP were analyzed based on this dataset.</p><p><strong>Keywords: </strong>Soil moisture; Remote Sensing; Dataset; GRNN; ECV; Tibetan Plateau</p>

scholarly journals Mapping Snow for Area Where was Detected Ground Deplacement: A Case Studi of Iaşi County

2018 ◽  
Vol 12 (2) ◽  
pp. 167-174
Author(s):  
Paul Macarof ◽  
Cezarina Georgiana Bartic Lazăr ◽  
Florian Statescu
Keyword(s):  
Ground Deformation ◽  
European Space Agency ◽  
Vertical Displacement ◽  
Landsat 8 ◽  
Ground Displacement ◽  
Space Agency ◽  
Numerical Indicator ◽  
Normalized Difference Snow Index ◽  
Differential Radar Interferometry ◽  
Wide Swath

Abstract The main goal of this paper is to detect snow in areas where was detecting and mapping, using Differential Radar Interferometry (DInSAR) technique, ground displacement. DInSAR is a powerful tool to detect and monitor ground deformation. Iaşi county is considered as study area in this research. Study area is geographically situated on latitude 46°48’N to 47°35’N and longitude 26°29’E to 28°07’E. For this paper, to detect and mapping grond displacement, was used Sentinel – 1 images, provided free by The European Space Agency (ESA), for January 2018, with vertical polarization (VV), ascending orbit and Interferometric Wide swath (IW) mode operated. SNAP was used to process the Sentinel – 1 images. Landsat-8 OLI was taken to detect areas cover with snow using Normalized Difference Snow Index (NDSI) - a numerical indicator that shows snow cover over land areas. ArcMap was used to create NDSI map after Landsat-8 data was preprocessed. The presence of snow has been observed both in the areas where it exists vertical displacement positive and negative.

scholarly journals IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY

2019 ◽  
Vol 13 (2) ◽  
pp. 179-186
Author(s):  
Paul Macarof ◽  
Florian Statescu ◽  
Cristian Iulian Birlica ◽  
Paul Gherasim
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
European Space Agency ◽  
Condition Index ◽  
Landsat 8 ◽  
Natural Phenomena ◽  
Space Agency ◽  
Vegetation Condition Index ◽  
Sentinel 2

In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.

Assessing the RST_VOLC algorithm implementation on infrared Sentinel 3 SLSTR data

2020 ◽  
Author(s):  
Alfredo Falconieri ◽  
Francesco Marchese ◽  
Giuseppe Mazzeo ◽  
Nicola Pergola ◽  
Valerio Tramutoli
Keyword(s):  
Land Surface ◽  
European Space Agency ◽  
Space Agency ◽  
Mt Etna ◽  
Multi Temporal ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
New Generation ◽  
Algorithm Implementation

<p>RSTVOLC is a multi-temporal algorithm developed for detecting volcanic hotspots that was successfully used to monitor active volcanoes located in different geographic areas exploiting both polar and geostationary satellite data. The algorithm runs operationally at the Institute of Methodologies for Environmental Analysis (IMAA) to monitor Italian volcanoes in near-real time by means of Advanced Very-High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. In this study, we assess the possible RSTVOLC implementation on data from the Sea and Land Surface Temperature Radiometer (SLSTR). The latter is a new generation sensor flying onboard the ESA (European Space Agency) Sentinel-3 mission, offering some spectral channels in the infrared bands particularly suited to identify high temperature surfaces such as lava flows. Here, we verify the RSTVOLC implementation on SLSTR data despite the absence of a multiannual time series of satellite records, by using synthetic spectral reference fields. Results achieved by investigating recent eruptions of Mt. Etna and Stromboli (Italy) volcanoes are presented and discussed.</p>

scholarly journals Household food waste disposal in South Africa: A case study of Johannesburg and Ekurhuleni

2018 ◽  
Vol 114 (5/6) ◽  
Author(s):  
Suzan Oelofse ◽  
Aubrey Muswema ◽  
Fhumulani Ramukhwatho
Keyword(s):  
South Africa ◽  
Developing Countries ◽  
Waste Disposal ◽  
Food Waste ◽  
Municipal Waste ◽  
Sub Saharan Africa ◽  
Household Waste ◽  
Household Food ◽  
Sub Saharan

Food waste is becoming an important issue in light of population growth and global food security concerns. However, data on food wastage are limited, especially for developing countries. Global estimates suggest that households in developed countries waste more food than those in developing countries, but these estimates are based on assumptions that have not been tested. We therefore set out to present primary data relating to household food waste disposal for South Africa within the sub-Saharan African context. As the Gauteng Province contributes about 45% of the total municipal waste generated in South Africa, the case study area covers two of the large urban metropolitan municipalities in Gauteng, namely Ekurhuleni and Johannesburg, with a combined population of 8.33 million, representing nearly 15% of the South African population. Municipal solid waste characterisation studies using bulk sampling with randomised grab sub-sampling were undertaken over a 6-week period during summer in 2014 (Johannesburg) and 2016 (Ekurhuleni), covering a representative sample of the municipal waste collection routes from households in each of the two surveyed municipalities. The food waste component of the household waste (excluding garden waste) was 3% in Ekurhuleni and 7% in Johannesburg. The results indicate that an average of 0.48 kg (Ekurhuleni) and 0.69 kg (Johannesburg) of food waste (including inedible parts) is disposed of into the municipal bin per household per week in the two municipalities, respectively. This translates into per capita food waste disposal of 8 kg and 12 kg per annum, respectively, in South Africa as compared to the estimated 6–11 kg per annum in sub-Saharan Africa and South and Southeast Asia.

scholarly journals Assessment of the Hail Damage on Agricultural Crops from North of Baragan Plain, Romania, Using Remote Sensing Data

2021 ◽  
Author(s):  
Claudiu Valeriu Angearu ◽  
Irina Ontel ◽  
Anisoara Irimescu ◽  
Burcea Sorin
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Weather Radar ◽  
Radar Data ◽  
Landsat 8 ◽  
Water Index ◽  
Hail Damage ◽  
Sentinel 2A

Abstract Hail is one of the dangerous meteorological phenomena facing society. The present study aims to analyze the hail event from 20 July 2020, which affected the villages of Urleasca, Traian, Silistraru and Căldăruşa from the Traian commune, Baragan Plain. The analysis was performed on agricultural lands, using satellite images in the optical domain: Sentinel-2A, Landsat-8, Terra MODIS, as well as the satellite product in the radar domain: Soil Water Index (SWI), and weather radar data. Based on Sentinel-2A images, a threshold of 0.05 of the Normalized Difference Vegetation Index (NDVI) difference was established between the two moments of time analyzed (14 and 21 July), thus it was found that about 4000 ha were affected. The results show that the intensity of the hail damage was directly proportional to the Land Surface Temperature (LST) difference values in Landsat-8, from 15 and 31 July. Thus, the LST difference values higher than 12° C were in the areas where NDVI suffered a decrease of 0.4-0.5. The overlap of the hail mask extracted from NDVI with the SWI difference situation at a depth of 2 cm from 14 and 21 July confirms that the phenomenon recorded especially in the west of the analyzed area, highlighted by the large values (greater than 55 dBZ) of weather radar reflectivity as well, indicating medium–large hail size. This research also reveals that satellite data is useful for cross validation of surface-based weather reports and weather radar derived products.

scholarly journals KappaMask: AI-Based Cloudmask Processor for Sentinel-2

2021 ◽  
Vol 13 (20) ◽  
pp. 4100
Author(s):  
Marharyta Domnich ◽  
Indrek Sünter ◽  
Heido Trofimov ◽  
Olga Wold ◽  
Fariha Harun ◽  
...  
Keyword(s):  
Land Surface ◽  
European Space Agency ◽  
Optical Data ◽  
Dice Coefficient ◽  
Test Dataset ◽  
Space Agency ◽  
Northern Latitudes ◽  
Agricultural Applications ◽  
Cloud Mask ◽  
Sentinel 2

The Copernicus Sentinel-2 mission operated by the European Space Agency (ESA) provides comprehensive and continuous multi-spectral observations of all the Earth’s land surface since mid-2015. Clouds and cloud shadows significantly decrease the usability of optical satellite data, especially in agricultural applications; therefore, an accurate and reliable cloud mask is mandatory for effective EO optical data exploitation. During the last few years, image segmentation techniques have developed rapidly with the exploitation of neural network capabilities. With this perspective, the KappaMask processor using U-Net architecture was developed with the ability to generate a classification mask over northern latitudes into the following classes: clear, cloud shadow, semi-transparent cloud (thin clouds), cloud and invalid. For training, a Sentinel-2 dataset covering the Northern European terrestrial area was labelled. KappaMask provides a 10 m classification mask for Sentinel-2 Level-2A (L2A) and Level-1C (L1C) products. The total dice coefficient on the test dataset, which was not seen by the model at any stage, was 80% for KappaMask L2A and 76% for KappaMask L1C for clear, cloud shadow, semi-transparent and cloud classes. A comparison with rule-based cloud mask methods was then performed on the same test dataset, where Sen2Cor reached 59% dice coefficient for clear, cloud shadow, semi-transparent and cloud classes, Fmask reached 61% for clear, cloud shadow and cloud classes and Maja reached 51% for clear and cloud classes. The closest machine learning open-source cloud classification mask, S2cloudless, had a 63% dice coefficient providing only cloud and clear classes, while KappaMask L2A, with a more complex classification schema, outperformed S2cloudless by 17%.

scholarly journals Terrain subsidence detected by satellite radar scanning of the Copenhagen area, Denmark, and its relation to the tectonic framework

1969 ◽  
Vol 28 ◽  
pp. 25-28 ◽  
Author(s):  
Peter Roll Jakobsen ◽  
Urs Wegmuller ◽  
Ren Capes ◽  
Stig A. Schack Pedersen
Keyword(s):  
European Space Agency ◽  
Information Service ◽  
Radar Data ◽  
Coastal Protection ◽  
The European Union ◽  
Persistent Scatterer Interferometry ◽  
Space Agency ◽  
Satellite Radar ◽  
Flooding Risk ◽  
Eu Project

In the European Union (EU) project Terrafirma, which is supported by the European Space Agency to stimulate the Global Monitoring Environment System, we are using the latest technology to measure terrain motion on the basis of satellite radar data. The technique we employ is known as persistent scatterer interferometry (PSI); in Denmark, it was previously used to map areas of subsidence susceptible to flooding in the Danish part of the Wadden Sea (Vadehavet) area (Pedersen et al. 2011). That study was part of the flooding risk theme under the TerraFirma Extension project. Another coastal protection monitoring activity in the EU seventh framework project SubCoast followed, in which the low-lying south coast of Lolland, prone to flooding, was studied. The Geological Survey of Denmark and Greenland (GEUS) is also involved in the three-year EU collaborative project PanGeo in which GEUS is one of 27 EU national geological surveys. The objective of PanGeo is to provide free and open access to geohazard information in support of the Global Monitoring Environment System. This will be achieved by providing a free, online geohazard information service for the two largest cities in each EU country, i.e. 52 towns throughout Europe with c. 13% of EU’s population.

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