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 LAND-COVER CLASSIFICATION USING FREELY AVAILABLE MULTITEMPORAL SAR DATA (WORK IN PROGRESS)

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 133-138
Author(s):  
M. Rajngewerc ◽  
R. Grimson ◽  
J. L. Bali ◽  
P. Minotti ◽  
P. Kandus
Keyword(s):  
Land Cover ◽  
European Space Agency ◽  
Weather Conditions ◽  
Land Cover Classification ◽  
Free Access ◽  
Kappa Index ◽  
Sar Images ◽  
Space Agency ◽  
Different Seasons ◽  
Sar Data

Abstract. Synthetic Aperture Radar (SAR) images are a valuable tool for wetlands monitoring since they are able to detect water below the vegetation. Furthermore, SAR images can be acquired regardless of the weather conditions. The monitoring and study of wetlands have become increasingly important due to the social and ecological benefits they provide and the constant pressures they are subject to. The Sentinel-1 mission from the European Space Agency enables the possibility of having free access to multitemporal SAR data. This study aims to investigate the use of multitemporal Sentinel-1 data for wetlands land-cover classification. To perform this assessment, we acquired 76 Sentinel-1 images from a portion of the Lower Delta of the Paraná River, and considering different seasons, texture measurements, and polarization, 30 datasets were created. For each dataset, a Random Forest classifier was trained. Our experiments show that datasets that included the winter dates achieved kappa index values (κ) higher than 0.8. Including textures measurements showed improvements in the classifications: for the summer datasets, the κ increased more than 14%, whereas, for Winter datasets in the VH and Dual polarization, the improvements were lower than 4%. Our results suggest that for the analyzed land-cover classes, winter is the most informative season. Moreover, for Summer datasets, the textures measurements provide complementary information.

scholarly journals Mapping and Quantifying the Human-Environment Interactions in Middle Egypt Using Machine Learning and Satellite Data Fusion Techniques

2020 ◽  
Vol 12 (3) ◽  
pp. 584
Author(s):  
José Manuel Delgado Blasco ◽  
Fabio Cian ◽  
Ramon F. Hanssen ◽  
Gert Verstraeten
Keyword(s):  
Land Cover ◽  
Data Fusion ◽  
Satellite Data ◽  
Rural Areas ◽  
European Space Agency ◽  
Optical Remote Sensing ◽  
Joint Research ◽  
Human Environment ◽  
Multi Temporal ◽  
Agricultural Areas

Population growth in rural areas of Egypt is rapidly transforming the landscape. New cities are appearing in desert areas while existing cities and villages within the Nile floodplain are growing and pushing agricultural areas into the desert. To enable control and planning of the urban transformation, these rapid changes need to be mapped with high precision and frequency. Urban detection in rural areas in optical remote sensing is problematic when urban structures are built using the same materials as their surroundings. To overcome this limitation, we propose a multi-temporal classification approach based on satellite data fusion and artificial neural networks. We applied the proposed methodology to data of the Egyptian regions of El-Minya and part of Asyut governorates collected from 1998 until 2015. The produced multi-temporal land cover maps capture the evolution of the area and improve the urban detection of the European Space Agency (ESA) Climate Change Initiative Sentinel-2 Prototype Land Cover 20 m map of Africa and the Global Human Settlements Layer from the Joint Research Center (JRC). The extension of urban and agricultural areas increased over 65 km2 and 200 km2, respectively, during the entire period, with an accelerated increase analysed during the last period (2010–2015). Finally, we identified the trends in urban population density as well as the relationship between farmed and built-up land.

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>

scholarly journals Brief communication: Hurricane Dorian: automated near-real-time mapping of the “unprecedented” flooding in the Bahamas using synthetic aperture radar

2020 ◽  
Vol 20 (5) ◽  
pp. 1463-1468
Author(s):  
Diego Cerrai ◽  
Qing Yang ◽  
Xinyi Shen ◽  
Marika Koukoula ◽  
Emmanouil N. Anagnostou
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
European Space Agency ◽  
Synthetic Aperture ◽  
Management Service ◽  
Sar Images ◽  
The Bahamas ◽  
Space Agency ◽  
Continental Area ◽  
Aperture Radar

Abstract. In this communication, we present application of the automated near-real-time (NRT) system called RAdar-Produced Inundation Diary (RAPID) to European Space Agency Sentinel-1 synthetic aperture radar (SAR) images to produce flooding maps for Hurricane Dorian in the northern Bahamas. RAPID maps, released 2 d after the event, show that coastal flooding in the Bahamas reached areas located more than 10 km inland, covering more than 3000 km2 of continental area. RAPID flood estimates from subsequent SAR images show the recession of the flood across the islands and present high agreement scores when compared to Copernicus Emergency Management Service (Copernicus EMS) estimates.

scholarly journals OPERATING SCALE OF ANTHROPIZATION EXTENT FOR PHYSICAL-GEOGRAPHIC TAXONS OF UKRAINE AND ITS VERIFYING REALIZATION

2017 ◽  
pp. 54-66
Author(s):  
V. Samoilenko ◽  
V. Plaskalnyi
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
European Space Agency ◽  
Forest Steppe ◽  
Model Assessment ◽  
Space Agency ◽  
Geographic Regions ◽  
Land Cover Maps ◽  
Cumulative Curves

In order to progress previously proposed interoperable for Ukrainian and all-European approaches procedure of anthropization extent analysis for Ukrainian landscapes, new operating scale of anthropization extent for physical-geographic taxons of Ukraine was substantiated and developed. The operating scale of anthropization extent relies, first of all, on created geoinformation basis, which is accessible for area of examination selected for the scale realization. Such area consists of physical-geographic regions and districts as plain landscape aggregations for zones of mixed and broad-leaved forests and forest-steppe. The geoinformation basis was organized by application and appropriate processing of up-to-date open digital spatial data sources. These sources contain, in particular, interactive raster land cover maps of European Space Agency (2015) and National Geomatics Center of China (2011), data of cartographic web-service OpenStreetMap, subject raster electronic maps collected in the National Atlas of Ukraine and other representative sources. There were stated peculiarities of development and implementation for the operating scale of anthropization extent, which embodies 55 operating land use and/or land cover (LULC) systems causing determinate anthropization extent, presented by corresponding to mentioned systems categories and indexes. Initial verifying realization of the anthropization extent operating scale was executed for the examination area, namely for its 25 physical-geographic regions, considering 130 physical-geographic districts, which form these regions. Realization of the scale proved, for the first, overall for examination area unfavorable geoecological situation in land use. Under such situation most of investigated regions and districts are indicated by categories of moderate-great and great anthropization (or β-euhemerobic and α-euhemerobic degree). For the second, there was constructed classed choropleth of anthropization extent categories’ fields, which were simulated for 1 km grid. For the third, there were typified percent distributions by regions for total LULC systems’ areas according to categories of these systems defined by their geoecological favorableness / unfavorableness (or degree of naturalness). Verifying-analogous comparison obtained model anthropization indicators with adequate representative foreign European results (concerning Germany and plain territory of central and west parts of Europe on the whole) proved their coincidence by content. All these jointly verify the objectivity of tools, proposed for model assessment of anthropization extent, and implementation validity of these tools. Prospects for further research were defined, aimed at detailed anthropization extent analysis, first of all by analysis of anthropization extent categories’ fields within physical-geographic districts especially by application of appropriate quasi-spectra and cumulative curves for anthropization indexes and areas.

scholarly journals Oil Spill Index (OSI) to Sentinel-2 Satellite Data: QU in International Contribution

2021 ◽  
Author(s):  
S Rajendran ◽  
AS Fahad ◽  
FN Sadooni ◽  
HAS Al-Kuwari ◽  
P Vethamony ◽  
...  
Keyword(s):  
Oil Spill ◽  
Satellite Data ◽  
Emergency Preparedness ◽  
Oil Spills ◽  
European Space Agency ◽  
Optical Data ◽  
Spectral Absorption ◽  
Spectral Bands ◽  
Space Agency ◽  
Sentinel 2

An Oil Spill Index (OSI = (B3+B4)/B2) was developed and applied to Sentinel-2 optical satellite data of the European Space Agency (ESA) to map marine oil spills using spectral absorption characters of spectral bands of the Sentinel-2. The potential application of OSI and derived indices [i. (5+6)/7, (3+4)/2, (11+12)/8 and ii. 3/2, (3+4)/2, (6+7)/5] were demonstrated to the oil spills that occurred off Mauritius, Indian Ocean, on August 06, 2020, and Norilsk region, Russia on May 29, 2020, and the results were published in the peer-reviewed research journals. Recently (August 19, 2021), our methodology was recognized by the Sentinel-Hub (a repository of custom scripts) https://custom-scripts.sentinel-hub.com/sentinel-2/oil-spill-index/ for OSI calculation. We validated the remote sensing results with the drone images taken during the incident. Our OSI index is the first to be applied to Sentinel-2 optical data to map oil spills. We proved the potential of indices and the capability of Sentinel sensors to detect, map, monitor, and assess the oil spill, which can be used for emergency preparedness of oil spills.

scholarly journals Evaluation of the Worldwide Wave Energy Distribution Based on ERA5 Data and Altimeter Measurements

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 394
Author(s):  
Liliana Rusu ◽  
Eugen Rusu
Keyword(s):  
Renewable Energy ◽  
Energy Density ◽  
Satellite Data ◽  
Wave Energy ◽  
European Space Agency ◽  
Clean Energy ◽  
Wave Crest ◽  
Wave Energy Density ◽  
Space Agency ◽  
The Mean

There is an increasing necessity in reducing CO2 emissions and implementing clean energy technologies, and over the years the marine environment has shown a huge potential in terms of renewable energy. From this perspective, extracting marine renewable energy represents one of the most important technological challenges of the 21st century. In this context, the objective of the present work is to provide a new and comprehensive understanding concerning the global wave energy resources based on the most recent results coming from two different databases, ERA5 and the European Space Agency Climate Change Initiative for Sea State. In this study, an analysis was first made based only on the ERA5 data and concerns the 30-year period of 1989–2018. The mean wave power, defined as the energy flux per unit of wave-crest length, was evaluated at this step. Besides the spatial distribution of this parameter, its seasonal, inter, and mean annual variability was also assessed on a global scale. As a second step, the mean wave energy density per unit horizontal area was analyzed for a 27-year period (1992–2018) with both ERA5 and the satellite data from the European Space Agency being considered. The comparison indicates a relatively good concordance between the results provided by the two databases in terms of mean wave energy density, although the satellite data indicate slightly higher energy values.

scholarly journals Land Cover Changes in Ghana over the Past 24 Years

2021 ◽  
Vol 13 (9) ◽  
pp. 4951
Author(s):  
Peter A. Y. Ampim ◽  
Michael Ogbe ◽  
Eric Obeng ◽  
Edwin K. Akley ◽  
Dilys S. MacCarthy
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
European Space Agency ◽  
Original Data ◽  
Bare Area ◽  
The Past ◽  
Space Agency ◽  
Land Cover Data ◽  
Emerging Issues ◽  
Wide Scale

Changes in land cover (LC) can lead to environmental challenges, but few studies have investigated LC changes at a country wide scale in Ghana. Tracking LC changes at such a scale overtime is relevant for devising solutions to emerging issues. This study examined LC changes in Ghana for the past almost two and half decades covering 1995–2019 to highlight significant changes and opportunities for sustainable development. The study used land cover data for six selected years (1995, 2000, 2005, 2010, 2015, and 2019) obtained from the European Space Agency. The data was analyzed using R, ArcGIS Pro and Microsoft Excel 365 ProPlus. The original data was reclassified into eight LC categories, namely: agriculture, bare area, built-up, forest, grassland, other vegetation, waterbody, and wetland. On average, the results revealed 0.7%, 131.7%, 23.3%, 46.9%, and 11.2% increases for agriculture, built-up, forest, waterbody, and wetland, respectively, across the nation. However, losses were observed for bare area (92.8%), grassland (51.1%), and other vegetation (41%) LCs overall. Notably, agricultural land use increased up to 2015 and decreased subsequently but this did not affect production of the major staple foods. These findings reveal the importance of LC monitoring and the need for strategic efforts to address the causes of undesirable change.

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