Satellite monitoring of the environment in the area of the Iskitim coal mines in 2013–2020

Mapping Intimacies ◽

10.25743/sdm.2021.39.39.042 ◽

2021 ◽

Author(s):

N.V. Rodionova

Keyword(s):

Data Analysis ◽

Reflection Coefficient ◽

Coal Mines ◽

Open Pit ◽

Satellite Monitoring ◽

Landsat 8 ◽

Visualization System ◽

Multispectral Data ◽

Novosibirsk Region ◽

Sentinel 2

The paper considers the use of multispectral data from the Landsat-8, Sentinel-2, Aqua and Terra satellites for monitoring pollution in the areas of open-pit coal mines in the Iskitim district of the Novosibirsk region for the period 2013–2020. The change in the values of the reflection coefficient (RC) from the surface and water bodies, the snow index NDSI during the snowmelt period, the change of NDVI in the summer, in the area of Kolyvansky and Vostochny coal mines and in the area of the Linevo village are considered. The dynamics of the aerosol optical thickness (AOT) changes, CO and CH4 concentrations in the atmosphere of the Iskitim district using the Giovanni data analysis and visualization system are shown.

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Soil Organic Carbon Mapping Using Multispectral Remote Sensing Data: Prediction Ability of Data with Different Spatial and Spectral Resolutions

Remote Sensing ◽

10.3390/rs11242947 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2947 ◽

Cited By ~ 1

Author(s):

Daniel Žížala ◽

Robert Minařík ◽

Tereza Zádorová

Keyword(s):

Prediction Accuracy ◽

Remote Sensing Data ◽

Cost Effective ◽

Hyperspectral Data ◽

Landsat 8 ◽

Prediction Ability ◽

Multispectral Data ◽

Multispectral Remote Sensing ◽

Data Source ◽

Sentinel 2

The image spectral data, particularly hyperspectral data, has been proven as an efficient data source for mapping of the spatial variability of soil organic carbon (SOC). Multispectral satellite data are readily available and cost-effective sources of spectral data compared to costly and technically demanding processing of hyperspectral data. Moreover, their continuous acquisition allows to develop a composite from time-series, increasing the spatial coverage of SOC maps. In this study, an evaluation of the prediction ability of models assessing SOC using real multispectral remote sensing data from different platforms was performed. The study was conducted on a study plot (1.45 km2) in the Chernozem region of South Moravia (Czechia). The adopted methods included field sampling and predictive modeling using satellite multispectral Sentinel-2, Landsat-8, and PlanetScope data, and multispectral UAS Parrot Sequoia data. Furthermore, the performance of a soil reflectance composite image from Sentinel-2 data was analyzed. Aerial hyperspectral CASI 1500 and SASI 600 data was used as a reference. Random forest, support vector machine, and the cubist regression technique were applied in the predictive modeling. The prediction accuracy of models using multispectral data, including Sentinel-2 composite, was lower (RPD range from 1.16 to 1.65; RPIQ range from 1.53 to 2.17) compared to the reference model using hyperspectral data (RPD = 2.26; RPIQ = 3.34). The obtained results show very similar prediction accuracy for all spaceborne sensors (Sentinel-2, Landsat-8, and PlanetScope). However, the spatial correlation between the reference mapping results obtained from the hyperspectral data and other maps using multispectral data was moderately strong. UAS sensors and freely available satellite multispectral data can represent an alternative cost-effective data source for remote SOC mapping on the local scale.

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Contribution of Sentinel-2 data for applications in vegetation monitoring

ACTA IMEKO ◽

10.21014/acta_imeko.v5i2.352 ◽

2016 ◽

Vol 5 (2) ◽

pp. 44 ◽

Cited By ~ 26

Author(s):

Pia Addabbo ◽

Mariano Focareta ◽

Salvo Marcuccio ◽

Claudio Votto ◽

Silvia Liberata Ullo

Keyword(s):

Added Value ◽

Landsat 8 ◽

Vegetation Monitoring ◽

Multispectral Data ◽

Monitoring Applications ◽

Green Areas ◽

Land Surfaces ◽

Project Data ◽

Different Sources ◽

Sentinel 2

<p class="Abstract"><span lang="EN-US">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.</span></p>

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Multispectral Data Analysis for Semantic Assessment—A SNAP Framework for Sentinel-2 Use Case Scenarios

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.3013091 ◽

2020 ◽

Vol 13 ◽

pp. 4429-4442

Author(s):

Alexandru Cosmin Grivei ◽

Iulia Coca Neagoe ◽

Florin Andrei Georgescu ◽

Andreea Griparis ◽

Corina Vaduva ◽

...

Keyword(s):

Data Analysis ◽

Use Case ◽

Multispectral Data ◽

Sentinel 2

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Medium and High Resolution Multispectral Data from Landsat-8 and Sentinel-2 for Active Fire Monitoring and Post-Fire Assessment of Burned Areas: A Case Study on Vesuvius

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2018.8517322 ◽

2018 ◽

Author(s):

L. Circula ◽

N. Fiscante ◽

C.V. Angelino ◽

S. Parrilli

Keyword(s):

High Resolution ◽

Landsat 8 ◽

Multispectral Data ◽

Fire Monitoring ◽

Burned Areas ◽

Active Fire ◽

Sentinel 2

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A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

Applied Sciences ◽

10.3390/app112311486 ◽

2021 ◽

Vol 11 (23) ◽

pp. 11486

Author(s):

Shahab Ud Din ◽

Khan Muhammad ◽

Muhammad Fawad Akbar Khan ◽

Shahid Bashir ◽

Muhammad Sajid ◽

...

Keyword(s):

Principal Component ◽

Cost Effective ◽

Thermal Infrared ◽

Google Earth ◽

Engineering Properties ◽

Petrographic Analysis ◽

Landsat 8 ◽

Khyber Pakhtunkhwa ◽

Multispectral Data ◽

Sentinel 2

Despite low spatial resolutions, thermal infrared bands (TIRs) are generally more suitable for mineral mapping due to fundamental tones and high penetration in vegetated areas compared to shortwave infrared (SWIR) bands. However, the weak overtone combinations of SWIR bands for minerals can be compensated by fusing SWIR-bearing data (Sentinel-2 and Landsat-8) with other multispectral data containing fundamental tones from TIR bands. In this paper, marble in a granitic complex in Mardan District (Khyber Pakhtunkhwa) in Pakistan is discriminated by fusing feature-oriented principal component selection (FPCS) obtained from the ASTER, Landsat-8 Operational Land Imager (OLI), Thermal Infrared Sensor (TIRS) and Sentinel-2 MSI data. Cloud computing from Google Earth Engine (GEE) was used to apply FPCS before and after the decorrelation stretching of Landsat-8, ASTER, and Sentinel-2 MSI data containing five (5) bands in the Landsat-8 OLI and TIRS and six (6) bands each in the ASTER and Sentinel-2 MSI datasets, resulting in 34 components (i.e., 2 × 17 components). A weighted linear combination of selected three components was used to map granite and marble. The samples collected during field visits and petrographic analysis confirmed the remote sensing results by revealing the region’s precise contact and extent of marble and granite rock types. The experimental results reflected the theoretical advantages of the proposed approach compared with the conventional stacking of band data for PCA-based fusion. The proposed methodology was also applied to delineate granite deposits in Karoonjhar Mountains, Nagarparker (Sindh province) and the Kotah Dome, Malakand (Khyber Pakhtunkhwa Province) in Pakistan. The paper presents a cost-effective methodology by the fusion of FPCS components for granite/marble mapping during mineral resource estimation. The importance of SWIR-bearing components in fusion represents minor minerals present in granite that could be used to model the engineering properties of the rock mass.

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Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets

Remote Sensing ◽

10.3390/rs13173525 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3525

Author(s):

Carmela Cavallo ◽

Maria Nicolina Papa ◽

Massimiliano Gargiulo ◽

Guillermo Palau-Salvador ◽

Paolo Vezza ◽

...

Keyword(s):

Land Cover ◽

Continuous Monitoring ◽

Open Water ◽

Bare Soil ◽

Landsat 8 ◽

Multispectral Data ◽

Spatially Extended ◽

Moderate Resolution ◽

Sentinel 2

Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments.

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Spectral Unmixing for Mapping a Hydrothermal Field in a Volcanic Environment Applied on ASTER, Landsat-8/OLI, and Sentinel-2 MSI Satellite Multispectral Data: The Nisyros (Greece) Case Study

Remote Sensing ◽

10.3390/rs12244180 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4180

Author(s):

Athanasia-Maria Tompolidi ◽

Olga Sykioti ◽

Konstantinos Koutroumbas ◽

Issaak Parcharidis

Keyword(s):

Elemental Sulfur ◽

Spectral Unmixing ◽

Hydrothermal Field ◽

Landsat 8 ◽

Landsat 8 Oli ◽

Pyroclastic Deposits ◽

Multispectral Data ◽

Geological Map ◽

Sentinel 2

The aim of this study was to propose a methodology that provides a detailed description of the argillic zone of a hydrothermal field, based on satellite multispectral data. More specifically, we developed a method based on spectral unmixing where hydroxyl-bearing alteration is represented by a single endmember (representing clays) and the three (nearly) non-altered primary volcanic lithologies, namely, two types of lava flows (basic and acidic compositions) and the loose materials (alluvial/beach deposits, scree, pyroclastic deposits, etc.), are represented by three endmembers. We also used one endmember representing elemental sulfur that is present in fumarolic vents hosted by active hydrothermal craters. The methodology was applied in the south part of Lakki plain inside the Nisyros volcano caldera (Greece), using Sentinel-2, Landsat-8/OLI, and ASTER satellite multispectral datasets. Specifically, it was applied separately to each one of the three datasets. The spectral unmixing results, combined with the relative geological map, provide quantitative estimations of the primary volcanic and loose material areas affected by alteration. In addition, pixels with high abundance values of hydroxyl-bearing alteration corresponded to mapped areas with strong hydrothermal alteration. The developed methodology is superior to conventional approaches (e.g., alteration spectral index) in terms of its ability to describe the overall pattern of the hydrothermal field. The most accurate results were taken when applied to ASTER or Sentinel-2 MSI data.

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Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry

Remote Sensing ◽

10.3390/rs10111687 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1687 ◽

Cited By ~ 20

Author(s):

Joan-Cristian Padró ◽

Francisco-Javier Muñoz ◽

Luis Ávila ◽

Lluís Pesquer ◽

Xavier Pons

Keyword(s):

Near Infrared ◽

Field Measurements ◽

Systematic Sampling ◽

Landsat 8 ◽

Radiometric Correction ◽

Multispectral Data ◽

Central Hypothesis ◽

Sentinel 2A ◽

Sentinel 2

The main objective of this research is to apply unmanned aerial system (UAS) data in synergy with field spectroradiometry for the accurate radiometric correction of Landsat-8 (L8) and Sentinel-2 (S2) imagery. The central hypothesis is that imagery acquired with multispectral UAS sensors that are well calibrated with highly accurate field measurements can fill in the scale gap between satellite imagery and conventional in situ measurements; this can be possible by sampling a larger area, including difficult-to-access land covers, in less time while simultaneously providing good radiometric quality. With this aim and by using near-coincident L8 and S2 imagery, we applied an upscaling workflow, whereby: (a) UAS-acquired multispectral data was empirically fitted to the reflectance of field measurements, with an extensive set of radiometric references distributed across the spectral domain; (b) drone data was resampled to satellite grids for comparison with the radiometrically corrected L8 and S2 official products (6S-LaSRC and Sen2Cor-SNAP, respectively) and the CorRad-MiraMon algorithm using pseudo-invariant areas, such as reflectance references (PIA-MiraMon), to examine their overall accuracy; (c) then, a subset of UAS data was used as reflectance references, in combination with the CorRad-MiraMon algorithm (UAS-MiraMon), to radiometrically correct the matching bands of UAS, L8, and S2; and (d) radiometrically corrected L8 and S2 scenes obtained with UAS-MiraMon were intercompared (intersensor coherence). In the first upscaling step, the results showed a good correlation between the field spectroradiometric measurements and the drone data in all evaluated bands (R2 > 0.946). In the second upscaling step, drone data indicated good agreement (estimated from root mean square error, RMSE) with the satellite official products in visible (VIS) bands (RMSEVIS < 2.484%), but yielded poor results in the near-infrared (NIR) band (RMSENIR > 6.688% was not very good due to spectral sensor response differences). In the third step, UAS-MiraMon indicated better agreement (RMSEVIS < 2.018%) than the other satellite radiometric correction methods in visible bands (6S-LaSRC (RMSE < 2.680%), Sen2Cor-SNAP (RMSE < 2.192%), and PIA-MiraMon (RMSE < 3.130%), but did not achieve sufficient results in the NIR band (RMSENIR < 7.530%); this also occurred with all other methods. In the intercomparison step, the UAS-MiraMon method achieved an excellent intersensor (L8-S2) coherence (RMSEVIS < 1%). The UAS-sampled area involved 51 L8 (30 m) pixels, 143 S2 (20 m) pixels, and 517 S2 (10 m) pixels. The drone time needed to cover this area was only 10 min, including areas that were difficult to access. The systematic sampling of the study area was achieved with a pixel size of 6 cm, and the raster nature of the sampling allowed for an easy but rigorous resampling of UAS data to the different satellite grids. These advances improve human capacities for conventional field spectroradiometry samplings. However, our study also shows that field spectroradiometry is the backbone that supports the full upscaling workflow. In conclusion, the synergy between field spectroradiometry, UAS sensors, and Landsat-like satellite data can be a useful tool for accurate radiometric corrections used in local environmental studies or the monitoring of protected areas around the world.

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