scholarly journals EXTRACTION OF BUILT-UP AREA USING HIGH RESOLUTION SENTINEL-2A AND GOOGLE SATELLITE IMAGERY

2018 ◽  
Vol XLII-4/W9 ◽  
pp. 165-169 ◽  
Author(s):  
S. Vigneshwaran ◽  
S. Vasantha Kumar
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Accurate Information ◽  
Open Source Data ◽  
Kappa Value ◽  
Moderate Resolution ◽  
Sentinel 2A ◽  
Index Value ◽  
Normalized Difference Index

<p><strong>Abstract.</strong> Accurate information about the built-up area in a city or town is essential for urban planners for proper planning of urban infrastructure facilities and other basic amenities. The normalized difference indices available in literature for built-up area extraction are mostly based on moderate resolution images such as Landsat Thematic Mapper (TM) and enhanced TM (ETM+) and may not be applicable for high resolution images such as Sentinel-2A. In the present study, an attempt has been made to extract the built-up area from Sentinel-2A satellite data of Chennai, India using normalized difference index (NDI) with different band combinations. It was found that the built-up area was clearly distinguishable when the index value ranges between &amp;minus;0.29 and &amp;minus;0.09 in blue and near-infrared (NIR) band combination. Post extraction editing using Google satellite imagery was also attempted to improve the extraction results. The results showed an overall accuracy of 90% and Kappa value of 0.785. Same approach when applied for another area also yields good results with overall accuracy of 92% and Kappa value of 0.83. As the proposed approach is simple to understand, yields accurate results and requires only open source data, the same can be used for extracting the built-up area using Sentinel-2A and Google satellite imagery.</p>

A Council Circle at Etzanoa? Multi-sensor Drone Survey at an Ancestral Wichita Settlement in Southeastern Kansas

2020 ◽  
Vol 85 (4) ◽  
pp. 761-780
Author(s):  
Jesse Casana ◽  
Elise Jakoby Laugier ◽  
Austin Chad Hill ◽  
Donald Blakeslee
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Great Plains ◽  
Large Scale ◽  
Near Infrared ◽  
Archaeological Site ◽  
Lidar Data ◽  
Historical Evidence ◽  
Population Center ◽  
Archaeological Landscape

This article presents results of a multi-sensor drone survey at an ancestral Wichita archaeological site in southeastern Kansas, originally recorded in the 1930s and believed by some scholars to be the location of historical “Etzanoa,” a major settlement reportedly encountered by Spanish conquistador Juan de Oñate in 1601. We used high-resolution, drone-acquired thermal and multispectral (color and near-infrared) imagery, alongside publicly available lidar data and satellite imagery, to prospect for archaeological features across a relatively undisturbed 18 ha area of the site. Results reveal a feature that is best interpreted as the remains of a large, circular earthwork, similar to so-called council circles documented at five other contemporary sites of the Great Bend aspect cultural assemblage. We also located several features that may be remains of house basins, the size and configuration of which conform with historical evidence. These findings point to major investment in the construction of large-scale ritual, elite, or defensive structures, lending support to the interpretation of the cluster of Great Bend aspect sites in the lower Walnut River as a single, sprawling population center, as well as demonstrating the potential for thermal and multispectral surveys to reveal archaeological landscape features in the Great Plains and beyond.

scholarly journals Assessment and prediction of natural hazards from satellite imagery

2007 ◽  
Vol 31 (5) ◽  
pp. 459-470 ◽  
Author(s):  
Thomas W. Gillespie ◽  
Jasmine Chu ◽  
Elizabeth Frankenberg ◽  
Duncan Thomas
Keyword(s):  
High Resolution ◽  
Natural Hazards ◽  
Satellite Imagery ◽  
Flood Damage ◽  
Time Data ◽  
Active Sensors ◽  
Pixel Resolution ◽  
Spatial And Temporal Scales ◽  
Moderate Resolution ◽  
Passive Sensors

Since 2000, there have been a number of spaceborne satellites that have changed the way we assess and predict natural hazards. These satellites are able to quantify physical geographic phenomena associated with the movements of the earth's surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires). Most of these satellites contain active or passive sensors that can be utilized by the scientific community for the remote sensing of natural hazards over a number of spatial and temporal scales. The most useful satellite imagery for the assessment of earthquake damage comes from high-resolution (0.6 m to 1 m pixel size) passive sensors and moderate resolution active sensors that can quantify the vertical and horizontal movement of the earth's surface. High-resolution passive sensors have been used successfully to assess flood damage while predictive maps of flood vulnerability areas are possible based on physical variables collected from passive and active sensors. Recent moderate resolution sensors are able to provide near real-time data on fires and provide quantitative data used in fire behavior models. Limitations currently exist due to atmospheric interference, pixel resolution, and revisit times. However, a number of new microsatellites and constellations of satellites will be launched in the next five years that contain increased resolution (0.5 m to 1 m pixel resolution for active sensors) and revisit times (daily < 2.5 m resolution images from passive sensors) that will significantly improve our ability to assess and predict natural hazards from space.

scholarly journals Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump

2021 ◽  
Author(s):  
Vahid Khosravi ◽  
Faramarz Doulati Ardejani ◽  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Near Infrared ◽  
Prediction Models ◽  
Fitness Function ◽  
Reflectance Spectroscopy ◽  
Landsat 8 ◽  
Least Squares Regression ◽  
Cr Concentration ◽  
Sentinel 2A

Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible--Near Infrared--Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky–Golay First-Derivative (SG-FD) and Savitzky–Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using correlation analysis, Partial Least Squares Regression (PLSR) and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg.kg-1 and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using Ordinary Kriging (OK) technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. The findings of this study indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered as a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas of Sarcheshmeh in Iran.

scholarly journals Sharpening of Worldview-3 Satellite Images by Generating Optimal High-Spatial-Resolution Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7313
Author(s):  
Honglyun Park ◽  
Namkyung Kim ◽  
Sangwook Park ◽  
Jaewan Choi
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Near Infrared ◽  
Spectral Distortion ◽  
Swir Band ◽  
Sentinel 2A ◽  
Types Of Information ◽  
Shortwave Infrared ◽  
Distortion Reduction

Compared to using images in the visible and near-infrared (VNIR) wavelength range only, remotely sensed satellite imagery from the spectral wavelengths of both VNIR and shortwave infrared (SWIR), such as Sentinel-2A and Worldview-3, is more effective for analyzing various types of information for tasks such as land cover mapping, environmental monitoring and land use change detection. In this manuscript, a new sharpening technique to enhance the spatial resolution of Worldview-3 satellite imagery with various spatial and spectral resolutions is proposed. Selected and synthesized band schemes were used to produce optimal panchromatic images; then, sharpened images were generated by applying the Gram-Schmidt adaptive (GSA) and Gram-Schmidt 2 (GS2) techniques, which are component substitution (CS)- and multiresolution analysis (MRA)-based algorithms, respectively. In addition, to minimize the spectral distortion of the initial sharpened image, a postprocessing methodology for spectral distortion reduction was developed. Qualitative and quantitative evaluation of the sharpened images showed that the pansharpening performance using the GS2 technique based on the selected band scheme and spectral distortion reduction was the best. To confirm the usability of the SWIR band, supervised classification based on machine learning was performed on the pansharpened images obtained by applying the technique proposed in this study and on the pansharpened images obtained by the VNIR bands only. The classification accuracy of the results using SWIR bands was higher than that of VNIR bands only. In particular, it was confirmed that the accuracy of the classification of artificial facilities known to be effective for SWIR bands was greatly improved.

scholarly journals Feasibility of Cloud Screening Using Proxy Photon Pathlength Distributions Derived from High-Resolution Spectra in the Near Infrared

2010 ◽  
Vol 27 (1) ◽  
pp. 135-146 ◽  
Author(s):  
D. M. O’Brien ◽  
Igor Polonsky ◽  
Philip Stephens ◽  
Thomas E. Taylor
Keyword(s):  
High Resolution ◽  
Rayleigh Scattering ◽  
Near Infrared ◽  
Distribution Functions ◽  
High Resolution Spectrum ◽  
Direct Path ◽  
Clear Sky ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Clear Cases

Abstract High-resolution spectra of reflected sunlight in the 2-μm absorption band of CO2 are simulated at the top of the atmosphere using cloud profiles and particle sizes from CloudSat analyzed meteorology from ECMWF, surface bidirectional distribution functions over land derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), and a facet model of ocean reflectance. It is argued that in clear sky the photons will follow the direct path from sun to surface to satellite, because Rayleigh scattering is negligible at 2 μm, so the distribution of photon pathlengths will be a δ function. A proxy for the photon pathlength distribution under any sky condition is recovered from the high-resolution spectrum by representing the distribution as a weighted sum of δ functions. Scenes are classified as clear or cloudy according to how closely the distribution approximates the ideal single δ function for the direct path. The algorithm has an efficiency of approximately 75%, meaning that 25% of the clear cases will be rejected as cloudy. For scenes that pass the clear-sky test, the probability that the prediction will be correct is typically 95%. The algorithm appears to be robust, insensitive to instrument noise and to errors in the surface pressure and profiles of temperature and water vapor. The efficiency and confidence level of the algorithm are almost unchanged for bright surfaces such as sun glint.

scholarly journals Preliminary Assessment of Turbidity and Chlorophyll Impact on Bathymetry Derived from Sentinel-2A and Sentinel-3A Satellites in South Florida

2019 ◽  
Vol 11 (6) ◽  
pp. 645 ◽  
Author(s):  
Isabel Caballero ◽  
Richard Stumpf ◽  
Andrew Meredith
Keyword(s):  
Near Infrared ◽  
Chlorophyll Concentration ◽  
South Florida ◽  
Depth Limit ◽  
Key West ◽  
Spectral Bands ◽  
Red Edge ◽  
Moderate Resolution ◽  
Sentinel 2A ◽  
The Impact

Evaluation of the impact of turbidity on satellite-derived bathymetry (SDB) is a crucial step for selecting optimal scenes and for addressing the limitations of SDB. This study examines the relatively high-resolution MultiSpectral instrument (MSI) onboard Sentinel-2A (10–20–60 m) and the moderate-resolution Ocean and Land Color instrument (OLCI) onboard Sentinel-3A (300 m) for generating bathymetric maps through a conventional ratio transform model in environments with some turbidity in South Florida. Both sensors incorporate additional spectral bands in the red-edge near infrared (NIR) region, allowing turbidity detection in optically shallow waters. The ratio model only requires two calibration parameters for vertical referencing using available chart data, whereas independent lidar surveys are used for validation and error analysis. The MSI retrieves bathymetry at 10 m with errors of 0.58 m at depths ranging between 0–18 m (limit of lidar survey) in West Palm Beach and of 0.22 m at depths ranging between 0–5 m in Key West, in conditions with low turbidity. In addition, this research presents an assessment of the SDB depth limit caused by turbidity as determined with the reflectance of the red-edge bands at 709 nm (OLCI) and 704 nm (MSI) and a standard ocean color chlorophyll concentration. OLCI and MSI results are comparable, indicating the potential of the two optical missions as interchangeable sensors that can help determine the selection of the optimal scenes for SDB mapping. OLCI can provide temporal data to identify water quality characteristics and general SDB patterns. The relationship of turbidity with depth detection may help to enhance the operational use of SDB over environments with varying water transparency conditions, particularly in remote and inaccessible regions of the world.

scholarly journals The Massive M31 Cluster G1: Detailed Chemical Abundances from Integrated Light Spectroscopy*

2021 ◽  
Author(s):  
Charli M Sakari ◽  
Matthew D Shetrone ◽  
Andrew McWilliam ◽  
George Wallerstein
Keyword(s):  
High Resolution ◽  
Near Infrared ◽  
Chemical Properties ◽  
Star Clusters ◽  
High Resolution Spectrum ◽  
Subsequent Paper ◽  
Chemical Abundances ◽  
Moderate Resolution ◽  
Astrophysical Research ◽  
Detailed Chemical

Abstract G1, also known as Mayall II, is one of the most massive star clusters in M31. Its mass, ellipticity, and location in the outer halo make it a compelling candidate for a former nuclear star cluster. This paper presents an integrated light abundance analysis of G1, based on a moderately high-resolution (R = 15, 000) spectrum obtained with the High Resolution Spectrograph on the Hobby-Eberly Telescope in 2007 and 2008. To independently determine the metallicity, a moderate resolution (R ∼ 4, 000) spectrum of the calcium-II triplet lines in the near-infrared was also obtained with the Astrophysical Research Consortium’s 3.5-m telescope at Apache Point Observatory. From the high-resolution spectrum, G1 is found to be a moderately metal-poor cluster, with $[\rm {Fe/H}]~=~-0.98\pm 0.05$. G1 also shows signs of α-enhancement (based on Mg, Ca, and Ti) and lacks the s-process enhancements seen in dwarf galaxies (based on comparisons of Y, Ba, and Eu), indicating that it originated in a fairly massive galaxy. Intriguingly, G1 also exhibits signs of Na and Al enhancement, a unique signature of GCs—this suggests that G1’s formation is intimately connected with GC formation. G1’s high [Na/Fe] also extends previous trends with cluster velocity dispersion to an even higher mass regime, implying that higher mass clusters are more able to retain Na-enhanced ejecta. The effects of intracluster abundance spreads are discussed in a subsequent paper. Ultimately, G1’s chemical properties are found to resemble other M31 GCs, though it also shares some similarities with extragalactic nuclear star clusters.

scholarly journals Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump

2021 ◽  
Vol 13 (7) ◽  
pp. 1277
Author(s):  
Vahid Khosravi ◽  
Faramarz Doulati Ardejani ◽  
Asa Gholizadeh ◽  
Mohammadmehdi Saberioon
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Near Infrared ◽  
Prediction Models ◽  
Fitness Function ◽  
Reflectance Spectroscopy ◽  
Landsat 8 ◽  
Least Squares Regression ◽  
Cr Concentration ◽  
Sentinel 2A

Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples’ mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible–Near Infrared–Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky-Golay First-Derivative (SG-FD) and Savitzky-Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using Partial Least Squares Regression (PLSR) coefficients and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A, and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with R2 = 0.91, RMSE = 8.73 mg/kg and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using the ordinary kriging technique. Using satellite images, SD-SMLR provided the best prediction models with R2 values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. This study’s findings indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas.

SHALLOW WATER HABITAT MAPPING AND REEF FISH STOCK ESTIMATION USING HIGH RESOLUTION SATELLITE DATA

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Vincentius P. Siregar ◽  
Sam Wouthuyzen ◽  
Andriani Sunuddin ◽  
Ari Anggoro ◽  
Ade Ayu Mustika
Keyword(s):  
High Resolution ◽  
Reef Fish ◽  
Satellite Imagery ◽  
Field Survey ◽  
Coral Cover ◽  
Fish Density ◽  
Fish Stock ◽  
Benthic Habitat ◽  
Live Coral ◽  
Stock Estimation

Shallow marine waters comprise diverse benthic types forming habitats for reef fish community, which important for the livelihood of coastal and small island inhabitants. Satellite imagery provide synoptic map of benthic habitat and further utilized to estimate reef fish stock. The objective of this research was to estimate reef fish stock in complex coral reef of Pulau Pari, by utilizing high resolution satellite imagery of the WorldView-2 in combination with field data such as visual census of reef fish. Field survey was conducted between May-August 2013 with 160 sampling points representing four sites (north, south, west, and east). The image was analy-zed and grouped into five classes of benthic habitats i.e., live coral (LC), dead coral (DC), sand (Sa), seagrass (Sg), and mix (Mx) (combination seagrass+coral and seagrass+sand). The overall accuracy of benthic habitat map was 78%. Field survey revealed that the highest live coral cover (58%) was found at the north site with fish density 3.69 and 1.50 ind/m2at 3 and 10 m depth, respectively. Meanwhile, the lowest live coral cover (18%) was found at the south site with fish density 2.79 and 2.18  ind/m2 at 3 and 10 m depth, respectively. Interpolation on fish density data in each habitat class resulted in standing stock reef fish estimation:  LC (5,340,698 ind), DC (56,254,356 ind), Sa (13,370,154 ind), Sg (1,776,195 ind) and Mx (14,557,680 ind). Keywords: mapping, satellite imagery, benthic habitat, reef fish, stock estimation

Sign in / Sign up

Export Citation Format

Share Document