scholarly journals Comparison of Multi-Resolution Optical Landsat-8, Sentinel-2 and Radar Sentinel-1 Data for Automatic Lineament Extraction: A Case Study of Alichur Area, SE Pamir

2019 ◽  
Vol 11 (7) ◽  
pp. 778 ◽  
Author(s):  
Aminov Javhar ◽  
Xi Chen ◽  
Anming Bao ◽  
Aminov Jamshed ◽  
Mamadjanov Yunus ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Accuracy Assessment ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Optical Data ◽  
Landsat 8 ◽  
Geological Investigation ◽  
Lineament Extraction ◽  
Automatic Lineament Extraction ◽  
Sentinel 2A

Lineament mapping, which is an important part of any structural geological investigation, is made more efficient and easier by the availability of optical as well as radar remote sensing data, such as Landsat and Sentinel with medium and high spatial resolutions. However, the results from these multi-resolution data vary due to their difference in spatial resolution and sensitivity to soil occupation. The accuracy and quality of extracted lineaments depend strongly on the spatial resolution of the imagery. Therefore, the aim of this study was to compare the optical Landsat-8, Sentinel-2A, and radar Sentinel-1A satellite data for automatic lineament extraction. The framework of automatic approach includes defining the optimal parameters for automatic lineament extraction with a combination of edge detection and line-linking algorithms and determining suitable bands from optical data suited for lineament mapping in the study area. For the result validation, the extracted lineaments are compared against the manually obtained lineaments through the application of directional filtering and edge enhancement as well as to the lineaments digitized from the existing geological maps of the study area. In addition, a digital elevation model (DEM) has been utilized for an accuracy assessment followed by the field verification. The obtained results show that the best correlation between automatically extracted lineaments, manual interpretation, and the preexisting lineament map is achieved from the radar Sentinel-1A images. The tests indicate that the radar data used in this study, with 5872 and 5865 lineaments extracted from VH and VV polarizations respectively, is more efficient for structural lineament mapping than the Landsat-8 and Sentinel-2A optical imagery, from which 2338 and 4745 lineaments were extracted respectively.

scholarly journals POSSIBILITIES OF THE JOINT USE OF OPTICAL AND RADAR DATA IN FLOOD SPACE MONITORING

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 67-73
Author(s):  
O. P. Arkhipkin ◽  
G. N. Sagatdinova
Keyword(s):  
Spatial Resolution ◽  
Complex Analysis ◽  
Remote Sensing Data ◽  
Weather Conditions ◽  
High Water ◽  
Radar Data ◽  
Time Of Day ◽  
Optical Data ◽  
Additional Information ◽  
Good Spatial Resolution

<p><strong>Abstract.</strong> The article gives a brief description of the system of space monitoring of high water and floods. Its main tasks are the operational dynamics of snow and ice cover melting and the passage of flood waters. The solution of these tasks is carried out in three levels corresponding to the low, medium and high resolution of remote sensing data. An important role in monitoring is given to radar data. This is due to the features of the radar survey: independence from weather conditions and time of day, regularity, good spatial resolution, the possibility of using polarimetric properties (including phase information). The use of radar data also provides additional information, including the allocation of wet soils, flooded vegetation and infrastructure. The presence of large time periods of repeated survey, interference (cloudiness, haze, noise, etc.), different spatial resolution necessitates a complex analysis of optical and radar data in flood space monitoring. Such analysis makes it possible to better observe the flood dynamics, more precisely identify of flooding zones and determine their structure. Features of radar survey (transparency of dry snow and change of reflected signal during snowmelt) allow using them to determine the beginning of snow melt and determine the degree of water content in it. Optical data are also used to determine the area and structure of the snow cover. Method of detecting the beginning of the snowmelt period consists in the comparison of the current radar image with a base image created as an average image from the winter images with dry snow.</p>

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 Fuzzy machine learning approach for transitioned building footprints extraction using dual-sensor temporal data

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Narayan Shankar Hamde ◽  
Anil Kumar ◽  
Sandeep Maithani
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Data Sets ◽  
Fuzzy Approach ◽  
Dual Sensor ◽  
Sentinel 2A ◽  
Temporal Indices

AbstractThis study presents a fuzzy approach, for detection of transitioned building footprints in urban area using medium resolution datasets. Multi-temporal remote sensing data sets from Landsat-8 Operational Land Imager and Sentinel-2A were used for generation of temporal indices database. The database was generated using class-based sensor independent-normalized difference vegetation index approach, with an aim to reduce spectral dimensionality of each image and maintain temporal dimensionality. The temporal indices database was subsequently used as input in Modified Possibilistic c-means classifier for transitioned building footprints extraction. The identified transitioned building locations were validated using ground samples as well as from Google images at four different test sites. For accuracy assessment, F-measure was calculated and its value was 0.75 or higher for all training and testing sites. Thus, using proposed fuzzy approach, transitioned building footprints were accurately identified compared to traditional techniques.

scholarly journals A Meta-Methodology for Improving Land Cover and Land Use Classification with SAR Imagery

2020 ◽  
Vol 12 (6) ◽  
pp. 961 ◽  
Author(s):  
Marinalva Dias Soares ◽  
Luciano Vieira Dutra ◽  
Gilson Alexandre Ostwald Pedro da Costa ◽  
Raul Queiroz Feitosa ◽  
Rogério Galante Negri ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Data Classification ◽  
Test Site ◽  
Radar Data ◽  
Input Image ◽  
Optical Data ◽  
Land Use Classification ◽  
Object Based ◽  
Sar Imagery ◽  
Small Change

Per-point classification is a traditional method for remote sensing data classification, and for radar data in particular. Compared with optical data, the discriminative power of radar data is quite limited, for most applications. A way of trying to overcome these difficulties is to use Region-Based Classification (RBC), also referred to as Geographical Object-Based Image Analysis (GEOBIA). RBC methods first aggregate pixels into homogeneous objects, or regions, using a segmentation procedure. Moreover, segmentation is known to be an ill-conditioned problem because it admits multiple solutions, and a small change in the input image, or segmentation parameters, may lead to significant changes in the image partitioning. In this context, this paper proposes and evaluates novel approaches for SAR data classification, which rely on specialized segmentations, and on the combination of partial maps produced by classification ensembles. Such approaches comprise a meta-methodology, in the sense that they are independent from segmentation and classification algorithms, and optimization procedures. Results are shown that improve the classification accuracy from Kappa = 0.4 (baseline method) to a Kappa = 0.77 with the presented method. Another test site presented an improvement from Kappa = 0.36 to a maximum of 0.66 also with radar data.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping

2019 ◽  
Vol 11 (22) ◽  
pp. 2603
Author(s):  
George Xian ◽  
Hua Shi ◽  
Cody Anderson ◽  
Zhuoting Wu
Keyword(s):  
Spatial Resolution ◽  
Signal To Noise Ratio ◽  
Spatial Scales ◽  
Regression Tree ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Landsat 8 ◽  
Signal To Noise ◽  
Study Results ◽  
The Impact

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.

Application of optical and radar data for lineaments mapping in Kerdous inlier of the Anti Atlas belt, Morocco

2021 ◽  
Author(s):  
Amine Jellouli ◽  
Abderrazak El Harti ◽  
Zakaria Adiri ◽  
Mohcine Chakouri ◽  
Jaouad El Hachimi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Ore Deposits ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Alos Palsar ◽  
Geological Map ◽  
Color Composite ◽  
Manual Extraction

&lt;p&gt;Lineament mapping is an important step for lithological and hydrothermal alterations mapping. It is considered as an efficient research task which can be a part of structural investigation and mineral ore deposits identification. The availability of optical as well as radar remote sensing data, such as Landsat 8 OLI, Terra ASTER and ALOS PALSAR data, allows lineaments mapping at regional and national scale. The accuracy of the obtained results depends strongly on the spatial and spectral resolution of the data. The aim of this study was to compare Landsat 8 OLI, Terra ASTER, and radar ALOS PALSAR satellite data for automatic and manual lineaments extraction. The module Line of PCI Geomatica software was applied on PC1 OLI, PC3 ASTER and HH and HV polarization images to automatically extract geological lineaments. However, the manual extraction was achieved using the RGB color composite of the directional filtered images N - S (0&amp;#176;), NE - SW (45&amp;#176;) and E - W (90&amp;#176;) of the OLI panchromatic band 8. The obtained lineaments from automatic and manual extraction were compared against the faults and photo-geological lineaments digitized from the existing geological map of the study area. The extracted lineaments from PC1 OLI and ALOS PALSAR polarizations images showed the best correlation with faults and photo-geological lineaments. The results indicate that the lineaments extracted from HH and HV polarizations of ALOS PALSAR radar data used in this study, with 1499 and 1507 extracted lineaments, were more efficient for structural lineament mapping, as well as the PC1 OLI image with 1057 lineaments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt; Remote Sensing . OLI. ALOS PALSAR . ASTER . Kerdous Inlier . Anti Atlas&lt;/p&gt;

scholarly journals Automatic Lineaments Extraction using the Line Algorithm in the Denguélé District (North West of Ivory Coast)

2021 ◽  
pp. 45-58
Keyword(s):  
Principal Component ◽  
Input Image ◽  
Contour Length ◽  
Landsat 8 ◽  
Difference Threshold ◽  
North West ◽  
Extraction Algorithm ◽  
Cumulative Length ◽  
Lineament Extraction ◽  
Automatic Lineament Extraction

The aim of this work was to apply the LINE Algorithm (Segment Extraction Algorithm) on Landsat 8 images for automatic lineament extraction in the Denguélé district. The Landsat 8 images had previously been subjected to the technique of Principal Component Analysis (PCA). After that, we implemented the LINE algorithm. Indeed, the LINE algorithm uses the following six (6) parameters : RADI (Radius of the filter) for improving the quality of the input image, GTHR (Threshold of the contour gradient), LTHR (Threshold of the contour length), FTHR (Threshold of mounting error), ATHR (Angular difference threshold between two contours ) and DTHR (Distance chaining threshold to link two contours ) for lineament discrimination. Analysis of the principal components PCA 1, PCA2 and PCA3 of bands 1, 2, 3, 4, 5 and 7 of the Landsat 8 images shows that they contain respectively 79.57; 15.88 and 2.15%, this represents overall 97.6% of all channels. 3468 lineaments were extracted. The minimum and maximum lengths of the lineaments extracted are respectively 4201.08 m and 16167.59 m and their cumulative length is 18 919 517.9 m. The lineaments average lengths are 5.55 km; 5.75 km; 5.6 km and 5.40 km respectively for NE-SW, NS, E-W and NW-SE directions. The analysis of the directions of the lineaments using a rose diagram with 10 ° of frequency, shows that the dominant directions are NE-SW (31.83% of the total lineaments), EW (28.71% of the total lineaments) and NS (27.91% of the total lineaments).

scholarly journals Urban thermal micro-mapping using satellite imagery and ground-truth measurements: Kyiv city area case study

2019 ◽  
pp. 40-48
Author(s):  
Iryna Piestova ◽  
Mykola Lubskyi ◽  
Mykhailo Svideniuk ◽  
Stanislav Golubov ◽  
Oleksandr Laptiev
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Thermal Conditions ◽  
Ground Truth ◽  
Bare Soil ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
City Area ◽  
Thermal Emissivity ◽  
Sentinel 2A

The aim of this research is to enhance approaches existing for the assessment of cities thermal conditions under climate change impact by using multispectral satellite data for Kyiv city area. This paper describes the method and results of the Earth’s surface temperature (LST) and thermal emissivity calculation. Particularly, the thermal distribution was estimated based on spectral densities according to Planck’s law for “grey bodies” by using the Landsat-8 TIRS and Sentinel-2 MSI satellite imagery. Furthermore, the result was calibrated by ground data collected during the ground-truth measurements of the typical city surfaces temperature and thermal emissivity. The spatial resolution of the LST images obtained was enhanced by using the approach of subpixel processing, that is the pairs of invariant images shifted with subpixel accuracy. As a result, such an approach allowed to enhance the spatial resolution of the image up 46%, which is much higher than the potential performance of the thermal imaging sensors existing. The interrelation between the Earth’s surface type and the temperature was revealed by the results of the Sentinel-2A MSI image of 21 August 2017 supervised classification. Thus, the image was divided into the six major classes of the urban environment: building’s rooftops, roads surface, bare soil, grass, wood, and water. As a result, surfaces with vegetation much more cool next to artificial ones. The time-series analysis of 18 thermal images (Landsat TM and Landsat-8 TIRS) of Kyiv for the period from 6 Jun 1985 till 1 June 2018 was done for spatiotemporal changes investigation. Therefore, the sites of the LST thermal anomalies caused by landscape changes were developed. Among them are the sites of increased LST where thw “Olimpiyskiy” national sport center and adjacent parking was built and the site of decreased LST where the tram depot was liquidated and the territory was flooded.

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