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    Wyświetlanie 1-6 z 6
    Tytuł:
    Mapping and Assessment of Geological Lineaments with the Contribution of Earth Observation Data: A Case Study of the Zaer Granite Massif, Western Moroccan Meseta
    Autorzy:
    Zoraa, Noura
    Raji, Mohammed
    El Hadi, Hassan
    Maimouni, Soufiane
    Mhamdi, Hicham
    Reddad, Aicha
    Zahour, Ghalem
    Ait-Yazza, Achraf
    Powiązania:
    https://bibliotekanauki.pl/articles/27314299.pdf
    Data publikacji:
    2023
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    lineament extraction
    Landsat 8
    ASTER
    Sentinel
    Zaer
    Opis:
    The Zaer granitic massif is one of the most important Variscan granitoids in the Central Zone of the Western Moroccan Meseta. It is characterized by a deformation which is manifested by a network of fractures of different scales. Thanks to the technology currently available, many geological studies rely heavily on the mapping of geological lineaments, especially in structural geology. This has become more reliable with access to earth observation data using optical and radar sensors as well as the various remote sensing techniques. Therefore, the objective of this work is to determine the potential of Landsat 8, ASTER, Sentinel 2 and radar Sentinel 1 datasets using the automatic method to extract lineaments. Furthermore, this work focuses on quantitative lineament analysis to determine lineament trends and subsequently compare them with global and regional tectonic movement trends. The lineaments obtained through different satellite images were validated by including the shaded relief maps, the slope map, the correlation with the pre-existing faults in the geological maps as well as the field investigation. Comparison of these results indicates that Sentinel 1 imagery provides a better correlation between automated extraction lineaments and major fault zones. Thus, Sentinel 1 data is more effective in mapping geological lineaments. The final lineament map obtained from the VH and VV polarizations shows two major fault systems, mainly oriented NE-SW and NW-SE to NNW-SSE.
    Źródło:
    Geomatics and Environmental Engineering; 2023, 17, 5; 107--144
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    The Application of Remote Sensing Techniques and Spectral Analyzes to Assess the Content of Heavy Metals in Soil – A Case Study of Barania Góra Reserve, Poland
    Autorzy:
    Sobura, Szymon
    Widłak, Małgorzata
    Hejmanowska, Beata
    Muszyńska, Joanna
    Powiązania:
    https://bibliotekanauki.pl/articles/2174645.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    remote sensing
    heavy metals
    Sentinel-2
    soil
    spectral indices
    Opis:
    The understanding of the spatial and temporal dynamics of farmland processes is essential to ensure the proper crop monitoring and early decision making needed to support efficient resource management in agriculture. By creating appropriate crop management strategies, one can increase harvest efficiency while reducing costs, waste, chemical spraying, and inhibiting the impact of biotic and abiotic factors on crop stress. Only reliable spatial information makes it possible to comprehend the influence of various factors on the environment. The main objective of the research presented in the paper was to assess the possibility of using maps of vegetation and soil indices, such as NDVI, SAVI, IRECI, CIred-edge, PSRI and HMSSI, calculated on the basis of images from the Sentinel-2 satellite, to qualitatively determine the increased amount of heavy metals in the soil in the areas of small agricultural plots around the Barania Góra nature reserve in Poland. The conducted pilot project shows that the spectral indices: NDVI, SAVI, IRECI, CIred-edge, PSRI, and HMSSI, calculated on the basis of images from Sentinel-2, have the potential to assess the content of nickel zinc, chromium and cobalt in the soil on agricultural plots. However, the confirmation of the obtained results requires continuation of the research.
    Źródło:
    Geomatics and Environmental Engineering; 2022, 16, 4; 187--213
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Forest Community Mapping Using Hyperspectral (CHRIS/PROBA) and Sentinel-2 Multispectral Images
    Autorzy:
    Głowienka, Ewa
    Zembol, Nicole
    Powiązania:
    https://bibliotekanauki.pl/articles/2174650.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    hyperspectral
    pre-processing
    multispectral
    Sentinel-2
    CHRIS/PROBA
    machine learning
    Opis:
    The possibility to use hyperspectral images (CHRIS/PROBA) and multispectral images (Sentinel-2) in the classification of forest communities is assessed in this article. The pre-processing of CHRIS/PROBA image included: noise reduction, radiometric correction, atmospheric correction, geometric correction. Due to MNF transformation the number of the hyperspectral image channels was reduced (to 10 channels) and smiling errors were removed. Sentinel-2 image (level 2A) did not require pre-processing. Three tree genera occurring in the study area were selected for the classification: pine (Pinus), alder (Alnus) and birch (Betula). Image classification was carried out with three methods: SAM (Spectral Angle Mapper ), MTMF (Mixture Tuned Matched Filtering), SVM (Support Vector Machine). For the CHRIS/PROBA image, the algorithm SVM turned out to be the best. Its overall accuracy (OA) was 72%. The poorest result (OA = 52%) was for the MTMF classifier. In the classification of Sentinel-2 multispectral image the best result was for the MTMF method: OA = 82%, kappa coefficient 0.7. For other methods, the overall accuracy exceeded 65%. Among the classified genera, the highest producer’s accuracy was obtained for pine (PA = 96%), and the broad-leaf genera: alder and birch had PA ranging from 42% to 85%.
    Źródło:
    Geomatics and Environmental Engineering; 2022, 16, 4; 103--117
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Monitoring of Water Bodies and Non-vegetated Areas in Selenica ‑ Albania with Sar and Optical Images
    Autorzy:
    Belba, Pietro
    Kucaj, Spartak
    Thanas, Jorgaq
    Powiązania:
    https://bibliotekanauki.pl/articles/2105519.pdf
    Data publikacji:
    2022
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    SAR image
    optical image
    collocated image
    water bodies
    NDVI
    Sentinel images
    Opis:
    The availability of Sentinel satellites for providing open data with optical and SAR imagery leads to better opportunities related to Earth surface mapping and monitoring. Recently, optical fusion with radar data has shown improvement in classification quality and the accuracy of information acquired. In this setting, the main objective of this research is to monitor the environmental impact of an open-pit mine on water, vegetation, and non-vegetation areas by exploring the single and combined use of Sentinel-1 and Sentinel-2 data. The data utilized in this paper were collected from the European Space Agency Copernicus program. After selecting the Selenica region, we explored the products in the Sentinel Application Platform. According to our data, Sentinel-2 misses the small water ponds but successfully identifies the river and open-pit areas. It mistakenly identifies urban structures and cloud areas as non-vegetated and does not identify non-vegetated areas which correspond to mining operation areas. Sentinel-1 identifies very small water ponds and delivers additional information in the cloudy areas, but misses a part of the river. Alongside the strong contribution in identifying the vegetation, it also roughly identifies the non-vegetation areas of mining operations.
    Źródło:
    Geomatics and Environmental Engineering; 2022, 16, 3; 5--25
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Mapping of Surface Deformation and Displacement Associated with the 6.5 Magnitude Botswana Earthquake of 3 April 2017 Using DInSAR Analysis
    Autorzy:
    Thomas, Abraham
    Powiązania:
    https://bibliotekanauki.pl/articles/1837989.pdf
    Data publikacji:
    2020
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    differential interferometric synthetic aperture radar (DInSAR)
    interferometry
    Sentinel-1
    Botswana earthquake
    Opis:
    The DInSAR analysis was performed for mapping surface deformation and displacement associated with the 6.5 magnitude Botswana earthquake of 3 April 2017 using Sentinel 1 data and SNAP. The analyses involved: coregistration of SAR images, interferogram formation, debursting, merging of sub swaths, topographic phase removal, phase filtering, phase unwrapping, orthorectification and calculation of vertical displacement for two situations (unmasked and masked with a layer of coherence ≥0.6). The vertical displacement for the unmasked situation ranged from −122 mm to +136 mm whereas in the masked layer it ranged from −84 mm to +122 mm. Negative surface deformation (subsidence) is seen in the epicentre region and eastern, north eastern, northern areas of the image whereas major positive surface deformations (uplift) are seen in the south western, western and north western corner part. Comparison of displacements with geology revealed that major deformation occurred in the Karoo basalts and lesser surface deformation has occurred in the Lebung Group rocks of the northern, NE and SW region. The elongated shape of deformation near the epicentre and positive vertical displacement seen towards the SW of the epicentre and negative vertical displacement seen towards NE of the epicentre reveals that the region has undergone uplifting and subsidence on either side of the area close to the epicentre (similar to faulting in a NW or SE direction). The boundaries of the uplift and subsidence regions inferred as long lineaments were digitised as faults. Comparison of the deformation with existing seismotectonic map revealed the existence of some north westerly faults seen in the region.
    Źródło:
    Geomatics and Environmental Engineering; 2020, 14, 4; 81-100
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq
    Autorzy:
    Alwan, Imzahim A.
    Aziz, Nadia A.
    Powiązania:
    https://bibliotekanauki.pl/articles/1838006.pdf
    Data publikacji:
    2021
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    land cover mapping
    Sentinel 2
    supervised classification
    maximum likelihood
    Support Vector Machine (SVM)
    confusion matrix
    Opis:
    Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al Hawizeh marsh/Iraq Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.
    Źródło:
    Geomatics and Environmental Engineering; 2021, 15, 1; 5-21
    1898-1135
    Pojawia się w:
    Geomatics and Environmental Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-6 z 6

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