• An in-depth statistical analysis of the rainstorms erosivity in Europe

    Gini coefficients (G) for the 1181 stations across Europe using all rainfall erosive events included in the REDES database. As a background map, the six K-means clusters as defined by Ballabio et al. (2017) are shown.

    Nejc Bezaka, Matjaž Mikoša, Pasquale Borrellibc, Leonidas Liakosd, Panos Panagosd. 2021. “An In-Depth Statistical Analysis of the Rainstorms Erosivity in Europe.” CATENA 206 (November): 105577. https://doi.org/10.1016/j.catena.2021.105577.

    a .University of Ljubljana, Faculty of Civil and Geodetic Engineering, Slovenia
    b. Department of Earth and Environmental Sciences, University of Pavia, Italy
    c. Department of Biological Environment, Kangwon National University, Chuncheon 24341, Republic of Korea
    d. European Commission, Joint Research Centre (JRC), Ispra, Italy

    Heavy rainstorms play a central role in the water-driving soil erosion processes. An in-depth knowledge about temporal and spatial erosivity of rainfall events is required to gain a better understanding of soil erosion processes and optimize soil protection measures efficiency. In this study, the spatiotemporal distribution of more than 300,000 erosive events measured at 1181 locations, part of the Rainfall Erosivity Database at European Scale (REDES) database, is studied to shed some new light on the rainfall erosivity in Europe. Rainfall erosive events are statistically investigated through the Lorenz curve and derived coefficients such as the Gini coefficient (G). Additionally, seasonal characteristics of the most and the less erosive events are compared to investigate seasonal characteristics of rainstorms across Europe. The G shows largest values of inequality of the inter-annual temporal distribution of the rainfall erosive events in the Alpine region, mostly due to the large number of rainfall events with smaller rainfall erosivity. While for other parts of Europe, the inequality described by the G is mostly due to a small number of high erosive events. The G slightly decreases from south to north while no clear regional patterns can be detected. Additionally, in Europe, on average 11% (ranging from 1 to 24%) of all erosive events contribute to form 50% of the total rainfall erosivity. Furthermore, higher erosive rainfall events tend to occur later in the year compared to less erosive events that take place earlier. To our knowledge, this study is the first one addressing event scale rainfall erosivity distribution using more than 300,000 rainfall erosivity events and covering almost a whole continent. Scientifically our findings represent a major step towards large-scale process-based erosion modelling while, practically, they provide new elements that can support national and local soil erosion monitoring programs.

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  • Further adjustment of the Defense Meteorological Satellite Program-Operational Linescan System using radar data

    Fig. 2

    Stathakis, Demetris, and Leonidas Liakos. 2019. “Further Adjustment of the Defense Meteorological Satellite Program-Operational Linescan System Using Radar Data.” Journal of Applied Remote Sensing 13 (03): 034507/1-9. https://doi.org/10.1117/1.JRS.13.034507.

    University of Thessaly, Spatial Analysis/GIS and Thematic Mapping Laboratory,
    Department of Planning and Regional Development, Sekeri & Alamanas, Volos, Greece

    In the past, the Defense Meteorological Satellite Program—Operational Linescan
    System (DSMP/OLS) data have been successfully adjusted based on the presence of vegetation
    in each pixel. We propose a further adjustment based on radar data obtained by the SeaWinds
    scatterometer. It is shown that the adjustment results in substantial accuracy gains, measured as
    increased correlation with the more accurate Visible Infrared Imaging Radiometer Suite data as
    well as with the existing instances of radiance calibrated DSMP/OLS data. The proposed adjust-
    ment is easy to implement. The improvement of the proposed method is particularly significant
    in arid regions where vegetation is relatively sparse.

  • Median Shift Lunar Correction for VIIRS

    Fig.5

    Stathakis, Demetris*, and Leonidas Liakos*. 2020. “Median Shift Lunar Correction for VIIRS.” IEEE Geoscience and Remote Sensing Letters, 1–5. https://doi.org/10.1109/LGRS.2020.3007965.

    * Planning and Development Department, University of Thessaly, 38334 Volos, Greece

    Visible Infrared Imaging Radiometer Suite (VIIRS) 24-h data are substantially affected by lunar illumination. A new method is proposed to correct this distortion and be able to form a consistent time series. The method has the advantage of being relatively simple in its deployment while at the same time effective. The results show that the proposed method removes periodical illumination noise due to the lunar circle while preserving meaningful information, in the test sites applied.

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  • Raster και Raster Stack στην R

    Η ανάρτηση αυτή περιλαμβάνει το περιεχόμενο από το workshop στο FOSSCOMM 2019 στην Λαμία. Για περισσότερες λεπτομέρειες ανατρέξτε στο github repository.

    Στόχος του εργαστηρίου είναι η εξοικείωση του χρήστη με το πακέτο raster της R το οποίο προσφέρει την δυνατότητα ανάγνωσης ψηφιδωτών δεδομένων (raster) και επεξεργασίας τους (cropreclassifyreprojectresample κτλ.).

    Επιπλέον, θα επικεντρωθούμε στην κλάση raster stack η οποία δημιουργεί συστοιχίες ψηφιδωτών δεδομένων, κατάλληλες για χρονοσειρές και πολυκαναλικές εικόνες.

    Η διεξαγωγή του εργαστηρίου θα γίνει μέσω παραδειγμάτων και με την χρήση δεδομένων νυκτερινών φώτων DMSP-OLS Nighttime Lights Time Series (Stable Lights Version 4). Θα προηγηθεί μια σύντομη παρουσίαση των βημάτων και της διαδικασίας ώστε οι χρήστες να αποκτήσουν μια σύντομη αλλά περιεκτική εικόνα του στόχου του εργαστηρίου και των δυνατοτητων που προσφέρει ο προγραμματισμός με την R.

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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported
This work by Leonidas Liakos is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported.