Exploring structured scripting cartographic technique of GMT for ocean seafloor modeling: A case of the East Indian Ocean

Lemenkova, Polina

doi:10.33175/mtr.2021.248158

Cited by 3 publications

(3 citation statements)

References 66 publications

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“…Spatial distribution modeling of the earthquakes in Jordan (Figure 8) has been performed by the GMT module 'psxy' with the database of IRIS for the period 1974-2020 as the event data with magnitude and depth of the foci, to analyze the epicenters of the earthquakes. The data visualization has been performed by following the code: 'gmt psxy -R -J quakes_Jordan.gmt -Wfaint -i4,3,6,6s0.1 -h3 -Scc -Csteps.cpt -O -K >> $ps' where the visualized points show the magnitude of the events using the available method (Lemenkova, 2021).…”

Section: Gmt Scripting / Gmt Skriptementioning

confidence: 99%

Big Earth Data Processing Using Machine Learning for Integrated Mapping of the Dead Sea Fault, Jordan

Lemenkova

2022

GSFUBL

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In this research, an integrated framework on the big Earth data analysis has been developed in the context of the geomorphology of Jordan. The research explores the correlation between several thematic datasets, including machine learning and multidisciplinary geospatial data. GIS mapping is widely used in geological mapping as the most adequate technical tool for data visualization and analysis. GIS applications encourage geological prospective modeling by visualizing data aimed at the prognosis of mineral resources. However, automatization using machine learning for big Earth data processing provides the speed and accurate processing of multisource massive datasets. This is enabled by the application of scripting and programming in cartographic techniques. This study presents the combined machine learning methods of cartographic analysis and big Earth data modeling. The objective is to analyze a correlation between the factors affecting the geomorphological shape of Jordan with respects to the Dead Sea Fault and geological evolution. The technical methodology includes the following three independent tools: 1) Generic Mapping Tools (GMT); 2) Selected libraries of R programming language; 3) QGIS. Specifically, the GMT scripting program was used for topographic, seismic and geophysical mapping, while QGIS was used for geologic mapping and R language for geomorphometric modeling. Accordingly, the workflow is logically structured through these three technical tools, representing different cartographic approaches for data processing. Data and materials include multisource datasets of the various resolution, spatial extent, origin and formats. The results presented cartographic layouts of qualitative and quantitative maps with statistical summaries (histograms). The novelty of this approach is explained by the need to close a technical gap between the traditional GIS and scripting mapping, which is wider for big data mapping and where the crucial factors are speed and precision of data handling, as well as effective visualization achieved by the machine graphics. The paper analyzes the underlying geologic processes affecting the formation of geomorphological landforms in Jordan with a 3D visualization of the selected fragment of the Dead Sea Fault zone. The research presents an extended description in methodology, including the explanations of code snippets from the GMT modules and examples of the use of R libraries ‘raster’ and ‘tmap’. The results revealed strong correlation between the geological and geophysical settings which affect geomorphological patterns. Integrated study of the geomorphology of Jordan was based on multisource datasets processed by scripting. A thorough analysis presented regional correlations between the geomorphological, geological and tectonic settings in Jordan. The paper contributed both to the development of cartographic engineering by introducing scripting techniques and to the regional studies of Jordan including the Dead Sea Fault as a special region of Jordan. The results include 12 new thematic maps including a 3D model.

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Section: Gmt Scripting / Gmt Skriptementioning

confidence: 99%

Big Earth Data Processing Using Machine Learning for Integrated Mapping of the Dead Sea Fault, Jordan

Lemenkova

2022

GSFUBL

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“…The selected files in netCDF format have been produced for each year presenting a variable combination of the monthly data in the TerraClimate dataset. The topographic map in Figure 1 has been plotted using GEBCO grid of the Earth's relief (Schenke, 2016), largely used in geosciences and topographic mapping (Lemenkova, 2020d. This study is based on the methodology of GMT developed by Wessel et al (2019) with case applications documented in relevant studies (Gauger et al 2007;Lemenkova, 2020aLemenkova, , 2020bLemenkova, , 2021a.…”

Section: Datamentioning

confidence: 99%

Mapping environmental and climate variations by GMT: A case of Zambia, Central Africa

Lemenkova¹

2021

Zemljište i biljka

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Zambia recently experienced several environmental threats from climate change such as droughts, temperature rise and occasional flooding and they all affect agricultural sustainability and people wellbeing through negative effects on plants and growing crops. This paper is aimed at showing variations in several climate and environmental parameters in Zambia showing spatial variability and trends in different regions of Zambia's key environmental areas (Zambezi River and tributaries), Livingstone near the Victoria Falls and central region with Muchinga Mountains. A series of 10 maps was plotted using data from TerraClimate dataset: precipitation, soil moisture, Palmer Drought Severity Index (PDSI), downward surface shortwave radiation, vapor pressure deficit and anomalies, potential and actual evapotranspiration and wind speed with relation to the topographic distribution of elevations in Zambia plotted using GEBCO/SRTM data. The data range of the PDSI according to the index values ranged from minimum at -5.7 to the maximum at 16.6 and mean at 7.169, with standard deviation at 4.278. The PDSI is effective in quantifying drought in long-term period. Because PDSI index applies temperature data and water balance model, it indicates the effect of climate warming on drought by correlation with potential evapotranspiration. The maximum values for soil moisture of Zambia show minimum at 1 mm/m, maximum at 413 mm/m, mean at 173 mm/m. This study is technically based on using the Generic Mapping Tools (GMT) as cartographic scripting toolset. The paper contributes to the environmental monitoring of Zambia by presenting a series of climate and environmental maps that are beneficial for agricultural mapping of Zambia.

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“…To undertake complex thematic mapping of Mongolia, advanced cartographic methods of data representation and visualization are required. Methodological applications of GIS were under investigation both in pure cartographic research focused on technical issues of geoinformatics and data representation (Elidrissi et al 2020;Gauger et al 2007;Le Fillâtre et al 2021;Araújo et al 2019;Lemenkova, 2021, Höhle, 2021 and from the geological point of view (Gohl et al 2006a(Gohl et al , 2006bHolguín and Sternberg, 2018;Graf et al 2018). Difficulties in selecting suitable and optimal software for geologic mapping are caused both by the technical approaches of GIS (console-or menu-based) and by specific file format requirements (e.g., the QGIS reads the ArcGIS native .shp format, while GMT better operates with raster files, R 'raster' package enables direct SRTM file capture).…”

Section: Original Articlementioning

confidence: 99%

Gobi Altai, Khangai and Khentii Mountains mapped by a mixed-method cartographic approach for comparative geophysical analysis

Lemenkova

2021

Mong. geosci.

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Geologic and geophysical mapping has been so far limited to the traditional single-method GIS-based mapping. A new approach combining integrated analysis of data on geology, gravity, topography and geomorphology is presented for regional characterization of the geophysical setting in Mongolia: the Gobi Altai Mountains, the Khangai Mountains and Khentii Mountains with surrounding areas. Nine new maps have been produced from the high-resolution datasets: GEBCO, gravity raster, USGS geological data and SRTM-90 DEM geomorphological grid. Methodology includes three tools for cartographic data visualization: i) Generic Mapping Tools (GMT), ii) R programming language (‘raster’ and ‘tmap’ libraries); iii) QGIS. The results demonstrated strong agreement between the estimated values in gravity and topography grids, distribution of geological units and provinces over the country and geomorphological landforms with respect to the mountain ranges: Altai, Khangai and Khentii Mountains. The highest values in the gravity anomalies correspond to the mountain ranges in the Altai Mountains and Khangai Mountains (<80 mGal); high values correspond to the Khentii Mountains (20–60 mGal). Contrariwise, the basins of the Uvs Nuur and Khyargas Nuur show negative values (<-80 mGal). The NE- to NNE-oriented faulting and rift basins are clearly visible in the geophysical grids and geologic maps. The geomorphometric analysis performed based on the SRTM-90 DEM using R scripting demonstrated (1) slope, (2) aspect, (3) hillshade and (4) elevation models of Mongolia supported by histograms of data distribution and frequency. The study contributed to the cartographic methods and regional geological studies of Mongolia.

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Exploring structured scripting cartographic technique of GMT for ocean seafloor modeling: A case of the East Indian Ocean

Cited by 3 publications

References 66 publications

Big Earth Data Processing Using Machine Learning for Integrated Mapping of the Dead Sea Fault, Jordan

Big Earth Data Processing Using Machine Learning for Integrated Mapping of the Dead Sea Fault, Jordan

Mapping environmental and climate variations by GMT: A case of Zambia, Central Africa

Gobi Altai, Khangai and Khentii Mountains mapped by a mixed-method cartographic approach for comparative geophysical analysis

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