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Geoid model of the Somali Sea basin, the Seychelles Bank, the Madagascar and East Africa. Mapping: GMT. Map source: authors
Source publication
Evaluation of the representative cartographic techniques demonstrated that there are still considerable challenges facing the methods of marine geodetic, geophysical and bathymetric data visualisation. In an oceanic seafloor formation, the interaction between the geological structural elements and topographical relief can be analysed by advanced ma...
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... Ma (Collier et al. 2008). There are common traits in geologic history and magmatism of the Seychelles and southern India that are reflected in the geoid geopotential model ( Figure 3). This corresponds to the similarity in trace element composition between the Seychelles suite and the Deccan alkaline felsic, granite and ultramafic rocks (Engel and Fisher, 1975). ...
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... a cartographic simulation, visualisation of the raster grids was approximated by the 'grdimage' module, e.g. for the gravity map (Figure 3), with the following example of the code: 'gmt grdimage ss_grav.nc -Ccolors.cpt ...
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... the southern part of the region, the continental slopes of Kenya and Tanzania gradually transform to a very wide continental foot with dominating depths from -3500 up to approximately -5000 m, according to the GEBCO grid inspected by GDAL (dark blue in Figure 1). The satellite-derived marine free-air gravity anomalies ( Figure 3) are mapped using satellite-based data. Such high-resolution raster grids enable to detect various tectonic features of the seafloor ocean and contribute to a better understanding of regional geological setting and features: faults, fracture zones, mid-ocean ridges, rises, trenches, seamounts. ...
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... is especially true for the areas of basins covered by the thick layer sediments. Comparing Figure 3 with Figure 1 enables to detect such lines and specific topographic and geomorphological structures that mirror the tectonic lines in the region (Bird, 2003). ...
Citations
... Another problem in the commercial GIS is data format handling. In contrast, scripting methods of cartographic approach present more in-depth ways of data processing and visualization (Lemenkova and Debeir, 2022b, 2023c, 2023d. Scripting ensures an effective solution to automation in data processing. ...
This study presents new maps of the topographic and geophysical setting and seismicity in the region of the Gulf of Panama. The spatial analysis is based on the comparative analysis of the datasets on geoid, free-air gravity anomaly, topography and earthquakes. The cartographic framework is developed using the Generic Mapping Tools (GMT) scripting toolset. Seismic activity in the Central America is high due to the complex geologic setting, tectonic activity and lithosphere plate subduction. The data include the Earth Gravitational Model (EGM2008), the General Bathymetric Chart of the Oceans (GEBCO) and gravity grids. The seismicity data were collected from the Incorporated Research Institutions for Seismology (IRIS) catalogue on 1970-2021. The variations in data were compared to analyse correlations between the geophysical, seismic and topographic parameters. Free-air gravity, geoid and topographic data derived from the high-resolution datasets were used to investigate their effects on the main seismic sources in the region. The comparison of the maps showed that the distribution of the shallow earthquakes in the Pacific segment of Panama coincides with negative free-air anomalies and lower geoid values. The results revealed high values of geoid in the high mountainous regions of Panama (Cordilliera de Talamanca, southern coast of Peninsula de Azuero and eastern Panama, 77.5-78.5°W), which correspond to the topographic roughness in the highlands. Negative values of geoid are found over the Caribbean Sea basin (−4 to 0 m). The analyses of seismicity showed 1740 earthquake events varying by magnitudes from 2.9 to 7.8 at the depths up to 225 m (near the west coast of Colombia). A high concentration of the earthquakes is in the western region of the Panama's shelf waters (~82-83.5°W), and on the border with Colombia (~77-78.5°W). High gravity anomalies (over 220 mGal) are found in the mountainous regions which match the geodynamic processes associated with the Earth structure and geodetic and geophysical effects. The regions of the high seismicity were defined in the Gulf of Chiriqui and eastern part of the Gulf of Panama.
... The geologic maps were built upon the USGS data using QGIS software version 3.22.1 [80], while the other datasets were processed separately by the GMT scripts by codes presented in appendix A. The programming approaches used for mapping present an advanced alternative to the traditional cartography [81][82][83]. They operate with scripts which can either be used as a sequence for codes employing diverse modules used for plotting the maps [84][85][86][87], or as instruments in remote-sensing data processing [88][89][90][91]. ...
The interactions between the geophysical processes and geodynamics of the lithosphere play a crucial role in the geologic structure of the Earth’s crust. The Bangui magnetic anomaly is a notable feature in the lithospheric structure of the Central African Republic (CAR) resulting from a complex tectonic evolution. This study reports on the coherence in the geophysical data and magnetic anomaly field analysed from a series of maps. The data used here include raster grids on free-air altimetric gravity, magnetic EMAG2 maps, geoid EGM2008 model and topographic SRTM/ETOPO1 relief. The data were processed to analyse the correspondence between the geophysical and geologic setting in the CAR region. Histogram equalization of the topographic grids was implemented by partition of the raster grids into equal-area patches of data ranged by the segments with relative highs and lows of the relief. The original data were compared with the equalized, normalized and quadratic models. The scripts used for cartographic data processing are presented and commented. The consistency and equalization of topography, gravity and geoid data were based using GMT modules ‘grdfft’ and ‘grdhisteq’ modules. Using GMT scripts for mapping the geophysical and gravity data over CAR shows an advanced approach to multi-source data visualization to reveal the relationships in the geophysical and topographic processes in central Africa. The results highlighted the correlation between the distribution of rocks with high magnetism in the central part of the Bangui anomaly, and distribution of granites, greenstone belts, and metamorphosed basalts as rock exposure. The correspondence between the negative Bouguer anomaly (<−80 mGal), low geoid values (<−12 m) and the extent of the magnetic anomaly with extreme negative values ranging from −1000 to −200 nT is identified. The integration of the multi-source data provides new insights into the analysis of crustal thicknesses and the average density of the Earth in CAR, as well as the magnitude of the magnetic fields with notable deviations caused by the magnetic flux density in the Bangui area related to the distribution of mineral resources in CAR.
... However, there is a deficiency in functionality of the traditional GIS: slow-speed data processing, manual operational workflow and subjectivity and biased data analysis. In contrast, using programming techniques support accurate computations and mapping [25][26][27][28][29] presenting more advanced tools for environmental monitoring of mangroves. This paper is a continuation of our previous work [30] on the use of GRASS GIS scripts for cartographic data processing, where spatial data were processed by the GRASS GIS modules. ...
This paper addresses the issue of the satellite image processing using GRASS GIS in the mangrove forests of the Niger River Delta, southern Nigeria. The estuary of the Niger River Delta in the Gulf of Guinea is an essential hotspot of biodiversity on the western coast of Africa. At the same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems and result in the rapid decline of mangrove habitats. This motivates monitoring of the vegetation patterns using advanced cartographic methods and data analysis. As a response to this need, this study aimed to calculate and map several vegetation indices (VI) using scripts as advanced programming methods integrated in geospatial studies. The data include four Landsat 8-9 OLI/TIRS images covering the western segment of the Niger River Delta in the Bight of Benin for 2013, 2015, 2021, and 2022. The techniques included the ’i.vi’, ’i.landsat.toar’ and other modules of the GRASS GIS. Based on the GRASS GIS ’i.vi’ module, ten VI were computed and mapped for the western segment of the Niger
River Delta estuary: Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Second Modified Soil Adjusted Vegetation Index (MSAVI2), Infrared Percentage Vegetation Index (IPVI), and Enhanced Vegetation Index (EVI). The results showed variations in the vegetation patterns in mangrove habitats situated in the Niger River Delta over the last decade as well as the increase in urban areas (Onitsha, Sapele, Warri and Benin City) and settlements in the Delta State due to urbanization. The advanced techniques of the GRASS GIS of satellite image processing and analysis enabled us to identify and visualize changes in vegetation patterns. The
technical excellence of the GRASS GIS in image processing and analysis was demonstrated in the scripts used in this study.
... Among the advantages of using the high-resolution space-borne data as a source of information, one can mention the improved quality of spatial visualization [11,12], and the enabled access to remotely located places and areas otherwise inaccessible, such as deserts [13]. For such places, satellite imagery presents a valuable source of information. ...
With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite image processing. In this paper, we primarily focus on the methods of the unsupervised classification of the Landsat OLI/TIRS images covering the region of the Qena governorate in Upper Egypt. The Qena Bend of the Nile River presents a remarkable morphological feature in Upper Egypt, including a dense drainage network of wadi aquifer systems and plateaus largely dissected by numerous valleys of dry rivers. To identify the fluvial structure and stream network of the Wadi Qena region, this study addresses the problem of interpreting the relevant space-borne data using R, with an aim to visualize the land surface structures corresponding to various land cover types. To this effect, high-resolution 2D and 3D topographic and geologic maps were used for the analysis of the geomorphological setting of the Qena region. The information was extracted from the space-borne data for the comparative analysis of the distribution of wadi streams in the Qena Bend area over several years: 2013, 2015, 2016, 2019, 2022, and 2023. Six images were processed using computer vision methods made available by R libraries. The results of the k-means clustering of each scene retrieved from the multi-temporal images covering the Qena Bend of the Nile River were thus compared to visualize changes in landforms caused by the cumulative effects of geomorphological disasters and climate-environmental processes. The proposed method, tied together through the use of R scripts, runs effectively and performs favorably in computer vision tasks aimed at geospatial image processing and the analysis of remote sensing data.
... Resembling the scripts in Unix shell, R and Python environments [59], GRASS GIS and GMT scripts are used to automate the workflow repeatedly performed for different maps using the same algorithm for various study areas. Other examples of automation of the cartographic workflow are diverse and include cases of rapid vectorisation of the isolines via machine learning [60], morphometric landscape analysis [61,62], geospatial analysis-based scripting libraries [63], topographic mapping using the GMT and QGIS [64], and fractal modelling quantifying the repetitive patterns [65]. ...
Libraries with pre-written codes optimize the workflow in cartography and reduce labour intensive data processing by iteratively applying scripts to implementing mapping tasks. Most existing Geographic Information System (GIS) approaches are based on traditional software with a graphical user's interface which significantly limits their performance. Although plugins are proposed to improve the functionality of many GIS programs, they are usually ad hoc in finding specific mapping solutions, e.g., cartographic projections and data conversion. We address this limitation by applying the principled approach of Geospatial Data Abstraction Library (GDAL), library for conversions between cartographic projections (PROJ) and Geographic Resources Analysis Support System (GRASS) GIS for geospatial data processing and morphometric analysis. This research presents topographic analysis of the dataset using scripting methods which include several tools: (1) GDAL, a translator library for raster and vector geospatial data formats used for converting Earth Global Relief Model (ETOPO1) GeoTIFF in XY Cartesian coordinates into World Geodetic System 1984 (WGS84) by the 'gdalwarp' utility; (2) PROJ projection transformation library used for converting ETOPO1 WGS84 grid to cartographic projections (Cassini-Soldner equirectangular, Equal Area Cylindrical, Two-Point Equidistant Azimuthal, and Oblique Mercator); and (3) GRASS GIS by sequential use of the following modules: r.info, d.mon, d.rast, r.colors, d.rast.leg, d.legend, d.northarrow, d.grid, d.text, g.region, and r.contour. The depth frequency was analysed by the module 'd.histogram'. The proposed approach provided a systematic way for morphometric measuring of topographic data and combine the advantages of the GDAL, PROJ, and GRASS GIS tools that include the informativeness, effectiveness, and representativeness in spatial data processing. The morphometric analysis included the computed slope, aspect, profile, and tangential curvature of the study area. The data analysis revealed the distribution pattern in topographic data: 24% of data with elevations below 400 m, 13% of data with depths −5000 to −6000 m, 4% of depths have values −3000 to −4000 m, the least frequent data (−6000 to 7000 m) <1%, 2% of depths have values −2000 to 3000 m in the basin, while other values are distributed proportionally. Further, by incorporating the generic coordinate transformation software library PROJ, the raster grid was transformed into various cartographic projections to demonstrate distortions in shape and area. Scripting techniques of GRASS GIS are demonstrated for applications in topographic modelling and raster data processing. The GRASS GIS shows the effectiveness for mapping and visualization, compatibility with libraries (GDAL, PROJ), technical flexibility in combining Graphical User Interface (GUI), and command-line data processing. The research contributes to the technical cartographic development.
My professional portfolio briefly summarizes my skills in in Cartography and Remote Sensing. It includes the selected examples of my works on RS data processing, including satellite image processing, cartographic modelling using DEM and terrain data, case studies on thematic mapping and modelling Earth Observation (EO) data. The illustrations are copied from my published papers.
This presentation proposes research on mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. The presentation is held on 13 June at the University of Salzburg. The presented research covers the problem of extracting knowledge and information from Earth Observation (EO) data which requires advanced technical cartographic tools. In particular, I presented the use of methods of machine learning (ML) and algorithms deep learning (DL) as well as scripting approaches to geospatial data handling. The concept of the study: Landscapes of Africa. Research focus: land surface of the African continent where diverse environmental processes interplay. Understanding landscape dynamics requires modelling and mapping the complexity of factors that affect the shape of the Earth using advanced methods of EO data processing. Landscape dynamics was analysed on several case study that demonstrate the evaluation of spatio-temporal changes caused by human and natural forces across various countries of Africa. Applications of landscape ecology and environmental monitoring of Africa were discussed on the example of landscape monitoring. Possible applications include land management (urban planning), diverse goals of sustainable development (food resources, agriculture) and theoretical issues of cartography and geoinformatics. Factors affecting formation of landscapes are reviewed in the published papers. These incldue geologic-tectonic setting, climate processes, anthropogenic activities in various countries across the African continent which is notable for different relief, soil and vegetation setting.
This paper presents an R-based approach to mapping dynamics of the flooded areas in the Inner Niger Delta (IND), Mali, using time series analysis of Landsat 8-9 satellite images. As the largest inland wetland in West Africa, the habitats of IND offers high potential for biodiversity of the flood-dependent e c o systems. IND is one of the most productive areas in West Africa. Mapping flooded areas based on satellite images enables to provide strategies for land management and rice planting and modelling vegetation types of IND. Our approach is based on using libraries of R programming language for processing six Landsat images, and each image was taken on November from 2013 to 2022. By capturing spatial and temporal structures of the satellite images on 2013, 2015, 2018, 2020, 2021 and 2022, the remote sensing data are combined to yield estimates of landscape dynamics that is temporally coherent, while helping to analyse fluctuations of spatial extent in fluvial wetlands caused by the hydrological processes of seasonal flooding. Further, by allowing packages of R to support image processing, an approach to mapping vegetation by NDVI, SAVI and EVI indices and visualising changes in distribution of different land cover classes over time is realised. In this context, processing Earth observation data by advanced scripting tools of R language provides new insights into complex interlace of climate-hydrological processes and vegetation responses. Our study contributes to the sustainable management of natural resources and improving knowledge on the functioning of IND ecosystems in Mali, West Africa.
In this paper, the climate and environmental datasets were processed by the scripts of Generic Mapping Tools (GMT) and R to evaluate changes in climate parameters, vegetation patters and land cover types in Burkina Faso. Located in the southern Sahel zone, Burkina Faso experiences one of the most extreme climatic hazards in sub-saharan Africa varying from the extreme floods in Volta River Basin, to desertification and recurrent droughts.. The data include the TerraClimate dataset and satellite images Landsat 8-9 Operational Land Imager (OLI) and Thermal Infrared (TIRS) C2 L1. The dynamics of target climate characteristics of Burkina Faso was visualised for 2013-2022 using remote sensing data. To evaluate the environmental dynamics the TerraClimate data were used for visualizing key climate parameter: extreme temperatures, precipitation, soil moisture, downward surface shortwave radiation, vapour pressure deficit and anomaly. The Palmer Drought Severity Index (PDSI) was modelled over the study area to estimate soil water balance related to the soil moisture conditions as a prerequisites for vegetation growth. The land cover types were mapped using the k-means clustering by R. Two vegetation indices were computed to evaluate the changes in vegetation patterns over recent decade. These included the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI) The scripts used for cartographic workflow are presented and discussed. This study contributes to the environmental mapping of Burkina Faso with aim to highlight the links between the climate processes and vegetation dynamics in West Africa.
