Tanzania Craton, Serengeti Plain and Eastern Rift Valley: mapping of geospatial data by scripting techniques

2023

Geomatics

The volcanic Kerguelen Islands are formed on one of the world’s largest submarine plateaus. Located in the remote segment of the southern Indian Ocean close to Antarctica, the Kerguelen Plateau is notable for a complex tectonic origin and geologic formation related to the Cretaceous history of the continents. This is reflected in the varying age of the oceanic crust adjacent to the plateau and the highly heterogeneous bathymetry of the Kerguelen Plateau, with seafloor structure differing for the southern and northern segments. Remote sensing data derived from marine gravity and satellite radar altimetry surveys serve as an important source of information for mapping complex seafloor features. This study incorporates geospatial information from NOAA, EMAG2, WDMAM, ETOPO1, and EGM96 datasets to refine the extent and distribution of the extracted seafloor features. The cartographic joint analysis of topography, magnetic anomalies, tectonic and gravity grids is based on the integrated mapping performed using the Generic Mapping Tools (GMT) programming suite. Mapping of the submerged features (Broken Ridge, Crozet Islands, seafloor fabric, orientation, and frequency of magnetic anomalies) enables analysis of their correspondence with free-air gravity and magnetic anomalies, geodynamic setting, and seabed structure in the southwest Indian Ocean. The results show that integrating the datasets using advanced cartographic scripting language improves identification and visualization of the seabed objects. The results include 11 new maps of the region covering the Kerguelen Plateau and southwest Indian Ocean. This study contributes to increasing the knowledge of the seafloor structure in the French Southern and Antarctic Lands.

Section: Methodsmentioning

confidence: 99%

Seafloor and Ocean Crust Structure of the Kerguelen Plateau from Marine Geophysical and Satellite Altimetry Datasets

2023

Geomatics

“…The methodology of recent environmental studies is mostly focused on utilising the traditional approaches of data modelling associated with the GUI-based GIS software [82][83][84][85][86][87][88][89][90][91][92]. In contrast, this study is inspired by the approach of GMT [8], which performs illustrative mapping through running the scripts via the command line from the console, e.g., Figure 6.…”

Section: Methodsmentioning

confidence: 99%

Mapping Climate Parameters over the Territory of Botswana Using GMT and Gridded Surface Data from TerraClimate

2022

IJGI

This articles presents a new series of maps showing the climate and environmental variability of Botswana. Situated in southern Africa, Botswana has an arid to semi-arid climate, which significantly varies in its different regions: Kalahari Desert, Makgadikgadi Pan and Okavango Delta. While desert regions are prone to droughts and periods of extreme heat during the summer months, other regions experience heavy downpours, as well as episodic and unpredictable rains that affect agricultural activities. Such climatic variations affect social and economic aspects of life in Botswana. This study aimed to visualise the non-linear correlations between the topography and climate setting at the country’s scale. Variables included T °C min, T °C max, precipitation, soil moisture, evapotranspiration (PET and AET), downward surface shortwave radiation, vapour pressure and vapour pressure deficit (VPD), wind speed and Palmer Drought Severity Index (PDSI). The dataset was taken from the TerraClimate source and GEBCO for topographic mapping. The mapping approach included the use of Generic Mapping Tools (GMT), a console-based scripting toolset, which enables the use of a scripting method of automated mapping. Several GMT modules were used to derive a set of climate parameters for Botswana. The data were supplemented with the adjusted cartographic elements and inspected by the Geospatial Data Abstraction Library (GDAL). The PDSI in Botswana in 2018 shows stepwise variation with seven areas of drought: (1) −3.7 to −2.2. (extreme); (2) −2.2 to −0.8 (strong, southern Kalahari); (3) −0.8 to 0.7 (significant, central Kalahari; (4) 0.7 to 2.1 (moderate); (5) 2.1 to 3.5 (lesser); (6) 3.5 to 4.9 (low); (7) 4.9 to 6.4 (least). The VPD has a general trend towards the south-western region (Kalahari Desert, up to 3.3), while it is lower in the north-eastern region of Botswana (up to 1.4). Other values vary respectively, as demonstrated in the presented 12 maps of climate and environmental inventory in Botswana.

“…The images were processed, analyzed, and stored in a working directory for further processing by the GRASS GIS. Additional mapping was performed using the Generic Mapping Tools (GMT) scripting toolset [102], following the existing technical cartographic workflow [103,104]. The metadata from Landsat images were collected by the EarthExplorer file with the .xml extension, as summarized in Table 1.…”

Section: Datamentioning

confidence: 99%

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria

2023

ASI

Automated classification of satellite images is a challenging task that enables the use of remote sensing data for environmental modeling of Earth’s landscapes. In this document, we implement a GRASS GIS-based framework for discriminating land cover types to identify changes in the endorheic basins of the ephemeral salt lakes Chott Melrhir and Chott Merouane, Algeria; we employ embedded algorithms for image processing. This study presents a dataset of the nine Landsat 8–9 OLI/TIRS satellite images obtained from the USGS for a 9-year period, from 2014 to 2022. The images were analyzed to detect changes in water levels in ephemeral lakes that experience temporal fluctuations; these lakes are dry most of the time and are fed with water during rainy periods. The unsupervised classification of images was performed using GRASS GIS algorithms through several modules: `i.cluster’ was used to generate image classes; `i.maxlik’ was used for classification using the maximal likelihood discriminant analysis, and auxiliary modules, such as `i.group’, `r.support’, `r.import’, etc., were used. This document includes technical descriptions of the scripts used for image processing with detailed comments on the functionalities of the GRASS GIS modules. The results include the identified variations in the ephemeral salt lakes within the Algerian part of the Sahara over a 9-year period (2014–2022), using a time series of Landsat OLI/TIRS multispectral images that were classified using GRASS GIS. The main strengths of the GRASS GIS framework are the high speed, accuracy, and effectiveness of the programming codes for image processing in environmental monitoring. The presented GitHub repository, which contains scripts used for the satellite image analysis, serves as a reference for the interpretation of remote sensing data for the environmental monitoring of arid and semi-arid areas of Africa.