Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d'Ivoire, West Africa

Image-based vegetation analysis of desertified area by using a combination of ImageJ and Photoshop software

Fractional vegetation cover (FVC) is a crucial indicator to estimate degradation and desertification for grasslands. However, traditional small-scale FVC analysis methods, such as visual estimation and point-sampling, are cumbersome and imprecise. Innovative methods like image-based FVC analysis methods, while accurate, face challenges such as complex analytical procedures and the necessary training for operations. Therefore, in this study, a combined application of ImageJ and Photoshop was employed to achieve a more effective analysis of FVC values in desertification areas. Our results showed that the FVC results obtained by combination of Photoshop and ImageJ were dependable and precise (R2 > 0.98), demonstrating equivalency to results obtained through either visual estimation or Photoshop-based methods. Furthermore, even in the face of background interference and varied shooting angles, the combination of ImageJ and Photoshop software was still able to maintain a low error rate when analyzing FVC values (average error rate =  - 2.6%). In conclusion, the imaged-based combined FVC analysis method employed in our research was an effective, precise, and efficient technique for analyzing small-scale FVC, promising substantial improvement over conventional methods.

Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa

Desertification is one of the most destructive climate-related issues in the Sudan-Sahel region of Africa. As the assessment of desertification is possible by satellite image analysis using vegetation indices (VIs), this study reports on the technical advantages and capabilities of scripting the 'raster' and 'terra' R-language packages for computing the VIs. The test area which was considered includes the region of the confluence between the Blue and White Niles in Khartoum, southern Sudan, northeast Africa and the Landsat 8-9 OLI/TIRS images taken for the years 2013, 2018 and 2022, which were chosen as test datasets. The VIs used here are robust indicators of plant greenness, and combined with vegetation coverage, are essential parameters for environmental analytics. Five VIs were calculated to compare both the status and dynamics of vegetation through the differences between the images collected within the nine-year span. Using scripts for computing and visualising the VIs over Sudan demonstrates previously unreported patterns of vegetation to reveal climate-vegetation relationships. The ability of the R packages 'raster' and 'terra' to process spatial data was enhanced through scripting to automate image analysis and mapping, and choosing Sudan for the case study enables us to present new perspectives for image processing.

Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.