<figure><img src="image_1.png" /></figure>

HAL
open science

# Portfolio in Cartography and Remote Sensing

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Portfolio in Cartography and Remote Sensing. Doctoral. Salzburg, Austria, Austria. 2023, pp.24. [hal-04612624](https://hal.science/hal-04612624)

HAL Id: hal-04612624

https://hal.science/hal-04612624

Submitted on 14 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
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<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
<figure><img src="image_1.png" /></figure>

HAL
open science

# Portfolio in Cartography and Remote Sensing

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Portfolio in Cartography and Remote Sensing. Doctoral. Salzburg, Austria, Austria. 2023, pp.24. hal-04612624

HAL Id: hal-04612624

https://hal.science/hal-04612624v1

Submitted on 14 Jun 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
Portfolio in Cartography and Remote Sensing

RS: Satellite image processing
Cartography: DEM and terrain modelling
Mapping and modelling Earth Observation (EO) data

Polina Lemenkova

December 21, 2023

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# Portfolio in Cartography and Remote Sensing

RS: Satellite image processing
Cartography: DEM and terrain modelling
Mapping and modelling Earth Observation (EO) data

Polina Lemenkova

December 21, 2023

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## Summary

### Image processing

* Image processing

* Image segmentation

* Parametric classification

* Image analysis

* Color composites

* Time series analysis

* Computing vegetation indices

* Image classification

* Sequential pattern recognition

* Multi-source RS data processing

### Programming

* Scripting and applied programming

* Python for data analysis, spatial and statistical modelling

* R language for terrain mapping

* Workflow modelling

### Remote sensing methods in geophysics

* Technical skills in geophysical data processing

* Topographic 3D modelling by GMT scripting toolset

* Modelling cross-sections by GMT for analysis of slope steepness

### Data science

* Statistical data analysis

### Publications

### Bibliography

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## Summary

### Image processing

- Image processing

- Image segmentation

- Parametric classification

- Image analysis

- Color composites

- Time series analysis

- Computing vegetation indices

- Image classification

- Sequential pattern recognition

- Multi-source RS data processing

### Programming

- Scripting and applied programming

- Python for data analysis, spatial and statistical modelling

- R language for terrain mapping

- Workflow modelling

### Remote sensing methods in geophysics

- Technical skills in geophysical data processing

- Topographic 3D modelling by GMT scripting toolset

- Modelling cross-sections by GMT for analysis of slope steepness

### Data science

- Statistical data analysis

### Publications

### Bibliography

<figure><img src="image_12.png" /></figure>

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# My technical skills:

## Professional background

⇒ Background in RS, image processing, applied geophysics and physical geography, geology, climate-related issues, environmental analysis and monitoring.

⇒ Strong extensive experience in RS data processing: satellite image classification, clustering, segmentation, analysis and interpretation;

⇒ Satellite images I worked with: Landsat 7 TM/ETM+, 8-9 OLI/TIRS (NASA/USGS), Sentinel-2A (Copernicus satellite missions), VHR SPOT, World Imagery, Pléiades-1A and 1B products of ESA.

⇒ Strong cartographic skills (print-quality maps, graphical plots and illustrations).

⇒ Programming and scripting skills; console-based data processing: Python and R for image processing and statistical analysis, modelling and visualization.

# Software skills

✓ Strong practical experience in GIS: GRASS GIS, GMT, QGIS, SAGA GIS, Erdas Imagine, ENVI GIS, ArcGIS, Idrisi, eCognition, GeoMedia.

# Example

Examples of my works and links to papers with DOI are in the following slides.
# My technical skills:

## Professional background

⇒ Background in RS, image processing, applied geophysics and physical geography, geology, climate-related issues, environmental analysis and monitoring.

⇒ Strong extensive experience in RS data processing: satellite image classification, clustering, segmentation, analysis and interpretation;

⇒ Satellite images I worked with: Landsat 7 TM/ETM+, 8-9 OLI/TIRS (NASA/USGS), Sentinel-2A (Copernicus satellite missions), VHR SPOT, World Imagery, Pléiades-1A and 1B products of ESA.

⇒ Strong cartographic skills (print-quality maps, graphical plots and illustrations).

⇒ Programming and scripting skills; console-based data processing: Python and R for image processing and statistical analysis, modelling and visualization.

# Software skills

✓ Strong practical experience in GIS: GRASS GIS, GMT, QGIS, SAGA GIS, Erdas Imagine, ENVI GIS, ArcGIS, Idrisi, eCognition, GeoMedia.

# Example

Examples of my works and links to papers with DOI are in the following slides.
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## Image processing

Using satellite images for environmental mapping as a source of geoinformation: mapping land cover types and recognizing land features and structures. Technical steps: image preprocessing (import, enhancement and noise abatement), image processing (pattern classification for comparative analysis), cartographic visualization.

<figure><img src="image_32.png" /></figure>

(a) 2013

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(b) 2015

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(c) 2016

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(a) 2016: Bands 5-6-4

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(b) 2016: Bands 7-5-4

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(c) 2016: Clustering

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(d) 2019

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(e) 2022

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(f) 2023

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(d) 2019: Bands 7-6-4

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(e) 2019: Bands 7-5-3

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(f) 2019: Clustering

(a) Original dataset: Landsat 8-9 OLI/TIRS

(b) Classified scenes and color composites

## Example

**Source:** Recognizing the Wadi Fluvial Structure and Stream Network in the Qena Bend of the Nile River, Egypt, on Landsat 8-9 OLI Images, Information. 2023; 14(4):249; DOI: 10.3390/info14040249.

<figure><img src="image_44.png" /></figure>
## Image processing

Using satellite images for environmental mapping as a source of geoinformation: mapping land cover types and recognizing land features and structures. Technical steps: image preprocessing (import, enhancement and noise abatement), image processing (pattern classification for comparative analysis), cartographic visualization.

<figure><img src="image_28.png" /><figcaption>(a) Original dataset: Landsat 8-9 OLI/TIRS</figcaption></figure>

<figure><img src="image_29.png" /><figcaption>(b) Classified scenes and color composites</figcaption></figure>

## Example

Source: Recognizing the Wadi Fluvial Structure and Stream Network in the Qena Bend of the Nile River, Egypt, on Landsat 8-9 OLI Images, Information. 2023; 14(4):249; DOI: 10.3390/info14040249.

<figure><img src="image_30.png" /></figure>
## Image segmentation: Landsat 8-9 OLI/TIRS

Segmentation of the image using techniques of thresholding (similarity analysis) using GRASS GIS module 'i segment'. Edge detection, texture analysis, template matching and tracking for analysis of random segments using seeds at various levels.

<figure><img src="image_45.png" /><figcaption>(a) 2023, segmentation threshold = 0.90</figcaption></figure>

<figure><img src="image_46.png" /><figcaption>(b) 2023, segmentation threshold = 0.05</figcaption></figure>

## Highlight

Segmentation the Landsat 8-9 OLI/TIRS image of Sudd area (South Sudan) with varied parameters: (a) minsize=5, threshold=0.90, (b) minsize=100, threshold=0.05.

## Example

Source: Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts, Analytics, 2(3), 745-780): DOI: 10.3390/analytics2030040.

<figure><img src="image_47.png" /></figure>
## Image segmentation: Landsat 8-9 OLI/TIRS

Segmentation of the image using techniques of thresholding (similarity analysis) using GRASS GIS module 'i segment'. Edge detection, texture analysis, template matching and tracking for analysis of random segments using seeds at various levels.

<figure><img src="image_31.png" /><figcaption>(a) 2023, segmentation threshold = 0.90</figcaption></figure>

<figure><img src="image_32.png" /><figcaption>(b) 2023, segmentation threshold = 0.05</figcaption></figure>

## Highlight

Segmentation the Landsat 8-9 OLI/TIRS image of Sudd area (South Sudan) with varied parameters: (a) minsize=5, threshold=0.90, (b) minsize=100, threshold=0.05.

## Example

Source: Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts, Analytics, 2(3), 745-780): DOI: 10.3390/analytics2030040.

<figure><img src="image_33.png" /></figure>
## Parametric classification in image segmentation using eCognition

Estimation of object parameters for object based image analysis (OBIA) using multi-scale segmentation of data derived from the satellite images with varied parameters and scale.

<figure><img src="image_48.png" /></figure>

Examples of the VHR Pléiades ESA archive image processing using eCognition: a case of Brussels, Belgium.

## Example

Source: *Topology, Homogeneity and Scale Factors for Object Detection: Application of eCognition Software for Urban Mapping using Multispectral Satellite Image*, INS02015 Proceedings: DOI: 10.48550/arXiv.1901.00726.
## Parametric classification in image segmentation using eCognition

Estimation of object parameters for object based image analysis (OBIA) using multi-scale segmentation of data derived from the satellite images with varied parameters and scale.

<figure><img src="image_34.png" /></figure>

Examples of the VHR Pléiades ESA archive image processing using eCognition: a case of Brussels, Belgium.

## Example

Source: *Topology, Homogeneity and Scale Factors for Object Detection: Application of eCognition Software for Urban Mapping using Multispectral Satellite Image*, INS02015 Proceedings: DOI: 10.48550/arXiv.1901.00726.
## Image analysis: Landsat 8-9 OLI/TIRS

<figure><img src="image_49.png" /></figure>

### Highlight

Libraries of the R language - RToolbox, raster and terra - used to classify satellite images by k-means clustering and dynamics of vegetation in tropical rainforests.

### Example

Source: R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC. Appl. Sci. 2022; 12(24):12554. DOI: 10.3390/app122412554.

<figure><img src="image_50.png" /></figure>
## Image analysis: Landsat 8-9 OLI/TIRS

<figure><img src="image_35.png" /></figure>

### Highlight

Libraries of the R language - RToolbox, raster and terra - used to classify satellite images by k-means clustering and dynamics of vegetation in tropical rainforests.

### Example

Source: R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC. Appl. Sci. 2022; 12(24):12554. DOI: 10.3390/app122412554.

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## Colour composite images: associating spectral monochrome bands to a primary colour triplets (RGB)

Creating natural or true/false color composites of an image using R to display a combination of visible Red, Green and Blue bands to the corresponding channels.

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## Highlight

The generated images reveal spatial details due to the intensity of pixels in false colour composites for water areas and sharpened view of urban areas in natural colour composites.

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Colour composite images: associating spectral monochrome bands to a
primary colour triplets (RGB)

Creating natural or true/false color composites of an image using R to display a combination of visible Red, Green and Blue bands to the corresponding channels.

<figure><img src="image_44.png" /></figure>

Highlight

The generated images reveal spatial details due to the intensity of pixels in false colour composites for water areas and sharpened view of urban areas in natural colour composites.

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

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# Time series analysis for mapping landscape dynamic

A set of satellite images taken from the same area at different times, time series analysis supports investigating the dynamic processes in ecosystem monitoring.

<figure><img src="image_65.png" /></figure>

## Highlight

Comparative analysis of images using GRASS GIS for mapping lacustrine environment in arid landscapes to evaluate the effects of seasonal fluctuations in land cover types.

## Example

*Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS.* Land. 2023; 12(11):1995. DOI: 10.3390/land12111995.

<figure><img src="image_66.png" /></figure>
# Time series analysis for mapping landscape dynamic

A set of satellite images taken from the same area at different times, time series analysis supports investigating the dynamic processes in ecosystem monitoring.

<figure><img src="image_51.png" /></figure>

## Highlight

Comparative analysis of images using GRASS GIS for mapping lacustrine environment in arid landscapes to evaluate the effects of seasonal fluctuations in land cover types.

## Example

*Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS.* Land. 2023; 12(11):1995. DOI: 10.3390/land12111995.

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# Computing Vegetation Indices (VI) using GRASS GIS

<figure><img src="image_74.png" /></figure>

Left: code written in GRASS GIS for image analysis. Right: image classification for 2013, 2015, 2021 and 2022.

## Highlight

RS data analysis for analysis of vegetation state and ecosystem monitoring, e.g., deforestation of mangrove forests. Computing VI using GRASS GIS: Normalized Difference Vegetation Index (NDVI), Atmospherically Resistant Vegetation Index (ARVI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), Infrared Percentage Vegetation Index (IPVI), etc.

## Example

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria, J. Mar. Sci. Eng., 11(4), 871; DOI: 10.3390/jmse11040871.
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Computing Vegetation Indices (VI) using GRASS GIS

<figure><img src="image_65.png" /></figure>

Left: code written in GRASS GIS for image analysis. Right: image classification for 2013, 2015, 2021 and 2022.

## Highlight

RS data analysis for analysis of vegetation state and ecosystem monitoring, e.g., deforestation of mangrove forests. Computing VI using GRASS GIS: Normalized Difference Vegetation Index (NDVI), Atmospherically Resistant Vegetation Index (ARVI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI), Infrared Percentage Vegetation Index (IPVI), etc.

## Example

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria, J. Mar. Sci. Eng., 11(4), 871; DOI: 10.3390/jmse11040871.
## Image classification and quality assessment

<figure><img src="image_75.png" /></figure>

Classification of pixels into 10 classes according to landscape types (coloured scenes), and computing rejection probability values with pixel classification confidence levels (monochrome images).

### Highlight

RS data processing by GRASS GIS scripts for landscape analysis using k-means clustering technique.

### Example

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria, Appl. Syst. Innov. 2023, 6(4), 61; DOI: 10.3390/asi6040061

<figure><img src="image_76.png" /></figure>

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<figure><img src="image_70.png" /></figure>

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# Image classification and quality assessment

<figure><img src="image_73.png" /></figure>

Classification of pixels into 10 classes according to landscape types (coloured scenes), and computing rejection probability values with pixel classification confidence levels (monochrome images).

## Highlight

RS data processing by GRASS GIS scripts for landscape analysis using k-means clustering technique.

## Example

A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria, Appl. Syst. Innov. 2023, 6(4), 61; DOI: 10.3390/asi6040061

<figure><img src="image_74.png" /></figure>

<figure><img src="image_75.png" /></figure>

<figure><img src="image_76.png" /></figure>

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /></figure>

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# Sequential pattern recognition

<figure><img src="image_95.png" /></figure>

## Highlight

Unsupervised automatic classification techniques for RS data processing using "i.maxlik" GRASS GIS module: analysis of lake shrinkage during recent decade.

## Example

Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa, 2023. Die Bodenkultur Journal of Land Management Food and Environment 74(1):49-64: DOI: 110.2478/boku-2023-0005.

<figure><img src="image_96.png" /></figure>

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<figure><img src="image_103.png" /></figure>
# Sequential pattern recognition

<figure><img src="image_81.png" /></figure>

## Highlight

Unsupervised automatic classification techniques for RS data processing using "i.maxlik" GRASS GIS module: analysis of lake shrinkage during recent decade.

## Example

Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa, 2023. Die Bodenkultur Journal of Land Management Food and Environment 74(1):49-64: DOI: 110.2478/boku-2023-0005.

<figure><img src="image_82.png" /></figure>

<figure><img src="image_83.png" /></figure>

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# Multi-source geospatial data analysis: satellite and aerial images, maps

## RS Data Analysis

Overlay of high-resolution satellite images, maps and historical aerial photographs enables to reveal land cover structures otherwise hidden from the eyes.

<figure><img src="image_111.png" /></figure>

<figure><img src="image_112.png" /></figure>

<figure><img src="image_113.png" /></figure>

<figure><img src="image_114.png" /></figure>

## Highlight

Different parameters of RS data enables to adjust scale-depending mapping.
Example: Landsat 8-9 OLI/TIRS has narrower spectral bands, higher calibration, finer radiometric resolution and geometry and better signal-to-noise parameters compared to Landsat-7 TM/ETM+. In turn, aerial photographs enable to enlarge target regions.

## Example

Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa, Journal of Imaging. 2023; 9(5):98: DOI: 10.3390/jimaging9050098.

<figure><img src="image_115.png" /></figure>

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<figure><img src="image_117.png" /></figure>

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# Multi-source geospatial data analysis: satellite and aerial images, maps

## RS Data Analysis

Overlay of high-resolution satellite images, maps and historical aerial photographs enables to reveal land cover structures otherwise hidden from the eyes.

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## Highlight

Different parameters of RS data enables to adjust scale-depending mapping. Example: Landsat 8-9 OLI/TIRS has narrower spectral bands, higher calibration, finer radiometric resolution and geometry and better signal-to-noise parameters compared to Landsat-7 TM/ETM+. In turn, aerial photographs enable to enlarge target regions.

## Example

*Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa, Journal of Imaging. 2023; 9(5):98: DOI: 10.3390/jimaging9050098.*

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# My GitHub repository with programming codes and scripts:
https://github.com/paulinelemenkova

## My skills in applied programming and scripting languages:

* ✓ Python (Matplotlib, terra, Seaborn, NumPy, SciPy, Pandas and other libraries)

* ✓ R (a wide variety of packages)

* ✓ MATLAB and its open source analogue Octave language

* ✓ AWK and pother Unix utilities for console-based data processing

* ✓ Shell scripting in GMT and GRASS GIS syntax

* ✓ Data converting, reprojecting and formatting by GDAL and PROJ.

## My skills in markup languages:

* ✓ LATEX, TEX. Compilers: LualATEX, XeATEX. HTML/CSS, XML, gedit, LyX document processor, Notepad++.

* ✓ Bibliographies: BibLATEX, Biber, MakelIndex.

* ✓ Strong experience in LATEX=> submission of scientific papers

## My skills in standard Office suite:

* ✓ Proficiency in Word, Excel, Access, PowerPoint for usual document processing.
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# My GitHub repository with programming codes and scripts:
https://github.com/paulinelemenkova

## My skills in applied programming and scripting languages:

* ✓ Python (Matplotlib, terra, Seaborn, NumPy, SciPy, Pandas and other libraries)

* ✓ R (a wide variety of packages)

* ✓ MATLAB and its open source analogue Octave language

* ✓ AWK and pother Unix utilities for console-based data processing

* ✓ Shell scripting in GMT and GRASS GIS syntax

* ✓ Data converting, reprojecting and formatting by GDAL and PROJ.

## My skills in markup languages:

* ✓ LATEX, TEX. Compilers: LualATEX, XeATEX. HTML/CSS, XML, gedit, LyX document processor, Notepad++.

* ✓ Bibliographies: BibLATEX, Biber, MakelIndex.

* ✓ Strong experience in LATEX=> submission of scientific papers

## My skills in standard Office suite:

* ✓ Proficiency in Word, Excel, Access, PowerPoint for usual document processing.
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# I use Python for spatial and statistical data analysis and modelling

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Examples of my mapping DEM and graphical plotting using libraries of Python for spatial data analysis.

## Highlight

I use various libraries of Python for data analysis, modelling and visualization. I have experience in DEM modelling by library 'EarthPy' for terrain analysis (image left). I also worked with diverse libraries (Matplotlib, Seaborn, NumPy, Plotly) for statistical data visualization and various technique of plotting (image right). Python is a very useful tool in multi-disciplinary research applications, and I use it in my research.

## Example

Example paper: *Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d'Ivoire, West Africa*, in: *Journal of Imaging, 8(12), 317: DOI: 10.3390/jimaging8120317*.

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Examples of my mapping DEM and graphical plotting using libraries of Python for spatial data analysis.

## Highlight

I use various libraries of Python for data analysis, modelling and visualization. I have experience in DEM modelling by library 'EarthPy' for terrain analysis (image left). I also worked with diverse libraries (Matplotlib, Seaborn, NumPy, Plotly) for statistical data visualization and various technique of plotting (image right). Python is a very useful tool in multi-disciplinary research applications, and I use it in my research.

## Example

Example paper: *Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d'Ivoire, West Africa*, in: *Journal of Imaging, 8(12), 317: DOI: 10.3390/jimaging8120317*.

## I use Python for spatial and statistical data analysis and modelling

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# R programming for terrain modelling (DEM, slope/aspect, relief hillshade)

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## Highlight

I have advanced skills in R: diverse packages for DEM modelling, satellite image processing, statistical data analysis and plotting. I apply R for geomorphological modelling and geospatial data analysis or topographically diverse surfaces.

## Example

Example of my research based on R for data analysis and modelling: *Tanzania Craton, Serengeti Plain and Eastern Rift Valley: mapping of geospatial data by scripting techniques*, in: *Estonian Journal of Earth Sciences*, 71(2), 61–79: DOI: 10.3176/earth.2022.05.

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# R programming for terrain modelling (DEM, slope/aspect, relief hillshade)

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## Highlight

I have advanced skills in R: diverse packages for DEM modelling, satellite image processing, statistical data analysis and plotting. I apply R for geomorphological modelling and geospatial data analysis or topographically diverse surfaces.

## Example

Example of my research based on R for data analysis and modelling: *Tanzania Craton, Serengeti Plain and Eastern Rift Valley: mapping of geospatial data by scripting techniques*, in: *Estonian Journal of Earth Sciences*, 71(2), 61–79: DOI: 10.3176/earth.2022.05.

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# Conceptual workflow modelling for organising multiple tasks

## Workflow modelling and organizational flowcharts

I use R packages *DiagrammeR* and *mermaid* for plotting workflow graphs when processing RS data and organising projects on satellite image processing.

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Conceptual workflow modelling for organising multiple tasks

Workflow modelling and organizational flowcharts

I use R packages *DiagrammeR* and *mermaid* for plotting workflow graphs when processing RS data and organising projects on satellite image processing.

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# Acoustic remote sensing data and methods for geophysical signal processing

## Geophysical Data Analysis

I worked in Schmidt Institute of Physics of the Earth, RAS, Dept. of Natural Disasters, Anthropogenic Hazards and Seismicity of the Earth. Lab. of Regional Geophysics and Natural Disasters =>

I have a strong hands-on experience in processing geophysical RS data (acoustic signals) and theoretical background in processes related to soil properties and inner structure of the Earth. I process acoustical signals by applied methods of RS data analysis to characterise land structures and properties of materials.

## Examples

1. Ultrasonic P- and S-Wave Reflection and CPT Soundings for Measuring Shear Strength in Soil Stabilized by Deep Lime/Cement Columns in Stockholm Norvik Port. Archives of Acoustics, 48(3), 325-346. DOI: 10.24425/aoa.2023.145243.

2. Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium. Sensors. 2023; 23(1):56. DOI: 10.3390/s23010056.

3. Simplex Lattice Design and X-ray Diffraction for Analysis of Soil Structure: A Case of Cement-Stabilised Compacted Tills Reinforced with Steel Slag and Slaked Lime, Electronics, 11(22):3726, 2022. DOI: 10.3390/electronics11223726.

4. Dynamics of Strength Gain in Sandy Soil Stabilised with Mixed Binders Evaluated by Elastic P-Waves during Compressive Loading. Materials 15(21):7798, 2022. DOI: 10.3390/ma15217798.

5. Coherence of Bangui Magnetic Anomaly with Topographic and Gravity Contrasts across Central African Republic, Minerals, 13(5), 604. DOI: 10.3390/min13050604.

6. Shear bond and compressive strength of clay stabilised with lime/cement jet grouting and deep mixing: A case of Norvik, Nynäshamn, Nonlinear Engineering 11(1):693-710, 2022. DOI: 10.1515/nleng-2022-0269.

7. Seismic monitoring of strength in stabilized foundations by P-wave reflection and downhole geophysical logging for drill borehole core. Journal of the Mechanical Behavior of Materials, 32(1), 2023, 20220290. 10.1515/jmbm-2022-0290

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# Acoustic remote sensing data and methods for geophysical signal processing

## Geophysical Data Analysis

I worked in Schmidt Institute of Physics of the Earth, RAS, Dept. of Natural Disasters, Anthropogenic Hazards and Seismicity of the Earth. Lab. of Regional Geophysics and Natural Disasters =>

I have a strong hands-on experience in processing geophysical RS data (acoustic signals) and theoretical background in processes related to soil properties and inner structure of the Earth. I process acoustical signals by applied methods of RS data analysis to characterise land structures and properties of materials.

## Examples

1. Ultrasonic P- and S-Wave Reflection and CPT Soundings for Measuring Shear Strength in Soil Stabilized by Deep Lime/Cement Columns in Stockholm Norvik Port. Archives of Acoustics, 48(3), 325-346. DOI: 10.24425/aoa.2023.145243.

2. Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium. Sensors. 2023; 23(1):56. DOI: 10.3390/s23010056

3. Simplex Lattice Design and X-ray Diffraction for Analysis of Soil Structure: A Case of Cement-Stabilised Compacted Tills Reinforced with Steel Slag and Slaked Lime, Electronics, 11(22):3726, 2022. DOI: 10.3390/electronics11223726.

4. Dynamics of Strength Gain in Sandy Soil Stabilised with Mixed Binders Evaluated by Elastic P-Waves during Compressive Loading. Materials 15(21):7798, 2022. DOI: 10.3390/ma15217798.

5. Coherence of Bangui Magnetic Anomaly with Topographic and Gravity Contrasts across Central African Republic, Minerals, 13(5), 604. DOI: 10.3390/min13050604.

6. Shear bond and compressive strength of clay stabilised with lime/cement jet grouting and deep mixing: A case of Norvik, Nynäshamn, Nonlinear Engineering 11(1):693-710, 2022. DOI: 10.1515/nleng-2022-0269.

7. Seismic monitoring of strength in stabilized foundations by P-wave reflection and downhole geophysical logging for drill borehole core. Journal of the Mechanical Behavior of Materials, 32(1), 2023, 20220290. 10.1515/jmbm-2022-0290

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# Topographic 3D modelling by GMT scripting toolset

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Examples of my 3D mapping of Japan Archipelago and terrain grid using ETOPO2 data and GMT.

## Highlight

I can map 3D and perspective plots by GMT scripting for visualization of the terrain. I use various data: GEBCO/SRTM and ETOPO1 grids for geomorphological mapping.

## Example

Full paper: *Quantitative Morphometric 3D Terrain Analysis of Japan Using Scripts of GMT and R*, in: Land, 12(1), 261: DOI: 10.3390/land12010261.
# Topographic 3D modelling by GMT scripting toolset

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Examples of my 3D mapping of Japan Archipelago and terrain grid using ETOPO2 data and GMT.

## Highlight

I can map 3D and perspective plots by GMT scripting for visualization of the terrain. I use various data: GEBCO/SRTM and ETOPO1 grids for geomorphological mapping.

## Example

Full paper: *Quantitative Morphometric 3D Terrain Analysis of Japan Using Scripts of GMT and R*, in: Land, 12(1), 261: DOI: 10.3390/land12010261.
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Modelling cross-sections by GMT for analysis of slope steepness

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Examples of my modelling of the cross-section profiles using GMT.

## Highlight

I can map cross-sections by GMT to visualise a complex sequence of terraces and deposits including ancient layers in structural terraces. It is useful for analysis of landscape dynamics from buried fill terraces to present. Cross-sections can be used for modelling the effects of erosion, floodplains or hydrological analysis of river valleys.

## Example

Example paper: *Variations in the bathymetry and bottom morphology of the Izu-Bonin Trench modelled by GMT*, in: Bulletin of Geography. Physical Geography Series, 18(1), 41–60: DOI: 10.2478/bgeo-2020-0004.

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Modelling cross-sections by GMT for analysis of slope steepness

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Examples of my modelling of the cross-section profiles using GMT.

Highlight

I can map cross-sections by GMT to visualise a complex sequence of terraces and deposits including ancient layers in structural terraces. It is useful for analysis of landscape dynamics from buried fill terraces to present. Cross-sections can be used for modelling the effects of erosion, floodplains or hydrological analysis of river valleys.

Example

Example paper: *Variations in the bathymetry and bottom morphology of the Izu-Bonin Trench modelled by GMT*, in: Bulletin of Geography. Physical Geography Series, 18(1), 41–60: DOI: 10.2478/bgeo-2020-0004.

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# Statistical data modelling and analysis

## Statistical Analysis

My skills in statistical analysis include a variety of data modelling techniques using tabular data format (processing tables): plotting histograms, box plots, QQ quantiles, regression analysis, cluster analysis (dendrograms, data grouping, k-means), ANOVA, correlation matrices, PCA, density curves KDE, 3D scatter plots, pie charts, etc.

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Examples of my plotting using libraries of Python and R for data analysis (here: submarine gearchaeology analysed by cross-section profiles).

## Examples

Paper: *Statistical Analysis of the Mariana Trench geomorphology Using R Programming Language, Geodesy and Cartography, 45(2), 57-84: DOI: 10.3846/gac.2019.3785.*

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# Statistical data modelling and analysis

## Statistical Analysis

My skills in statistical analysis include a variety of data modelling techniques using tabular data format (processing tables): plotting histograms, box plots, QQ quantiles, regression analysis, cluster analysis (dendrograms, data grouping, k-means), ANOVA, correlation matrices, PCA, density curves KDE, 3D scatter plots, pie charts, etc.

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Examples of my plotting using libraries of Python and R for data analysis (here: submarine gearchaeology analysed by cross-section profiles).

## Examples

Paper: *Statistical Analysis of the Mariana Trench geomorphology Using R Programming Language, Geodesy and Cartography, 45(2), 57-84: DOI: 10.3846/gac.2019.3785.*

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# Publications: my experience on RS data processing reported in journals

## I continuously publish my research on satellite image analysis

* I have a strong publication record and experience in publishing scientific papers.

* I have strong skills in academic writing on RS and Earth Observation topics. In my papers I report the whole process of performed research, logical structure and organising of workflow, performing related tasks (literature review on RS data processing, flowcharts, RS data analysis, visualization of the processed satellite images and mapping, description of results).

* The most of my articles are published in journals indexed in Scopus/WoS.

* My papers are publicly available in open access mode on ResearchGate, Academia.edu, Google Scholar and other research-related websites of academic and professional community.

* To promote papers, I retweet them in Twitter and LinkedIn, and post on Facebook. Sharing helps to improve metrics, gain citations, attract social feedback, and have a greater impact.

* Selected examples of my recent publications are provided in this presentation.

## Soft skills:

* Motivation for cross-disciplinary research: cartographic visualization and mapping + remote sensing + GIS + Earth Observation data

* Working in international and multi-disciplinary teams with different educational backgrounds.

* Time management skills: attention to deadlines, multi-task regime, communication.

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# Publications: my experience on RS data processing reported in journals

## I continuously publish my research on satellite image analysis

* I have a strong publication record and experience in publishing scientific papers.

* I have strong skills in academic writing on RS and Earth Observation topics. In my papers I report the whole process of performed research, logical structure and organising of workflow, performing related tasks (literature review on RS data processing, flowcharts, RS data analysis, visualization of the processed satellite images and mapping, description of results).

* The most of my articles are published in journals indexed in Scopus/WoS.

* My papers are publicly available in open access mode on ResearchGate, Academia.edu, Google Scholar and other research-related websites of academic and professional community.

* To promote papers, I retweet them in Twitter and LinkedIn, and post on Facebook. Sharing helps to improve metrics, gain citations, attract social feedback, and have a greater impact.

* Selected examples of my recent publications are provided in this presentation.

## Soft skills:

* Motivation for cross-disciplinary research: cartographic visualization and mapping + remote sensing + GIS + Earth Observation data

* Working in international and multi-disciplinary teams with different educational backgrounds.

* Time management skills: attention to deadlines, multi-task regime, communication.

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[13] P. Lemenkova and O. Debeir. "Satellite Altimetry and Geimetry Data for Mapping Marine Geodetic and Geophysical Setting of the Seychelles and the Somali Sea, Indian Ocean". In: *Journal of Applied Engineering Sciences* 12(25) (2 2022), pp. 191-192. DOI: 10.2478/java-2022-0026

[14] P. Lemenkova and O. Debeir. "Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data from Côte d'Ivoire, West Africa". In: *Journal of Imaging* 8 (12 Nov. 2022), p. 317. DOI: 10.3390/jiimaging8120317

[15] P. Lemenkova and O. Debeir. "Seismotectonics of Shallow-Focus Earthquakes in Venezuela with Links to Gravity Anomalies and Geologic Heterogeneity Mapped by a GMT Scripting Language". In: *Sustainability* 14 (23 Nov. 2022), p. 15966. DOI: 10.3390/su142315966

[16] P. Lemenkova, R. D. Plaen, T. Lecocq, and O. Debeir. "Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium". In: *Sensors* 23 (1 Nov. 2022), p. 56. DOI: 10.3390/s23010056

[17] P. Lemenkova. "Geodynamic setting of Scotia Sea and its effects on geomorphology of South Sandwich Trench, Southern Ocean". In: *Polish Polar Research* 42 (1 Mar. 2021), pp. 1-23. DOI: 10.24425/ppz.2021.136810

[18] P. Lemenkova. "Submarinic tectonic geomorphology of the Pliny and Hellenic Trenches reflecting geologic evolution of the southern Greece". In: *Rudarsko-geoliko-nartni zbornik* 36 (4 Sept. 2021), pp. 33-48. DOI: 10.17794/rgzn.2021.4.4.

[19] P. Lemenkova. "The visualization of geophysical and geomorphologic data from the area of Weddell Sea by the Generic Mapping Tools". In: *Studia Quaternaria* 38 (1 Apr. 2021), pp. 19-32. DOI: 10.24425/eq.2020.133759

[20] P. Lemenkova. "GEBCO Gridded Bathymetric Datasets for Mapping Japan Trench Geomorphology by Means of GMT Scripting Toolset". In: *Geodesy and Cartography* 46 (3 2020), pp. 98-112. DOI: 10.3846/gac.2020.11524

[21] P. Lemenkova. "Geomorphology of the Puerto Rico Trench and Cayman Trench in the Context of the Geological Evolution of the Caribbean Sea". In: *Annales Universitatis Mariae Curie-Skłodowska, sectio B – Geographia, Geologia, Mineralogia et Petrographia* 75 (Nov. 2020), pp. 115-141. DOI: 10.17951/b.2020.75.115-141

[22] P. Lemenkova. "GMT Based Comparative Geomorphological Analysis of the Vityaz and Vanuatu Trenches, Fiji Basin". In: *Geodetski List* 74.1 (97 Mar 2020), pp. 19-39. DOI: 10.5281/zenodo.3794155

[23] P. Lemenkova. "Java and Sumatra Segments of the Sunda Trench: Geomorphology and Geophysical Settings Analysed and Visualized by GMT". In: *Glasnik Srpskog Geografskog Drustva* 100 (2 Dec. 2020), pp. 1-23. DOI: 10.2298/68020020011

[24] P. Lemenkova. "The geomorphology of the Makran Trench in the context of the geological and geophysical settings of the Arabian Sea". In: *Geology, Geophysics and Environment* 46 (3 Oct. 2020), pp. 205-222. DOI: 10.7494/geo1.2020.46.3.205

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