# Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. Doctoral. Salzburg, Austria. 2024, pp.43. hal-04611990

HAL Id: hal-04611990

https://hal.science/hal-04611990

Submitted on 14 Jun 2024

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

Distributed under a Creative Commons Attribution 4.0 International License
# Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. Doctoral. Salzburg, Austria. 2024, pp.43. hal-04611990

HAL Id: hal-04611990

https://hal.science/hal-04611990v1

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_1.png" /></figure>
# Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images

Polina Lemenkova

June 13, 2024

<figure><img src="image_2.png" /></figure>
# Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images

Polina Lemenkova

June 13, 2024

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

* **Concept:** Landscapes of Africa: land surface where diverse environmental processes interplay

* **Understanding landscape dynamics:** modelling and mapping complexity of factors that affect the shape of the Earth

* **Dynamics:** spatio-temporal changes caused by human and natural forces

* Applications of landscape ecology and environmental monitoring of Africa:

* Landscape monitoring

* Land management (urban planning)

* Sustainable development (food resources, agriculture)

* **Nature:** Factors affecting formation of landscapes: geologic-geological setting, climate processes, anthropogenic activities => different relief, soil and vegetation

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
# Introduction

* **Concept:** Landscapes of Africa: land surface where diverse environmental processes interplay

* **Understanding landscape dynamics:** modelling and mapping complexity of factors that affect the shape of the Earth

* **Dynamics:** spatio-temporal changes caused by human and natural forces

* Applications of landscape ecology and environmental monitoring of Africa:

* Landscape monitoring

* Land management (urban planning)

* Sustainable development (food resources, agriculture)

* **Nature:** Factors affecting formation of landscapes: geologic-geological setting, climate processes, anthropogenic activities = > different relief, soil and vegetation

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>
# Concept

## EO Data as an Opportunity

* **Possibilities:** Technical revolution in the amount of Earth data (satellite imagery, digital raster and vector datasets, tabular data) enabled smooth and quick access to monitoring of the large regions of the Earth, such as African continent

* **Access:** Open datasets and repositories available online enable to access, download, examine, and map RS and cartographic spatial data for mapping Africa

* **Coverage:** EO data provide information on spatial events across local, regional, and global scales of African landscapes: e.g. risk assessment, tsunami caused by monsoons, earthquakes (East African rift), landslides

## Machine Learning for Processing big Earth data

* **Problem:** Extracting knowledge and information from EO data requires advanced technical tools => need for ML/DL algorithms and scripting approaches to data handling

* **Paradigm:** ML and scripts present advanced methods of EO data analysis that automate data processing, modelling, mapping and visualization.

* **Hierarchy:** ML is a branch of Artificial Intelligence (AI) => GIS *learns from data and extracts information*. Identification of landscape patterns and detecting land cover changes

<figure><img src="image_5.png" /></figure>
# Concept

## EO Data as an Opportunity

* **Possibilities:** Technical revolution in the amount of Earth data (satellite imagery, digital raster and vector datasets, tabular data) enabled smooth and quick access to monitoring of the large regions of the Earth, such as African continent

* **Access:** Open datasets and repositories available online enable to access, download, examine, and map RS and cartographic spatial data for mapping Africa

* **Coverage:** EO data provide information on spatial events across local, regional, and global scales of African landscapes: e.g. risk assessment, tsunami caused by monsoons, earthquakes (East African rift), landslides

## Machine Learning for Processing big Earth data

* **Problem:** Extracting knowledge and information from EO data requires advanced technical tools => need for ML/DL algorithms and scripting approaches to data handling

* **Paradigm:** ML and scripts present advanced methods of EO data analysis that automate data processing, modelling, mapping and visualization.

* **Hierarchy:** ML is a branch of Artificial Intelligence (AI) => GIS *learns from data and extracts information*. Identification of landscape patterns and detecting land cover changes

<figure><img src="image_5.png" /></figure>
# Objectives and Goals

## Machine Learning in Earth data processing for mapping Africa

* **Mapping:** Environmental analysis of African landscapes through processing of RS data

* **Cartography:** Generic Mapping Tools (GMT): special syntax enabling to quickly operate with cartographic data using shell scripts

* **Examples:** Automation of data processing: programming languages (Python and R), scripting GIS and scripting toolsets (GRASS GIS, GMT)

## Remote sensing

* Using scripting algorithms for RS data (satellite images, e.g., Landsat) to extract environmental information for mapping variability of landscapes across Africa

## Research Techniques

* This research includes algorithms of Generic Mapping Tools (GMT) that has a scripting syntax enabling to quickly operate with spatial data using shell scripts

<figure><img src="image_6.png" /></figure>
# Objectives and Goals

## Machine Learning in Earth data processing for mapping Africa

* **Mapping:** Environmental analysis of African landscapes through processing of RS data

* **Cartography:** Generic Mapping Tools (GMT): special syntax enabling to quickly operate with cartographic data using shell scripts

* **Examples:** Automation of data processing: programming languages (Python and R), scripting GIS and scripting toolsets (GRASS GIS, GMT)

## Remote sensing

* Using scripting algorithms for RS data (satellite images, e.g., Landsat) to extract environmental information for mapping variability of landscapes across Africa

## Research Techniques

* This research includes algorithms of Generic Mapping Tools (GMT) that has a scripting syntax enabling to quickly operate with spatial data using shell scripts

<figure><img src="image_6.png" /></figure>
# Research Highlights: Variability of African landscapes

* **Complexity:** variability of regional geographic setting on the African continent

* **Scale:** Handling multi-source spatial data for local, regional and continental scales

* **Data-driven research:** Variety of RS data (Landsat TM/ETM+ OLI/TIRS, Sentinel-2A, SPOT), weather data as time series, climate forecasts, ecological monitoring, seismic observations (hazard assessment).

* **Methods:** Processing spatial data by advanced tools and methods (scripts, programming algorithms)

Landscape ecology of diverse regions of Africa reflects a variety of impact factors: tectonic-geologic setting, climate change and anthropogenic activities

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
# Research Highlights: Variability of African landscapes

* **Complexity:** variability of regional geographic setting on the African continent

* **Scale:** Handling multi-source spatial data for local, regional and continental scales

* **Data-driven research:** Variety of RS data (Landsat TM/ETM+ OLI/TIRS, Sentinel-2A, SPOT), weather data as time series, climate forecasts, ecological monitoring, seismic observations (hazard assessment).

* **Methods:** Processing spatial data by advanced tools and methods (scripts, programming algorithms)

Landscape ecology of diverse regions of Africa reflects a variety of impact factors: tectonic-geologic setting, climate change and anthropogenic activities

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
Research Progress: Current state of the project

Current state: mapping of 27 African countries is published in 32 journal articles

<table><thead><tr><td>No</td><td>Name</td><td>Publication</td><td>Link</td></tr></thead><tbody><tr><td>1</td><td>Algeria</td><td>[9]</td><td>doi: 10.3390/asi6040061</td></tr><tr><td>2</td><td>Botswana</td><td>[12]</td><td>doi: 10.3390/ijgi11090473</td></tr><tr><td>3</td><td>Burkina Faso</td><td>[25]</td><td>doi: 10.24427/agg.2023.146157</td></tr><tr><td>4</td><td>Cameroon</td><td>[23]</td><td>doi: 10.47246/CEJGSD.2020.2.2.4</td></tr><tr><td>5</td><td>Central Africa Republic</td><td>[28]</td><td>doi: 10.3390/min13050604</td></tr><tr><td>6</td><td>Chad</td><td>[5]</td><td>doi: 10.2478/boku-2023-0005</td></tr><tr><td>7</td><td>D.R.C. Congo</td><td>[31]</td><td>doi: 10.3390/app122412554</td></tr><tr><td>8</td><td>Egypt</td><td>[27]</td><td>doi: 10.3390/infol4040249</td></tr><tr><td>9</td><td>Ethiopia</td><td>[11, 15]</td><td>doi: 10.24425/jwld.2022.141573; doi: 10.5755/j01.erem.78.1.29963</td></tr><tr><td>10</td><td>Ghana</td><td>[21, 19]</td><td>doi: 10.4314/sinet.v4il.9; doi: 10.2478/abmj-2021-0010</td></tr><tr><td>11</td><td>Ghana</td><td>[14, 17]</td><td>doi: 10.24425/gac.2022.141169; doi: 10.32909/kg.20.36.2</td></tr><tr><td>12</td><td>Guinea</td><td>[4]</td><td>doi: 10.5281/zenodo.10673287</td></tr><tr><td>13</td><td>Ivory Coast (Côte d'Ivoire)</td><td>[32]</td><td>doi: 10.3390/jimaging8120317</td></tr><tr><td>14</td><td>Kenya</td><td>[8]</td><td>doi: 10.2478/trser-2023-0008</td></tr><tr><td>15</td><td>Malawi</td><td>[13, 18]</td><td>doi: 10.5937/tehnika22021831; doi: 10.5281/zenodo.5772315</td></tr><tr><td>16</td><td>Mali</td><td>[24]</td><td>doi: 10.2478/arsa-2023-0011</td></tr><tr><td>17</td><td>Mozambique</td><td>[3]</td><td>doi: 10.3390/coasts4010008</td></tr><tr><td>18</td><td>Nigeria</td><td>[29]</td><td>doi: 10.3390/jmse11040871</td></tr><tr><td>19</td><td>Rwanda</td><td>[10]</td><td>doi: 10.5281/zenodo.7113414</td></tr><tr><td>20</td><td>Sierra Leone</td><td>[1]</td><td>doi: 10.5281/zenodo.11473307</td></tr><tr><td>21</td><td>Somalia and Seychelles</td><td>[30]</td><td>doi: 10.2478/jaes-2022-0026</td></tr><tr><td>22</td><td>South Africa</td><td>[2]</td><td>doi: 10.4000/11pjy</td></tr><tr><td>23</td><td>South Sudan</td><td>[7]</td><td>doi: 10.3390/ analytics2030040</td></tr><tr><td>24</td><td>Sudan</td><td>[26]</td><td>doi: 10.3390/jimaging9050098</td></tr><tr><td>25</td><td>Tanzania</td><td>[16]</td><td>doi: 10.3176/earth.2022.05</td></tr><tr><td>26</td><td>Tunisia</td><td>[6]</td><td>doi: 10.3390/land12111995</td></tr><tr><td>27</td><td>Uganda</td><td>[20]</td><td>doi: 10.5281/zenodo.5082861</td></tr><tr><td>28</td><td>Zambia</td><td>[22]</td><td>doi: 10.5937/ZemBijl21011171L</td></tr></tbody></table>

<figure><img src="image_9.png" /></figure>
Research Progress: Current state of the project

Current state: mapping of 27 African countries is published in 32 journal articles

<table><thead><tr><td>No</td><td>Name</td><td>Publication</td><td>Link</td></tr></thead><tbody><tr><td>1</td><td>Algeria</td><td>[9]</td><td>doi: 10.3390/asi6040061</td></tr><tr><td>2</td><td>Botswana</td><td>[12]</td><td>doi: 10.3390/ijgi11090473</td></tr><tr><td>3</td><td>Burkina Faso</td><td>[25]</td><td>doi: 10.24427/agg.2023.146157</td></tr><tr><td>4</td><td>Cameroon</td><td>[23]</td><td>doi: 10.47246/CEJGSD.2020.2.2.4</td></tr><tr><td>5</td><td>Central Africa Republic</td><td>[28]</td><td>doi: 10.3390/min13050604</td></tr><tr><td>6</td><td>Chad</td><td>[5]</td><td>doi: 10.2478/boku-2023-0005</td></tr><tr><td>7</td><td>D.R.C. Congo</td><td>[31]</td><td>doi: 10.3390/app122412554</td></tr><tr><td>8</td><td>Egypt</td><td>[27]</td><td>doi: 10.3390/infl04040249</td></tr><tr><td>9</td><td>Ethiopia</td><td>[11, 15]</td><td>doi: 10.24425/jwld.2022.141573; doi: 10.5755/j01.erem.78.1.29963</td></tr><tr><td>10</td><td>Ghana</td><td>[21, 19]</td><td>doi: 10.4314/sinet.v4il.9; doi: 10.2478/abmj-2021-0010</td></tr><tr><td>11</td><td>Ghana</td><td>[14, 17]</td><td>doi: 10.24425/gac.2022.141169; doi: 10.32909/kg.20.36.2</td></tr><tr><td>12</td><td>Guinea</td><td>[4]</td><td>doi: 10.5281/zenodo.10673287</td></tr><tr><td>13</td><td>Ivory Coast (Côte d'Ivoire)</td><td>[32]</td><td>doi: 10.3390/jimaging8120317</td></tr><tr><td>14</td><td>Kenya</td><td>[8]</td><td>doi: 10.2478/trser-2023-0008</td></tr><tr><td>15</td><td>Malawi</td><td>[13, 18]</td><td>doi: 10.5937/tehnika22021831; doi: 10.5281/zenodo.5772315</td></tr><tr><td>16</td><td>Mali</td><td>[24]</td><td>doi: 10.2478/arsa-2023-0011</td></tr><tr><td>17</td><td>Mozambique</td><td>[3]</td><td>doi: 10.3390/coasts4010008</td></tr><tr><td>18</td><td>Nigeria</td><td>[29]</td><td>doi: 10.3390/jmse11040871</td></tr><tr><td>19</td><td>Rwanda</td><td>[10]</td><td>doi: 10.5281/zenodo.7113414</td></tr><tr><td>20</td><td>Sierra Leone</td><td>[1]</td><td>doi: 10.5281/zenodo.11473307</td></tr><tr><td>21</td><td>Somalia and Seychelles</td><td>[30]</td><td>doi: 10.2478/jaes-2022-0026</td></tr><tr><td>22</td><td>South Africa</td><td>[2]</td><td>doi: 10.4000/11pjy</td></tr><tr><td>23</td><td>South Sudan</td><td>[7]</td><td>doi: 10.3390/ analytics2030040</td></tr><tr><td>24</td><td>Sudan</td><td>[26]</td><td>doi: 10.3390/jimaging9050098</td></tr><tr><td>25</td><td>Tanzania</td><td>[16]</td><td>doi: 10.3176/earth.2022.05</td></tr><tr><td>26</td><td>Tunisia</td><td>[6]</td><td>doi: 10.3390/land12111995</td></tr><tr><td>27</td><td>Uganda</td><td>[20]</td><td>doi: 10.5281/zenodo.5082861</td></tr><tr><td>28</td><td>Zambia</td><td>[22]</td><td>doi: 10.5937/ZemBijl21011171L</td></tr></tbody></table>

<figure><img src="image_9.png" /></figure>
* Understanding complexity and variability of landscapes requires processing of large amounts of diverse datasets

* Multi-source data => effective, rapid yet accurate processing

* High-resolution data => at print-quality mapping and modelling. Accuracy of digital Earth data is of crucial importance, because it directly controls research output.

<table><caption>Table 1: Data used in this project: their sources, types and precision</caption><thead><tr><td>No</td><td>Data</td><td>Origine</td><td>Type, resolution</td></tr></thead><tbody><tr><td>1</td><td>Landsat 8-9 OLI/TIRS</td><td>USGS</td><td>satellite images</td></tr><tr><td>2</td><td>ETOP01</td><td>NOAA</td><td>1 arc-min GRM grid</td></tr><tr><td>3</td><td>ETOP05</td><td>NOAA</td><td>5 arc min GRM grid</td></tr><tr><td>4</td><td>GEBCO</td><td>BODC</td><td>15 arc sec DEM raster grid</td></tr><tr><td>5</td><td>SRTM</td><td>NASA</td><td>15-sec DEM raster grid</td></tr><tr><td>6</td><td>Geology</td><td>USGS</td><td>Vector layers for diverse African countries</td></tr><tr><td>7</td><td>Social data</td><td>FAO</td><td>Vector layers for administrative borders and population</td></tr><tr><td>8</td><td>Gravity</td><td>Scripps IO</td><td>CryoSat-2, Jason-1 grid</td></tr><tr><td>9</td><td>EGM96</td><td>Scripps IO</td><td>Geopotential data: geoid</td></tr><tr><td>10</td><td>EGM-2008</td><td>Scripps IO</td><td>Geoid model</td></tr><tr><td>11</td><td>TerraClimate</td><td>NOAA</td><td>Climate characteristics of African landscapes</td></tr><tr><td>12</td><td>GLOBE dataset</td><td>NOAA</td><td>Topography model</td></tr><tr><td>13</td><td>GEBCO</td><td>IHO-IOC</td><td>Geographic gazetteer</td></tr></tbody></table>

<figure><img src="image_10.png" /></figure>

<footnote>*30 arc second = ca. 1km cell size, 15 arc sec = ca. 500 m cell size, etc.</footnote>
* Understanding complexity and variability of landscapes requires processing of large amounts of diverse datasets

* Multi-source data => effective, rapid yet accurate processing

* High-resolution data => at print-quality mapping and modelling. Accuracy of digital Earth data is of crucial importance, because it directly controls research output.

<table><caption>Table 1: Data used in this project: their sources, types and precision</caption><thead><tr><td>No</td><td>Data</td><td>Origine</td><td>Type, resolution</td></tr></thead><tbody><tr><td>1</td><td>Landsat 8-9 OLI/TIRS</td><td>USGS</td><td>satellite images</td></tr><tr><td>2</td><td>ETOP01</td><td>NOAA</td><td>1 arc-min GRM grid</td></tr><tr><td>3</td><td>ETOP05</td><td>NOAA</td><td>5 arc min GRM grid</td></tr><tr><td>4</td><td>GEBCO</td><td>BODC</td><td>15 arc sec DEM raster grid</td></tr><tr><td>5</td><td>SRTM</td><td>NASA</td><td>15-sec DEM raster grid</td></tr><tr><td>6</td><td>Geology</td><td>USGS</td><td>Vector layers for diverse African countries</td></tr><tr><td>7</td><td>Social data</td><td>FAO</td><td>Vector layers for administrative borders and population</td></tr><tr><td>8</td><td>Gravity</td><td>Scripps IO</td><td>CryoSat-2, Jason-1 grid</td></tr><tr><td>9</td><td>EGM96</td><td>Scripps IO</td><td>Geopotential data: geoid</td></tr><tr><td>10</td><td>EGM-2008</td><td>Scripps IO</td><td>Geoid model</td></tr><tr><td>11</td><td>TerraClimate</td><td>NOAA</td><td>Climate characteristics of African landscapes</td></tr><tr><td>12</td><td>GLOBE dataset</td><td>NOAA</td><td>Topography model</td></tr><tr><td>13</td><td>GEBCO</td><td>IHO-IOC</td><td>Geographic gazetteer</td></tr></tbody></table>

<figure><img src="image_10.png" /></figure>

<footnote>*30 arc second = ca. 1km cell size, 15 arc sec = ca. 500 m cell size, etc.</footnote>
# Methods

## Cartographic tasks by scripts: Generic Mapping Tools (GMT)

<figure><img src="image_11.png" /><figcaption>Figure 1: Using GMT as integrated tools for cartographic workflow</figcaption></figure>

* Various GMT techniques and modules have been used in this research for rapid and accurate mapping

* gmtpsy for plotting graphs and adding annotations on raster map

* gmtdmakeopt for making color palette

* gmtdrgimage for generating raster image

* gmtpsscale for adding legend for adding scale

* gmtdrdcontour for adding isolines

* gmtpsbasemap for adding grid and essential cartographic marks

* gmtpsbasemap for adding scale and directional rose

* echo UNIX utility for adding text labels

* gmtpstext for adding subtitle

* gmtp logo for adding GMT logo

* gmtpconconvert for convert PS to image (by GhostScript interpreter)

* gmtp legend adding legend on maps with cartographic annotations

* gmtphistogram for statistical analysis of topographic data distribution (relief)

* gmtprose for plotting rose diagrams

* gmtpimg2grd converting geospatial formats

* gmtdrgcut selecting study area and clipping the region from the world map

These and many other modules of GMT were used in this research.

* Plotting a map by GMT becomes a rapid procedure, fully controlled by the cartographer

* Such automatisation by GMT facilitates mapping and decreases time of cartographic workflow

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

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>
# Methods

## Cartographic tasks by scripts: Generic Mapping Tools (GMT)

<figure><img src="image_11.png" /><figcaption>Figure 1: Using GMT as integrated tools for cartographic workflow</figcaption></figure>

* Various GMT techniques and modules have been used in this research for rapid and accurate mapping

* gmtpsy for plotting graphs and adding annotations on raster map

* gmtdmakeopt for making color palette

* gmtdrgimage for generating raster image

* gmtpsscale for adding legend for adding scale

* gmtdrdcontour for adding isolines

* gmtpsbasemap for adding grid and essential cartographic marks

* gmtpsbasemap for adding scale and directional rose

* echo UNIX utility for adding text labels

* gmtpstext for adding subtitle

* gmtp logo for adding GMT logo

* gmtpconconvert for convert PS to image (by GhostScript interpreter)

* gmtp legend adding legend on maps with cartographic annotations

* gmtphistogram for statistical analysis of topographic data distribution (relief)

* gmtprose for plotting rose diagrams

* gmtpimg2grd converting geospatial formats

* gmtdrdgrid selecting study area and clipping the region from the world map

These and many other modules of GMT were used in this research.

* Plotting a map by GMT becomes a rapid procedure, fully controlled by the cartographer

* Such automatisation by GMT facilitates mapping and decreases time of cartographic workflow

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

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>
# Algeria

* Algeria - the largest country in Africa and the Mediterranean Basin.

## Contrasting environment:

* south - a significant part of Sahara Desert;

* center - Tell Atlas Mts. (Saharan Atlas) + vast plains, highlands (e.g., Hoggar Mts) & mountain ranges

* coastal areas: hilly & mountainous landscapes, a few harbours.

* Diverse topographic setting + climate setting = contrasting precipitation patterns

* Salt lakes (saline sabkhas) in the semi-desert environment on border of Sahara

<figure><img src="image_32.png" /><figcaption>Figure 2: Topographic map of Algeria</figcaption></figure>

<figure><img src="image_33.png" /></figure>

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

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

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

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

* Algeria - the largest country in Africa and the Mediterranean Basin.

## Contrasting environment:

* south - a significant part of Sahara Desert;

* center - Tell Atlas Mts. (Saharan Atlas) + vast plains, highlands (e.g., Hoggar Mts) & mountain ranges

* coastal areas: hilly & mountainous landscapes, a few harbours.

* Diverse topographic setting + climate setting = contrasting precipitation patterns

* Salt lakes (saline sabkhas) in the semi-desert environment on border of Sahara

<figure><img src="image_29.png" /><figcaption>Figure 2: Topographic map of Algeria</figcaption></figure>

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# Algeria, Sahara: Sabkhas Salt Lakes

<figure><img src="image_50.png" /></figure>

Figure 3: Sabkha - coastal sandy flats of northern Algeria where evaporite and saline minerals accumulate. Sabkhas fluctuate seasonally and over years (subject to climate change)

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

Figure 4: Workflow used to map sabkhas of Algeria using RS data

* Sabkha of Algeria - in semiarid to arid climate (border of Sahara + impact of Mediterranean).

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# Algeria, Sahara: Sabkhas Salt Lakes

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

Figure 3: Sabkha - coastal sandy flats of northern Algeria where evaporite and saline minerals accumulate. Sabkhas fluctuate seasonally and over years (subject to climate change)

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

Figure 4: Workflow used to map sabkhas of Algeria using RS data

* Sabkha of Algeria - in semiarid to arid climate (border of Sahara + impact of Mediterranean).

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

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<figure><img src="image_56.png" /><figcaption>Figure 5: Topographic map of Botswana with specific geographic features.</figcaption></figure>

<figure><img src="image_57.png" /><figcaption>Figure 6: Right: climate variables over Botswana (here: droughts)</figcaption></figure>

* **Relief:** mostly flat, gently rolling tableland

* **Climate:** dominated by the Kalahari Desert (ca. 70% of its land surface)

* **Hydrology:** Okavango Delta - one of the world's largest inland river deltas (UNESCO World Heritage Site) is located in Botswana.

* **Environment:** Okavango Delta - oasis in an arid climate of Botswana => large wetland system

* **Ecology:** rich wildlife, swamp-dominant rare species. Habitats: salt inlands, lagoons.

<figure><img src="image_58.png" /></figure>
<figure><img src="image_55.png" /><figcaption>Figure 5: Topographic map of Botswana with specific geographic features.</figcaption></figure>

<figure><img src="image_56.png" /><figcaption>Figure 6: Right: climate variables over Botswana (here: droughts)</figcaption></figure>

* **Relief:** mostly flat, gently rolling tableland

* **Climate:** dominated by the Kalahari Desert (ca. 70% of its land surface)

* **Hydrology:** Okavango Delta - one of the world's largest inland river deltas (UNESCO World Heritage Site) is located in Botswana.

* **Environment:** Okavango Delta - oasis in an arid climate of Botswana => large wetland system

* **Ecology:** rich wildlife, swamp-dominant rare species. Habitats: salt inlands, lagoons.

<figure><img src="image_57.png" /></figure>
# Burkina Faso

## Food insecurity & Poverty ($1,000) per capita - IMF '23 Reasons for food instability:

* Environmental hazards: Burkina Faso experiences one of the most contrasting climatic variations in the world with environmental hazards varying from severe floods to extreme drought => agriculture instability

* Climate issues: tropical with two very distinct seasons – rainy and dry seasons. During dry period - effects from harmattan (hot dry Saharan winds)

* Agricultural vulnerability: insect attacks (locusts and crickets), which destroy crops

* Migration: people migrate to other countries to find better jobs and support for their families

## Topography: flat relief with 2 major types of landscapes influenced by geologic setting.

* Penplain: larger part of the country (gently undulating landscape and a few isolated hills) from Precambrian massif.

* Sandstone massif: on the southwest of the country with the highest peak, Ténakourow

<figure><img src="image_59.png" /></figure>
# Burkina Faso

## Food insecurity & Poverty ($1,000) per capita - IMF '23 Reasons for food instability:

* Environmental hazards: Burkina Faso experiences one of the most contrasting climatic variations in the world with environmental hazards varying from severe floods to extreme drought => agriculture instability

* Climate issues: tropical with two very distinct seasons – rainy and dry seasons. During dry period - effects from harmattan (hot dry Saharan winds)

* Agricultural vulnerability: insect attacks (locusts and crickets), which destroy crops

* Migration: people migrate to other countries to find better jobs and support for their families

## Topography: flat relief with 2 major types of landscapes influenced by geologic setting.

* Penplain: larger part of the country (gently undulating landscape and a few isolated hills) from Precambrian massif.

* Sandstone massif: on the southwest of the country with the highest peak, Ténakourow

<figure><img src="image_58.png" /></figure>
<figure><img src="image_60.png" /><figcaption>Figure 8: Topographic map of Cameroon</figcaption></figure>

## Cameroon – Africa in miniature

All major climates and vegetation of the continent:

* Landscapes: a mixture of coastal ecosystems, desert plains and mountains and savannah in the central regions, tropical rain forests in the south

## Social-environmental problems of Cameron

* Population growth, urbanisation and industrialisation

* Land degradation, water / air pollution

* Waste management (plastic pollution)

* Anglophone crisis: colonial history of Cameroon (France and UK shared Cameroon unequally – French area 80% of the country => two nations in one country)

<figure><img src="image_61.png" /></figure>
<figure><img src="image_59.png" /><figcaption>Figure 8: Topographic map of Cameroon</figcaption></figure>

## Cameroon – Africa in miniature

All major climates and vegetation of the continent:

* Landscapes: a mixture of coastal ecosystems, desert plains and mountains and savannah in the central regions, tropical rain forests in the south

## Social-environmental problems of Cameron

* Population growth, urbanisation and industrialisation

* Land degradation, water / air pollution

* Waste management (plastic pollution)

* Anglophone crisis: colonial history of Cameroon (France and UK shared Cameroon unequally – French area 80% of the country => two nations in one country)

<figure><img src="image_60.png" /></figure>
# Data Integration

## Mapping central African regions (complex geology)

### Multi-source data mixing and integration using GMT adjustments:

* Data mining from open sources (e.g. Landsat, GEBCO, ETOPO1, geological data, etc) for geographic analysis

* Predictive environmental analytics: forecasting and prognosis in hazard risk assessment based on climate data and FAO information on food security

* Mapping geophysical setting in central African countries, volcanic activity, seismicity, earthquakes

* Data analysis by GMT has potential to advance development of cartography

* It enables scientific discoveries, based on spatial analysis in geology

## Scripting techniques in cartography

* GMT-based mapping provides accurate visualization of the complex terrain as in African countries

## Solutions of GMT:

* Advanced level of cartographic functionality: variety of projections, data processing, import/export, etc

* High-quality cartographic output: colour palettes, layouts, grids, vector lines, advanced design approaches

* Flexibility of coding / shell scripting from the console

* Embedded data analysis and visualisation: mapping, modelling, statistical analysis, logical queries, import/export, vector and raster processing

<figure><img src="image_62.png" /></figure>
# Data Integration

## Mapping central African regions (complex geology)

### Multi-source data mixing and integration using GMT adjustments:

* Data mining from open sources (e.g. Landsat, GEBCO, ETOPO1, geological data, etc) for geographic analysis

* Predictive environmental analytics: forecasting and prognosis in hazard risk assessment based on climate data and FAO information on food security

* Mapping geophysical setting in central African countries, volcanic activity, seismicity, earthquakes

* Data analysis by GMT has potential to advance development of cartography

* It enables scientific discoveries, based on spatial analysis in geology

## Scripting techniques in cartography

* GMT-based mapping provides accurate visualization of the complex terrain as in African countries

## Solutions of GMT:

* Advanced level of cartographic functionality: variety of projections, data processing, import/export, etc

* High-quality cartographic output: colour palettes, layouts, grids, vector lines, advanced design approaches

* Flexibility of coding / shell scripting from the console

* Embedded data analysis and visualisation: mapping, modelling, statistical analysis, logical queries, import/export, vector and raster processing

<figure><img src="image_61.png" /></figure>
Central African Republic (CAR)

<figure><img src="image_63.png" /><figcaption>Figure 9: Snapshot of the GMT script for cartographic mapping</figcaption></figure>

<caption>Figure 10: Topographic map and location of the CAR to analyse correlation between topography and geophysical setting</caption>

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Central African Republic (CAR)

<figure><img src="image_62.png" /><figcaption>Figure 9: Snapshot of the GMT script for cartographic mapping</figcaption></figure>

<caption>Figure 10: Topographic map and location of the CAR to analyse correlation between topography and geophysical setting</caption>

<figure><img src="image_63.png" /></figure>

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# Chad

<figure><img src="image_67.png" /><figcaption>Figure 11: Topographic map of Chad</figcaption></figure>

## Chad: social-environmental problems

* **Poverty:** Chad – the 7th poorest country in the world, with 80% of the population living below the poverty line (Source: UN' Human Development Index)

* **Arid climate, droughts:** Landlocked country with arid climate. Terrestrial ecoregions: savannah, xeric woodlands, south Saharan steppes

* **Lake Chad:** important source of food and natural resources; yet subject to seasonal and climate fluctuations

## RS and cartography for landscape analysis

<figure><img src="image_68.png" /></figure>
# Chad

<figure><img src="image_66.png" /><figcaption>Figure 11: Topographic map of Chad</figcaption></figure>

## Chad: social-environmental problems

* **Poverty:** Chad – the 7th poorest country in the world, with 80% of the population living below the poverty line (Source: UN' Human Development Index)

* **Arid climate, droughts:** Landlocked country with arid climate. Terrestrial ecoregions: savannah, xeric woodlands, south Saharan steppes

* **Lake Chad:** important source of food and natural resources; yet subject to seasonal and climate fluctuations

## RS and cartography for landscape analysis

<figure><img src="image_67.png" /></figure>
# Congo (D.R.C)

<figure><img src="image_69.png" /><figcaption>Figure 13: Topographic map of Congo D.R.C.</figcaption></figure>

<figure><img src="image_70.png" /><figcaption>Figure 14: Advantages of R for satellite image processing (libraries)</figcaption></figure>

* **Climate hazards:** Congo: high precipitation and the highest frequency of thunderstorms in the world.

* **Environment:** Congolese rainforests – the 2nd largest rain forest in the world after the Amazon

* **Biodiversity of Congo:** Lush jungle rainforest + savannah + grasslands + mountainous terraces

* **Geomorphology:** Rwenzori Mountains – the source of the Nile River with unique (for Africa) alpine meadows

<figure><img src="image_71.png" /><figcaption>Figure 15: Workflow for research methodology</figcaption></figure>

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# Congo (D.R.C)

<figure><img src="image_68.png" /><figcaption>Figure 13: Topographic map of Congo D.R.C.</figcaption></figure>

<figure><img src="image_69.png" /><figcaption>Figure 14: Advantages of R for satellite image processing (libraries)</figcaption></figure>

* **Climate hazards:** Congo: high precipitation and the highest frequency of thunderstorms in the world.

* **Environment:** Congolese rainforests – the 2nd largest rain forest in the world after the Amazon

* **Biodiversity of Congo:** lush jungle rainforest + savannah + grasslands + mountainous terraces

* **Geomorphology:** Rwenzori Mountains – the source of the Nile River with unique (for Africa) alpine meadows

<figure><img src="image_70.png" /><figcaption>Figure 15: Workflow for research methodology</figcaption></figure>

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<figure><img src="image_89.png" /><figcaption>**Figure 16:** Snippet of the R code for unsupervised classification by k-means clustering (Basoko, Congo DRC)</figcaption></figure>

* Automatisation in RS data analysis for Congo: diverse geospatial libraries of R

<figure><img src="image_90.png" /><figcaption>**Figure 17:** k-means clustering classification algorithm in RStudio using the RStoolbox package for satellite image processing (Landsat images covering Congo DRC)</figcaption></figure>

* Automatic image processing by R to study changes in vegetation of Congo, to analyse their correlation with landforms (topography), detect correlation with climate settings

<figure><img src="image_91.png" /></figure>
<figure><img src="image_88.png" /><figcaption>**Figure 16:** Snippet of the R code for unsupervised classification by k-means clustering (Basoko, Congo DRC)</figcaption></figure>

* Automatisation in RS data analysis for Congo: diverse geospatial libraries of R

<figure><img src="image_89.png" /><figcaption>**Figure 17:** k-means clustering classification algorithm in RStudio using the RStoolbox package for satellite image processing (Landsat images covering Congo DRC)</figcaption></figure>

* Automatic image processing by R to study changes in vegetation of Congo, to analyse their correlation with landforms (topography), detect correlation with climate settings

<figure><img src="image_90.png" /></figure>
Egypt

<figure><img src="image_92.png" /><figcaption>Figure 18: Qena Bend as study area on the map of Egypt</figcaption></figure>

<figure><img src="image_93.png" /><figcaption>Figure 19: 3D perspective view of Qena Bend, Nile River, Upper Egypt</figcaption></figure>

Features of Qena Bend:

* Qena Bend – a remarkable geomorphological feature in southern Egypt.

* Qena Bend presents a great loop of the Nile River towards the east

* Qena Bend is bounded by limestone cliffs on both sides

* Qena Bend is one of the most structurally-complicated regions of the Nile Valley, where it intersects from the east with some tectonic trends: Wadi Qena and Qena-Safaga Shear Zone

* The geodynamic formation of Qena Bend is still not fully understood.

* Cartographic scripting by GMT is essential for 3D modelling and geomorphological mapping to capture the correlation between geological, topographic and environmental variables

* Console-based mapping techniques are script-based and data-driven
Egypt

<figure><img src="image_91.png" /><figcaption>Figure 18: Qena Bend as study area on the map of Egypt</figcaption></figure>

<figure><img src="image_92.png" /><figcaption>Figure 19: 3D perspective view of Qena Bend, Nile River, Upper Egypt</figcaption></figure>

Features of Qena Bend:

* Qena Bend – a remarkable geomorphological feature in southern Egypt.

* Qena Bend presents a great loop of the Nile River towards the east

* Qena Bend is bounded by limestone cliffs on both sides

* Qena Bend is one of the most structurally-complicated regions of the Nile Valley, where it intersects from the east with some tectonic trends: Wadi Qena and Qena-Safaga Shear Zone

* The geodynamic formation of Qena Bend is still not fully understood.

* Cartographic scripting by GMT is essential for 3D modelling and geomorphological mapping to capture the correlation between geological, topographic and environmental variables

* Console-based mapping techniques are script-based and data-driven
<figure><img src="image_94.png" /><figcaption>Figure 20: Geomorphic features of Ethiopia: slope, aspect, DEM and hillshade relief map mapped using R</figcaption></figure>

## Geologic mapping

* Advanced cartographic tools => mapping relief => understanding the links between geology, geomorphology, seismicity and topography of Ethiopia

* Lnk s among geomorphology, tectonics and geology => insights into geologic evolution of the African continent and current topography of the East African Rift region

## Important geologic-geographic features of Ethiopia

* The major part of Ethiopia is located in the Horn of Africa, - the easternmost part of the African landmass

* Great Rift Valley divides Ethiopia into two parts of landscapes with a vast highland complex of mountains and dissected plateaus

* Great Rift Valley - branch of the East African Rift which stretches in SW-NE direction from the Afar Triple Junction.

* Afar Triple Junction is a unique example on the Earth of continental rifting (as opposed to seafloor spreading)

* In brief, the geologic reason for Afar Triple junction - extension of lithosphere crust, caused by mantle upwelling => seismically active region

* Great Rift Valley is surrounded by lowlands, steppes, or semi-desert.

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<figure><img src="image_93.png" /><figcaption>Figure 20: Geomorphic features of Ethiopia: slope, aspect, DEM and hillshade relief map mapped using R</figcaption></figure>

## Geologic mapping

* Advanced cartographic tools => mapping relief => understanding the links between geology, geomorphology, seismicity and topography of Ethiopia

* Lnk s among geomorphology, tectonics and geology => insights into geologic evolution of the African continent and current topography of the East African Rift region

## Important geologic-geographic features of Ethiopia

* The major part of Ethiopia is located in the Horn of Africa, - the easternmost part of the African landmass

* Great Rift Valley divides Ethiopia into two parts of landscapes with a vast highland complex of mountains and dissected plateaus

* Great Rift Valley - branch of the East African Rift which stretches in SW-NE direction from the Afar Triple Junction.

* Afar Triple Junction is a unique example on the Earth of continental rifting (as opposed to seafloor spreading)

* In brief, the geologic reason for Afar Triple junction - extension of lithosphere crust, caused by mantle upwelling => seismically active region

* Great Rift Valley is surrounded by lowlands, steppes, or semi-desert.

<figure><img src="image_94.png" /></figure>

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## Hydrology of Ghana

* White Volta River + its tributary Black Volta, flowing to Lake Volta – the world's third-largest reservoir by volume and largest by surface area,

* => hydroelectric Akosombo Dam – major electricity producer.

## Ecoregions of Ghana

* 5 terrestrial ecoregions of Ghana: Eastern Guinean forests, Guinean forest–savanna mosaic, West Sudanian savanna, Central African mangroves, and Guinean mangroves.

* Mapping using diverse data (geologic, topographic, land cover types) helps get better insights into the underlying processes of landscape formation which affect the distribution of vegetation types.
<figure><img src="image_112.png" /></figure>

## Hydrology of Ghana

* White Volta River + its tributary Black Volta, flowing to Lake Volta – the world's third-largest reservoir by volume and largest by surface area,

* => hydroelectric Akosombo Dam – major electricity producer.

## Ecoregions of Ghana

* 5 terrestrial ecoregions of Ghana: Eastern Guinean forests, Guinean forest–savanna mosaic, West Sudanian savanna, Central African mangroves, and Guinean mangroves.

* Mapping using diverse data (geologic, topographic, land cover types) helps get better insights into the underlying processes of landscape formation which affect the distribution of vegetation types.
# Guinea

<figure><img src="image_113.png" /><figcaption>Figure 22: Topographic map of Guinea</figcaption></figure>

<figure><img src="image_114.png" /><figcaption>Figure 23: Landscape types of Guinea</figcaption></figure>

## Environmental alerts of Guinea:

* Reduced wildlife due to human encroachment and hunting (e.g., large mammals)

* overfishing => t threat to the nation's marine life.

* Threatened species: the African elephant, Diana monkey, and Nimba otter-shrew.

* A nature reserve Mount Nimba Strict Nature Reserve is established on Mt. Nimba: protected area and UNESCO World Heritage Site

## Environmental features of Guinea:

* Guinean Forests of West Africa Biodiversity hotspot - southern part of Guinea

* Dry savanna woodlands (north-eastern region)

* Guinea is home to 5 ecoregions: Guinean montane forests, Western Guinean lowland forests, Guinean forest-savanna mosaic, West Sudanian savanna, and Guinean mangroves.

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

<figure><img src="image_113.png" /><figcaption>Figure 22: Topographic map of Guinea</figcaption></figure>

<figure><img src="image_114.png" /><figcaption>Figure 23: Landscape types of Guinea</figcaption></figure>

## Environmental alerts of Guinea:

* Reduced wildlife due to human encroachment and hunting (e.g., large mammals)

* overfishing > t threat to the nation's marine life.

* Threatened species: the African elephant, Diana monkey, and Nimba otter-shrew.

* A nature reserve Mount Nimba Strict Nature Reserve is established on Mt. Nimba: protected area and UNESCO World Heritage Site

## Environmental features of Guinea:

* Guinean Forests of West Africa Biodiversity hotspot - southern part of Guinea

* Dry savanna woodlands (north-eastern region)

* Guinea is home to 5 ecoregions: Guinean montane forests, Western Guinean lowland forests, Guinean forest-savanna mosaic, West Sudanian savanna, and Guinean mangroves.

<figure><img src="image_115.png" /></figure>
<figure><img src="image_116.png" /><figcaption>Côte d'Ivoire (Ivory Coast)</figcaption></figure>

Figure 24: Satellite image processing and DEM of Ivory Coast visualised using Python (fragment)

## Environmental highlights of Ivory Coast:

* The most biodiverse country in West Africa (over 1,200 animal species, 223 mammals, 702 birds, 161 reptiles, 85 amphibians, 111 fish species, 4,700 plant species, of which 3,927 species of terrestrial plants)

* The world's largest exporter of cocoa beans;

* The largest economy in the West African Economic and Monetary Union (40% total GDP)

* 9 national parks (the largest - Assgny National Park); 6 terrestrial ecoregions

* Economy grows fast; high-income country; highly diversified agriculture (cocoa, cashews, coffee, rubber, cotton, palm oil, bananas, etc)

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

Figure 25: Topographic map of Ivory Coast, West Africa

## Environmental problems and alerts in Ivory Coast:

* High deforestation rates due to high agriculture activities and economic activities: logging of timber, mineral exploration

* Loss of biodiversity - declining variety of flora and fauna

<figure><img src="image_118.png" /></figure>
<figure><img src="image_116.png" /><figcaption>Figure 24: Satellite image processing and DEM of Ivory Coast visualised using Python (fragment)</figcaption></figure>

<figure><img src="image_117.png" /><figcaption>Figure 25: Topographic map of Ivory Coast, West Africa</figcaption></figure>

## Environmental highlights of Ivory Coast:

* The most biodiverse country in West Africa (over 1,200 animal species, 223 mammals, 702 birds, 161 reptiles, 85 amphibians, 111 fish species, 4,700 plant species, of which 3,927 species of terrestrial plants)

* The world's largest exporter of cocoa beans;

* The largest economy in the West African Economic and Monetary Union (40% total GDP)

* 9 national parks (the largest - Assaly National Park); 6 terrestrial ecoregions

* Economy grows fast; high-income country; highly diversified agriculture (cocoa, cashews, coffee, rubber, cotton, palm oil, bananas, etc)

## Environmental problems and alerts in Ivory Coast:

* High deforestation rates due to high agriculture activities and economic activities: logging of timber, mineral exploration

* Loss of biodiversity - declining variety of flora and fauna

<figure><img src="image_118.png" /></figure>
# Kenya

<figure><img src="image_119.png" /><figcaption>Figure 26: Map of Kenya performed using GMT and GEBCO data</figcaption></figure>

<figure><img src="image_120.png" /><figcaption>Figure 27: Geological map of Kenya. Source:</figcaption></figure>

## Multi-source EO data

* **Analysis** of geological data to understand topographic structure which affects distribution of vegetation and landscape formation through relief forms.

* EO data present an **integration** of geospatial data, collection of RS resources (Landsat images, topographic and geologic data, climate, vegetation) => information extraction and knowledge mining

* **Data heterogeneity:** multi-source data of different formats and scales => data integration and analysis.

## East African Rift

**Tectonics:** active continental rift zone in East Africa which affected countries => active and dormant volcanoes (incl. Mt. Kilimanjaro, Mt. Kenya etc).
# Kenya

<figure><img src="image_119.png" /><figcaption>Figure 26: Map of Kenya performed using GMT and GEBCO data</figcaption></figure>

## East African Rift

**Tectonics:** active continental rift zone in East Africa which affected countries => active and dormant volcanoes (incl. Mt. Kilimanjaro, Mt. Kenya etc).

<figure><img src="image_120.png" /><figcaption>Figure 27: Geological map of Kenya. Source:</figcaption></figure>

## Multi-source EO data

* **Analysis** of geological data to understand topographic structure which affects distribution of vegetation and landscape formation through relief forms.

* EO data present an **integration** of geospatial data, collection of RS resources (Landsat images, topographic and geologic data, climate, vegetation) => information extraction and knowledge mining

* **Data heterogeneity:** multi-source data of different formats and scales => data integration and analysis.

<figure><img src="image_121.png" /></figure>

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<figure><img src="image_121.png" /><figcaption>Figure 28: Script used for 3D mapping</figcaption></figure>

<figure><img src="image_122.png" /><figcaption>Figure 29: 3D mapping of Tanzania</figcaption></figure>

## 3D modelling as important cartographic approach in EO data analysis

1. 3D topographic scene effectively illustrates the relief representation

2. Advanced GMT functionalities specify different cartographic elements on the map for each layer by a combination of the GMT modules (grdview, grdccontour, pscoast, ptextext).

3. Cartographic processing includes

- visualisation of layers (raster grids vector lines)

- text annotations

- subplots, hierarchical sub-levels of the elements

- coordinate axis labels and grids, translucency of layers

- general layout setting

4. The outcome of the GMT cartographic processing: **print-quality maps**

5. 3D modelling of Earth data creates potential for cartographic opportunities in **visual data analytics** and interpretation of the seafloor structure

6. GMT enables **flexible, accurate and rapid visualisation of** the 2D and 3D EO data

7. Data selection: 3D modelling of terrain landscapes is limited by massive data processing (3D arrays of cells) $=> ETOPO5$ presents better

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<figure><img src="image_130.png" /><figcaption>Figure 28: Script used for 3D mapping</figcaption></figure>

<figure><img src="image_131.png" /><figcaption>Figure 29: 3D mapping of Tanzania</figcaption></figure>

## 3D modelling as important cartographic approach in EO data analysis

1. 3D topographic scene effectively illustrates the relief representation

2. Advanced GMT functionalities specify different cartographic elements on the map for each layer by a combination of the GMT modules (grdview, grdccontour, pscoast, ptextext).

3. Cartographic processing includes

- visualisation of layers (raster grids vector lines)

- text annotations

- subplots, hierarchical sub-levels of the elements

- coordinate axis labels and grids, translucency of layers

- general layout setting

4. The outcome of the GMT cartographic processing: **print-quality maps**

5. 3D modelling of Earth data creates potential for cartographic opportunities in **visual data analytics** and interpretation of the seafloor structure

6. GMT enables **flexible, accurate and rapid visualisation of** the 2D and 3D EO data

7. Data selection: 3D modelling of terrain landscapes is limited by massive data processing (3D arrays of cells) $=> ETOPO5$ presents better

<figure><img src="image_132.png" /></figure>
<figure><img src="image_142.png" /><figcaption>**Figure 30:** Topographic and seismic maps of Malawi (based on IRIS data on deep earthquakes).</figcaption></figure>

## Geographic features of Malawi:

* The Great Rift Valley runs through the country from N to S

* Lake Malawi – (a.k.a. Lake Nyasa) – 3/4 of Malawi's eastern boundary; one of the major Rift Valley ancient lakes

* Lake Malawi – unique meromictic lake (layers of water that don't intermix and don't circulate) with specific hydrology – permanently stratified water layers

* Lake Malawi – 4th largest freshwater lake in the world by volume, the 9th largest lake in the world by area and the 3rd largest and 2nd deepest lake in Africa.

* Lake Malawi National Park created to protect unique ecosystems of the lake

## Problems in Malawi:

* Tanzania–Malawi dispute about the borders of the Malawi Lake

* Deforestation – serious environmental issue

* Scarcity of natural resources (landlocked)

* Extreme poverty and rapidly growing population dependent on resources

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<figure><img src="image_133.png" /><figcaption>**Figure 30:** Topographic and seismic maps of Malawi (based on IRIS data on deep earthquakes).</figcaption></figure>

## Geographic features of Malawi:

* The Great Rift Valley runs through the country from N to S

* Lake Malawi – (a.k.a. Lake Nyasa) – 3/4 of Malawi's eastern boundary; one of the major Rift Valley ancient lakes

* Lake Malawi – unique meromictic lake (layers of water that don't intermix and don't circulate) with specific hydrology – permanently stratified water layers

* Lake Malawi – 4th largest freshwater lake in the world by volume, the 9th largest lake in the world by area and the 3rd largest and 2nd deepest lake in Africa.

* Lake Malawi National Park created to protect unique ecosystems of the lake

## Problems in Malawi:

* Tanzania–Malawi dispute about the borders of the Malawi Lake

* Deforestation – serious environmental issue

* Scarcity of natural resources (landlocked)

* Extreme poverty and rapidly growing population dependent on resources

<figure><img src="image_134.png" /></figure>
Mali

<figure><img src="image_149.png" /><figcaption>Figure 31: Topographic map of Mali plotted using GMT scripts and GEBCO data. The green rotated square shows the location of the Landsat satellite images used for image analysis. These images cover the area of Inner Niger Delta.</figcaption></figure>

<figure><img src="image_150.png" /><figcaption>Figure 32: Scheme of information extraction from RS data to analyse vegetation indices for ecological monitoring of Niger Inner Delta, Mali. For time series analysis, images are collected in various years.</figcaption></figure>

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<figure><img src="image_135.png" /><figcaption>Figure 31: Topographic map of Mali plotted using GMT scripts and GEBCO data. The green rotated square shows the location of the Landsat satellite images used for image analysis. These images cover the area of Inner Niger Delta.</figcaption></figure>

<figure><img src="image_136.png" /><figcaption>Figure 32: Scheme of information extraction from RS data to analyse vegetation indices for ecological monitoring of Niger Inner Delta, Mali. For time series analysis, images are collected in various years.</figcaption></figure>

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# Mali: Inner Niger Delta

<figure><img src="image_152.png" /><figcaption>Figure 33: Landsat 8-9 OLI/TIRS images: showing Niger Inner Delta, Mali in natural colours.</figcaption></figure>

<figure><img src="image_153.png" /><figcaption>Figure 34: Unsupervised classification by k-means clustering: Inner Niger Delta</figcaption></figure>

## Image processing by R

* Analysing land cover types by R-based satellite image processing => visualising heterogeneity of landscape features formed under various environmental setting:

* Example of data analysis by R: computing vegetation indices in Mali; clustering for image classification.

* Data analysis in R is based on a full range of algorithms used in RS data processing.
# Mali: Inner Niger Delta

<figure><img src="image_138.png" /><figcaption>Figure 33: Landsat 8-9 OLI/TIRS images: showing Niger Inner Delta, Mali in natural colours.</figcaption></figure>

<figure><img src="image_139.png" /><figcaption>Figure 34: Unsupervised classification by k-means clustering: Inner Niger Delta</figcaption></figure>

## Image processing by R

* Analysing land cover types by R-based satellite image processing => visualising heterogeneity of landscape features formed under various environmental setting:

* Example of data analysis by R: computing vegetation indices in Mali; clustering for image classification.

* Data analysis in R is based on a full range of algorithms used in RS data processing.
# Mozambique

<figure><img src="image_154.png" /><figcaption>Figure 35: Workflow used for RS data classification using Random Forest (RF) in GRASS GIS: A case of Mozambique</figcaption></figure>

<figure><img src="image_155.png" /><figcaption>Figure 6: Classified LandSat-8-9 OLI/TIRS images on the coastal region of Beira, Mozambique, partitioned into 10 classes using k-means clustering approach (a) 2015, (b) 2016, (c) 2023.</figcaption></figure>

<figure><img src="image_156.png" /><figcaption>Figure 8: Kansan forest classification using machine learning (ML) approach of GRASS GIS for the LandSat-8-9 OLI/TIRS images on a coastal region of Beira, Mozambique. (a) 2015, (b) 2016, (c) 2023.</figcaption></figure>

## Environmental problems in Mozambique

## Climate setting in Mozambique

* Tropical climate with two seasons: wet (Oct-Mar) and dry (Apr-Sep); => responses of vegetation.

* Climatic conditions vary with altitude.

* Rainfall is heavy along the coast and decreases in the north and south of Mozambique.

* Deforestation & loss of natural habitats; disrupted coastal ecosystems, land cover change, land degradation

* Mangroves and wetlands are being lost and converted into rice farms, aquaculture and housing

* Climate change and high population growth

* Destructive fishing practices (e.g. use of dynamite & fine mesh nets) => declining fish stocks;
# Mozambique

<figure><img src="image_140.png" /><figcaption>Figure 35: Workflow used for RS data classification using Random Forest (RF) in GRASS GIS: A case of Mozambique</figcaption></figure>

<figure><img src="image_141.png" /><figcaption>Figure 6: Classified Landat 8-9 OLI/TIRS images on the coastal region of Beira, Mozambique, partitioned into 10 classes using k-means clustering approach (a) 2015, (b) 2016, (c) 2023.</figcaption></figure>

<figure><img src="image_142.png" /><figcaption>Figure 8: Kansan forest classification using machine learning (ML) approach of GRASS GIS for the Landat 8-9 OLI/TIRS images on a coastal region of Beira, Mozambique. (a) 2015, (b) 2016, (c) 2023.</figcaption></figure>

## Environmental problems in Mozambique

* Deforestation & loss of natural habitats; disrupted coastal ecosystems, land cover change, land degradation

* Mangroves and wetlands are being lost and converted into rice farms, aquaculture and housing

* Climate change and high population growth

* Destructive fishing practices (e.g. use of dynamite & fine mesh nets) => declining fish stocks;

<caption>Figure 36: RS data classification using Random Forest in GRASS GIS</caption>

## Climate setting in Mozambique

* Tropical climate with two seasons: wet (Oct-Mar) and dry (Apr-Sep); => responses of vegetation.

* Climatic conditions vary with altitude.

* Rainfall is heavy along the coast and decreases in the north and south of Mozambique.

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# Deep Learning / Machine Learning

<figure><img src="image_157.png" /><figcaption>Figure 37: Conceptual scheme of ML/DL algorithms</figcaption></figure>

<figure><img src="image_158.png" /><figcaption>Figure 38: Methodological scheme of ANN</figcaption></figure>

## ML in cartography and geoinformatics

→ Rapid development of ML/DL = Cartographic knowledge encoding with algorithms of data handling

→ Using cartographic patterns as classification/generalisation rules for learning models (as seed) for processing RS data

→ DL is a new, rapidly evolving field of ML and consists in advanced algorithms of computer vision and analysis.

→ ML algorithms include, among others, methods of Artificial Neural Networks (ANN), Random Forest (RF), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), Support Vector Machines (SVM)

## ML opportunities for satellite image processing

→ ML techniques (Python, R, GRASS GIS) can help visualise land cover patterns in landscapes using classification of RS data

→ Land cover types of the diverse landscapes are inherently linked with regional topographic, geologic and climate setting

→ ML enables to get insights into land cover patterns (variability, dynamics using time series analysis, fragmentation) formed in various environmental conditions in diverse ecoregions of Africa
# Deep Learning / Machine Learning

<figure><img src="image_161.png" /><figcaption>Figure 37: Conceptual scheme of ML/DL algorithms</figcaption></figure>

## ML in cartography and geoinformatics

→ Rapid development of ML/DL = Cartographic knowledge encoding with algorithms of data handling

→ Using cartographic patterns as classification/generalisation rules for learning models (as seed) for processing RS data

→ DL is a new, rapidly evolving field of ML and consists in advanced algorithms of computer vision and analysis.

→ ML algorithms include, among others, methods of Artificial Neural Networks (ANN), Random Forest (RF), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), Support Vector Machines (SVM)

<figure><img src="image_162.png" /><figcaption>Figure 38: Methodological scheme of ANN</figcaption></figure>

## ML opportunities for satellite image processing

→ ML techniques (Python, R, GRASS GIS) can help visualise land cover patterns in landscapes using classification of RS data

→ Land cover types of the diverse landscapes are inherently linked with regional topographic, geologic and climate setting

→ ML enables to get insights into land cover patterns (variability, dynamics using time series analysis, fragmentation) formed in various environmental conditions in diverse ecoregions of Africa
<figure><img src="image_159.png" /><figcaption>Figure 39: Topographic map of Nigeria with shown study area (region of southern coastal Nigeria with mangroves as aquatic plants)</figcaption></figure>

## Environmental problems in Nigeria

* **Deforestation** due to extensive and rapid clearing of forests (reasons: expanding agriculture, logging, urbanisation, and infrastructure development)

* **Niger Delta pollution** due to oil exploration / petroleum industry => Nigeria's Delta region is one of the most oil-polluted aquaries in the world

* **Decline of mangroves** in coastal areas due to oil pollution (mangroves are precious ecosystems and unique saltwater-tolerant plants)

* **Land cover changes** (important types: tropical forest zones in the south, wetlands, mangroves and freshwater swamps in the Atlantic coasts)

* **Waste management** including sewage treatment => water pollution, soil contamination (untreated wastes go to groundwater)

* **Population growth** => non-systematic industrial planning, increased urbanisation, increased of urban spaces

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<figure><img src="image_163.png" /><figcaption>Figure 39: Topographic map of Nigeria with shown study area (region of southern coastal Nigeria with mangroves as aquatic plants)</figcaption></figure>

## Environmental problems in Nigeria

* Deforestation due to extensive and rapid clearing of forests (reasons: expanding agriculture, logging, urbanisation, and infrastructure development)

* Niger Delta pollution due to oil exploration / petroleum industry => Nigeria's Delta region is one of the most oil-polluted aquaries in the world

* Decline of mangroves in coastal areas due to oil pollution (mangroves are precious ecosystems and unique saltwater-tolerant plants)

* Land cover changes (important types: tropical forest zones in the south, wetlands, mangroves and freshwater swamps in the Atlantic coasts)

* Waste management including sewage treatment => water pollution, soil contamination (untreated wastes go to groundwater)

* Population growth => non-systematic industrial planning, increased urbanisation, increased of urban spaces

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<figure><img src="image_179.png" /><figcaption>Figure 40: Snippet of GRASS GIS script for image processing</figcaption></figure>

<figure><img src="image_180.png" /><figcaption>Figure 41: Landsat OLI/TIRS images classified using GRASS GIS</figcaption></figure>

RS data processing by GRASS GIS for
analysis of land cover changes in the
coastal regions of Nigeria (monitoring
the decline of mangroves).

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<figure><img src="image_181.png" /><figcaption>Figure 40: Snippet of GRASS GIS script for image processing</figcaption></figure>

RS data processing by GRASS GIS for
analysis of land cover changes in the
coastal regions of Nigeria (monitoring
the decline of mangroves).

<figure><img src="image_182.png" /><figcaption>Figure 41: Landsat OLI/TIRS images classified using GRASS GIS</figcaption></figure>

<figure><img src="image_183.png" /></figure>
# Rwanda

<figure><img src="image_182.png" /><figcaption>Figure 42: Geophysical maps of Rwanda: seismicity (deep-focus earthquakes according to IRIS), inundations of the geoid model and free-air gravity (Faye's)</figcaption></figure>

## Geographic features and particularities of Rwanda

* **High-altitude relief:** the entire country is located in uplands and highlands. The lowest point is the Rusizi River (950 m a.s.l.)

* **Mountains of Rwanda** - part of the Albertine Rift Mountains (branch of the East African Rift) = > complex geologic and tectonic setting, high seismicity ("Land of a thousand hills").

* **Temperate tropical highland climate** = > montane forests, terraced agriculture

* **Volcanoes National Park** = > rare species (1/3 of the worldwide mountain gorilla population) living in bamboo forests; also: rhinos, lions, chimpanzees, and other protected animal species.

* **Nyungwe Forest:** rare species endemic to Albertine Rift

* **3 terrestrial ecoregions** = > Albertine Rift montane forests, Victoria Basin forest-savanna mosaic, and Ruwenzori-Virunga montane moorlands.

<figure><img src="image_183.png" /></figure>
# Rwanda

<figure><img src="image_184.png" /><figcaption>Figure 42: Geophysical maps of Rwanda: seismicity (deep-focus earthquakes according to IRIS), inundations of the geoid model and free-air gravity (Faye's)</figcaption></figure>

## Geographic features and particularities of Rwanda

* **High-altitude relief:** the entire country is located in uplands and highlands. The lowest point is the Rusizi River (950 m a.s.l.)

* **Mountains of Rwanda** - part of the Albertine Rift Mountains (branch of the East African Rift) => complex geologic and tectonic setting, high seismicity ("Land of a thousand hills").

* **Temperate tropical highland climate** => montane forests, terraced agriculture

* **Volcanoes National Park** => rare species (1/3 of the worldwide mountain gorilla population) living in bamboo forests; also: rhinos, lions, chimpanzees, and other protected animal species.

* **Nyungwe Forest:** rare species endemic to Albertine Rift

* **3 terrestrial ecoregions** => Albertine Rift montane forests, Victoria Basin forest-savanna mosaic, and Ruwenzori-Virunga montane moorlands.

<figure><img src="image_185.png" /></figure>
# South Sudan

<figure><img src="image_184.png" /><figcaption>Figure 43: Image segmentation performed using GRASS GIS for Landsat images covering South Sudan</figcaption></figure>

## Highlights of South Sudan

* Civil war => destruction and displacement > 2M people died, and > 4 M became refugees or displaced persons

* **Origine of Nile – White Nile** passes through the country, passing by Juba. Name "White" due to clay sediment contained in the water => water color pale

* **Rich wildlife** => tropical forests – habitat for rare species: bongo, giant forest hogs, red river hogs, forest elephants, chimpanzees, forest monkeys, etc.

* **Bandingilo National Park** protected area – the 2nd-largest wildlife migration in the world

* **Ecoregions of South Sudan** => tropical forest, swamps, and grassland
# South Sudan

<figure><img src="image_186.png" /><figcaption>Figure 43: Image segmentation performed using GRASS GIS for Landsat images covering South Sudan</figcaption></figure>

## Highlights of South Sudan

* Civil war => destruction and displacement > 2M people died, and > 4 M became refugees or displaced persons

* **Origine of Nile – White Nile** passes through the country, passing by Juba. Name "White" due to clay sediment contained in the water => water color pale

* **Rich wildlife** => tropical forests – habitat for rare species: bongo, giant forest hogs, red river hogs, forest elephants, chimpanzees, forest monkeys, etc.

* **Bandingilo National Park** protected area – the 2nd-largest wildlife migration in the world

* **Ecoregions of South Sudan** => tropical forest, swamps, and grassland
# Sudan

<figure><img src="image_185.png" /><figcaption>Figure 44: Aerial images showing the confluence of the Blue Nile and White Nile which meet in Khartoum to form the Nile</figcaption></figure>

## Highlights of Sudan

* **5 Cataracts of Nile:** 5 out of 6 Cataracts of Nile are located in Sudan (from 2nd to 6th, with only the 1st being in Egypt)

* **Land cover changes in Sudan:** Desertification, deforestation, soil erosion => displacement of vegetation

* **Environmental problems:** water & air pollution, waste management, trash and plastic disposal

* **Human activities:** unsustainable agricultural expansion => soil desiccation, lowering of soil fertility and water table

* **Threats to wildlife:** through poaching and illegal hunting

* **Natural hazards:** sandstorms in northern dry regions ("haboob").

<figure><img src="image_186.png" /><figcaption>Figure 45: Cataracts of Nile on the topographic map of Sudan</figcaption></figure>

## Cataract-related landscape formation

Region of northern Sudan is tectonically active => Nubian Swell has diverted the Nile's course => formation of the cataracts, shallow depth of Nile => black soil/deposits in arid areas of cataracts => shallow alkaline pans => swampy environment in cataracts' areas, northern Sudan
# Sudan

<figure><img src="image_187.png" /><figcaption>Figure 44: Aerial images showing the confluence of the Blue Nile and White Nile which meet in Khartoum to form the Nile</figcaption></figure>

## Highlights of Sudan

* **5 Cataracts of Nile:** 5 out of 6 Cataracts of Nile are located in Sudan (from 2nd to 6th, with only the 1st being in Egypt)

* **Land cover changes in Sudan:** Desertification, deforestation, soil erosion => displacement of vegetation

* **Environmental problems:** water & air pollution, waste management, trash and plastic disposal

* **Human activities:** unsustainable agricultural expansion => soil desiccation, lowering of soil fertility and water table

* **Threats to wildlife:** through poaching and illegal hunting

* **Natural hazards:** sandstorms in northern dry regions ("haboob").

<figure><img src="image_188.png" /><figcaption>Figure 45: Cataracts of Nile on the topographic map of Sudan</figcaption></figure>

## Cataract-related landscape formation

Region of northern Sudan is tectonically active => Nubian Swell has diverted the Nile's course => formation of the cataracts, shallow depth of Nile => black soil/deposits in arid areas of cataracts => shallow alkaline pans => swampy environment in cataracts' areas, northern Sudan
<figure><img src="image_187.png" /><figcaption>Figure 46: RS data processing to compare two gulfs of Tunisia (Gulf of Gabes and Gulf of Hammamet): A GRASS GIS approach</figcaption></figure>

<figure><img src="image_188.png" /></figure>

<figure><img src="image_189.png" /></figure>
<figure><img src="image_189.png" /><figcaption>Figure 46: RS data processing to compare two gulfs of Tunisia (Gulf of Gabes and Gulf of Hammamet): A GRASS GIS approach</figcaption></figure>

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Digital elevation data: GEBCO/SGRTM, 15 sec (x, y, z) resolution grid

<figure><img src="image_190.png" /></figure>

DEM: SGRTM/GEBCO, 15 sec. grid. Earthquakes: BSI Seismic Event Database

<figure><img src="image_191.png" /></figure>

## Highlights of Uganda

* **Tectonic setting:** The Great Rift Valley of East Africa Rift System (EARS) – a unique combination of graben basins forming a complex Afro-Arabian rift system.

* **Geographic location:** Uganda is located in the eastern part of the EARS => diverse geomorphology, consisting of volcanic hills, mountains, dense hydrologic network of lakes and river basins.

* **Geology:** The Albertine Graben is a geologically important region of Uganda and one of the most petroliferous rifts in Africa.

* **Mountainous geomorphology:** Rwenzori mountains – the highest non-volcanic, non-organic mountains in the world.

* **Biodiversity:** high biodiversity of Rwenzori Mountains National Park (World Heritage Site) => vegetation ranging from tropical rainforest to alpine meadows.

* **Landscape mixture:** 5 overlapping vegetation zones in the Ruwenzori Mts: the evergreen forests, the bamboo; the weather; the alpine; and, the nival zone

* **Climate change:** impact of global warming and rise in T° on the Ruwenzori's glaciers => snow and glaciers melt

* **Environmental issues:** deforestation. Reasons: population growth, agricultural expansion, and the demand for natural resources (timber and charcoal).

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Digital elevation data: GEBCO/SGRTM, 15 sec (see. 450 m) resolution grid

<figure><img src="image_192.png" /></figure>

DEM: SGRTM/GEBCO, 15 sec. grid. Earthquakes: BSI Seismic Event Database

<figure><img src="image_193.png" /></figure>

## Highlights of Uganda

* **Tectonic setting:** The Great Rift Valley of East Africa Rift System (EARS) - a unique combination of graben basins forming a complex Afro-Arabian rift system.

* **Geographic location:** Uganda is located in the eastern part of the EARS => diverse geomorphology, consisting of volcanic hills, mountains, dense hydrologic network of lakes and river basins.

* **Geology:** The Albertine Graben is a geologically important region of Uganda and one of the most petroliferous rifts in Africa.

* **Mountainous geomorphology:** Rwenzori mountains - the highest non-volcanic, non-organic mountains in the world.

* **Biodiversity:** high biodiversity of Rwenzori Mountains National Park (World Heritage Site) => vegetation ranging from tropical rainforest to alpine meadows.

* **Landscape mixture:** 5 overlapping vegetation zones in the Ruwenzori Mts: the evergreen forests, the bamboo; the weather; the alpine; and, the nival zone

* **Climate change:** impact of global warming and rise in T° on the Ruwenzori's glaciers => snow and glaciers melt

* **Environmental issues:** deforestation. Reasons: population growth, agricultural expansion, and the demand for natural resources (timber and charcoal).

<figure><img src="image_194.png" /></figure>

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# Zambia

<figure><img src="image_211.png" /><figcaption>Figure 49: Topographic location of Zambia</figcaption></figure>

## Zambia: geographic and environmental features

* **Climate:** humid subtropical or tropical wet and dry (Source: Köppen climate classification).

* **Lication:** Landlocked country in southern Africa which influences types of vegetation

* **Biodiversity:** ecosystems in Zambia include forest, thicket, woodland and grassland vegetation

* **Geomorphology:** high plateaus with some hills and mountains, dissected by river valleys

## GMT-based mapping

* Scripting techniques have the advantage of increased speed, accuracy and precision of mapping which enables rapid EO and RS data processing

* Validity and utility of data models in GMT

* Embedded mathematical algorithms of data modelling and cartographic principles

* GMT => deeper understanding of the underlying processes affecting the Earth's landscapes across Africa

* With increasing rise and constant updates in EO data availability in environmental analysis, it is essential to apply data-driven mapping and script-based cartographic visualisation

* ML facilitates cartographic workflow

* Processing large volumes of heterogeneous and rapidly arriving digital geodata using scripts and ML supports GIS.

* GMT-based mapping uses scripts which optimises cartographic workflow

<figure><img src="image_212.png" /></figure>
# Zambia

<figure><img src="image_214.png" /><figcaption>Figure 49: Topographic location of Zambia</figcaption></figure>

## Zambia: geographic and environmental features

* **Climate:** humid subtropical or tropical wet and dry (Source: Köppen climate classification).

* **Lication:** Landlocked country in southern Africa which influences types of vegetation

* **Biodiversity:** ecosystems in Zambia include forest, thicket, woodland and grassland vegetation

* **Geomorphology:** high plateaus with some hills and mountains, dissected by river valleys

## GMT-based mapping

* Scripting techniques have the advantage of increased speed, accuracy and precision of mapping which enables rapid EO and RS data processing

* Validity and utility of data models in GMT

* Embedded mathematical algorithms of data modelling and cartographic principles

* GMT => deeper understanding of the underlying processes affecting the Earth's landscapes across Africa

* With increasing rise and constant updates in EO data availability in environmental analysis, it is essential to apply data-driven mapping and script-based cartographic visualisation

* ML facilitates cartographic workflow

* Processing large volumes of heterogeneous and rapidly arriving digital geodata using scripts and ML supports GIS.

* GMT-based mapping uses scripts which optimises cartographic workflow

<figure><img src="image_215.png" /></figure>
# Conclusions

## Monitoring landscapes of Africa using EO data and integration of advanced cartographic tools

* ✓ African landscape are very complex. In this research proposal, selected examples of these ecosystems were presented to illustrate current state of the research

* ✓ Next steps in the proposed research include publishing 3-4 articles (reviews) that will summarise the variability of African landscapes with focus on climate change issues, environmental dynamics, anthropogenic impacts, topographic-geologic setting which shape the face of the African countries and help get more insights in its unique landscapes

* ✓ In the era of big EO data, these research aims at integrated monitoring of African ecosystems using advanced cartographic tools such as GMT scripts, R and Python for image processing (computing vegetation indices, image segmentation) and ML algorithms for image classification

* ✓ ML methods of GRASS GIS were used with a case of Random Forest for the cases of Mozambique land cover types

* ✓ The demands from environmental landscape analysis for effective methods of EO data processing is explained by the overwhelming increase of data that should be processed rapidly yet effectively for the scale of African continent

* ✓ The study presented cartographic challenges faced in large-scale environmental approach: A case of diverse landscape and ecoregions of Africa

* ✓ Benefits of scripting tools and programming algorithms used for cartographic data processing are discussed

* ✓ Advanced software ensure processing of multi-source data with diverse formats (export, converting and import via GDAL), diverse geospatial data type (raster, vector and tabular), high processing speed, and mapping accuracy

* ✓ Cartographic development opportunities in the era of big EO data are related to the machine-based data processing, that is scripting and algorithms of automatisation of satellite image processing and classification

* ✓ Modelling landscape of Africa starts with diversified steps of processing and includes advanced algorithms of data handling

* ✓ GRASS GIS, GMT, Python and R – all these software present a smart methodological combination for RS data processing

* ✓ The use of such technologies in landscape analysis results in the accurate processing large amounts of geospatial data including satellite images which enable to reveal complex heterogeneous characteristics of the African landscapes

<figure><img src="image_213.png" /></figure>

<figure><img src="image_214.png" /></figure>

<figure><img src="image_215.png" /></figure>

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<figure><img src="image_221.png" /></figure>

<figure><img src="image_222.png" /></figure>
# Conclusions

## Monitoring landscapes of Africa using EO data and integration of advanced cartographic tools

* ✓ African landscape are very complex. In this research proposal, selected examples of these ecosystems were presented to illustrate current state of the research

* ✓ Next steps in the proposed research include publishing 3-4 articles (reviews) that will summarise the variability of African landscapes with focus on climate change issues, environmental dynamics, anthropogenic impacts, topographic-geologic setting which shape the face of the African countries and help get more insights in its unique landscapes

* ✓ In the era of big EO data, these research aims at integrated monitoring of African ecosystems using advanced cartographic tools such as GMT scripts, R and Python for image processing (computing vegetation indices, image segmentation) and ML algorithms for image classification

* ✓ ML methods of GRASS GIS were used with a case of Random Forest for the cases of Mozambique land cover types

* ✓ The demands from environmental landscape analysis for effective methods of EO data processing is explained by the overwhelming increase of data that should be processed rapidly yet effectively for the scale of African continent

* ✓ The study presented cartographic challenges faced in large-scale environmental approach: A case of diverse landscape and ecoregions of Africa

* ✓ Benefits of scripting tools and programming algorithms used for cartographic data processing are discussed

* ✓ Advanced software ensure processing of multi-source data with diverse formats (export, converting and import via GDAL), diverse geospatial data type (raster, vector and tabular), high processing speed, and mapping accuracy

* ✓ Cartographic development opportunities in the era of big EO data are related to the machine-based data processing, that is scripting and algorithms of automatisation of satellite image processing and classification

* ✓ Modelling landscape of Africa starts with diversified steps of processing and includes advanced algorithms of data handling

* ✓ GRASS GIS, GMT, Python and R – all these software present a smart methodological combination for RS data processing

* ✓ The use of such technologies in landscape analysis results in the accurate processing large amounts of geospatial data including satellite images which enable to reveal complex heterogeneous characteristics of the African landscapes

<figure><img src="image_216.png" /></figure>

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Thank you for attention !

Questions ?

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Thank you for attention !

Questions ?

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<figure><img src="image_227.png" /></figure>
[30] P. Lemenkova and O. Debeir. "Satellite Altimetry and Gravimetry 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 Dec. 2022), pp. 191-202. DOI: 10.2478/jaes-2022-0026.

[31] P. Lemenkova and O. Debeir. "R Libraries for Remote Sensing Data Classification by k-means Clustering and NDVI Computation in Congo River Basin, DRC". In: *Applied Sciences* 12 (24 Dec. 2022), p. 12554. DOI: 10.3390/app122412554.

[32] P. Lemenkova and O. Debeir. "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 Nov. 2022), p. 317. DOI: 10.3390/jimaging8120317.

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[30] P. Lemenkova and O. Debeir. "Satellite Altimetry and Gravimetry 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 Dec. 2022), pp. 191-202. DOI: 10.2478/jaes-2022-0026.

[31] P. Lemenkova and O. Debeir. "R Libraries for Remote Sensing Data Classification by k-means Clustering and NDVI Computation in Congo River Basin, DRC". In: *Applied Sciences* 12 (24 Dec. 2022), p. 12554. DOI: 10.3390/app122412554.

[32] P. Lemenkova and O. Debeir. "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 Nov. 2022), p. 317. DOI: 10.3390/jimaging8120317.

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