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

This presentation proposes research on mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite images. The presentation is held on 13 June at the University of Salzburg. The presented research covers the problem of extracting knowledge and information from Earth Observation (EO) data which requires advanced technical cartographic tools. In particular, I presented the use of methods of machine learning (ML) and algorithms deep learning (DL) as well as scripting approaches to geospatial data handling. The concept of the study: Landscapes of Africa. Research focus: land surface of the African continent where diverse environmental processes interplay. Understanding landscape dynamics requires modelling and mapping the complexity of factors that affect the shape of the Earth using advanced methods of EO data processing. Landscape dynamics was analysed on several case study that demonstrate the evaluation of spatio-temporal changes caused by human and natural forces across various countries of Africa. Applications of landscape ecology and environmental monitoring of Africa were discussed on the example of landscape monitoring. Possible applications include land management (urban planning), diverse goals of sustainable development (food resources, agriculture) and theoretical issues of cartography and geoinformatics. Factors affecting formation of landscapes are reviewed in the published papers. These incldue geologic-tectonic setting, climate processes, anthropogenic activities in various countries across the African continent which is notable for different relief, soil and vegetation setting

1. Mapping landscapes of Africa using remote sensing data:
detecting spatio-temporal environmental dynamics
from the satellite images
Polina Lemenkova
June 13, 2024
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 1 / 40

2. Introduction
Introduction
Concept: Landscapes of Africa: land surface
where diverse environmental processes interplay
Understanding<> landscape dynamics: modelling
and mapping complexity of factors that aect
the shape of the Earth
Dynamics: spatio-temporal changes caused byhuman<> 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 aecting formation of
landscapes: geologic-tectonic setting, climate
processes, anthropogenic activities =>dierent
relief, soil and vegetation
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 2 / 40

3. Introduction
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 globalscales 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 technicaltools =>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 Articial Intelligence (AI) =>GISlearns from data and
extracts information. Identication of landscape patterns and detecting land cover changes
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 3 / 40

4. Introduction
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 extractenvironmental 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
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 4 / 40

5. AF
Research Highlights: Variability of African landscapes
Complexity: variability of regional
geographic setting on the African
continent
Scale: Handling multi-sourcespatial 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 reects a
variety of impact factors: tectonic-geologic setting,
climate change and anthropogenic activitiesPolina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 5 / 40

6. AF
Research Progress: Current state of the project
Current state: mapping of 27 African countries is published in 32 journal articles
NoName Publication Link
1Algeria [9]<> doi: 10.3390/asi6040061
2Botswana [<>12]<> doi: 10.3390/ijgi11090473
3Burkina Faso [25]<> doi: 10.24425/agg.2023.146157
4Cameroon [23]<> doi: 10.47246/CEJGSD.2020.2.2.4
5Central Africa Republic [28]<> doi: 10.3390/min13050604
6Chad [5]<> doi: 10.2478/boku-2023-0005
7D.R.C. Congo [31]<> doi: 10.3390/app122412554
8Egypt [27]<> doi: 10.3390/info14040249
9
Ethiopia
[11,<> 15]<> doi: 10.24425/jwld.2022.141573;<> doi: 10.5755/j01.erem.78.1.29963
10 [21,<> 19]<> doi: 10.4314/sinet.v44i1.9;<> doi: 10.2478/abmj-2021-0010
11Ghana [14,<> 17]<> doi: 10.24425/gac.2022.141169;<> doi: 10.32909/kg.20.36.2
12Guinea [4]<> doi: 10.5281/zenodo.10673287
13Ivory Coast (C^ote d'Ivoire) [32]<> doi: 10.3390/jimaging8120317
14Kenya [<>8<>]<> doi: 10.2478/trser-2023-0008
15Malawi [13,<> 18]<> doi: 10.5937/tehnika2202183L;<> doi: 10.5281/zenodo.5772315
16Mali [24]<> doi: 10.2478/arsa-2023-0011
17Mozambique [3]<> doi: 10.3390/coasts4010008
18Nigeria [29]<> doi: 10.3390/jmse11040871
19Rwanda [10]<> doi: 10.5281/zenodo.7113414
20Sierra Leone [1]<> doi: 10.5281/zenodo.11473307
21Somalia and Seychelles [30]<> doi: 10.2478/jaes-2022-0026
22South Africa [2]<> doi: 10.4000/11pyj
23South Sudan [7]<> doi: 10.3390/analytics2030040
24Sudan [26]<> doi: 10.3390/jimaging9050098
25Tanzania [16]<> doi: 10.3176/earth.2022.05
26Tunisia [6]<> doi: 10.3390/land12111995
27Uganda [20]<> doi: 10.5281/zenodo.5082861
28Zambia [22]<> doi: 10.5937/ZemBilj2101117L
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 6 / 40

7. Data
Data
Understanding complexity and variability of landscapes requires processing of large amounts of diverse datasets
Multi-source data =>eective, 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 1:<> Data used in this project: their sources, types and precision
NoData Origine Type, resolution
1Landsat 8-9 OLI/TIRS USGS satellite images
2ETOPO1 NOAA 1 arc-min GRM grid
3ETOPO5 NOAA 5 arc min GRM grid
4GEBCO BODC 15 arc sec DEM raster grid
5SRTM NASA 15-sec DEM raster grid
6Geology USGS Vector layers for diverse African countries
7Social data FAO Vector layers for administrative borders and population
8Gravity Scripps IO CryoSat-2, Jason-1 grid
9EGM96 Scripps IO Geopotential data: geoid
10EGM-2008 Scripps IO Geoid model
11TerraClimate NOAA Climate characteristics of African landscapes
12GLOBE dataset NOAA Topography model
13GEBCO IHO-IOC Geographic gazetteer
*30 arc second = ca. 1km cell size, 15 arc sec = ca. 500 m cell size, etc.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 7 / 40

8. Methods
Methods
Cartographic tasks by scripts: Generic Mapping
Tools (GMT)
Figure 1:<> Using GMT as integrated tools for cartographic workow
Plotting a map by GMT becomes a rapid procedure, fully
controlled by the cartographer
Such automatisation by GMT facilitates mapping anddecreases time of cartographic workow
Various GMT techniques and modules have beenused in this research for rapid and accurate mapping
àgmt psxy<> for plotting graphs and addingannotations on raster map
àgmt makecpt<> for making color palette
àgmt grdimage<> for generating raster image
àgmt psscale<> for adding legend for adding scale
àgmt grdcontour<> for adding isolines
àgmt psbasemap<> for adding grid and essentialcartographic marks
àgmt psbasemap<> for adding scale anddirectional rose
àecho<> UNIX utility for adding text labels
àgmt pstext<> for adding subtitle
àgmt logo<> for adding GMT logo
àgmt psconvert<> for convert PS to image (byGhostScript interpreter)
àgmt legend<> adding legend on maps withcartographic annotations
àgmt pshistogram<> for statistical analysis oftopographic data distribution (relief)
àgmt psrose<> for plotting rose diagrams
àgmt img2grd<> converting geospatial formats
àgmt grdcut<> selecting study area and clippingthe region from the world map
These and many other modules of GMT
were used in this research.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 8 / 40

9. DZ
Algeria
Algeria { the largest country in
Africa and the Mediterranean
Basin.
Contrasting environment:
south<> - a signicant part of
Sahara Desert;
center<> { Tell Atlas Mts. (SaharanAtlas) + vast plains, highlands(e.g., Hoggar Mts) & mountainranges
coastal<> areas: hilly &mountainous landscapes, a few
harbours.
Diverse topographic setting +
climate setting = contrasting
precipitation patterns
Salt lakes (saline sabkhas) in thesemi-desert environment on
border of Sahara
Figure 2:<> Topographic map of AlgeriaPolina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 9 / 40

10. DZ
Algeria, Sahara: Sabkhas Salt Lakes
Figure 3:<> Sabkha - coastal sandy ats of northern Algeria where evaporite and
saline minerals accumulate. Sabkhas uctuate seasonally and over years
(subject to climate change)
Figure 4:<> Workow used to map sabkhas of Algeria using RS
data
Sabkha of Algeria - in semiarid to
arid climate (border of Sahara +
impact of Mediterranean).
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 10 / 40

11. BW
Botswana
Figure 5:<> Topographic map of Botswana with specic geographic features.
Relief: mostly at, gently rolling tableland
Climate: dominated by the Kalahari Desert (ca. 70% of its
land surface)
Hydrology: Okavango Delta { one of the world's largestinland river deltas (UNESCO World Heritage Site) islocated in Botswana.
Figure 6:<> Right: climate variables over Botswana (here: droughts)
Environment<> Okavango Delta { oasis in an arid climate ofBotswana =>large wetland system
Ecology<> rich wildlife, swamp-dominant rare species.
Habitats: salt inlands, lagoons.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 11 / 40

12. BF
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 oods to extreme drought =>agriculture instability
Climate issues: tropical with two very distinct seasons { rainy and dry seasons. During dry period - eects from harmattan(hot dry Saharan winds )
Agricultural vulnerability: insect attacks (locusts and crickets), which destroy crops
Migration: people migrate to other countries to nd better jobs and support for their families
Topography: at relief with 2 major types of landscapes inuenced by geologic setting.
Peneplain: 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, Tenakourou
Figure 7:<> Data capture from the EarthExplorer repository of the USGS: Landsat-8 OLI/TIRS satellite image, central Burkina Faso, Ouagadougou.
Background image: ESRI World imagery. Source:
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 12 / 40

13. CM
Cameroon
Figure 8:<> Topographic map of Camerron
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 ofCameroon (France and UK shared
Cameroon unequally { French area 80% of
the country =>two nations in one country
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 13 / 40

14. Data
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 scientic 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, vectorand raster processing
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 14 / 40

15. CAR
Central African Republic (CAR)
Figure 9:<> Snippet of the GMT script for cartographic mappingFigure 10:<> Topographic map and location of the CAR to analyse correlation
between topography and geophysical setting
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 15 / 40

16. TD
Chad
Figure 11:<> Topographic map of Chad
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 Saharansteppes
Lake Chad: important source of food and natural resources; yet subjectto seasonal and climate uctuations
RS and cartography for landscape analysis
Figure 12:<> Topographic map of the PCT. Source:
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 16 / 40

17. CG
Congo (D.R.C)
Figure 13:<> Topographic map of Congo D.R.C.
Climate hazards: Congo: high precipitation
and the highest frequency of thunderstorms
in the world.
Environment: Congolese rainforests { the 2ndlargest rain forest in the world after theAmazon
Biodiversity<> of Congo: lush jungle rainforest
+ savannah + grasslands + mountainous
terraces
Geomorphology: Rwenzori Mountains { thesource of the Nile River with unique (forAfrica) alpine meadows
Figure 14:<> Advantages of R for satellite image processing (libraries)Figure 15:<> Workow for research methodology
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 17 / 40

18. CG
Congo (D.R.C.)
Figure 16:<> Snippet of the R code for unsupervised
classication by k-means clustering (Basoko, Congo DRC)
.
Automatisation in RS data
analysis for Congo: diverse
geospatial libraries of R
Figure 17:<> k-means clustering classication algorithm in RStudio using the
RStoolbox package for satellite image processing (Landsat images covering
Congo DRC)
Automatic image processing by R to study
changes in vegetation of Congo, to analyse
their correlation with landforms
(topography), detect correlation with
climate settings
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 18 / 40

19. EG
Egypt
Figure 18:<> Qena Bend as study area on the map of Egypt
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 arescript-based<> and<> data-driven
Figure 19:<> 3D perspective view of Qena Bend, Nile River, Upper Egypt
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 clis on both sides
Qena Bend is one of the most structurally-complicated regions of theNile 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.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 19 / 40

20. ET
Ethiopia
Figure 20:<> Geomorphic features of Ethiopia: slope, aspect, DEM and hillshade
relief map mapped using R
Geologic mapping
Advanced cartographic tools =>mapping relief =>
understanding the links between geology, geomorphology,
seismicity and topography of Ethiopia
Lnks 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 twoparts of landscapes with a vast highlandcomplex of mountains and dissectedplateaus
Great Rift Valley { branch of the EastAfrican Rift which stretches in SW-NEdirection from the Afar Triple Junction.
Afar Triple Junction is a unique exampleon the Earth of continental rifting (as
opposed to seaoor 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 bylowlands, steppes, or semi-desert.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 20 / 40

21. GH
Ghana
Figure 21:<> Thematic maps of Ghana plotted using GMT
Hydrology of Ghana
White Volta River + its tributary Black Volta, owing 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 theunderlying processes of landscape formation which aect the distribution of vegetation types.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 21 / 40

22. GN
Guinea
Figure 22:<> Topographic map of Guinea
Environmental alerts of Guinea:
Reduced wildlife due to human encroachment and
hunting (e.g., large mammals)
overshing =>t hreat to the nation's marine life.
Threatened species: the African elephant, Dianamonkey, and Nimba otter-shrew.
A nature reserve MountNimba Strict Nature
Reserveis established on Mt. Nimba: protected
area and UNESCO World Heritage Site
Figure 23:<> Landscape types of Guinea
Environmental features of Guinea:
Guinean Forests of West Africa Biodiversityhotspot - southern part of Guinea
Dry savanna woodlands (north-eastern region)
Guinea is home to<> 5 ecoregions: Guinean montane
forests, Western Guinean lowland forests, Guineanforest-savanna mosaic, West Sudanian savanna,and Guinean mangroves.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 22 / 40

23. CI
C^ote d'Ivoire (Ivory Coast)
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 sh 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 AfricanEconomic and Monetary Union (40% total GDP)
9 national parks<> (the largest { Assgny NationalPark);<> 6 terrestrial ecoregions
Economy grows fast; high-income country; highlydiversied agriculture (cocoa, cashews, coee,rubber, cotton, palm oil, bananas, etc)
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 varietyof ora and fauna
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 23 / 40

24. KE
Kenya
Figure 26:<> Map of Kenya performed using GMT and GEBCO data
East African Rift
Tectonics: active continental rift zone in East
Africa which aected countries =>active and
dormant volcanoes (incl. Mt. Kilimanjaro, Mt.
Kenya etc).
Figure 27:<> Geological map of Kenya. Source:
Multi-source EO data
Analysis<> of geological data to understand
topographic structure which aects 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 dierentformats and scales =>data integration and
analysis.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 24 / 40

25. TZ
Tanzania
Figure 28:<> Script used for 3D mappingFigure 29:<> 3D mapping of Tanzania
3D modelling as important cartographic
approach in EO data analysis
x3D topographic scene eectively illustrates the relief
representation
xAdvanced<> GMT functionalities<> specify dierent
cartographic elements on the map for each layer by a
combination of the GMT modules (grdview, grdcontour,
pscoast, pstext).
xCartographic processing includes
åvisualisation of layers (raster grids vector lines)
åtext annotations
åsubplots, hierarchical sub-levels of the elements
åcoordinate axis labels and grids, translucency of layers
ågeneral layout setting
xThe outcome of the GMT cartographic processing:
print-quality maps
x3D modelling of Earth data creates potential for
cartographic opportunities in<> visual data analytics<> and
interpretation of the seaoor structure
xGMT enables<> exible, accurate and rapid visualisation<> of
the 2D and 3D EO data
xData selection: 3D modelling of terrain landscapes is
limited by massive data processing (3D arrays of cells)
=>ETOPO5 presents better
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 25 / 40

26. MW
Malawi
Figure 30:<> Topographic and seismic maps of Malawi (based on IRIS
data on deep earthquakes).
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 (layersof water that don't intermix and don'tcirculate) with specic hydrology {
permanently stratied water layers
Lake Malawi<> - 4
th
largest freshwater lake in
the world by volume, the 9
th
largest lake in
the world by area and the 3
rd
largest and 2
nd
deepest lake in Africa.
Lake Malawi National Park<> created to protect
unique ecosystems of the lake
Problems in Malawi:
Tanzania{Malawi dispute<> about the bordersof the Malawi Lake
Deforestation<> - serious environmental issue
Scarcity of natural resources<> (landlocked)
Extreme poverty<> and rapidly growingpopulation dependent on resources
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 26 / 40

27. ML
Mali
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.
Figure 32:<> Scheme of information extraction from RS data to analysevegetation indices for ecological monitoring of Niger Inner Delta, Mali. For
time series analysis, images are collected in various years.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 27 / 40

28. ML
Mali: Inner Niger Delta
Figure 33:<> Landsat 8-9 OLI/TIRS images: showing Niger Inner Delta, Mali in
natural colours.
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 classication.
Data analysis in R is based on a full range of algorithmsused in RS data processing.
Figure 34:<> Unsupervised classication by k-means clustering: Inner Niger Delta
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 28 / 40

29. MZ
Mozambique
Figure 35:<> Workow used for RS data classication using Random Forest (RF)
in GRASS GIS: A case of Mozambique
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 intorice farms, aquaculture and housing
Climate change<> and high population growth
Destructive shing<> practices (e.g. use of dynamite & ne
mesh nets) =>declining sh stocks;
Figure 36:<> RS data classication using Random Forest in GRASS GIS
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 inthe north and south of Mozambique.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 29 / 40

30. ML
Deep Learning / Machine Learning
Figure 37:<> Conceptual scheme of ML/DL algorithms
ML in cartography and geoinformatics
ÙRapid development of ML/DL =>Cartographic
knowledge encoding with algorithms of data handling
ÙUsing cartographic patterns as classication/generalisation
rules for learning models (as seed) for processing RS data
ÙDL is a new, rapidly evolving eld of ML and consists in
advanced algorithms of computer vision and analysis.
ÙML algorithms include, among others, methods of
Articial Neural Networks (ANN), Random Forest (RF),
Convolution Neural Networks (CNN) and Recurrent Neural
Networks (RNN), Support Vector Machines (SVM)
Figure 38:<> Methodological scheme of ANN
ML opportunities for satellite image processing
ÙML techniques<> (Python, R, GRASS GIS) can help visualiseland cover patterns in landscapes using classication of RSdata
ÙLand cover types of the diverse landscapes are inherentlylinked with regional topographic, geologic and climate
setting
ÙML 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
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 30 / 40

31. NG
Nigeria
Figure 39:<> Topographic map of Nigeria with shown study area (region of southern coastal
Nigeria with mangroves as aquatic plants)
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-pollutedaquatories in the world
Decline of mangroves<> in coastal areas due tooil pollution (mangroves are preciousecosystems and unique saltwater-tolerantplants)
Land cover changes<> (important types:tropical forest zones in the south, wetlands,
mangroves and freshwater swamps in the
Atlantic coasts)
Waste management<> including sewagetreatment =>water pollution, soil
contamination (untreated wastes go to
groundwater)
Population growth<> = >non-systematic
industrial planning, increased urbanisation,
increased of urban spaces
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 31 / 40

32. NG
Nigeria
Figure 40:<> Snippet of GRASS GIS script for image processing
RS data processing by GRASS GIS<> for
analysis of land cover changes in the
coastal regions of Nigeria (monitoring
the decline of mangroves).
Figure 41:<> Landsat OLI/TIRS images classied using GRASS GIS
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 32 / 40

33. RW
Rwanda
Figure 42:<> Geophysical maps of Rwanda: seismicity (deep-focus earthquakes according to IRIS), inundations of the geoid model and free-air gravity (Faye's)
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.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 33 / 40

34. SS
South Sudan
Figure 43:<> Image segmentation performed using GRASS GIS for Landsat images covering South Sudan
Highlights of South Sudan
`Civil war<> = >destruction and displacement>2Mpeople 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 2
nd
-largest wildlife migration in the world
`Ecoregions of South Sudan<> = >tropical forest, swamps, and grassland
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 34 / 40

35. SD
Sudan
Figure 44:<> Aerial images showing the conuence of the Blue
Nile and White Nile which meet in Khartoum to form the Nile
Highlights of Sudan
T5 Cataracts of Nile: 5 out of 6 Cataracts of
Nile are located in Sudan (from 2
nd
to 6
th
,
with only the 1
st
being in Egypt)
TLand cover changes in Sudan:
Desertication, deforestation, soil erosion
=>displacement of vegetation
TEnvironmental problems: water & air
pollution, waste management, trash and
plastic disposal
THuman activities: unsustainable agricultural
expansion =>soil desiccation, lowering of
soil fertility and water table
TThreats to wildlife<> through poaching and
illegal hunting
TNatural hazards: sandstorms in northern dry
regions ("haboob").
Figure 45:<> Cataracts of Nile on the topographic map of Sudan
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's areas, northern Sudan
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 35 / 40

36. TN
Tunisia
Figure 46:<> RS data processing to compared two gulfs of Tunisia (Gulf of Gabes and Gulf of Hammamet): A GRASS GIS approach
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 36 / 40

37. UG
Uganda
Figure 47:<> Topographic map of UgandaFigure 48:<> Seismicity in Uganda
Highlights of Uganda
TTectonic setting: The Great Rift Valley of East Africa Rift
System (EARS) { a unique combination of graben basins
forming a complex Afro-Arabian rift system.
TGeographic 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.
TGeology: The Albertine Graben is a geologically important
region of Uganda and one of the most petroliferous rifts in
Africa.
TMountainous geomorphology: Rwenzori mountains { the
highest non-volcanic, non-orogenic mountains in the world.
TBiodiversity<> high biodiversity of Rwenzori Mountains
National Park (World Heritage Site) =>vegetation
ranging from tropical rainforest to alpine meadows.
TLandscape mixture: 5 overlapping vegetation zones in the
Ruwenzori Mts:the evergreen forests, the bamboo; the
heather; the alpine; and, the nival zone
TClimate change: impact of global warming and rise in T

on the Ruwenzori's glaciers =>snow and glaciers melt
TEnvironmental issues: deforestation. Reasons: population
growth, agricultural expansion, and the demand for natural
resources (timber and charcoal).
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 37 / 40

38. ZM
Zambia
Figure 49:<> Topographic location of Zambia
Zambia: geographic and environmental features
Climate: humid subtropical or tropical wet and dry
(Source: Koppen climate classication).
Lication: Landlocked country in southern Africa whichinuences types of vegetation
Biodiversity: ecosystems in Zambia include forest, thicket,woodland and grassland vegetation
Geomorphology: high plateaus with some hills andmountains, 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 datamodelling and cartographic principles
GMT =>deeper understanding of the underlying
processes aecting the Earth's landscapes acrossAfrica
With increasing rise and constant updates in EOdata availability in environmental analysis, it isessential to apply data-driven mapping and
script-based cartographic visualisation
ML facilitates cartographic workow
Processing large volumes of heterogeneous and
rapidly arriving digital geodata using scripts and
ML supports GIS.
GMT-based mapping uses scripts which optimises
cartographic workow
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 38 / 40

39. Con
Conclusions
Monitoring landscapes of Africa using EO data and integration of advanced cartographic tools
3African landscape are very complex. In this research proposal, selected examples of these ecosystems were presented to
illustrate current state of the research
3Next 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
3In 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 classication
3ML methods of GRASS GIS were used with a case of Random Forest for the cases of Mozambique land cover types
3The demands from environmental landscape analysis for eective methods of EO data processing is explained by the
overwhelming increase of data that should be processed rapidly yet eectively for the scale of African continent
3The study presented cartographic challenges faced in large-scale environmental approach: A case of diverse landscape and
ecoregions of Africa
3Benets of scripting tools and programming algorithms used for cartographic data processing are discussed
3Advanced 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
3Cartographic 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 classication
3Modelling landscape of Africa starts with diversied steps of processing and includes advanced algorithms of data handling
3GRASS GIS, GMT, Python and R { all these software present a smart methodological combination for RS data processing
3The 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
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 39 / 40

40. Thanks
Thanks
Thank you for attention !
Questions ?
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 40 / 40

41. References
Author's publications
[1]<> P. Lemenkova.<> \Landscape Fragmentation and Deforestation in Sierra Leone, West Africa, Analysed Using Satellite Images".<> In: Transylvanian
Review of Systematical and Ecological Research26 (1 June 2024), pp. 13{26.issn: 2344-3219.doi:10.5281/zenodo.11473307.url:
https://magazines.ulbsibiu.ro/trser/trser26/trser_26.1- 2024- contents.pdf.
[2]<> P. Lemenkova.<> \Exploitation d'images satellitaires Landsat de la region du Cap (Afrique du Sud) pour le calcul et la cartographie d'indices de
vegetation a l'aide du logiciel GRASS GIS".<> In: Physio-Geo20 (May 2024), pp. 113{129.issn: 1958-573X.doi:10.4000/11pyj.url:
https://journals.openedition.org/physio- geo/17018.
[3]<> P. Lemenkova.<> \Random Forest Classier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite
Image Processing: Case Study of Bight of Sofala, Mozambique".<> In: Coasts4 (1 Feb. 2024), pp. 127{149.doi:10.3390/coasts4010008.
[4]<> P. Lemenkova.<> \An automated algorithm of GRASS GIS to retrieve the data on land cover types in Guinea, West Africa, from Landsat-8 OLI/TIRS
images".<> In: Ovidius University Annals of Constanta - Series: Civil Engineering25 (1 Dec. 2023), pp. 19{36.doi:10.5281/zenodo.10673287.
[5]<> P. Lemenkova.<> \Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa".<> In: Die
Bodenkultur: Journal of Land Management, Food and Environment74 (1 Nov. 2023), pp. 49{64.doi:10.2478/boku- 2023- 0005.
[6]<> P. Lemenkova.<> \Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS".<> In: Land12
(11 Oct. 2023), p. 1995.doi:10.3390/land12111995.
[7]<> P. Lemenkova.<> \Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts".<> In: Analytics2 (3
Sept. 2023), pp. 745{780.doi:10.3390/analytics2030040.
[8]<> P. Lemenkova.<> \Mapping Wetlands of Kenya Using Geographic Resources Analysis Support System (GRASS GIS) with Remote Sensing Data".<> In:
Transylvanian Review of Systematical and Ecological Research25 (2 July 2023), pp. 1{18.doi:10.2478/trser- 2023- 0008.
[9]<> P. Lemenkova.<> \A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria".
In:Applied System Innovation6 (4 June 2023), p. 61.doi:10.3390/asi6040061.
[10]<> P. Lemenkova.<> \Command-Line Cartographic Data Processing for Geophysical Plotting of Rwanda Using GMT and R Scripts".<> In: Romanian Journal
of Physics67 (7{8 Sept. 2022), pp. 1{21.doi:10.5281/zenodo.7113414.
[11]<> P. Lemenkova.<> \Evapotranspiration, vapour pressure and climatic water decit in Ethiopia mapped using GMT and TerraClimate dataset".<> In:
Journal of Water and Land Development54 (7{9 Sept. 2022), pp. 201{209.doi:10.24425/jwld.2022.141573.
[12]<> P. Lemenkova.<> \Mapping Climate Parameters over the Territory of Botswana Using GMT and Gridded Surface Data from TerraClimate".<> In: ISPRS
International Journal of Geo-Information11 (9 Aug. 2022), p. 473.doi:10.3390/ijgi11090473.
[13]<> P. Lemenkova.<> \Cartographic Scripting for Geophysical Mapping of Malawi Rift Zone".<> In: Tehnika77 (2 May 2022), pp. 183{191.doi:
10.5937/tehnika2202183L.
[14]<> P. Lemenkova.<> \Mapping Ghana by GMT and R scripting: advanced cartographic approaches to visualize correlations between the topography,
climate and environmental setting".<> In: Advances in Geodesy and Geoinformation71 (1 May 2022). Article no. e16, pp. 1{20.doi:
10.24425/gac.2022.141169.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 40 / 40

42. References
[15]<> P. Lemenkova.<> \Seismicity in the Afar Depression and Great Rift Valley, Ethiopia".<> In: Environmental Research, Engineering and Management78 (1
Apr. 2022), pp. 83{96.doi:10.5755/j01.erem.78.1.29963.
[16]<> P. Lemenkova.<> \Tanzania Craton, Serengeti Plain and Eastern Rift Valley: mapping of geospatial data by scripting techniques".<> In: Estonian Journal
of Earth Sciences71 (2 Apr. 2022), pp. 61{79.doi:10.3176/earth.2022.05.
[17]<> P. Lemenkova.<> \Geophysical Mapping of Ghana Using Advanced Cartographic Tool GMT".<> In: Kartograja i Geoinformacije20 (36 Dec. 2021),
pp. 16{37.doi:10.32909/kg.20.36.2.
[18]<> P. Lemenkova.<> \Mapping Earthquakes in Malawi Using Incorporated Research Institutions for Seismology (IRIS) Catalogue for 1972{2021".<> In:
Malawi Journal of Science and Technology13 (2 Dec. 2021), pp. 31{50.doi:10.5281/zenodo.5772315.
[19]<> P. Lemenkova.<> \Evaluating the Performance of Palmer Drought Severity Index (PDSI) in Various Vegetation Regions of the Ethiopian Highlands".
In:Acta Biologica Marisiensis4 (2 Dec. 2021), pp. 14{31.doi:10.2478/abmj- 2021- 0010.
[20]<> P. Lemenkova.<> \Application of scripting cartographic methods to geophysical mapping and seismicity in Rwenzori mountains and Albertine Graben,
Uganda".<> In: Makerere University Journal of Agricultural and Environmental Sciences10 (1 July 2021), pp. 1{21.doi:10.5281/zenodo.5082861.
[21]<> P. Lemenkova.<> \Scripting methods in topographic data processing on the example of Ethiopia".<> In: SINET Ethiopian Journal of Science44 (1 June
2021), pp. 91{107.doi:10.4314/sinet.v44i1.9.
[22]<> P. Lemenkova.<> \Mapping environmental and climate variations by GMT: a case of Zambia, Central Africa".<> In: Zemljiste i biljka70 (1 May 2021),
pp. 117{136.doi:10.5937/ZemBilj2101117L.
[23]<> P. Lemenkova.<> \Object Based Image Segmentation Algorithm of SAGA GIS for Detecting Urban Spaces in Yaounde, Cameroon".<> In: Central
European Journal of Geography and Sustainable Development2 (2 Dec. 2020), pp. 38{51.doi:10.47246/CEJGSD.2020.2.2.4.
[24]<> P. Lemenkova and O. Debeir.<> \Time Series Analysis of Landsat Images for Monitoring Flooded Areas in the Inner Niger Delta, Mali".<> In: Articial
Satellites58 (4 Dec. 2023), pp. 278{313.doi:10.2478/arsa- 2023- 0011.
[25]<> P. Lemenkova and O. Debeir.<> \Environmental mapping of Burkina Faso using TerraClimate data and satellite images by GMT and R scripts".<> In:
Advances in Geodesy and Geoinformation72 (2 Oct. 2023), pp. 1{32.doi:10.24425/agg.2023.146157.
[26]<> P. Lemenkova and O. Debeir.<> \Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan,
Northeast Africa".<> In: Journal of Imaging9 (5 May 2023), p. 98.doi:10.3390/jimaging9050098.
[27]<> P. Lemenkova and O. Debeir.<> \Recognizing the Wadi Fluvial Structure and Stream Network in the Qena Bend of the Nile River, Egypt, on Landsat
8-9 OLI Images".<> In: Information14 (4 Apr. 2023), p. 249.doi:10.3390/info14040249.
[28]<> P. Lemenkova and O. Debeir.<> \Coherence of Bangui Magnetic Anomaly with Topographic and Gravity Contrasts across Central African Republic".
In:Minerals13 (5 Apr. 2023), p. 604.doi:10.3390/min13050604.
[29]<> P. Lemenkova and O. Debeir.<> \Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests
in the Coastal Landscapes of Niger Delta, Nigeria".<> In: Journal of Marine Science and Engineering11 (4 Apr. 2023), p. 871.doi:
10.3390/jmse11040871.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 40 / 40

43. Bibliography
[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 Sciences12(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 Classication by k-means Clustering and NDVI Computation in Congo River
Basin, DRC".<> In: Applied Sciences12 (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 C^ote d'Ivoire,
West Africa".<> In: Journal of Imaging8 (12 Nov. 2022), p. 317.doi:10.3390/jimaging8120317.
Polina Lemenkova Mapping landscapes of Africa using remote sensing data: detecting spatio-temporal environmental dynamics from the satellite imagesJune 13, 2024<> 40 / 40