# Thematic mapping of Burundi using geospatial data and satellite images processed by geoinformatics methods

Polina Lemenkova

► To cite this version:

Polina Lemenkova. Thematic mapping of Burundi using geospatial data and satellite images processed by geoinformatics methods. Geografares, 2024, 4 (39), pp.1-26. 10.47456/geo.v4i39.46177.eng . hal-04792292

HAL Id: hal-04792292

https://hal.science/hal-04792292v1

Submitted on 20 Nov 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>

Distributed under a Creative Commons Attribution 4.0 International License
# Thematic mapping of Burundi using geospatial data and satellite images processed by geoinformatics methods

Mapeamento temático do Burundi utilizando dados geoespaciais e imagens de satélite processadas por métodos geoinformáticos

Cartografía temática de Burundi utilizando datos geoespaciales e imágenes de satélite procesadas mediante métodos geoinformáticos

Cartographie thématique du Burundi à l'aide de données géospatiales et d'images satellitaires traitées par les méthodes de géoinformatique

Polina Lemenkova*

Alma Mater Studiorum – University of Bologna
Bologna, Emilia-Romagna, Italia
Free University of Bozen-Bolzano
Bolzano, South Tyrol, Italy
polina.lemenkova2@unibo.it

## ABSTRACT

This paper presents the implementation of the integrated cartographic approach for environmental mapping of Burundi, East Africa. Monitoring different types of land cover in Africa by remote sensing is presented using GRASS SIG methods. The series of thematic maps of vegetation and habitat types, landscapes, topographic, geomorphic and geological context of Burundi is created in QGIS and GMT software. The methodological issues concerning the processes of cartographic scripting are discussed with commented snippets of programming codes using syntax of GRASS GIS. Several modules are used for satellite image mosaic, processing vector and raster data, and classification. The paper documents environmental features of Burundi, such as vegetation types, land cover patterns, geologic setting and landscape distribution using digital cartographic tools. A series of the thematic maps is proposed to support environmental policies on agricultural management in Burundi.

**KEYWORDS:** remote sensing; mapping; satellite images.
## RESUMO

Este artigo apresenta a implementação da abordagem cartográfica integrada para mapeamento ambiental do Burundi, África Oriental. O monitoramento de diferentes tipos de cobertura de terra na África por sensoriamento remoto é apresentado usando métodos GRASS SIG. A série de mapas temáticos de tipos de vegetação e habitat, paisagens, contexto topográfico, geomórfico e geológico do Burundi é criada no software QGIS e GMT. As questões metodológicas relativas aos processos de script cartográfico são discutidas com trechos comentados de códigos de programação usando a sintaxe do GRASS GIS. Vários módulos são usados para mosaico de imagens de satélite, processamento de dados vetoriais e raster e classificação. O artigo documenta características ambientais do Burundi, como tipos de vegetação, padrões de cobertura de terra, cenário geológico e distribuição de paisagens usando ferramentas cartográficas digitais. Uma série de mapas temáticos é proposta para dar suporte a políticas ambientais sobre gestão agrícola no Burundi.

**PALAVRAS-CHAVE:** sensoriamento remoto; mapeamento; imagens de satélite.

## RESUMEN

En este artículo se presenta la implementación de un enfoque cartográfico integrado para la cartografía ambiental de Burundi, África Oriental. Se presenta el monitoreo de diferentes tipos de cobertura terrestre en África mediante teledetección utilizando métodos GRASS SIG. La serie de mapas temáticos de vegetación y tipos de hábitat, paisajes, contexto topográfico, geomorfológico y geológico de Burundi se crea en el software QGIS y GMT. Se discuten las cuestiones metodológicas relacionadas con los procesos de creación de guiones cartográficos con fragmentos comentados de códigos de programación utilizando la sintaxis de GRASS GIS. Se utilizan varios módulos para el mosaico de imágenes satelitales, el procesamiento de datos vectoriales y raster y la clasificación. El artículo documenta las características ambientales de Burundi, como los tipos de vegetación, los patrones de cobertura terrestre, el entorno geológico y la distribución del paisaje utilizando herramientas cartográficas digitales. Se propone una serie de mapas temáticos para respaldar las políticas ambientales sobre la gestión agrícola en Burundi.

**PALABRAS CLAVE:** teledetección; cartografía; imágenes satelitales.

## RÉSUMÉ

Cet article présente la mise en œuvre des approches cartographiques intégrées pour la cartographie environnementale du Burundi, Afrique de l'Est. Sui-

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

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

<figure><img src="image_4.png" /></figure>
vi des différents types de couverture terrestre par télédétection est présenté
dans cet article par les méthodes de GRASS SIG. Les questions méthodologi-
ques des scripts cartographiques sont discutées avec des extraits de codes
de programmation utilisant la syntaxe de GRASS GIS commentés. Plusieurs
modules de GRASS GIS sont utilisés pour la mosaïque d'images satellitaires,
le traitement des données vectorielles et raster, ainsi que la classification. La
série de cartes thématiques du Burundi, créée en utilisant le logiciel QGIS et
GMT, comprend des cartes des types de végétation et d'habitats, des paysa-
ges, du contexte topographique, géomorphologique et géologique du pays.
Ainsi, l'article examine les caractéristiques environnementales du Burundi,
telles que les types de végétation, les modèles de couverture terrestre, le ca-
dre géologique et la répartition du paysage à l'aide d'outils cartographiques
numériques. Une série de cartes thématiques est proposée pour soutenir les
politiques environnementales de gestion agricole au Burundi.

MOTS-CLÉS : télédétection ; cartographie ; images satellitaires.
# INTRODUCTION

Since the development of remote sensing and geoinformatics techniques in the 1970s, a long tradition of geographical research has seen satellite images and geospatial data as both a source of information and a challenge for mapping methods: it is possible to use accurate and reliable data but using advanced and technically sound methods. Information from remote sensing and geospatial data is essential for environmental preservation. Their usefulness lies in their ability to map landscape dynamics, which allows representing vegetation cover at different scales of time and space. But the use of geospatial data, even by traditional GIS users, is still hampered by its methods and approaches (Robert and Gangneron 2015; Sellin et al. 2015).

Environmental monitoring can be carried out efficiently using satellite images. However, image processing is not efficient with conventional GIS methods due to the presence of a large number of manual operations. It is therefore preferable to adopt an autonomous approach to computer vision using automatic image processing algorithms. The question that arises is that when we see satellite images, we wonder whether they are images of objective landscapes and how the landscapes visible on the Earth's surface can be interpreted and correlated with other geospatial data, such as the geological framework, the hydrographic network, the relief and geomorphological processes, land use, regional variations in vegetation cover, etc. (Cabral et al. 2006; Diédhiou et al. 2020; Maswi and Mshiu, 2024).

In a geographical analysis, this issue becomes of paramount importance. On this occasion, the interpretation of data covering the study area in the integrated context is an essential approach for the analysis of environmental dynamics and the linkages between geological and geographical variables (El-Awady et al. 2016). Remote sensing has long been used for these purposes in various regions of Africa. However, the application of spatial data and remote sensing in Burundi is limited. It has mainly been used to map general land cover types using traditional GIS.

Using efficient processing of geospatial data, it is possible to represent the complexity of social and environmental systems. In this context, an additional ambiguity of the links between the elements of the systems arises. On the one hand, there are social developments such as demographic and nutritional transitions; on the other hand, landscape transitions (Cuq 2009). From the countryside to urban areas, we observe the dynamics of land covers visible from space using geospatial data.
Several aspects of the links between them can be distinguished: geologi-
cal-geomorphological, environmental-vegetative, social-nutritional. The geo-
logical and geomorphological context determines the diversity of vegetation
in the landscapes of East Africa. The regions of East Africa consist of coastal
plains, tropical rainforests in the central regions, swamps and wetlands do-
minant in the mountainous regions, grassy and semi-deciduous forests in
the central regions. The distinction between these different types of vegeta-
tion can be identified using remote sensing and geospatial data.

There is no doubt that satellite images have contributed greatly to the
study of East Africa, improving the understanding of environmental com-
plexity at continental, regional and local levels. The analysis and understand-
ting of East African landscapes in the context of the complex geological
setting deeply linked to the East African Rift, with a variety of regional soil
and vegetation features, has been supported by the results obtained from
the visualization of cartographic data, initially composed of maps and later
(since the 1970s) of images obtained from continuously operating satellite
missions, such as Landsat.

The objective of this study is to integrate satellite images and geospatial
data in vector and raster formats in Burundi using several cartographic sof-
tware tools. Three software were used for data processing. The main sof-
tware is GRASS GIS which is used for processing Landsat satellite images.
Generic Mapping Tools (GMT) software is used for a topographic map, and
Quantum GIS (QGIS) software is used for GIS applications. A procedure is
developed to combine multi-source data including geological, landscape,
vegetation and geomorphological data with classified satellite images pro-
cessed in mosaic form.

The variety of data used as thematic layers allows incorporating informa-
tion on the environmental properties of East Africa. The performance of sa-
tellite image classification was presented by scripting methods and descri-
bed with comments on the functionalities of selected GRASS GIS modules.
GRASS GIS programming and mosaic techniques of multiple satellite images
are explained. Integrated information on Burundi, derived from data collec-
ted, presented, analyzed and visualized using cartographic techniques, is
evaluated to understand the links between the geological and environmental
parameters of the country. Therefore, this study provides a description and
visualization of land cover types, habitat and vegetation patterns of lands-
capes, and thematic maps of Burundi. The geographical analysis of Burundi
using remote sensing data and cartographic information, presented in this study, contributes to the environmental monitoring of East Africa.

In the remainder of the paper, after a brief discussion on the particular nature of the landscape in Burundi and the current environmental problems related to geology, environmental and climatic context, and anthropogenic activities, we will develop the working steps for spatial data analysis using GRASS GIS and explain the methodology using its programming modules with examples of scripts provided. The selected functional codes were based on existing works with examples from GRASS GIS. We used GRASS GIS scripts to process medium-resolution Landsat satellite images with a case study of the Burundi region.

## GEOGRAPHIC LOCATION OF STUDY AREA:
BURUNDI, EAST AFRICA

The study is located in Burundi, East Africa. The country is bordered by Rwanda to the north, Tanzania to the east and the Democratic Republic of Congo to the west, Figure 1. Lake Tanganyika which belongs to the African Great Lakes forms the southern border of the country.

<figure><img src="image_5.png" /><figcaption>**Figure 1** - Topographic map of study area: Burundi</figcaption></figure>

Source: author.
Recently, socioeconomic and demographic processes in Burundi have led to environmental challenges that affect biodiversity, ecosystem habitats (El-Hassanin et al. 1993), trigger land cover changes (Hennebert et al. 1996) and cause fragmentation of landscape patches (Nyirarwasa et al. 2024). Land cover and vegetation in Burundi have undergone rapid changes in recent years due to environmental and climatic impacts, as well as human activities.

The food crisis and the consequences of climate change, such as droughts and environmental scarcity, have led to overexploitation of local farmers' resources in order to preserve crops and ensure food security (Ejigu, 2009; Bununu et al. 2023). Due to climate change and poverty of the population, food insecurity is becoming one of the major challenges in Burundi, especially in rural areas (Niragira et al., 2022). However, land degradation and deforestation have been driven by uncontrolled agricultural activities, as reported in previous research (Ndagijimana et al., 2019; Jacques et al., 1993).

Recent environmental challenges in Burundi include increasing pressure on land and natural resources due to rapid population growth and uncontrolled and unsustainable land use. According to Placide and colleagues (2021), population growth in Burundi is 11%, which implies increased pressure on natural resources and agricultural activities in Burundi: rural poverty increased by 70% and malnutrition by 67% in Burundi, making it one of the poorest countries in the world.

The geological peculiarity of Burundi lies in its location in the Albertine Rift Valley with Holocene alluvial deposits in the valley bottoms (Chuwa et al. 2023). In addition, it is a western extension of the East African Great Rift, which defines its rich geological setting (Evans, 2015) and its heavily vegetated terrain. Petrology and geochemistry are distinguished by complex mineral compositions including crystallized rocks and outcrops from the area between the Kibaran Belt and the Tanzania Craton (Midende et al. 2014). Among other geological resources, Burundi is distinguished by its deposits of rare earth elements (Buyse et al. 2020). For example, rare earth element mineralization in the Neoproterozoic Gakara in Burundi is located along fracture zones and in the upper crust of Mesoproterozoic sediments of the Gikoro Group, overlain by gneissic basement rocks (Decrée et al. 2015).

# DATA AND METHODS

## Variables, data and procedures for spatial environmental analysis
The advantage of using multi-source data (Figure 2) lies in their compa-
rability and compatibility. Satellite images were exported into a GRASS GIS
environment to create a geospatial data mosaic that was enhanced by the
addition of topographic data (localities, roads, hydrographic network and
country boundaries), land cover map based on spatial data from the Food
and Agriculture Organization (FAO) and geological maps.

During geospatial analysis, satellite images and thematic maps can be
compared to distinguish correlations between geological and environmental
settings: distribution of vegetation types, geomorphological forms, tectonic
structures such as the East African Rift. These correlations have been dis-
cussed earlier in the geological settings that are reflected in the landscape
(Emishaw and Abdelsalam, 2023; Lemenkova and Debeir, 2023a).

<figure><img src="image_6.png" /><figcaption>**Figure 2** – Overlaid Landsat multispectral satellite images covering the territory of Burundi</figcaption></figure>

Source: USGS. Compilation: author.

Goals and motivation

In this study, the GMT scripting toolset was used to map Figure 1; QGIS for the preparation of thematic maps in Figures 2, 3, 4, and 9; and GRASS GIS software for processing satellite images (Figures 7 and 8). For satellite image processing, we used Landsat 8-9 OLI/TIRS images covering Burundi from the USGS website. The advantages of remote sensing data for environmental mapping and land cover type distinction lie in their reliability and objectivity, as discussed in many previous works (Lemenkova and Debeir, 2022b, Lemenkova, 2024).

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

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>
Thus, this study illustrates various cartographic approaches to process
and visualize multi-source geospatial data by different software. We used
two types of geospatial data covering the Burundi region: raster data (sa-
tellite images and DEM topographic data) and vector data (thematic files on
landscape, soils and administrative divisions).

Processing of remote sensing data

The geographic database is formed from Landsat image records with
30-meter resolution in multispectral channels that are presented in RGB co-
lors in Figure 3.

<figure><img src="image_10.png" /><figcaption>**Figure 3** – Four original Landsat 8-9 OLI/TIRS satellite images over Burundi</figcaption></figure>

Source: USGS. Compilation: author.

The long history of Landsat sensors is one of the strengths of Landsat mission. Launched in 1982, it provides a valuable data source for environmental
study in Africa. In addition, the open source availability of Landsat images is a complementary advantage. Finally, the 185 km band of Landsat images in multispectral cameras has the advantage of obtaining frequent global coverage. Such a large band of Landsat images is beneficial in regions of Burundi where clouds are a major obstacle to image acquisition in clear weather. In this context, satellite images, as accurate and current data, can be contrasted with descriptive data that may be subjective or outdated (e.g., old maps). Landsat multispectral imagery consists of spectral bands that are located in the visible and infrared spectra. The combination of three of these spectral bands is necessary for the constitution of RGB images or thematic classification.

Programming methods in mapping

The topographic map (Figure 1) was produced using Generic Mapping Tools (GMT) which can be defined as a computer scripting tool for storing, processing and representing geographic information using cartographic modules.

<figure><img src="image_11.png" /><figcaption>**Figure 4** - Mosaic of Landsat 8-9 OLI/TIRS monochrome color satellite images, SWIR-1 and SWIR-2 bands, overlaid with vector data, covering Burundi</figcaption></figure>

Source: author. Software: GRASS GIS.

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

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

<figure><img src="image_14.png" /></figure>
The use of GRASS GIS modules plays a vital role in the processing of remo-
te sensing data. The techniques used in the field of visualization are based on
the algorithms that constitute the structure of the program. The satellite ima-
ge processing process included the following steps. First, the satellite images
were taken on the USGS site using a cloud mask of less than 10% for each
image. The country of Burundi is covered by the four images. This requires
a mosaic technique for representation by several merged images. This pro-
blem was solved by using the GRASS GIS module 'g.region', Figure 4.

The images were imported into the program using the 'r.import' module using the code presented in Note 1. In a satellite image, the color information is decomposed into different spectral bands. But just as the representation of images is twofold, so is the interpretation: it is partly related to the identification and description of vegetation types, and partly to the precise detection of land cover types (Lemenkova and Debeir, 2023b).

Of course, the fact of processing satellite images and interpreting them by
classification are not the same, but what we keep in mapping using remote
sensing data is a classified image with the types of land cover detected and
identified, and a representation of the perceived objects. However, this be-
comes more complicated when we have to take into account the seasonal
and natural changes in land cover that are found first in the "wet" and "dry"
seasons in tropical climates. Therefore, it is necessary to take comparable
images covering the same period of the season for reasons of comparability.
For this reason, we took Landsat images on the following dates: images 1 and
2 on August 19, 2023; image 3 on August 10, 2023 and image 4 on July 25,
2023 depending on the availability of the swath scene covering Burundi with
minimal cloudiness (less than 10%), Figure 6. The mosaic of the 4 images is
presented in Figures 4 and 5.

For countries with very small spatial extent and coverage, such as Buruni-
di, the problem often arises when using satellite images. Some parts of the
country may not be covered by the images and the country is located on the
border of several images. Therefore, it is not possible in such cases to use a
single image. Instead, it is necessary to stitch multiple images together using
geoprocessing methods that analyze the coordinates of the target region.

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

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

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

<footnote n="1">1 'r.import input=/Users/path/grassdata/Burundi/LC08_<...>_02_T1_SR_B1.TIF output=L_2023a_01' Here, the example is given for band 1 of the image taken on August 19th. The same code, modified for the image name and file output name, has been used respectively for all Landsat multispectral bands and four images.</footnote>
<figure><img src="image_18.png" /><figcaption>**Figure 5** - Classification of Landsat 8-9 OLI/TIRS satellite images according to the 'Maximal Likelihood' method over the territory of Burundi</figcaption></figure>

Source: author. Software: GRASS SIG.

In GRASS GIS, several approaches are possible to solve this problem. In this study, we used three possibilities to stitch satellite images together: 'g.region', 'i.image.mosaic' and 'r.patch', Figure 5. First, the coordinate extent was checked using the 'r.info' module, for example using the command 'r.info L_2023a_07'. The result is received as a geographic coverage for the west-east and south-north extent <fnref n="2" />. Another approach to mosaicking images can be achieved by using the 'i.image.mosaic' module which mosaics multiple images and extends their color palette <fnref n="3" />.

Yet another way to stitch multiple images together is to use the 'r.patch' module which creates a mosaic of maps using bash syntax. Thus, it creates a list of maps matching a pattern, extends the region to include all of them,

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

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

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

<footnote n="2">2 In the same way, the coordinates of the four images were inspected and the area of greatest coverage comprising the four images was applied for the SIG GRASS project using the command 'g.region w=84585 e=345915 n=-204285 s=-596415'. The images were then visualized step by step as a mosaic of scenes.</footnote>

<footnote n="3">3 SIG GRASS code for mosaicing satellite images: 'i.image.mosaic input=L_2023a_06,L_2023b_06,<...>L_2023c_06,L_2023d_06 output=mosaic_2023_SWIR1'.</footnote>
and stitches them together to create a mosaic. In this approach, overlapping
maps will be used in the order given. For this method, the following code was
tested for all four images from (a) to (d): using the code in Note 4. Once the
images were assembled using mosaic techniques, the vector data in ArcGIS
'.shp' format was added as overlay layers.

So the file containing the country borders was imported using the import
of vector files into GRASS GIS as presented in the note 5. Here, the command
uses the 'v.in.ogr' module that allows importing vector data into GRASS GIS
using the OGR (Open Source Geospatial Resources) library. Similarly, vector
data has been imported for visualization of major geographic features, such
as cities, lakes, rivers, text annotations, etc.

There are many approaches of two types of satellite image processing that
can be used: supervised and unsupervised classification (Kim et al. 2013).
In this study, we used clustering algorithms because they have reliable and
simple methods used in many similar studies before. Therefore, image classi-
fication was performed using a combination of GRASS GIS modules "i.group",
"i.cluster" and "i.maxlik" following the methodology described in previous
studies (Lemenkova, 2023b, 2023c).

RESULTS

The environmental challenge in Burundi:
agricultural development, food supply for population, and
sustainable economy

Technically, this study offered us the opportunity to evaluate the results of
the GRASS GIS programming approach in the processing of remote sensing
data. The environmental study was used to monitor the biodiversity of diffe-
rent land cover classes in Burundi. These methods can be applied to study
ecosystems in Africa using the GRASS GIS raster data format and scripting
approach. Therefore, Landsat-8 OLI/TIRS data can be used in similar studies
using the presented programming codes as a continuous data source for ve-
getation monitoring and geological mapping.

<footnote n="4">4 The code for merging several satellite images using r.patch ' module of SIG GRASS: "r.patch in=L_2023a_01,<...>,L_2023d_01 out=mosaic_2023_Burundi."</footnote>

<footnote n="5">5 The code for importing country border data into SIG GRASS: "v.in.ogr input=/Users/<...>/ne_10m_admin_0_countries.shp output=world_countries -o".</footnote>
Geospatial data analysis then allows us to detect important environmental challenges in Burundi. Food insecurity and climate effects such as droughts and environmental scarcity have led to overexploitation of local farmers' resources to protect crops and secure food resources. Increased by climate effects and triggered by the poverty of the population, food insecurity is becoming one of the major challenges in Burundi, especially in rural areas. Therefore, we also recall the importance of analyzing landscape change and land use planning processes, both from a social ethics and environmental point of view. This has led to land degradation and deforestation due to uncontrolled agricultural activities, as reported in previous studies.

The mapping was based on processed and visualized remote sensing data. The subregions of the coastal zone of Lake Tanganyika are visualized and compared using the mosaic of satellite images. The results indicate the different land cover classes automatically detected by GRASS GIS methods. The visualization of vector data was performed in GRASS GIS using the 'd.vect' module <fnref n="6" />. Land cover type classes for Burundi are presented in Figure 6.

<figure><img src="image_22.png" /><figcaption>**Figure 6** – Map of land cover types</figcaption></figure>

Source: author.

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

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

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

<footnote n="6">6 For example, Lake Tanganyika on figure 9 was visualized using the following code: "d.vect map=Burundi_lakes display=shape type=area color=white fill_color=blue width=1 attrib_column=name label_color=white". The grid in figures 7 and 8 was added using the 'd.grid' module with the command "d.grid -g size=00:30:00 color=red width=0.1 fontsize=10 text_color=blue".</footnote>
For example, among the recent environmental problems of Burundi, there
is increasing pressure on land and natural resources due to rapid population
growth and an uncontrolled and unsustainable land use system. Population
growth in Burundi suggests increased pressure on natural resources and
agricultural activities in Burundi. Agricultural pressure is particularly notable
in the provinces of Bururu, Bujumbura and Bubanza, Figure 7.

<figure><img src="image_26.png" /><figcaption>**Figure 7 - Administrative division of Burundi**</figcaption></figure>

Source: author. Software: QGIS.

In addition, civil unrest and the recent war have had devastating effects
on the economy of Burundi, with negative consequences on the population.
In addition, there is an increase in rural poverty and malnutrition in Burundi,
making it one of the poorest countries in the world. Such social indicators
naturally push farmers to intensify agriculture and adapt more land to crops,
leading to unsustainable development and land depletion, forming a vicious
circle in socio-natural systems.

The geographical and geological framework of Burundi:
the main tectonic formations, rock type and location

Burundi is distinguished by its location in the Albertine Rift Valley, as shown in Figure 8, which represents the western extension of the Great East African Rift. This explains its rich geological setting and its highly vegetated terrain. The geological setting of Burundi is presented by a wide distribution of sedimentary and granitic rocks belonging to the Karagwe-Ankolean belt, with
Holocene alluvial deposits in the valley bottoms. The crystallized rocks and outcrops of the area between the Kibaran belt and the Tanzania craton are complex mineral components that characterize the petrology and geochemistry of Burundi, Figure 8.

**Figure 8** - Geological and geomorphological map of Burundi: reliefs, lithological types and general physical characteristics of rocks in Burundi according to the United Nations Africover Project

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

Geology: JTr - Jurassic and Triassic; PC - Permian-Carboniferous; pCm - Precambrian; Q - Quaternary; Qe - Holocene; QT - Cenozoic; Qv - Quaternary extrusive and intrusive rocks.

Source: author. Software: QGIS.

Burundi is distinguished by geological and mineral resources, including regional reserves of rare earth elements. The availability of rare earths can be an important economic factor for mining. Various applications of these elements as electronic components, magnetic materials and industrial processes can contribute to the economic development and wealth of the country. However, their proper geological exploration is lacking due to limited resources of Burundi.

The geological context of the country is strongly linked to the tectonic context. Consider the example of the rare earth element mineralization of the Neoproterozoic Gakara in Burundi, which is located along fracture zones and in the upper crust of the Mesoproterozoic sediments of the Gikoro Group, which are underlain by gneissic basement rocks.

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

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

<figure><img src="image_30.png" /></figure>
The environmental and social framework of Burundi

The adaptive determination of the ecological and environmental context of the country by geographic maps is a new aspect of this study implemented using GRASS SIS scripts. Indeed, landscape monitoring using remote sensing and geospatial data is an important research topic for future studies on climate-environment relationships in Africa, because the automatic processing of satellite images using computer cartographic approaches is a very complex task. After describing the geological aspects and the geomorphological framework of the country, it is appropriate to define the plant diseases and types (Figure 4) and the agricultural human activities that relate to this geological factor of Burundi. They form, as just noted, a continuum of environmental factors within which regional distinctions must be made in Burundi and to determine what is related to regional environmental specificities, Figure 9.

<figure><img src="image_31.png" /><figcaption>Figure 9 - Terrestrial habitat map of Burundi</figcaption></figure>

Source: author.

One of the smallest countries in Africa with a tiny territorial extent of Burundi (about 2° in the west-east and south-north directions), Burundi nevertheless has a great variety of landscapes formed under an equatorial climate. Plateaux and highlands cover more than half of the country, which affects the specific distribution of habitats such as mosaic vegetation types, broadleaf, herbaceous and deciduous forests. The major units have been identified as follows: urban area, forest, forest-savanna mosaic, savanna and steppe, aquatic vegetation and marsh (Henri 2009).
To protect rare species and conserve wildlife populations, two national parks have been created in Burundi: Kibira National Park, which preserves 40,000 ha of tropical rainforest in the northwest of the country, and Ruvubu National Park, located along the Rurubu River in the northeast of the country (Peyrot 1997). The park preserves the natural grassland ecosystem that covered the vast majority of the northeast.

## The links between the geological framework and the environmental context of Burundi: challenges for agriculture and environmental dynamics

The location of Burundi near the margins of the Tanzanian Craton determines its geological setting and defines the mineral content of geological outcrops which are dominated by Precambrian basement rocks, mostly of Proterozoic age. Thus, sediments comprising mainly alluvial sands, silts, gravels and clays fill the main tectonic rift valley in western Burundi and descend to Lake Tanganyika. Such a geological setting leads to the distribution of acidic soils with "strong" to "very strong" acidity (pH ≤ 5.5). At the same time, acidic soils constitute a serious factor of land degradation that impacts agricultural productivity and sustainable agriculture.

As a result, agriculture in Burundi is limited to arable land in only half of the country, and only about a third is suitable for grazing. Agricultural plantations are occupied by simple crops such as beans, maize, cassava and sorghum, as well as some export crops (coffee, cotton and tea). The complex interrelationships between geological setting, cover and vegetation types result from the effects of rock and mineral geochemistry that affect soil content, plasticity, acidity and structure (Hicintuka and Masilya, 2013). In addition, regional geomorphology is expressed in the relief, curvature and steepness of slopes overlying crystalline rocks belonging to the geological structures of the East African Rift. Therefore, the geomorphological setting and exposure control soil stability and properties which, in turn, contribute to the distribution of certain vegetation types in the country (Nkunzimana et al. 2020).

## Environmental monitoring of Burundi

In response to limited natural resources and growing population, Burundian farmers are adjusting and intensifying their agricultural activities to mitigate the impact of environmental scarcity and weather effects on crop yields. For example, they are diversifying crop types, using more productive and
climate-resilient crops, adopting different farming practices and techniques to increase harvests through multiple cropping, supplemental irrigation, and high-yielding varieties (Niragira et al. 2021).

However, such intensification of agricultural activities leads to fragmen-
tation of landscapes and overexploitation of land. In this regard, Turimu-
bumwe et al. (2024) recently noted the need for land use and management
planning through quantitative data for soil monitoring in Burundi. Therefore,
it is necessary for geospatial data integrity to link the country's geological and
environmental framework to produce integral analysis and monitoring (Lwa-
sa 2019). This integrity is the data representation that receives in the form of
satellite images and thematic mapping what comes from the environmental
complexity of East Africa.

DISCUSSION

The study of land cover properties for landscape monitoring in Burundi in-
volves cartographic visualization of data collected from various sources such
as spatial imagery, vector and raster data, as well as a descriptive analysis of
existing works. Examples of complex GIS approaches are presented in selec-
ted works that combine satellite images with geological, soil and vegetation
maps (Lemenkova, 2023a) or climate data (Ge et al. 2008). Moreover, 'in situ'
fieldwork is difficult in this country and data are scarce.

However, these studies use traditional GIS mapping methods for data
analysis and visualization. To continue and extend these studies, this work
presents the application of script mapping techniques for integrated ma-
pping that uses various data for a comprehensive analysis of the environ-
mental context of Burundi. By continuing this study, it is possible to achieve a
graphical representation of land use changes using classification. For exam-
ple, the upcoming study could explore segmentation techniques that could
be used to analyze satellite images at different scales. The development of
this method could include automatic object discrimination, segmentation,
and feature extraction of land cover types in Burundi landscapes.

Land monitoring and an effective landscape management system can con-
tribute to the development of a more efficient agricultural system. For exam-
ple, using multi-temporal image sequence, the dynamics of Burundi landscap-
es can be analyzed in more detailed spatio-temporal aspects. The content
of this information can be derived from satellite imagery for the analysis of
suitable and sufficient features for landscape monitoring.
In this context, this paper highlights the importance of integrating program-
ming algorithms, scripting techniques and GRASS GIS computational methods
for spatial data analysis in landscape studies. Furthermore, it offers oppor-
tunities for the development of new methods that could complement classi-
cal cartographic tools. Furthermore, this study applied interactive spatial data
analysis combining logical queries on raster data regarding various landscape
characteristics, with exploratory and confirmatory analysis, and cartographic
visualization by GRASS GIS scripts. Considering this, this paper contributed to
the thematic mapping of Burundi through the presentation of a new series of
maps based on multi-source geospatial data and satellite images.

CONCLUSION

This article presented the environmental analysis of Burundi as well as
the principles of using various GIS software, to see the differences between
cartographic applications through multiple geospatial data on Burundi. So,
the different ways of approaching the visualization of geospatial data and sa-
tellite images via QGIS, GRASS GIS and GMT software are analyzed. As for the
best software in this study, our choice would be GRASS GIS followed by GMT.
Even if it does not show completely easy approaches, it is categorically faster
thanks to the programming algorithms used for processing cartographic and
remote sensing data. So, the use of scripts in the context of environmental
monitoring can be considered a very great advantage.

On the one hand, the integration of geospatial data and satellite images
using GRASS GIS scripts and traditional GIS approaches aimed to support a ge-
ographic analysis process with the aim of assessing environmental dynamics
in East Africa. On the other hand, the demonstrated cartographic capabilities
of the scripts executed in GRASS GIS contribute to the development of techni-
cal methods of geoinformatics and cartography. Thus, this paper achieved the
dual objective of this study by combining the technical processing of cartogra-
phic data by scripts and the environmental analysis of landscapes.

In addition, this paper illustrates the importance of applying programming
for remote sensing data processing using spectral and texture feature analy-
sis of satellite images. The programming approach has yielded relatively
good results on Landsat-8 OLI/TIRS images with simple color texture covering
the territory of Burundi. Satellite image analysis and thematic mapping are
advantageous for environmental monitoring, and the integration of cartogra-
phic and programming approaches allows to extract maximum information
from these data. The classification of Landsat 8-9 OLI/TIRS satellite images
performed using GRASS GIS software is useful for learning Burundi landsca-
pe patterns in the form of geospatial structures detected and identified by
the script algorithms.

The social development of the country is generally strongly linked to its
natural geographical setting and available land resources. This is particularly
true for Burundi whose limited economic potential is affected by the extent of
the landlocked and small country. For example, the existing relationship be-
tween land productivity and farm size, which is affected by the fragmentation
and heterogeneity of the landscape. At the same time, a good understanding
of the environmental and geological context of the country helps to reveal
the potential of its resources. For example, geological mapping is useful for
modeling regional hydraulic parameters and discovering the thickness of the
aquifer. In turn, this is useful for the analysis of the groundwater resources
of Burundi which are essential sources of drinking water for the population.
In this regard, the production of new maps using reliable geospatial data and advanced
methods contributes to a better understanding of the country's natural and environmental
resources. Several authors have highlighted the successful applications of programming
and data integration methods for environmental and geographic analysis (Fikadu and
Olika 2023; Cloete et al. 2024). In the presented study, such integrated environmental
analysis fills the gap in the existing literature on Burundi that lacks approaches on pro-
gramming cartographic techniques used with GIS software. ●

BIBLIOGRAPHIC REFERENCES

BETGE, D. A Holistic Approach to Address Food Security Risks and Climate Change Adaptation—Insights from Burundi. In: Leal Filho, W., Djekic, I., Smetana, S., Kovaleva, M. (eds) **Handbook of Climate Change Across the Food Supply Chain**. Climate Change Management. Springer, Cham, 2022. https://doi.org/10.1007/978-3-030-87934-1_20.

BUNUNU, Y. A.; BELLO, A.; AHMED, A. Land cover, land use, climate change and food security. **Sustainable Earth Reviews**, v. 6, n. 16, 2023. https://doi.org/10.1186/s42055-023-00065-4.

BUYSE, F.; DEWAELE, S.; DECRÉE, S.; MEES. F. Mineralogical and geochemical study of the rare earth element mineralization at Gakara (Burundi). **Ore Geology Reviews**, v. 124, n. 103659, 2020. https://doi.org/10.1016/j.oregeorev.2020.103659.

CABRAL, A. I. R.; VASCONCELOS, M. J. P.; PEREIRA, J. M. C.; MARTINS, E.; É. BARTHOLOMÉ.
A land cover map of southern hemisphere Africa based on SPOT-4 Vegetation data.
**International Journal of Remote Sensing**, v. 27, n. 6, p. 1053-1074, 2006. https://doi.org/10.1080/01431160500307409.

CHUWA, G. L.; KASANZU, C. H.; APEN, F. E.; KAZIMOTO, E. O. Geochemistry and U-Pb zircon geochronology of S-type granites in the Karagwe Ankolean Belt, northwestern Tanzania: Implications for rare-metals mineralization. **Journal of African Earth Sciences**, v. 208, n. 105078, 2023. https://doi.org/10.1016/j.jafrearsci.2023.105078.

CLOETE, D. N.; SHOKO, C.; DUBE, T.; SUMAYA C. Remote sensing-based land use land cover classification for the Heuningnes Catchment, Cape Agulhas, South Africa. **Physics and Chemistry of the Earth, Parts A/B/C**, v. 103559, 2024. https://doi.org/10.1016/j.pce.2024.103559.

CUQ, F. Actes des journées. Télédétection en Sciences Humaines », **Cybergeo: European Journal of Geography** [En ligne], Dossiers, 2009. https://doi.org/10.4000/cybergeo.614.

DECRÉE, S.; BOULVAIS, P.; COBERT, C.; BAELE, J.-M.; MIDENDE, G.; GARDIEN, V.; TACK, L.; NIMPAGARITSE, G.; DEMAIFFE, D. Structurally-controlled hydrothermal alteration in the syntectonic Neoproterozoic Upper Ruvubu Alkaline Plutonic Complex (Burundi): Implications for REE and HFSE mobilities. **Precambrian Research**, v. 269, p. 281-295, 2015. https://doi.org/10.1016/j.precamres.2015.08.016.

DIÉDHIOU, I.; MERING, C.; SY, O.; TIDIANE, S. Cartographier par télédétection l'occupation du sol et ses changements. **EchoGéo** [En ligne], v. 54, 2020. https://doi.org/10.4000/echogeo.20510.

EJIGU, M. Environmental Scarcity, Insecurity and Conflict: The Cases of Uganda, Rwanda, Ethiopia and Burundi. In: BRAUCH, H.G., et al. Facing Global Environmental Change. **Hexagon Series on Human and Environmental Security and Peace**, v. 4. Springer, Berlin, Heidelberg, 2009. https://doi.org/10.1007/978-3-540-68488-6_68.

EL-AWADY, M. M.; EL-BADRAWY, H. T.; ABUO EL-ELA, A. M.; SOLIMAAN, M. R.; ALREFAEE, H. A.; ELBOWAB, M. Integrated geophysical studies on the area east of Abu Gharadig basin, southern Cairo, Egypt, using potential field data. **NRIAG Journal of Astronomy and Geophysics**, v. 5, n. 2, p. 351-361, 2016. https://doi.org/10.1016/j.nrjag.2016.05.002.

EL-HASSANIN, A. S.; LABIB, T. M.; GABER, E. I. Effect of vegetation cover and land slope on runoff and soil losses from the watersheds of Burundi. **Agriculture, Ecosystems & Environment**, v. 43, n. 3, p. 301-308, 1993. https://doi.org/10.1016/0167-8809(93)90093-5.

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

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

<figure><img src="image_34.png" /></figure>
EMISHAW, L.; ABDELSALAM, M. G. Satellite gravity data for mapping lithospheric structure of Precambrian tectonic blocks in Africa: The advantages and limitations. **Journal of African Earth Sciences**, v. 197, n. 104775, 2023. https://doi.org/10.1016/j.jafrearsci.2022.104775.

EVANS, D. M. Metamorphic modifications of the Muremera mafic–ultramafic intrusions, eastern Burundi, and their effect on chromite compositions. **Journal of African Earth Sciences**, v. 101, p. 19-34, 2015. https://doi.org/10.1016/j.jafrearsci.2014.09.004.

FIKADU, G.; OLIKA G. Impact of land use land cover change using remote sensing with integration of socio-economic data on Rural Livelihoods in the Nashe watershed, Ethiopia. **Heliyon**, v. 9, n. 3, p. e13746, 2023.

GE, J.; QI, J.; LOFGREN, B. Use of vegetation properties from EOS observations for land-climate modeling in East Africa. **Journal of Geophysical Research**, v. 113, n. D15101, 2008. https://doi.org/10.1029/2007JD009628.

HENRI, N. Progrès de la connaissance du Congo, du Rwanda et du Burundi de 1993 à 2008. **Belgeo**, v. 3-4, 2009. https://doi.org/10.4000/belgeo.7306.

HENNEBERT, P. A.; TESSENS, E.; TOURENNE, D.; DELVAUX, B. Validation of a FAO land evaluation method by comparison of observed and predicted yields of five food crops in Burundi. **Soil Use and Management**, v. 12, p. 134-142, 1996.

HICINTUKA, C.; MASILYA, P. M. Gestion optimale et intégrée de la fertilité des sols acides du Burundi. **VertigO**, v. 17, 2013. https://doi.org/10.4000/vertigo.13898.

JACQUES, P. ; MASSART, M. ; WILMET, J. Intérêt de l'analyse spatiale dans le traitement des données satellitaires pour un suivi agricole en Afrique Centrale. **Innovations et développement rural dans les pays tropicaux.** Bordeaux : Presses Universitaires de Bordeaux, pp. 205-212, 1993. (Espaces tropicaux, 8).

KIM, D.-Y.; THOMAS, V.; OLSON, J.; WILLIAMS, M.; CLEMENTS, N. Statistical trend and change-point analysis of land-cover-change patterns in East Africa. **International Journal of Remote Sensing**, v. 34, n. 19, p. 6636-6650, 2013.

LEMENKOVA, P. Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa. **Die Bodenkultur**: Journal of Land Management, Food and Environment, v. 74, n. 1, p. 49-64, 2023a.

LEMENKOVA, P. A GRASS GIS Scripting Framework for Monitoring Changes in the Ephemeral Salt Lakes of Chotts Melrhir and Merouane, Algeria. **Applied System Innovation**, v. 6, n. 4, p. 61, 2023b. https://doi.org/10.3390/asi6040061.
LEMENKOVA, P. Monitoring Seasonal Fluctuations in Saline Lakes of Tunisia Using Earth Observation Data Processed by GRASS GIS. **Land**, v. 12, n. 11, p. 1995, 2023c. https://doi.org/10.3390/land12111995.

LEMENKOVA, P. Mapping Woodlands in Angola, Tropical Africa: Calculation of Vegetation Indices From Remote Sensing Data. **Agriculture and Forestry**, v. 70, n. 3, p. 185-202, 2024. https://doi.org/10.17707/AgricultForest.70.3.13.

LEMENKOVA, P.; DEBEIR, O. R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC. **Applied Sciences**, v. 12, n. 24, p. 12554, 2022a. https://doi.org/10.3390/app122412554.

LEMENKOVA, P.; DEBEIR, O. Satellite Image Processing by Python and R Using Landsat 9 OLI/TIRS and SRTM DEM Data on Côte d'Ivoire, West Africa. **Journal of Imaging**, v. 8, n. 12, p. 317, 2022b. https://doi.org/10.3390/jimaging8120317.

LEMENKOVA, P.; DEBEIR, O. Coherence of Bangui Magnetic Anomaly with Topographic and Gravity Contrasts across Central African Republic. **Minerals**, v. 13, n. 5, p. 604, 2023a. https://doi.org/10.3390/min13050604.

LEMENKOVA, P.; DEBEIR, O. Multispectral Satellite Image Analysis for Computing Vegetation Indices by R in the Khartoum Region of Sudan, Northeast Africa. **Journal of Imaging**, v. 9, n. 5, p. 98, 2023b. https://doi.org/10.3390/jimaging9050098.

LWASA, S. Appreciating the heterogeneity in the unity of Africa: A socio-ecological perspective on Africa's geographies. **The Canadian Geographer / Le Géographe canadien**, v. 63, p. 594-602, 2019. https://doi.org/10.1111/cag.12576.

MASWI, M. S.; MSHIU, E. E. Remote sensing mapping of geothermal systems around Lake Natron in the east Africa rift system, northeastern Tanzania. **Geothermics**, v. 119, n. 102930, 2024. https://doi.org/10.1016/j.geothermics.2024.102930.

MIDENDE, G.; BOULVAIS, P.; TACK, L.; MELCHER, F.; GERDES, A.; DEWAELE, S.; DEMAIFFE, D.; DECRÉE, S. Petrography, geochemistry and U-Pb zircon age of the Matongo carbonatite Massif (Burundi): Implication for the Neoproterozoic geodynamic evolution of Central Africa. **Journal of African Earth Sciences**, v. 100, p. 656-674, 2014. https://doi.org/10.1016/j.jafrearsci.2014.08.010.

NDAGIJIMANA, M.; KESSLER, A.; ASSELDONK, M. Understanding farmers' investments in sustainable land management in Burundi: A case-study in the provinces of Gitega and Muyinga. **Land Degradation & Development**, v. 30, n. 417-425, 2019.
NIRAGIRA, S.; D'HAESE, M.; BUYSSE, J. et al. Historical changes in the traditional agrarian systems of Burundi: endogenous drive to survive from food insecurity. **GeoJournal**, v. 86, p. 865-884, 2021. https://doi.org/10.1007/s10708-019-10102-5

NIRAGIRA, S.; NDIMUBANDI, J.; VAN ORSHOVEN, J. et al. Modelling crop portfolios that minimize human macronutrient deficiency on subsistence farms in Burundi. **Food Security**, v. 14, p. 23–37, 2022. https://doi.org/10.1007/s12571-021-01216-1

NKUNZIMANA, A.; BI, S.; ALRIAH, M. A. A.; ZHI, T.; KUR, N. A. D. Comparative analysis of the performance of satellite-based rainfall products over various topographical unities in central East Africa: Case of Burundi. **Earth and Space Science**, v. 7, n. e2019EA000834, 2020. https://doi.org/10.1029/2019EA000834.

NYIRARWASA, A.; HAN, F.; YANG, Z.; MPEREJEKUMANA, P.; DUFATANYE, UMWALI E.; NSENGIYUMVA, J. N.; HABIBULLOEV, S. Evaluating the Impact of Environmental Performance and Socioeconomic and Demographic Factors on Land Use and Land Cover Changes in Kibira National Park, **Burundi. Sustainability**, v. 16, n. 2, p. 473, 2024. https://doi.org/10.3390/su16020473.

PEYROT, B. Dynamiques paléoécologique et anthropogène de la forêt ombrophile de la dorsale Congo-Nil au Burundi. **Les Cahiers d'Outre-Mer**, v. 199, pp. 271-292, 1997. https://doi.org/10.3406/caoum.1997.3656.

PLACIDE, G.; LOLLCHUND, M. R.; DALSO, G. A. Wind Energy Potential Assessment of some Sites in Burundi using Statistical Modelling, **2021 IEEE PES/IAS PowerAfrica**, Nairobi, Kenya, p. 1-5, 2021. https://doi.org/10.1109/PowerAfrica52236.2021.9543186.

SELLIN, V.; MAGNANON, S.; GOURMELON, F.; DEBAINE, F.; NABUCET. J. Etude expérimentale en cartographie de la végétation par télédétection. **Cybergeo: European Journal of Geography, Cartographie, Imagerie, SIG**, v. 730, 2015. DOI: https://doi.org/10.4000/cybergeo.27067.

ROBERT, E.; GANGNERON, F. Un « SIG à dires d'acteurs » : décryptage des vulnérabilités environnementales des agro-éleveurs et pasteurs au Bénin. **Cybergeo: European Journal of Geography** [En ligne], Cartographie, Imagerie, SIG, v. 748, 2015. https://doi.org/10.4000/cybergeo.27285.

TURIMUBUMWE, P.; ADAM, A. G.; ALEMIE, B. K. Managing public urban lands for sustainable urban development in Bujumbura, Burundi: The role of land administration system. **GeoJournal**, v. 89, p. 10, 2024. https://doi.org/10.1007/s10708-024-10998-8.
ARTICLE EDITOR

Cláudio Luiz Zanotelli.

Received: 09/27/2024

Accepted: 11/06/2024

Available online: 11/18/2024