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Citation: Mensah, F.; Mushtaq, F.;
Bartel, P.; Abramowitz, J.;
Cherrington, E.; Mahamane, M.;
Mamane, B.; Dieye, A.M.; Sanou, P.;
Enaruvbe, G.; et al. Land Cover
Mapping in West Africa: A
Collaborative Process. Land 2024,13,
1712. https://doi.org/10.3390/
land13101712
Academic Editor: Shicheng Li
Received: 4 September 2024
Revised: 4 October 2024
Accepted: 6 October 2024
Published: 19 October 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
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Attribution (CC BY) license (https://
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4.0/).
land
Article
Land Cover Mapping in West Africa: A Collaborative Process
Foster Mensah 1, *, Fatima Mushtaq 2, Paul Bartel 3, Jacob Abramowitz 4,5 , Emil Cherrington 4,5 ,
Mansour Mahamane 6, Bako Mamane 6, Amadou Moctar Dieye 7, Patrice Sanou 8, Glory Enaruvbe 9
and Ndeye Fatou Mar 10
1Center for Remote Sensing and Geographic Information Services (CERSGIS), University of Ghana,
Legon, Accra PMB L17, Ghana
2Food and Agriculture Organization of the United Nations (FAO), Viale delle Terme di Caracalla,
00153 Rome, Italy; fatima.mushtaq@fao.org
3SERVIR West Africa, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT),
Cantonments, Accra PMB CT 112, Ghana; paul.bartel@icrisat.org
4Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL 35805, USA;
jca0030@uah.edu (J.A.); eac0021@uah.edu (E.C.)
5NASA SERVIR Science Coordination Office, Marshall Space Flight Centre, Earth Science Branch,
Huntsville, AL 35805, USA
6CILSS/AGRHYMET Regional Center, 425 Boulevard de l’Université, Niamey BP 11011, Niger;
mansour.mahamane@cilss.int (M.M.); mamane.bako@cilss.int (B.M.)
7Centre de Suivie Ecologique, Rue Leon Gontran Damas, Fann Residence Dakar, Dakar BP 15532, Senegal;
dieye@cse.sn
8
Higher Institute of Space Studies and Telecommunications (ISESTEL), Parcelle 13, Secteur 21, Arrondissement
5-Dassasgho, Ouagadougou 03 BP 7021, Burkina Faso; patrice.sanou@isestel.org
9African Regional Institute for Geospatial Information Science and Technology (AFRIGIST), Off Road 1,
Obafemi Awolowo University Campus, Ife Central, Ile Ife PMB 5545, Nigeria; enaruvbe@afrigist.org
10 Observatoire du Sahara et du Sahel (OSS), Boulevard du Leader Yasser Arafat BP 31 Tunis Carthage,
Tunis 1080, Tunisia; fatou.mar@oss.org.tn
*Correspondence: fmensah@ug.edu.gh
Abstract: The availability of current land cover and land use (LCLU) information for monitoring
the status of land resources has considerable value in ensuring sustainable land use planning and
development. Similarly, the need to provide updated information on the extent of LCLU change in
West Africa has become apparent, given the increasing demand for land resources driven by rapid
population growth. Over the past decade, multiple projects have been undertaken to produce regional
and national land cover maps. However, using different classification systems and legends has made
updating and sharing land cover information challenging. This has resulted in the inefficient use of
human and financial resources. The development of the Land Cover Meta Language (LCML) based
on International Organization for Standardization (ISO) standards offers an opportunity to create
a standardized classification system. This system would enable easier integration of regional and
national data, efficient management of information, and better resource utilization in West Africa.
This article emphasizes the process and the need for multistakeholder collaboration in developing
a standardized land cover classification system for West Africa, which is currently nonexistent. It
presents the survey data collected to evaluate historical, current, and future land cover mapping
projects in the region and provides relevant use cases as examples for operationalizing a standardized
land cover classification legend for West Africa.
Keywords: land cover classification; data harmonization; semantic interoperability; land cover meta
language; West Africa; geospatial
1. Introduction
1.1. The Relevance of a Land Cover Harmonization Framework for West Africa
Accurate and up-to-date information on land cover and land use (LCLU) is crucial
for tracking land-based indicators of the Sustainable Development Goals (SDGs). Thus,
Land 2024,13, 1712. https://doi.org/10.3390/land13101712 https://www.mdpi.com/journal/land
Land 2024,13, 1712 2 of 15
the United Nations recognizes land cover information as a fundamental geospatial data
theme layer [
1
]. The African Union (AU) Agenda 2063 envisions productive agriculture and
healthy ecosystems in a continent resilient to climate change [
2
]. Similarly, the Economic
Community of West African States (ECOWAS) Regional Environmental Action Plan (EAP),
adopted in 2008 and revised in 2020, seeks to “reverse trends of natural resource degrada-
tion and depletion to guarantee to the people of the sub-region, a healthy environment that
consequently improves on the living conditions of the population” [
3
]. The EAP highlights
the presence of “ineffective collaboration, linkages and coordination among institutions of
environmental management” and calls for the “minimization of duplications, to ensure
synergy and coherence amongst the various actors”. These noble ideas and objectives
require a common semantic framework for land cover assessment, the dynamics of its use,
and impacts on land, agriculture, forests, and water resources. However, land cover maps
of West Africa are based on different land cover classification systems and methodologies.
Additionally, there is a lack of documentation, and in cases where attempts are made to
characterize land features, class descriptions are not provided or unclear where provided.
Sometimes, different terminologies are used for the same concept, and, sometimes, the same
terminologies are used for heterogenous concepts. This limits the usability and integration
of different land cover datasets at the regional or even national levels.
Due to the unavailability of up-to-date land cover and land use information, global
datasets are used for regional and national land change assessment and impact studies.
Therefore, there is an urgent need for a regional semantic interoperability framework that
incorporates different map scales and production objectives and unambiguously defines
land cover classes using internationally accepted standards. The development of a Land
Cover Meta Language (LCML) by the Food and Agriculture Organization of the United
Nations (FAO) based on the International Organization for Standardization (ISO) standards
offers an opportunity to create a standardized classification system. Also, the development
and adoption of a West African Land Cover Reference System (WALCRS) would enable
easier integration of regional and national data, efficient management of information,
and better resource utilization in West Africa. Land cover legends compatible with the
International Organization for Standardization (ISO) international nomenclature would
facilitate map comparability, accuracy assessment, and area estimates of land cover classes.
An ontology-based semantic mapping approach for integrating land-cover products has
been effectively demonstrated by Zhu et al. [
4
]. Thus, the semantic similarity between
heterogeneous land-cover products can be achieved by combining land cover attributes
for real-time land-cover mapping for the subregion. Kinoshita et al. [
5
] implemented an
approach using ground truth data to integrate global land cover maps. They used the
same legend as the ground truth data to create a lookup and achieved a higher accuracy
than the existing maps. The corresponding probability map showed the probabilities
of the chosen land cover categories. In their case study of conterminous United States,
Zhu et al. [
6
] proposed an integration method based on fuzzy theory and combined three
land cover datasets, namely the National Land Cover Database 2011, Fine Resolution
Observation and Monitoring of Global Land Cover Segmentation 2010 [
7
], and the global
forest cover data (treecover 2010) [
8
,
9
], using the European Environment Information and
Observation Network Action Group on Land Monitoring in the European (EAGLE) system
of semantic translation [
10
]. These existing land cover datasets were integrated to improve
the GlobeLand30 [
11
] forest class data to obtain second-level classification, i.e., broadleaf,
coniferous, and mixed forest classes over the coterminous United States. They showed
that land cover product accuracy can be improved by effectively integrating multiple land
cover datasets. This example indicates that, while the inconsistency between the source
data and the target data classification system can be addressed, the accuracy of each source
data can also be efficiently improved. Invariably, the classification scheme of each land
cover map is inconsistent with others because of the variations in legend definitions. While
a land cover classification scheme is essential for land cover mapping [
12
–
14
], several of
these classification schemes are significantly different in class names, class definitions,
Land 2024,13, 1712 3 of 15
level of detail, and compatibility. However, many studies have directly compared and
transformed land cover legends during integration. The different classification legends
used by West African countries for mapping land cover present an opportunity for a
standardized classification system that facilitates semantic interoperability. While this
article does not discuss the land cover integration process per se, it points out the unique
characteristics of the West African Classification Reference System (WALCRS).
The objective of this article is to emphasize the process and the need for multistake-
holder collaboration in developing a standardized land cover classification system for
West Africa and presents the data collection methodology for evaluating historical, current,
and future land cover mapping projects in the region and provides relevant use cases as
examples for operationalizing a standardized land cover classification legend for West
Africa. The goal is to facilitate collaboration among diverse stakeholders in West Africa,
including development, regional and national agencies, environmental organizations, and
domain experts, to create a standardized land cover legend for the sub-region.
1.2. Regional and National Land Cover Mapping Initiatives in West Africa
Given the unprecedented land cover change in recent decades, West Africa has at-
tracted the attention of project initiatives seeking to strengthen the capacities of regional and
national institutions to produce current LCLU-related datasets for monitoring land change
and vegetation-based indicators. Within the scope of the ESA-GlobCover 2005 project, the
300 m global land cover map for 2005 was updated in 2010 by the European Space Agency
(ESA) in partnership with the Joint Research Centre (JRC), European Environment Agency
(EEA), Food and Agriculture Organization (FAO), United Nations Environment Programme
(UNEP), Global Observation for Forest Cover and Land Dynamics (GOFC-GOLD) and
International Geosphere-Biosphere Programme (IGBP) to produce a land cover map at the
scale of all of Africa [
15
] based on the FAO land cover classification system (LCCS). In 2016,
the Agrometeorology, Hydrology, and Meteorology Regional Center (AGRHYMET) collab-
orated with the United States Geological Survey (USGS) to jointly publish an atlas of West
Africa’s land cover [
16
]. The atlas utilized National Aeronautics and Space Administration
(NASA) and USGS Landsat imagery to classify the region’s land cover patterns for 1975,
2000, and 2013 and was updated using AGRHYMET to 2018 (Figure 1). The Sahara and Sa-
hel Observatory (OSS) has also developed land cover maps for North and West Africa based
on data from Sentinel-2 satellites [
17
]. Likewise, the West African Science Service Centre
on Climate Change and Adapted Land Use (WASCAL) has supported the development of
LULC maps at both regional [
18
], national [
19
], and subnational [
20
] scales. In its second
Research and Action Plan (WRAP 2.0), WASCAL is supporting high-resolution LULC
mapping through two projects, namely “greenhouse gas (GHG) emissions and mitigation
options under climate and land use change in West Africa concerted regional modeling and
assessment (CONCERT WEST AFRICA)” and “land surface processes as a determinant
of climate change in Africa—scenarios, high-resolution modeling and development of a
stakeholder data portal (LANDSURF)” [
21
]. NASA’s SERVIR applied science teams have
recently supported future land use scenario development projects and the integration of
time series Earth observation (EO) data for land use planning [
22
,
23
]. They are currently
developing workflows for near real-time monitoring of forest disturbance in West Africa,
as well as integration of socioeconomic and EO data to characterize conflict precursors and
land degradation dynamics in Ghana. The diversity of land cover mapping projects without
a harmonized classification system necessitated the creation of the West Africa Land Cover
Task Force (WALC-TF) to develop collaborative initiatives for land cover mapping and
monitoring and ensured the harmonization of disparate legends in West Africa.
Land 2024,13, 1712 4 of 15
Land2024,13,xFORPEERREVIEW4of16
andlanddegradationdynamicsinGhana.Thediversityoflandcovermappingpro-
jectswithoutaharmonizedclassificationsystemnecessitatedthecreationoftheWest
AfricaLandCoverTaskForce(WALC-TF)todevelopcollaborativeinitiativesforland
covermappingandmonitoringandensuredtheharmonizationofdisparatelegends
inWestAfrica.
Figure1.TheWestAfricalanduseandlandcovertimeseriesmaps,jointlyproducedbyUSGSand
AGRHYMET.
1.3.TheWestAfricaLandCoverTaskForce
InJune2018,undertheauspicesofECOWAS,SERVIRWestAfricahostedare-
gionalconferenceonlandcoverclassificationandmethodologiesinAccra,Ghana,to
engageregionalandinternationalexpertsontheharmonizationoflandcoverclassi-
ficationsystemsandmethodsusedinthesubregion.Followingtheconference,an
inter-agencymultistakeholdergroup,theWALC-TF,wasestablishedtoaddressthe
conferencerecommendations:harmonizinglandcoverproductsacrosstheregion
anddevelopingstandardsfordescribingproductaccuracycompatibilitywithinter-
nationalstandards.Thetaskforcewasalsomandatedtocoordinateandfacilitateac-
cesstosuitablecapacitydevelopmentprogramsforregionalresearchers,practition-
ers,andpolicymakers.ThetaskforceconsistsofmembersfromtheSERVIR-WestAf-
ricaconsortium,namelytheAgrometeorology,Hydrology,MeteorologyRegional
Center(AGRHYMET),theAfricanRegionalInstituteforGeospatialInformationSci-
enceandTec hnology(AFRIGIST),theCentredeSuiviÉcologique,(CSE)inSenegal,
theCentreforRemoteSensingandGeographicInformationServices(CERSGIS)in
Ghana,andtheHigherInstituteofSpaceStudiesandTelecommunications(ISESTEL)
inBurkinaFaso.OtherinternationalinstitutionsoutsideoftheSERVIRWestAfrica
consortiumincludetheWestAfricanScienceServiceCenteronClimateChangeand
AdaptedLandUse(WASCAL),theSahel-SaharanObservatory(OSS),theGlobalOb-
servationofForestCoverandLandDynamics-WestAfricaRegionalNetwork
(GOFC/GOLD-WARN),theJointResearchCenteroftheEuropeanCommission(JRC),
andtheFoodandAgricultureOrganizationoftheUnitedNations(FAO)[24].
Kilometer
s
0 750 1500375
Figure 1. The West Africa land use and land cover time series maps, jointly produced by USGS and
AGRHYMET.
1.3. The West Africa Land Cover Task Force
In June 2018, under the auspices of ECOWAS, SERVIR West Africa hosted a regional
conference on land cover classification and methodologies in Accra, Ghana, to engage
regional and international experts on the harmonization of land cover classification systems
and methods used in the subregion. Following the conference, an inter-agency multistake-
holder group, the WALC-TF, was established to address the conference recommendations:
harmonizing land cover products across the region and developing standards for describ-
ing product accuracy compatibility with international standards. The task force was also
mandated to coordinate and facilitate access to suitable capacity development programs
for regional researchers, practitioners, and policymakers. The task force consists of mem-
bers from the SERVIR-West Africa consortium, namely the Agrometeorology, Hydrology,
Meteorology Regional Center (AGRHYMET), the African Regional Institute for Geospatial
Information Science and Technology (AFRIGIST), the Centre de Suivi Écologique, (CSE) in
Senegal, the Centre for Remote Sensing and Geographic Information Services (CERSGIS)
in Ghana, and the Higher Institute of Space Studies and Telecommunications (ISESTEL)
in Burkina Faso. Other international institutions outside of the SERVIR West Africa con-
sortium include the West African Science Service Center on Climate Change and Adapted
Land Use (WASCAL), the Sahel-Saharan Observatory (OSS), the Global Observation of
Forest Cover and Land Dynamics-West Africa Regional Network (GOFC/GOLD-WARN),
the Joint Research Center of the European Commission (JRC), and the Food and Agriculture
Organization of the United Nations (FAO) [24].
2. Study Area
West Africa is a heterogeneous and rapidly developing subregion that has experienced
significant socioeconomic and biophysical changes over the past decades [
25
]. This rapid
transformation of West African landscapes can be described as anthropogenic, given the
extensive exploitation of natural resources and the rapid modification of land cover [
25
,
26
].
The subregion, which is about 8 million square kilometers, can be classified broadly into
Land 2024,13, 1712 5 of 15
five ecological zones, based on climate and vegetation characteristics, from the humid
southern coast to the Sahara as Guineo-Congolian, Guinean, Sudanian, Sahelian, and
Saharan (Figure 2). The Sudanian zone is dominated by vegetation, which ranges from
open tree savannah to wooded savannah and open woodland. The grassland is often taller
than in the Sahel region. The vegetation in the Guinean region is dominated by wet-and-dry
deciduous or semi-deciduous forests with dense and closed forest canopy, which often
forms an understory with very high trees. The vegetation in the Guineo-Congolian region
is rich and dense forest, with trees reaching over 60 m with intermingling crowns. The
biodiversity in this zone is considered relatively rich [
27
]. Although smallholder crop
cultivation is the dominant land use in the region, land cover types and land use systems
vary across the different ecological zones. Various land cover legends have therefore been
designed and implemented, such as Yangambi [
28
], FAO-Land Cover Classification System
(LCCS) [
29
], Intergovernmental Panel on Climate Change (IPCC) land cover classification
scheme [
30
], and various national forest classification systems for specific ecological zones
with multiresolution satellite data.
Land2024,13,xFORPEERREVIEW5of16
2.StudyArea
WestAfricaisaheterogeneousandrapidlydevelopingsubregionthathasexpe-
riencedsignificantsocioeconomicandbiophysicalchangesoverthepastdecades[25].
ThisrapidtransformationofWestAfricanlandscapescanbedescribedasanthropo-
genic,giventheextensiveexploitationofnaturalresourcesandtherapidmodification
oflandcover[25,26].Thesubregion,whichisabout8millionsquarekilometers,can
beclassifiedbroadlyintofiveecologicalzones,basedonclimateandvegetationchar-
acteristics,fromthehumidsoutherncoasttotheSaharaasGuineo-Congolian,Guin-
ean,Sudanian,Sahelian,andSaharan(Figure2).TheSudanianzoneisdominatedby
vegetation,whichrangesfromopentreesavannahtowoodedsavannahandopen
woodland.ThegrasslandisoftentallerthanintheSahelregion.Thevegetationinthe
Guineanregionisdominatedbywet-and-drydeciduousorsemi-deciduousforests
withdenseandclosedforestcanopy,whichoftenformsanunderstorywithveryhigh
trees.ThevegetationintheGuineo-Congolianregionisrichanddenseforest,with
treesreachingover60mwithinterminglingcrowns.Thebiodiversityinthiszoneis
consideredrelativelyrich[27].Althoughsmallholdercropcultivationisthedominant
landuseintheregion,landcovertypesandlandusesystemsvaryacrossthedifferent
ecologicalzones.Vario uslandcoverlegendshavethereforebeendesignedandimple-
mented,suchasYang a mbi[28],FAO-LandCoverClassificationSystem(LCCS)[29],
IntergovernmentalPanelonClimateChange(IPCC)landcoverclassificationscheme
[30],andvariousnationalforestclassificationsystemsforspecificecologicalzones
withmultiresolutionsatellitedata.
Figure2.Thestudyareaandbioclimaticzones.
3.Methodology
Aspartofthelandcoverdataharmonizationprocess,regionalconsultations
wereorganizedtostrengthenthecollaborationamongnational,regional,andinter-
nationalstakeholdersandpreparedfordevelopingtheregionalreferencesystemfor
thesubregion.Thesystemdevelopmentalsoinvolvedaliteraturereviewandunder-
standingofvariouslandcoversystemsusedintheregiontoidentifyareaswherehar-
monizationisrequiredforinteroperability.Datainventorysurveyandstakeholder
engagementwerecriticalinseingaframeworkfortheWestAfricanLandCoverRef-
erenceSystem(WALCRS).
Additionally,theroleofsubjectmaerexpertsfromtheFAO
wasinstrumentalincreating
therelevantbuildingblocksforthereferencesystem.
Thesurveywasconductedtocollecttheexistinglandcoverdatasets,landcover
legends,landcoverclassdefinitions,andrelevantdocumentationinthesubregion.
Expertsfromacademiaandthepublicsectorfromnationalandregionalorganizations
respondedtotheinvitationtoparticipateinthesurvey.Thesurveyquestionnairein
supportoftheregionallandcoverharmonizationprocesswaspreparedincollabora-
tionwiththeWALC-TFtoevaluatetheinconsistencyandnoninteroperabilitybe-
tweenlandcovermaplegends.Thequestionnairewassharedwith229potentialre-
spondentsacross
Stud
y
Area
E
Km0 2500 50001250
E
Figure 2. The study area and bioclimatic zones.
3. Methodology
As part of the land cover data harmonization process, regional consultations were
organized to strengthen the collaboration among national, regional, and international
stakeholders and prepared for developing the regional reference system for the subregion.
The system development also involved a literature review and understanding of various
land cover systems used in the region to identify areas where harmonization is required for
interoperability. Data inventory survey and stakeholder engagement were critical in setting
a framework for the West African Land Cover Reference System (WALCRS). Additionally,
the role of subject matter experts from the FAO was instrumental in creating the relevant
building blocks for the reference system.
The survey was conducted to collect the existing land cover datasets, land cover
legends, land cover class definitions, and relevant documentation in the subregion. Experts
from academia and the public sector from national and regional organizations responded
to the invitation to participate in the survey. The survey questionnaire in support of the
regional land cover harmonization process was prepared in collaboration with the WALC-
TF to evaluate the inconsistency and noninteroperability between land cover map legends.
The questionnaire was shared with 229 potential respondents across
West Africa from institutions working in the environment and forestry sectors, tertiary
institutions, and research centers. The questionnaire was administered with Google Form to
relevant partner institutions in the 17 member countries of ECOWAS. The questionnaire was
administered in English and French, the two main languages in West Africa. While contact
persons were not physically interviewed, the online inventory fostered initial engagement
with institutional partners for the collaborative process of developing a regional semantic
framework for mapping land cover. The questionnaire was categorized into sections to
collect data on respondents’ contribution to any land use/cover mapping activities in West
Africa, the use of FAO’s Land Cover Classification System (LCCS), country-level land cover
Land 2024,13, 1712 6 of 15
datasets (i.e., date of production, the institution responsible for the data production, access
to the data, and contact address), and corresponding legends if available.
4. Results
Out of representatives in the 17 countries contacted, respondents from 11 answered
the questionnaire, giving a country-level response rate of 65%. A total of 116 land cover
datasets and 33 land cover legends were collected through a survey (Figure 3).
Land2024,13,xFORPEERREVIEW6of16
WestAfricafrominstitutionsworkingintheenvironmentandforestrysectors,
tertiaryinstitutions,andresearchcenters.Thequestionnairewasadministeredwith
GoogleFormtorelevantpartnerinstitutionsinthe17membercountriesofECOWAS.
ThequestionnairewasadministeredinEnglishandFrench,thetwomainlanguages
inWestAfrica.Whilecontactpersonswerenotphysicallyinterviewed,theonlinein-
ventoryfosteredinitialengagementwithinstitutionalpartnersforthecollaborative
processofdevelopingaregionalsemanticframeworkformappinglandcover.The
questionnairewascategorizedintosectionstocollectdataonrespondents’contribu-
tiontoanylanduse/covermappingactivitiesinWestAfrica,theuseofFAO’sLand
CoverClassificationSystem(LCCS),country-levellandcoverdatasets(i.e.,dateofpro-
duction,theinstitutionresponsibleforthedataproduction,accesstothedata,and
contactaddress),andcorrespondinglegendsifavailable.
4.Results
Outofrepresentativesinthe17countriescontacted,respondentsfrom11an-
sweredthequestionnaire,givingacountry-levelresponserateof65%.Atotalof116
landcoverdatasetsand33landcoverlegendswerecollectedthroughasurvey(Figure
3).
Figure3.Numberofresponsesreceivedfromthe17WestAfricancountries.
Thesurveyshowedthatmostrespondentsinthesubregionhavebeeninvolvedin
landuseandlandcoveractivities,butfewerthan50%haveusedtheFAOlandcoverclas-
sificationsystem(LCCS)[24].Whilelanduseandlandcoverdatasetsareavailableand
accessible,thecorrespondinglegendsarenot,makingthemapscompliantwithinterna-
tionalstandards.Asaresult,theWestAfricanLandCoverReferenceSystemwillhelp
establishastandardnamingsystemforregionalandnationallandcoverclassification
Figure 3. Number of responses received from the 17 West African countries.
The survey showed that most respondents in the subregion have been involved in
land use and land cover activities, but fewer than 50% have used the FAO land cover
classification system (LCCS) [
24
]. While land use and land cover datasets are available
and accessible, the corresponding legends are not, making the maps compliant with in-
ternational standards. As a result, the West African Land Cover Reference System will
help establish a standard naming system for regional and national land cover classification
schemes based on international ISO standards to address this issue. The land cover ref-
erence system will provide an unambiguous ordering of the land features by eliminating
inconsistencies in the definition of different land features (Figure 4).
For example, in a tree-dominated area (Figure 5), two classes are expected to be classi-
fied as closed-tree formation and open-tree formation, subdivided according to the different
cover ranges that characterize the dominant element tree. The closed tree formation will
constitute a mandatory stratum that defines the overall class structure as a tree, with at-
tributes such as tree cover expressed as a percentage of tree crown covering the ground
(70–100%), tree height, and vegetation naturalness or artificiality, which, in this case, is
defined as natural vegetation. Similarly, the open-tree formation will constitute a basic class
with one mandatory strata that defines the overall class aspect. However, while its tree
cover attribute is expressed in percentages (20–70%), it is still defined as natural vegetation.
A further subdivision can be made for both closed- and open-tree formations. At this level,
the classes will have structural aspects and other thematic details as necessary.
Land 2024,13, 1712 7 of 15
The reference system is based on an object-oriented land cover meta language (LCML).
The LCML provides a set of ISO standard elements for land features that can be enriched
with different attributes to describe complex semantics of land cover classes in different
land cover-related applications. The LCML objects are represented with unified modeling
language (UML) (Figure 6), which makes it easier to describe real-world characteristics and
is straightforward to use in a GIS modeling environment to generate consistent dynamic
land cover datasets [31].
Land2024,13,xFORPEERREVIEW7of16
schemesbasedoninternationalISOstandardstoaddressthisissue.Thelandcoverrefer-
encesystemwillprovideanunambiguousorderingofthelandfeaturesbyeliminating
inconsistenciesinthedefinitionofdifferentlandfeatures(Figure4).
Forexample,inatree-dominatedarea(Figure5),twoclassesareexpectedtobeclas-
sifiedasclosed-treeformationandopen-treeformation,subdividedaccordingtothedif-
ferentcoverrangesthatcharacterizethedominantelementtree.Theclosedtreeformation
willconstituteamandatorystratumthatdefinestheoverallclassstructureasatree,with
aributessuchastreecoverexpressedasapercentageoftreecrowncoveringtheground
(70–100%),treeheight,andvegetationnaturalnessorartificiality,which,inthiscase,is
definedasnaturalvegetation.Similarly,theopen-treeformationwillconstituteabasic
classwithonemandatorystratathatdefinestheoverallclassaspect.However,whileits
treecoveraributeisexpressedinpercentages(20–70%),itisstilldefinedasnaturalveg-
etation.Afurthersubdivisioncanbemadeforbothclosed-andopen-treeformations.At
thislevel,theclasseswillhavestructuralaspectsandotherthematicdetailsasnecessary.
Thereferencesystemisbasedonanobject-orientedlandcovermetalanguage
(LCML).TheLCMLprovidesasetofISOstandardelementsforlandfeaturesthatcanbe
enrichedwithdifferentaributestodescribecomplexsemanticsoflandcoverclassesin
differentlandcover-relatedapplications.TheLCMLobjectsarerepresentedwithunified
modelinglanguage(UML)(Figure6),whichmakesiteasiertodescribereal-worldchar-
acteristicsandisstraightforwardtouseinaGISmodelingenvironmenttogeneratecon-
sistentdynamiclandcoverdatasets[31].
Figure4.Regionalreferencesystemcomponentsandtheirfunctionalrelationships[32].
Figure 4. Regional reference system components and their functional relationships [32].
Land2024,13,xFORPEERREVIEW8of16
Figure5.Structureoftree-dominatedareas[31].
Figure6.UMLdiagramrepresentingthedescriptionofalandcoverclassfortreesandshrubs[31].
Thesystemisdynamicintermsoforganizinglandfeaturesatmanylevelsandthe
correspondinglandcoverlegend.Furthermore,thelandcovermetalanguageprovidesa
dictionaryofaributesdepictedinthegraphicalmodel(grayboxes)inFigure7.Theresult
isanISO-basedlandcoverdatasetgeneratedwithLCML-basedlandcharacterizationsoft-
ware[32,33].Thelandcharacterizationsystemsoftwareprovidestoolstocharacterize
landcoverwithpredefinedelementsliketrees,shrubs,herbs,buildings,andothersthat
canbecombinedtorepresentreal-worldfeatures.
Figure 5. Structure of tree-dominated areas [31].
Land 2024,13, 1712 8 of 15
Land2024,13,xFORPEERREVIEW8of16
Figure5.Structureoftree-dominatedareas[31].
Figure6.UMLdiagramrepresentingthedescriptionofalandcoverclassfortreesandshrubs[31].
Thesystemisdynamicintermsoforganizinglandfeaturesatmanylevelsandthe
correspondinglandcoverlegend.Furthermore,thelandcovermetalanguageprovidesa
dictionaryofaributesdepictedinthegraphicalmodel(grayboxes)inFigure7.Theresult
isanISO-basedlandcoverdatasetgeneratedwithLCML-basedlandcharacterizationsoft-
ware[32,33].Thelandcharacterizationsystemsoftwareprovidestoolstocharacterize
landcoverwithpredefinedelementsliketrees,shrubs,herbs,buildings,andothersthat
canbecombinedtorepresentreal-worldfeatures.
Figure 6. UML diagram representing the description of a land cover class for trees and shrubs [31].
The system is dynamic in terms of organizing land features at many levels and the
corresponding land cover legend. Furthermore, the land cover meta language provides
a dictionary of attributes depicted in the graphical model (gray boxes) in Figure 7. The
result is an ISO-based land cover dataset generated with LCML-based land characterization
software [32,33]. The land characterization system software provides tools to characterize
land cover with predefined elements like trees, shrubs, herbs, buildings, and others that
can be combined to represent real-world features.
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Figure7.Anillustrationofthelandcovermetalanguageaributes[31].
5.UseCases
Theusecasespresentedinthissectionarelandcover-relatedproductsgenerated
byregionalandnationalWestAfricanorganizationsworkingtogethertoestablisha
standardizedlandcoverlegendforWestAfrica.Theywereprovidedtohighlightex-
istinglandcover-relatedproductscreatedinthesubregionfordifferentpurposesthat
couldbeusedasexamplestooperationalizetheWestAfricanLandCoverReference
System(WALCRS).Theseexamplesmayalsobeusedtocreatespecializedlegends
forpilotprojectsonwatershedmanagement,ecosystemrestoration,biodiversitycon-
servation,andforestcarbonstockassessmentatnationalandregionallevelsbyfol-
lowingaconsistentregionallandcoverreferencesystem.
5.1.AssessingWestAfrica’sCurrentForestCarbonStock
TheSERVIRCarbonPilot(S-CAP)isanactivityimplementedbySERVIRtomon-
itorforestcarbonin11SERVIRpilotcountries,including
Ghana.Theactivityisbased
onEarthobservationdataandinsituobservations
toimproveGHGemissionsmonitor-
ing.TheS-CAP
toolusesanensembleapproachtoestimateandvisualizecarbonemis-
sionsbasedonIPCCGuidelinesforNationalGreenhouseGasInventories
.Thetoolfo-
cusesonchangesinforestcovertoestimatechangesinabove-groundbiomassand
carbonstock.Theinitialresulthighlightstheimportanceoflandcoverandlandcover
changedataandhowforestcarbonstockvariessignificantlydependingonthelevelof
landcoverchange.
hps://s-cap.servirglobal.net/pilot/8/
(accessedon27August2024).
5.2.MonitoringArtisanalSmall-ScaleGoldMiningandClimateChangeResilienceinGhana
GhanaisoneoftheleadingproducersofgoldinAfricaandtheworld[34].How-
ever,about35percentofthegoldisextractedinformallythroughartisanalmining.
Althoughtheseinformalminingsitescoverlessarea,thenegativeimpactsonland,
forest,andfreshwaterresourcesoutweighthoseoflarge-scalemines.TheCenterfor
RemoteSensingandGeographicInformationServices(CERSGIS),aconsortiummem-
beroftheSERVIR-WestAfricaprogram,hasbeenusingfreelyavailablesatellite-based
earthobservationdatatoidentify,map,andquantifyinformalartisanalsmall-scale
miningactivitiesinGhana.ThemapsgeneratedbyCERSGIS(Figure8)serveasad-
vocacyanddecision-makingtoolsfornationalandlocalauthorities,enablingthemto
identifyareasthatrequireaentionandintervention.
Figure 7. An illustration of the land cover meta language attributes [31].
5. Use Cases
The use cases presented in this section are land cover-related products generated by
regional and national West African organizations working together to establish a stan-
dardized land cover legend for West Africa. They were provided to highlight existing
land cover-related products created in the sub region for different purposes that could
be used as examples to operationalize the West African Land Cover Reference System
(WALCRS). These examples may also be used to create specialized legends for pilot projects
on watershed management, ecosystem restoration, biodiversity conservation, and forest
carbon stock assessment at national and regional levels by following a consistent regional
land cover reference system.
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5.1. Assessing West Africa’s Current Forest Carbon Stock
The SERVIR Carbon Pilot (S-CAP) is an activity implemented by SERVIR to monitor
forest carbon in 11 SERVIR pilot countries, including Ghana. The activity is based on
Earth observation data and in situ observations to improve GHG emissions monitoring.
The S-CAP tool uses an ensemble approach to estimate and visualize carbon emissions
based on IPCC Guidelines for National Greenhouse Gas Inventories. The tool focuses on
changes in forest cover to estimate changes in above-ground biomass and carbon stock.
The initial result highlights the importance of land cover and land cover change data and
how forest carbon stock varies significantly depending on the level of land cover change.
https://s-cap.servirglobal.net/pilot/8/ (accessed on 27 August 2024).
5.2. Monitoring Artisanal Small-Scale Gold Mining and Climate Change Resilience in Ghana
Ghana is one of the leading producers of gold in Africa and the world [
34
]. However,
about 35 percent of the gold is extracted informally through artisanal mining. Although
these informal mining sites cover less area, the negative impacts on land, forest, and
freshwater resources outweigh those of large-scale mines. The Center for Remote Sensing
and Geographic Information Services (CERSGIS), a consortium member of the SERVIR-
West Africa program, has been using freely available satellite-based earth observation data
to identify, map, and quantify informal artisanal small-scale mining activities in Ghana.
The maps generated by CERSGIS (Figure 8) serve as advocacy and decision-making tools
for national and local authorities, enabling them to identify areas that require attention
and intervention.
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Figure8.Footprintsofsmall-scalegoldminingintheforestzoneofGhana.
Similarly,theMinistryofEnvironment,Science,TechnologyandInnovationin
Ghanaisdevelopingnature-basedsolutionstoenhancetheclimate-changeresilience
ofinfrastructuresystemsacrossthetransport,water,andenergysectors.Insupport
ofthisinitiative,CERSGIShasprovidedlandcoverandlandusedatatoassessthe
impactsofsuchinfrastructuresystemsonecosystemserviceswhiledevelopingprior-
itizationoptionsforclimatechangeadaptation.
5.3.MappingCharcoalProductionSitestoAssessEcosystemDegradationinGhana
Closeto80percentofAfricanurbanhouseholdsusecharcoalastheirprimary
sourceofcookingfuel[35].Thegrowingenergyneedsoftheurbanpopulationinsub-
SaharanAfricaposeanincreasingpressureonforestresources[35].Whileunsustain-
ablecharcoalproductiondegradesenvironmentalservices,italsocontributestobio-
diversityloss,wildfires,andpoorairquality.InGhana,suitabletreespeciesforchar-
coalproductionaredeclininginthemaincharcoal-producingareas,andproducers
arealreadyharvestingtreespeciesthatarelesssuitableforcharcoalproduction.Thus,
TheCentreforRemoteSensingandGeographicInformationServices(CERSGIS)uses
high-resolutionearthobservationsatelliteimagestomaptheevolutionofcharcoal
productionsitesovertimeandestimatesabovegroundbiomassremovalsresulting
fromcharcoalproduction.ThemapsproducedwithCERSGIS(Figure9)areusedby
nationalandlocalauthoritiestomonitorandplanlandscaperestorationinthetarget
areas.
Figure 8. Footprints of small-scale gold mining in the forest zone of Ghana.
Similarly, the Ministry of Environment, Science, Technology and Innovation in Ghana
is developing nature-based solutions to enhance the climate-change resilience of infrastruc-
ture systems across the transport, water, and energy sectors. In support of this initiative,
CERSGIS has provided land cover and land use data to assess the impacts of such infras-
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tructure systems on ecosystem services while developing prioritization options for climate
change adaptation.
5.3. Mapping Charcoal Production Sites to Assess Ecosystem Degradation in Ghana
Close to 80 percent of African urban households use charcoal as their primary source
of cooking fuel [
35
]. The growing energy needs of the urban population in sub-Saharan
Africa pose an increasing pressure on forest resources [
35
]. While unsustainable charcoal
production degrades environmental services, it also contributes to biodiversity loss, wild-
fires, and poor air quality. In Ghana, suitable tree species for charcoal production are
declining in the main charcoal-producing areas, and producers are already harvesting tree
species that are less suitable for charcoal production. Thus, The Centre for Remote Sensing
and Geographic Information Services (CERSGIS) uses high-resolution earth observation
satellite images to map the evolution of charcoal production sites over time and estimates
aboveground biomass removals resulting from charcoal production. The maps produced
with CERSGIS (Figure 9) are used by national and local authorities to monitor and plan
landscape restoration in the target areas.
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Figure9.TheevolutionofcharcoalproductionkilndensityinGhana’sNorthernSavannalandscape
from2017to2020.
5.4.SustainableNaturalResourcesManagement
TheGlobalMonitoringforEnvironmentandSecurityandAfrica(GMESandAf-
rica)Initiative,fundedbytheEuropeanUnion,aimstoimprovethecapacitiesinAf-
ricatopromotethesustainablemanagementofnaturalresourcesthroughtheuseof
Earthobservationdata.AconsortiumofinstitutionsineightWestAfricancountries
(includingBurkinaFaso,Côted’Ivoire,Gambia,Ghana,Guinee,Mali,Niger,andSen-
egal),ledbytheCentredeSuiviEcologique,Senegal,amemberoftheSERVIR-West
Africaconsortium,isimplementingamonitoringsystemforthesustainablemanage-
mentofwetlandstoenhanceecosystemresilienceinWestAfrica.Theprojecthasde-
velopedanddisseminatedaportfoliooflandcover-relatedproductsandservicesto
improvedecision-makingonthesustainablemanagementofwetlandsinWestAfrica.
Twowetlandswereselectedineachfocuscountrytodevelopproductsandservices
forthebenefitofusers.Theinitiativehasalsobuiltthecapacitiesofsitemanagerstoim-
plementsustainablewetlandmanagementpolicies.Inaddition,theprojecthasdevel-
opedanobservatory(geo-portal,hp://gdzhao.gmes.cse.sn(accessedon27August
2024))intendedtoprovidehigh-qualitymapsandservices,includingtheidentifica-
tionanddelimitationofwetlands,monitoringofsurfacewaterdynamics,monitoring
ofwaterqualityandinvasiveaquaticvegetation,mangroveinventory,andthemap-
pingandmonitoringofwetlands(Figure10).
Figure 9. The evolution of charcoal production kiln density in Ghana’s Northern Savanna landscape
from 2017 to 2020.
5.4. Sustainable Natural Resources Management
The Global Monitoring for Environment and Security and Africa (GMES and Africa)
Initiative, funded by the European Union, aims to improve the capacities in Africa to
promote the sustainable management of natural resources through the use of Earth ob-
servation data. A consortium of institutions in eight West African countries (including
Burkina Faso, Côte d’Ivoire, Gambia, Ghana, Guinee, Mali, Niger, and Senegal), led by the
Centre de Suivi Ecologique, Senegal, a member of the SERVIR-West Africa consortium, is
implementing a monitoring system for the sustainable management of wetlands to enhance
ecosystem resilience in West Africa. The project has developed and disseminated a portfolio
of land cover-related products and services to improve decision-making on the sustainable
management of wetlands in West Africa. Two wetlands were selected in each focus country
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to develop products and services for the benefit of users. The initiative has also built the
capacities of site managers to implement sustainable wetland management policies. In
addition, the project has developed an observatory (geo-portal, http://gdzhao.gmes.cse.sn
(accessed on 27 August 2024)) intended to provide high-quality maps and services, includ-
ing the identification and delimitation of wetlands, monitoring of surface water dynamics,
monitoring of water quality and invasive aquatic vegetation, mangrove inventory, and the
mapping and monitoring of wetlands (Figure 10).
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Figure10.WorkflowdevelopedbytheGMESandAfricaprojectforproductionofthematicmaps.
6.Discussion
ManylandcovermappinginstitutionsinWestAfricausedifferentlandcover
classificationsystemsandlegends.Thus,developingastandardlegendwithclearly
definedlandcoverclassesbasedonISOstandardsisessentialforinternationalcon-
sistency.TheISO-basedFAOLandCoverClassificationSystemprovidesasolidbasis
fordevelopingaclassificationsystemcompatiblewithinternationalstandards.Given
thediverseecosystemsinthesubregion,thestandardizedsystemshouldaccountfor
ecosystemvariations.Thiswillrealisticallyreflectthedifferentecologicalzonesand
theregion’sdynamiclandcovertypes.Whileitisvitalthattheclassificationsystem
accommodatesvaryinglevelsofdetail,allowingforlocal,national,andregional
scaleswillensurethatusecasescanbedevelopedatanyscalenecessary,fromland
managementtopolicy-making.LandcoverinWestAfricaismainlyinfluencedby
humanactivitieslikeurbanization,mining,agriculture,andnaturalresourceextrac-
tion.Thus,thesystemmustintegratelandusetypesthataccountforpracticeslike
smallholderfarming,shiftingcultivation,pastoralism,charcoalproduction,andarti-
sanalandsmall-scalemineralextraction.Thiswillreflectcommonlandusetypesin
thesubregion.Furthermore,thesystemmustincludeculturallanduseclasseslike
sacredgrovesandcommunalforests.Sustainablelandandwaterresourcemanage-
mentpracticesalignedwiththeSustainableDevelopmentGoals(SDGs),especially
formonitoringwetlands,soilfertilitydepletion,deforestation,andlanddegradation,
willmakethesystemadaptabletochanginglandscapes[36,37].Integratingmachine
learningandartificialintelligencetoolstoimprovetheautomationofworkflowsand
classificationaccuracywillbeacost-effectivewayofprocessinglargedatasets.Lastly,
cooperationamonggovernments,regionalbodies,andresearchinstitutionsiscritical
foradoptionandsustainability.Thesystemshould,therefore,alignwithlanduseand
environmentalpoliciesfordecision-making.
6.1.Opportunities
Figure 10. Workflow developed by the GMES and Africa project for production of thematic maps.
6. Discussion
Many land cover mapping institutions in West Africa use different land cover classi-
fication systems and legends. Thus, developing a standard legend with clearly defined
land cover classes based on ISO standards is essential for international consistency. The
ISO-based FAO Land Cover Classification System pr