sustainability

Article Land Cover Changes in over the Past 24 Years

Peter A. Y. Ampim 1,* , Michael Ogbe 2, Eric Obeng 1, Edwin K. Akley 3 and Dilys S. MacCarthy 4

1 Department of Agriculture, Nutrition and Human Ecology and Cooperative Agricultural Research Center, College of Agriculture and Human Sciences, Prairie View A&M University, P.O. Box 519, MS 2008, Prairie View, TX 77446, USA; [email protected] 2 Department of Geography, Norwegian University of Science and Technology, 7491 Trondheim, Norway; [email protected] 3 CSIR-Savanna Agricultural Research Institute (SARI), TL 52 Tamale, Ghana; [email protected] 4 Soil and Irrigation Research Center, School of Agriculture, College of Basic and Applied Science (CBAS), University of Ghana, LG 62 Legon, Ghana; [email protected] * Correspondence: [email protected]; Tel.: +1-936-261-5096

Abstract: Changes in land cover (LC) can lead to environmental challenges, but few studies have investigated LC changes at a country wide scale in Ghana. Tracking LC changes at such a scale overtime is relevant for devising solutions to emerging issues. This study examined LC changes in Ghana for the past almost two and half decades covering 1995–2019 to highlight significant changes and opportunities for sustainable development. The study used land cover data for six selected years (1995, 2000, 2005, 2010, 2015, and 2019) obtained from the European Space Agency. The data was analyzed using R, ArcGIS Pro and Microsoft Excel 365 ProPlus. The original data was reclassified into eight LC categories, namely: agriculture, bare area, built-up, forest, grassland, other vegetation, waterbody, and wetland. On average, the results revealed 0.7%, 131.7%, 23.3%, 46.9%, and 11.2%  increases for agriculture, built-up, forest, waterbody, and wetland, respectively, across the nation.  However, losses were observed for bare area (92.8%), grassland (51.1%), and other vegetation (41%)

Citation: Ampim, P.A.Y.; Ogbe, M.; LCs overall. Notably, agricultural land use increased up to 2015 and decreased subsequently but Obeng, E.; Akley, E.K.; MacCarthy, this did not affect production of the major staple foods. These findings reveal the importance of LC D.S. Land Cover Changes in Ghana monitoring and the need for strategic efforts to address the causes of undesirable change. over the Past 24 Years. Sustainability 2021, 13, 4951. https://doi.org/ Keywords: Ghana; land cover; sustainable development; urbanization; natural resources; population 10.3390/su13094951

Academic Editor: Miguel Amado 1. Introduction Received: 2 March 2021 Changes in land cover and land use (LULC) is currently an important subject of dis- Accepted: 22 April 2021 Published: 28 April 2021 cussion worldwide as it is regarded as one of the leading factors causing global change [1]. Though most people in the world now live in urban areas [2], the majority of the urban

Publisher’s Note: MDPI stays neutral growth is projected to occur in developing regions of the world [3] with Africa being at the with regard to jurisdictional claims in forefront of this growth [4,5]. In fact, it is forecasted that about 56% of people in Africa will published maps and institutional affil- live in urban areas by 2050. iations. Ghana, similar to most developing countries, is growing rapidly in population, as projected. It has grown in population from about 5 million during independence in 1957 to 31 million in 2020 [5] representing an addition of about 26 million people over its 64 years of existence as a sovereign nation. Significant changes in LULC have accompanied this population growth including the expansion of cities, especially , , Tamale, Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. and Sekondi-Takoradi [1,4–7]. Since 2010, more than 50% of Ghanaians have been living in This article is an open access article urban areas and this population is expected to grow to about 70% by 2050 [8]. distributed under the terms and On a broad scale, the drivers of LULC are grouped into two: those originating from conditions of the Creative Commons human activities and those from natural forces. A significant human activity is the growth Attribution (CC BY) license (https:// and size of the human population, whose growth can be moderated by institutional factors, creativecommons.org/licenses/by/ national, and regional policies as well as globalization activities [9]. This implies that LULC 4.0/). transformation is often complex, comprising interactions between several factors including

Sustainability 2021, 13, 4951. https://doi.org/10.3390/su13094951 https://www.mdpi.com/journal/sustainability Sustainability 2021, 13, 4951 2 of 23

economic, political, demographic, and environmental, but political and economic factors are by far considered to be the most important [10]. Reasons identified in the literature as influencers of LULC changes in Ghana mirror the contention of McNeill et al. [10]. They are primarily spinoffs of economic and/or political drivers including economic growth, commercialization, industrialization, rural-urban migration, and globalization. For example, the growth of the Greater Accra Metropolitan Area comprising Accra, the National Capital, and the Port City of owes its expansion to government microeconomic policies and modernization efforts including the establishment of Tema, industrial estates and the accommodation of several corporate offices of major financial and industrial companies [4]. Similarly, the expansion of the Sekondi-Takoradi metropolitan area is attributed firstly to the expansion of the Takoradi harbor under Ghana’s Economic Recovery Program, which was executed under the Structural Adjustment Programs carried out by the World Bank and the International Monetary Fund [1]. Secondly, the discovery of offshore oil deposits in commercial quantities near the area in 2007 resulted in a second wave of urban expansion [11]. The expansion of Kumasi is attributed largely to rural-urban migration owing to the growth in industrial and commercial activities within the city and its environs [9]. Similarly, growth in Tamale is credited to migration from poor rural areas in the northern parts of Ghana [12]. LULC changes have negative impacts [2,13,14] including deforestation, loss of bio- diversity, mitigated ability of land in urban areas to provide ecosystem services and loss of agricultural land. Hence, it is imperative to continuously monitor LULC changes to identify resulting challenges and needs for action. Since geospatial technologies are not well developed in most developing countries including Ghana, Remote Sensing and Geo- graphic Information System techniques are essential tools for studying LULC at various spatiotemporal scales [15–17]. Several authors have applied these methods to study LULC changes in Ghana across various spatial and temporal scales spanning regional, district, city/municipal and river basin scales and covering between few and several years. Pub- lished regional LULC studies comprise land use appraisal and modeling future change dynamics [18], analysis of LULC spatial patterns and identification of spatial determi- nants [19–21], transformations in forests both inside and outside protected areas [22], determining classes of LULC around the coastal cities [23], determining relationships between LULC and demographic changes [24]. The District level studies span issues including evaluating the impacts of surface mining on LULC [15,25] and determining the drivers of LULC changes in peri-urban areas [26–28]. LULC studies conducted at the city/municipal level in Ghana mostly capture LULC change patterns, the drivers and impacts and implications in different locations across the country including Accra and Kumasi metropolitan areas [4,5], Sekondi-Takoradi metro area [1,29], New [30] and Kintampo North Municipality [31]. Some of the city/municipal level studies compared the patterns of urban development between cities in the northern and southern parts of Ghana [32]. The river basin scale studies covering the Lake Bosomtwe Basin, White and River Basins and the Pra River Basin had different foci including assessment of land cover transitions and dynamics [33]; evaluating the impacts of climate change and LULC change patterns and impacts on hydrology [34,35] and an examination of land cover changes emanating from widespread gold mining, urbanization, and agriculture [36]. Other authors [37] have reviewed studies on changes in land use on sedimentation of water bodies but few studies have evaluated the LULC changes across Ghana over a significant period. More so, Ghana’s economy is largely dependent on agricultural activities and the extraction of mineral resources, which pose environmental challenges and expose the necessity for routine monitoring of LULC changes across the country to identify emerging challenges and need for action. We expect changes in land cover (LC) over time, because of the population increase and its associated pressures on resource use and economic activities. As a result, the objective of this study is to examine LC changes in Ghana for the past almost two and half decades spanning 1995–2019 to highlight the significant changes and opportunities for sustainable development. Sustainability 2021, 13, x FOR PEER REVIEW 3 of 23

the objective of this study is to examine LC changes in Ghana for the past almost two and Sustainability 2021, 13, 4951 3 of 23 half decades spanning 1995–2019 to highlight the significant changes and opportunities for sustainable development.

2. Materials2. and Materials Methods and Methods 2.1. Study Area2.1. Study Area The study areaThe is study the country area is theof Ghana country (Fig of Ghanaure 1). (FigureGhana 1is). locate Ghanad ison located the west on the west coast of of Africa andAfrica shares and its shares eastern, its western, eastern, western,and northern and northern borders borderswith , with Côte Togo, d’Ivoire, Côte d’Ivoire, and and BurkinaBurkina Faso, respectively. Faso, respectively. Its southern Its southern border border is the Gulf is the of Gulf Guinea of Guinea and the and Atlantic the Atlantic Ocean. Ocean. The UnitedThe United Nations Nations Population Population Division Division [5] estimated [5] estimated Ghana’s Ghana’s 2020 population 2020 population to to be ◦ ◦ be around 31,073,000.around 31,073,000. The country, The whose country, geog whoseraphical geographical coordinates coordinates are 7.9465° are N, 7.9465 1.0232°N, 1.0232 W, 2 W, occupiesoccupies an area anof 238,533 area of 238,533km2 [9] km and[ 9has] and a tropical has a tropical climate. climate. The northern The northern part of part of Ghana Ghana has ahas Sudanian a Sudanian type type climate climate characterized characterized by bya short a short rainy rainy season season (unimodal) (unimodal) while the while the southernsouthern part part experiences experiences higher higher rainfall rainfall regimes regimes (bimodal) (bimodal) and and is isforested forested es- especially in pecially in thethe southwest southwest [9]. [9 ].The The climate climate conditions conditions here here have have characteristics characteristics of the of Guin- the Guinean and ean and Guineo-CongolianGuineo-Congolian type type climate. climate. Agricu Agriculturelture is an is animportant important economic economic sector sector in in Ghana as Ghana as it itemploys employs about about half half of of the the population. population. Ghana Ghana is isalso also endowed endowed with with natural natural resources resources includingincluding timber, timber, gold, gold, diamonds, diamonds, ba bauxite,uxite, manganese, manganese, and and oil. oil. Since Since Ghana’s Ghana’s economic economic developmentdevelopment is is directly directly dependent dependent on the extractionextraction ofofnatural natural resources, resources, the the country faces country facestroubling troubling environmental environmental challenges challenges [9 ].[9].

Figure 1. The currentFigure 161. The Administrative current 16 Administrative Regions in Ghana Regions (Map inA )Ghana and the (Map previous A) and 10 the Regions, previous from 10 which Regions, additional six Regions were carvedfrom which from (MapadditionalB). Ahafo, six Regi Bonoons and were Bono carved East from Regions (Map were B). createdAhafo, Bono from theand former Bono East Brong Regions . were created from the former Brong Ahafo Region. Similarly, the former was Similarly, the former Northern Region was split into Northern, North East and Savannah Regions while the former Western split into Northern, North East and Savannah Regions while the former was sepa- Region was separated into Western and Western North Regions and the former was divided into Volta and rated into Western and Western North Regions and the former Volta Region was divided into Oti Regions. Volta and Oti Regions. 2.2. Data Used 2.2. Data Used Landcover data for six selected years obtained from the European Space Agency was Landcoveranalyzed data for for six this selected study. years The years obtained include from 1995, the European 2000, 2005, Space 2010, Agency 2015, and was 2019. All the analyzed foryears this study. selected The are years at five-year include intervals 1995, 2000, except 2005, 2019, 2010, because 2015, 2020and 2019. data wasAll notthe available at years selectedthe are time at offive-year the data intervals acquisition. except Awotwi 2019, etbecause al. [36] 2020 also useddata satellitewas not imageryavailable data ranging at the time offrom the data three acquisition. to five years Awotwi to investigate et al. [36] LULCalso used changes satellite in imagery the Pra Riverdata rang- Basin in Ghana. ing from threeThey to arguedfive years that to while investigate 10-year LULC timeframes changes are in typical, the Pra there River is Basin a likelihood in Ghana. of considerable They arguedchange that while occurring 10-year within timeframes that period are typical, because there of human is a likelihood activity of which considera- is by far the most ble change occurringimpactful driverwithin ofthat LULC period according because to of a CILSShuman [9 activity] report. which is by far the most impactful driverIn this of study, LULC LCaccording data for to 1995a CILSS is considered [9] report. as a baseline. The global landcover data was downloaded from the European Space Agency (ESA) Climate Change Initiative (CCI) Copernicus Climate Change Service (C3S) Climate Data Store (CDS) in NetCDF formats (Table1). The LC data acquired has a spatial resolution of 300 m with a geographic coordinate system based on the World Geodetic System 84 (WGS84) reference ellipsoid. Sustainability 2021, 13, 4951 4 of 23

Each of the raster’s pixel value corresponds to the label of a LC class based on the UN Land Cover Classification System (LCCS), developed by the United Nations Food and Agriculture Organization [38]. The following satellite sensors captured the land cover datasets; Advanced Very High-Resolution Radiometer (AVHRR) for 1995, Satellite Pour l’Observation de la Terre—Vegetation (SPOT-VGT) for 2000, Medium Resolution Imaging Spectrometer (MERIS) Full Resolution (FR) and Reduced Resolution (RR) for 2005 and 2010, and Project for On-Board Autonomy, with the V standing for Vegetation (PROBA-V) for 2015 and 2019.

Table 1. Original and reclassified land cover categories used †.

Original Category Reclassified Category Landcover Code Landcover Code Urban areas 190 Built-up 190 Mosaic herbaceous cover 110 Grassland 110 Grassland 130 Shrubland 120, 122 Sparse vegetation 150, 153 Other Vegetation 120 Shrub or herbaceous cover 180 Bare areas 200, 201, 202 Bare Area 200 Rainfed cropland 10, 11 Irrigated cropland 20 Agriculture 10 Mosaic cropland (>50% vegetation) 30 Mosaic natural vegetation 40 Tree cover (broadleaved evergreen) 50 Tree cover (broadleaved deciduous) 60, 62 Forest 50 Mosaic tree and shrub 100 Tree cover (flooded, fresh, or brackish water) 160 Wetland 170 Tree cover (saline water) 170 Water bodies 210 Water body 210 † The original land cover data set was acquired from the European Space Agency (ESA) and the reclassification approach used was influenced by ESA [38].

Figure2 illustrates the logical model underlying this processing chain. The processing combined the GlobCover unsupervised classification chain from Bicheron et al. [39] and a machine learning algorithm on a multiple-year strategy from Bontemps et al. [40]. Detailed information on the processing is provided by the ESA [38]. Based on the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (CEOS- WGCV), the overall accuracy values weighted by the area proportions of the LC classes were 71.7% [38]. The geospatial data used in the study, which are shapefiles for towns and Ghana’s regional administrative boundaries were obtained from Maphouse Ghana Limited in Accra, Ghana. Crop production data for 2011 and 2019 were obtained from the Ministry of Food and Agriculture [41,42]. Sustainability 2021, 13, x FOR PEER REVIEW 5 of 23

as lichens and mosses, sparse vegetation and flooded forest with fresh water can be af- fected by errors. Second, to allow the detection of change from an old class to a new class, the method developed needs to observe the new class for at least two consecutive years [38]. Thus, abrupt changes are better captured than gradual ones. Abrupt changes are sudden LC transitions from one Intergovernmental Panel on Climate Change IPCC class to another, which most often last more than two years (e.g., a forest loss to an agriculture class). However, since five-year intervals were used, the change over time is more gradual than abrupt. Finally, though a 300 m dataset may seem to be coarse, the spatio-temporal Sustainability 2021, 13, 4951 5 of 23 scale of mapping employed with a complete and consistent Copernicus dataset from the ESA was more beneficial than problematic.

Figure 2. Schematic representation of the land cover classificationclassification chain, created byby authorsauthors basedbased onon [[38].38].

2.3. DataThough Analysis the data set used in this study may have some limitations, it is important to note the following. First, the overall accuracy of the dataset must be balanced by the fact that theFigure quality 3 outlines of the the landcover data analysis maps varyapproach according used tofor the this thematic study. The class. data Land collected cover wasclasses processed such as rainfedand analyzed and irrigated using three croplands, software broadleaved packages: evergreen R version forest, 4.0.2 [43] urban (R areas,Core Team,bare areas, 2013), and ArcGIS water Pro bodies 2.7 (Environmental are quite accurately Systems mapped. Research On Institute, the other Redlands, hand, classes CA, USA)such as[44], lichens and andExcel mosses, 365 ProPlus sparse (Microsoft vegetation Corporation, and flooded forestRedmond, with freshWA, waterUSA) [45]. can beR wasaffected used by to errors.subset the Second, Ghana to landcover allow the data detection from the of changeglobal datasets. from an The old classNetCDF to a land- new coverclass, files the method were converted developed to raster needs data to observe and ma thesked new to the class size for of atGhana least with two consecutivethe regional boundaryyears [38]. shapefiles Thus, abrupt (through changes the are ‘raster’, better captured‘ncdf4’, and than the gradual ‘rgdal’ ones. packages) Abrupt because changes it are is fastersudden than LC doing transitions so in fromArcGIS one Pro. Intergovernmental The rest of the Panelgeospatial on Climate processing, Change analyses, IPCC class and visualizationto another, which were most done often using last ArcGIS more Pro. than To two activate years (e.g.,the attribute a forest tables loss to for an the agriculture masked rasterclass). data, However, the data since was five-year reclassi intervalsfied using were the reclassify used, the changetool from over their time pixel is more values gradual to the codedthan abrupt. ones (Table Finally, 1). thoughThis reclassification a 300 m dataset was may informed seem to by be ESA coarse, [38]. the The spatio-temporal areas (km2) for thescale respective of mapping land employed covers were with acalculated complete using and consistent the Zonal Copernicus Geometrydataset as a table from tool. the ChangesESA was between more beneficial the various than land problematic. covers over time was achieved by intersecting for ex- ample, the 1995 and the 2019 land covers. Next, all the LC changes were mapped with ArcGIS2.3. Data Pro. Analysis The zonal geometry attributes were exported as DBF tables and further ana- lyzedFigure in Excel3 outlines to obtain the statistical data analysis values approach and figures. used for this study. The data collected was processed and analyzed using three software packages: R version 4.0.2 [43] (R Core Team, 2013), ArcGIS Pro 2.7 (Environmental Systems Research Institute, Redlands, CA, USA) [44], and Excel 365 ProPlus (Microsoft Corporation, Redmond, WA, USA) [45]. R was used to subset the Ghana landcover data from the global datasets. The NetCDF landcover files were converted to raster data and masked to the size of Ghana with the regional boundary shapefiles (through the ‘raster’, ‘ncdf4’, and the ‘rgdal’ packages) because it is faster than doing so in ArcGIS Pro. The rest of the geospatial processing, analyses, and visualization were done using ArcGIS Pro. To activate the attribute tables for the masked raster data, the data was reclassified using the reclassify tool from their pixel values to the coded ones (Table1). This reclassification was informed by ESA [ 38]. The areas (km2) for the respective land covers were calculated using the Zonal Geometry as a table tool. Changes between the various land covers over time was achieved by intersecting for example, the 1995 and Sustainability 2021, 13, 4951 6 of 23

the 2019 land covers. Next, all the LC changes were mapped with ArcGIS Pro. The zonal Sustainability 2021, 13, x FOR PEER REVIEW geometry attributes were exported as DBF tables and further analyzed in6 of Excel 23 to obtain Sustainability 2021, 13, x FOR PEER REVIEW 6 of 23 statistical values and figures.

Figure 3. TheFigure land cover 3.Figure The data land 3. analysisThe cover land data flow cover analysis chart. data analysisflow chart. flow chart.

3. Results 3.3. Results Results The results TheTheof the results study of are the presented study are in presented Figures 4–9 in Figures and Tables4 4–9–9 and and2 and TablesTables 3. 2 2and and3. 3.

Figure 4. Land cover maps for 1995 and 2019. Figure 4. Land cover maps for 1995 and 2019.

Figure 4 showsFigure the 4 reclassifiedshows the reclassified eight land coeightvers land for 1995,covers which for 1995, serves which as the serves base- as the base- line and 2019,line which and 2019, is the which last year is the within last year the periodwithin consideredthe period consideredfor the study. for In the general, study. In general, agriculturalagricultural LC increased LC slightly increased by 0.7%slightly from by 19950.7% to from 2019 1995 across to 2019 Ghana across corresponding Ghana corresponding 2 to about 736.79to about km 736.79(Figure km 5).2 However,(Figure 5). a However, periodical a analysis periodical of analysisthe change of therevealed change that revealed that this LC increasedthis LC only increased up to 2015only andup to decreased 2015 and afterwards decreased afterwards(Table 2). A (Table regional 2). analysisA regional analysis indicates thatindicates most of that the most increment