Environmental Challenges 10 (2023) 100664 Contents lists available at ScienceDirect Environmental Challenges journal homepage: www.elsevier.com/locate/envc Land use and land cover change detection and prediction based on CA-Markov chain in the savannah ecological zone of Ghana Philip Aniaha, ∗ , Simon Bawakyillenuob, Samuel Nii Ardey Codjoec, Fred Mawunyo Dzankub a Department of Sustainable Development Studies, University for Development Studies, P. O. Box TL 1350, Tamale, Northern Region, Ghana b Institute of Statistical, Social and Economic Research, University of Ghana, Box LG 74, Legon, Accra, Ghana c Regional Institute for Population Studies, University of Ghana, Legon, Ghana a r t i c l e i n f o a b s t r a c t Keywords: Environmental problems have accompanied the accelerated land use and land cover change (LULCC), yet few LULCC local level studies make an attempt to assess the dynamics of LULCC. This work employed GIS and remote sensing Dynamics to quantify the past and predict future dynamics of LULCC based on the synergy Cellular Automata (CA) - Markov CA-Markov model Chain Model (MCM). The results revealed that agricultural land in the Bongo district witnessed the greatest expan- Remote sensing sion from 10.03% to 27.17% of total area from 1990 to 2019, while wooded savannah area witnessed the greatest Savannah zone Ghana decline from a share of 42.26% to 15.51% of total area from 1990 to 2019. In the Kassena-Nankana West (KNW) district, shrub and tree savannah and agricultural land expanded from 32.91% to 54.2% and 9.44% to 18.16% of the total area, respectively, at the expense of wooded savannah area (-32.9% of total area) between 1990 and 2019. Future predictions based on prevailing socio-economic development demonstrate that the observed trend would continue till the 2050 period. In the Bongo district, the settlement area will witness the highest proportion of net increase in total area 2 2 (5.63 km ) at the expense of wooded savannah (-11.26 km ) between 2019 and 2050. Conversely, in the KNW district, the shrub and tree savannah area will experience the highest proportion of 2 2 net gain in total area (156.02 km ) at the expense of wooded savannah area (-111.49 km ) between 2019 and 2050. This result is an indication that the synergy CA-MCM have effectively captured the spatiotemporal trend in LULCC in this study. 1 i s 2 e r c fi f i p s p c M t i 2 f m l ( v c b o n A u ( i e a h R 2 ( . Introduction For a very long time, humanity has transformed landscapes in their fforts to enhance the quantity, quality, and security of environmental esources vital to human well-being, such as medicinal products, food, bre and freshwater ( Potts et al., 2016 ; Wang et al., 2021 ). Through the ncreased use of technology and innovation, human populations have, lowly at first, and at a progressively rapid pace, later on, increased their apacity to obtain diverse services from the ecosystem and expand its erritory ( Song et al., 2018 ). The magnitude and rate of anthropogenic activities on the land sur- ace have accelerated during the last 300 years ( Potts et al., 2016 ). More and has been converted for human use than before, and already con- erted land in the earlier times was managed more intensively to in- rease the yields of agricultural and forest products. The phenomenon f landscape change has also shifted spatially over time. While Europe, sia and America experienced significant landscape change in time past around the 20th century) and have now witnessed a steady trend in for- st land cover, Africa and other developing countries (e.g. Latin Amer-∗ Corresponding author. E-mail address: philipaniah@yahoo.co.uk (P. Aniah) . ttps://doi.org/10.1016/j.envc.2022.100664 eceived 14 November 2022; Received in revised form 22 November 2022; Accepted 667-0100/© 2022 The Author(s). Published by Elsevier B.V. This is an open access http://creativecommons.org/licenses/by-nc-nd/4.0/ ) ca, Southeast Asia) are currently witnessing an exponential shift in land- cape and associated ecosystem depletion ( Ellis et al., 2013 ; Peng et al., 017 ; Wang et al., 2021 ). The accelerated landscape changes have been accompanied by lo- al and global environmental problems ( Meyfroidt et al., 2013 ), raising ears that the sustainable limits of the planet earth to support the human opulation without causing irreparable damage has reached its tipping oint and threshold ( Meadows & Randers, 2012 ). Various reports of the illennium Ecosystem Assessment team have added further credibil- ty to the indications of apparent global landscape changes ( MA, 2003 , 005 ). The key underlying trigger of past and recent global environ- ental changes has been anthropgenic activities on the land surface Meyfroidt et al., 2013 ). In the context of this paper, land cover denotes the physical and iological attributes of the land surface. Land use on the other hand de- otes any human management activity associated with the land. Land- se change therefore implies a shift from one land use to another or the ntensification of the existing land use ( Kleeman et al., 2017 ). Land use nd land cover change (LULCC) directly mirror the utilisation of land 6 December 2022 article under the CC BY-NC-ND license P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Fig. 1. Location of the study areas. r p t c s c i 2 ( 2 2 c T s m l b ( s ( i f s i 2 s T d c f m c z u l d b V c b n t c p a s L t l o gesources in an area and is an important component of the environmen- al systems ( Kleeman et al., 2017 ). LULCC has emerged as a threat to ocial and ecological systems ( Lambin and Meyfroidt, 2010 ) and can ause many ecological crises at various spatial scales such as shortages n natural resources, widespread and irreversible losses of biodiversity Tolessa et al., 2017 ; Karki et al., 2018 ; Bounoua et al., 2018 ; Lu et al., 019 ). Generally, several studies on LULCC dynamics in Africa have been arried out ( Brandt et al., 2016 ; Greiner, 2016 ; Koranteng et al., 2016 ; olessa et al., 2017 ; Timm-Hoffman et al., 2018 ). For instance, docu- entation of LULCC using satellite data have been carried out in Africa y Mohajane et al. (2018) and Tavares et al. (2019) . Despite the many tudies on LULCC dynamics in Africa, information about LULCC dynam- cs in the savannah ecological zone of Ghana remains scanty. Other tudies on LULCC in Ghana (e.g. Basommi et al., 2015 ; Kleeman et al., 017 ; Antwi-Agyei et al., 2019 ; Osumanu et al., 2019 ; Larbi et al., 2019 ; uffour-Mills et al., 2020 ; Osumanu & Akomgbangre, 2020 ) have been onducted on a broader scale with a larger spatial recommendation do- ain, and usually do not differentiate between vulnerable and resilient ones. Thus, there is the need to conduct location-specific and local level andscape change assessments to fill this gap. Such information would e useful in managing landscapes and resources and inform future lo- al level policies required for addressing peculiar local socio-ecological eeds. Therefore, this paper seeks to fill this research gap by analysing and redicting LULCC dynamics from 1990 and 2050 in the two distinct tudy sites within the Savanna ecological zone. Following the presenta- ion of the introduction in Section 1 , Section 2 provides a brief profile f the study area and the methodological approaches employed in this2 aper. Section 3 provides results and discussion. Section 4 presents con- lusion of the study. . Methodological approaches .1. Study area This study was conducted in the Upper East Region (UER) in the avannah ecological zone of Ghana. The extension of the study area ies between longitude 0° and 1° West, and latitudes 10° 30 ′ N and 11°N GSS, 2021 ). Two case study districts (Bongo and Kassena-Nankana West KNW) districts) were selected ( Fig. 1 ). Due to the semi-arid and physical eatures of the zone, it is extremely vulnerable to climate and ecolog- cal changes ( GSS, 2021 ). The increasing effects of environmental and ocio-economic factors, such as LULCC, climate and ecological change, rought, floods, and bushfires ( Klutse et al., 2020 ) presents severe ef- ects on the livelihoods of rural dwellers most especially rain-fed agri- ulture dependant households ( Boafo et al., 2020 ). The primary reason nderlying the selection of the study region (UER) and districts is the epiction of typical cases of LULCC through a Normalized Difference egetation Index (NDVI) analysis of the UER. Bongo District is selected ecause it is the most vulnerable area in the UER to severe LULCC since he 1980s. Conversely, KNW District is the area in the UER that largely ontains very high amounts of vegetated cover in the form of gallery nd riparian forest, and was therefore selected as a resilient district to ULCC ( Fig. 1 ). KNW District’s population density of 97.64/km2 is the owest 2 compared to Bongo District’s density of 289.8/km and the re- ional (UER) 2 density of 129/km . P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Table 1 2 Details of remote sensing data used in this study. b Bongo i s Year Satellite Sensor Date Bands used Spatial Resolution R 1990 Landsat 4 MSS 30/11/1990 2,3,4 30m a 2000 Landsat 7 ETM 09/11/2000 2,3,4 30m a 2010 Landsat 5 TM 29/11/2010 2,3,4 30m a 2019 Landsat 8 OLI 22/11/2019 3,4,5 30m m KNW u Year Satellite Date Bands used Spatial Resolution 1990 Landsat 4 TM 13/11/1990 2,3,4 30m t TM 22/11/1990 2,3,4 30m e 2000 Landsat 7 ETM 09/11/2000 2,3,4 30m t ETM 09/11/2000 2,3,4 30m l 2010 Landsat 5 TM 29/11/2010 2,3,4 30m r TM 20/11/2010 2,3,4 30m 2019 Landsat 8 OLI 22/11/2019 3,4,5 30m E OLI 29/11/2019 3,4,5 30m t w 2 2 s n e h s 2 U r l d L 2 c l 1 s o r w p b s c s g t A T i 1 o 8 ( ( t c ( s s 2 l m A p 2 f o p t f t M e c w L i t t t ( T .2. Acquisition of satellite imagery Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), En- anced Thematic Mapper (ETM + ) and Operational Land Imager (OLI) atellite data were used in this study. These were downloaded from the SGS Global Visualization Viewer website ( Table 1 ). To better visualise oads and rivers, Google Earth was used to create an overlapping vector ayer of existing roads and rivers for use in the display of the results. .3. Data pre-processing and classification To achieve accurate surface information from satellite data, it is es- ential to apply radiometric and atmospheric corrections. Radiomet- ic and atmospheric corrections were performed using the ENVI 5.2 latform prior to the classification. The imagery scenes were then mo- aicked, and the study area extracted. Our study combined various bands uch as RGB 5, 4, 3 for OLI and RGB 4, 3, 2 for TM to facilitate the athering of training data required for the classification of the images. lso, in describing the feature classes, we made use of historical and ex- sting expert knowledge on the physical patterns of the study sites and ther relevant supplementary data we deemed useful were incorporated Khwarahm et al., 2021 ). The Random Forest Algorithm (RFA), a non-parametric supervised lassification method was employed in our study. The RFA produces everal classification trees that makes contribution through a voting ystem for classification of data. RFA can produce moderate accuracy and cover maps even when employed with coarse data ( Breiman, 2001 ; bdullah et al., 2019 ; Santos et al., 2022 ). Our study employed the RFA ackage in R to classify LULCC maps for 4 years (1990, 2000, 2010 and 019) for the 2 study sites. Six (6) LULCC classes were identified based n researchers’ prior local knowledge, physiographical knowledge of he study areas, visual interpretation using google earth historical func- ion and supportive supplementary data. The Google Earth images were mployed to extract reference data and a random sampling technique as used to gather 150 ground control points. The data were divided nto test (70%) and training (30%) datasets. The RFA was fitted on the raining data for every study year using the number of decision trees ntree) = 600 and the number of input features (mtry) = 3 ( Santos et al.,Table 2 Accuracy assessment for Bongo and KNW Districts for the years 1990 Bongo district 1990 2000 2010 20 Overall accuracy 85.0 78.0 84.0 91 Kappa Statistic 0.7984 0.7098 0.7990 0.8 3 022 ). We employed fixed mtry. The preference of avoiding the possi- ility of overfitting the trained model was the key guiding principle that nformed our choice of the hyperparameters. The trained model was sub- equently employed to predict LULCC classes in the test datasets. The FA performance was examined by employing the user accuracy values nd the kappa coefficients ( Congalton, 1991 ; Hayes et al., 2014 ). A sep- rate RFA classification was undertaken on the same dataset to further ssess the performance of the RFA classifier. Several trials with different try (e.g. 3, 6, 9) and ntree (e.g. 200, 700, 900) parameterizations were ndertaken to asses out-of-bag (OOB) error levels. Lastly, the RFA was trained at ntree value of 600 with a default set- ing for mtry and this helped produce a low OOB error level. We then mployed the trained model to predict the 6 LULCC classes based on the est datasets for every study year. To determine genuine LULCC from ikely inaccurate changes due to errors in classification, per class accu- acy indices and error matrices were calculated. We employed Google arth images to randomly sample 150 points for every study year and heir equivalent LULCC classes were determined. The validated classes ere employed to assess producer and user accuracy ( Abdullah et al., 019 ; Santos et al., 2022 ). The following LULCC classes were identified: ettlement, agriculture, bare soil, water bodies, shrub and tree savan- ah, and wooded savannah. Four LULCC maps were then generated for ach of the 2 study sites with a spatial resolution of 30 m. .4. Accuracy assessment It is important to perform an accuracy assessment between reference ata and classified data.The accuracy of 1990, 2000, 2010 and 2019 ULCC maps were assessed by independent datasets. The kappa coeffi- ient, overall accuracy and producer’s and user’s accuracy were calcu- ated from the error matrix. The dynamics of LULCC for each target year 990, 2000, 2010 and 2019 was determined by calculating the quantity f each specific class category per time window ( Butt et al., 2015 ) after e performed the accuracy assessment for the generated LULCC maps etween 1990 and 2019. Cross-tabulation matrices were performed for hange detection. Table 2 illustrate the overall accuracy, and the kappa statistics for he years 1990, 2000, 2010 and 2019 for the Bongo and KNW districts. he results revealed that the overall accuracy and kappa statistics for the 990, 2000, 2010 and 2019 images were 85% (0.7984), 78% (0.7098), 4% (0.7990) and 91% (0.8838), respectively for the Bongo district Table 2 ). The overall accuracy and kappa statistics for the KNW dis- rict for the years 1990, 2000, 2010 and 2019 were 82% (0.7569), 87% 0.8317), 87% (0.8356) and 90% (0.8664), respectively ( Table 2 ). .5. Markov chain model, cellular automata (CA) model and CA-Markov odels The CA methods are tools for imitating complex spatial processes ounded on simple decision rules. The Markov Chain model (MCM) ex- lains and reveals the quantification of conversion states and the trans- er rate between different land-use types ( Khwarahm et al., 2021 ). The CM, which depends on the stochastic process, has been widely ac- epted to stimulate and predict LULCC over time ( Guan et al., 2011 ). ULCC prediction normally depend on the transition probability and the ransition area matrixes from time one (t0) to time two (t1) as well as he generated LULCC maps ( Guan et al., 2011 ; Khwarahm et al., 2021 ). he CA model, which consist of even grid-cells with each single grid-cell, 2000, 2010 and 2019 classification. KNW district 19 1990 2000 2010 2019 .0 82.0 87.0 87.0 90.0 838 0.7569 0.8317 0.8356 0.8664 P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Fig. 2. Simulated and actual LULCC maps for Bongo District. i 2 s m b d o m C 1 M l 2 m t d o K c e ( r s m t w o l G s e u t e 2 O w M 2 K 2 n a fixed number of positions, is capable of characterizing non-linear patially distributed LULCC class categories ( Mishra & Rai, 2016 ). The ain principle of the CA model is that changes in cell condition can e described by current condition and changes in adjacent cells based n the previous condition ( Mitsova et al., 2011 ; Liping et al., 2018 ). ombining the MCM with the CA methods solves the limitation of the CM by adding a spatial dimension to the CA model ( Mitsova et al., 011 ; Khwarahm et al., 2021 ). The CA–Markov chain model is an effec- ive combination between the MCM and the CA model for prediction f spatial LULCC. Therefore, the integrated CA–Markov chain model an be used to effectively predict spatiotemporal changes in LULCC Halmy et al., 2015 ). Transition suitability images and Markov tran- ition parameters which determines probability of change and the fil- er contiguity description (kernel size 4 4, 6 6, 8 8) as well as number f iterations (about 600 iterations) were used ( Arsanjani et al., 2011 ; uan et al., 2011 ). Many categories of filter contiguity and several it- rations were assessed to obtain the best kernel dimension (6 6) and he iterations number (400 iterations). The simulated LULCC map for 019 was compared with the actual LULCC map of 2019. Kappa index as employed to validate the accuracy of the simulated maps. The CA- arkov model was employed to predict the LULCC in the years 2030, 040 and 2050 for the Bongo and KNW districts based on 1990, 2000, 010 and 2019 LULCC maps. 4 .6. Model validation Model validation is a critical step in evaluating the accuracy of pre- icted data. In this paper, the kappa coefficient was calculated, and the odel was validated after simulating the 2019 LULCC conditions using 990, 2000, 2010 and 2019 LULCC maps. The accuracy of the simu- ated 2019 LULCC map was assessed by estimating the degree of agree- ent between modelled and reference map of 2019 using the Kappa In- ex of Agreement. The Kappa indices (Kappa for no information (Kno), appa for location (Klocation), and Kappa for standard (Kstandard) as xpressed in Eqs. (1) to (3) determine the overall success rate of the esults. A Kappa coefficient of between 0.75 and 1 implies a high agree- ent while a Kappa coefficient greater than 0.5 but less than 0.75 falls ithin the medium agreement range. However, if Kappa coefficient is ess than 0.5, it implies there is rarely any agreement. After obtaining uccessful Kappa values, the CA–Markov model was employed to sim- late the LULCC maps of 2030, 2040, and 2050. The equations below xpress the summary statistics for the Kappa variations according to mar et al. (2014) . ( no = M ( 𝑚 )N ( 𝑛 ) − 𝑁 (𝑛 ) (1) Р ( 𝑝 ) P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Fig. 3. Simulated and actual LULCC maps for KNW District. 3 K t A v a w f m c s o S 3 8 3 t A w i r B H a c S s fi w a t o L f g ( location = M ( 𝑚 )𝑁 (𝑛 ) − 𝑁 (𝑛 ) (2) 𝑃 ( 𝑝 ) ( ndKstandard = 𝑀 ( 𝑚 )𝑁 (𝑛 ) − 𝑁 (𝑛 ) (3) 𝑃 ( 𝑝 ) here no information is defined by N(n), medium grid cell-level infor- ation by M(m), and perfect grid cell-level information across the land- cape by P(p). . Results and discussion .1. Classification accuracy Figs. 2 and 3 show a comparison of the simulated and actual maps n 2019 for both Bongo and KNW Districts. In the simulated map for the ongo District, the kappa values of Kno = 0.7794, Klocation = 0.7903, nd Kstandard = 0.7409 illustrate a good performance of the model. imilarly, In the simulated map for the KNW District, the Kappa coef- cients of Kno = 0.7331, Klocation = 0.6823, and Kstandard = 0.6247 lso demonstare good performance of the model. The good agreement f all Kappa values confirm that the model has enough ability to forecast uture LULCC dynamics soundly. 5 .2. Dynamics in LULCC in Bongo and KNW Districts The total land area of Bongo District is about 41,647.53 Ha. Around he year of 1990, shrub and tree savannah (45.88%) and wooded sa- annah (42.26%) areas occupied the greatest portion of the total land rea ( Fig. 4 ). Agriculture land was the third greatest land use and cover orm (10.03% of total area). By the turn of 2019 as shown in Fig. 4 , agri- ulture land witnessed the greatest expansion (27.17%) at the expense f wooded savannah (15.51% of total area) between 1990 and 2019. ettlement, bare soil, and water bodies increased by about three-fold. Conversely, the total land area of KNW District is approximately 3,992.85 Ha. Wooded savannah and shrub and tree savannah consti- uted 56.16% and 32.91% of total area, respectively in 1990 ( Fig. 5 ). griculture area occupied 9.44% (7,931.61 Ha) of the total land area hile settlement, bare soil, and water bodies (0.35%, 0.45% and 0.69%, espectfully) each occupied less than 1% of the total land area in 1990. owever, by the 2019 period, Wooded savannah area significantly de- lined to 23.26% of total land area (net loss of 32.9%). Conversely, hrub and tree savannah area expanded to 54.2% of the total area, hile agricultural area expanded to 18.16% of total land area. Similar rends have been observed in previous studies ( Osumanu et al., 2019 ; arbi et al., 2019 ; Tuffour-Mills et al., 2020 ; Osumanu and Akomgban- re, 2020 ). Declines in wooded savannah areas is more noticeable in P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Fig. 4. LULCC map of Bongo District (1990-2019). Table 3 Class percentage change for 1990-2019 in Bongo and KNW districts. Bongo District KNW District 1990-2000 2000-2010 2010-2019 1990-2000 2000-2010 2010-2019 LULCC Type Change Percent Change Percent Change Percent Change Percent Change Percent Change Percent (Ha) Change (Ha) Change (Ha) Change (Ha) Change (Ha) Change (Ha) Change Settlement 61.56 0.15 540.99 1.31 385.83 0.93 401.49 0.48 573.03 0.68 69.3 0.08 Agriculture 3398.94 8.16 2449.81 5.88 1289.6 3.10 3900.29 4.64 1951.3 2.32 1472.9 1.75 Bare Soil 264.24 0.64 148.05 0.36 754.11 1.81 –98.91 –0.12 –63.9 –0.08 907.83 1.08 Water Body 158.4 0.38 9.9 0.02 630 1.51 167.22 0.20 455.04 0.54 16.02 0.02 Shrub and Tree –416.7 –1.00 –1680.9 –4.04 3148.9 7.56 –3303.9 –3.93 12707.5 15.13 8479.9 10.10 Savannah Wooded Savannah –3466.5 –8.32 –1467.8 –3.52 –6208.49 –14.91 –1067.2 –1.27 –15621.9 –18.61 –10945.9 –13.03 B t p 2 t K W a c t c m r 2 r o 3 a D B t L a b ongo District than KNW District, owing to land use intensification, high opulation density and smaller total land area compared to KNW Dis- rict ( Larbi et al., 2019 ). The findings of this study also mirror that of ardell et al. (2003) and Kleemann et al. (2017) which disclosed de- lining trends in wooded savannah areas and marked expansion in agri- ulture and settlement areas since the 1980s in the UER. The minimal egeneration of fallow areas to wooded savannah area occurred around iver blindness-endemic areas along the sissili river in the KNW District. .3. Land use and land cover class percentage change for Bongo and KNW istricts between 1990 and 2019 Table 3 presents LULCC in hectares and percentage change for Bongo nd KNW Districts between 1990 and 2019. In Bongo District, the set-6 lement area increased by 0.15% between 1990 and 2000, by 1.3% from 000 to 2010 and by 0.93% from 2010 to 2019. The settlement area in NW District increased by 0.48% between 1990 and 2000. Settlement rea in KNW District continued to expand from 2000 to 2010 and 2010 o 2019 by 0.68% and 0.08%, respectively. Distinct expansion in settle- ent area occurred in Bongo District than KNW District from 2000 to 019. Agricultural area in Bongo District witnessed significant expansion f 8.16% (representing 3398.94 Ha) of total land area between 1990 nd 2000. From 2000 to 2010 and 2010 to 2019, agriculture area in ongo District increased by 5.9% (2449.81 Ha) and 3.1% (1289.6 Ha) of otal land area, respectively. Similarly, in KNW District, the mainstream ULCC class expansion was seen in agricultural land, which expanded y 4.6% of the total land area (3900.3 Ha) from 1990 to 2000 and by P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Fig. 5. LULCC map of KNW District (1990-2019). 2 t a 2 a u w h a o t c a a i 2 B d 1 B 2 s f W c b K t t a w a 3 a b 2 D n s ( w v w t t a u b a a I .3% (1951.3 Ha) between 2000 and 2010. From 2010 to 2019, the griculture area increased by 1.8% of the total land area. Although the griculture area witnessed the highest expansion relative to other land se and cover types in both study districts, Bongo District witnessed a igher expansion of agricultural land area relative to the total land area f Bongo District than KNW District between 1990 and 2019. Bare soil and water bodies increased by less than 1% between 1990 nd 2010 in Bongo District. However, both bare soil and water bod- es increased by 1.8% and 1.5%, respectively, between 2010 and 2019. are soil in KNW District decreased by -0.12% and -0.08% between 990-2000 and 2000-2010, respectively, but increased by 1.1% between 010 and 2019. Water bodies marginally increased by less than 1% rom 1990 to 2019 in KNW District. Whereas bare soil consistently in- reased between 1990 and 2019 in Bongo District, bare soil decreased in NW District. The increases in bare land areas in Bongo District are at- ributable to increased use of agrochemicals and bad farming practices, hich exacerbate land degradation, while increases in water bodies are ttributable to past and present Government irrigation programs. Shrub nd tree savannah declined for the periods 1990 to 2000 and 2000 to 010 by -1% (-416.7 Ha) and -4% (-1680.9 Ha), respectively, in Bongo istrict. Along the same period in Bongo District, the wooded savan- ah area also declined considerably by -8% (-3466.5 Ha) and -3.5% -1467.8 Ha), respectively. From 2010 to 2019, while the wooded sa- annah area decreased considerably by -14.9% (6208.5 Ha), shrub and ree savannah increased by 7.6% (3148.9 Ha). In KNW District however, part from 1990 to 2000, in which shrub and tree savannah decreased y -3.9%, shrub and tree savannah areas significantly increased by 15% nd 10.1% from 2000 to 2010 and 2010 to 2019, respectively, owing7 o significant declines in wooded savannah -18.6% (-15621.9 Ha) from 000 to 2010 and -13% (-10945.9 Ha) from 2010 to 2019. Analogous to this present study, Ruelland et al. (2010) observed that ooded savannah and shrub and tree savannah areas declined from 34% nd 18% of total land area in 1967 to 11% and 6%, respectively, of to- al land area in 2003 in the UER. Agriculture and degraded areas in- reased from 11% to 23% of total land area and 9% to 27% of total land rea, respectively, between 1967 and 2003 in the UER ( Ruelland et al., 010 ; Larbi et al., 2019 ). This findings further confirm the widespread eforestation and loss of forest cover in the UER ( Yiran et al., 2012 ; oateng, 2017 ; Larbi et al., 2019 ). The remaining areas in the UER with ubstantial forest cover are uninhabited areas along the Red, Black and hite Volta Rivers ( Yiran et al., 2012 ; Boateng, 2017 ). The relative sta- ility of the shrub & tree savannah areas in Bongo and KNW Districts in he UER is attributed to the gradual degradation of wooded savannah reas ( Gessner et al., 2015 ; Dimobe et al., 2017 ; Larbi et al., 2019 ). .4. Projected LULCC patterns and trends in Bongo and KNW districts etween 2019 and 2030 In Bongo District, the CA–Markov model based on business-as-usual cenarios (in which poverty levels will keep rising, population growth ill continue unabated, and inhabitants will continue to collect fire- ood and produce charcoal) forecast that between 2019 and 2030, set- lement 2 area will increase by 7.30 km at the expense of other land se and cover forms, which represents a change of 24.48% of the total rea ( Table 4 ). Overall, the 2 net change in settlement area is 3.26 km . n KNW District, settlement and bare lands will witness net gains of P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Table 4 Projected LULCC in Bongo and KNW Districts between 2019 and 2030. LULCC Bongo District KNW District class loss km2 gain km2 net change km2 % change 2 2 2 loss km gain km net change km % change Settlement –4.04 7.30 3.26 24.48 –2.04 10.23 8.19 38.25 Agriculture –26.67 44.88 18.21 14.00 –54.32 7.01 –47.31 –45.35 Bare land –7.03 2.59 –4.43 –46.24 –4.20 8.66 4.47 28.64 Water Body –2.05 1.18 –4.43 –46.24 –0.95 0.76 –0.20 –1.73 Shrub and tree Savannah –2.05 1.18 –0.87 –7.67 –12.25 162.85 150.60 24.97 Wooded Savannah –29.32 13.37 –15.95 –33.90 –117.06 1.31 –115.75 –148.74 Fig. 6. Projected LULCC maps for Bongo District between 2019 and 2050. 8 2 a a a i c m s a 2 l ( b n w e d b t s n d i 1 v r a t t 1 s B 3 2 a e c a .19 km2 and 4.47 km2 , accounting for 38.25% and 28.64% of the total rea, respectively. In Bongo District, a net change of 18.21 km2 is expected to occur n agriculture areas, signalling a 14% change in the total area. The re- aining land use and cover classes such as bare land, water Body, shrub nd tree savannah and wooded savannah are projected to decline by net osses of -4.43 km2 , 2 2 -0.87 km2, -0.21 km , and -15.95 km , respectively etween 2019 and 2030 in Bongo District. These net losses in bare land, ater Body, shrub and tree savannah and wooded savannah represent eclines in total area by -46.24%, -46.24%, -7%, and -33.90%, respec- ively ( Table 4 ). In KNW District, agriculture and wooded savannah areas are pre- icted to witness significant net decreases of -47.31 km2 and - 15.75km2, accounting for -45.35% and -148.74% of total area change, espectively. The highest proportion of net gain in land area be- ween 2019 and 2030 is shrub and tree savannah, with a net gain of 50.60 km2 and this net gain accounts for 24.97% of total land area. .5. Projected LULCC in Bongo and KNW Districts between 2030 and 040 From 2030 to 2040 in Bongo District, settlement area and agri- ulture lands are projected 2 to increase by net gains of 5.44 km and8 2 km2 , respectively, accounting for 29.01% and 14.47% of the total rea, respectively. Bare land, water bodies, shrub and tree savannah nd wooded savannah areas in Bongo District are expected to decline onsiderably by -7.34%, -1.88%, -1.63%, and -73,26% of total area, re- pectively ( Table 5 ). The decline in shrub and tree Savannah between 030 and 2040 in Bongo District will constitute the highest proportion 21.82 km2) due to expansion in agriculture and settlement area. The et change of -19.90 km2 in wooded savannah areas will be the great- st proportion of net change in a particular land use and cover type etween 2030 and 2040. Although the highest proportion of loss is in hrub and tree savannah area, the net change in shrub and tree savan- ah 2 area (-3.15 km ) is considerably low compared to the net change n wooded savannah areas (-19.90 2 km ) because the shrub and tree sa- annah area will gain a significant amount (18.67 2 km ) of the total area t the expense of the wooded savannah area. Bare lands are projected o decrease by a net change of -4.18 km2 due to the implementation of ustainable land-use practices, reclamation, and regeneration efforts in ongo District. In Kassena Nankana West District however, between 2030 and 2040, griculture, bare land, water bodies, and wooded savannah areas are xpected to witness a net change of -6.17 km2 , -0.84 km2 , -0.24 2 km , nd -3.84 km2 , respectively, which translate to -6.14%, -5.71%, -2.15% P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Table 5 Projected LULCC in Bongo and KNW Districts between 2030 and 2040. LULCC Bongo District KNW District class loss km2 gain km2 net change km2 % change loss km2 gain km2 net change km2 % change Settlement –0.62 6.06 5.44 29.01 –2.94 3.69 0.76 3.41 Agriculture –3.39 25.38 22.00 14.47 –12.68 6.51 –6.17 –6.14 Bareland –4.21 0.03 –4.18 –7.34 3.72 2.88 –0.84 –5.71 Water Body –0.21 0.00 –0.21 –1.88 –0.51 0.27 –0.24 –2.15 Shrub and tree Savannah –21.82 18.67 –3.15 –1.63 –7.03 17.36 10.34 1.69 Wooded Savannah –20.85 0.95 –19.90 –73.26 –8.12 4.28 –3.84 –4.76 Fig. 7. Projected LULCC maps for KNW Districts between 2019 and 2050. a 3 e a t 0 l 2 a 3 t 2 o a 2 D m a o o r D w - o v c s ( 2 ( t c o t nd -4.76% of total area change, respectively ( Table 5 ). The only areas xpected to witness significant expansion in KNW District are shrub and ree savannah and settlement, with the net change of 10.34 km2 and .76 2 km , respectively, accounting for 1.69% and 3.41% of the total and area change. .6. Projected LULCC in Bongo and KNW Districts between 2040 and 050 Similar dynamics observed over the period 2019 to 2040 in Bongo istrict are expected to continue through to the 2050 period. Settle- ent area and agricultural land is projected to increase at the expense f other land use and cover forms at 23.1% and 10.97% of total area, espectively. Bare land, water bodies, shrub and tree savannah and ooded savannah areas are equally projected to decline by -86.73%, 1.89%, -5.68% and -70.83% of total area, respectively ( Table 6 ). Con- ersely, in KNW District, between 2040 and 2050, agriculture, wooded avannah and bare land are expected to expand with a net change of .19 2 km (2.20%), 3.12 km2 (3.73%) and 0.14 2 km (0.94%), respec- ively ( Table 6 ). Shrub and tree savannah will decrease by a net change f -4.92km2 (-0.18%). 9 .7. Projected LULCC Maps for Bongo and KNW Districts between 2019 nd 2050 Fig. 6 presents LULCC maps for Bongo District between 2019 and 050. It can be observed that the highest proportion of net loss in the rea is wooded savannah, with a net decrease of -11.26 km2 (-70.83% of he total area) between 2019 and 2050. The second highest proportion f net decrease in area is shrub and tree savannah, which accounts for net decrease of -10.39 km2 (-5.68%) of total area between 2019 and 050. Bare land 2 will witness a net decrease of -2.51 km between 2019 nd 2050. Settlement and Agriculture lands will witness net increases f 5.63 km2 (23.10%) and 18.73 km2 (10.97%) of the total area in Bongo istrict between 2019 and 2050 ( Fig. 6 ). In KNW District, Shrub and tree savannah area will witness a net gain f 156.02 km2 (25.64%) while settlement and bare land areas will in- rease by 2 2 a net gain of 8.60 km and 3.77 km of the total area change Fig. 7 ). Wooded savannah will decrease at a net loss of -111.49 km2 -135.83%) of total area change. Likewise, the agriculture area will de- rease by 2 a net loss of -56.25 km (-58.99%) of total area change at he expense of settlement area between 2019 and 2050. This finding is P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 Table 6 Projected LULCC in Bongo and KNW Districts between 2040 and 2050. LULCC Bongo District KNW District class loss km2 gain km2 net change km2 % change loss 2 km gain km2 net 2 change km % change Settlement –0.23 5.86 5.63 23.10 –2.43 2.09 –0.35 –1.58 Agriculture –3.00 21.73 18.73 10.97 –2.85 5.05 2.19 2.20 Bare land –2.52 0.00 –2.51 –86.73 –2.17 2.31 0.14 0.94 Water Body –0.21 0.00 –0.21 –1.89 –0.39 0.19 –0.20 –1.80 Shrub and tree Savannah –20.68 10.29 –10.39 –5.68 –9.23 4.32 –4.92 –0.81 Wooded Savannah –11.77 0.51 –11.26 –70.83 –3.64 6.77 3.12 3.73 c A c fi A d i B 4 B c e B w i B a i B s w B 2 B 2 a C o c D c a e E a G o p G a a G m i G T b H s H D K i t K D K K R K A L L A L A ontrary to previous studies which project significant increases in agri- ulture area by the 2050 period in Ghana ( Aduah et al., 2018 ). The ndings however confirm the forecast that wooded savannah area will ecline considerably by the 2050 ( Larbi et al., 2019 ) as well as likely ncreases in settlement area ( Ackom et al., 2020 ). . Conclusions Analysing and predicting past and future land use dynamics and land over changes have become a prerequisite for sustainable landscape gov- rnance, particularly in the drylands. Geospatial data in combination ith land surface modellers (e.g. CA-Markov chain) are effective tools n providing valuable information on the dynamics of LULCC over space nd time. Understanding the dynamics of LULCC offers guidance in pol- cy formulation in sustainable landscape governance. Agriculture and ettlement areas in both study districts have expanded at the expense of ooded savannah and shrub and tree savannah areas between 1990 and 019. Future predictions point to a continuous trend (i.e. from 2019 to 050), in which substantial expansion of agriculture and the settlement rea will occur at the expense of wooded savannah areas. The paper rec- mmends that the government should implement necessary steps to re- laim and protect vulnerable landscapes from further degradation. This ould be achieved through collaboration amongst key stakeholders such s forestry commission, Environmental Protection Agency (EPA), For- st Research Institute, traditional authorities and the Ministry of Lands nd Natural Resources. The landscape governance actions should focus n protecting forest areas and promoting sustainable land management ractices in the savannah ecological zone of Ghana. The classification ccuracy results were not perfect due to presence of cloud cover which ffected the image pixel value. Also, it was cumbersome calibrating the odel due to the high intensity of changes in the LULCC that occurred n the period under analysis owing to expansion in agriculture activities. his notwithstanding, the findings of the study are important and can e applied to other areas in the savannah zone of Ghana with similar ocio-economic and ecological features. eclaration of Competing Interest The authors declare that they have no known competing financial nterests or personal relationships that could have appeared to influence he work reported in this paper. ata availability Data will be made available on request. eferences bdullah, A.Y.M., Masrur, A., Adnan, M.S.G., Baky, M.A.A., Hassan, Q.K., Dewan, A., 2019. Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sens. 11 (7), 790. ckom, E.K., Adjei, K.A., Odai, S.N., 2020. Monitoring land-use and land-cover changes due to extensive urbanization in the Odaw River Basin of Accra, Ghana, 1991–2030. Model. Earth Syst. Environ. 6 (2), 1131–1143 . duah, M.S., Toucher, M.L., Jewitt, G.P., 2018. Estimating potential future (2030 and 2040) land use in the Bonsa catchment, Ghana, West Africa. S. Afr. J. Geomat. 7 (3), 279–291 . 10 ntwi-Agyei, P., Kpenekuu, F., Hogarh, J.N., Obiri-Danso, K., Abaidoo, R.C., Jeppesen, E., Andersen, M.N., 2019. Land use and land cover changes in the owabi reservoir catch- ment, Ghana: implications for livelihoods and management. Geosciences 9 (7), 286 . rsanjani, J.J., Kainz, W., Mousivand, A.J., 2011. Tracking dynamic land-use change using spatially explicit Markov Chain based on cellular automata: the case of Tehran. Int. J. Image Data Fusion 2 (4), 329–345 . asommi, P.L., Guan, Q., Cheng, D., 2015. Exploring land use and land cover change in the mining areas of Wa East District, Ghana using Satellite Imagery. Open Geosciences 1, 618–626 . oafo, Y.A., Saito, O., Jasaw, G.S., Yiran, G.A., Lam, R.D., Mohan, G., Kranjac- Berisavlje- vic, G., 2020. Perceived community resilience to floods and droughts induced by cli- mate change in semi-arid Ghana. In: Sustainability Challenges in Sub- Saharan Africa I. Springer, Singapore, pp. 191–219 . oateng, P., 2017. Land access, agricultural land use changes and narratives about Land degradation in the Savannahs of Northeast Ghana during the pre-colonial and colonial periods. Soc. Sci. 6 (1), 35 . ounoua, L., Nigro, J., Zhang, P., Thome, K., Lachir, A., 2018. Mapping urbanization in the United States from 2001 to 2011. Appl. Geogr. 90, 123–133 . randt, M., Hiernaux, P., Rasmussen, K., Mbow, C., Kergoat, L., Tagesson, T., Fensholt, R., 2016. Assessing woody vegetation trends in Sahelian drylands using MODIS based seasonal metrics. Remote Sens. Environ. 183, 215–225 . reiman, L., 2001. Random forests. Mach. Learn. 45, 5–32 . utt, A., Shabbir, R., Ahmad, S.S., Aziz, N., 2015. Land use change mapping and analysis using remote sensing and GIS: a case study of Simly watershed, Islamabad, Pakistan. Egypt. J. Remote Sens. Space Sci. 18 (2), 251–259 . ongalton, R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37 (1), 35–46 . imobe, K., Goetze, D., Ouédraogo, A., Forkuor, G., Wala, K., Porembski, S., Thiom- biano, A., 2017. Spatio-temporal dynamics in land use and habitat fragmentation within a protected area dedicated to tourism in a Sudanian savanna of West Africa. J. Landsc. Ecol. 10 (1), 75–95 . llis, E.C., Kaplan, J.O., Fuller, D.Q., Vavrus, S., Goldewijk, K.K., Verburg, P.H., 2013. Used planet: a global history. Proc. Natl Acad. Sci. 110 (20), 7978–7985 . essner, U., Knauer, K., Kuenzer, C., Dech, S., 2015. Land surface phenology in a west african savanna: impact of land use, land cover and fire. In: Remote Sensing Time Series. Springer, Cham, pp. 203–223 . hana Statistical Service (GSS), (2021). 2021 population and housing census. Regional Analytical report. Upper East Region. Accra, Ghana: Ghana Statistical Service. reiner, C., 2016. Land-use change, territorial restructuring, and economies of anticipa- tion in dryland Kenya. J. Eastern Afr. Stud. 10 (3), 530–547 . uan, D., Li, H., Inohae, T., Su, W., Nagaie, T., Hokao, K., 2011. Modeling urban land use change by the integration of cellular automaton and Markov model. Ecol. Modell. 222, 3761–3772 20-22 . almy, M.W.A., Gessler, P.E., Hicke, J.A., Salem, B.B., 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using Markov-CA. Appl. Geogr. 63, 101–112 . ayes, M.M., Miller, S.N., Murphy, M.A., 2014. High-resolution landcover classification using random forest. Remote Sens. Lett. 5 (2), 112–121 . arki, S., Thandar, A.M., Uddin, K., Tun, S., Aye, W.M., Aryal, K., Chettri, N., 2018. Im- pact of land use land cover change on ecosystem services: a comparative analysis on observed data and people’s perception in Inle Lake, Myanmar. Environ. Syst. Res. 7 (1), 1–15 . hwarahm, N.R., Qader, S., Ararat, K., Al-Quraishi, A.M.F., 2021. Predicting and mapping land cover/land use changes in Erbil/Iraq using CA-Markov synergy model. Earth Sci. Inf. 14 (1), 393–406 . leemann, J., Baysal, G., Bulley, H.N., Fürst, C., 2017. Assessing driving forces of land use and land cover change by a mixed-method approach in north-eastern Ghana, West Africa. J. Environ. Manag. 196, 411–442 . lutse, N.A.B., Owusu, K., Boafo, Y.A., 2020. Projected temperature increases over north- ern Ghana. SN Appl. Sci. 2 (8), 1–14 . oranteng, A., Zawila-Niedzwiecki, T., Adu-Poku, I., 2016. Remote sensing study of land use/cover change in West Africa. J. Environ. Prot. Sustain. Dev. 2, 17–31 . ambin, E.F., Meyfroidt, P., 2010. Land use transitions: socio-ecological feedback versus socio-economic change. Land Use Policy 27 (2), 108–118 . arbi, I., Forkuor, G., Hountondji, F.C., Agyare, W.A., Mama, D., 2019. Predictive land use change under business-as-usual and afforestation scenarios in the vea catchment, West Africa. Int. J. Adv. Remote Sens. GIS 8, 3011–3029 . iping, C., Yujun, S., Saeed, S., 2018. Monitoring and predicting land use and land cover changes using remote sensing and GIS techniques-a case study of a hilly area, Jiangle, China. PLoS One 13 (7), e0200493 . P. Aniah, S. Bawakyillenuo, S.N.A. Codjoe et al. Environmental Challenges 10 (2023) 100664 L M R M S M M S M T M T . T O T W O O W P Y P u, Y., Wu, P., Ma, X., Li, X., 2019. Detection and prediction of land use/land cover change using spatiotemporal data fusion and the cellular automata–Markov model. Environ. Monit. Assess. 191 (2), 68 . illennium Ecosystem Assessment (MA), 2003. Ecosystem and Human Well-Being: A Framework For Assessment. World Resources Institute, Washington, DC . illennium Ecosystem Assessment (MA), 2005. Ecosystems and Human Well-Being: Wet- lands and Water. World Resources Institute . eadows, D., Randers, J., 2012. The Limits to Growth: The 30-Year Update. Routledge . eyfroidt, P., Lambin, E.F., Erb, K.H., Hertel, T.W., 2013. Globalization of land use: distant drivers of land change and geographic displacement of land use. Curr. Opin. Environ. Sustain. 5 (5), 438–444 . ishra, V.N., Rai, P.K., 2016. A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arabian J. Geosci. 9 (4), 249 . itsova, D., Shuster, W., Wang, X., 2011. A cellular automata model of land cover change to integrate urban growth with open space conservation. Landsc. Urban Plan. 99 (2), 141–153 . .. & Mohajane, M., Essahlaoui, A., Oudija, F., Hafyani, M.E., Hmaidi, A.E., Ouali, A.E., Teodoro, A.C., 2018. Land use/land cover (LULC) using landsat data series (MSS, TM, ETM + and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments 5 (12), 131 . mar, N.Q., Ahamad, M.S.S., Hussin, W.M.A.W., Samat, N., Ahmad, S.Z., 2014. Markov CA, multi regression, and multiple decision making for modeling historical changes in Kirkuk city. Iraq. J. Indian Soc. Remote Sens. 42, 165–178 . sumanu, I.K., Akomgbangre, J.N., 2020. A growing city: patterns and ramifications of urban change in Wa, Ghana. Spat. Inf. Res. 28 (5), 523–536 . sumanu, I.K., Akongbangre, J.N., Gordon, N., Tuu, Y., Owusu-Sekyere, E., 2019. From patches of villages to a municipality: time, space, and expansion of Wa, Ghana. In: Urban Forum, 30. Springer, Netherlands, pp. 57–74 . eng, S., Ciais, P., Maignan, F., Li, W., Chang, J., Wang, T., Yue, C., 2017. Sensitivity of land use change emission estimates to historical land use and land cover mapping. Global Biogeochem. Cycles 31 (4), 626–643 . otts, S.G., Ngo, H.T., Biesmeijer, J.C., Breeze, T.D., Dicks, L.V., Garibaldi, L.A., Van- bergen, A. (2016). The assessment report of the Intergovernmental Science-Policy11 Platform on Biodiversity and Ecosystem Services on pollinators, pollination, and food production. Bonn, Germany, Secretariat of the Intergovernmental Science-Policy Plat- form on Biodiversity and Ecosystem Services, 556pp. uelland, D., Levavasseur, F., Tribotté, A., 2010. Patterns and dynamics of land-cover changes since the 1960s over three experimental areas in Mali. Int. J. Appl. Earth Obs. Geoinf. 12, S11–S17 . antos, D., Cardoso-Fernandes, J., Lima, A., Müller, A., Brönner, M., Teodoro, A.C., 2022. Spectral analysis to improve inputs to random forest and other boosted ensemble tree-based algorithms for detecting NYF pegmatites in Tysfjord, Norway. Remote Sens. 14 (15), 3532 . ong, X.P., Hansen, M.C., Stehman, S.V., Potapov, P.V., Tyukavina, A., Vermote, E.F., Townshend, J.R., 2018. Global land change from 1982 to 2016. Nature 560 (7720), 639–643 . avares, P.A., Beltrão, N.E.S., Guimarães, U.S., Teodoro, A.C., 2019. Integration of sen- tinel-1 and sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon. Sensors 19 (5), 1140 . imm Hoffman, M., Skowno, A., Bell, W., Mashele, S., 2018. Long-term changes in land use, land cover and vegetation in the Karoo drylands of South Africa: implications for degradation monitoring. Afr. J. Range Forage Sci. 35 (3-4), 209–221 . olessa, T., Senbeta, F., Kidane, M., 2017. The impact of land use/land cover change on ecosystem services in the central highlands of Ethiopia. Ecosyst. Serv. 23, 47–54 . uffour-Mills, D., Antwi-Agyei, P., Addo-Fordjour, P., 2020. Trends and drivers of land cover changes in a tropical urban forest in Ghana. Trees Forests People 2, 100040 . ang, S.W., Munkhnasan, L., Lee, W.K., 2021. Land use and land cover change detection and prediction in Bhutan’s high altitude city of Thimphu, using cellular automata and Markov chain. Environ. Chall. 2, 100017 . ardell, D.A., Reenberg, A., Tøttrup, C., 2003. Historical footprints in contemporary land use systems: forest cover changes in savannah woodlands in the Sudano-Sahelian zone. Glob. Environ. Change 13 (4), 235–254 . iran, G.A.B., Kusimi, J.M., Kufogbe, S.K., 2012. A synthesis of remote sensing and local knowledge approaches in land degradation assessment in the Bawku East District, Ghana. Int. J. Appl. Earth Obs. Geoinf. 14 (1), 204–213 .