Towards a concrete landscape: Assessing the efficiency of land consumption in the Greater Accra Region, Ghana.

Most existing studies on land consumption have used a reactive approach to assess the phenomenon. However, for evidence-based policies, an initiative-taking forecast has been touted to be more appropriate. This study, therefore, assessed current trends and efficiency of land consumption in the Greater Accra Region from 1987 to 2017, and predicted a 30-year future land consumption in a "business-as-usual" scenario. The study adopted maximum likelihood image classification techniques and "combinatorial or" to model land cover change for Greater Accra from 1987 to 2017 while the UN-Habitat land efficiency index was employed to model efficiency of land consumption. In addition, Leo-Breiman Forest based regression, was used to model a future land cover by using the 30 years land cover change as a dependant variable and a series of natural and anthropogenic factors as independent variables. Results showed that artificial surfaces increased from 4.2% to 33.1%, with an annual growth rate of 22.1% in 30 years. Land consumption was highly inefficient as only 4.2% of the region had a good proportion of population per land area. Factors which influenced artificial surface growth were population, distance from water bodies, poverty index, distance from sacred groves, proportion of agriculture population with a small margin of influence from soil and geology type. Landscape prediction showed that artificial surfaces will increase to 92.6% as more places are coated with concrete. The high rate of land inefficiency provides an opportunity for re-zoning by the Land Use and Spatial Planning Authority of Ghana to accommodate the growing population.

Introduction

Human transformation and its impacts on the earth’s biophysical environment are becoming more glaring and intense in the 21st century, with an estimated 37.5% and 1.3% of the world’s land mass being agriculture landscape and urban landscape respectively [1]. Causes of land consumption include increasing population, industrialisation, economic growth, unplanned development, unsecured land tenure, governmental policies and food demand [2-5]. Between agriculture and urban areas, the latter has serious dents on the land because once instituted, it becomes irreversible to earlier natural ecology.

The non-renewability of urban landscapes is due to the concretisation of surfaces which causes land sealing; thus, preventing the growth of flora and fauna [6, 7]. It also affects hydrological cycles as it disrupts percolation and groundwater recharge, thereby increasing the incidence of flooding [8]. It also leads to wetland loss, sequestration of carbon and loss of biodiversity due to high irreplaceable fauna and flora populations [9]. Urban land consumption is associated with long commuting distances and time, heat island effect, and air pollution [10]. Also, the increase in urban concrete surfaces contributes to increasing psychological stress because urban folks become detached from nature [11]. The negative effects of land consumption make it undesirable and a threat to the achievement of a sustainable world as captured in the Sustainable Development Goal 11. It is, thus, essential that patterns of land consumption are readily available to planners for strategic land use planning.

Although predicting future land consumption has a great advantage for streamlining development in less developed areas, further analysis in spatial land efficiency helps to support planning in already land-consumed areas [12]. Land efficiency is the measure of a unit area of physically developed land as a function of socio-economic activities [13]. Factors influencing land efficiency are economic growth, foreign direct investment, institutional capacity, government policy, productivity, consumption, labour, property rights and urbanisation [12, 14]. Measuring land efficiency provides a means to ascertain if land consumption is of ecological, environmental or social good. Also, land efficiency assessment serves as a guide to measure population growth and land availability and forge proper planning schemes [3]. One weakness of land efficiency is that land consumption at the initial stage produces efficient agglomeration but, but without planning, it later transgresses to agglomeration diseconomies [4]. Future diseconomies of land consumption can be tackled through land use planning systems supported by efficient land markets and policing [4, 5]. Policies such as setting urban boundaries and green zones with greater enforcement best control land consumption for efficiency [15].

Land consumption in Greater Accra Region has been on the increase with limited studies on the spatial growth of the entire region as existing studies mostly focus on the districts and watershed zones within the region [16-18]. Such haphazard growth can be attributed to the structural adjustment policy in Ghana which detached government from housing without providing a guided spatial planning policy [17]. Despite the acceptance of the rapid spatial growth of the region, efficiency of the growth is unknown, which inhibits spatial planning/re-demarcation of already built zones and outward planning of natural lands. Based on these gaps, the study proposes the following hypotheses:

H1: There is no significant difference in artificial surfaces consumption and other dominant land cover types in Greater Accra Region from 1987 to 2017.

H2. Artificial land consumption in the region is not efficient.

H3: Artificial surface (concrete landscape) is less likely to consume all remaining terrestrial land covers in Greater Accra Region.

This study aims to provide spatial information for planners by identifying areas with low land efficiency for rezoning and re-development to reverse trends of diseconomies of agglomeration of Greater Accra Region and the consequences of not reversing the current trends. Business-as-usual physical development in Greater Accra has the propensity to result in natural environmental disasters for ecosystems, with reciprocal effects on inhabitants of the region.

Land change and consumption: Theoretical perspectives

Several hypotheses including natural, Malthusian, urban and regional economic growth and structural change explain land cover change and consumption. According to the natural theory, non-human forces were the most powerful drivers of land cover change before the anthropcene. Natural fires can damage vegetated land, converting it to bare land covering [19], whereas flooding leaves silt and sand deposits [20]. Volcanic eruptions, as well, have been observed to damage existing natural covers with sima [21] while elevation and soil factors can impact land cover types, since mountain locations with shallow soil might restrict vegetation growth [22]. On the other hand, the Malthusian hypothesis of land cover change explains that growing populations lead to the population outpacing food output, resulting in natural land consumption for farms and urban/built land cover [23].

Furthermore, population expansion drives the majority of agricultural households into off-farm jobs as a means of survival, freeing up space for built land cover. Also, market factors determine land consumption according to urban and regional economic theories [24]. They illustrate how land usage extends from the centre with industry, administrative buildings, and dwellings. Finally, the structural theory describes how interactions across institutional components (political, legal, administrative, economic and traditional) impact land consumption with policies at the national, regional or local levels having different effects. Market liberalisation, privatisation and currency depreciation are examples of structural adjustment policies that have the potential to increase growth and demand for land [25]. Furthermore, the central government’s policies on lending, land rent, housing and subsidies to specific sectors of the economy may result in fast land consumption [26].

Resource efficiency theory

Resource efficiency is an estimation of the output of a process as the ratio of achieved effect and the resources used to achieve the outcome [27]. The main tenet of resource efficiency theory is to gain more with less [28]; that is, using less resources such as land, water and energy to produce more of human desired goods and services. Resource efficiency advocates resource conservation and protection to reduce environmental impact and ensure sustainable development [28]. The land efficiency concept takes its roots from resource efficiency theory. Using land more efficiently involves using smaller areas of land to produce the same product or service [27]. The concept of land efficiency is based on the output or input-oriented perspectives [4]. The output-oriented perspective measures land efficiency as economic output per land unit [4]. Specifically, the output-oriented approach assesses the value added to land by secondary and tertiary industries per unit of square kilometer [4]. Output-oriented approaches ignore the social and environmental dimension of land consumption which are resolved by the input-output approaches [29].

Factors which influence land efficiency are land price, transportation networks, social services, public road facilities, industrial clusters and government policies [4, 14]. Ecologically, variables such as landscape diversity and fragmentation are essential in land efficiency measures [30]. An appropriate measure of land efficiency demands compositing of economic, social, environmental and even institutional indicators to generate a weighted measure [3, 4]. The essence is to provide a more comprehensive, unbiased and rigorous outcome.

However, the [31] supports a simple measure of land efficiency based on population growth in the absence of complex models and data availability. The measure seeks to supply data and output to guide development [31]. In countries, especially developing countries, where spatial data availability is a challenge, the measure is still relevant in assessing land efficiency with the growing population and the spatial growth of cities. The United Nations Habitat’s simple measure of land efficiency was adopted for our study because of the unavailability of spatial economic data to support complex spatial modelling. In total, the land efficiency model is a comparable indicator; thus, serving as an indicator for an achievable goal and an indicator of consumable resources.

Methodology

Study area

The Greater Accra Region is one of the 16 administrative regions of Ghana, and it is bounded to the west by the Central Region of Ghana, east and north by the Volta Region and Eastern Region respectively and south, by the Gulf of Guinea (Fig 1). Geographic coordinates show that the region lies within the bounding box of 6°6’34.07’’ N and 0°30’28.76’’ W to the North-East and 5°28’25.76’’ N and 0°37’28.21’’ W to the South-West. It covers about 370,390.6 hectares (ha) of land. The region houses the national capital of Ghana (Accra), making it highly influenced by formal government policies, economic pressures and urbanisation. The population of the region is about four million people, with a growth rate of 3.1% and about 75% of the populace being migrants [32].

Fig 1

Map of the Greater Accra Region of Ghana.

Source: [36].

On the local front, the region is the traditional abode for the Ga/Dangme ethnic group. The main drainage system in the region comprises the Korle, Kphesie, Sakumono and Songo lagoons among others. It also houses rivers like the Akonyador, Ado, Densu, Odaw, Kyekudor, Kyekubor, Nasakyir, Oludor and Sege which are mostly degraded or choked by physical development and refuse. These rivers flow on a geological bed of quartz, schist, fluvial and lacustrine sediments for wetland areas [33]. The dominant soils in the region are vertisols, solonetz, luvisols and acrisols [33].

The region falls within the Dry Equatorial Climatic Zone. It has an average rainfall amount of 787mm to 1,200mm yearly [34] while temperatures are often in the range of 22°C to 32°C with a mean of 26.5°C. The vegetation has adapted to the low rainfall and high temperature with a coastal savannah vegetation cover dominated by shrubs, grass and a few mangrove patches. Greater Accra Region is the economic hub of Ghana; urban centres in the region are service-oriented while rural districts are primarily of agriculture production and fishing.

Data: Sources, processing and analysis

Data used for land cover mapping were the Landsat images of 1987, 2005 and 2017 for Greater Accra Region. The images were cleaned for haze and noise with the Erdas Imagine 2013 software. They were, further, projected from the UTM 30 North coordinated system to the Ghana Metre Grid Systems and clipped to the regional boundary of Greater Accra in Erdas Imagine 2013 software. Using the maximum likelihood classification method, and based on [35] land cover scheme (https://www.fao.org/3/X0596E/x0596e01f.htm#p381_40252), the study generated land covers such as Natural & Semi-Natural Terrestrial Vegetation [NSTV-OF] (areas where the vegetative cover is in balance with the biotope’s abiotic and biotic forces. This describes vegetation which has not been planted by people but has been impacted by their activity and mainly of trees; Natural & Semi-Natural Terrestrial Vegetation [NSTV-SG] (areas where the vegetative cover is in balance with the biotope’s abiotic and biotic forces. It consists of vegetation which has not been planted by people but has been impacted by their activities. It composes mainly of grass.); Natural & Semi-Natural Aquatic Vegetation [NSAV] (areas that are transitional between pure terrestrial and aquatic systems and where the water table, vegetation cover and floods are greatly impacted by water. For e.g., mangroves, marshes, swamps and aquatic beds); Natural & Semi-Natural Waterbodies [NSW] (areas that are naturally covered by water such as lakes and rivers); Artificial surfaces [AS] [areas with a man-made cover due to human activity such as construction (cities, towns, transportation), extraction (open mines and quarries), or waste disposal]; Cultivated and Managed Terrestrial Areas [CMTA] (areas where natural vegetation has been removed or changed and replaced with different forms of artificial vegetative cover, including crops planted for harvest) and, lastly, Bare Areas [BA] (areas that do not have an artificial cover as a result of human activities. They include bare rock areas and sands)

The 2017 land cover map had the highest accuracy in terms of producer, customer and Kappa. The least accuracy assessment result was recorded for the land cover map of 1987, as it had a customer accuracy of 72%, 68% for producer and 0.7 Kappa (Table 1). Therefore, a change-detection map was generated using the 1987 land cover map against the 2017 map.

Table 1

Accuracy assessments of land cover classes.

Land cover (Year)	Customer accuracy (%)	Producer Accuracy	Kappa (K)
1987	72	68	0.72
2005	84	70	0.68
2017	92	94	0.96

Source: Authors’ construct, 2021.

Land consumption was calculated as with y as years under study, Urbt as urban spatial extent in km2 for past/initial year, Urb (t+n) as extent for the current year. Efficiency of land based on population growth rate (LCPC) was estimated as , where Urb = Built area and P = Population. Areas with population between 1–150 people per hectare are, thus, seen as inefficient, 151–250 as moderately efficient and 251 people and above per hectare as efficient [31].

The research considered two major factors of land cover change: natural and anthropogenic factors. The natural variables consisted of distance away from water bodies, elevation, geology, rainfall, slope, soil type and temperature (Fig 2). Distance from water bodies was generated as euclidean function of all river bodies in the region. Elevation was cropped from SRTM dataset from the [37] after it was processed for sinks and fills. Geology and soil type datasets were sourced from the Ghana Geological Service. Slope data was a derivative from the elevation data using the generate slope tool in ArcPro. Anthropogenic factors used in this study were proportion of agricultural population, distance from road, distance from sacred groves/land, land value, distance from market, distance from government residential facilities (housing), poverty index (population below poverty line) and population (Fig 3).

Fig 2

Natural variables for land cover change in the Greater Accra Region.

Source: [36].

Fig 3

Anthropogenic variables of land cover change in the Greater Accra Region.

Source: [36].

These factors were identified from the categorisation of causes of land cover change in Greater Accra by [17, 38, 39]. Also, the research adopted these causes because of their spatial measurability. Land prices were computed from a meta-analysis of land prices of lands in the Greater Accra Region as advertised by websites in Ghana. The average price of land advertised for a community was used and mapped spatially and later interpolated based on the moving average interpolation method. Distance from water bodies, road and nodal towns were all based on the Euclidean distance model in ArcPro. All datasets were clipped to fit the boundary of the study area and projected to the Ghana Metre Grid.

The study adopted a Maximum Variance Inflation Factor (VIF) cut-off of >7.5 to assess the multicollinearity of anthropogenic and natural factors of land cover change. Distance from the Accra Central Business District (CBD), Distance from government residential facilities and Distance from Market had VIF above the 7.5 threshold (Table 2). Distance from Accra CBD was dropped because of the high covariance with Distance from government residential facilities and Distance from Market.

Table 2

Multicollinearity and importance of exploratory variables.

Variables	VIF	Covariates	VIF with Distance from Accra CBD	Sig%	Negative%	Positive%
Elevation	2.68	-	2.66	93.20	19.24	80.76
Distance from water	2.47	-	2.47	92.59	29.57	70.43
Population	3.97	-	3.67	97.21	78.04	21.96
Distance from Accra CBD	27.11	Distance from Market	-
Distance from Roads	1.16	-	1.16	78.11	50.85	49.15
Proportion of Agricultural Population	2.79	-	2.54	100	100	-
Distance from Market	14.54	Distance from Accra CBD	5.68	93.13	23.45	76.55
Distance from Government Residential Facilities	7.96	Distance from Accra CBD	4.23	92.52	48.20	51.80
Rainfall	2.26	-	2.26	99.86	4.49	95.51
Poverty Index	1.99	-	1.95	99.25	1.29	98.71
Slope	1.79	-	1.79	97.21	78.04	21.96
Land Value	3.49	-	3.49	99.66	79.27	20.73
Temperature	1.17	-	1.15	100	-	100
Distance from Sacred Groves/land	1.83	-	1.83	97.82	66.89	33.11
Geology	1.66	-	1.66	93.13	18.63	81.37
Soil	1.89	-	1.89	93.95	18.22	81.78
Percentage of criterion passed		Trails	No. passed	%passed
Min Adjusted R-Squared > 0.50		4943	1471	29.76
Max Coefficient p-value < 0.05		4943	3949	79.89
Max VIF Value < 7.50		4943	4943	100.00
Min Jarque-Bera p-value > 0.10		4943	-	-
Min Spatial Autocorrelation p-value > 0.10		17	-	-

Source: Authors’ construct, 2021.

Two variables (proportion of agriculture population and temperature) had a 100% importance significant value for the 4943 trails, with the least variable being Distance from Roads (78.11%) (Table 2). Five variables (distance from water bodies, proportion of agriculture population, temperature, distance from sacred groves and geology) produced the highest adjusted R-square value of 0.93 with Akaike’s Information Criterion (-49037.44), Jarque-Bera p-value (0.00), Koenker (BP) Statistic p-value (0.00), Max Variance Inflation Factor (1.15) and minimum Spatial Autocorrelation p-value (0.1). Further, a Leo-Breiman Forest Based classification and regression (creates a model based on known values from a training dataset and later uses it to forecast unknown values with the same associated explanatory factors. It generates a large number of decision trees, known as forest, each tree creates its own forecast, which is then utilised as part of a voting mechanism to determine final predictions) [40] with the exploratory variables to predict between AS and CMTA (which is likely to consume the remaining terrestrial land cover in Greater Accra Region) were done.

Using the random forest regression, the future land consumption modelling generated 100 classification trees, 1 leaf size, 3 depth range of 4,083 to 4,404 and mean tree depth of 4,269. The model also had 3 randomly sampled variables, with about 30% of the dataset excluded for validation after training the data with the independent variables’ prediction for 30 years future land cover for Greater Accra Region. For the 30-year future land cover modelling, the study used the "business-as-usual" scenario, based on the assumption that the forces influencing land cover change are semi-permanent and result from all social and economic elements at play. Government has the greater influence to alter the trajectory of land cover change but with its less influence on housing and land ownership in the region since 1900’s, such outcome is not expected [41, 42].

Results

In 1987, the predominant land cover class was natural and semi-natural terrestrial vegetation (NSTV-SG). It covered an area of 161,628.9 ha (43.6%) (Table 3 and Fig 4). Cultivated managed and terrestrial areas (CMTA) was the second-largest cover for 1987 while natural & semi-natural waterbodies (NSW) had the least area of 3.41%. Natural & semi-natural terrestrial vegetation-shrubs and grass (NSTV-SG) remained the dominant land cover in 2005, but in 2017, artificial surfaces (AS) was the largest land cover with an area size of 122,650.0 ha (33.1%) (Table 3). AS land consumption from 1987 to 2005 was about 26,222.4 ha, giving an annual consumption rate of 9.07%. The level of AS consumption increased from 42,291.6 ha in 2005 to 122,650.0 ha in 2017. The annual growth rate of AS from 2005 to 2017 was 15.3%. But cumulative AS from 1987 to 2017 was 106,580.8 ha with an annual growth rate of 22.11%.

Table 3

Landcover statistics of the Greater Accra Region for 1987 to 2017.

	1987		2005		2017
Land Cover	Ha	%	Ha	%	Ha	%
AS	16069.2	4.3	42291.6	11.4	122650.0	33.1
BA	13034.8	3.5	12677.9	3.4	17028.1	4.6
CMTA	72881.3	19.7	128480.4	34.7	110350.2	29.8
NSAV	26819.7	7.2	8423.3	2.3	18350.3	5.0
NSTV-SG	161628.9	43.6	164720.2	44.5	90169.7	24.3
NSTV-OF	67343.4	18.2	7520.8	2.0	2310.5	0.6
NSW	12613.3	3.4	6276.4	1.7	9531.8	2.6
Total	370390.6	100	370390.6	100	370390.6	100

Source: Authors’ construct, 2021.

Fig 4

Land cover and change maps for the study area for 1987, 2005 and 2017.

Source: Authors’ construct with base map from [37].

CMTA consumed a total of 37,468.9 ha of land area with an annual growth rate of 1.7% from 1987 to 2017.

The study extracted areas which have changed from other land covers to AS and CMTA for 1987 to 2005, 2005 to 2017 and 1987 to 2017 because they were the largest land cover types transforming the landscape of the study area. A test of independence was performed to assess whether there were significant differences between the means of areas changing to AS and CMTA. The study found statistically significant differences in the means of areas changing to AS (M = 3366.90m, SD = 28217.81) and CMTA (M = 6384.01m, SD = 24487.7); t (201097) = -3.46, p = 0.01 for 1987 to 2005 while for 1987 to 2017 in terms of differences in means of areas changing to AS (M = 19614.07m, SD = 76004) and CMTA (M = 7132.38m, SD = 214856.78); t(1355664) = -4.48, p = 0.00.

Based on the [31] land efficiency claification, the study identified three categories of land efficiency in Greater Accra Region (Fig 5). Efficiency values ranged from inefficient (1–150 people/hectare) to moderately efficient (151–250 people/hectare) and efficient (251 people and above/hectare). Efficient land use areas based on population were around the Accra Central and Gbawe enclave covering about 13,258.46 ha; that is, 10.81% of the entire AS in the Greater Accra Region.

Fig 5

Land consumption in Greater Accra Region.

Source: Authors construct with base map from [37].

Inefficient zone represented about 64.76% of the AS with the remaining 24.43% as moderately efficient areas. AS areas from Teshie to Ada Foah were in inefficient land use zones.

Future land consumption of the Greater Accra Region (30 years from 2017)

The factors used for the Leo-Breiman forest based classification and regression model generated various levels of predictive importance, with population having the highest level of importance, predicting land cover change by 21.19%. The least important variable was temperature (0.0%) (Fig 6).

Fig 6

Level of importance for the variables.

Source: Authors’ construct, 2021.

The model did check for validation of the training data by matching the results with 30% of the data not included in the training sample. The results for the validation data generated an F-Score of 0.92 and 0.95 for both AS and CMTA respectively. The sensitivity value reduced by a small margin to 0.95 and 0.93 for AS and CMTA and a Matthew Correlation Coefficient (MCC) value of 0.95 for both AS and CMTA. However, the explanatory variables had high validation shares within the range of 0.99 to 1 (Table 4).

Table 4

Accuracy of prediction of training and validation for the exploratory variables.

Variables	Training		Validation		Training Share	Validation Share
	Min	Max	Min	Max
Distance from waterbodies	5.03	23387.72	14.51	23197.39	1	0.99
Population	0.00	1837290.13	0.00	1838587.88	1	1
Proportion of Agricultural Population	0.00	36.00	0.00	36.00	1	1
Poverty Index	2	55.00	2.00	55.00	1	1
Temperature	0.00	30.37	0.00	30.07	1	0.99
Distance from Sacred Groves	35.50	31349.59	51.03	31271.64	1	1
Geology	1	7.00	1	7.00	1	1
Soil	1	9	1	9	1	1

Source: Authors construct, 2021.

Per the Leo-Breiman Forest based classification and regression, AS and CMTA will be the only terrestrial land covers in the future (Fig 7). Of the remaining 238,208.8ha of terrestrial lands remaining in the region, about 220,569.8ha will be consumed by AS. This is going to take the entire land cover for AS in the region to an area size of 343,219.8ha (92.6% of the region). CMTA will reduce in size from 110,350.2ha to occupy an area of 17,639ha (about 84.01% of its original size). AS will consume all available terrestrial landscape into a homogenous concrete landscape.

Fig 7

Future land cover for Greater Accra Region in business-as-usual scenario.

Source: Authors’ construct with base map from [37].

Discussion

The main purpose of this study was to assess the efficiency of land consumption based on the population of the Greater Accra Region using classified Landsat imagery for over 30 years. The natural land cover classes NSAV, NSTV-SG and NSTV-OF were dominant from 1987 to 2005, covering more than two-thirds of the region. The dominant natural land cover classes in 1987 reduced as artificial surfaces gradually increased in 2005 to become the largest single land cover in 2017. A study by the [37] showed that Greater Accra Region was the only region in Ghana with highest growth of artificial surfaces as against agricultural landscape. This has rendered the land consumption in the region highly inefficient. Meanwhile, it has been established that high rate of artificial surface land consumption is a normal trend in developing cities across the world such as Beijing (China), Mumbai (India), Cairo (Egypt), and Lagos (Nigeria) [43-45].

[39] Population growth is the prime cause of the changing land cover in the Greater Accra Region [39]. This was confirmed by the random forest regression results in our current study. The findings were also in agreement with those of [3] in China [46], in Europe [47], in USA and [48] in South Africa all of whom declared population growth as the main determinant of land consumption in the world. In the Greater Accra Region, the increasing population is a result of in-migration rather than natural growth [49]. The pull factor of the region as the preferred destination for migrants is related to colonial and post-colonial government biased policies towards the region. Colonial rulers adopted Accra as their seat of governance; hence, provided the areas with better transport systems and educational, health, political and commercial infrastructure than other regions. Independent Ghana did not move from the spatially biased development of colonial leaders, but rather entrenched it with upgrading existing infrastructure in the region to the detriment of other regions [50, 51].

Although population is a major determinant of artificial surface growth in Greater Accra Region [52], claimed that structural policies in Ghana and the region also have a greater effect on the efficiency of land consumption. The structural adjustment programme adopted by Ghana in the 1980s led to the neglect of housing by government, a gap filled by low-income housing [52]. Also, loosely implemented land and spatial planning policies worsened the housing deficit, leading to a horizontal and non-uniform growth in the region. Weak institutions like the Land Use and Spatial Planning Authority, Forestry Commission and Environmental Protection Agency had little control over spatial growth [53] as artificial surfaces engulfed wetlands, groves and forest areas [18]. A well planned and executed spatial plan for Greater Accra could have reduced the large areas of land inefficiency in relation to population growth.

In this regard, [15] advocate the implementation of green belts to curtail land consumption while [54] advocates tradable land certificates to ensure land efficiency. Tradable land certificates are yet to be implemented in the study region and Ghana in general even though it is highlighted in the Land Policy of Ghana [55]. Also, the land management system in Greater Accra is traditionally communal; the government has limited direct control to enforce land trading and bonds. As a result [4], assert that in areas where governments have less control over land tenure systems, land consumption is high; hence, efficiency is low. With the absence of direct land control, an alternative is physically restricting horizontal growth while enforcing building codes and re-zoning and re-demarcating inefficient zones in the region [3].

With current trends of land consumption and land use inefficiency, firm action is needed to restrict the horizontal growth of the region into a homogenous concrete surface. Planners must focus on re-zoning inefficient land-use areas to improve the number of people per hectare as that will prevent rapid horizontal growth and encourage intensification. Per the forest-based modelling, it was seen that if horizontal growth is not curtailed, Greater Accra will develop into a homogenous concrete surface. This has serious physical, environmental and health effects in the region as espoused by [3] and [4]. That, in effect, will worsen current levels of urban heat [56], air quality [57], psychological health [58] and the dwindling small mammal population [59].

Conclusions and recommendations

Urban land is a small fraction of natural lands degraded by humans compared to agricultural lands. Despite its limited spatial extent, urban growth leads to permanent concrete sealing of land, preventing ecological growth within, on and around it. The effects of urban land consumption are even worsened with cyclical effects on human societies as it reduces resilience to climate change, flooding, urban heats and associated deaths and health complications. This study sought to assess the efficiency of urban land consumption in Greater Accra Region using remotely sensed datasets at three points in a 30-year interval. The study further modelled future land consumptions in the region based on a “Business-as-Usual” growth of urban artificial surfaces. Results showed that growth in urban land consumption was significantly higher than the growth of any other land cover in the Greater Accra Region. As result, land consumption in region was found to be moderately efficient but largely inefficient. This suggests a need for policy and management actions to constrain horizontal growth. The results point to potential areas for re-demarcation and re-zoning to improve efficiency. The scale of potential expansion of artificial surfaces in the future (based on the current trends) is huge and the consequences are alarming. It is extremely important, if not urgent, to adopt measures to reduce the rate of artificial surface expansion and coverage in the interest of sustainable development, ecosystem services and human wellbeing. The human and environmental costs of allowing the concretisation of the region will be far greater for government and society. Going forward, green infrastructure should be integral to physical development planning in the region.

4 Dec 2021

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Reviewers' comments:

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Comments to the Author

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Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: I Don't Know

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This paper raised a concern of low land consumption efficiency in Greater Accra Region, by mapping the land cover change in 30 years, the authors calculated the land consumption efficiency using UN habitat’s LCPC and found that the land consumption in the region was highly inefficient. By running random forest model, the authors also identified variables that are associated with land consumption and predicted the future land cover change. The paper has solid sections in background and data, but relative weak in terms of the method and result interpretations.

Overall, the paper may have significant implications for the urban development in Greater Accra Region, especially how to balance the development of urban and population growth. However, I would say that neither the method nor the concluding remarks offer significant contribution to the existing literature, neither in theoretical nor practical domain. For international readers, we would like some take-away from the paper, but I failed to do so. I would suggest put more focus on the interpretation of the associations between land consumption and the influencing factors, and what can we learn from the results. Also, I am not convinced by the prediction results from the random forest model, as the method and model are not well introduced, and the results still require more in-depth interpretation.

Some other questions or comments are as follow:

�  Line 174: maybe give some brief introduction to each term, such as what is cultivated and managed terrestrial areas, or just provide a link to the FAO documents.

�  Line 180, I don’t quite understand how you decided to use the 1987 and 2017 maps to detect land cover change, as you said the accuracy of 1987 is the weakest.

�  Line 184: the efficiency of land is calculated here as the ratio between two growth rate, but why later was defined as population density (land consumption per capita)? Such as 1-150 people per hectare as inefficient. Also, I did not see the results of the LCRPGR in the rest of the paper, but only the LCPC.

�  Line 204: although you listed the references that support the selection of independent variables, I suggest add some explanations, especially the theoretical underpinnings behind the potential associations.

�  Line 237: should be Table 3.

�  Line 247, use cumulative “AS”, and the number should be 106580.8

�  Reformate Table 2 and Table 4 to fit the pages.

�  Line 278: the random forest modelling should be provided with more details. I am not an expert on random forest model and correct me if I am wrong. I understand that you were using the random forest to regress the correlations between land cover change and the influencing factors, and then using such correlations to predict the future change of land cover under “business as usual” scenario, however, more information should be provided, such as what is the target year of your prediction, have you set any thresholds of the influencing factors, such as the population growth (such as the world population projected by UN), climate change (maybe also from UN’s climate report?).

�  Line 283: the interpretation of the Fig 5, while I can tell from the Figure that population has the highest importance, but the text said poverty index has the highest level of importance.

�  I have read paper using population projection to predict land use change, or verse vise, but why do you think a random forest that includes all factors in the model is better?

�  Line 303: it should be “Leo-Breiman”.

�  Line 342: the authors pointed out the planning and protection policies intensified the land consumption, but how to include such influences in the predicting model?

�  What is the unit of analysis, especially when calculating the land efficiency? grid cells?

�  Actually, you were using the land consumption per capita (LCPC) from UN Habitat (https://unhabitat.org/sites/default/files/2021/08/indicator_11.3.1_training_module_land_use_efficiency.pdf) to calculate the “land use efficiency”, rather than the LCRPGR you mentioned (whether the increase of urbanized land is compatible with the increase of population, line 184). In addition, I did not find the threshold of low, moderately and efficient from the reference you provided (line 188, https://apo.org.au/sites/default/files/resource-files/2018-07/apo-nid182836.pdf).

Reviewer #2: This manuscript “TOWARDS A CONCRETE LANDSCAPE? ASSESSMENT OF EFFICIENCY OF LAND CONSUMPTION IN GREATER ACCRA REGION, GHANA” assess the efficiency of land consumption based on population in the Greater Accra Region using classified Landsat imagery over 30 years. Based on the prediction, Artificial surfaces and Cultivated and Managed Terrestrial Areas will be the dominant terrestrial land types in the future. The authors used the Landsat images of Greater Accra Region of 1987, 2005 and 2017 and Resource Efficiency Theory presented the study. Though the results were relatively simple, the contents and thoughts were a bit interesting. After some revision, I would recommend it for publication in PLOS ONE. Detailed revision listed below.

In the abstract part, studied methods and studied years (1987, 2005 and 2017) were not presented.

Many minor problems need to be revised, such as the “Li, Ye, Song, 2020;” in line 47, “Fig 1” in line 139, “(Yan, Peng & Wu, 2020).” in line 111, “(Ahmad, Pandey, & Kumar, 2019).” In line 53, “Guo and Xiong, (2014)” in 114, “Liu et al,” in line 120, “Grekousis and Mountrakis, (2015)” in line 327, in line 349, “Fig 1: Map …” in line 147 and in all the figure captions.

Please unify the tense you used. Such as “Stewart and Haaga (2018) assert that increasing” in line 56-57, and all the primary tenses when you cited and described other reports.

This manuscript had two “Fig. 3” in line 207 and in line 252, respectively, pleased revise them as well as in the text.

In line 282-285, “…predicting land cover change by 13.91% while the least important variables were rainfall (9.53%) and slope (6.60%) …” why the data “13.91%, 9.53% and 6.60%” was disagreed with them in Fig. 5?

Line 251, delete the second “organic”

Line 289, please revise “in at the experimental site”

Reviewer #3: The manuscript is well-developed and well-organized. The article is well-written with the analyses quite useful to identify land consumption of an area. I think it is suitable to be published in PLOS ONE.

I am looking forward to reading the revised paper after the following suggest edits.

* For which year land cover trends is simulated need to be precisely stated.

* Future trends of land consumption is evaluated using “business as usual scenario”. Why business as usual scenario was chosen for the analysis need to be properly justified. Given that it is unlikely to continue the existing growth rate for prolonged time. And, this model is susceptible for portraying an upper bound benchmark for urban growth.

* Few caption of table and tables were misplaced. Need to be corrected.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Farhan Asaf Abir

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

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Submitted filename: PONE review.docx

Click here for additional data file.

1 Apr 2022

REVIEWER 1

Comment 1

Line 174: maybe give some brief introduction to each term, such as what is cultivated and managed terrestrial areas, or just provide a link to the FAO documents.

Response 1

Marked Line 208-226

Unmarked Line 195-210

Brief explanation is provided for each land cover type. Also link to assess in-depth meanings is provided

Comment 2

Line 180, I don’t quite understand how you decided to use the 1987 and 2017 maps to detect land cover change, as you said the accuracy of 1987 is the weakest.

Response 2

Satellite image for the area starts from 1987. The year 2017 was used because at the time of the study that was the current year and it also provided a 30 year span for the study. The 1987 had few bands compared with the 2005 and 2017 possibly accounting for less accuracy.

Comment 3

Line 184: the efficiency of land is calculated here as the ratio between two growth rate, but why later was defined as population density (land consumption per capita)? Such as 1-150 people per hectare as inefficient. Also, I did not see the results of the LCRPGR in the rest of the paper, but only the LCPC

Response 3

Marked Line 236

Unmarked Line 222

Function modified to Land consumption per capita mean built up area divided by population

Comment 4

Line 204: although you listed the references that support the selection of independent variables, I suggest add some explanations, especially the theoretical underpinnings behind the potential associations.

Response 4

Marked line 112

Unmarked line 103

Section on theory added

Heading: Land cover change and consumption

Comment 5

Line 237: should be Table 3

Response 5

Marked line 312

Unmarked 300

Corrected to Table 3

Comment 6

Line 247, use cumulative “AS”, and the number should be 106580.8

Response 6

Corrected

Comment 7

Reformate Table 2 and Table 4 to fit the pages

Response 7

Tables made to fit

Comment 8

Line 278: the random forest modelling should be provided with more details. I am not an expert on random forest model and correct me if I am wrong. I understand that you were using the random forest to regress the correlations between land cover change and the influencing factors, and then using such correlations to predict the future change of land cover under “business as usual” scenario, however, more information should be provided, such as what is the target year of your prediction, have you set any thresholds of the influencing factors, such as the population growth (such as the world population projected by UN), climate change (maybe also from UN’s climate report?).

Response 8

Marked line 281-284

A basic explanation is provided

[creates a model based on known values from a training dataset and later uses it to forecast unknown values with the same associated explanatory factors. It generates a large number of decision trees, known as forest, each tree creates its own forecast, which is then utilised as part of a voting mechanism to determine final predictions] (Bühlmann, 2010).

Marked line 287-296

Unmarked line 274-278

Using the random forest regression, the future land consumption modelling generated 100 classification trees, 1 leaf size, 3 depth range of 4083 to 4404, and mean tree depth of 4269. The model also had 3 randomly sampled variables, with about 30% of the dataset excluded for validation. After training the data with the independent variables, prediction for 30 years future land cover for Greater Accra Region.

Comment 9

Line 283: the interpretation of the Fig 5, while I can tell from the Figure that population has the highest importance, but the text said poverty index has the highest level of importance.

Response 9

Marked Line 347

Unmarked line 333

Rewriting: The factors used for the model generated various levels of predictive importance, with population having the highest level of importance, predicting land cover change by 21.19% while the least important variables was temperature (0.0%) (Fig 6).

Comment 10

I have read paper using population projection to predict land use change, but why do you think a random forest that includes all factors in the model is better?

Response 10

The factors which influence land cover change are theoretically and empirically anthropogenic and natural. Most studies depend on anthropogenic by using population growth but there are other factors as the modelling indicated distance from water bodies, poverty and distance from sacred groves influence growth. These are micro level factors which needs to be accounted for.

Comment 11

Line 303: it should be “Leo-Breiman

Response 11

Correction made

Comment 12

Line 342: the authors pointed out the planning and protection policies intensified the land consumption, but how to include such influences in the predicting model?

Response 12

Modelling policies was difficult

Proxy variables were distance from road, distance from government housing

Comment 13

What is the unit of analysis, especially when calculating the land efficiency? grid cells?

Response 13

Hectare from

https://archive.unescwa.org/sites/www.unescwa.org/files/u593/module_3_land_consumption_edited_23-03-2018.pdf

The grid cell was 30*30 meters because of the Landsat image used for classification

Comment 14

Actually, you were using the land consumption per capita (LCPC) from UN Habitat (https://unhabitat.org/sites/default/files/2021/08/indicator_11.3.1_training_module_land_use_efficiency.pdf) to calculate the “land use efficiency”, rather than the LCRPGR you mentioned (whether the increase of urbanized land is compatible with the increase of population, line 184). In addition, I did not find the threshold of low, moderately and efficient from the reference you provided (line 188, https://apo.org.au/sites/default/files/resource-files/2018-07/apo-nid182836.pdf).

Response 14

Mode to LCPC

Correction made in methodology

Interpretation for the levels of land efficiency can be found here

https://archive.unescwa.org/sites/www.unescwa.org/files/u593/module_3_land_consumption_edited_23-03-2018.pdf

REVIEWER 2

Comment 1

In the abstract part, studied methods and studied years (1987, 2005 and 2017) were not presented.

Response 1

The study adopted maximum likelihood image classification techniques and “combinatorial or “to model land cover change for Greater Accra from 1987 to 2017. While the UN-Habitat land efficiency index was employed to model land consumption and efficient of land cover change. Leo-Breiman Forest based regression was used to model future land cover by using the 30 years land cover change as dependant variable a series of natural and anthropogenic as independent variables.

Comment 2

Many minor problems need to be revised, such as the “Li, Ye, Song, 2020;” in line 47, “Fig 1” in line 139, “(Yan, Peng & Wu, 2020).” in line 111, “(Ahmad, Pandey, & Kumar, 2019).” In line 53, “Guo and Xiong, (2014)” in 114, “Liu et al,” in line 120, “Grekousis and Mountrakis, (2015)” in line 327, in line 349, “Fig 1: Map …” in line 147 and in all the figure captions.

Response 2

Corrected

Comment 3

Please unify the tense you used. Such as “Stewart and Haaga (2018) assert that increasing” in line 56-57, and all the primary tenses when you cited and described other reports.

Response 3

Well noted and corrected

Comment 4

This manuscript had two “Fig. 3” in line 207 and in line 252, respectively, pleased revise them as well as in the text.

Response 4

Tables and Figure numbers verified and corrected

Comment 5

In line 282-285, “…predicting land cover change by 13.91% while the least important variables were rainfall (9.53%) and slope (6.60%) …” why the data “13.91%, 9.53% and 6.60%” was disagreed with them in Fig. 5?

Response 5

The factors used for the Leo-Breiman forest based classification and regression model generated various levels of predictive importance, with population having the highest level of importance, predicting land cover change by 21.19% while the least important variables was temperature (0.0%) (Fig 6).

Comment 6

Line 251, delete the second “organic”

Response 6

No organic

Comment 7

Line 289, please revise “in at the experimental site”

Response 7

However, the explanatory variables had high validation shares within the range of 0.99 to 1 (Table 4).

REVIEWER 3

Comment 1

For which year land cover trends is simulated need to be precisely stated.

Response 1

Marked Line 201-202

Unmarked line 190-191

Data used for land cover mapping were the Landsat images of 1987, 2005 and 2017 for Greater Accra Region

Comment 2

Future trends of land consumption is evaluated using “business as usual scenario”. Why business as usual scenario was chosen for the analysis need to be properly justified. Given that it is unlikely to continue the existing growth rate for prolonged time. And, this model is susceptible for portraying an upper bound benchmark for urban growth.

Response 2

Marked line 291-296

Unmarked line 278-283

For the 30-year future land cover modelling, the study used the "business as usual" scenario, assuming that the forces influencing land cover change are semi-permanent and the result of all social and economic elements at play. Government has the greater influence to alter the trajectory of land cover change but with less influence on housing and land ownership since 1900’s such outcome is not expected (Gough & Yankson, 2000; Quarcoopome, 1992).

Comment 3

Few caption of table and tables were misplaced. Need to be corrected.

Response 3

Corrected

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

16 May 2022

TOWARDS A CONCRETE LANDSCAPE: ASSESSMENT OF EFFICIENCY OF LAND CONSUMPTION IN GREATER ACCRA REGION, GHANA

PONE-D-21-33010R1

Dear Dr. Osman,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Academic Editor

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Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: In this revised manuscript, I think all comments have been well addressed, and I have no further questions.

**********

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Reviewer #1: No

27 May 2022

PONE-D-21-33010R1

TOWARDS A CONCRETE LANDSCAPE: ASSESSING THE EFFICIENCY OF LAND CONSUMPTION IN THE GREATER ACCRA REGION, GHANA

Dear Dr. Osman:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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