Managing Urban Waterbodies and Wetlands in Sunyani, Ghana

Analysis of geospatial data towards enhanced management

Abstract

The spatial scope of water bodies and wetlands, as well as their degradation and the effectiveness of institutional responses to their protection, remain poorly documented in Ghana. Using a case study of Sunyani, Ghana, this study draws on mixed methods to address two research objectives: (1) to identify and examine the status of water bodies and wetlands; and (2) to assess the effectiveness of management systems aimed at protecting these resources. The results show a shift from vegetative cover and waterbody land categories to built-up areas over the past two decades (2003–2023). In 2003, water covered 7.95% of the total area, but this declined sharply to 4.37% in 2013 and 2.79% in 2023. The study also found weak collaboration between stakeholders in the management of water resources, with little or no involvement from local communities. Addressing these challenges requires that Sunyani’s municipal government incorporate local knowledge and community experiences into their policies and programmes that protect water resources and wetlands. This approach could support a more sustainable urban environment that balances development with the preservation of essential ecosystems.

Introduction

The rapid urban development and growth of cities is increasingly drawing attention to the relevance of wetlands and water bodies in building resilient and livable communities (Cai, Lin et al., 2024; Wang, X., Jiang et al., 2022). Wetlands are dynamic ecosystems that provide critical, indispensable and highly valued life-supporting functions for human societies (Ghous and Siddiqui, 2022). These ecosystems offer significant economic value, including water filtration, habitat for wildlife, and carbon storage (Gallant, Withey et al., 2020; Saraswat, Pipralia et al., 2024). Wetlands also render urban areas more liveable by mitigating existing urban threats such as flooding, heat island effects, and groundwater replenishment; contributing to cleaner air and providing essential green spaces in urbanized areas (Dooley and Stelk, 2021). In addition to these, wetlands serve as habitats for diverse aquatic species, many of which are threatened or endangered (Nilsson, Destouni et al., 2005).

Freshwater ecosystems, covering less than 1 percent of the Earth’s surface, are home to more than 10 percent of known animal species and about one-third of all vertebrates (World Wildlife Fund, 2018). However, from 1970 and 2018, the animal populace of freshwater have declined by 83 percent , twice the rate of decline seen in marine or terrestrial ecosystems (Almond, Grooten et al., 2022). In fact, the persistent decline in freshwater fishes account for the highest extinction rate among vertebrates in the twentieth century (Burkhead, 2012). Although various factors contribute to the rapid decline in fish stocks, substantial evidence in literature (see UNFCCC (2018); Ye, Cochrane et al. (2013)) identifies human activities, such as unsustainable fishing practices, and shifting consumption patterns, as the primary drivers of this decline (World Wildlife Fund, 2018).

Wetlands are among the largest carbon reservoirs on Earth, yet their role in carbon sequestration is often underestimated (Fennessy and Lei, 2018). According to the 1971 United Nations Convention on Wetlands of International Importance especially as Waterfowl Habitat (Ramsar Convention), wetlands include areas such as marshes, fens, peatlands, and rivers, whether natural or artificial, permanent or temporary, with static or flowing water that may be fresh, brackish or salty. Importantly, this definition extends to marine water zones where the depth at low tide does not exceed six meters (Ramsar Convention Secretariat, 2013). In adapting definitions by the U.S. Army Corps of Engineers and the U.S. Environmental Protection Agency, Campion and Owusu-Boateng (2013) conceptualized wetlands as areas that are regularly inundated or saturated by surface or groundwater, with sufficient frequency and duration sufficient to support vegetation typically adapted for saturated soil conditions. Similarly, Owusu, Asumadu-Sarkodie et al. (2016) defined water resource management as the skilful and efficient allocation of the scarce freshwater resources to meet the needs of the entire human population.

The importance of urban wetlands management and water resource management has gained attention in recent years (Awuku, 2016). According to The World Bank (2022), water resources management (WRM) encompasses the planning, development, and regulation of water resources in terms of both water quantity and quality across all uses. In sum, effective water resource management is crucial for sustainable development worldwide (Owusu, Asumadu-Sarkodie et al., 2016). The 2018 special report of the Intergovernmental Panel on Climate Change (IPCC) contends that global climate change poses irreversible risks to human well-being, the built environment, and ecosystems (IPCC, 2022). Land use decisions, particularly those related to urban development and agricultural expansion, directly impact water supply, wastewater management and stormwater control systems. As urban infrastructure expands, so too does the demand for energy, which typically requires large volumes of water (Loucks and Van Beek, 2017).

Furthermore, the clearing and preparation of land for these developments, combined with increased withdrawal of fresh water, are among the primary drivers of inland wetlands degradation, leading to the loss of critical ecosystems such as swamps, marshes, rivers, and associated floodplain (Millennium Ecosystem Assessment, 2005). For example, by 1985, an estimated 56−65 percent of inland and coastal marshes in Europe and North America —including ponds and small lakes — had been drained for agricultural activities. In other regions, wetlands loss was also significant, with approximately 2% drained in Africa, 27% in Asia, and 6 % in South America (Millennium Ecosystem Assessment, 2005). Currently, global water pollution is projected to continue rising due to economic development driven by urbanization, industrialization, and intensive agriculture, unless substantial progress in regulation and enforcement are made (Cap-Net, 2016; Wang, J., Chen et al., 2024).

The impacts of climate change also exacerbate the degradation of urban wetlands. Reduced precipitation levels linked to climate change, for instance, are expected to water scarcity and heighten the already intense competition for water resources (Millennium Ecosystem Assessment, 2005; Moumen, El Idrissi et al., 2019; UNICEF, 2021; WWAP, 2015). As water management is essential to addressing these challenges, it forms a central part of the United Nations Sustainable Development Goals (SDGs 6) and intersects with several other SDGs. Specifically, effective water management underpins sustainable urban agriculture (SDG 11), efficient land use (SDG 15), environmental restoration (SDG 13), biodiversity preservation (SDG 15), carbon sequestration (SDG 13), and sustainable soil and catchment management (SDG 2 and SDG 15) (Keesstra, Nunes et al., 2018).

At the national level, Ghana is well endowed with water resources. However, water availability fluctuates markedly between seasons, and the distribution across the country is uneven, with the south-western parts receiving more water than the coastal and northern regions (Water Resources Commision, 2023). Wetlands in Ghana are ecologically valuable, supporting diverse wildlife and offering critical life-supporting services. They act as food sources, roosting and nesting sites for thousands of migratory and resident birds, habitats for marine turtles and fish species, and plant genetic materials for research. Additionally, wetlands serve as a major source of income for economically disadvantaged communities in catchment areas (Kwei, 2005). Despite this ecological importance, approximately 60 percent of Ghana’s surface water remains polluted (Yeleliere, Cobbina et al., 2018).

According to the Ministry of Environment, Science, Technology and Innovation (MESTI, 2018), pollution has resulted in significant biodiversity loss in wetlands and water bodies, particularly in coastal lagoons such as Korle, Kpeshie, and Sakumo wetlands, as well as inland waters. Mining activities, especially in the south-western river systems (e.g., Ankobra, Bia, Birim, Offin, Pra, and Tano), have raised national concerns following the heavy metals contamination and discoloration of surface and groundwater sources mainly by illegal miners (Agodzo, Bessah et al., 2023). Increasing pollution directly reduces the amount of water that is safe for operational use, further straining Ghana’s water resources (Yeleliere, Cobbina et al., 2018).

In the past decade, there has been a growing consensus on the need for integrated water resource management (IWRM) to address these challenges (Agyenim and Gupta, 2012). Ghana’s National Water Policy (2007) represents a deliberate effort to achieve sustainable development through the equitable and efficient management of the nation’s water resources (Klutse, 2022). However, artisanal and small-scale mining continues to have severe negative impacts on water and land quality in many communities, despite government’s efforts to regulate these activities (Ofosu, Dittmann et al., 2020). The negative impacts of these activities include loss of vegetation cover, mass destruction of water bodies, biodiversity loss, land-use changes, food insecurity, increased social vices and conflicts, high cost of living, and air pollution (Worlanyo and Jiangfeng, 2021).

Studies by Eduful (2014) and Anane (2013) further reveal that improper waste disposal and encroachment of riverine ecosystems significantly contribute to water pollution in Ghana’s urban centres. Reports by Zhou, Appiah et al. (2022) corroborate these findings that waste from agricultural, industrial and mining activities has both immediate and long-term impacts on urban communities, leading to degraded groundwater and soil quality. Bawakyillenuo (2020) attributes these issues to the scarcity of recycling facilities and inefficient waste management systems, which result in more waste ending up in drainage channels and ultimately polluting water resources. A considerable body of research address the management of wetlands and water resources. For example, Campion and Owusu-Boateng (2013) reported that encroachment by low-income populations seeking affordable land in urban areas has led to significant alterations in the hydrology and water chemistry of urban water systems and wetlands. Also, Bawakyillenuo (2020) and Adjomah (2010) argue that these issues stem from inadequate enforcement of laws intended to protect urban water resources, compounded by the lack of political will by governments, incapacitated responsible institutions and ineffective management strategies.

While many studies have been on the pollution of water bodies and wetlands and the shortcomings in government-led sustainable management discourse, research specifically addressing the management of water bodies and wetlands in Sunyani Municipality remains sparse. Despite its reputation as Ghana’s cleanest city, Sunyani is gradually losing its status due to growing population and inadequate waste management delivery services (Miwornunyuie, Kabo-Bah et al., 2016). This study therefore uses Sunyani, a rapidly growing city in the Bono region of Ghana, as a case study to (1) identify and examine the status of existing and endangered water bodies and wetlands in Sunyani and its peripherals; and (2) evaluate the effectiveness of the current management systems in protecting these critical resources.

Materials and Methods

Study area

Adopting a case study research approach, this study uses Sunyani, the capital town of Ghana’s Bono region to explore the management of urban wetlands and water bodies. Sunyani Municipality is one of the 11 administrative districts in the Bono Region. Geographically, Sunyani lies between Latitudes 7 °20’N and 7 °05’N and Longitudes 2 °30’W and 2°10’W. It shares boundaries with Sunyani West Municipality to the north, Dormaa East District to the west, Asutifi North District to the south and Tano North Municipality to the east (MOFA, 2021) (See Figure 1).

With a population of 193,595 — comprising 96,358 males and 97,237 females — Sunyani has a population density of 382.9 persons per square kilometre (Bono Regional Coordinating Council, 2022) . The study area is nestled between gently sloping hills, with the Tano River, the largest river in the region, located southeast of the city centre. Sunyani is also characterized by several smaller streams, including the Sunyani, Agyei, and Akokora Kwadwo Rivers. These streams typically dry up during the dry season but serve as surface runoff channels in the rainy season, ultimately feeding into the Tano River (Anane, 2013; Höflinger, Antwi et al., 2020).

The Tano River acts as the main source of treated water for Sunyani, with water distribution covering 26.3 percent for rural households and other domestic activities, compared to 1.3 percent for urban households at (Ghana Statistical Service, 2014). See Figure 1.

Figure 1. Map of the Study Area

The Sunyani municipal waste dumpsite significantly affects the water quality of the Awuo Kojo stream, which is a primary water source for nearby communities and ultimately flows into the Tano River, a major water source for the entire municipality (Miwornunyuie, Kabo-Bah et al., 2016). This contamination has led to increased pollution levels in the Tano River, raising the cost of water treatment for municipal use (Sunyani Municipal Assembly, 2019). Additionally, illegal activities such as logging along river banks and mining have exacerbated environmental degradation, prompting the adoption of the Tano Basin Integrated Water Resource Management Plan to support sustainable management of the basin (Awuku, 2016).

Research Design and Data Collection Methods

The study employs a mixed-methods research design, incorporating a review of secondary data, the use of the Normalized Difference Vegetation Index (NDVI) to assess the status of water bodies and wetlands, and semi-structured interviews with the staff from relevant institutions. Also, a secondary data review was conducted, covering over 35 relevant publications such as journal articles, books, book chapters, government and institutional reports, to complement the empirical data. For the change detection analysis, satellite imageries were acquired from the United States Geological Survey (USGS) portal.

To achieve the second study, which assesses the effectiveness of management systems for protecting water bodies and wetlands, semi-structured interviews were conducted with five (5) key institutional representatives. These included the municipal coordinating director of the Sunyani Municipal Assembly (SMA), responsible for decentralized land use and development planning; the Physical Planning Department, in charge of city zoning and protected areas; the Environmental Protection Agency, focused on environmental conservation; Ghana Water Company Limited, a state-owned company responsible for urban potable water supply; and the Water Resources Commission, which manages Ghana's water resources and coordinates relevant government policies. The interviews were conducted in September, 2023, lasted between 45 and 60 minutes each.

Interview questions centred on two main themes: the condition of urban water bodies and wetlands; and institutional initiatives to manage urban green space (UGS). The NVIVO 10 software was used to code and categorize interview data, with initial codes derived from themes of institutional responses, collaboration and coordination. Additional categories, such as drivers of degradation and encroachment of water bodies and wetlands were deductively produced based on the secondary data analysis. For comprehensive analysis, codes and categories were refined and combined to generate conceptual themes based on recurring associations.

Remote Sensing Data Processing

To address the first objective — identifying and examining the status of existing and endangered water bodies and wetlands in Sunyani and its peripherals — geospatial data were obtained from open-sources online databases. A Sunyani shapefile was created using Google Earth Pro, while shapefiles for Ghana, including its 16 administrative regions and district boundaries, were acquired from DIVA-GIS. Remote sensing images utilized for change detection analysis were sourced from the United States Geological Survey (USGS) i.e. https://earthexplorer.usgs.gov/.

Specifically, Landsat 7 (Enhanced Thematic Mapper – ETM) imagery from 2003 and Landsat 8 (Operational Land Manager – OLI) imagery from 2013 and 2023 were acquired to detect changes in water bodies and wetlands over time. were retrieved, while those for 2013 and 2023 were obtained from. These satellite images were chosen based on accessibility, spatial resolution, and image quality, with priority given to those with minimal cloud cover and scene obstruction.

Each of the satellite images — 2003 Landsat 7 ETM+ and 2013 and 2023 Landsat 8 OLI — were pre-processed against atmospheric, and radiometric distortions to enhance interpretability and image quality. The individual bands were then layer stacked, and the Sunyani shapefile from Google Earth Pro was used to extract the area of interest from the Landsat images. Table 2 provides a summary of the satellite images downloaded from the USGS website. To create Land Use and Land Cover (LULC) maps for Sunyani for 2003, 2013 and 2023, supervised classification was applied using the Maximum Likelihood algorithm in ArcMap. This classification mapped images into four LULC categories: Water, Built-up Areas, Vegetation, and Bare Lands. Supervised classification was used to accurately identify and categorize both visible and subtle features of water bodies and wetlands, facilitating detailed change detection. An accuracy assessment was then done to evaluate the reliability and the quality of the classification results. ArcMap was further used to calculate the area of each classified category, quantifying the level of change in different land covers, with a focal lens on water bodies.

Landsat Image Classification Design

The supervised classification method employed for change detection was based on researchers’ knowledge of the study area and the findings from the field survey to identify areas of land cover change between the imaging dates through ArcGIS 10.5. Band combinations, visual interpretation, GPS data from the field survey helped in determining LULC classes. Table 1 outlines the characteristics of each LULC class, while Land use and land cover (LULC) maps and class statistics for 2003, 2013, and 2023 were generated using ArcMap v.10.5, as shown in Figures 3.

Post-Classification

This stage involves post-classification change detection techniques, an analysis of land cover type areas across the three (3) classified images. Post-classification was used to identify changes in water bodies and other land cover classes. First, raster data were converted into vector layers using the raster-to-polygon tool in ArcMap 10.5, and LULC classes were then categorized accordingly. LULC maps for the years 2003, 2013, and 2023 were generated, enabling a comparative analysis of changes in land cover within the study area. To analyse LULC changes over time classification from two different years were merged, allowing for a clear identification of differences across the timeframe (Figure 4).

Accuracy assessment

The accuracy assessment evaluated the quality and reliability of the LULC classification using remotely sensed datasets. This assessment is essential validating classification approaches and identifying potential errors (Abbas and Jaber, 2020). It is noteworthy that the classification accuracy of a specific set of trained classes may differ from the overall accuracy when classifying all classes in the entire region of interest (Foody, 2021). Classification accuracy is considered poor if reference points used for validation are highly inaccurate.

In all, a total of 200 training sampling points were established for each year, (2003, 2013, and 2023) to validate the classification results, with both accuracy and Kappa statistics calculated for each classified image. Fifty training points were assigned for each of the four (4) classes; Built-up Areas, Vegetation, Water and Bare Land) using the editing toolbar in ArcMap 10.5. A confusion matrix was generated to compare the true classes with the mapped classes for each year, and statistics such as the overall accuracy, producer, and accuracy were computed using equations shown below. Additionally, Kappa statistics values were computed to measure the degree of coherence between the classified data and the reference data. The overall accuracy and Kappa values for the various LULCs are summarized in Table 4.

Accuracy Metrics

  

$$1.\mathrm{\text{Overall Accuracy}}\mathrm{\text{=}}\frac{\mathrm{\text{Total Number of Correctly Classified Pixels}}\left(\mathrm{\text{Diagonal}}\right)}{\mathrm{\text{Total Number of Reference Pixels}}}\mathrm{\text{*}}\mathrm{\text{100\%}}$$

2. $\mathrm{\text{User’s Accuracy}}\mathrm{\text{=}}\frac{\mathrm{\text{Total number of pixels in a category}}}{\mathrm{\text{Total number of pixels of that category derived from the reference data (i.e. row total)}}}$

  

$$3.𝐏\mathrm{\text{roducer Accuracy}}\mathrm{\text{=}}\frac{\mathrm{\text{Total number of pixels in a category}}}{\mathrm{\text{Total number of pixels of that category derived from the reference data (i.e. column total)}}}$$

  

$$\mathrm{\text{4. Kappa Index}}\mathrm{\text{=}}\frac{\mathrm{\text{Sum of correct predictions - Total number of correct classifications attributed to chance}}}{\mathrm{\text{Total Predictions - Total number of correct classifications attributed to chance}}}$$

Table 1. Description of LULC classes

NO	LULC CLASSES	DESCRIPTION
1	Built-up Areas	A densely populated region marked by a continuous growth of residential, commercial, and industrial structures, characterized by settlements, tarred roads, and concretized surfaces.
2	Vegetation	These areas encompass diverse plant life, from dense forest cover to sparsely spread trees, shrubs, herbaceous perennials, grasses, patches of forests, and agricultural land, constituting a significant portion of the study area
3	Water	These areas pertain to various liquid bodies like rivers, lakes, reservoirs, and other water features.
4	Bare Land	These are areas devoid of vegetation, encompassing burnt areas, untarred roads, and similar locations lacking vegetative cover.

Source: Classified Images of the years 2003, 2013, and 2023

Figure 2. Flow chart on methodology for remote sensing analysis

Table 2. Data types and sources

Landsat Sensor	Date of Acquisition	Spatial Resolution (m)	Source
Landsat 7 ETM+	14th March, 2003	30	USGS
Landsat 8 OLI TIRS	20th May, 2013	30	USGS
Landsat 8 OLI TIRS	8th January, 2023	30	USGS

Source: Metadata file of the years 2003, 2013, and 2023.

Results and Discussion

Analysis of LULC variations for 2003, 2013 and 2023

The analysis reveals significant variations in the LULC classes over the 20-year period, with a clear trend of increasing built-up areas at the expense of natural land covers like vegetation and water. Figure 3 displays the classified LULC maps for the years 2003, 2013, and 2023, organized into four categories: Built-up Areas, Vegetation, Water, and Bare Land. In 2003, vegetation was the predominant land cover, occupying 8013.94 ha, representing 56.46% of the total land area. Built-up areas covered 4976.30.67 ha (i.e. 35.06%), indicating some degree of urban development but with limited expansion. Water bodies, encompassing 1128.04 ha (7.95%), represented a significant component of the landscape, while bare land was minimal, covering only 75.33 ha (0.53%). This distribution suggests a relatively balanced landscape with considerable natural cover at the beginning of the study period.

By 2013, there was a notable shift in LULC, particularly in the reduction of vegetation and the expansion of built-up areas. Vegetation decreased to 6352.99 ha (44.77%), reflecting a substantial loss of nearly 1660 ha over ten years, largely due to increased human activity. Conversely, built-up areas expanded to 5820.75 ha, accounting for 41.01% of the area. Notably, there was also a significant increase in bare land, which rose to 1397.51 ha (9.85%), likely driven by land clearing, deforestation, bush burning, mining, and urban development process. Water, bodies experienced a reduction, occupying only 620.51 ha (4.37%), a drop of nearly 508 ha from 2003, suggesting encroachment on water resources as urbanization progressed see (Figure 3).

In 2023, the transformation of the landscape was even more pronounced. Built-up areas surged to 9174.10 ha, representing 64.63% of the total land area, indicating rapid urban expansion within the municipality. Vegetation further declined to 4351.97 ha, constituting 30.66% of the area, a decrease of approximately 2000 ha since 2013. Water bodies continued to shrink, now covering only 338.74 ha (2.39%), marking a further reduction of nearly 282 ha from 2013. Bare land also diminished to 328.93 ha (2.32%), suggesting that previously cleared areas were converted into built-up spaces as urbanization intensified. The data in Table 3 illustrates the exponential increase in built-up areas at the cost of natural land cover like vegetation and water. This pattern aligns with a trend of urban expansion that is common in rapidly developing regions, where natural landscapes are increasingly converted to accommodate urban infrastructure and housing needs.

Additionally, the sharp decline in water bodies, from 7.95% in 2003 to 2.39% in 2023, highlights the vulnerability of these resources to encroachment and urban pressures. Such a reduction not only reflects environmental degradation but also poses challenges for water resource management in the municipality, particularly in terms of sustainability and access to clean water. These findings reveal a pressing ned for sustainable land management practices to balance urban development with the preservation of natural resources. The results suggest that without immediate intervention, the current trajectory of urban expansion may further compromise the availability of green spaces, water resources, and biodiversity within Sunyani and its surroundings.

Figure 3. Classified LULC of Sunyani (2003 – Landsat 7 ETM+, 2013 - Landsat 8 OLI and 2023 - Landsat 8 OLI

Table 3. Land use land cover areas in 2003, 2013, and 2023

Years	2003		2013		2023
Class Name	Area (ha)	Area (%)	Area (ha)	Area (%)	Area (ha)	Area (%)
Built-up Areas	4976.30.67	35.06	5820.75	41.01	9174.10	64.63
Vegetation	8013.94	56.46	6352.99	44.77	4351.97	30.66
Water	1128.04	7.95	620.51	4.37	338.74	2.39
Bare Land	75.33	0.53	1397.51	9.85	328.93	2.32

Accuracy assessment

The overall accuracy rates for the classified images in 2003, 2013, and 2023, were 88%, 86%, and 88.5%, respectively, with corresponding Kappa statistics of 0.84, 0.78, and 0.77 (as presented in Table 4). These values indicate a generally high level of classification accuracy across the years. Among the land use and land cover (LULC) categories, Bare Land consistently demonstrated the lowest accuracy, with a producer’s accuracy of 70% and a user’s accuracy of 72% in 2013. In contrast, Vegetation and Water exhibited the highest classification accuracy across the years, achieving producer’s accuracy rates of 99% and 95%, respectively, in 2003. Notably, the Kappa Statistics for 2003 (0.84) indicates a substantial level of agreement between the classified image and the reference data, exceeding the 0.80 threshold is commonly associated with high accuracy. Comparatively, the Kappa values for the reference years 2013 and 2023 (0.78 and 0.77) reflect a moderate but noteworthy level of agreement.

User accuracy values also reveal differences among the LULC classes. Bare Land had the lowest accuracy across the study years, with 72% in 2013. Built-up Areas, however, consistently exhibited high user accuracy, reaching 95 %. In the 2023 classification, vegetation recorded a lower user accuracy of 75% while Built-up Areas, Water, and Bare Land achieved user accuracy values of 92 %, 93%, and 92%, respectively, reflecting strong classification reliability for these classes.

Table 4. Summary of the Accuracy Assessment

	2003		2013		2023
Land Cover classes	Producers Accuracy (%)	Users Accuracy (%)	Producers Accuracy (%)	Users Accuracy (%)	Producers Accuracy (%)	Users Accuracy (%)
Built-up Areas	79	95	78	89	88	92
Vegetation	96	89	99	88	87	75
Water	93	86	75	86	74	93
Bare Lands	87	76	70	72	92	92
Overall Accuracy	88%		86%		88.5%
Overall Kappa statistics	0.84		0.78		0.77

Source: Classified images of the years 2003, 2013, and 2023

Change Detection Analysis – Post Classification

The post-classification change detection analysis reveals twelve (12) distinct types of land conversions over the 20-year study period. The most significant change observed was the transformation of 4219.81 ha of vegetation into built-up areas between 2003 and 2023, reflecting substantial urban expansion (Table 5).

Table 5. Detection of Land Use Land Cover Modifications with Post-Classification Techniques

LULC Change	Hectares (ha)	LULC Change	Hectares (ha)	LULC Change	Hectares (ha)
2003 - 2013		2013 - 2023		2003 - 2023
BL - BA	31.46	BL - BA	836.29	BL - BA	54.39
BL - V	26.19	BL - V	421.10	BL - V	15.53
BL - W	2.17	BL - W	51.59	BL - W	1.50
BA - BL	333.89	BA - BL	57.99	BA - BL	119.68
BA - V	719.06	BA - V	362.95	BA - V	417.22
BA - W	182.11	BA - W	106.20	BA - W	130.92
V - BL	842.84	V - BL	166.11	V - BL	157.98
V - BA	1745.54	V - BA	2667.74	V - BA	4219.81
V - W	357.92	V - W	149.17	V - W	169.72
W - BL	202.95	W - BL	18.55	W - BL	47.16
W - BA	301.37	W - BA	375.01	W - BA	588.58
W - V	543.80	W - V	194.48	W - V	453.37

Note: Bare Land (BL), Built-up Areas (BA), Water (W), Vegetation (V)

Figure 4. Major LULC conversion from 2003 to 2023.

The finalized LULC maps for 2003, 2013, and 2023 indicate a continuous decline in urban wetlands and water bodies over the study period. The increase in the bare lands and built areas between 2013 and 2023 suggest intensified encroachment and the rezoning of wetlands for residential development. In response to these changes, there are rules and regulations adopted by the municipal government aimed at managing both protected and open-access water bodies and wetlands. According to an official from Ghana Water Company Limited:

These measures aim not only to safeguard a limited number of wetlands and water bodies from further degradation but also to ensure the safety of those who use them for various purposes. (Official, Ghana Water Company Limited)

Further insight from an Environmental Protection Agency representative revealed that municipal bye-laws and policies, such as the 1994 Environmental Protection Agency’s Act and the Water Resources Commission Act, govern urban wetlands and water bodies. Although these Acts and the Land Use and Spatial Planning Act of 2016 include sanctions for encroachment, enforcement has reportedly been inconsistent. This sentiment was echoed by the Director of the Physical Planning Department, who highlighted gaps in enforcement as a contributing factor to ongoing wetland encroachment.

Furthermore, the official from the Water Resources Commission disclosed that they collaborate with the municipal government, EPA and the Lands Commission, as well as communities, to establish buffer zones and protective fences in vulnerable areas. According to the Commission’s representative, these interventions have been effectively in protecting certain water bodies from further degradation, although this could not be independently confirmed through field observations.

The municipal planning officer acknowledged that the rapid urbanization and weak enforcement of zoning regulations have led to extensive encroachments on wetlands for construction. The officer noted that the municipal government faces a myriad of challenges such as inadequate resources, weak law enforcement, limited staff, and a lack of a comprehensive management tools, all of which hinder effective protection of urban wetlands and water bodies. The head of the Physical Planning Department further reported that

While the municipal assembly currently lacks specific initiatives for wetland conservation and management of urban water bodies and wetlands, it implements general management practices, such as educating landowners on the importance of wetlands in urban designs and demolishing structures that do not comply with approved layouts. (Head of the Physical Planning Department)

However, these efforts are hampered by limited labour for monitoring and insufficient funding for routine management activities. Participants consistently linked the degradation of water bodies to urbanization pressures. Despite the existence of national legislation and municipal bye-laws, the observed encroachments indicate that urban water bodies and wetlands are increasingly used for construction and irrigation, thereby accelerating their rate of degradation. In light of these findings, the municipal coordinating director emphasized the importance of addressing the contributors to water body pollution and wetland degradation, such as improper waste disposal and encroachment for residential development. He asserted that tackling these root causes through improved resource allocation, stricter enforcement, and community engagement would significantly enhance the preservation and quality of Sunyani’s water bodies and wetlands.

Discussion

The evidence from the study shows that waterbodies and wetlands within the Sunyani municipality are under severe stress. This is primarily due to the growing demand for land to accommodate residential, commercial and industrial development, in response to the needs of the rapidly growing urban population. Consequently, there has been a significant loss of vegetative land and water resources. Specifically, the municipality has experienced a steep decline in the area covered by waterbodies and wetlands, from a about 8% in 2003 to just about 2% in 2023. These trends are particularly concerning given the relative inadequacy of freshwater resources to meet the increasing demands of the urban population. If current trends continue, the municipality may face a dire shortage of freshwater resources due to the drying up of aquifers. To add on, the lack of wetlands will render the catchment susceptible to deadly floods, as these wetlands act as sponges to readily absorb excess water from rainstorms and flash floods.

The situation in the Sunyani municipality is not different and mirrors the challenges faced by many countries in the Global South. A study by Wantzen, Alves et al (2019) on stream and wetland restoration in the Global South found that the many urban water systems in developing countries have been substituted with concrete and asphalt to accommodate infrastructural development. Similarly, McRae, Deinet et al. (2017) observed that freshwater systems, including rivers, ponds and groundwater resources, have been severely impacted by the developments taking place in the urban sphere. They further reported that the shrinkage of waterbodies has led to an 81% decline in the population of freshwater species, many of which are essential for maintaining important ecosystem services. Walsh, Roy et al. (2005) coined the term “urban stream syndrome” to denote the changing characteristics of urban water bodies, which include “flashier hydrographs, elevated nutrients and contaminants concentrations, altered channel morphology, reduced biotic richness, and an increased dominance of tolerant species.

The study also highlights the weak collaboration between the Sunyani Municipal Assembly and local communities in the management of water resources and wetlands. While this claim contradicts assertions made by the Water Resources Commission, it warrants further investigation, especially considering the Sunyani Municipal Assembly’s (SMA) role in the municipality’s governance. The SMA holds decentralized executive authority and is responsible for ensuring spatial order and functional efficiency through its Physical Planning Department. This corroborates the findings of Wantzen, Alves et al (2019), who noted that in many Global South regions, local communities are often excluded from identifying problems, analysing solutions, or making decisions about water resource management. However, these developments do not foreclose the possibility of restoration and reversal of the current trends. Indigenous knowledge on land use, community experiences, emotional connections to the environment are still etched in the memories of the people and can be leveraged to restore and rejuvenate wetlands and water bodies (Kondolf and Pinto, 2017). To achieve this, the SMA reevaluates its working relationship with communities and other stakeholders must reassess its relationships with communities and other stakeholders, and work collaboratively to implement blue-green infrastructure that will enhance urban resilience (Ghofrani, Sposito et al., 2017).

The results also reveal that while several institutions, including the Sunyani Municipal Assembly, Ghana Water Company Limited, the Water Resources Commission and the Environmental Protection Agency, are involved in the management of water resources and wetlands, there is little to no collaboration among them. For example, while Environmental Protection Agency officers are aware of the existence of bye laws, they do not fully understand or appreciate their content. Additionally, although the Water Resources Commission works with local community members, the SMA has no community-initiated or collaborative projects concerning water resources management. Meanwhile the Ghana Water Company Limited also paints an entirely different picture of the SMA’s role in the water resource management, contrasting with the views held by the Environmental Protection Agency. These differing narratives suggest significant institutional framework and weak collaborations among key stakeholders in the water resource management ecosystem.

This study’s findings are consistent with other studies on weak institutional collaboration in the Global South. For instance, Materu, Urban et al. (2018) found that in Tanzania, legal and policy provisions for managing wetlands and water resources were enacted in an uncoordinated manner. Such that, they were too limited in scope to effectively address the destruction of wetlands. In contrast, Ghana, as a signatory to on the United Nations Convention on Wetlands of International Importance (Ramsar Convention), has a national water policy dating back to 2007. However, the extent to which these national and international conventions, legislation and policies permeate the decentralized governance system remains unclear. This is reflected in the weak institutional and regulatory regime for wetlands and water resources management in the Sunyani Municipality.

Conclusion

This study sought to understand the measures put in place by the Sunyani municipal government and other stakeholders to effectively manage water resources and wetlands. Geospatial tools were coupled with key informant interviews to collect both quantitative and qualitative data. The results show a notable shift from vegetative cover and waterbody land cover categories to built-up areas over the past two decades (2003–2023). In 2003, water constituted 7.95 percent of the total area, but this declined sharply to 4.37 percent in 2013 and further to 2.79 percent in 2023. Similarly, vegetative cover experienced a significant decrease, plummeting from 56.46 percent in 2003 to 44.77 percent in 2013, and further down to 30.66 percent in 2023. Conversely, built-up areas witnessed an upward trajectory, increasing from 35.06 percent in 2003 to 41.01 percent in 2013, eventually soaring to 64.63 percent in 2023.Bare land underwent a rapid escalation from 0.53 percent in 2003 to 9.85 percent in 2013 before subsiding to 2.32 percent in 2023.

Additionally, the study also highlighted the weak collaboration among stakeholders in the water resource management, with little or no involvement from local communities. To enhance the management of water resources and wetlands in Sunyani, international and national conventions that have been adopted, or promulgated by the national government must be integrated into the country’s decentralized governance system. This integration would contribute to enriching local bye-laws and promote more effective policy implementation at the local level. Furthermore, the Sunyani municipal government and its stakeholders should explore ways of harnessing and incorporating indigenous knowledge on land-uses, community experiences, and the emotional connections between people and their environment into water resource and wetland management policies. Such an approach could significantly support the assembly in developing a unique agenda for blue-green urban infrastructure, ultimately increasing the municipality’s resilience.

Author Contributions

Conceptualization, M.A., D.A., P.A., J.A.A.; methodology, P.A., M.A., P.K., S.N.S.K.; software, P.A., P.K., S.N.S.K.; investigation, P.A., M.A., P.K., S.N.S.K.; resources, M.A., D.A., P.A., J.A.A.; data curation, P.A., M.A., P.K., S.N.S.K.; writing original draft preparation, P.A., M.A., P.K., S.N.S.K., D.E.A., F.A.; writing-review and editing, P.A., M.A., P.K., S.N.S.K., D.A., J.A.A.; supervision, M.A., D.A., P.A., J.A.A. All authors have read and agreed to the published version of the manuscript.

Ethics Declaration

The authors declare that they have no conflicts of interest regarding the publication of the paper.

Acknowledgments

The authors acknowledge the contribution of the teaching assistants at the Department of Geography and Sustainability Science of the University of Energy and Natural Resources for actively participating in the Sustainable Development Seminar which shaped the conceptualization and writing of this paper.

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