1. Introduction
Urbanization has enormous impacts on changes in population characteristics and land use and land cover (LULC) class transformation [
1]. More than half of the world’s population lives in urban areas, and it is projected to grow by 2.5 billion between 2018 and 2050, with associated environmental repercussions [
2]. In 2000, 26 percent of the world’s population in low-income countries lived in urban areas, and this is expected to double by 2030 [
3]. Most urban areas have experienced tremendous LULC changes due to rapid urbanization and urban growth caused by, among other things, an excess of births over deaths and internal and external migration [
4]. Against this background, a thorough understanding of human-induced spatiotemporal LULC changes is required to manage environmental changes and improve urban sustainability [
5].
Urbanization also plays an important role in the development of nations, as urban areas serve multiple functions in society and drive economic growth and technological advances [
6]. Urbanization has led to the conversion of LULC features, such as forested areas and bare land, into built-up areas for residential and commercial use, roads, pavements, and other modern infrastructure [
7]. This transformation is often associated with the loss of agricultural land, pollution in the air, land and water, flooding, and the depletion of surface water bodies and groundwater sources [
8]. Urbanization has negative effects on nations, serving as a breeding ground for unemployment, poverty, and inequality, among others [
9]. It has also led to food insecurity [
10], high crime rates, environmental degradation, the construction of unplanned settlements, and the uncontrolled haphazard growth of cities without proper planning, which has become a problem for governments and city dwellers [
11]. However, it is also worth noting that with urbanization comes economic development and growth, especially when coupled with proper planning: a gain that is currently unrecognized in most countries of the Global South due to weak institutions and ill-resourced local planning authorities.
The effective management of urbanization resulting from LULC changes and associated environmental systems requires evidence-based approaches to mitigate and adopt undesirable changes [
5]. The central city in Lesotho, Maseru, is situated just over the Caledon (Mohokare) River from South Africa. The city was first established as a police camp on the river’s eastern edge following the 1869 Treaty of Aliwal North between the British Empire and the Orange Free State Boer Republic. Maseru has since grown as a small town to provide commercial, educational and health functions. After independence in 1966, the city underwent significant changes, including the growth of government buildings. Maseru eventually became home to 60% of Lesotho’s urban population in 1986. The main factor of this rapid urbanization is a combination of natural increase and internal migration. The city recently introduced a 2050 urban plan that was established in 2017 for effective natural resources management through rehabilitating wetlands and rivers and restoring deteriorating landscapes to promote sustainable urban development per the sustainable development goals (SDGs) (
https://www.gov.ls/maseru-2050-urban-plan/ accessed on 14 March 2022). Gathering evidence of urban LULC changes using traditional methods such as field surveys is time-consuming and resource-intensive [
12]. This process requires the application of appropriate methods to analyze the drivers and impacts of urbanization on LULC over time. Remote sensing using both air- and space-borne sensors has provided an inexpensive, timeous, and effective way to analyze the impacts of urbanization on LULC changes over time and predict future urban growth [
12,
13]. This information is lacking in most metropolitan areas, particularly in sub–Saharan Africa. This limits the realization of sustainable cities and communities, preventing most urban areas in sub-Saharan Africa from achieving economic growth in terms of zero hunger, low poverty levels and job security. Therefore, it is crucial to understand the dynamics of these growth(s) in Sub-Saharan Africa and their trajectories to support proper planning and promote regional integration.
Several previous studies have analyzed the impact of urbanization on LULC and predicted urban growth using remote sensing techniques [
14,
15,
16,
17]. Wang and Maduako [
15] analyzed urban growth for 31 years between 1984 and 2015 in Lagos, Nigeria. They predicted urban growth for 2050, finding massive changes in urban LULC and forecasts in built-up areas with a 25.14% (120,790 km
2) increase. Abudu et al. [
14] quantified an urban sprawl between 2001 and 2016 and projected urban growth for 2021 in the Arua Municipality, Uganda. Their results showed an increase in built-up areas from 18.2% in 2001 to 40.9% of the total area.
There is growing interest in using the artificial neural network cellular automata (ANN-CA) to model spatiotemporal transitions and urban growth. This has been found to be effective for analyzing nonlinear complex LULC phenomena and avoiding the automatic acquisition of conversion rules during transitional computations [
18,
19]. Using self-organization, self-learning, association, and memory, ANN is capable of simplifying the acquisition of CA model conversion rules, extracting CA conversion rules from the original training data, and eliminating subjective factors, thus improving simulation accuracy [
18]. Mansour et al. [
16] analyzed spatiotemporal changes in the LULC between 2008 and 2018 and predicted urban expansion in 2038 using the MCA in the city of Nizwa, Oman. Their results showed that the city had changed by 418.5%, and by 2038 there would be an urban growth of 37,465 ha. Furthermore, the study projected a 10% decline in agricultural land and an increase of 6% in built-up areas in 2031. Most of the previous research studies used various spatial models to estimate LULC changes with models such as the Land Transformation Model [
18] and the CLUE (Conversion of Land Use and its Effects) model [
19]. In the present study, we used ANN-CA in the city of Maseru, Lesotho, where no previous studies have been conducted to monitor the impact of urbanization on LULC changes and to predict future urban growth.
Urbanization has a positive as well as negative impact on LULC in countries in sub-Saharan Africa, which includes Lesotho [
20]. Maseru is the capital of Lesotho, where a high rate of urbanization has led to massive urban growth, with the population doubling since the early 2000s [
20], mainly due to high rates of immigration from other rural and urban places in Lesotho [
21,
22]. Maseru is Lesotho’s most developed urban center, with high employment opportunities, public services, and infrastructure facilities [
22]. One of the fundamentals of sustainable urban planning is the availability of LULC data to support the monitoring and forecasting of urban growth, which is crucial for decisions and policymaking [
23]. Monitoring and forecasting city growth requires the use of historical and current remote sensing data for future city management [
24]. A shortage of data often poses challenges in such regions. The lack of research studies in Maseru on the impact of urbanization on LULC changes and future urban growth brings a gap in the city’s literature. In response, our objective in this research was: (i) to characterize LULC classes from 1988 to 2019 with Landsat data, (ii) to quantify LULC changes over the study period, and (iii) to simulate and forecast future urban growth using ANN-CA in the city of Maseru, Lesotho, for 2050.
5. Conclusions
Our results show that urbanization has been the main driver of LULC changes in the city of Maseru, Lesotho, between 1988 and 2019. Our results have validated the value of Landsat data with spatial variables and the MOLUSCE model, which effectively simulated and predicted the LULC changes in Maseru city. Based on these results, we concluded that:
Landsat products provide satisfactory results in classifying and mapping LULC changes from 1988 to 2019. The overall accuracy ranged from 88% to 95%, with kappa values between 0.84 and 0.94.
Remarkable LULC changes occurred in Maseru from 1988 to 2019, with the built-up area increasing from 15.3% to almost half (48%) of the city, much of which consumed pristine classes such as agricultural lands and grasslands.
In 2050, built-up areas are projected to increase further, while the area covered by agricultural land, bare soil, water bodies and woody vegetation is expected to decrease.
Overall, our study provides useful insights for land management authorities in the city of Maseru, Lesotho, so that proactive planning strategies can be formulated to achieve sustainable development. Our results are also valuable for implementing the Maseru 2050 urban plan. Using high-resolution imagery such as unmanned aerial vehicles (UAV)-drone-derived LULC is recommended to provide more detailed spatial data for urban planning. However, the use of UAVs is limited by the large area, big data processing capacity, and legal regulation of UAV operations. Future studies should consider economic factors in the simulation of LULC in 2050 and beyond and also incorporate the latest deep-learning methods.