Some of the most dynamic places on planet Earth are urbanized locations developing across multiple dimensions. Globally, the expansion of urban forms is documented to be a principal front in habitat destruction which begins with habitat loss and later results in species extinction [1
]. Notwithstanding their importance, their growth is associated with huge impacts on contiguous ecosystems [2
]. For instance, the loss of arable agricultural land to urbanization, especially in developing nations, is flagged to be a result of prevalent anthropogenic activities [3
]. Similarly, the unprecedented transformation of natural landscapes into urban settings significantly affects the natural functioning of ecosystems [5
]. Hence, urbanization has been the foremost human led land-use anthropogenic activity with huge and irreversible impacts. It is a major force that drives changes such as land-use land-cover change (LULCC), biodiversity loss, the biogeochemical cycle, hydrological systems and climate [6
]. Another prominent agent that can be linked to the unprecedented growth witnessed in urban expansion is population increase [7
]. One hundred years ago, of every 10 persons, two resided in urban areas. By 2030, the number of people living in urban areas is likely to hit six, and by 2050 seven out of every 10 [8
]. Since the 1950s, the number of global urban inhabitants has increased, and by 2050 a two fold increase is anticipated from an approximate value of 3.4 thousands of million as of 2009 to 6.4 thousands of million by 2050 [8
]. The year 2020 is projected to be when the majority of mega cities in the world will be in developing countries due to differential population growth and anthropogenic activities such as change in LULC [9
]. Therefore, governments in West African countries and Nigeria in particular must act fast to better understand spatial and urban growth patterns for improved municipal planning.
The pathway to understanding the process of urbanization can be established by deliberate monitoring of biophysical and socioeconomic conditions of the existing and transformed urban areas [11
]. However, reliable information on the biophysical dimensions of urban landscapes, especially the urban LULC of the built environment, can be difficult to obtain. Hence, remotely sensed data and its applications can provide critical information about urbanization in order to advance urban science for improved policy and decision making. However, having access to requisite biophysical, socio-economic characteristics of the urban areas is difficult because gaps exist between these data streams. Moreover, linkages between data collection time lags, administrative and landscape units and spatio-temporal scales pose great challenges.
A paucity of reliable information has coincided with a period when substantial growth in urban areas has been witnessed worldwide, and hopes are high among policy makers and research groups that this information gap can be filled. These groups hope that fine-scale information will be increasingly made available to comprehend the impacts of urbanization on local environments and human security such as temperature variability linked to urban heat and cold islands and local environmental and climate change/variability. This has paved the way lately for more attention being directed towards urbanization science with new directions in data collection and analysis [12
], as well as monitoring changes in urban LULC considering its strong influence on ecosystems [13
]. Remarkably, remote sensing scientists are responding to the call for linked environmental and socio-economic information through the advancement of remotely sensed data application and methods [14
The support vector machine (SVM) method is one of the latest additions to the existing catalogue of superior and robust image classification techniques for handling multispectral satellite images that support LULC analysis, considering their non-linearity and multidimensionality [18
]. The SVM-based approach is a non-parametric machine learning algorithm that uses hyperplanes to separate features of different categories with a maximum distance margin located close to it [21
]. The best generalization is achieved when the margin distance is farthest from vectors from both classes despite minimal training samples which previously limited numerous remote sensing applications [19
One of the standard ways of simplifying reality is the use of models and, in spatial analysis, LULCC mapping, monitoring and modelling can be regarded as models used for decision support to assess the root causes and implications. Modelling LULCC leads to improved understanding of human–environmental systems’ interaction toward sustainable and spatially framed land-use policy development and planning [22
]. Hence, such spatial models are suitable for assessing arrays of complex biophysical and socioeconomic drivers of spatiotemporal patterns of LULCC and measuring the change consequences [24
]. Lately, different models such as the cellular automata-based, agent-based, machine learning, and spatially explicit approaches have been applied to study changes in LULC [25
]. Similarly, the land change modeler (LCM) has been used to model urban sprawl and growth [28
]. Hua et al
] ran the SELUTH model which derives its name from input data requirements to run the model slope, land use, exclusion, urban extent, transportation and hill shade over Jimei in Fujian Province, China. Other approaches such as artificial neural networks have also been applied for urban modeling studies [31
]. The LCM approach is embedded in the IDRISI package which is an integrated environment suitable for analysis, prediction and validation of LULCC [34
]. LCM uses categorical gridded images with the same land-cover types sequentially similar in order of arrangement as input for modelling LULCC [35
]. Land-cover changes are evaluated across multiple time lines, and change results are calculated and presented in the form of graphs and maps. The subsequent step is predicting the future by generating LULC maps based on the transition potential maps [35
], trusting the Multi-Layer Perception (MLP) neural networks output [36
]. For short time frames, the LCM performed better with good prediction accuracies particularly with stable land-covers types against rapid conversion [35
]. Also, comparing LCM outputs to other LULCC models that predict change based on supervised classifiers such as the weights of evidence (WoE) approach that uses user defined weighting, more accurate change maps are generated. This is because the final change map uses the overall change potential maps which are based on neural network outputs that are capable of expressing changes in various land-cover types much better than single probabilities derived from the WoE approach [36
The federal capital territory (FCT) of Nigeria was established in 1976 but physical development only began in 1980 [37
], and it has been characterized as one of the fastest growing cities in West Africa [38
]. The territory has experienced rapid LULC changes, urban spatial expansion and transportation infrastructure expansion over the last 30 years and is the major focus of urban spatial analysis in this study. Over time, urban growth significantly changed in Abuja which gave way to complex urban dynamics such as conversion of agricultural land to settlement, road and infrastructure [37
], population growth with an estimated annual growth rate of 9.4% [39
]. Table 1
presents an overview of how significant population growth in the federal capital city (FCC) differs from other parts of the FCT, which indicates that rapid population growth is a major driver to consider in this study. For instance, the decrease in agricultural activities and concomitant loss of cultivated land is likely to contribute to landlessness and food shortages and put the livelihood of inhabitants in jeopardy. To date, obtaining information on the environmental and socio-economic sustainability of Abuja, which is essential for development planning, has received relatively little attention. So far, no available systematic study of the spatiotemporal dynamics of urban growth changes in Abuja in the context of climate impacts has been conducted. In terms of short, medium and long-term development, this current study is appropriate to bridge the knowledge lacuna between these urban events. One of the few studies that has previously been conducted was detecting Land-use Land-cover change (LULCC) in Abuja and that was based on the maximum likelihood classifier [40
This study aims to integrate remotely sensed and ancillary data such as a master plan, digital elevation model and the population to detect LULCC from 1984 to 2014, and spatiotemporally analyze the settlement expansion pattern and model changes to project the LULC in 2050 using LCM in the context of climate impact. Major themes considered to achieve the goal of this study included biophysical information extracted from remotely sensed data as a baseline for subsequent applications such as analyzing settlement expansion which is the focus of this work. Other aspects investigated are the development of a land use change index and modelling urban growth (from the past into the future using LCM which can be contextualized for potential climate change impacts). The subsequent subsection presents and describes the study area considering the anthropogenic land-use situation and potential climate change impacts.
The FCC of Nigeria, Abuja, was used as the test site for this study using integrated remote sensing datasets and GIS modelling approaches. It was established that significant LULC change and settlement expansion has occurred. Due to the study timeframe and there being limited cloud free datasets available from a single Landsat sensor, the multi-sensor and multi-temporal images of Landsat (TM, ETM+ and OLI for 1986, 2001 and 2014) were used for this study. The utility of these datasets was realized through rigorous image pre-processing to ensure that real change over time was measured and the results obtained were reliable and fit for further use (e.g., informed policy and decision-making). Also, the application of a robust information extraction algorithm such as SVM required limited training samples and yielded good image classification. In future, the SVM and other advanced methods such as object oriented image analysis and random forest can either be integrated or compared to assess their performance in problematic or heterogeneous areas.
The core objective was to spatio-temporally analyze LULC for 1986, 2001 and 2014. Based on the pixel count error matrix, overall accuracies of the three LULC maps ranged from 82% to 94%. Contrary to these statistics, the adjusted area error gave a somewhat different accuracy measure that ranged from 69% to 91%. The study found that computing area information directly from pixel count for accuracy assessment can be misleading. Hence, in producing local maps that can be useful for evaluating the accuracy of global maps, climate modelling and other relevant applications, assessing error propagation in regional maps by applying the error adjusted area estimator yields a more reliable and informative result including confidence intervals and uncertainty measures. The different components of LULC change analysis allowed better understanding of the transformation processes, especially for the transition from the bare/arable land and vegetation categories to the built-up area class. The computed indices provided empirical insight into a realistic spatio-temporal situation, and detailed annual land use and urban spatial expansion change rates in the study timeframe with major impacts on the landscape and potential influence on the local climate of Abuja. The forces governing such a momentous expansion might be numerous, however, apparent drivers in the case of Abuja included suitable topography, availability of public infrastructure, such as social amenities, and population growth, particularly in the past two decades.
Two salient aspects ascertained by this research are that massive impervious surface development is occurring due to urbanization, which may lead to elevated urban temperatures known as the UHI phenomenon. This phenomenon, is one of the likely climate control factors at a local scale highlighted by the Fifth Assessment Report of the IPCC [77
], and COP 21 [78
]. Complementary to the UHI is the change in drainage geography that may also increase surface runoff which translates to flash flood events in cities. With rapid urban expansion in Abuja, when rainfall increases, surface runoff is expected to increase and can trigger flash flood events in areas deficient of adequate drainage, especially looking at the 2050 built-up area projection in the context of climate change. From this study, further research is needed to empirically verify present and future impacts of urbanization on Abuja city.