1. Introduction
From the steam era till the present, the human way of living has changed dramatically [
1]. The evidence of this rapid change is manifested by the growth of urban areas and the population living in cities. According to a United Nations [
2] estimation, 68% of the world’s population is expected to live in urban areas by 2050. However, rapid urbanization has many consequences collectively grouped as social and environmental problems. The social aspect comprises but is not limited to traffic congestion, crowdedness, racial disparities, and crime [
3,
4]. On the other hand, the environmental consequences of urbanization include but are not limited to, air pollution, water pollution, resource depletion, waste, and loss of green area [
5,
6]. In this context, the quality of urban life and sustainable urbanization has drawn the attention of researchers, politicians, and residents at large. Moreover, urban quality of life also outlined in the United Nations Sustainable Development Goal number one (no poverty), two (zero hunger), ten (reduce inequalities), and eleven (sustainable cities and communities). In view of all that has been mentioned so far, the assessment of urban quality of life is indispensable to ensuring sustainable development within urban areas.
By definition, assessing the quality of urban life includes quantification of the relationship between the social and urban characteristics of a place in comparison to the perceived quality of life [
7]. Urban quality of life can be explained using different aspects such as quantitative estimations, qualitative descriptions, perceptions, subjective wellbeing, and landscape features [
8,
9,
10]. Furthermore, urban quality of life is the result of the interaction of various factors such as environmental, social, ecological, cultural, and so on. Due to the multi-dimensional nature of quality-of-life, there is no standard definition for urban quality of life [
11,
12]. Generally, researchers commonly use two approaches to assess the urban quality of life: objective and subjective approach [
11]. In subjective approach people perception, attitude, and happiness are measured [
13]. While in objective approach urban quality of life is assessed using indicators derived from observation, census, and satellite images [
14]. For example, a study done by Tuan Seik [
15] collected questionnaire from over 3000 people to estimate their overall life satisfaction. Some of the aspects of life measured are social life, family life, public services, environment, housing and health care. On the other hand, quality of life can be measured objectively by assessing the environmental and physical factors that could impact the livelihood of urban dwellers. For instance, environmental factors such as temperature, building density, greenness, accessibility to public services, and population density [
16,
17].
Urban quality of life is complex and has many dimensions, a single parameter analysis would not describe the complex nature of urban quality of life. Therefore, it is imperative to integrate more than one aspect of urban quality of life such as environmental, ecological, psychological, political, socioeconomic, and sociodemographic [
18]. Field-based collections of quality-of-life indicators such as building density, green area, air quality, water availability, and surface temperature are very limited in space and has been a challenge. On the other hand, most of the socioeconomic and sociodemographic data are observed and collected directly from the site, which is costly and time consuming. Advances in remote sensing has improved the capability of remotely sensed images to be utilized for objective assessment of urban quality of life. As a result, urban quality of life assessment using remote sensing has received great attention. There are two main reasons for that: (1) remote sensing data with modest spatial resolution are publicly available and (2) the improvement in remote sensing image resolution and computation speed. Moreover, urban quality of life has different dimensions such as socioeconomic, spatial, and physical aspects. Hence, remote sensing provides superiority in providing the majority of the aspects of urban quality of life with spatially dense data [
8]. Datasets derived from remote sensing are mainly grouped into two environmental indicators: biophysical and ecological. The biophysical indicators are, but are not limited to, normalized vegetation index (NDVI), normalized built-up index (NDBI), land surface temperature (LST), and land use land cover (LULC), while the ecological aspect includes aerosol optical depth [
19,
20,
21,
22]. Further processing of the data derived from remote sensing can be used to infer the socioeconomic aspect of urban life such as greening of the area, building density, and green area to building ratio. A study by Nichol and Wong [
22] used remote sensing to produce a detailed map showing the urban quality of life in the Kowloon Peninsula, Hong Kong. Most importantly, the study found that relatively vegetation density explains most of the variability in urban quality of life. Similarly, a study done in Tehran indicated that remote sensing-derived parameters adequately captured the spatiotemporal variability of urban quality of life [
11]. The finding is important because the urban quality of life is complicated and time-dependent that requires a holistic approach. Regardless of the climate and the extent of the study region, Nichol and Wong [
23] showed that remote sensing-derived indicators are a reliable means of measuring the urban quality of life in tropical regions. Moreover, previous studies have shown that land use land cover can significantly affect ecosystem services and household wealth [
24,
25]. Collectively, these studies outline a critical role of remote sensing-derived indicators in assessing quality of life in urban setting.
One major challenge is to integrate the indicators and generate urban quality of life index that is easily interpretable and signify their relative importance. Recent evidence suggests that there are two main approaches to integrate the indicators: weighted overlay and principal component analysis (PCA). In weighted overlay, each parameter is given arbitrary weight then the indicators are overlaid to produce quality of life index [
26]. The main drawback of weighted overlay is the bias associated with giving weights for each indicator. On the other hand, PCA provides robust weighting criteria based on the percentage of variation explained by each component [
7,
8,
27].
In this research, an objective assessment of urban quality of life from indicators derived from remote sensing was performed for Al Ain city. The objective approach is chosen because the indicators are easily accessible and spatially dense. Moreover, the quality-of-life indicators derived from remote sensing proved to provide satisfactory estimation of urban quality-of-life [
7,
28]. Although urban quality of life has been studied in different climatic zones, little attention has been paid to urban settings in arid environments. Moreover, with the advancement of cloud computation and different machine learning algorithms, satellite-derived urban quality of life indicators can be obtained with great accuracy. Urban life indicators such as build-up density and green area estimation depend on the classification accuracy of LULC images. Therefore, it is important to utilize models with the capability of classifying LULC with higher accuracy. Machine Learning (ML) is one of the models with the capability to take large data sets, structure them for relevance, mine them for insights, and ultimately create predictive/clustering capabilities based on models and algorithms [
29]. ML has wide applications such as in text recognition, speech recognition, and image recognition [
30,
31]. ML was utilized in this study to classify the satellite images and produce land use land cover (LULC) Map. The LULC map was one of the components that were employed to determine urban quality of life (
Section 3.3) and derive two indices (green to build-up ratio and building to bare soil ratio). In addition, the LULC data were correlated with urban quality of life indicators. Therefore, the LULC map is considered a crucial component in determining urban quality of life and the use of machine learning algorithm enhanced the classification accuracy.
There are two types of satellite image classification approaches: (a) traditional pixel-based image analysis and (b) object-based image analysis (OBIA). The OBIA classification has superiority on the counterpart pixel-based image analysis on the following aspects: it operates on similar pixels produced by image segmentation and more layers can be added to be used in classification; the object is composed of a group of pixels that has similar texture and shape; hence, object statistics can be computed and used to differentiate land cover classes with similar spectral signature [
32]. In the same vein, Meneguzzo et al.’s [
33] comparative study between OBIA and pixel-based image classification suggested that OBIA-based classification has better accuracy result [
32,
34,
35]. For example, Nasir et al. [
36] used object-based image analysis for LULC classification and change detection. The research found that OBIA has greater potential on classifying images derived from different sensors and resolution. Therefore, to implement LULC classification, OBIA-based supervised classification was chosen.
Furthermore, there are many ML algorithms (classifiers) such as minimum distance, random forest (RF), and support vector machines (SVM). The application of any algorithm depends on many factors such as data under study, spatial, and temporal characteristics. Hence, it is necessary to choose a classifier that suits the purpose of the analysis. Random forest (RF) classifier was used because previous studies showed its effectiveness in comparison to minimum distance and support vector machines (SVM). For example, Tokar et al. [
37] used RF classification to classify Landsat 8 imagery and obtained classification accuracy of 80% within a reasonable speed. Moreover, utilization of RF and SVM for multiple identical datasets indicated that overall, RF classification has higher accuracy than the SVM algorithm [
38].
The specific objective of this study was to investigate urban quality of life (UQoL) for Al Ain city from parameters derived from remotely sensed images and geographical information system (GIS). This study used the inductive approach where general quality of life in the city is determined based on specific indicators such as land use, infrastructure facility, land surface temperature, aerosol index, NDVI, and so on [
11]. The authors familiarized themselves with the data, coded it, generated themes using classification, assigned names to the classes, and interpreted the classes (thematic analysis). To enhance the quality of indicators derived from remote sensing, object-based image classification using random forest classifier (RF) was implemented. The RF algorithm is an ensemble learning approach, where the classifier is made of a group of decision trees and each tree is produced from the training set. The RF algorithm is non-parametric and requires two parameters to set up the model [
39]. Moreover, RF decision-making is simple to understand and the algorithm is robust for noise and outliers [
40]. Furthermore, the present study integrates data derived from different satellites that include biophysical, ecological, and infrastructure facility variables to build a quality-of-life model. The results are expected to support urban sustainability efforts and provide more informed decisions for policymakers.
4. Discussion
The UQoL study carried out in Al Ain city shows association between urban structure, environmental factors, and quality-of-life dimensions. Principal component analysis of all the variables used in the study indicates four components that explain 75.3% of the variability in UQoL. PC1 (biophysical indicators) has strong negative loading with SAVI (−0.963), NDWI (−0.764), and NDVI (−0.969). These variables indicate the urban greening and water availability. Greening and water availability provide various ecosystem service that indirectly improve human well-being. A subjective assessment of quality-of-life from a coastal desert megacity in Saudi Arabia found that urban green spaces have a positive impact on the well-being of urban dwellers [
60]. The benefits are mental refreshment, physical activity, interaction with their children and relatives, and experience beauty of the nature.
In contrast, PC1 has strong positive loading with two factors: NDBI (0.699) and ENDISI (0.828). This factor highlights adverse effect of land use; hence, the higher the score of this component, the lowest the urban quality of life [
59]. On the other hand, PC2 (infrastructure facility indicator) has strong positive loading with urban structure (distance to schools, roads, parks, and hospitals) indicating the importance of infrastructure on UQoL [
20]. PC3 (ecological indicator) has strong positive loading with AI and MNDWI, while negative loading with LST. This component represents the effect of ecological indicators, and it has paramount relevance in the context of urban areas in desertic environment. Quality of life in desert environment is affected by the occurrence of dust storms, heat islands, and water shortage, which are sometimes referred as environmental factors [
61]. PC4 has strong positive loading with slope indicating the accessibility of the area to different public facilities. However, variability of slope within the study area is negligible. The exception is in the southern part of city (Highland area) where population density is very low (
Figure 3).
Spatial variations of UQoL in Al Ain city is related to greening and human settlement. The reason is that the outskirts are covered by sand dunes, which have no human settlement or vegetation. Thus, the UQoL score for the areas with no dwellers is low (very poor to poor). In contrast, the highest UQoL scored areas are located in the central and northern parts of the city. Since the city is in the desert, the higher UQoL score among the inhabited areas indicates urban greening and/or socioeconomic factors. People with higher income tend to live in big houses with compound and garden, which positively affect the overall quality-of-life. A study done by Ma et al. [
62] showed that quality of life has positive relationship with peoples level of income in both rural and urban setting. This result may be explained by the fact that green areas provide cooling effect, reduce exposure to air pollutants, and mental wellbeing [
63]. The spatial variability of UQoL (poor to moderate) within the central part might be due disparity in income level which could affect the preference of large house with garden. These results further support the idea of urban quality of life has strong association with income levels of individuals [
64]. In line with the present finding, a study conducted by Giannico et al. [
65] found that the presence of green space positively predicts the overall perception of quality of life. On the contrary, there are few residential areas scored very low (very poor) located outside the central part of the city. The low score is due to inaccessibility to facilities and public services. In accordance with the present results, previous studies have demonstrated that improved public transport, easy access to facilities and services greatly improve urban quality-of-life [
10].
Furthermore, the spatial pattern of high NDVI value and low LST value in Al Ain correlated well, which are important aspect of comfort in urban areas (
Figure A1). The relationship indicates the cooling effect of vegetation, which attributes positively to urban quality life in desert environment. These results are in accordance with recent studies indicating that urban land cover controls spatial LST variation, which affects human quality of life [
66]. A study done by Musse et al. [
59] suggests, the income level of household greatly attributes to the spatial variability of urban quality-of-life. Furthermore, oasis expansion and desert sand stabilization greatly contribute to the greening of the desert [
28]. All these factors add on to the improvement of quality of life in the desert environment. For this reason, the above-mentioned factors might attribute to the variability of UQoL within the inhabited area. From a visual inspection of the UQoL, vegetation cover variability can be noticed. Comparison of the findings with those of other studies confirms the importance of greening for improving UQoL [
10,
63,
65]. The finding suggests that city planners need to focus on urban greening and urban infrastructure to improve the overall livelihood of the city residents.
5. Conclusions
Using traditional methods to assess quality of life in a city is time-consuming, costly, and inefficient. The improvement in remote sensing such as spatial resolution and development of new algorithms such as machine learning, and cloud computation has revolutionized extraction of quality-of-life indicators. In the present study, urban quality of life for Al Ain city was assessed using parameters derived from remote sensing and GIS vector data. The datasets derived from remote sensing are as follows: Sentinel 2A (NDVI, SAVI, NDWI, NDBI, ENDISI, MNDWI, and LULC), Sentinel 5P (AI), Landsat 8 (LST), and SRTM (slope). The GIS vector file contains infrastructure facility data, namely: distance to schools, roads, parks, and distance to hospitals. All parameters were normalized using Z-score before further analysis. Then the datasets from remote sensing and GIS vector data were integrated using PCA analysis and developed into a model to estimate the UQoL index. Based on PCA analysis, four components that have eigenvalue greater than 1 have accounted for 75% of the variability of the input datasets. In summary, the result of the present analysis showed that the UQoL of Al Ain city varies from moderate to very good in the central region. On the contrary, the outskirts of the city have the lowest index (poor to very poor). There are many parameters that could affect the variability within the city. However, the greenness factor has the highest influence, and densely vegetated areas have a better UQoL index. The present finding can be used by urban planners, decision-makers, and charitable organizations to ensure the quality of life and sustainable development of urban areas. Further work is inevitable to establish and standardize the main factors that control urban-quality-of-life.