1. Introduction
Sustainable development is dependent on land use/land cover (LULC) change, which is one of the major factors causing environmental change on a worldwide scale. These alterations in the landscape must be examined from varied aspects in order to analyze their causes, progression, and effects [
1]. Urban growth, which is a sign of economic development, has contributed to such landscape related changes for a longer period. The geographical pattern of land development that occurred in order to meet the demands of the anthropogenic factors is considered urban dynamics, and it has a major impact on the land use pattern [
2]. Statistically from 1901 to 2011, India’s urban population in comparison with the total population has expanded dramatically from 10.8% to 31.16%. The main elements in the current situation that could assist in managing the natural resources and environmental changes is the consistent monitoring of land use and land cover change [
3]. Analyzing the patterns of urban heat island (UHI) formation [
4,
5], population sprawl patterns [
6,
7], and environmental variations are the outcomes of an effective change detection study. Geographic information systems (GIS) and remote sensing (RS) consist of large database of high-resolution imageries and innovative tools for sophisticated ecosystem management. The gathering of remotely sensed data makes it easier to conduct regional and global synoptic assessments of earth features. Such data paves way for intensive and localized research for the conservation of ecology and environment. Geographic Information Systems (GIS) is used to the conversion of remote sensing data at all forms into tangible and spatial information. The combination of these technologies allows urban planners to better comprehend how the landscape is changing and to provide associated environmental monitoring systems in view of upcoming developments [
8,
9]. With the availability of huge repositories of image datasets and data extraction algorithms, efficient multiscale analysis [
10] can be done through the creation of land -use/land cover maps.
A time-series of five SPOT HRV pictures for a region of the State of Rondonia were used in a study on the analysis of land cover changes [
11].In this study, image classification approaches and Geographic Information System (GIS) tools were used to calculate the total deforested area, and the percentage of land left for secondary vegetation were calculated. The development of novel spatial information extraction techniques and specialized geographical analysis tools for supporting the formulation and implementation of urban planning were made possible by the integrated use of remote sensing, GIS, and urban planning models [
12]. Landsat (satellite) images of sensor Multispectral Scanner (MSS) and Enhanced Thematic Mapper (ETM) were employed to assess the areal changes that incurred in the agricultural lands, urban encroachment, and water areas [
13]. Information systems shall be used for the interpretation and evaluation of physical data and other socio-economic data and thereby providing an important source of information for guidance in the overall planning process [
14]. Three digital images were used to analyze the main trajectories of land use change in Bhubaneshwar city and employed entropy approach to find the degree of randomness in the sprawl pattern [
15]. In addition to the utilization of standard supervised classification frameworks like Maximum Likelihood Classifier [
16], several researches related to LULC analysis employed Artificial Neural Network (ANN), Machine Learning algorithms, Cellular Automata, and Markov chain analysis [
17,
18,
19]. The urban agriculture of Chicago is classified into public and private places with the help of Google Earth imagery [
20]. Using Elshayal Smart open source software, approximately 340 distinct tiles from Google Earth pictures covering the Vellore region of southern India were retrieved for urban mapping [
21]. In a case study on land use characterization of Wuhan, China, the authors stated that Google Earth imagery has certain benefits for mapping in terms of good geometric, morphological, and contextual spatial properties [
22]. In recent times, Google Earth Engine (GEE) [
23] is employed to generate higher level LULC maps. Simulation or prediction of LULC dynamics is necessary for visualizing the interaction of natural habitat and future developmental scenarios. MOLUSCE plugin Quantum GIS (QGIS) has been used in recent studies to validate predicted LULC maps [
24,
25].
Simulating dynamically changing environment is highly imperative for analyzing the growth aspects of a city [
26,
27,
28]. The challenges that occur due to a rapidly changing city’s landscape need to be analyzed for improved planning and management [
29]. Several research [
2,
30,
31] has highlighted the urban dynamics and related growth model analysis for Chennai region, one of the four major metropolitan cities in India, and is located in the southeastern part of the country. The average rate of population growth of the city is 25% per decade and this is recurrently reducing the green-covered area in the city. During the post-economic liberalization, it is seen that this urban region is trying to balance its environmental quotient with respect to its infrastructural development. The urban expansion in Chennai city varies in a periodic manner. The urban area of Chennai has increased from 1.46 % to 18.55% in two decades (1991–2012), in which 22% of vegetation land has reduced and around 36% of the total area was converted to urban area in 2016. According to another research, the amount of land covered by wetland classes between 2005 and 2016 reduced by 10.05 km
2 (0.98%). Simultaneously, from 2005 to 2016, the built-up area significantly rose by 363.99 km
2 (10.13%) [
32]. In addition, the urban heat island scenario was also studied by characterizing the land surface temperature of Chennai using Support Vector Machine and Maximum Likelihood Classifiers [
33]. Though urban expansion relates to economic development, it also influences the standards of living, land rate and nature of urban planning [
2,
30]. The outer ring road (ORR) is a major transport corridor along Chennai Metropolitan Area which was initiated by 2010. At present, the nature of urban dynamics around the corridor needs to be studied to evaluate the related land use change in the region. Four study sites were selected along the ORR region based on their prominence in terms of urbanization and population dynamics for the study. These sites, which are considered to be hotspots along the initial phase of the ORR, need to be studied for the land use/land cover dynamics.
This study was aimed at characterizing the spatiotemporal changes that incurred in the land use/land cover aspects and relate them with the environmental, socio-economic parameters related to the study area. Instead of utilizing the temporal satellite datasets, the study relies on the history imagery available in the Google Earth database, which is of higher resolution thereby facilitating improve data extraction. In addition, the prediction of future LULC dynamics has been done using the MOLUSCE plugin available with the open-source GIS software (QGIS).
3. Results and Discussion
Spatiotemporal analysis is the study of changes in land cover of a particular area between two or more years in terms of area quantity. The change of land cover from one class to another between different years indicates the growth of the area. The common increase and decrease in every segment are observed in urban and agriculture land, respectively. Environmental analysis of a land area is the change in the green belt of the area. It indicates the rate of decrease or increase of green cover on that area and the reason for the change can be identified in this analysis. The agricultural land and the other vegetation land are considered the green cover of the segment. The other vegetation lands will change to barren and vice versa from season to season. Economic growth analysis is the study of forces that determine the distribution of scarce resources. It provides insight into how markets operate and offers methods for attempting to predict future market behavior in response to events, trends, and cycles.
3.1. Chikkarayapuram Segment
The agriculture land area in this segment during 2009, 2012, and 2016 are 1,260,000 sq. m, 801,360 sq. m, and 655,740 sq. m, respectively (
Table 2). The change difference during 2009–2012 was more when compared to 2012–2016. During the period (2009–2012), the reduction in agriculture land is because of the construction process carried out in the outer ring road. From
Figure 7a, it was observed that the land areas nearby the road alignment were fully utilized for the construction work. The other agriculture lands, which were present on the sides of outer ring road, were converted to other vegetation and barren lands. During the period (2012–2016), the reduction in agricultural land is minimum because the outer ring road was constructed completely before 2016. The agricultural lands were mostly converted to empty barren lands and layouts that were taken up for residential purpose. A part of agricultural land was also converted into other vegetation on the left side of the segment (
Figure 7b). The other vegetation land decreased to some extent since this classes increases and decreases from season to season, as there is a random growth in this segment. In the economic aspect, the guideline value for this segment from the year 2007–2012 is Rs.200 per sq. ft and from 2012, the revised guideline value increased between Rs.1300–1600 per sq. ft. These guideline values are obtained from the State Land Registration Portal for the respective years.
3.2. Nazarathpettai Segment
The area of agriculture and other vegetation class can be referred from
Table 3. In this segment, the overall amount of agricultural land is very low with respect to the total area. The reason for this is that Nazarathpettai is the main point to enter Chennai from the north side, hence urban development is already high in that region. During the period 2009 to 2012, the agriculture land was cleared for the construction of the outer ring road to some extent, and a part was converted into other vegetation. From 2012 to 2016, the land was converted into layouts on the left side along the outer ring road which resulted in the reduction during this period. From 2009 to 2012 (
Figure 8a), there is a smaller amount of reduction in the land area which is due to the area occupied by the outer ring road. During the period of 2012 to 2016 (
Figure 8b), there is great change in other vegetation, particularly on the right side of the segment near Shrirdi Sai Nagar where most of the land was converted into urban class. Similarly, in Annai Indra streets and Annai Theresa streets, there is a transformation to urban land from the other vegetation. On the left side of the segment, most of the vegetation land was converted as barren land for layout, partly due to seasonal effects. The guideline value for Nazarathpettai segment from the year 2007–2012 is Rs.300 per sq. ft, and from 2012, the revised guideline value is between Rs.1200 per sq. ft.
3.3. Meppur Segment
In this segment, the green cover is high with respect to the total area when compared with other segments. From the period of 2009 to 2012 (
Figure 9a), the decline in agricultural land is much less when compared to other segments. There is an increase in other vegetation land because most of the barren lands near Bakthavathsalam Nagar and Mahalakshmi Nagar in 2009 were converted into vegetation parcels. During the period of 2012 to 2016 (
Figure 9b), there is large amount of reduction in agriculture land cover. On the left side of the segment, the agricultural parcel was converted to urban regions and layouts. Further, there was a much smaller amount of change in other vegetation land. The guideline value for Meppur segment from the year 2007–2012 is Rs.280 per sq. ft, and from 2012, the revised guideline value is between Rs.600–700 per sq. ft.
3.4. Perungalathur Segment
From the period of 2009 to 2012 (
Figure 10a), there is random growth in agriculture land, as a part of other vegetation land was cleared out to pave way for agricultural activity. In this period from 2012 to 2016 (
Figure 10b), the agricultural land was decreased which is lesser than 2009 due to urban sectors built near Shanthi Nagar and Parvathi Nagar. In 2009, the other vegetation class on the outer ring road alignment and the land which occupied this region were cleared for the construction. Further, there was a small decrease in other vegetation class due to negligible level of urbanization in this segment that occurred from 2012 to 2016.The guideline value for Perungalathur segment from the year 2007–2012 is Rs.300 per sq. ft, and from 2012, the revised guideline value is between Rs.1500–2400 per sq. ft.
3.5. Analysis of Predicted Output
Each segment of the outer ring road is predicted for future land cover change for the year 2022. The urban growth is estimated by adding the industrial class and residential class. The model gives the urban growth of the segments, and the land cover change occurred due to the urban growth. By comparing the predicted changes with the past condition, the environmental impacts and its related land issues can be analyzed. Further, a validation of the predicted outputs was carried out using ground control points on the Google Earth interface. A stratified random sampling approach was taken up to validate 100 control points in each segment for assessing the accuracy of the predicted output. From this validation, an urban growth of around 53.95% was estimated with respect to the total land area. An error matrix was derived to compute the omission and commission error thereby resulting in the overall accuracy of the digitized map. A prediction accuracy of 84.56%, 83.59%, 85.00%, and 86.12% was estimated through the MOLUSCE plugin for the segments, namely Chikkarayapuram, Nazarethpettai, Meppur, and Perungalathur, respectively.
3.5.1. Chikkarayapuram Segment
Based on the prediction map, the segment (
Figure 11a) of each class is calculated and shown in
Table 3. The total land area of Chikkarayapuram segment is 3,627,810 sq. m in which the urban contribution in the year 2009 was 5.29%, and in 2016 it was 19.55%. In the year 2022, it is expected to be nearly 32.88%. Due to this increase in the urban land, the vegetation land is reduced up to 11.69% and water body is reduced up to 1.955% from its land cover (2016). The impact due to urban growth is minimum in the water body, but there is a moderate decrease in vegetation land, which is a normal impact based on the development of Chennai city. The outer ring road plays a minor role in the urban development of the Chikkarayapuram segment. The barren land and swamp area has undergone changes, but does not have a great impact in environmental problems. These observations are confirmed by a detailed variation in area as shown in
Figure 12.
3.5.2. Nazarathpettai Segment
Based on the prediction map (
Figure 11b) the area of each class is calculated and shown in
Table 3. The total land area of Nazarathpettai segment is 2,796,220 sq. m in which the urban contribution in 2009 was 19.63%, and in 2016 it was 32.52%. In the year 2022, it is expected to be nearly 45.86%. The urban growth was already high in 2009 and 2016 because Nazarathpettai is a main junction through which the north side route vehicles can enter the city. Due to the increase of urban land, the vegetation and water body gets reduced up to 7.03% and 1.04%, respectively, from its present land cover (2016). The increase in urban growth between (2016–2022) is more when compared with previous urban growth (
Figure 13).
3.5.3. Meppur Segment
The total land area of Meppur segment (
Table 4) is 2,950,272 sq. m in which the urban contribution in 2009 and 2016 was 6.764% and 23.35%, respectively. In 2022 (
Figure 11c), it is expected to be 32.20%. In this segment, barren land is reduced very high up to 13.311% based on the land cover (2016). The vegetation land and water body has lesser amount of change, thereforethere will be no environmental impact due to these land covers. The urban growth in this segment is an expected development based on normal land development criteria. These observations are confirmed by detailed variations in area-wise parameters as shown in
Figure 14.
3.5.4. Perungalathur Segment
Based on the prediction map (
Figure 11d), the area of each class is calculated and shown in
Table 5. The total land area in Perungalathur segment is 2,756,916 sq. m in which the urban contribution in 2009 and 2016 was 11.694% and 39.77%, respectively. In 2022, it is expected to be around 50.55% (as shown in
Figure 15). In this segment, the urban growth is already high in 2009 and 2016, because Perungalathur is a main junction through which the entire south side route vehicles can enter the city. Due to increased urbanization, the impact on natural resources needs to be managed in a prior manner.