A systematic review of urban sprawl and land use/land cover change studies in India

ABSTRACT

The extensive modification in different land operations fueled by rapid urbanization is a matter of great concern. It is not just a simple process of exchange within different classes as we think; moreover, it is one of the most responsible factors for the change in biological rotations of the natural system. India, one of the fastest-urbanizing nations, is expected to be the home of the most urban residents by 2050, likely resulting in various undesirable issues in emerging and existing cities. The review highlights the apprehensive sides of sprawl and land use/land cover (LULC) changes in the Indian context. It also aims to assist the researchers in exploring the scope and relevance of sprawl measurement techniques for emerging urban settlements through the collected literature. A multi-stage sampling method is used to scrutinize the secondary source-based information regarding the research work. In total, 58 studies of recognized journals have been appraised systematically from 1981 to 2022. The findings reveal that increasing accidental growth caused by rapid urbanization is the leading cause of the change in LULC and irreparable environmental loss. 77.58 percent of studies have reported that geospatial technology, models, and numerical methods are more relevant to urban planners and officials for sprawl measurement and LULC change detection. So, the administration should address the accidental urban growth in its initial phase with a priority for sustainable urbanization.

KEYWORDS:

• Sprawl
• LULC
• irreparable
• geospatial technology
• numerical
• sustainable

1. Introduction

Throughout civilization, the earth has been amended many times. Cut-burn-hoe-weed (cutting the plants from roots and clearing the soil for farming) practice in shifting cultivation during early evolution was perhaps the first step in this sequence. It was the need of contemporary humans, so altering the natural coverage into artificial practices was not a matter of apprehension. However, when it converted into greed and endless desires, it resulted in irreparable loss to nature and the human itself. Such thoughtless use of natural resources continued, and the situation became more complicated with time. Global concern about land use/land cover studies emerged several years ago after knowing the significant impacts of land use/land cover changes on natural aspects like geo-chemical rounds, primarily carbon and water cycles, intensity and frequency of natural episodes from regional to intercontinental scales (Woodwell et al., 1983; Eltahir & Bras, 1996; Mahmood et al., 2014). Recently, several places have witnessed extremities and uncertainties in natural climatic events, acceleration in disaster vulnerability, the extinction of biotic diversity, exhausting non-biotic resources, and declining soil capacity to nourish all living organisms (Lambin & Geist, 2008). Similarly, in a new group of hundred years, the global landscape has passed with significant modifications in natural phenomena to satisfy human desires (Foley et al., 2005). This process has resulted in extensive and vulnerable adjustment to bio-network and its arrangements in spaces, organisms, atmosphere, and privileged circumstances (Ogle et al., 2017; Tyson et al., 2001). Across the world, several socio-economic and biophysical determinants are involved in shaping and scaling to varying degrees of changes in different land operations, and sprawl is one of them (Feranec et al., 2017; Fuchs et al., 2015; Sharma & Kumar, 2023).

Though sprawl is considered an inevitable and parallel outcome of rapid urbanization, the concept has confusion and controversy in urban geography, especially in land use and planning (Maier et al., 2006). Several associated facts like conceptual confusion and misunderstanding about causes and consequences, combined with urban growth, suburbanization, outgrowth, and lack of uniform, accurate, and scientific measurements, make the occurrence and handling of sprawl more intricate. It is a fundamental dynamic phenomenon related to urban space, but the prevailing literature lacks all-agreed definitions and measuring methods for urban sprawl owing to its abstruse nature (Barnes et al., 2001; Johnson, 2001; Clifton et al., 2008; Liu et al., 2018). So, different disciplines and scholars have viewed, categorized, and interpreted it with different perspectives. The term was first addressed in 1937 by Earle Sumner Draper, a town planner, landscape designer, and director of Tennessee Valley Authority of United States, as the diffusion or bursting in urban boundaries that creates an unpleasant appearance over land (Black, 1996). The word ‘sprawl’ was first used by William Whyte, a famous sociologist, urbanist, and writer, in his writing ‘The Exploding Metropolis’ in 1958 to describe the various urban particularities (Wassmer & Edwards, 2005). Conceptually, it is the encroachment of urban limits on other land categories like vegetation and farming lands, water bodies, and open spaces (Sharma & Kumar, 2023; Zhang, 2004). It is a heterogeneous and low-density area with essential urban features developed at the margin of a well-developed urban center and surrounded by open space or agricultural land (Saini & Tiwari, 2020). In simple terms, a rapid increase in sub-urban parts or the periphery of a developed area in improper pattern and discontinuity is labeled as urban sprawl (Bhatta, 2010; Cheng, 2003). This disorganized and unmonitored growth leads to off-limits changes in different land arrangements (LU/LC) over the terrain, which is not a better sign for the environment. Here, the LULC changes refer to numerical addition or subtraction in spatial limits of land use or land cover in a particular region on a temporal basis. Knowledge about these changes is vital in modeling and comprehending global landscape dynamics, controlling natural hazards and disasters, estimating and supervising messy urban growth, and ensuring sustainable development (Anderson, 1976). While on one side land use gets affected by the social, economic, political, ethical, and historical progression of humans over time, on the other side the land cover that determines the functioning of the ecosystem by the presence of various biophysical attributes gets altered by land-use changes (Turner et al., 1995). However, sprawl-induced land use/cover change is mainly linked to regional conditions. It is regarded as an essential constituent of micro-level environmental transformation (Mendoza-Ponce et al., 2018) that further converts into macro. Some common side-effects of these land-use shifts are missing of natural habitats, shrinkage of water resources and wetlands, loss of biodiversity, more frequent happenings of natural calamities, severe soil erosion, increasing sedimentation in the sea (mainly in coastal regions), reducing, fragmenting as well as degrading farming lands and vanishing the green cover (Lambin & Meyfroidt, 2011; Huang et al., 2018; Sharma & Kumar, 2022). Such changes from one category to another increase the vulnerability of both giver and receiver classes. So, chronological appraisal of sprawl and regional LULC changes is an essential need of the hour. Growth and encroachment are the expected outcomes of the urbanizing world that cannot be avoided. The issue is not to avoid; however, the concern is to handle these progressions sustainably and innovatively to get the benefit of development rather than facing adversity. The task may be possible by identifying, delineating, monitoring, and mapping the nature, trends, and direction of sprawl and modifications in different land categories that further need intensive research.

2. Research gap and objectives

The presented literature regarding sprawl mainly discusses its causes, consequences, and faces and the extent of its impact on growing urban centers. However, the growing urban population has been marked as the main factor responsible for sprawl in common (directly or indirectly), whether the studies have been conducted prior or later. The studies of the initial phase have generally used conventional methods to delineate the sprawl and measure the change in land use/land cover owing to some technical limitations (Goyal et al., 1986; Nath, 1986; Ramesh et al., 1989). Advancing with time, new research work (especially of the 21^st century) has presented the use of spatial measurements parameters, models (cellular automata model (Srinivasan, 2005), urban expansion intensity, landscape matrices (Farooq & Ahmad, 2008), Shannon entropy’s value (Aithal & Ramachandra, 2016; Hasnine et al., 2020; Sharma & Kumar, 2022; Sudhira et al., 2003), analytical hierarchy model (Anugya & Jain, 2017), normalized difference built-up index (NDBI), built-up area index (Badlani et al., 2017; Sahana et al., 2018), and real-time data-based geospatial technology are more effective and accurate for identification, monitoring and managing the sprawl and change in LU/LC as its consequences. The present study wants to fill the gap by presenting a systematic review of both kinds of literature under the sub-head of Decadal Appraisal of Key Studies regarding Urban Sprawl and LULC. The study desires to achieve the following objectives:

1. To analyze the various studies related to urban sprawl and land use/land cover changes conducted in Indian cities periodically.

2. To compare the various approaches adopted to measure the sprawl, its causes, and consequences regarding the urban spaces of different hierarchies to determine the scope of adaptability and their relevance in emerging urban centers.

3. Make available the literature on one platform to assist the researchers and the concerned urban administration in formulating and implementing plans and policies.

3. Study area and research methodology

India, along with different administrative units, has been selected as the study area for the present study (Figure 1). According to area coverage, India is the world’s seventh-largest country (3287263 km²). It accommodates the second-largest number (121.0 crores) of people with an overall literacy rate of 74 percent, a sex ratio of 943 females per thousand males, and a proportion of 31.16 percent urban population settled in 7935 different-order towns or cities as per the census 2011 (Census of India, 2011). The country has been forecasted to be home to 416 million new urban residents by 2050, and this increase is expected to be concentrated mainly along the capital cities (United Nations, 2018). In such conditions, multiplying urban development is anticipated to encroach on the natural habitat of flora and fauna. Various challenges for urban centers will emerge that must be handled through experimental prior urban studies. For example, the residuals of various ancient towns in India are not only the material heritage reflecting the economic prosperity, structured social milieu, and cultural excellence of the nation but also offer ideas for town planning and management in the present scenario (Sharma, 2001). So, through this appraisal, the authors wish to offer a comprehensive source of concerning issues to the seekers, researchers, and planners, especially in the Indian context. These various case studies of cities may assist them in exploring the scope and relevance regarding means and methods for new urban centers.

Figure 1. Location of major urban areas in India.

The present study is based on secondary data and follows the multi-stage approach for data collection and other methodological procedures.

1. The first review criterion is selecting the research paper in English, published in recognized peer-reviewed or other specified indexed journals, to determine the authenticity of data and ensure academic reliability. In this sequence, Google Scholar, Research Gate, J-store, Web of Science, and Scopus catalog have been approached.

2. Second, the targeted literature has been accessed by using abbreviated titles like ‘land use/land cover studies’, ‘mapping and monitoring of sprawl’, ‘causes and consequences of urban sprawl’, ‘quantitative methods used for sprawl assessment’, and ‘geospatial technology and urban growth’ in the Indian context. Through this search, the authors reached nearly a hundred studies, all fully or partially related to the subject.

3. In the third stage, the internal validity of quantitative (data sources, techniques, and methods) and qualitative (way of representation, relevance, description of outcomes with facts) aspects of papers or articles have been checked through deep study. Further, considering the objectives, 58 studies highly associated with the subject matter have been scrutinized for review.

4. At the next level, the authors have decided to review the literature from the decade 1981 as sprawl and its negative impressions on LU/LC started to emerge as the apprehensions in the country owing to rapid population growth and unorganized expansion in urban centers. The justification behind selecting decades rather than years is that a decade is enough to identify the changes and compare techniques and methods in the relevant field. Further classification of decades has been done according to the guidelines of the Census of India. The final categories of decades are 1981-91, 1991-2001, 2001-2011, 2011-2021, and dual years 2021-2022 (by July).

5. Finally, the collected research papers or articles have been categorized into different years and set under specific decades. In order to keep mutual exclusiveness (which means one study falls under one specified category of the decade), the studies published in the first year of any decade have been reviewed (presented in Figure 2 also) under the next decadal category rather than in ending the decade with the same year. Among all, five studies authored by Jain et al. (1991), Bisht and Kothyari (2001), Dinda et al. (2021), Shikary and Rudra (2021), and Sarif and Gupta (2021) ensure the above selectiveness.

   Figure 2. Percentage of reviewed studies per decade and in two years.

   

The proportion of studies reviewed by decades is presented in Figure 2, where the category of 2011–2021 shows the maximum proportion (39.65 percent) of reviewed studies. The detailed description is mentioned in Tables 1, 2.

Table 1. Characteristics of literature

Sr No.	Author’s Name	Study Year	Studied Period	Satellite and Conventional Data Source	City/town/area
1.	Gautam and Narayan	1983	1975	Landsat satellite Data	Andhra Pradesh
2.	Sharma et al.	1984	1972–77	Landsat satellite Data	Dehradun - Roorkee
3.	Mohan	1985	1961–81	Population statistics	India
4.	Natarajan et al.	1986	1963–65	Aerial photographs	Mewat, Haryana
5.	Nath	1986	1951–81	Population statistics	India
6.	Goyal et al.	1986	1977	Landsat satellite Data	Western Haryana
7.	Mohan and Mehta	1988	1984	Landsat MSS	Punjab Plains
8.	Ramesh et al.	1989	1988	Aerial photographs, guide map, toposheets	Kanpur City
9.	Sokhi et al.	1989	1975–88	Landsat MSS & TM	Delhi
10.	Jain et al.	1991	1965–1986	Aerial photographs & Landsat TM	Hisar City
11.	Dhinwa et al.	1992	1986–1989	Landsat TM, LISS-II satellites data & Toposheet	Bharatpur District
12.	Pathan et al.	1992	1960–1990	Landsat TM, LISS-II satellites data & SPOT MLA	Calcutta Metropolitan
13.	Routray et al.	1996	1974–1990	Population statistics and secondary data	Bhubaneswar city
14.	Palaniyandi and Nagarathinam	1997	1986–1990	Landsat TM, LISS II, topographic sheets	Chennai-MGR
15.	Taragi and Pundir	1997	1972–1992	Landsat MSS, SPOT-HRV-I, LISS-II, toposheets	Lucknow City
16.	Chaurasia and Sharma	1999	1964–1997	LISS-II, IRS PAN, toposheets	Dehlon Block, Ludhiana
17.	Fazal	2000	1988–1998	PAN, aerial photographs	Saharanpur city
18.	Bisht and Kothyari	2001	1963–1996	Landsat TM, toposheets	Gurur Ganga Watershed
19.	Jayakumar and Arockiasamy	2003	1990–1999	LISS III, Landsat TM & toposheets	Kolli Hills, Tamil Nadu
20.	Sudhira et al.	2003	1972–1998	LISS III & toposheets	Bangalore-Mysore highway
21.	Sharma and Kushwaha	2005	1975–2007	Landsat MSS, Landsat TM, Landsat ETM + & LISS II	Jaintia Hills, Meghalaya
22.	Srinivasan	2005	1962–1999	Satellite data	Delhi City
23.	Farooq and Ahmad	2008	1971–2006	Toposheet, Landsat TM, Landsat ETM+ & Quick bird	Aligarh City
24.	Jat et al.	2008	1977–2005	Toposheets, Landsat TM, LISS-II & LISS-III	Ajmer City
25.	Bhatta	2009^a	1971–2005	Landsat MSS, Landsat TM, Landsat ETM+ & LISS III	Kolkata city
26.	Bhatta	2009^b	1975–2005	Landsat MSS, Landsat TM, & LISS III	Kolkata Urban Agglomeration
27.	Chowdhury and Maithani	2010	2001–2007	DMSP-OLS and MODIS	Indo-Gangetic Plain
28.	Chadchan and Shankar	2012	Post-1991	Secondary data	Urban India
29.	Rahman et al.	2012	1972–2003	ASTER & toposheets	Northwest Districts of Delhi
30.	Acharjee et al.	2013	1974–2009	Landsat satellite data	Urban Regions, Assam
31	Wakode et. al.	2014	1989–2011	Landsat TM & ETM	Hyderabad City
32.	Pandey and Seto	2015	2001–2011	MODIS	India
33.	Gibson et al.	2015	1992–2012	Satellite data	India Urban Agglomerations
34.	Aithal and Ramachandra	2016	1991–2010	Landsat TM, IRS P6 – LISS III, Topographical maps & ASTER	Chennai City
35.	Chatterjee et al.	2016	1972–2014	Landsat MSS & Landsat 8 OLI &TIRS	Bhubaneswar city
36.	Anugya et. al.	2017	2001–02	IKONOS and LISS-III	Lucknow City
37.	Bhat et al.	2017	2004–2014	LISS-IV & toposheets	Dehradun
38.	Badlani et al.	2017	1989–2015	Landsat TM & MSS	Gandhinagar
39.	Kumar and Sharma	2017	1973–2011	Landsat TM & LISS III	Rohtak city
40.	Verma et al.	2017	1991–2016	Landsat Satellite Data	Bengaluru city
41.	Bharath et al.	2018	1991–2011	Landsat TM, Landsat ETM + & Toposheets	Delhi, Mumbai, Chennai, Pune, Coimbatore,
42.	Sahana et al.	2018	1990–2015	Landsat TM and Landsat 8 OLI	Kolkata Urban Agglomeration
43.	Sharma and Kumar	2019	1972–2017	Landsat MSS, Landsat TM, & Landsat 7 ETM+	Hisar City
44.	Tripathy and Kumar	2019	1989–2014	Landsat TM, Landsat 7 ETM+ & Landsat 8 OLI &TIRS	Delhi City
45.	Anees et al.	2019	1992–2016	Landsat TM, Landsat 7 ETM+ & Landsat 8 OLI &TIRS	Kurukshetra City
46.	Prokop	2020	1965–2017	Pan-sharpened image & Google Earth satellite image	Meghalaya Plateau
47.	Dhanaraj and Angadi	2020	1972–2018	Landsat MSS, TM, ETM +, & OLI & TIRS	Mangaluru Urban Agglomeration
48.	Hasnine and Rukhsana	2020	1990–2017	Landsat TM, ETM+ & OLI/TIRS	Barasat Municipality
49.	Maithani	2020	2000–2019	Landsat TM, & OLI/TIRS	Dehradun Urban agglomeration
50.	Vani and Prasad	2020	1990–2018	Landsat TM, & OLI	Vijayawada City
51.	Dinda et al.	2021	1980–2018	Landsat MSS, TM, ETM + & OLI	Kolkata City
52.	Shikary and Rudra	2021	1998–2018	Landsat TM, & OLI	Purulia Municipality
53.	Sharma and Kumar	2022	1991–2021	Landsat TM, & ETM+	Rohtak City
54.	Parihar et al.	2022	2010–2020	Landsat TM, Landsat 7 ETM+ & Landsat 8 OLI	Bhilangana Basin
55.	Krishnaveni and Kumar	2022	1988–2021	Satellite data	Kochi Urban Area
56.	Sarif and Gupta	2021	1988–2018	Landsat TM, & OLI/TIRS	Prayagraj City
57.	Mitra et al.	2022	2000–2021	Landsat 7 ETM+ & Landsat 8 OLI &TIRS	Agartala City
58.	Neog	2022	2000–2020	Landsat TM & Landsat 8 OLI &TIRS	Dimapur Urban Area

Source: compiled by authors.

Here: Study year refers to the publication year, whereas studied period refers to assessed years for the study.

Landsat MSS- Landsat multi-spectral scanners; Landsat TM- Landsat thematic mapper; Landsat ETM- Landsat enhanced thematic mapper; LISS II- Linear image self-scanning; SPOT MLA- multi-spectral; SPOT HRV-High-Resolution Visible; DMSP OLS- Defense meteorological program- operational line-scan system; MODIS- Moderate-resolution imaging spectroradiometer; ASTER- Advanced spaceborne thermal emission and reflection radiometer; OLI- Operational land imager, TIRS- Thermal infrared sensor.

Table 2. Methods and nature-based detailed description of literature

Sr No.	Nature of Study	Sub-theme/Issue	Adopted Methods & Techniques	Study’s Particulars
1.	Interpretation	LULC description	Image interpretation and ground truthing; aerial photo interpretation, toposheets analysis	Gautam and Narayan (1983). Andhra Pradesh; Natarajan et al. (1986), Mewat; Goyal et al. (1986), Western Haryana; S. Mohan and Mehta (1988), Punjab plain; Ramesh et al. (1989), Kanpur, Pathan et al. (1992), Kolkatta; Parihar et al. (2022), Bhilangana basin.
2.	Risk identification and assessment	Urban sprawl and LULC changes detection and measurement using various techniques	Visual image interpretation, ground truthing, and conventional methods	Sharma et al. (1984), Dehradun Roorkee; Sokhi et al. (1989), Delhi; Jain et al. (1991), Hisar; Dhinwa et al. (1992), Bharatpur; Routray et al. (1996), Bhubaneswar; Taragi and Pundir (1997), of Lucknow; Chaurasia and Sharma (1999), Dehlon; Fazal (2000), Saharanpur; Acharjee et al. (2013), Assam; Bhat et al. (2017), Dehradun; Kumar and Sharma (2017), Rohtak; Prokop (2020), Meghalaya.
			FCC image analysis and superimposing the maps	Palaniyandi and Nagarathinam (1997), Tiruvallur.
			Digital image processing in ERDAS IMAGINE	Jayakumar and Arockiasamy (2003), Tamilnadu; Maithani (2020), Dehradun.
			Shannon’s entropy, maximum likelihood method, landscape metrics for sprawl measurement, gradient approach, LCR, LAC	Sudhira et al. (2003), Bangalore-Mysore; Jat et al. (2008), Ajmer city; Chatterjee et. al. (2016), Bhuvneshwar; Sharma and Kumar (2019), Hisar; Anees et al. (2019), Kurukshetra; Vani and Prasad (2020), Vijayawada; Sharma and Kumar (2022), Rohtak.
			Toposheets interpretation, image classification, fragmentation analysis	Sarma and Kushwaha (2005), Jaintia.
			Cellular automaton and markov model, discrete choice model	Srinivasan (2005), Delhi.
			Spatial metrics and image classification, AHP	Farooq and Ahmad (2008), Aligarh; Anugya and Jain (2017), Lucknow.
			Digital Image processing in ERDAS IMAGINE, accuracy assessment, toposheets interpretation, ground truthing, MLC	Bhatta (2009b), Kolkata; Rahman et al. (2012), Delhi; Wakode et al. (2014), Hyderabad; Mitra et al. (2022), Agartala.
			Hierarchical classification method and time-series analysis, population Statistics	Pandey and Seto (2015), India.
			HSI, OLS, and NDVI	Chowdhury and Maithani (2010), Indo-Gangetic plain.
			Shannon’s entropy, AHP, landscape metrics, CA model, accuracy assessment	Aithal and Ramachandra (2016), Chennai; Verma et al. (2017), Bengaluru; Tripathy and Kumar (2019), Delhi; Shikary and Rudra (2021), Purulia.
			NDVI, NDBI, Built-up index, image interpretation, ground survey, landscape metrics, urban heat intensity, UTFVI	Badlani et al. (2017), Gandhinagar; Sahana et al. (2018), Kolkatta, Krishnaveni and Anil Kumar (2022), Kochi; Sarif and Gupta (2021), Prayagraj; Neog (2022), Dimapur.
			ANN, SVM, (MLC), MC approach, accuracy assessment, CA model, AHP	Dinda et al. (2021), Kolkatta
3.	Advisory and prediction	Strategi for sprawl management and future forecast	Population statistics	Mohan (1985), India; Chadchan and Shankar (2012), India; Gibson et al. (2015), India; Biswas et al. (2017), India; Bharath et al. (2018), Delhi, Mumbai, Chennai, Pune and Coimbatore
			Shannon’s entropy, UII, and MLC	Hasnine et al. (2020), Barasat Municipality.

Source: compiled by the author.

Here: FCC- False colour composite; LCR- Land consumption rate; LAC- Land absorption ratio; AHP- Analytic hierarchy process; HIS- Human settlement index; OLS and NDVI- Normalized difference vegetation index; OLS- Operational line-scan system; CA- cellular automata; NDBI- Normalized Difference Built-up Index; UTFVI- Urban thermal field variance index; ANN- Artificial neural network; SVM- Support vector machine; MLC- Maximum likelihood classifier; MC-Markov chain; UII- Urban intensity index.

4. Decadal appraisal of key studies regarding urban sprawl and LULC

Reviewing the existing chronological literature helps to understand the trends and pattern of urban growth, its consequences like sprawl, and change in land use categories over the periods. It also assists in selecting appropriate techniques to identify and delineate the urban penalties, formulation, and implementation of urban plans and policies. In the context of Indian cities, many urban geographers and scholars have studied urban issues from different perspectives. Some studies are location-based and focus on a specific place like a city, town, or country. They emphasize specific area characteristics like geographical conditions, climate and environment, and population aspects. So, the scope to apply more advanced methods and techniques for understanding the urban processes and ongoing modifications of urban space is rare and associated with other methods. In simple words, they are partially techniques-based. Another perspective is that applications have different limitations and scopes to measure urban processes. Because each metropolitan area, city, or other level’s urban center has a complex and individual landscape, one or two metrics do not apply to every space. Considering these restrictions, the researchers try to choose appropriate techniques or indices to measure the urbanization levels, sprawl, land use, land cover change detections, and other urban aspects. The authors have presented a brief systematic synopsis of both kinds of literature with an overview of applied means, methods, and techniques (in tables and reviewed part) of sprawl measurement (Figure 2, Tables 1 & 2). Compared to earlier phase studies, the empirical and quantitative measurement-based work dominates the later periods. In all, 77.58 percent of studies are related to risk identification assessment dealing with measuring sprawl and LULCC using various quantitative aspects and models (Figure 3).

Figure 3. Methods and nature-based proportion of reviewed studies.

5. Decade: 1981–1991

The reviewed studies of this decade mainly focused on qualitative aspects or partial use of quantitative techniques. However, these consist of significant information regarding contemporary urban status that can be useful in comparative studies and exploring the possible apprehension. The imagery-based observations inventory of Andhra Pradesh for the year 1973 reported that agricultural land, including dry and wet fallow lands, was dominant in the state with 69.86 percent, whereas forests (mixed and shrubs) were the second leading class with 24.36 percent area coverage of total (Gautam & Narayan, 1983). In the Dehradun-Roorkee region during 1972–77, the built-up class was at the bottom with 0.32 percent. In 1977, the forests and sand/open space categories were reduced by 4.6 and 0.9 percent, respectively.

In contrast, major urban structures and the farming area received an addition of 0.03 and 2.10 percent area in that order (Sharma et al., 1984). Prediction-based statistics revealed that the increasing rural-urban migration in India will augment the built-up area and decline the rural share in the future. It will further reduce the agricultural sector’s contribution to the Indian economic pool (Mohan, 1985). The aerial photographs and remote sensing data-based interpretation of the Mewat region of Haryana revealed that the agriculture category (both irrigated and unirrigated) was dominant (81.72 percent) among all land use, followed by barren land and forest area. The built-up area comprised the second lowest proportion (2.05 percent) after waterbodies (Natarajan et al., 1986). The growing industries in metro cities will result in rapid urban growth with dispersed evolution of built-up areas. There will be the emergence of low-cost houses and illegal occupancies or slums, creating extra pressure on available basic urban amenities (Nath, 1986). While describing the land use of western Haryana, it was found that the dominance of sandy soil generated less intensive or sparse cultivation with grazing, fallow lands, and scrubs and bushes as vegetation (Goyal et al., 1986). The image interpretation of Punjab plains (1984) confirmed the occurrence of intensive agricultural land, water bodies in the form of streams and ponds, dense vegetation, and compact built-up structures in the central part and scattered construction towards the edges of cities (Mohan & Mehta, 1988). In the case of Kanpur city, the maximum development was traced in the CBD part or along the road network. There was also the appearance of slums or temporary commercial structures along the transport routes or in open spaces. The existence of vacant lands in the CBD area may be an essential space for urban planners to execute future development plans in the city (Ramesh et al., 1989). Regarding the capital city of Delhi, residential built-up areas dominated the class, followed by wasteland, forest cover, and water bodies between 1975 and 1987. While measuring the sprawl, it has been found that, from the concentration in the central part in the initial year, the city expanded towards the edges along the rail-road network in concentric or in a ring shape in later phases (Sokhi et al., 1989).

6. Decade: 1991–2001

In the Hisar city of Haryana, the increasing built-up area (residential and commercial) augmented the unplanned structure from 1965 to 1986. Dispersed urbanization has appeared far from the old city in patches along the transport network, mainly Delhi Road, in the initial decade. Later, the city expanded on outside open land in the southwest and northeast (Jain et al., 1991). A different land use pattern from the general form of the state was observed in the Bharatpur district. Farming dominated all land use classes, possessing 75 percent of the total. The area under tree crops and pastures is also insignificant in the region, whereas forest cover area has reported a depletion of 2.5 percent in a short span of three years (1986–89). In the district, more land uses are found for the non-agricultural categories like built-up areas, transport networks, and water bodies (Dhinwa et al., 1992). A prediction study showed that if the built-up area in Calcutta metropolitan increases at the same speed (10 km² from the 1960s onwards), the entire city will be filled with construction without green space in the next 30–40 years. The maximum possible growth is likely to occur in linear form along with the banks of river Hooghly (due to high land on both sides) and radial patterns around the transport networks. So, the ground with little or no physical restrictions can be an appropriate site for future urban development with the strict rejection of heavy constructions (Pathan et al., 1992). The review of the various urban dynamics and determinants reveals that despite the planned development, Odisha’s capital city has experienced unintended growth in slums and squatters and environmental deterioration during 1974–1990. The population doubled in the residential area, resulting in increased built-up categories like commercial, transport, industrial, administration, and recreational. The growth in developed land consumed a significant part of 9.34 percent of vegetative and 1.93 percent of farming land. The other important observation is that Bhubaneswar City was a part of Chandaka Forest in the past. Its sprawling nature in the present has led to large-scale deforestation in the surrounding, resulting in poor quality and quantity of groundwater, drying of natural water resources (springs), improper drainage, and loss of wildlife and flora (Routray et al., 1996). The outcomes regarding the Tiruvallur area reveal an increase of 270 hectares in the settlement area from 1986 to 1990. The agricultural area (cropland, fallow, and plantation land) has also reported nominal growth in its place, with some significant ups and downs in the sub-categories level. The area remained almost untouched in forest type, but open and dense forests have lost 885 and 177 hectares, respectively (Palaniyandi & Nagarathinam, 1997).

Lucknow Metropolitan had a five-fold growth in the built-up area category. This sprawl negatively affected agricultural land and orchards, forest cover, water bodies, and wasteland, significantly reducing area in respective categories. With the restriction of cantonment in the east and southeast, the city expanded mainly in the northeast and southwest sides along with the major transport routes (Taragi & Pundir, 1997). Identifying the sprawl impacts regarding the Dehlon block of district Ludhiana, Punjab, it was found that agricultural land has received the highest decline due to urban expansion. The reduction in water bodies (canals and ponds) was negligible. The forest area has received a little addition due to the plantation strip (76 meters wide) along the Bathinda and Abohar distributaries of the Sirhind Canal. The changing scenario of crop cultivation with more water-dominated crops may lead to mismanagement of water resources, primarily underground water (Chaurasia & Sharma, 1999). With a 34 percent reduction in Saharanpur City, agricultural land has encountered irreparable loss due to unplanned urban expansion. This land use for non-agricultural activities resulted in the area’s food production loss. Second, because of encroachment over fertile farming land, the canals and distributaries are converted into garbage and waste disposal sites for urban centers (Fazal, 2000).

7. Decade: 2001–2011

The land cover change of the Gurur Ganga watershed in Uttaranchal reported a decline of 5.07 percent in the area of forest cover and barren land. The periphery has witnessed maximum loss in these categories where large-scale felling of pine trees has been carried out illegally. This mass destruction of dense and open forests and the shifting to residential and commercial creations have significantly affected the social and environmental sustainability of the region (Bisht & Kothyari, 2001). The LU/LC study of Kolli Hills of the Eastern Ghats in Tamil Nadu state revealed that the coverage of forests covers more than half (54 percent) of the area of the study region. In contrast, the rest is used for other operations. Apart from the insignificant increase in built-up area, the wasteland category has met with maximum transformation and shifted to other human uses. However, abandoned agricultural and woodlands (trees cutting for fuel use and uncontrolled grazing of animals) are subsequently transferred to scrubland (Jayakumar & Arockiasamy, 2003).

The growth pattern along the Bangalore-Mysore highway (1972–98) reported highly dispersed growth in the proximity area of Bangalore, and the north and south segments of the city noticed a maximum increase in built-up area from the seventies to the late nineties. The ribbon-like urban growth along with the transport route declines with a distance from Bangalore towards Mysore (Sudhira et al., 2003). About four times increases in the mining area (3.26 to 11.20 percent) from 1975 to 2007 have resulted in severe loss of natural vegetation in the Jaintia Hills district of Meghalaya. In 1975, 25.26 percent of the area under dense and open forests was reduced by 15.1 percent in 2007. The forest area has encountered the highest conversion and resulted in fragmentation at a large scale (Sarma & Kushwaha, 2005). In the case of land use and transportation linkage in a rapidly urbanized city, Delhi (1962 and 2019), it was found that the proximities to major transport routes, road density, and office locations have significantly affected the land practices in the capital city concerning concentration and variability. In this phase, the city observed the shifting of open space (including farming land, area of green belt, and non-cultivable land) to residential and commercial construction, mainly in the northern direction. Between 1962 and 1990, the area near highways was likely to intensify more. However, the area with a high population density and closeness to Central Business Districts (CBD) is continuously strengthening, yet it has less probability of losing open spaces (Srinivasan, 2005). Aligarh city has become highly dense as the urban area has increased three times, and main transportation corridors (connecting with Delhi, Agra, Mathura, Moradabad, and Kanpur) have played a significant role in this ribbon, like expansion. In one and half decades (1991–2006), the built-up area has increased by 73.52 percent against the population growth of 64.31 percent, and the agricultural area was consumed for different urban purposes (Farooq & Ahmad, 2008). The results revealed that the city’s massive growth of built-up areas had remained faster (three times) than the population growth rate. Generally, increasing urbanization leads to a high concentration and less space availability. Still, in the case of Ajmer city in the last three decades, exceptionally, the per capita consumption of land has augmented for all categories. The entropy analysis points out that dispersed growth in the town occurred due to the merging of more peripheral areas into municipal boundaries by the administration. The prediction metric is clear that one-third of the city’s land (33.95 percent) will become impervious by the year 2051, and it will put pressure on vegetation and open land within and outside the city limits (Jat et al., 2008).

Urban growth significantly impacts the overall development of Kolkata itself and its periphery. So remote sensing and GIS-based analytical models are needed to regularly monitor growth to avoid the annoying consequences of urbanization (Bhatta, 2009a). At the same time, modeling of the urban growth boundary (UGB) of Kolkata agglomeration using geo-informatics offers a new concept of Ideal Urban Radial Proximity (IURP)to urban planners and officials for balanced urban growth and preserving natural possessions. The model promotes compressed urban development within a circular zone to save the urban water bodies, vegetation cover, farming land, and other sensitive areas. The simple, flexible, and practical approach will be helpful in strategic development and invention and applications of future urban plans (Bhatta, 2009b). The area of the Indo-Gangetic plain is highly dense, and constant population growth leads to the conversion of rich cultivated land into settlements and other uses. In Punjab, Haryana, West Bengal, and the capital city of Delhi, the built-up area will continuously intensify as the brightness values are high in OLS images related to these areas (Chowdhury & Maithani, 2010).

8. Decade: 2011–2021

After post-economic reforms, India’s urbanization is mainly characterized by high speed. The urban population has increased to 260.3 million in a century. The emerging new towns and megacities have consumed a significant portion of fertile farming land, vacant spaces, and vegetation cover. The high population pressure has also led to the origin of slums and squatters in urban centers, the absence of basic facilities, overcrowding, jamming, increased road accidents and crime rates, and a decline in the quality of urban life (Chadchan & Shankar, 2012).

It is found that agriculture has remained the dominant practice in the northwest districts of Delhi (1972–2003), as this area mainly belongs to villages. However, from 1972, the arable land has lost more than one-fourth (27.35 percent) of the total area in 2003. The main factor for this reduction is the augmentation in new constructions for residential, commercial, and other economic purposes due to a substantial increase (3.09 to 28.40 percent) in the built-up area (Rahman et al., 2012). In urban regions of Assam, increasing migration from rural areas for employment and other facilities increased the development of urban and semi-urban areas in the surroundings of the city. The conditions of Guwahati, Dibrugarh, and Sibsagar cities are worse, where a fast-growing urban population has resulted in deforestation and damage to the mountainous ecosystem. The shifting of farming and forest land to housing and commercial construction increased the risk of wetlands, groundwater, wildlife habitat, flooding, and solid waste generation in these regions (Acharjee et al., 2013). The urban settlement area in Hyderabad city had increased from 304.88 km² in 1989 to 740.34 km² in 2011. As a result of urban growth, the agriculture, open land, and aquatic vegetation categories have lost 282.7, 159.77, and 7.55 km² in that order from 1989 to 2011. The water bodies have also been reduced by 5.28, and ponds and tanks have been missed entirely in some parts. This significant loss in surface water resources has widened the gap between the demand and supply of drinking water in the city (Wakode et al., 2014). Due to rapid urbanization in India from 2001 to 2011, on average, urban centers have encroached on 0.7 million hectares of the agricultural sector. In the south, Maharashtra and Andhra Pradesh states (having a high share in the manufacturing sector) have a maximum proportion of converted land from farming to other urban uses because of the implementation of the special economic zones (SEZs) policy (Pandey & Seto, 2015). The rapid growth in urban population, mainly in the twenty-first century, has led to the expansion of existing towns and the rise of new ones. It increased the demand for land for more urban uses in urban agglomerations, and agricultural areas, forests, grasslands, and open land have been seen as easy targets to fulfill this requirement (Gibson et al., 2015). Chennai city has drastically changed land cover due to an increased urban population. The normalized difference vegetation index (NDVI) reported a reduction of 22 percent of the vegetation-covered area. This land has been shifted to the urban built-up area (for residential and commercial purposes), open space, and other practices. The compacted or concentrated growth pattern towards the central business district (CBD) area can be seen in all classified zones of the city. The peripheries are witnessing dispersed growth, whereas the city’s core area will be more compacted (Aithal & Ramachandra, 2016).

Even after being one of India’s most well-designed cities, Bhubaneswar’s fast-growing population generated significant effects on arable, vacant, and vegetative lands, especially in peripheral zones. In 1972, the expansion was linear from north to south along the Kuakhai River and transport routes. However, in 2014, a major built-up area appeared in the northwest and southwest that consumed open land (covered with shrubs and grass) and vacant agricultural lands. The river sand has drastically disappeared from many river banks (Chatterjee et al., 2016). In Lucknow city and its surroundings, intensive agriculture can be observed all around the city’s built-up area owing to a part of the great alluvial plain. Urban boundaries are expanding towards the farming area due to the increasing population (Anugya & Jain, 2017). While exploring the potentiality to achieve inclusive urban growth in India, it has been found that preliminary plans and executions have always subordinated India’s urban development strategy. However, urbanization is continuously increasing in the nation. Still, owing to sprawl, most urban residents are underprivileged from the primary urban amenities like the availability of safe drinking water, drainage, sanitation, and nutritional foods (Biswas et al., 2017). Dehradun City is witnessing rapid urban growth despite being a sensitive mountainous area. This unplanned growth threatens the ecosystem, water possessions, resource consumption, and quality of mountain life by converting hilly land into various urban uses. A remarkable decline of 39 percent has been noticed in the city’s agricultural area only in a decade (2004 to 2014). As an attractive tourist site, the city is experiencing urban interference in some protected areas (Bhat et al., 2017). Gandhinagar City has witnessed a significant increase of 73 percent in residential and commercial structures from 1989 to 2015. Even after the upsurge in urban uses, increasing green cover (tree clusters, parks, and gardens) is an achievement of efficient urban plans and their implementation. Defiantly, such greenery has helped the city achieve the title of ‘Tree Capital of India’ (Badlani et al., 2017).

Rohtak city in Haryana has witnessed a five-fold increase (14.98 km² in 1973 to 81.68 km² in 2011) in its built-up area. Though the city has grown in radial or star-like patterns, more sprawl was observed in the eastern side along with major roads, industrial township, and northern part due to the new government and private constructions for residential, administrative, and institutional purposes (Kumar & Sharma, 2017). The change detection shows a significant growth of 29 percent in the built-up area category in Bengaluru city. The city has been perceived to have equal radial growth in all parts (slightly higher in the east and southeast) along the major national highways (NH-7 and NH-4) and regional transport routes. The entropy value indicates that the core area is more compact, and towards the fringe, it has developed in a fragmented way. As a result, pasture land has encountered the highest loss of 19.53 percent, followed by 15.53 percent of dense vegetation, 7.75 percent of barren lands, and 1.42 percent in wetlands (Verma et al., 2017). Discussing the urban dynamics in the megacities of Delhi, Mumbai, Chennai, Pune, and Coimbatore in India, it has been found that the rapid urbanization process has caused changes in land use/land cover patterns, exhaustion and depletion of natural resources. The built-up area of the capital city of Delhi is estimated to be densified by 57 and 70 percent by 2025 and 2030, correspondingly. Only 3.76 percent of the area is considered vegetation cover for the same period. Such a human-vegetation ratio will be challenging from an environmental perspective and result in various health complications. In the same way, the residential and commercial structure of Mumbai, Pune, Chennai, and Coimbatore is also predicted to increase by 27, 50, 45.8, and 37 percent respectively for the same period (Bharath et al., 2018). Due to fast urban growth in Kolkata’s urban agglomeration, the farming land has lost a substantial part of 216 km² of fertile area. The 149 km² of this is directly transformed into a built-up area, and the rest might be converted into fallow land or used for other urban uses. The open space, vegetation/plantation land, aquatic vegetation, and wetlands have also reduced by 67.5, 18.6, 6, and 6 km² in that order in 25 years. The increasing ribbon-shaped construction for residential and economic purposes along both sides of the Hooghly River from north to south has shrieked the course (Sahana et al., 2018).

The increase in a built-up area in Hisar City resulted in extra pressure on urban resources and a high land consumption rate, unplanned growth, the origin of slums, and illegal occupancy within the city that negatively affected the quality of urban life (Sharma & Kumar, 2019). The eastern and central region of the capital city, Delhi, was already dense due to the presence of the Yamuna River. So, the later expansion occurred in the north, north-western, and southern sides as suitable sites for urban functions. The statistics also report reduced vegetation area, water bodies, and open space in the city (Tripathy & Kumar, 2019). In Kurukshetra city, the built-up area has developed faster, and maximum new construction has appeared in adjacent highly dense areas. This accelerating urbanization has resulted in a great loss of 69.72 and 57.67 percent for vacant land and tree cover classes, respectively, in the same period (Anees et al., 2019). In the Meghalaya plateau region, the grasslands (the largest land use category in 1965, covering 76.8 percent of the total) and forest cover have declined by 252 and 51 hectares, respectively, and the major loss has been identified near the residential areas. There was a negligible increase in cropland, while the area undermining operations doubled (69 to 160 hectares) from 1965 to 2017. The coal mining was scattered in the region’s north, northeast, and central parts, while the limestone extraction was concentrated in the south of the spur. The transformation of grasslands into bare land and deforestation is a major environmental concern for the region (Prokop, 2020). In the Mangaluru urban agglomeration of Karnataka, the developed urban area has increased six times (3.68 percent in 1972 to 18.79 percent in 2018), and the core has intensified due to in-fill growth. The dominancy of agricultural land has been destroyed by increasing urban boundaries, and this class has lost a remarkable area of 14,454 hectares within 46 years. The open land and water bodies have also declined by 437 and 187 hectares in the same span (Dhanaraj & Angadi, 2020). In Barasat Municipality (North 24 Parganas district of West Bengal), except for the increase of 22.87 and 3.39 percent in a built-up area and vegetation cover, respectively, in the past 27 years, all classes have witnessed a decline in the region due to urbanization. The main reduction has been observed in agricultural land (11.94 percent), followed by vacant lands (11.64 percent) and water bodies (2.55 percent) categories. The entropy model revealed the encroaching urban construction mainly towards the arable land and surrounding open space. As a result, peripheral villages are merging in urban areas, and seven villages, namely, Kanthalia, Mandalganti, Jirat, Bahera, Kashimpur, Kadambagachhi, and Bara, are found more sensitive to be census towns in the future due to an increasing population (Hasnine et al., 2020).

The outcomes regarding Dehradun urban agglomeration (DUA) from 2000–2019 stated that the urban built-up area has increased 126 percent in 19 years. The maximum conversion of 2353 hectares in the developed area has been seen in the suburban built-up class, whereas the scattered built-up area has decreased by 24 hectares simultaneously. The areas of urbanized open spaces and peripheral parts have also transferred to urban agglomeration. The rural open land has lost its 14,178 hectares due to the expanding boundaries of DUA. It has also led to the depletion of water resources in terms of quantity and quality (Maithani, 2020). The residential area in Vijayawada city has increased about five times (138.01 km² in 2018 from 28 km² in 1990), and it consumed mainly vegetative land. The directional growth analyses explained that the city had gained tentacle-type (feathery way) development. Significantly, the sprawl ignored the depletion of water resources, reducing and degrading the quantity and quality of water in the area. Secondly, the high land surface temperature led to more evaporation from the water bodies, which led to heat island formation over the city, especially in winter (Vani & Prasad, 2020).

9. Twain years: 2021–2022

Kolkata has witnessed a significant loss in urban green space (UGS) due to continued urban expansion from 1980 to 2018. The built-up area (high and low density) has increased by 55.59 km² from 1980 to 2018, occupying 80 percent of the total. However, it has expanded all over, yet the highly dense built-up area has developed mainly in the northwest part along the Hooghly River bank. Rapid growth has resulted in a drastic loss in vegetation cover (31.45 km²), wetlands, and water bodies in the same period. The highest green space index is found near the dispersed settlement area, continuously declining due to the direct transformation of vegetative and grasslands into built-up areas. The transition prospect predicts that the built-up area will cover more than 85 percent of the city by 2035, and vegetation cover and grassland have the highest possibility of 63.79 and 53.15 percent, corresponding to change (Dinda et al., 2021). The outcomes of urban growth in Purulia municipality from 1998–2018 demonstrate that more than doubled growth (5.79 to 12.87 km²) in land use has substantially modified the land use/land cover scenario of the region in the last 20 years. Vegetative land has been reduced by 65.41 percent while fulfilling the increasing demand for land for urban uses, and it is the highest area loss among all studied categories. Moreover, the physical expansion of the city across the municipal boundaries has consumed significant parts of the water body, bare land, and agriculture, i.e. 46.63, 31.55, and 10.69 percent, respectively. The residential and commercial areas have been developed at the cost of green space and open land affiliated with the water body. This growth pattern refers to the density of growth that goes to be dispersed as we move from the center to the periphery. Despite undergoing rapid urbanization, the region has dominancy over agriculture. Because of this, more land has been transferred to farming practices from bare ground, water bodies, and forests compared to the built-up class (Shikary & Rudra, 2021).

The rapid growth in built-up areas has caused a significant concern in Prayagraj. From one-fourth of the total area (25.40 percent) in 1988, it reached 64.61 percent in 2018. This mass evolution of built-up area consumed the area of other classes. As a result, farming land, forest, and barren land lost 28.45, 6.86, and 9.13 percent of the area individually in the same period. The maps depict that the one-sided concentration of built-up structures in the initial period fragmented later. It became dense in 2018 after consuming green surface and arable land from the center and the surrounding (Sarif & Gupta, 2021). In Rohtak City, the built-up area increased four times from 1991 to 2021. Due to this, the city’s surrounding land has met with enormous changes, which augmented the built-up area in both ways, intentionally and accidentally, in all directions. In 2021, the rapid growth in urbanization led to the progression of developed areas, and in 30 years, for the first time, it became the leading class with a proportion of 48.05 percent. The expansion cost was charged from farming, which resulted in the disappearance of a significant 35.03 percent of arable land in the studied period (Sharma & Kumar, 2022). While analyzing the impacts of land use dynamics on regional climate in the Bhilangana basin of Garhwal Himalaya, it has been found that after the construction of the Theri dam, the surrounding area has witnessed a significant change in its LULC as well as long-time atmospheric conditions. The dense forest is in the prime land use category, with an area coverage of 775.81 km² in 2020. However, it lost 84.71 km² (5.7 percent) of the absolute area between 2000 and 2010, restored by afforestation in 2020 to avoid environmental penalties. Agriculture, barren lands, and the exposed rocky surface have also met an insignificant decline of 0.28, 2.02, and 0.5 percent area, respectively. As climatic variability, owing to the formation of the reservoir, there is a continuous increase in monthly precipitation and temperature along with a short span of monsoon in the region from 1982 to 2021 (Parihar et al., 2022). In speedily urbanizing Kochi city, the developed area has increased more than five times (6.23 to 32.33 percent) from 1988–2021. The highest transformation of 186.94 km² to the built-up structure was confirmed by farming class, followed by 51 km² of forest land. Interestingly, the built-up area as a second-order class from the bottom in the initial year reached the top as per statistics of the recent period. This progression has ensued by sprawl in all directions, including coastal lines. The applied landscape metrics also confirmed the uncontrolled urban growth and emergence of fragmented urban centers in the peripheries throughout the period (Krishnaveni & Kumar, 2022).

In 2000, Agartala city grew in the core part, surrounded by several waterbodies that continuously declined in later periods and completely disappeared in 2021. In the same way, woodlands are also encroached by scattered structures initially and become denser after over the entire surface. However, the agricultural area has increased by 28.06 percent, but the farming area was eliminated from the central part for the impermeable or green surface (Mitra et al., 2022). The Dimapur area of Nagaland received a reduction of 13.77 percent in green area in the last two decades due to three times (14.74 percent in 2000 to 39.36 percent in 2020) increase in the built-up area. The considerable increase in the extension of urban confines affected the atmospheric conditions of the concerned area, which can be verified by mean land surface temperature (LST) statistics. The LST of the city has increased by 1.74 °C and 4.57 °C for winter and summer seasons, respectively, and various locations have been identified as an environmentally wrong or worse category (Neog, 2022).

10. Significance of sprawl and LULC change analysis in urban studies

Most cities in developed and developing nations are witnessing sprawl and its consequences for human being and the environment. The issue has aroused inclusive social attention for being responsible in queueing and delaying sustainable regional development plans. In the latter half of the 1990s, urban planners noticed public concern regarding the negative impacts of urban sprawl. It was also reflected in contemporary studies focused on forms, processes, sources, repercussions, and restorative measures of sprawl (Bengston et al., 2005; Bhatta, 2010). In growing urban centers, the authorities have continuously been resetting and trimming the earth’s ambiance to meet the immediate satisfaction of billions of people’s housing, food, clothing, and other necessities. This adjustment without knowing the future consequences is the first step of the initiation of sprawl. It initiates sluggish construction of farmsteads, business and manufacturing areas, unplanned urban settlements, and other uncharacterized land exercises towards rural perimeter or outside the city limits. This nature of growth further interferes unfavorably in urban environments and turns into an unessential feature that is neither acceptable for urban environments nor appropriate for the countryside (Rahman et al., 2008). The ignorance or soft approach towards sprawl in the initial stage becomes a big mistake of administration later. So, urban planners or researchers should thoroughly understand the sprawl for rational urban growth. Many urban studies have recently tried to develop a reliable approach and find its association with the specific geographical urban unit (Ewing et al., 2002; Galster et al., 2001). However, the sprawl issue requires more practical and inclusive research to comprehend.

While talking about the change in LULC, presently new ideas and techniques are employing for the maximum exploitation of earth’s resources. Its impressions are visible as the disappearance of green cover and woodlands, extinction of flora and fauna, and conversion of watercourses into paleochannels (Foley et al., 2005; Meyer & Turner, 1992; Newbold et al., 2015). Generally, the changes occur on undeveloped grounds with ideal environmental conditions. However, the regions with physical restrictions are not an exception and are getting almost equal conversion in this regard (Long et al., 2007). The problem is more complicated in developing nations as most cities are experiencing rapid growth in their urban population in a short span. It leads to expansion, and after the consumption of its open land, the urban area targets the hinterland for further infringement (Mosammam et al., 2017). Such changes in land aspects comprise spatial and temporal features. So, the chronological appraisal of regional LULC changes significantly helps urban studies. In this sequence, the knowledge about nature, site, and time of occurrence and its driving forces are primary desired information to understand the issue more innovatively (Lambin, 1997). The change detection in different land operations assists in formulating sustainable plans for the individual spatial unit (Ramachandra et al., 2012), along with determining the degree of changes in arrangements over land (Anderson, 1976). There are a variety of qualitative and quantitative techniques to measure the level and nature of land use and land cover changes. The practice of panchromatic aerial photographs with medium-scale since the 1940s is a significant part of this analysis (Lillesand et al., 2008). In continuation, satellite data and GIS tools-based geo-informatics techniques associated with cadastral approaches have emerged as an effective and more reliable tactic for mapping and monitoring the ongoing land operations and their transformations. However, the administration is facing a dearth of satellite data with sufficient spatial and temporal resolutions, which makes it challenging to analyze the land transformations and monitor the pattern and degree of growth in broader urban contexts (Jensen & Cowen, 1999; Sharma & Kumar, 2022; Theobald, 2001).

11. Conclusions

The above discussion has emphasized the temporal progress in the quality and quantity of research regarding urban sprawl, and LULC changes in the context of Indian cities. The availability and easy access to high-resolution remote sensing data and GIS software significantly assisted this. The review highlights that researchers or urban planners havE shifted their approach from conventional to numerical and technical parameters. For example, more than three-fourths (77.58 percent) of reviewed studies have focused on modern techniques, using various models and quantitative means to monitor and manage urban parameters like sprawl and LU/LC change. Next, 10.34 percent of studies have used population projections to forecast the urban sprawl in the country. Most studies have reported unrestricted growth oF urban centers and extensive changes in land use practices across the cities as common findings. These studies have also discussed the negative impacts of sprawl on biodiversity, habitation, and human well-being in terms of changing patterns of weather events, pollution-generated respiratory infections, congestion, etc.

The study also underlined the challenges and opportunities for sustainable urban planning and management in India. Despite being technique-based, further research must improve the methods’ accuracy, reliability, and relevance to know the dynamic nature of urban aspects and ensure the applicability of findings to other areas with similar or different characteristics. Another critical challenge is the need for extensive land use planning and enforcement of regulations to avoid unplanned and haphazard growth in many urban areas. However, there is also a scope to promote sustainable urbanization through innovative policies and planning approaches, such as transit-oriented development, green infrastructure, and participatory planning processes, but more is needed exclusively. So, the issue demands interdisciplinary and integrated approaches to address the multidimensional social, economic, and environmental challenges posed by rapid and unintentional urbanization. Overall, the appraisal shows that urban sprawl, land use/land cover, and change detection studies are critical research areas in an urbanizing country like India. This work will stimulate further research and contribute to developing more sustainable and inclusive urban features in the country.

12. Recommendation

The authors have reviewed the urban sprawl and LU/LC studies regarding Indian cities placed on different hierarchies by the census of India. It is clarified that the population size of these cities is separate from the subject matter in the study due to some limitations. So, implying the methods and techniques of one city (small or large) to another depends on the researcher or administrator’s consciousness.

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