Decadal changes in land use and land cover: impacts and their influence on urban ecosystem services

Populations in the present day are concentrated in urban areas, with more than 50% residing in the same (Gómez-Baggethun & Barton 2013); this is projected to increase to 60% in a decade (Avelar et al. 2009). Land use land cover (LULC) human-induced changes have increased massively over the last three decades (Joshi et al. 2016). These changes in LULC change cause urban expansion at a loss rate in prevailing natural capital. Hassan et al. (2016) narrate urban development as a process of LULC changes converting natural capital to the built-up area. Natural capital comprises forest land, hinterland, and water bodies, which are greatly affected due to urban expansion. This conversion process of natural capital into built-up areas alters the biochemical cycles, circulation pattern, and earth's energy equilibrium (Kaye et al. 2006; Zhang et al. 2009; Song et al. 2018), causing a loss in ecosystem services (ESs) (Foley et al. 2005; Barbier et al. 2011; Lawler et al. 2014; Delphin et al. 2016). Anthropogenic intervention plays a significant role in degrading ESs (MEA 2005; Jopke et al. 2015; Jew et al. 2019; Yang et al. 2019); also, specific ecological processes, such as quality of water and land, greenhouse gas (GHG) emission, biodiversity, habitat quality, etc., are altered (Weng 2007; Matteucci & Morello 2009; Ng et al. 2011; Su et al. 2011). The recent expansion of urban areas and conversion of the natural ecological capital to urban lands pressurizes the land demand phenomenon, especially in developing countries like India, China, and African countries, leading to a loss in the ESs enjoyed by people from urban ecological areas (UEA) (Pauleit et al. 2005; Güneralp & Seto 2008; Jomaa et al. 2008; Sundell-Turner & Rodewald 2008; Huang et al. 2009; Bohnet & Pert 2010; Li et al. 2010; Su et al. 2010; Seto et al. 2012). The loss of ES due to LULC changes is classified as man-made and natural; this impacts ES's functioning, which must be well documented (Chase et al. 2000). Demand to assess the existing ES and estimate the losses incurred in natural capital due to rapid urbanization plays a vital role in the sustainable development of urban areas at a regional scale and for future policy decision-making.

ESs are defined as services that people gain from natural capital directly or indirectly (Costanza et al. 1997; Daily Gretchen 1997; MEA 2005). Services that people gain benefit from the ecological landscape of urban areas, such as parks, gardens, water bodies, etc., are considered urban ecosystem services (UESs) (Gómez-Baggethun & Barton 2013). Rapid urbanization, resulting in LULC changes within cities, affects biochemical processes and the Earth's energy equilibrium. These alterations impact climatic conditions, hydrological cycles, and ecological biodiversity (Alberti 2005; Grimm et al. 2008; Song et al. 2018). Regulating ES, provisioning ES, supporting ES, and cultural ES are provided by the UESs in urban areas for the benefit of human beings. The work done by Sunita et al. (2023) describes two broad classifications of UESs, i.e., green spaces like parks, gardens, forests, etc., and blue spaces such as ponds, rivers, lakes, etc. Due to the urban population prevailing in urban areas, these ESs are used intensively.

Urban areas are hubs of various phenomena caused by rural migrants. This shift is causing a significant impact on the ESs, especially in developing countries like India. The transformation changes the physical space setting from rural land to urban land, bringing social, economic, and political changes into the picture. Even though we see growth regarding socio-economic and cultural development in the urban area due to migration, adverse effects are seen on ESs. Researchers are stressing the study of ESs to understand the projection of ecological changes for future sustainable development against global urbanization and climatic changes. Therefore, it is of prime importance to understand the relationship between urbanization and ES in sustainable management of the environment. As more than 50% of the population resides in urban areas, ecologists take particular interest in studying UESs (Costanza et al. 1997, 2014; Ahern et al. 2014). We find several studies that try to understand the relationship between ecosystem service and urbanization, with the root losses occurring in the same process (Wan et al. 2015; Alam et al. 2016). Therefore, it is critical to understand the impact of urbanization on UESs.

ESs, described in layman's terms, are the benefits humans obtain from nature (de Groot et al. 2002; Costanza et al. 2014). Human beings' dependency on nature and its services in an urban setting is UESs (Gómez-Baggethun & Barton 2013). In this study, we define UESs as the direct facilities provided by the nature of the urban setting (Gutman 2007; Gómez-Baggethun & Barton 2013; Jansson 2013). Researchers are recording evidence of large built-up areas covering the land surface, with a highly concentrated population leading to congestion and densification (Pickett et al. 2001; Farhadi et al. 2022). The urban ecosystem cannot be delineated with a boundary as ESs are celebrated beyond the spatial territory. Blue, green spaces, considered a fundamental part of the ecosystem, are tagged along with hinterlands, suburban, and peri-urban areas, which are directly affected by the changes in the ecosystem (Pickett et al. 2001; la Rosa & Privitera 2013). Therefore, in layman's and practical terms, UESs mean the services the urban population obtains from the urban blue-green areas or ecosystem clusters.

Evaluating the economic worth of ESs is a critical issue in the administration of the environment and ecosystem conservation. Critical disputes on the question of whether ESs need to be commodified are mostly motivated by ethical concerns, such as the belief that nature cannot be traded or sold. Many believe that the economic assessment of ESs does not set a price on nature, but rather provides additional knowledge for an open decision-making process and that it does not supplant nonmonetary qualities (Pham & Lin 2023).

As a result, many scholars have concentrated on quantifying and evaluating the global value of ESs. A group of scholars investigated the significance of ES and its influence on service appraisal by concentrating on the LULC change. Others investigated the internal relationship between change in LULC and value mapping methodologies. Others have examined how communities make value-changing judgments as a result of urbanization. The majority of such studies show a minor to significant drop in ESV as a result of LULC changes and urbanization. However, several investigations found no difference. Understanding how urban expansion affects ESs is critical for regional and global environmental sustainability (Haque et al. 2023).

Researchers empirically proved that changes in the natural spatial cover are brought in by urbanization. This phenomenon drifts the natural setting towards man-made settings by introducing impervious materials. However, understanding the impact of these impervious materials on the UESs plays a vital role in ES management strategies. This study is performed using the object-based image analysis (OBIA) technique, using nearest neighbour (NN) classification; this results in obtaining an accurate classification of the LULC classes. We find articles using maximum likelihood classification (MLC) to perform such tasks, the results obtained from these classifications are bound to be less accurate. The main aim of this paper is to examine the dynamics of urbanization and its impact on ESs by assessing the effect of LULC changes on the ESs for five decades, i.e., 1980, 1990, 2000, 2010, and 2022.

Mangaluru taluk is spread around 567 km² area. The population of Mangaluru city is more than half a million, i.e., 623,841, and is identified as a town under the category of Class I by the Census of India 2011. Due to urbanization, we find swift expansion of urban areas. This expansion is anticipated to go towards the city's South and Southeastern parts. Dhanaraj & Angadi (2021) affirm that a volatile and unsustainable form of urban expansion is occurring. Studies are necessary to understand the complexity of urbanization in Mangaluru, which would help urban planners interpret intricate urban landscapes to achieve sustainable urban growth (Dhanaraj & Angadi 2021).

Over a decade, Mangaluru experienced rapid urbanization, leading to changes in ESs. Urbanization in this area is mainly speculated due to its demand for the natural settings and commercial market, increasing population, and LULC changes. Researchers proved that these causes are the root of prevailing ecological changes and are associated with the difference in the surface land cover, creating a hindrance in the quality of human life in urban areas. Therefore, assessing UESs plays a vital role.

This study used multi-temporal Landsat satellite images, cloud-free with 30 m spatial resolution and path/row of 145/051 and 146/051 data sets. The images above 30 m resolution were resampled to 30 m and used for the study. These data sets were obtained from the United Nation Geological Survey (USGS) for five decades, i.e., 1980, 1990, 2000, 2010, and 2022 (as shown in Table 1). These data analyse the LULC classifications. The satellite imagery used is pre-georeferenced with the Universal Transverse Mercator (UTM) coordinate system using the datum of WGS 84 (World Geodetic System). To study LULC classifications (Table 2) along with corresponding biome identification, we used the OBIA approach.

LULC maps of the study area are mapped using ArcMap software, version 10.7. The map was image classified using supervised OBIA techniques, as this technique's accuracy is more reliable than MLC and other techniques (Kumar Shukla et al. 2018; Shukla et al. 2020). LULC categories were represented with around 30–40 spectral signatures. The kappa coefficient (KC) is used to check the accuracy of the assessment (Najafzadeh & Basirian 2023), with the help of reference points obtained from the Google Earth engine. For the study, 100 and 200 points were used as reference points to compute the map from 1980 to 2022 with the help of Google Earth engine. KC is used to cross-check the accuracy of the map (Table 3); KC's values range from 0 to 1, where 0 represents the least accuracy of the map and reality, whereas 1 illustrates the highest similarity of the map concerning the field. The idle range of KC should be more than 0.85 to indicate the feasible relation between map and reality (Table 4) (Monserud & Leemans 1992). Some researchers believe that KC is used as an advanced tool to understand interclass discrimination more than the accuracy level in object classification (Ma & Redmond 1995).

Table 3

Accuracy measures (Kumar Shukla et al. 2018)

Accuracy measures .	Formula .
Producer accuracy (⁠⁠)
User accuracy (⁠⁠)
Overall classification (⁠⁠)
Kappa coefficient (⁠⁠)

Accuracy measures .	Formula .
Producer accuracy (⁠⁠)
User accuracy (⁠⁠)
Overall classification (⁠⁠)
Kappa coefficient (⁠⁠)

is the sum of values in the ith row, is the sum of values in the jth column, N is the total number of reference points/samples, n is the total number of rows/columns, is the value of the ith row and jth column.

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The LULC map is assessed and calculated using the overall classification (⁠⁠), and the kappa coefficient (⁠⁠), after assessing the producer accuracy (⁠⁠), and user accuracy (⁠⁠) which is calculated for each class.

Valuation of ESs (17 biomes) was proposed to estimate the status of worldwide ESs in terms of global value coefficient (VC) (Costanza et al. 1997) (Tables 5–7). It is vital to study the ecosystem service values (ESV) to evaluate the effect of urbanization on ESs, triggering the change in the goodness of the services. To evaluate the same, in this paper, we quantified the total ESVs and functions of ESs at an individual level.

We used the classified LULC map to analyse the change in ESV. The LULC classifications do not entirely correspond to the represented biomes. In this research, agricultural land includes regions utilized for permanent and seasonal crops, irrigated areas, and dispersed rural communities. The presence of countryside in this classification, despite their dispersion, might not even correctly capture the ESV allocated to the agricultural biome. The research region is likewise densely forested, comprising acacia, other trees, and coniferous plants. In addition, the shrubland biome was reflected by the forest biome.

In the above equation, we have ⁠, and as the values and functions of ESs based on individual LULC. p represents the land use category comprising an area, talks about the VC and value coefficient of function ⁠.

In the formula we have, the average ESVs (US million/ha/year) denoted by ⁠, where is the total ecosystem service value for a particular study year and is the total geographical area of the urban setting (ha).

In the above formula, is the coefficient of sensitivity, stands for estimated ecosystem service value, stands for value coefficient, and j denotes the initial and adjusted values, respectively, and k represents the LULC category. When the value of CS is higher than one (>1), the evaluated ecosystem value is elastic (reasonably susceptible) in reply to the VC, and more care must be taken in calculating VC precisely. If the value of CS is below one (<1), the evaluated ESV is inelastic (relatively less sensitive) in reaction to the VC, and the resultant ESV is reliable.

Mangaluru city agglomeration is classified as a class I town under the Census of India 2011. The city is gradually getting urbanized (Table 8). The city's built-up area was spread over 13.56 km², then expanded to 224.70 km² by 2022. We find a 37.10% increase in the built-up area in the study region from 1980 to 2022, with an annual growth rate of 1.66%. The population of the study region increased gradually from 1,71,885 in 1981 to 2,30,919 in 1991, to 4,16,262 in 2001, to 4,88,968 in 2011, to 5,57,032 (estimated) in 2021, respectively. The percentage of population growth against the built-up area is given in Table 8.

Multi-temporal remotely sensed images were used to classify LULC accurately. The study shows how the LULC classifications (agricultural land, forest area, water body, built-up area, and barren land) have changed geographically between 1980, 1990, 2000, 2010, and 2022. According to the LULC percentage changes of these classes over the timeline above, agricultural land decreased by 9.54%. In comparison, forest areas increased by 35.83%, water bodies increased by 0.06%, and a significant increase in built-up area by 37.10% wasteland also reduced by 63.44%.

The results of the accuracy assessment are shown in Table 9. The accuracy is assessed using the Google Earth images and Survey of India (SoI) Toposheets. The accuracy assessments showcase the precise classification of the LULC maps. The accuracy estimation is done for the classified map of 2022, representing all other maps generated for the study. The results showed an overall accuracy of LULC classification of 91.61% and a kappa index of 0.89, which shows strong agreement between the classified LULC and the results (Table 10).

For the research, the value coefficient (VC) suggested by Costanza et al. (1997) is used to calculate the ESV for individual LULC classes and the predicted changes of ESVs for every decadal year from 1980, 1990, 2000, 2010, until 2022 (Table 2). Total ecosystem service value (⁠⁠) decreased in the first decade (1980–1990) by US$ 2.94 million/ha/year, later it decreased by US$ 27.47 million/ha/year in the next decade (1990–2000), in the decade (2000–2010), the ecosystem service increased by US$ 4.36 million/ha/year, again we find decrease in the decade (2010–2022) by US$ 5.69 million/ha/year. Across the previous 42 years of the research period, the net drop in ESV has been around US$ 31.74 million/ha/year related to decreases in shrubland/wasteland and agricultural land. Forest registered the highest ESVs among the five LULC classes because the highest VC of forest comprises approximately US$ 7.88 million/ha/year (1980) and US$ 48.81 million/ha/year (2022) of overall ESVs, respectively. Water bodies and forest areas are vital as ecosystem service providers in this study area.

Table 11 represents the contribution of ecosystem service value functions ⁠. We find regulating ESs contributed a lot in the study area, around US$ 43.89 million/ha/year in 1980 and around US$ 35.08 million/ha/year in 2022. It is preceded by supporting ESs, contributing around US$ 42.41 million/ha/year in 1980 and around US$ 27.69 million/ha/year in 2022. The provisioning ecosystem contributed to around US$ 24.73 million/ha/year in 1980 and around US$ 18.29 million/ha/year in 2022. The cultural ecosystem contributed to about US$ 5.85 million/ha/year in 1980 and around US$ 4.07 million/ha/year in 2022. We find a lot of variation in the contribution pattern of ESs in the study area. To sum it up, we can state that the highest contribution of each ecosystem service function to total ESVs was recorded by nutrient cycling followed by water regulation, raw materials, and erosion control (Tables 11 and 12).

Table 11

Estimated (US$ million/year) in the study area for representing equivalent biomes

Ecosystem services .	(US$ million/year) .
	1980 .	1990 .	2000 .	2010 .	2022 .
Provisioning services
Water supply	6.61	6.59	6.54	6.38	6.56
Food production	1.96	1.90	2.08	1.31	1.17
Raw material	14.30	13.84	9.33	10.36	9.35
Genetic resources	1.86	1.80	1.21	1.35	1.22
Regulating services
Water regulation	16.33	16.32	16.39	15.93	16.43
Water treatment	5.91	5.78	4.56	4.78	4.57
Erosion control	11.12	10.77	7.25	8.06	7.27
Climate regulation	10.12	9.80	6.60	7.33	6.62
Biological control	0.17	0.17	0.45	0.06	0.04
Gas regulation	0.00	0.00	0.00	0.00	0.00
Disturbance regulation	0.23	0.22	0.15	0.16	0.15
Supporting services
Nutrient cycling	41.86	40.52	27.29	30.32	27.37
Pollination	0.10	0.10	0.26	0.04	0.02
Soil formation	0.45	0.44	0.30	0.33	0.30
Habitat/refugia	0.00	0.00	0.00	0.00	0.00
Cultural services
Recreation	5.76	5.60	4.00	4.35	4.01
Cultural	0.09	0.09	0.06	0.07	0.06
Total	116.89	113.94	86.48	90.83	85.14

Ecosystem services .	(US$ million/year) .
	1980 .	1990 .	2000 .	2010 .	2022 .
Provisioning services
Water supply	6.61	6.59	6.54	6.38	6.56
Food production	1.96	1.90	2.08	1.31	1.17
Raw material	14.30	13.84	9.33	10.36	9.35
Genetic resources	1.86	1.80	1.21	1.35	1.22
Regulating services
Water regulation	16.33	16.32	16.39	15.93	16.43
Water treatment	5.91	5.78	4.56	4.78	4.57
Erosion control	11.12	10.77	7.25	8.06	7.27
Climate regulation	10.12	9.80	6.60	7.33	6.62
Biological control	0.17	0.17	0.45	0.06	0.04
Gas regulation	0.00	0.00	0.00	0.00	0.00
Disturbance regulation	0.23	0.22	0.15	0.16	0.15
Supporting services
Nutrient cycling	41.86	40.52	27.29	30.32	27.37
Pollination	0.10	0.10	0.26	0.04	0.02
Soil formation	0.45	0.44	0.30	0.33	0.30
Habitat/refugia	0.00	0.00	0.00	0.00	0.00
Cultural services
Recreation	5.76	5.60	4.00	4.35	4.01
Cultural	0.09	0.09	0.06	0.07	0.06
Total	116.89	113.94	86.48	90.83	85.14

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Table 12

Estimated (US$ million/year) changes in the study area for representing equivalent biomes

Ecosystem services .	(US$ million/year) .
	1980–1990 .	1990–2000 .	2000–2010 .	2010–2022 .	1980–2022 .
Provisioning services
Water supply	−0.012	−0.053	−0.161	0.176	−0.050
Food production	−0.058	0.179	−0.770	−0.146	−0.794
Raw material	−0.459	−4.517	1.034	−1.007	−4.949
Genetic resources	−0.060	−0.588	0.135	−0.131	−0.644
Regulating services
Water regulation	−0.009	0.072	−0.461	0.500	0.102
Water treatment	−0.127	−1.228	0.227	−0.215	−1.343
Erosion control	−0.357	−3.514	0.804	−0.783	−3.850
Climate regulation	−0.325	−3.198	0.732	−0.713	−3.504
Biological control	−0.005	0.283	−0.387	−0.021	−0.130
Gas regulation	0.000	0.000	0.000	0.000	0.000
Disturbance regulation	−0.007	−0.072	0.016	−0.016	−0.079
Supporting services
Nutrient cycling	−1.342	−13.222	3.026	−2.948	−14.487
Pollination	−0.003	0.165	−0.226	−0.012	−0.076
Soil formation	−0.015	−0.143	0.033	−0.032	−0.157
Habitat/refugia	0.000	0.000	0.000	0.000	0.000
Cultural services
Recreation	−0.163	−1.600	0.347	−0.336	−1.751
Cultural	−0.003	−0.029	0.007	−0.006	−0.031
Total	−2.94	−27.47	4.36	−5.69	−31.74

Ecosystem services .	(US$ million/year) .
	1980–1990 .	1990–2000 .	2000–2010 .	2010–2022 .	1980–2022 .
Provisioning services
Water supply	−0.012	−0.053	−0.161	0.176	−0.050
Food production	−0.058	0.179	−0.770	−0.146	−0.794
Raw material	−0.459	−4.517	1.034	−1.007	−4.949
Genetic resources	−0.060	−0.588	0.135	−0.131	−0.644
Regulating services
Water regulation	−0.009	0.072	−0.461	0.500	0.102
Water treatment	−0.127	−1.228	0.227	−0.215	−1.343
Erosion control	−0.357	−3.514	0.804	−0.783	−3.850
Climate regulation	−0.325	−3.198	0.732	−0.713	−3.504
Biological control	−0.005	0.283	−0.387	−0.021	−0.130
Gas regulation	0.000	0.000	0.000	0.000	0.000
Disturbance regulation	−0.007	−0.072	0.016	−0.016	−0.079
Supporting services
Nutrient cycling	−1.342	−13.222	3.026	−2.948	−14.487
Pollination	−0.003	0.165	−0.226	−0.012	−0.076
Soil formation	−0.015	−0.143	0.033	−0.032	−0.157
Habitat/refugia	0.000	0.000	0.000	0.000	0.000
Cultural services
Recreation	−0.163	−1.600	0.347	−0.336	−1.751
Cultural	−0.003	−0.029	0.007	−0.006	−0.031
Total	−2.94	−27.47	4.36	−5.69	−31.74

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The study reveals that the CS for all LULC classifications is greater than 1 (>1), indicating total estimated ESV is highly sensitive concerning VC (value coefficients). We find agriculture and shrubland/wasteland have recorded the highest and lowest CS hence VC for all LULC classes is adjusted to 50%, and it ranged from 1.96 to 1.96 and 0.55 to 1.72, respectively (Table 15). We find that the CS of all the LULC classes is high, pointing to attention that needs to be paid while considering the VC. Notwithstanding the uncertainty in the value coefficient, the overall results suggest that the CS determined in this research landscape is robust.

Table 15

ESV post VC and CS adjustment

Change in VC .	ESV .		Change (1980–2022) .	Effect of changing VC from the original value .
				1980 .		2022 .
	1980 .	2022 .		% .	CS .	% .	CS .
Forest +50	9.38	50.31	40.94	0.92	1.84	0.42	0.84
Forest −50	6.38	47.31	40.94
Agriculture +50	2.16	1.66	−0.50	0.98	1.96	0.98	1.96
Agriculture −50	−0.84	−1.34	−0.50
Water +50	26.56	26.87	0.31	0.77	1.55	0.69	1.38
Water −50	23.56	23.87	0.31
Shrubland/wasteland +50	84.79	12.30	−72.49	0.27	0.55	0.86	1.72
Shrubland/wasteland −50	81.79	9.30	−72.49

Change in VC .	ESV .		Change (1980–2022) .	Effect of changing VC from the original value .
				1980 .		2022 .
	1980 .	2022 .		% .	CS .	% .	CS .
Forest +50	9.38	50.31	40.94	0.92	1.84	0.42	0.84
Forest −50	6.38	47.31	40.94
Agriculture +50	2.16	1.66	−0.50	0.98	1.96	0.98	1.96
Agriculture −50	−0.84	−1.34	−0.50
Water +50	26.56	26.87	0.31	0.77	1.55	0.69	1.38
Water −50	23.56	23.87	0.31
Shrubland/wasteland +50	84.79	12.30	−72.49	0.27	0.55	0.86	1.72
Shrubland/wasteland −50	81.79	9.30	−72.49

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According to the findings, significant differences within the contribution of ESVs function are documented throughout the research period. Foremost statistically substantial changes were further investigated in ecosystem service value functions. such as water regulation, water supply, water treatment, climate regulation, erosion control, nutrient cycling, food production, biological control, and pollination, which seem to be the largest sources of overall ecosystem service functions (primarily regulating and provisioning services). The research of five time periods (1980, 1990, 2000, 2010, and 2022) and the complete study from 1980 to 2022 demonstrated that ESVs have altered because of the unpredictable nature of LULC changes. Specifically, the value of ESs has decreased during the previous 42 years using Costanza et al.'s value coefficient (1997). Over the last 42 years, agricultural land and shrubland/wasteland ESVs have fallen by varying percentages because of land use change, which is regarded as the primary provider of ESs. Early literature has demonstrated that changing the LULC has a harmful impact on the related ESs. The current research additionally discovered a decrease in ESVs from US$ 86.48 million to US$ 85.14 million between 1980 and 2022 due to declining agricultural land and wasteland/shrubland habitats.

Each ecosystem service function (⁠⁠) contributed very considerably to the research area. The most significant contributors of ESVs are regulating services (50.75% in 1980; 41.21% in 2022), supporting services (49.05% in 1980; 32.53% in 2022), and provisioning services (28.60% in 1980; 21.49% in 2022). Nonetheless, the top three contributors to ESs functions are nutrient cycling (45.64%), raw material (15.59%), and erosion control (12.13%).

Increasing urbanization in Indian cities poses a variety of major environmental issues. Environmental deterioration in Indian cities is a prevalent occurrence that requires immediate governmental intervention for sustainable urban growth and effective urban administration. Local governments worldwide have made an inventive effort to keep the biological environment as an intrinsic element of urban planning and policies. The primary cause of natural habitat deterioration is unregulated and chaotic urban expansion. No efficient measures are in place to preserve the biological setting of this medium-sized municipality. In this setting, research assistance would be critical in addressing environmental issues associated with increasing urbanization and assisting planners in implementing sustainable urban governance. The municipal government should also heed the decline in natural resources caused by urbanization. Some planning strategies to improve the Mangaluru city agglomeration would be (a) integration of ecological and spatial planning, (b) managing urban greens, and (c) conservation of water bodies.

This research examines the changes in LULC and their influence on ESs from 1980 to 2022, utilizing remote sensing (RS) data and GIS techniques relating to the global value coefficient (VC) developed by Costanza et al. (1997). The intensity of urbanization in the research field has grown by 37.10% in 42 years, from 1980 to 2022, and fluctuations in ESVs suggest that urban growth seems to influence overall ESVs in addition to particular ESVs throughout the research area. Because of the fast change of built-up areas, farmland, and shrubland/wasteland, key providers to ESs have reduced. During the previous 42 years, built-up areas and forests have expanded by 37.10 and 35.83%, respectively, while agricultural land and wasteland/shrubland have decreased by 9.54 and 63.44%. Total ecosystem service values (ESV_t) in the research area declined by US$ 116.89 million to US$ 85.14 million, while specific ESs altered owing to the loss of urban ecological land (UELs), mainly farmland and forest conversion into built-up areas, from 1980 to 2022. Within the research area, the constant growth of the built-up regions leads to the deterioration of natural land cover and the loss of ESs.

D.N. and A.K.S. conceptualized the whole article; D.N. and A.K.S. developed the methodology; D.N. and A.K.S. validated the article; D.N. rendered support in formal analysis; D.N. and A.K.S. investigated the work; D.N. wrote the original draft; A.K.S. wrote the review and edited the article; A.K.S. and N. R. D. supervised the work. All authors have read and agreed to the published version of the manuscript.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.