The steady growth of cities alters the urban environmental pattern and functions, posing significant challenges to urban ecological as well as environmental governance. Under this setting, analyzing the relationship between urban growth and ecological services is critical for management and policy-related sustainable urban development. The primary goal of this research is to analyse the dynamic urbanization and its influence on urban ecosystem services via changes in LULC of Mangaluru city agglomeration, India. For five decades (1980,1990, 2000, 2010, and 2022), the ecosystem service values (ESVs) are analysed using remote sensing data and Geographic Information System (GIS) techniques corresponding with the global value coefficient (VC) to estimate total ecosystem service values (ESVt) and individual ecosystem service function change. The study shows that ecosystem service values (ESVs) declined by US$ 116.89 million to US$ 85.14 million due to 9.54 and 63.44% decreases in agricultural land and wasteland/shrubland between 1980 and 2022. In terms of individual ecosystem service functions, regulating services increased from 1980 to 2022, with nutrient cycling (45.64%), raw material (15.59%), and erosion control (12.13%) contributing the most. The loss in total and certain individual ESV in the study landscape necessitates immediate action to improve urban ecosystem sustainability via proper planning and policy.

  • The quantification of ecosystem services (ES) plays an important role in properly understanding and efficiently managing social-ecological systems.

  • The decrease in agricultural land and shrubland/wasteland is the main reason for the degradation of ecosystem services.

  • The findings point out that regional or local-level ecosystem services-related work is immensely important and a hotspot of current research.

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.

Study area

Mangaluru is a port town located in the western coastal plains of India (Figure 1). The extent of the city is located with the help of GIS, ranging from 12° 48′ 0″ N to 13° 4′ 0″ N and 74° 48′ 0″ E to 74° 56′ 0″ E. The city spreads longitudinally from the north-south direction, with the Arabian Sea on the west and the ecological hotspot of the Western Ghats on the east. The town is spatially located by two rivers, Gurupura and Netravathi, in the north and south, respectively, with the central part taken over by the core city area. The city's urban agglomeration comprises a Municipal Corporation, two municipal councils, and one Town Panchayat. Mangaluru city is in the Mangaluru taluk, one of the administrative units. Mangaluru receives an annual rainfall of 4,464.47 mm, with a maximum temperature of 34.5 °C and a minimum temperature recorded is 16 °C for the year 2022.
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Close modal

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.

Data used

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.

Table 1

Details of Landsat imagery used for the study

Satellite senorSatellite No.Date of acquisitionSpatial resolution (m)Cloud coverage (%)
Multispectral scanner (MSS) Landsat 1–5 14 Feb 1980 60 
TM Landsat 4–5 16 Feb 1990 30 
Enhanced Thematic Mapper (ETM)+ Landsat 7 10 Feb 2000 30 
ETM+ Landsat 7 20 Feb 2010 30 
OLI-II/TIRS-II Landsat 9 12 Feb 2022 30 
Satellite senorSatellite No.Date of acquisitionSpatial resolution (m)Cloud coverage (%)
Multispectral scanner (MSS) Landsat 1–5 14 Feb 1980 60 
TM Landsat 4–5 16 Feb 1990 30 
Enhanced Thematic Mapper (ETM)+ Landsat 7 10 Feb 2000 30 
ETM+ Landsat 7 20 Feb 2010 30 
OLI-II/TIRS-II Landsat 9 12 Feb 2022 30 
Table 2

Types of LULC present in the study area

Types of land coverExplanation
Agriculture land The area is used as Hinterland in crop production. 
Built-up land Area covered by impervious structures. 
Water bodies Area covered with rivers, lakes, ponds, etc. 
Forest area Area covered with forest, parks, etc. 
Shrubland/wasteland Area covered with bushes, shrubs, etc. 
Types of land coverExplanation
Agriculture land The area is used as Hinterland in crop production. 
Built-up land Area covered by impervious structures. 
Water bodies Area covered with rivers, lakes, ponds, etc. 
Forest area Area covered with forest, parks, etc. 
Shrubland/wasteland Area covered with bushes, shrubs, etc. 

LULC classification and accuracy evaluation

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 measuresFormula
Producer accuracy ( 
User accuracy ( 
Overall classification ( 
Kappa coefficient ( 
Accuracy measuresFormula
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.

Table 4

Cohen's kappa statistics (Kumar Shukla et al. 2018)

Sl.NoKappa statisticsStrength of assessment
<0.00 Poor 
0.00–0.20 Slight 
0.21–0.40 Fair 
0.41–0.60 Moderate 
0.61–0.80 Substantial 
0.81–1.00 Almost perfect 
Sl.NoKappa statisticsStrength of assessment
<0.00 Poor 
0.00–0.20 Slight 
0.21–0.40 Fair 
0.41–0.60 Moderate 
0.61–0.80 Substantial 
0.81–1.00 Almost perfect 

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 (ESV)

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 57). 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.

Table 5

Equivalent biomes representing LULC classes and total ESV coefficients

LULC classesEquivalent biomeTotal ESV coefficient (USD ha−1 year−1)
Agriculture land Cropland 92 
Built-up land Urban 
Water bodies Lakes/rivers 8,498 
Forest area Tropical forest 2,008 
Shrubland/wasteland Tropical forest 2,008 
LULC classesEquivalent biomeTotal ESV coefficient (USD ha−1 year−1)
Agriculture land Cropland 92 
Built-up land Urban 
Water bodies Lakes/rivers 8,498 
Forest area Tropical forest 2,008 
Shrubland/wasteland Tropical forest 2,008 
Table 6

Classification of ecosystem services

Ecosystem servicesEquivalent types
Provisioning ES Raw materials, fresh water, food, etc. 
Regulating ES Local climate, extreme events, water treatment, soil erosion and fertility, pollination, etc. 
Supporting ES Habitat for species, genetic biodiversity, etc. 
Cultural ES Recreation, culture, aesthetic appreciation, etc. 
Ecosystem servicesEquivalent types
Provisioning ES Raw materials, fresh water, food, etc. 
Regulating ES Local climate, extreme events, water treatment, soil erosion and fertility, pollination, etc. 
Supporting ES Habitat for species, genetic biodiversity, etc. 
Cultural ES Recreation, culture, aesthetic appreciation, etc. 
Table 7

Annual (US$/year) of equivalent LULC biomes

Ecosystem servicesLULC types of ecosystem service values (US$/ha/year)
CroplandUrbanLakes/riversTropical forest
Provisioning services 
Water supply – 2,117 
Food production 54 41 32 
Raw material – – 315 
Genetic resources – – 41 
Regulating services 
Water regulation – 5,445 
Water treatment – 665 87 
Erosion control – – 245 
Climate regulation – – 223 
Biological control 24 – – 
Gas regulation – – – 
Disturbance regulation – – 
Supporting services 
Nutrient cycling – – 922 
Pollination 14 – – 
Soil formation – – 10 
Habitat/refugia – – 
Cultural services 
Recreation – 230 112 
Cultural – – – 
Total 92 8,498 2,008 
Ecosystem servicesLULC types of ecosystem service values (US$/ha/year)
CroplandUrbanLakes/riversTropical forest
Provisioning services 
Water supply – 2,117 
Food production 54 41 32 
Raw material – – 315 
Genetic resources – – 41 
Regulating services 
Water regulation – 5,445 
Water treatment – 665 87 
Erosion control – – 245 
Climate regulation – – 223 
Biological control 24 – – 
Gas regulation – – – 
Disturbance regulation – – 
Supporting services 
Nutrient cycling – – 922 
Pollination 14 – – 
Soil formation – – 10 
Habitat/refugia – – 
Cultural services 
Recreation – 230 112 
Cultural – – – 
Total 92 8,498 2,008 

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.

Furthermore, shrubland in the research region is less thick than forests and only covers areas filled with tiny trees, bushes, and shrubs, with grasses found in select locations. As a result, while the main constituents of the depicted forest and shrubland categories vary from those of the forest biome, they provide equivalent ESs (Gashaw et al. 2018). Similarly, the built-up region was represented by an urban biome. The built-up area in the research region did not reflect a highly urbanized place but rather just areas used for building sites and settlements. As a result, the functions and features of the built-up region differ from those of the urban biome, and built-up areas are predicted to offer superior ecosystem service than the depicted biome. However, the portrayed grassland biome is relatively similar; they are regions covered by grasses that are frequently utilized for grazing and that stay for a few months of the year. As a result, while the depicted biomes are not identical in terms of attributes and functions to the LULC in this situation, they may be utilized as a backup for calculating the ESV of the LULC classes for our study area. Many ESV research used proxies for LULC types and the respective biomes.
formula
(1)
formula
(2)
formula
(3)

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 .

The work first comprises estimating the total ecosystem services values (ESVs) using the formula mentioned above of the study area. In completion of this estimation, the average of ESVs is calculated using the following formula:
formula
(4)

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).

ES sensitivity analyses

Biomes are utilized as substitutes for specific LULC categories in this research, which are not precisely aligned well with biomes established by Costanza et al. (1997). As a result, there are ambiguities in calculating VC (value coefficient). As a result, a sensitivity analysis is required to estimate the variations (%) of ESV on the variations (%) of value coefficient (VC). The coefficient of sensitivity (CS) has been calculated using the economic notion of elasticity and the preceding formula (Kreuter et al. 2001).
formula
(5)

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.

Pattern of urban expansion in the study area

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.

Table 8

Trend of population growth concerning the built-up area of the study region (Census of India)

YearGrowth of built-up area (%)YearPopulation growth (%)
1980–1990 2.90 1981–1991 34.34 
1990–2000 4.40 1991–2001 80.26 
2000–2010 22.58 2001–2011 17.47 
2010–2022 6.94 2011–2021 13.92 
YearGrowth of built-up area (%)YearPopulation growth (%)
1980–1990 2.90 1981–1991 34.34 
1990–2000 4.40 1991–2001 80.26 
2000–2010 22.58 2001–2011 17.47 
2010–2022 6.94 2011–2021 13.92 

LULC change dynamics

For the thorough LULC classification, multi-temporal remotely sensed images were employed. Figure 2 shows cases of the classified map of 1980, 1990, 2000, 2010, and 2022 generated for the study. The classification includes water bodies, forests, wasteland/shrubland, agricultural land, and built-up areas in the decadal analysis can be visualized on the map. Through the decadal analysis, we observe that forest is the primary determinant of LULC classification, followed by built-up area and agricultural land. The water body constitutes the lowest percentage of the cover among all classes.
Figure 2

LULC map of Mangaluru city agglomeration: (a) 1980, (b) 1990, (c) 2000, (d) 2010, and (e) 2022.

Figure 2

LULC map of Mangaluru city agglomeration: (a) 1980, (b) 1990, (c) 2000, (d) 2010, and (e) 2022.

Close modal

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%.

Accuracy assessment

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).

Table 9

Aerial coverage of LULC classes of Mangaluru city

LULC class1980
1990
2000
2010
2022
Area (ha)(%)Area (ha)(%)Area (ha)(%)Area (ha)(%)Area (ha)(%)
Water bodies 2,949 5.18 2,949 5.18 2,978 5.23 2,890 5.08 2,985 5.25 
Forest 3,923 6.89 3,712 6.52 1,767 3.11 25,162 44.22 24,309 42.72 
Wasteland/Shrubland 41,478 72.90 40,233 70.71 27,837 48.92 7,724 13.58 5,379 9.45 
Agricultural land 7,184 12.63 6,980 12.27 18,772 32.99 2,631 4.62 1,756 3.09 
Built-up land 1,364 2.38 3,019 5.31 5,541 9.74 18,490 32.50 22,470 39.49 
Total (ha) 56,898 100 56,898 100 56,898 100 56,898 100 56,898 100 
LULC class1980
1990
2000
2010
2022
Area (ha)(%)Area (ha)(%)Area (ha)(%)Area (ha)(%)Area (ha)(%)
Water bodies 2,949 5.18 2,949 5.18 2,978 5.23 2,890 5.08 2,985 5.25 
Forest 3,923 6.89 3,712 6.52 1,767 3.11 25,162 44.22 24,309 42.72 
Wasteland/Shrubland 41,478 72.90 40,233 70.71 27,837 48.92 7,724 13.58 5,379 9.45 
Agricultural land 7,184 12.63 6,980 12.27 18,772 32.99 2,631 4.62 1,756 3.09 
Built-up land 1,364 2.38 3,019 5.31 5,541 9.74 18,490 32.50 22,470 39.49 
Total (ha) 56,898 100 56,898 100 56,898 100 56,898 100 56,898 100 
Table 10

Accuracy assessment of the 2022 LULC map produced from Landsat OLI-II/Thermal Infrared Sensor(TRIS)-II data representing both the confusion matrix and the kappa statistics

Classified dataReference data
Row totalUser's accuracy (%)Overall kappa statistics
AGBUFWL/SLWB
AG 120 128 93.75 0.89 
BU 57 63 90.48 
120 132 90.91 
WL/SL 82 91 90.11 
WB 58 63 92.06 
Column Total 131 62 129 91 64 477  
Producer's Accuracy (%) 91.60 91.94 93.02 90.11 90.63   
Overall Accuracy (%) 91.61  
Classified dataReference data
Row totalUser's accuracy (%)Overall kappa statistics
AGBUFWL/SLWB
AG 120 128 93.75 0.89 
BU 57 63 90.48 
120 132 90.91 
WL/SL 82 91 90.11 
WB 58 63 92.06 
Column Total 131 62 129 91 64 477  
Producer's Accuracy (%) 91.60 91.94 93.02 90.11 90.63   
Overall Accuracy (%) 91.61  

AG, agricultural land; BU, built-up land; F, forest; WL/SL, wasteland/shrubland; WB, water body.

Bold represents the accuracy level in pixels and ground verification.

Total ESV changes in the study area

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)
19801990200020102022
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)
19801990200020102022
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 
Table 12

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

Ecosystem services (US$ million/year)
1980–19901990–20002000–20102010–20221980–2022
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)
1980–19901990–20002000–20102010–20221980–2022
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 

Impact of LULC change on ESV

In the past four decades, noteworthy changes in the contribution of ESVs over a particular LULC category is shown in Table 9, and Figure 2. Marking LULC changes in the study area from 1980 to 2022, agricultural land and wasteland/ shrubland decreased by 9.54 and 63.44%, respectively. The built-up area increased in the study area by 37.10%, and we find that the green canopy of the forest area increased by 35.83% in the last four decades. Subsequently, we see a decline in total ESV from 1980 to 2022. The ESV of wasteland/shrubland and agricultural land decreased by 80.67 and 90.75% (Tables 13 and 14). These negative impacts of LULC on the ESVs influenced abundantly the total ESVs of the study area (Figure 3). The ecosystem service value of wasteland/shrubland and agricultural area decreased by US$ 72.99 million/ha/year while total ESVs in the study area declined by US$ 31.74 million/ha/year (Table 14).
Table 13

Total ecosystem service values ) for LULC classes

LULC classesESV (US million/ha/year)
19801990200020102022
Water bodies 25.06 25.06 25.31 24.56 25.37 
Forest 7.88 7.45 3.55 50.53 48.81 
Shrubland/wasteland 83.29 80.79 55.90 15.51 10.80 
Agricultural land 0.66 0.64 1.73 0.24 0.16 
Built-up land 0.00 0.00 0.00 0.00 0.00 
Total 116.89 113.94 86.48 90.83 85.14 
LULC classesESV (US million/ha/year)
19801990200020102022
Water bodies 25.06 25.06 25.31 24.56 25.37 
Forest 7.88 7.45 3.55 50.53 48.81 
Shrubland/wasteland 83.29 80.79 55.90 15.51 10.80 
Agricultural land 0.66 0.64 1.73 0.24 0.16 
Built-up land 0.00 0.00 0.00 0.00 0.00 
Total 116.89 113.94 86.48 90.83 85.14 
Table 14

Total ecosystem service changes during 1980–2022 for LULC classes

LULC classesESV change (US million/ha/year)
1980–19901990–20002000–20102010–20221980–2022
Water bodies 0.00 0.25 −0.75 0.81 0.31 
Forest 0.42 −3.91 46.98 −1.71 40.94 
Shrubland/wasteland 2.50 −24.89 −40.39 −4.71 −72.49 
Agricultural land 0.02 1.08 −1.48 −0.08 −0.50 
Built-up land 0.00 0.00 0.00 0.00 0.00 
Total 2.94 −27.47 4.36 −5.69 −31.74 
LULC classesESV change (US million/ha/year)
1980–19901990–20002000–20102010–20221980–2022
Water bodies 0.00 0.25 −0.75 0.81 0.31 
Forest 0.42 −3.91 46.98 −1.71 40.94 
Shrubland/wasteland 2.50 −24.89 −40.39 −4.71 −72.49 
Agricultural land 0.02 1.08 −1.48 −0.08 −0.50 
Built-up land 0.00 0.00 0.00 0.00 0.00 
Total 2.94 −27.47 4.36 −5.69 −31.74 
Figure 3

Impact of LULC on ESV.

Figure 3

Impact of LULC on ESV.

Close modal

Ecosystem sensitivity analysis

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 VCESV
Change (1980–2022)Effect of changing VC from the original value
1980
2022
19802022%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 VCESV
Change (1980–2022)Effect of changing VC from the original value
1980
2022
19802022%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     

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 (ESVt) 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.

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