Urban Expansion and Land Use Transformations in Midnapore City (2003–2024): Implications for Sustainable Development

Abstract

Amidst global shifts in land use patterns due to urbanization, this study focuses on the rapid land use and land cover (LULC) changes in Midnapore City during the periods 2003–2014 and 2014–2024. The study employs Landsat 5 and 8 imagery with 30 m spatial resolution which were processed through Maximum Likelihood Classifier (MLC) algorithms. The results were attained through ArcGIS 10.2.2 and ERDAS IMAGINE 2014 software, with ground-truth validation using data from 117, 111, and 116 points for 2024, 2014, and 2003, respectively. For the validation, the kappa coefficient was calculated and achieved 87.3%, 88.1%, and 81.7% for 2024, 2014, and 2003, indicating substantial accuracy. Using statistical measures such as change matrix union, binary logistic regression, and correlation matrix analysis applied to classified LULC outputs and spatial drivers, the research highlights significant transformations in the region. The study reveals significant transformations, notably the conversion of 77% of forest areas and 5% of fallow land to built-up land. The increased rate of agricultural land conversion to built-up areas is evident after 2014, indicating rapid urban growth. These factors led to the reduction of LULC classes possessing substantial ecological value like forests and scrub lands which are becoming more accessible due to the increasing population. The results point out the drastic alteration of these developments and recommend a planning approach responsive to environmental needs for safeguarded ecological impacts. The research highlights the importance of reforestation, preservation of water bodies, and socio-economic surveillance in fostering urban management and sustainable development in Midnapore City.

1. Introduction

Urbanization is a global trend expected to persist over the coming decades and the anthropogenic activities have significantly reshaped land use, leading to noticeable changes in land patterns and substantial environmental impacts [1,2]. Understanding these transformations is crucial for effective ecological and environmental planning and management to promote sustainable development [3,4,5,6]. Increasing population, industrialization, and variability in climate and natural disasters have made natural resource management increasingly complex [7,8]. Hence, assessing land use and land cover changes over time is vital for evaluating both current and future resources [1,9].

Various methodologies for land use and land cover (LULC) mapping and change detection have been developed and applied globally [10,11]. Differentiating between types of change, such as conversion and modification, is essential for understanding land dynamics [8,12,13,14]. LULC changes result from complex interactions of factors operating at different spatial and temporal scales. These factors are categorized into proximate causes that include agricultural expansion and infrastructure development and underlying drivers such as social, demographic, political, economic, technological, and biophysical factors [15].

Remote sensing and Geographic Information Systems (GIS) are highly effective tools for managing land and natural resources. These technologies provide a cost-effective and accurate means for analyzing landscape dynamics and offer valuable insights into spatial and temporal changes, supporting informed decision-making [9,15,16,17,18]. Binary logistic regression is highly valued for land-cover classification and detecting LULC changes due to its straightforward application and high classification accuracy. Its capability to provide clear and interpretable results makes it particularly useful in contexts where transparency and precision are crucial for accurately identifying and analyzing LULC changes [19]. Numerous studies have utilized this method to examine LULC change determinants [19,20,21,22,23,24]. Union matrix analysis further explores LULC changes by considering a range of factors categorized into natural (slope, aspect, elevation, soil types, wetlands) and socioeconomic (proximity to rivers, roads, urban centers, green spaces, agricultural and forest lands, residential zones, industrial areas, and population density) [25,26,27].

Its historical importance, coupled with rapid population growth, has driven the expansion of built-up areas, making it an important case study for examining the effects of urbanization on land use and socio-economic development. This shift reflects both positive and negative impacts of various variables [28,29]. The city’s urban growth is primarily influenced by factors such as low land prices, availability of reclaimed land, benefits of open spaces on the urban fringe, and economic opportunities. These factors have accelerated land-use changes, resulting in increased urban development and settlement [30]. Consequently, few landscapes in the city remain in their original state. This land use system highlights the intricate relationship between the biosphere and socio-economic structures [31].

Global studies on LULC changes exist, but there is a gap in research pertaining to medium-sized Indian towns like Midnapore. Literature focusing on Indian cities tends to highlight larger metropolitan regions or focus on singular environmental components without holistic, statistical analyses to reveal the factors that drive LULC changes. This research addresses the identified gap to analyze the spatio-temporal changes in Midnapore City from 2003 to 2024 by applying an integrated methodology based on Maximum Likelihood Classifier (MLC), change matrix, binary logistic regression, and correlation analyses. This research enables the planning and management of land resources by revealing the trends and causes of land use and land cover changes, highlighting Midnapore’s planning requirements.

There has been a significant advancement in LULC classification and in detecting changes pertaining to it. Various techniques have been put forward over the recent years. People have tended to favor older techniques such as Maximum Likelihood Classification (MLC), Support Vector Machines (SVM), and Random Forest (RF) classifiers because they have consistently performed well from a statistical point of view and are simpler to automate in supervised classification processes. People generally use change detection approaches such as post-classification comparison, cross-tabulation matrices, and infuse spatial models (e.g., Markov Chains or Cellular Automata) to project scenarios for the future. In further research, NDVI along with other spectral indices are included in the study in question so as to improve classification accuracy and monitor changes in vegetation over a defined period of time. While progressing and advancing through these phases, there is still a lack of cohesive systems that unite proven techniques of statistical landform modelling with visual representation to explain the complex transitions of developed and undeveloped areas within small and medium-scale cities in India.

To fill this gap, we use the change matrix union of binary logistic regression and Sankey Diagram as a framework to explain the extent of LULC transitions within the context of two decades across the years 2000 and 2020. This method improves spatial change quantification by integrating multiple dimensions of spatial change, transforming how urbanization and land stress are conveyed amid brisk development in the Midnapore region.

The objective of this study is to analyze the spatio-temporal changes in Midnapore City from 2003 to 2024 and the factors driving urban growth. The study uses GIS and remote sensing technologies, combined with statistical methods such as change matrix analysis, binary logistic regression, and correlation matrix analysis, to provide a comprehensive evaluation of these dynamics.

2. Methods and Methodology

2.1. Study Area

It is located in the state of West Bengal, India, and is situated at 22°424′ N 87°319′ E, with an elevation of 2 m above mean sea level; it serves as the district headquarters for Paschim Midnapore. It is the second-largest town in the district and a key commercial and administrative center. The city’s strategic location also makes it well-connected to other regions, further boosting its growth and urbanization. Population density has been a major factor in the conversion of agricultural land to built-up areas, particularly between 2014 and 2024. The municipality is divided into 25 wards, covering an area of 18.65 sq. km., and is situated on the banks of the Kangsabati River. Midnapore experiences a hot and humid tropical monsoon climate. During the summer months, from April to mid-June, temperatures typically rise to the upper 30 °C and can reach the mid-40 °C, with nighttime temperatures staying around the low 30 °C. According to the 2011 census, Midnapore Municipality had a total population of 169,127, with 85,362 males and 83,765 females. The literacy rate in the area was recorded at 90.01%. Figure 1 shows the location map of the study area.

2.2. Topography

Midnapore town features a generally flat terrain with gentle slopes, particularly towards the eastern part and the southern fringes. The town covers an area of 18.65 square kilometers and is situated on the border between the Midnapore West and Midnapore East districts.

2.3. Transport System

Midnapore is well-connected to both larger cities in the region and smaller towns, villages, and districts. The Midnapore railway station lies on the Howrah route, with several express, local, and passenger trains operating throughout the day between Howrah and Midnapore, as well as Adra and Midnapore. The town also hosts a central bus stand along with several other bus stands situated within the town, facilitating extensive road connectivity. Significant infrastructure developments along the town’s main roads have further enhanced its accessibility and spurred economic growth in the region, contributing to Midnapore’s role as an administrative and commercial center in the Paschim Medinipur district (Figure 2b).

2.4. Methodology

2.4.1. Maximum Likelihood Classifier (MLC)

The Maximum Likelihood Classifier (MLC) is a supervised classification algorithm grounded in Bayesian probability theory. It operates under the assumption that spectral data for each land cover class follow a multivariate normal distribution. For a pixel with spectral values x, the equation below is the probability of it belonging to class $\omega$, which is calculated using the probability density function.

$P(x|\omega )={\displaystyle \frac{1}{{\left(2\pi \right)}^{\raisebox{1ex}{$n$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}{\left|{\mathsf{\Sigma }}_{\omega }\right|}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.}}}\exp (-{\displaystyle \frac{1}{2}}{(x-{\mu }_{\omega })}^T{\sum }_{\omega }^{-1}(x-{\mu }_{\omega }))$

The MLC operates on the principle of Bayesian probability, assuming that the spectral data for each land cover class follow a multivariate normal distribution. For a given pixel with spectral values x, the probability of it belonging to class ω is calculated using the probability density function, where ${\mu }_{\omega }$ represents the mean vector of class ω (estimated from training data), ${\mathsf{\Sigma }}_{\omega }$ is the covariance matrix of class $\omega$ (capturing spectral variability), and n denotes the number of spectral bands. The classifier assigns the pixel to the class with the highest posterior probability. In our study, training samples for each LULC class (e.g., forest, built-up, agriculture) were derived from high-resolution Google Earth imagery and field surveys. These samples were used to compute class-specific statistics (mean and covariance), enabling the model to distinguish spectral signatures across heterogeneous urban and peri-urban landscapes. This approach ensured accurate classification by leveraging the distinct spectral characteristics of each land cover type, even in complex environments where spectral overlap between classes (e.g., fallow land vs. scrub) is common [32,33].

Although machine learning and deep learning models such as Random Forest and Convolutional Neural Networks (CNNs) have demonstrated superior classification performance in many land use and land cover (LULC) studies, the Maximum Likelihood Classifier (MLC) was purposefully selected in this study for several practical and methodological reasons. First, the moderate spatial resolution of the Landsat imagery (30 m), combined with a relatively small and balanced dataset, favors parametric approaches like MLC, which are statistically robust under such conditions. Second, MLC offers clear probabilistic outputs and straightforward interpretability, which are advantageous when the results are intended to inform policy and planning decisions that require transparency and reproducibility. Third, unlike complex models that may require extensive tuning, large training datasets, or high computational power, MLC is computationally efficient and well-suited for long-term, multi-temporal analyses, ensuring methodological consistency across two decades of imagery. Finally, the study prioritizes comparability over maximal accuracy, and previous research has validated the reliability of MLC for similar urban environments, particularly when classification accuracy exceeds 80%, as achieved here.

2.4.2. Data Source

The methodology of this study involved a comprehensive approach to analyse land use and land cover (LULC) changes in Midnapore City over three distinct periods: 2003, 2014, and 2024 (Figure 3). The analysis utilized a moderate-resolution of 30 m satellite imagery from the United States Geological Survey (USGS) Earth Explorer, specifically LANDSAT-5 and LANDSAT-8 OLI datasets for the years 2003, 2014, and 2024. The datasets were complemented with ancillary data such as population statistics, topographical maps, and socio-economic data to provide a contextual understanding of land use and land cover (LULC) changes. In this study, ancillary geospatial data such as proximity to roads, slope, and elevation were used as explanatory variables in the binary logistic regression model to identify the spatial drivers of LULC changes. These layers were derived from topographic datasets and processed using spatial analysis tools in ArcGIS. Although not directly used in the classification process, these variables played a key role in modelling and interpreting land transformation dynamics across Midnapore City.

2.4.3. Data Preprocessing and Image Classification

For a better analysis, we first correct all the errors from the satellite imagery with the help of image pre-processing methods which involve band ratio, layer stacking, geometric and radiometric corrections, and image enhancement. The raster LULC maps for each year were classified with distinct colors and labels using ArcGIS 10.2.2 to differentiate between various land cover types. Training samples were created for the model to enhance the accuracy of the classification. The Maximum Likelihood Classifier, for its higher accuracy, was used for the LULC classification. Maximum Likelihood Classifier is very useful for such complex urban topography because of its ability to use high-dimensional data and produce a precise classification.

2.4.4. Change Detection and Statistical Analysis

Using ERDAS IMAGINE 2014, a matrix union was performed to compare LULC changes between 2003 and 2014 and between 2014 and 2024. A change matrix was generated from these attributes, and the area of each LULC class was calculated using the following formula: (=first count value of the table/1,000,000). The extent of LULC changes for the periods 2003–2014 and 2014–2024 was quantified to assess the magnitude of transformations [6].

To identify significant changes in LULC classes, binary logistic regression was applied, with ‘1’ indicating substantial changes and ‘0’ minimal changes. The analysis used XLSTAT, where the data raster was extracted to points, binary values were assigned, and Euclidean distances, along with other variables, were incorporated into MS Excel for modeling [34]. Correlation matrices were generated to understand the relationships between different variables and their influence on LULC changes.

2.4.5. Validation, Visualization and Mapping

Ground-truthing and accuracy assessments were conducted to validate classification outputs. Cross-tabulation with field survey data ensured reliability in detecting LULC changes. Further Kappa Coefficient was calculated to check and validate the accuracy of the LULC classification with 117, 111, and 116 points for 2024, 2014, and 2003, respectively. It came out as 87.3% for 2024, 88.1% for 2014, and 81.7% for 2003. A kappa coefficient above 80% is considered to be sufficient, which is achieved in all the three LULC classifications. The study employed ArcGIS 10.2.2 to create and classify LULC maps [34,35,36], ERDAS IMAGINE 2014 for matrix union and change matrix calculations [37,38], and XLSTAT to perform binary logistic regression analysis [6,39,40], allowing for a detailed examination of LULC changes and patterns.

2.4.6. Binary Logistic Regression Modeling

Binary logistic regression (BLR) was used to identify the drivers of land use and land cover (LULC) changes, particularly transitions to built-up areas during 2003–2014 and 2014–2024. The dependent variable was binary: 1 for pixels that changed to built-up land and 0 for those that did not. Predictor variables included distance to roads, water bodies, forest, and agricultural and built-up areas, along with slope. The model estimates the probability of LULC conversion using the logistic function. Large coefficient values reflect the sensitivity of land change to proximity factors and were converted to odds ratios (exp(β)) for easier interpretation. Changes in coefficient signs between periods, such as for water-to-agriculture conversion, indicate shifts in land use dynamics over time. The analysis was performed in XLSTAT after spatial data preprocessing in ArcGIS.

3. Results

3.1. Land Use and Land Cover Change Detection

Results from the analysis of LULC conducted in ArcGIS for Midnapore City demonstrate the crucial transformations that took place during the periods of 2003, 2014, and 2024 (Figure 4). The present LULC highlight maps denote large changes including built-up areas which appear when a strong urbanization combined with a growth in population becomes noteworthy and agricultural land, forest, and fallow land are converted. Notably, the forest areas were transformed to the greatest extent, followed by agricultural and then ultimately fallow land. The proximity to roads and the slope of the terrain were some of the factors that significantly influenced the changes, as demonstrated by the logistic regression analysis. The Kappa coefficient for the LULC classifications was calculated and found to be 81.7% for 2003, 88.1% for 2014, and 87.3% for 2024, thus determining the accuracy of the classification. The changes during the periods of two decades are very evident from Figure 5a,b, demonstrating changes in hundredths for urban expansion as well as the city’s topography in a span of just a few years.

In the years 2003 and 2024, changes in land use and land cover were noticed due to forest cover, agricultural land, fallow land, and water bodies decreasing while built-up areas increased. Forest cover national park suffered a net loss of over 75% due to changes to the built-up area. Agricultural land equally suffered a deficit of 23% because of significant changes in settlements. Expansion of urban areas also resulted in a reduction of 38% in fallow land. Some water bodies have been changed into farming areas, resulting in a decline of 34%. The built-up areas, in comparison, received a population increase along with urbanization which expanded by 37% in that time frame. Changes in urban expansion greatly influenced the alteration of land cover. These transformations indicate the degree of change in land cover which depends on human activity and the development of cities.

Extensive research has been conducted on various models of land use and land cover change. These models typically incorporate three critical dimensions. The first two dimensions, time and space, provide a common framework within which all biophysical and human processes occur. The third dimension is human decision-making, which plays a crucial role in influencing LULC change.

3.2. Change Matrix of LULC

3.2.1. For 2003–2014

The study conducted on Midnapore city during the period 2003–2014 utilized the Supervised Digital Classification method, specifically the Maximum Likelihood Classifier, to identify the land use and land cover (LULC) classes. A two-way cross-matrix served as the primary analytical tool for determining changes within the study area. To quantify the conversions from one land class to another, cross-tabulation analysis was employed. The analysis of changes among the seven LULC classes from 2003 to 2014 reveals distinct trends in land use transitions, as presented in Table 1 and Figure 5a. The change matrix shows that water bodies transitioned to other classes, including agricultural land (0.18) and built-up areas (0.01), while some remained as water bodies (0.21). Forests saw significant conversions, primarily to agricultural land (0.68), with smaller areas shifting to fallow land (0.01) and built-up land (0.18). Agricultural land largely persisted (11.93) but also transitioned to scrub land (0.24), fallow land (0.78), and built-up areas (0.18).

Scrub land exhibited limited change, with small portions converting to forest (0.02) and agriculture (0.06). Fallow land underwent notable shifts, transitioning primarily to agriculture (0.49), but a portion also remained as fallow land (1.02) and converted to built-up land (0.02). Barren land transitioned to agricultural land (0.08), while smaller areas became barren land (0.04) or built-up land (0.29). Built-up land remained relatively stable (0.89).

The highest percentages of change were observed in agricultural land, fallow land, and built-up land, highlighting the increasing urbanization and shifts in land use during this period.

3.2.2. For 2014–2024

The analysis of land use and land cover (LULC) changes between 2014 and 2024 reveals significant transformations across the seven identified LULC classes, as detailed in Table 1 and Figure 5b. Water bodies largely remained unchanged (0.18), but some portions were converted to agricultural land (0.08). Forests experienced notable reductions, transitioning to agricultural land (0.21) and, more significantly, built-up land (0.81), indicating substantial forest loss due to urban expansion.

Agricultural land primarily persisted (10.70), with smaller areas transforming to scrub land (0.21), fallow land (0.09), and built-up land (2.36).

Scrub land mostly remained stable (0.29) but saw conversions into forests (0.12) and built-up land (0.21). Fallow land also remained mostly unchanged (0.66), with some transitions to agricultural land (1.05), scrub land (0.02), and built-up areas (0.06). Barren land showed minimal change, mostly remaining barren (0.05), while some areas were converted to agricultural land (0.03) and built-up land (0.08). Built-up land remained largely stable (2.04), reflecting the growing urbanization and the corresponding impact on land use patterns.

Both agricultural land and built-up land experienced significant changes during this period. A considerable portion of forest, scrub, and fallow land was converted into agricultural land, reflecting ongoing pressure on natural resources due to anthropogenic activities. Built-up land also expanded notably, indicating significant urban development and growth in the study area.

Forest areas suffered considerable reductions, with large portions being converted into agricultural and built-up land. This loss highlights the environmental impact of urbanization and agricultural expansion. Water bodies also experienced changes, with parts being converted into agricultural land, which could have implications for local ecosystems.

To evaluate the change processes in land use and land cover (LULC) and determine whether there are any acceleration trends, the annual conversion rates were calculated for the major transitions spanning 2003–2014 and 2014–2024. Observations indicated a pronounced acceleration of urban sprawl in the latter period. The annual conversion rate of forested land to urban infrastructure increased sharply from 0.016% per annum between 2003 and 2014 to 0.081% per annum between 2014 and 2024. This suggests an increasing trend of urban infrastructure development deforesting land at an accelerated rate. Likewise, the annual conversion rate of agricultural land to urban infrastructure also saw an increase from 0.196%/year to 0.236%/year, demonstrating relentless urban expansion onto farmable land.

The rate of converting fallow land into urbanized built-up areas increased from 0.0018%/year during the first period to 0.006%/year during the second period, which indicates growing encroachment on unused cultivated land. In contrast, barren land to built-up conversion declined, dropping from 0.026%/year (2003–2014) to 0.008%/year (2014–2024), which may reflect limited development interest or saturation of barren zones. Likewise, conversion of water bodies to agricultural land also decelerated, falling from 0.016%/year to 0.008%/year, possibly due to greater awareness or regulation of aquatic ecosystem preservation.

These metrics clearly demonstrate a temporal acceleration of urbanization in ecologically productive zones like forests and agricultural land, whereas less-productive or sensitive zones (barren lands, water bodies) saw a relative decline in conversion rates during 2014–2024. This nuanced pattern reinforces the urgent need for balanced land use planning, green infrastructure promotion, and ecosystem conservation in the Midnapore region.

Figure 6 displays Sankey diagrams depicting the LULC changes over Midnapore City from 2003–2014 and 2014–2024. These diagrams show how land was converted from one type to another and the relative area of each flow, which is represented through the width of the flow and is proportional to the transition area. During the first period (2003–2014), high amounts of forest and agricultural land turned into built-up areas, which marks the beginning of urban encroachment. A notable change of forest into agricultural land also signifies a certain level of deforestation as a result of expanding cultivation. During the second period (2014–2024), the trend sharpens as there is greater expansion of built-up land at the cost of forest land, agricultural land, and scrub lands, showing an increase in urbanization and infrastructure development. These changes mark not only the loss of valuable ecological land but also the two-decades-worth of expansion of urban areas. The Sankey diagrams clearly mark the need for efficient and sustainable intervention concerning land resources and highlight the constant and dire changing patterns of land cover in the area.

3.3. Matrix Union to Logistic Regression Result

The analysis of the change matrix suggests there were considerable land use changes between 2003 and 2024. The greatest of these changes was the conversion of agricultural land to built-up land, with probability values of −39.506 (2003–2014) and −60.629 (2014–2024) (Table A1). This pattern is also supported by the R-value (1.00).

As for the fallow land, a similar conversion was observed, with probability values for conversion to built-up land at −0.020 (2003–2014) and −0.385 (2014–2024), indicating an overall increase in urban expansion over time (Table A1). Moderate probability values (−539.111) were registered for water bodies being converted to agricultural land between 2003–2014, and a stronger probability (−1966.626) was observed for the latter period (2014–2024), indicating a more pronounced shift towards agricultural use during this period (Table A1).

The correlation matrix presented in Table A2 lists the factors that have an impact on the correlation with the process of land conversion. The correlation between distance to water and distance to agriculture exhibits a negative correlation (−0.363), which implies that regions nearer to water bodies were progressively transformed into agricultural lands. Further, the correlation between distance to forest and distance to agriculture (−0.462) during the period of 2014–2024 suggests that deforestation was, indeed, taking place for the purposes of expanding agriculture.

Alterations in slope and road networks had also changed, with the change in the base term of the road variable moving from 3.595 for the period of 2003–2014 to 41.348 for 2014–2024, confirming that higher levels of accessibility resulted in greater changes to the landscape.

4. Discussion

4.1. Summary

The analysis of land use and land cover (LULC) changes in Midnapore City over the years 2003 to 2024 demonstrates an alarming development, mainly resulting from population increase, economic expansion, and immigration. The study uses Supervised Digital Classification with the Maximum Likelihood Classifier, cross-tabulation, and matrix analysis to capture urban and infrastructure development within the two decades and the outcomes. As expected, there is a significant decline in forest and agricultural land due to increased urbanization and settlement expansion. Proximity to road networks and urban centers has also been a dominant factor driving land transformation, as shown through logistic regression analysis. This is also consistent with global urbanization patterns, which sustain the rationalization for readily available resources and economic opportunities driving infrastructure development and land use changes at an alarming pace [41,42].

Between 2003 and 2014, the flat geography of Midnapore is reflected in agricultural area slope change stagnation. However, built-up areas and their surroundings have undergone significant alterations from 2014 to 2024. There was a drastic decrease in water bodies, especially during the 2014 to 2024 period, indicating the increasingly alarming demand for land resources, which is being diverted from land previously designated as water bodies for farming and light horticulture. This trend may jeopardize groundwater replenishment, balance within the ecosystem, and the overall hydrology of the area in the long run. The increased deforestation in the first time period, 2003 to 2014, can be attributed to the spread of settlements and the expansion of agriculture. However, when most of the high-quality agricultural land was utilized, the rate of deforestation slowed. Afterward, new agricultural development turned their focus to more remote areas which increased urban fringe pressure. The expansion of road networks was crucial in facilitating these changes in land use; just as it is observed on a global scale, urban development often follows the expansion of available infrastructure. Areas with lower gradients and better access were especially prone to being transformed into built-up spaces [43]. This statistical evidence is strongly aligned with socio-economic realities. The positive relationship between built-up expansion and proximity to roads reflects how infrastructure development drives accessibility, attracting both residential and commercial growth. Similarly, the negative correlation with slope highlights the influence of terrain in guiding settlement patterns, with flatter areas being more economically viable and easier to develop.

These findings capture the importance of environment and social equity in supporting the sustainability of natural resources while urban planning to mitigate the impact of urban expansion [44]. The encroachment of urban centers on natural features such as green areas, forests, and water bodies poses serious challenges in terms of environmental sustainability, pollution, and ecosystem health. Furthermore, urban sprawl is likely to antagonize existing conditions through the polymorphic increase in transportation costs, degradation of infrastructure, and land. Diminishing vegetation cover contributes to the urban heat island effect, which heightens the need for effective climate-resilient strategies, urban planning, and design [31].

In addition, the impact of the study results touches in one way or another on biodiversity conservation, ecosystem services, and human well-being [45]. The growing agricultural and urban areas have a negative impact on biodiversity, while the increase in wastelands indicates land degradation that can impact food security and agricultural productivity. These changes illustrate the increasing need for policy and legislation on land use to be more ecologically sustainable [46].

In comparison with other studies dealing with India’s land use changes and other studies in different parts of the world, it appears that agricultural expansion and urbanization still remain the most common means of bringing about changes in LULC. However, this study shows greater changes in land use than other studies have done in other parts of the world, which suggests that these phenomena should be studied from both the environmental and the socio-economic angles [47].

4.2. Limitation and Recommendations

This study provides valuable information, but the reasons for urban growth and the migration, social, and economic activities that play a role were not captured fully by the research [48]. Urbanization is better understood through the integration of sociology, economics, and environmental science in developing approaches [49]. Applying factors like demographics, income distribution, and employment patterns would deepen the understanding of urban growth drivers and enhance their insights [50,51,52]. Nonetheless, this study does have one pertinent limitation; the utilization of Landsat data with a 30-metre spatial resolution captures only relatively coarse features and therefore lacks the ability to portray intricate details in peri-urban areas or built-up regions. The mix of set pixel types within a single mixed-urban fringe pixel results in coarse set resolution, which misclassifies cover type. This further complicates the identifying processes for small-scale fragmented settlements, land-transformation features like roads and water channels, and other linear features. Moreover, utilizing high-resolution remote sensing data would facilitate more accurate LULC classification and change detection [53]. The implementation of comprehensive field surveys would further refine the classification outputs and enhance data trustworthiness [54,55].

The use of AI tools would allow for the construction of artificial urban growth predictive models which would evaluate the implications of various policy changes [53,56]. It is equally important to carry out an exhaustive environmental impact assessment to evaluate the consequences of urban development on regional ecosystems and biodiversity [57,58]. Finally, engaging specialists in planning, sociology, economics, and environmental science would encourage a broader analysis of urban growth and its implications, fostering interdisciplinary collaboration [59,60,61,62].

Future research would be enhanced by the incorporation of scenario-based LULC model approaches like Markov Chain, Cellular Automata, or hybrid models with socio-economic and policy variables. Such an application would facilitate future land transitions to be modeled using different urban growth and environmental policy scenarios. Some researchers have already identified the benefits of combining remote sensing with machine learning and spatial simulation tools to provide enhanced forecasting of land use processes. The inclusion of such models in future research would offer in-depth information regarding the implications of urbanization and facilitate evidence-based land planning strategy [63,64,65].

5. Conclusions

The change matrix revealed that from 2003 to 2014, water bodies converted to agricultural land at a rate of 0.18%, while forest areas saw significant conversion to agricultural land (0.68%) and built-up land (0.18%). By 2014–2024, forest conversion accelerated with 0.21% to agricultural land and 0.81% to built-up land, indicating continued urban pressure. Further, matrix union logistic regression results highlighted a strong negative trend for agricultural land conversion to built-up areas (−39.506 and −60.629), underscoring significant urban expansion. Similarly, fallow land showed high conversion rates to built-up areas (−0.020 and −0.385), driven by population growth. Water bodies also experienced a notable increase in conversion to agricultural land, with coefficients of 539.111 in 2003–2014 and −1966.626 in 2014–2024, emphasizing the growing demand for agricultural space.

Overall, the study highlights the need for sustainable land management in Midnapore. The significant conversion of natural areas like forests and water bodies into agricultural and built-up land stresses the importance of balancing development with environmental conservation to mitigate ecological impacts, such as increased flood risks and loss of biodiversity. Effective land use planning and conservation strategies are essential to manage these ongoing changes and protect the region’s ecological balance.

To ensure sustainable development in Midnapore, it is essential to implement land use planning that protects natural areas, particularly forests and water bodies, from further conversion into built-up or agricultural land. Reforestation and conservation initiatives should be prioritized to maintain ecological balance. Water bodies need to be preserved and restored to enhance groundwater recharge and mitigate flooding. Infrastructure development should be ecologically sensitive, integrating green infrastructure to manage surface runoff. Additionally, continuous monitoring of land use changes, using tools like GIS, will help adapt planning strategies and mitigate the environmental impacts of urbanization. Future research may focus on addressing key gaps, such as the socio-economic drivers influencing land use changes, including livelihoods and access to resources and further exploration of ecological consequences of deforestation and biodiversity loss. Assessing the effectiveness of land use policies and community coping mechanisms can offer practical insights for sustainable development. Addressing these areas supports resilient land management practices.

Author Contributions

Conceptualization, R.R.T. and D.N.; methodology, A.K.S.; software, S.D.; validation, P.S., C.S. and A.K.S.; formal analysis, S.C.; investigation, D.N.; resources, A.K.S.; data curation, P.S.; writing—original draft preparation, R.R.T. and D.N. writing—review and editing, R.B., D.N. and A.K.S.; visualization, S.C.; supervision, R.R.T., A.K.S.; project administration, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

All data are available to the corresponding author upon reasonable request.

Acknowledgments

The research area’s base map was kindly provided to the authors by the Medinipur District. The authors would also like to thank all the researchers for their great work, and helpful suggestions for completing this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Table

Figure 1 Location map: (a) country map showing the national context of the study area (b) state map, (c) district map, (d) study area. Source: Survey of India (https://onlinemaps.surveyofindia.gov.in/ accessed on 5 January 2025).

Figure 2 (a) Slope map, (b) Road network map.

Figure 3 Methodology flow chart.

Figure 4 LULC maps for the years (a) 2003, (b) 2014, and (c) 2024.

Figure 5 Percentage change in LULC: (a) 2003–2014, (b) 2014–2024.

Figure 6 Sankey diagram of LULC transitions for Midnapore City during (a) 2003–2014 and (b) 2014–2024.

Table 1

Change matrix of LULC during the periods 2003–2014 and 2014–2024.

	2003–2014
	Water Bodies	Forest	Agricultural Land	Scrub Land	Fallow Land	Barren Land	Built-Up Land	Grand Total
Water Bodies	0.21	0.00	0.18	0.00	0.00	0.00	0.01	0.41
Forest	0.00	0.13	0.68	0.00	0.01	0.00	0.18	1.3
Agricultural Land	0.06	0.00	11.93	0.24	0.78	0.00	2.16	15.38
Scrub Land	0.00	0.02	0.06	0.06	0.00	0.00	0.00	0.14
Fallow Land	0.00	0.00	0.49	0.00	1.02	0.00	0.02	1.52
Barren Land	0.00	0.00	0.08	0.00	0.00	0.04	0.29	0.12
Built-up Land	0.00	0.00	0.00	0.00	0.00	0.00	0.89	1.37
Grand Total	0.27	0.32	13.88	0.60	1.81	0.09	3.26	20.23
	2014–2024
Water Bodies	0.18	0.00	0.08	0.00	0.00	0.00	0.01	0.27
Forest	0.00	0.09	0.21	0.02	0.00	0.00	0.81	0.32
Agricultural Land	0.01	0.00	10.70	0.21	0.09	0.05	2.36	13.88
Scrub Land	0.00	0.12	0.01	0.29	0.00	0.00	0.21	0.6
Fallow Land	0.00	0.01	1.05	0.02	0.66	0.00	0.06	1.81
Barren Land	0.00	0.00	0.03	0.00	0.00	0.05	0.08	0.09
Built-up Land	0.00	0.00	0.00	0.00	0.00	0.00	2.04	3.26
Grand Total	0.23	0.67	13.47	0.53	0.75	0.11	4.47	20.23

Appendix A

Table A1

Standard coefficient for years 2003–2014 and 2014–2024.

Agricultural Land
	Value		Standard Error		Wald Chi-Square		Pr > Chi2		Lower Bound (95%)		Upper Bound (95%)
	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024
Distance to Agricultural Land	262.183	246.508	33,380.489	78,909.069	0.00	0.00	0.994	0.998	−65,162.373	−154,412.425	65,686.74	154,905.441
Distance to Developed Land	−39.506	−60.629	10,913.161	97,936.02	0.00	0.00	0.997	1.00	−21,428.909	−192,011.701	21,349.897	191,890.444
Slope	0.186	−5.143	995.208	3009.465	0.00	0.00	1.00	0.999	−1950.387	−5903.586	1950.758	5893.299
Road	−0.26	1.071	1422.835	2459.607	0.00	0.00	1.00	1.00	−2788.966	−4819.67	2788.446	4821.812
Fallow Land
Distance to Fallow Land		42.974		4778.254		0.00		−9322.231		0.993		9408.179
Distance to Developed Land		−0.385		1296.537		0.00		−2541.551		1.000		2540.781
Slope		0.176		758.821		0.00		−1487.086		1.000		1487.438
Road		−0.072		857.779		0.00		−1681.288		1.000		1681.144
Water Body
Distance to Water	780.11	2365.223	201,296.127	632,182.068	0.00	0.00	0.997	0.997	−393,753.049	−1,236,688.861	395,313.269	1,241,419.308
Distance to Agricultural Land	539.111	−1966.626	102,218.787	301,618.184	0.00	0.00	0.996	0.995	−200,884.253	−593,127.403	199,806.03	589,194.152
Slope	15.165	−54.272	2919.267	9058.159	0.00	0.00	0.996	0.995	−5706.492	−17,807.937	5736.823	17,699.393
Road	3.595	41.348	1553.123	5516.36	0.00	0.00	0.998	0.994	−3040.469	−10,770.519	3047.66	10,853.215
Forest
Distance to Forest	3.547	6.752	2657.725	3223.23	0.00	0.00	0.999	0.998	−5205.499	−6310.662	5212.593	6324.167
Distance to Agricultural Land	53.911	−239.786	6673.254	20,943.657	0.00	0.00	0.994	0.991	−13,133.249	−41,288.599	13,025.427	40,809.027
Slope	−1.423	−3.533	1597.382	1507.614	0.00	0.00	0.999	0.998	−3132.235	−2958.403	3129.388	2951.337
Road	−0.928	17.919	1663.839	2647.621	0.00	0.00	1.00	0.995	−3261.992	−5171.323	3260.136	5207.162

Table A2

Correlation matrix for the years 2003–2014 and 2014–2024.

Agricultural Land
Variables	Distance to Agriculture		Distance to Built-Up Land		Slope		Road
	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024
Distance to Agricultural Land	1.000	1.000	−0.232	−0.210	0.041	0.134	−0.090	0.021
Distance to Developed Land	−0.232	−0.210	1.000	1.000	−0.095	0.033	0.343	0.257
Slope	0.041	0.134	−0.095	0.033	1.000	1.000	−0.085	−0.022
Road	−0.090	0.021	0.343	0.257	−0.085	−0.022	1.000	1.000
Fallow Land
Variables	Distance to Fallow Land		Distance to Built-Up Areas		Slope		Road
	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024
Distance to Fallow land	1.000	1.000	−0.158	−0.310	0.110	0.177	−0.058	−0.132
Distance to Developed Land	−0.158	−0.310	1.000	1.000	−0.230	−0.026	0.017	−0.283
Slope	0.110	0.177	−0.230	−0.026	1.000	1.000	−0.123	−0.177
Road	−0.058	−0.132	0.017	−0.283	−0.123	−0.177	1.000	1.000
Water Body
Variables	Distance to Water		Distance to Agriculture		slope		road
	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024
Distance to Water		1.000		−0.363		−0.036		−0.294
Distance to Agricultural Land		−0.363		1.000		0.150		0.045
Slope		−0.036		0.150		1.000		0.087
Road		−0.294		0.045		0.087		1.000
Forest
Variables	Distance to Forest		Distance to Agriculture		Slope		Road
	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024	2003–2014	2014–2024
Distance to Forest	1.000	1.000	−0.421	−0.462	0.267	0.073	0.458	0.255
Distance to Agricultural Land	−0.421	−0.462	1.000	1.000	0.016	0.099	−0.160	−0.191
Slope	0.267	0.073	0.016	0.099	1.000	1.000	0.271	−0.029
Road	0.458	0.255	−0.160	−0.191	0.271	−0.029	1.000	1.000

References

1. Wang, L.; Yang, Z.-L. Changes in Land Use Influenced by Anthropogenic Activity. Oxford Research Encyclopedia of Environmental Science; Oxford University Press: Oxford, UK, 2020.

2. Aryal, J.; Sitaula, C.; Frery, A.C. Land Use and Land Cover (LULC) Performance Modeling Using Machine Learning Algorithms: A Case Study of the City of Melbourne, Australia. Sci. Rep.; 2023; 13, 13510. [DOI: https://dx.doi.org/10.1038/s41598-023-40564-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37598272]

3. Fan, X.; Ma, Z.; Yang, Q.; Han, Y.; Mahmood, R. Land Use/Land Cover Changes and Regional Climate over the Loess Plateau during 2001–2009. Part II: Interrelationship from Observations. Clim. Change; 2015; 129, pp. 441-455. [DOI: https://dx.doi.org/10.1007/s10584-014-1068-5]

4. Zhao, G.X.; Lin, G.; Warner, T. Using Thematic Mapper Data for Change Detection and Sustainable Use of Cultivated Land: A Case Study in the Yellow River Delta, China. Int. J. Remote Sens.; 2004; 25, pp. 2509-2522. [DOI: https://dx.doi.org/10.1080/01431160310001619571]

5. Mishra, P.; Jena, D.; Samal, K.P.; Dibiat, N. Urbanization and Groundwater Quality: A Case of Bhubaneswar in Odisha, India. Turk. Online J. Qual. Inq.; 2021; 12, pp. 5520-5529.

6. Ellis, E.; Pontius, R. Land-Use and Land-Cover Change. Encycl. Earth; 2007; 1, pp. 1-4.

7. Hussain, S.; Mubeen, M.; Karuppannan, S. Land Use and Land Cover (LULC) Change Analysis Using TM, ETM+ and OLI Landsat Images in District of Okara, Punjab, Pakistan. Phys. Chem. Earth Parts A/B/C; 2022; 126, 103117. [DOI: https://dx.doi.org/10.1016/j.pce.2022.103117]

8. Wu, T.; Luo, J.; Fang, J.; Ma, J.; Song, X. Unsupervised Object-Based Change Detection via a Weibull Mixture Model-Based Binarization for High-Resolution Remote Sensing Images. IEEE Geosci. Remote Sens. Lett.; 2017; 15, pp. 63-67. [DOI: https://dx.doi.org/10.1109/LGRS.2017.2773118]

9. Gupta, A.; Shukla, A.K. Optimal approaches in global warming mitigation and adaptation strategies at city scale. Discov. Sustain.; 2024; 5, 272. [DOI: https://dx.doi.org/10.1007/s43621-024-00497-8]

10. Phiri, D.; Morgenroth, J. Developments in Landsat Land Cover Classification Methods: A Review. Remote Sens.; 2017; 9, 967. [DOI: https://dx.doi.org/10.3390/rs9090967]

11. Jin, S.; Yang, L.; Zhu, Z.; Homer, C. A Land Cover Change Detection and Classification Protocol for Updating Alaska NLCD 2001 to 2011. Remote Sens. Environ.; 2017; 195, pp. 44-55. [DOI: https://dx.doi.org/10.1016/j.rse.2017.04.021]

12. Felsenstein, D.; Lichter, M.; Ashbel, E.; Grinberger, A.Y. Land Use-Land Cover Dynamics at the Metropolitan Fringe. Modeling of Land-Use and Ecological Dynamics; Springer: Berlin/Heidelberg, Germany, 2013; pp. 143-165. ISBN 9783642401992

13. Dwivedi, R.S.; Sreenivas, K.; Ramana, K.V. Land-Use/Land-Cover Change Analysis in Part of Ethiopia Using Landsat Thematic Mapper Data. Int. J. Remote Sens.; 2005; 26, pp. 1285-1287. [DOI: https://dx.doi.org/10.1080/01431160512331337763]

14. Sekertekin, A.; Marangoz, A.M.; Akcin, H. Pixel-Based Classification Analysis of Land Use Land Cover Using Sentinel-2 and Landsat-8 Data. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.; 2017; 42, pp. 91-93. [DOI: https://dx.doi.org/10.5194/isprs-archives-XLII-4-W6-91-2017]

15. Lambin, E.F.; Geist, H.J.; Lepers, E. Dynamics of Land-Use and Land-Cover Change in Tropical Regions. Annu. Rev. Environ. Resour.; 2003; 28, pp. 205-241. [DOI: https://dx.doi.org/10.1146/annurev.energy.28.050302.105459]

16. Gour, D.; Soumen, D.; Sujan, S. An Application of Remote Sensing and GIS to Analyze Urban Expansion and Land Use Land Cover Change of Midnapore Municipality, WB, India. Int. Res. J. Earth Sci.; 2014; 2, pp. 8-20.

17. Lv, Z.; Liu, T.; Wan, Y.; Benediktsson, J.A.; Zhang, X. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sens.; 2018; 10, 472. [DOI: https://dx.doi.org/10.3390/rs10030472]

18. Thakur, R.R.; Kumar, P.; Palria, S. Monitoring Changes in Vegetation Cover of Bhitarkanika Marine National Park Region, Odisha, India Using Vegetation Indices of Multidate Satellite Data. Indian J. Geo-Mar. Sci.; 2019; 48, pp. 1916-1924.

19. Das, P.; Pandey, V. Use of Logistic Regression in Land-Cover Classification with Moderate-Resolution Multispectral Data. J. Indian Soc. Remote Sens.; 2019; 47, pp. 1443-1454. [DOI: https://dx.doi.org/10.1007/s12524-019-00986-8]

20. Arowolo, A.O.; Deng, X. Land Use/Land Cover Change and Statistical Modelling of Cultivated Land Change Drivers in Nigeria. Reg. Environ. Change; 2018; 18, pp. 247-259. [DOI: https://dx.doi.org/10.1007/s10113-017-1186-5]

21. Bhattacharya, R.K.; Das Chatterjee, N.; Das, K. Land Use and Land Cover Change and Its Resultant Erosion Susceptible Level: An Appraisal Using RUSLE and Logistic Regression in a Tropical Plateau Basin of West Bengal, India. Environ. Dev. Sustain.; 2021; 23, pp. 1411-1446. [DOI: https://dx.doi.org/10.1007/s10668-020-00628-x]

22. Kumar, R.; Nandy, S.; Agarwal, R.; Kushwaha, S.P.S. Forest Cover Dynamics Analysis and Prediction Modeling Using Logistic Regression Model. Ecol. Indic.; 2014; 45, pp. 444-455. [DOI: https://dx.doi.org/10.1016/j.ecolind.2014.05.003]

23. Buya, S.; Tongkumchum, P.; Owusu, B.E. Modelling of Land-Use Change in Thailand Using Binary Logistic Regression and Multinomial Logistic Regression. Arab. J. Geosci.; 2020; 13, 437. [DOI: https://dx.doi.org/10.1007/s12517-020-05451-2]

24. Mondal, I.; Srivastava, V.K.; Roy, P.S.; Talukdar, G. Using Logit Model to Identify the Drivers of Landuse Landcover Change in the Lower Gangetic Basin, India. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.; 2014; XL-8, pp. 853-859. [DOI: https://dx.doi.org/10.5194/isprsarchives-XL-8-853-2014]

25. Mustafa, A.; Heppenstall, A.; Omrani, H.; Saadi, I.; Cools, M.; Teller, J. Modelling Built-up Expansion and Densification with Multinomial Logistic Regression, Cellular Automata and Genetic Algorithm. Comput. Environ. Urban Syst.; 2018; 67, pp. 147-156. [DOI: https://dx.doi.org/10.1016/j.compenvurbsys.2017.09.009]

26. Alturk, B.; Konukcu, F. Modeling Land Use/Land Cover Change and Mapping Morphological Fragmentation of Agricultural Lands in Thrace Region/Turkey. Environ. Dev. Sustain.; 2020; 22, pp. 6379-6404. [DOI: https://dx.doi.org/10.1007/s10668-019-00485-3]

27. Matas, C. Urban Sprawl of Major Cities in the Baltic States. Archit. Urban Plan.; 2013; 7, pp. 72-79.

28. Pour, N.M.; Oja, T. Prediction Power of Logistic Regression (Lr) and Multi-Layer Perceptron (Mlp) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia. Sustainability; 2022; 14, 160. [DOI: https://dx.doi.org/10.3390/su14010160]

29. Gaur, S.; Singh, R. A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability; 2023; 15, 903. [DOI: https://dx.doi.org/10.3390/su15020903]

30. Dinda, S.; Das, K.; Chatterjee, N.D.; Ghosh, S. Integration of GIS and Statistical Approach in Mapping of Urban Sprawl and Predicting Future Growth in Midnapore Town, India. Model. Earth Syst. Environ.; 2019; 5, pp. 331-352. [DOI: https://dx.doi.org/10.1007/s40808-018-0536-8]

31. Feng, L.; Jiang, Y. Land Use Structure, Socioeconomic Structure and Macroeconomic Regulation—Empirical Analysis Based on Different Regions of China. Proceedings of the 2009 International Conference on Management Science and Engineering; Moscow, Russia, 14–16 September 2009; IEEE: Piscataway, NJ, USA, 2009; pp. 2060-2066.

32. Van der Meer, F. Remote-sensing image analysis and geostatistics. Int. J. Remote Sens.; 2012; 33, pp. 5644-5676. [DOI: https://dx.doi.org/10.1080/01431161.2012.666363]

33. Richards, J.A.; Richards, J.A. Remote Sensing Digital Image Analysis; Springer: Berlin/Heidelberg, Germany, 2022; Volume 5, pp. 256-258.

34. Chowdhury, M.; Hasan, M.E.; Abdullah-Al-Mamun, M.M. Land Use/Land Cover Change Assessment of Halda Watershed Using Remote Sensing and GIS. Egypt. J. Remote Sens. Space Sci.; 2020; 23, pp. 63-75. [DOI: https://dx.doi.org/10.1016/j.ejrs.2018.11.003]

35. Natarajan, S.; Radhakrishnan, N. Assessment of Rainfall–Runoff Due to the Impacts of Land-Use Changes by Integrated Geospatial Empirical Approach: Study on Koraiyar Basin, Tiruchirappalli City, India. J. Indian Soc. Remote Sens.; 2021; 49, pp. 793-812. [DOI: https://dx.doi.org/10.1007/s12524-020-01260-y]

36. Chatewutthiprapa, C.; Chotpantarat, S.; Yumuang, S. Land Use and Land Cover Change of Chanthaburi Watershed Following 1999, 2006 and 2013 Floods. Proceedings of the Geoinformatics for Sustainable Development in Asian Cities; Bangkok, Thailand, 19–20 July 2018; Springer: Berlin/Heidelberg, Germany, 2020; pp. 21-31.

37. Gupta, S.; Islam, S.; Hasan, M.M. Analysis of Impervious Land-Cover Expansion Using Remote Sensing and GIS: A Case Study of Sylhet Sadar Upazila. Appl. Geogr.; 2018; 98, pp. 156-165. [DOI: https://dx.doi.org/10.1016/j.apgeog.2018.07.012]

38. Kumar, D.; Singh, V.; Bahadur, R. Land Use/Land Cover Change Analysis Using Matrix Union Method. Bull. Pure Appl. Sci. -Geol.; 2022; 41, pp. 309-317. [DOI: https://dx.doi.org/10.5958/2320-3234.2022.00024.5]

39. Singh, M.C.; Satpute, S.; Prasad, V. Remote Sensing and GIS-Based Watershed Prioritization for Land and Water Conservation Planning and Management. Water Sci. Technol.; 2023; 88, pp. 233-265. [DOI: https://dx.doi.org/10.2166/wst.2023.207]

40. Ngandam Mfondoum, A.H.; Wokwenmendam Nguet, P.; Mefire Mfondoum, J.V.; Tchindjang, M.; Hakdaoui, S.; Cooper, R.; Gbetkom, P.G.; Penaye, J.; Bekoa, A.; Moudioh, C. Adapting Sudden Landslide Identification Product (SLIP) and Detecting Real-Time Increased Precipitation (DRIP) Algorithms to Map Rainfall-Triggered Landslides in Western Cameroon Highlands (Central-Africa). Geoenvironmental Disasters; 2021; 8, 17. [DOI: https://dx.doi.org/10.1186/s40677-021-00189-9]

41. Andersson, E. Urban Landscapes and Sustainable Cities. Ecol. Soc.; 2006; 11, 34. [DOI: https://dx.doi.org/10.5751/ES-01639-110134]

42. Patra, S.; Sahoo, S.; Mishra, P.; Mahapatra, S.C. Impacts of Urbanization on Land Use /Cover Changes and Its Probable Implications on Local Climate and Groundwater Level. J. Urban Manag.; 2018; 7, pp. 70-84. [DOI: https://dx.doi.org/10.1016/j.jum.2018.04.006]

43. Shen, G.; Yang, X.; Jin, Y.; Xu, B.; Zhou, Q. Remote Sensing and Evaluation of the Wetland Ecological Degradation Process of the Zoige Plateau Wetland in China. Ecol. Indic.; 2019; 104, pp. 48-58. [DOI: https://dx.doi.org/10.1016/j.ecolind.2019.04.063]

44. Basiago, A.D. Economic, Social, and Environmental Sustainability in Development Theory and Urban Planning Practice; Kluwer Academic Publishers: Dordrecht, The Netherlands, 1999; Volume 19.

45. Shen, L.Y.; Jorge Ochoa, J.; Shah, M.N.; Zhang, X. The Application of Urban Sustainability Indicators—A Comparison between Various Practices. Habitat Int.; 2011; 35, pp. 17-29. [DOI: https://dx.doi.org/10.1016/j.habitatint.2010.03.006]

46. Roy, N.C.; Thangaraj, V. Dynamic Interaction of Urban Development and Rural–Urban Migration: An Application of Integrated Urban Metabolism Analysis Tool (IUMAT) for Sustainable City Planning. Digit. Policy Regul. Gov.; 2022; 24, pp. 118-140. [DOI: https://dx.doi.org/10.1108/DPRG-06-2020-0085]

47. Kumar, S.S.; Pandey, M.; Shukla, A.K. Spatio-temporal analysis of riverbank changes using remote sensing and geographic information system. Phys. Chem. Earth Parts A/B/C; 2024; 136, 103692. [DOI: https://dx.doi.org/10.1016/j.pce.2024.103692]

48. Bharath, H.A.; Chandan, M.C.; Vinay, S.; Ramachandra, T.V. Modelling Urban Dynamics in Rapidly Urbanising Indian Cities. Egypt. J. Remote Sens. Space Sci.; 2018; 21, pp. 201-210. [DOI: https://dx.doi.org/10.1016/j.ejrs.2017.08.002]

49. Pearman, A.D. Scenario Construction for Transport Planning. Transp. Plan. Technol.; 1988; 12, pp. 73-85. [DOI: https://dx.doi.org/10.1080/03081068808717361]

50. Sarkar, R. Association of Urbanization with Demographic Dynamics in India. GeoJournal; 2020; 85, pp. 779-803. [DOI: https://dx.doi.org/10.1007/s10708-019-09988-y]

51. Shaban, A.; Kourtit, K.; Nijkamp, P. India’s Urban System: Sustainability and Imbalanced Growth of Cities. Sustainability; 2020; 12, 2941. [DOI: https://dx.doi.org/10.3390/su12072941]

52. Cohen, M.; Simet, L. Macroeconomy and Urban Productivity. Urban Planet; Cambridge University Press: Cambridge, UK, 2018; pp. 130-146.

53. Gao, J.; O’Neill, B.C. Mapping Global Urban Land for the 21st Century with Data-Driven Simulations and Shared Socioeconomic Pathways. Nat. Commun.; 2020; 11, 2302. [DOI: https://dx.doi.org/10.1038/s41467-020-15788-7]

54. Basha, U.; Pandey, M.; Nayak, D.; Shukla, S.; Shukla, A.K. Spatial–Temporal Assessment of Annual Water Yield and Impact of Land Use Changes on Upper Ganga Basin, India, Using InVEST Model. J. Hazard. Toxic Radioact. Waste; 2024; 28, 04024003. [DOI: https://dx.doi.org/10.1061/JHTRBP.HZENG-1245]

55. Verma, P.; Raghubanshi, A.; Srivastava, P.K.; Raghubanshi, A.S. Appraisal of Kappa-Based Metrics and Disagreement Indices of Accuracy Assessment for Parametric and Nonparametric Techniques Used in LULC Classification and Change Detection. Model. Earth Syst. Environ.; 2020; 6, pp. 1045-1059. [DOI: https://dx.doi.org/10.1007/s40808-020-00740-x]

56. Shi, W.; Zhang, M.; Zhang, R.; Chen, S.; Zhan, Z. Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Remote Sens.; 2020; 12, 1688. [DOI: https://dx.doi.org/10.3390/rs12101688]

57. Roy, P.S.; Ramachandran, R.M.; Paul, O.; Thakur, P.K.; Ravan, S.; Behera, M.D.; Sarangi, C.; Kanawade, V.P. Anthropogenic Land Use and Land Cover Changes—A Review on Its Environmental Consequences and Climate Change. J. Indian Soc. Remote Sens.; 2022; 50, pp. 1615-1640. [DOI: https://dx.doi.org/10.1007/s12524-022-01569-w]

58. Jung, M.; Scharlemann, J.P.W.; Rowhani, P. Landscape-Wide Changes in Land Use and Land Cover Correlate with, but Rarely Explain Local Biodiversity Change. Landsc. Ecol.; 2020; 35, pp. 2255-2273. [DOI: https://dx.doi.org/10.1007/s10980-020-01109-2]

59. Gomes, L.C.; Bianchi, F.J.J.A.; Cardoso, I.M.; Schulte, R.P.O.; Arts, B.J.M.; Fernandes Filho, E.I. Land Use and Land Cover Scenarios: An Interdisciplinary Approach Integrating Local Conditions and the Global Shared Socioeconomic Pathways. Land Use Policy; 2020; 97, 104723. [DOI: https://dx.doi.org/10.1016/j.landusepol.2020.104723]

60. Sharma, S.; Joshi, P.K.; Fürst, C. Exploring Multiscale Influence of Urban Growth on Landscape Patterns of Two Emerging Urban Centers in the Western Himalaya. Land; 2022; 11, 2281. [DOI: https://dx.doi.org/10.3390/land11122281]

61. Hale, R.C.; Gallo, K.P.; Loveland, T.R. Influences of Specific Land Use/Land Cover Conversions on Climatological Normals of Near-surface Temperature. J. Geophys. Res. Atmos.; 2008; 113, D14113. [DOI: https://dx.doi.org/10.1029/2007JD009548]

62. Asempah, M.; Sahwan, W.; Schütt, B. Assessment of Land Cover Dynamics and Drivers of Urban Expansion Using Geospatial and Logistic Regression Approach in Wa Municipality, Ghana. Land; 2021; 10, 1251. [DOI: https://dx.doi.org/10.3390/land10111251]

63. Amini, S.; Saber, M.; Rabiei-Dastjerdi, H.; Homayouni, S. Urban land use and land cover change analysis using random forest classification of landsat time series. Remote Sens.; 2022; 14, 2654. [DOI: https://dx.doi.org/10.3390/rs14112654]

64. Parracciani, C.; Gigante, D.; Mutanga, O.; Bonafoni, S.; Vizzari, M. Land cover changes in grassland landscapes: Combining enhanced Landsat data composition, LandTrendr, and machine learning classification in google earth engine with MLP-ANN scenario forecasting. GIScience Remote Sens.; 2024; 61, 2302221. [DOI: https://dx.doi.org/10.1080/15481603.2024.2302221]

65. Hansen, M.C.; Loveland, T.R. A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environ.; 2012; 122, pp. 66-74. [DOI: https://dx.doi.org/10.1016/j.rse.2011.08.024]