Land Use Dynamics Research Articles

Assessing and predicting the dynamics of land use/land cover in northern Bangladesh using cellular Automata-Markov chain model

ABSTRACT Rapid urbanization and land use changes significantly impact environmental sustainability and resource management, particularly in developing regions. Therefore, this study examines the spatiotemporal dynamics of land use and land cover (LULC) in Rangpur, Bangladesh, from 1991 to 2021 and projects future trends to 2041. Using supervised and unsupervised classification techniques, along with cellular automata and Markov-chain models, we assessed historical LULC changes and predicted future scenarios. Results show a 38.86% increase in built-up areas (BAs) and a 49.86% decrease in vegetation land (VL) during the study period, with classification accuracy above 87%. Projections indicate a further loss of over 210 km² of VL and an increase of more than 123 km² in urban areas by 2041. Notably, urban expansion is linked to the development of road networks, with significant growth from 115.06 km2 in 2021 to 124.33 km2 by 2041 within a 15-kilometer radius around the city center. These findings offer crucial insights for urban planning, emphasizing the need for sustainable strategies to manage urban expansion and protect environmental and socio-economic resilience in Rangpur.

Integrating Remote Sensing and GIS-Based Map Analysis in Determining Spread of Built-Ups and Land-Use Dynamics of Terrain of Onitsha Metropolis, Anambra State, Nigeria

Land scarcity in most cases hampers development and encourages the misuse of land. The suitability of land must be considered before appropriating or allocating land for any use. Land supports the livelihood of every being on the Earth and therefore determines survival, success, and sustainability (sustainable living). This study aimed at integrating remote sensing and GIS-based analysis to determine the rate at which built-up areas have spread across the terrain of Onitsha Metropolis, Anambra State, Nigeria, and the dynamics of other land uses. This research involved both primary and secondary data. The primary data included measurements, direct field observations, and key informant interviews to understand people’s perceptions of the land use in the area. The secondary data included satellite images of the area obtained from USGS and analyzed using ArcGIS 10.2 for variations in the terrain of the Onitsha Metropolis; to determine the land use and land cover change (LULCC) of the Onitsha Metropolis over 40 years, published and unpublished articles and books were also consulted. The geological analysis of the study showed that the area of the Ogwashi/Asaba formation is 318.57 km2; the areas of the Nanka sands and Bende-Ameke are 423.07 km2 and 259.42 km2, respectively. The Nanka sands and Bende-Ameke formations are best suited for engineering construction purposes, while the Ogwashi/Asaba formation is suitable for agriculture and should be designated as a buffer zone or park. However, due to the unavailability of land as a result of the growing population and the proximity of the area to the city center, the area is being encroached upon, and a large area (about 30.40%) has been converted to built-up areas as of 2022. Forecast analysis showed that if the trend continues, 158.28 km2 (49.68%) of the alluvium soils of the Ogwashi/Asaba formation will be covered with buildings by 2072. The geology and the terrain of the Onitsha Metropolis determine the soil characteristics and the land use suitability; mapping the geological formations and overlaying these with the land use and land cover change of the area revealed the extent of the encroachment on the Ogwashi/Asaba formation, which must be discouraged.

Spatiotemporal dynamics of LULC change and implications for sustainable urban growth in Saudi Arabia

This study investigates the spatiotemporal dynamics of land use and land cover (LULC) change in Saudi cities between 2013 and 2023. Using Landsat satellite imagery and a Maximum Likelihood Classifier, LULC changes are analyzed in nine cities to understand the impact of Saudi Arabia’s rapid urbanization following the oil crisis. The objectives are: (1) to evaluate the LULC changes in nine cities between 2013 and 2023; (2) to explore the spatiotemporal patterns of LULC changes in this period; and (3) to interpret the urban expansion that occurs in Saudi Arabia in the light of the kingdom sustainable development plan. The results identifiy four primary LULC change models: Urbanization, Vegetation Recovery, Land Degradation, and Ecological Transition Models. The Urbanization and Land Degradation Models determine Saudi Arabia’s urban growth trend, posing significant challenges to sustainable development. Most cities experienced substantial increases in impermeable surfaces and developed lands, while concurrently losing high terrain and barren areas. However, the small cities showed lower urbanization and land degradation rates than major cities, showing an opportunity to sustainably grow by adopting sustainable planning approaches. The study’s findings show that more targeted and comprehensive policies are needed to address the challenges of urbanization and land degradation, mitigate environmental impacts, and ensure long-term resilience. It underscores the importance of considering Ecological Urbanism in urban development plans.

An assessment of the seasonally dependent biophysical mechanism and dynamic land use change: A Research Effort from Emerging Urban Agglomeration of West Bengal

The behavior of the biophysical components needs to be closely observed while taking seasonal perspectives into account. This study focuses on seasonal analysis of biophysical components and dynamics of land use changes. This study examines the spatiotemporal seasonal variation among land surface temperature (LST), NDVI, and NDWI, with a focus on the two main sub-tropical climatic seasons summer and winter. We use correlation and regression analysis to assess the season-wise relationship between LST and biophysical indices with the help of grouped marginal plots. The comparison analyses of the biophysical components and land use change for a longer period from 1991-2021 with their matrix was undertaken in five urban agglomerations (UA) like Asansol, Siliguri, Kolkata, English Bazar and Kharagpur. The maximum likelihood classification technique was used to categorize multi-temporal Landsat images for each of the five growing UA in West Bengal. The most intriguing finding is that in all UAs, from 1991 to 2021, built-up area increased to become the predominant land. Thus, the vegetation and agricultural land are in danger in every location of West Bengal, whether it is plain or hilly, because of the land's gradual conversion to concrete or built-up areas over time. Additionally, the result shows that in summer and winter seasons, LST was observed growth due to the rapid urban built-up area expansion. It is also found that LST and NDVI have a close negative relationship, but LST and NDWI do not have any perfect relationship. Therefore, to lessen the warming effect of urban microclimates, policymakers should be concerned about future urban expansion that is both horizontal and vertical. An appropriate strategy for environmentally friendly construction, green buildings, and cool roof technologies should be developed.

Dynamics of Land Use and Coverage and its Impacts on the Upper Moquegua River Hydrographic Basin

Objective: Evaluate; identify and qualify the impacts of the dynamics of land use and coverage in the years 1973 and 2016 in the Hydrographic Basin and in the marginal strip of the Upper Moquegua River in Moquegua Peru. Theoretical framework: In the area studied, human activities and global climate change have disturbed the balance of natural systems, causing major impacts and complex consequences on land use and cover. Method: Digital image processing techniques and supervised classification using the maximum likelihood method were used, followed by matrix editing with refinement of the base and thematic cartography, reaching 94.4% accuracy in interpretation for the year 2016. Result and discussion: The main changes identified regarding the dynamics of land use and coverage were similar in the basin area and in the marginal strip. An increase was noted in the urban area of the entire basin of 568.53ha (1,097.96%) and 134.86ha (1,263.33%) in the marginal strip, which may have been largely influenced by mining activity, which was not observed in 1973, but present in 2016, occupying an area of 1,092.77ha throughout the basin, with 171.99ha in the marginal strip. The areas that had the greatest reduction were those of the glacier, with 2,275.39ha (-93.33%) in the entire basin and 83.77ha (-100.00%) in the marginal strip. Conclusion: With the results of this research, we hope to contribute to the literature related to the topic and to actions and public policies that encourage the appropriate use of land in the Upper Moquegua River Basin, especially in environmental protection areas.

Participatory topological mapping: A novel approach for exploring and communicating situated knowledge of complex socio-ecological systems

Land use change impacts on Sámi reindeer husbandry are well-documented, but existing maps often fail to capture socio-ecological relationships between herders, reindeer and nature. Conventional Geographic Information Systems (GIS) simplify these relationships, lacking local context and excluding valuable knowledge due to their rigid structure. This paper introduces participatory topological mapping, a low-tech, knowledge co-production method that allows communities to share geospatial insights without advanced software. Combining hand-drawn maps with a standardised topological structure facilitates the visualisation of geographical relationships and dynamic land use systems from a local perspective. Developed and assessed through a Swedish case study in collaboration with Vittangi and Malå reindeer herding communities, the method aims to reduce barriers to knowledge exchange and enhance dialogue between Indigenous knowledge-holders, researchers and decision-makers. The paper discusses the authors’ experiences with participatory topological mapping, emphasising its potential and limitations for knowledge exchange. The method is advocated for broader use, recognising its value in articulating concerns about land use change beyond research settings and reindeer husbandry. Participatory topological mapping enables knowledge-holders to control the map and its narrative, deciding what is included or excluded. The maps and accompanying stories are valuable sources for documenting, sharing and analysing local perspectives on landscapes and the effects of land use change. The method treats mapping as a social process with the potential to affect how we understanding the world. When thoughtfully applied, it offers a valuable avenue for knowledge co-production, fostering deeper engagement, supporting informed decision-making and empowering communities.

Soil reclamation of cranberry plantations on reclaimed lands in Arkhangelsk region

In the production conditions of the "Arkhangelsk cranberry" in the Arkhangelsk region, the land use analysis was carried out as a part of land use monitoring in this agricultural production. In 2015, the first cranberry plantations were planted, and in April 2023 about 40 hectares of peatlands were already in land use of the factory including 8 fields (checks) with an average area of 1 hectare. An assessment of the dynamics of change on the area of the created plantation was carried out for all indicators such as the length of roads, developed area and some separately designed structures (except of drained bogs). It was found that the soil indicators of peatlands are normal for this type of lands, and provide favorable conditions for growing cranberries. The analysis of the dynamics of land use in the period from 2015 to 2023 revealed the following dynamic indicators: an increase in the area of active land use by 418.97%, an increase in the area of drained swamps by 310.68%, 5.5 hectares were withdrawn from the forest fund, 42.31 hectares are under development (complex works), the total number of checks for planting increased by 23 parcels, an increase in the length of roads, including dirt roads, by 8.6 km, an increase in the number of fire ponds by 3 pieces, an increase in the number of sludge ponds by 3 pieces, and, finally, 1 recreational site was commissioned. The research has also shown that drainage does not lead to a significant change in the chemical composition of the organic part of peat and its agrochemical properties, so using drainage is not rational. In this case, a slight change in the water-air regime was observed together with the changes in the soil-forming process, and some activities of microorganisms, which was a prerequisite for an increase in the level of mineralization of the organic part of peat. Despite the gradual commissioning of peatlands, environmental sustainability has a trend towards an increase, which should be taken into account in the further development of the enterprise.

Evaluation and Comparison of Curve Numbers Based on Dynamic Land Use and Land Cover Changes

Evaluation and Comparison of Curve Numbers Based on Dynamic Land Use and Land Cover Changes

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Simulating the dynamics of cultivated land use in the farming regions of China: A social-economic-ecological system perspective

Greater comprehension of the intricate impacts of cultivated land use change on social and natural systems is of paramount importance for both immediate and long-term policy decisions and sustainable agricultural development. While prior research has examined specific elements of cultivated land use, there has been limited empirical research assessing interactions and feedback to examine the interplay among the intricacies of economic growth, social change, and their environmental impacts within a cultivated land use system. Hence, we introduce a nuanced system dynamics simulation model designed to evaluate the intricate interplay among social, economic, and ecological subsystems within the context of cultivated land use, with the Huang-Huai-Hai Plain of China serving as the case study. Our research elucidates that economic expansion tends to precipitate increased agrochemical application, highlighting that strategies to synchronize economic advancement with agricultural land conservation may alleviate the environmental stressors tied to regional land use. Furthermore, our analysis indicates that an integrative development strategy, which carefully balances economic acceleration with the reinforcement of agricultural land protection, can yield a measurable enhancement in agrarian environmental standards. This presents a feasible trajectory toward synergistic regional development.

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Prediction of land use/land cover and environmental estimation of carbon stocks in the Atlantic forest: A study in the state of Sergipe, Brazil

The forest ecosystems of the Atlantic Forest biome in Brazil are experiencing intense deforestation, contributing to climate change. Valuing the ecosystem service of CO2 sequestration can help in environmental policies to reduce deforestation and promote ecological restoration in this region. In this study, we sought to understand the dynamics of land use and land cover in the Atlantic Forest region of the state of Sergipe between 1985 and 2021 and to simulate land use and land cover for 2031 and 2051, assuming the current business as usual (BAU) trend was used as a reference scenario. The results showed that the agriculture-pasture mosaic class obtained the greatest percentage (5.27 %) and net (around 514.00 km²) increase among the land use and cover classes evaluated in the study area between 1985 and 2021. In the BAU scenario, total emissions reach 1,137,942.07 Mg CO2 in 2031 and 692,815.16 Mg CO2 in 2051, imposing an opportunity cost for society of US$ 7.94 million and US$ 4.35 million, respectively.

Construction of Landscape Ecological Risk Collaborative Management Network in Mountainous Cities—A Case Study of Zhangjiakou

The prevention of ecological risks is a critical determinant influencing sustainable development. Driven by rapid socio-economic development, the ecosystems of mountainous cities within agro-pastoral transition zones are increasingly vulnerable to complex disturbances, constituting a significant threat to sustainable development and human well-being. To help achieve sustainable development, it is essential to conduct research on addressing and mitigating ecological risks from the perspective of collaborative management networks in mountainous cities. Taking Zhangjiakou as the study area, this paper employed the land use transfer matrix and standard deviation ellipse methods to analyze the dynamic land use changes. Additionally, using Fragstats 4,2 to calculate the landscape indices with land use data, this paper evaluated the landscape ecological risk (LER) from 2000 to 2020. Furthermore, the social network analysis (SNA) method was utilized to explore the spatial correlation characteristics of the LER. The findings indicate that: (1) Cultivated land and grassland were the predominant land use types in Zhangjiakou. During 2000–2020, Zhangjiakou experienced significant changes in land use, dominated by the transfer among cultivated land, forestland, and grassland. It indicated that the issue of unstable ecological land use continued to exist. Affected by human activities, construction land showed a consistent upward trend, primarily concentrated in the urban built-up areas and areas along the Jing-Zhang Railway. (2) The LER of Zhangjiakou was predominantly characterized by low risk, medium risk, and high risk levels. In the transitional areas and foothills, the LER was relatively higher. During 2000–2020, Zhangjiakou showed a declining trend of LER. This suggested that the ecological protection policies in Zhangjiakou were effective, leading to an improvement in the local ecological environment. (3) The LER in Zhangjiakou demonstrated a spatial clustering pattern that exhibited an upward trend, which was supported by both spatial autocorrelation and the SNA analysis. In the LER collaborative management network, Xuanhua, Qiaodong, Qiaoxi, Wanquan and Zhangbei consistently upheld pivotal roles. Based on the number of inward and outward connections, 16 counties in Zhangjiakou were classified into four categories and three zones accompanied by corresponding recommendations. The findings of this study can serve as a valuable reference for subsequent landscape pattern optimization and ecological restoration in Zhangjiakou.

Assessing the impact of land use and land cover change on the Densu Delta wetland using Markov chain modeling and artificial neural networks

This study investigates the dynamics of land use and land cover (LULC) changes in the Densu Delta wetlands, a critical ecosystem in Ghana. Here, satellite images spanning from 1998 to 2023 were used to analyse the spatio-temporal patterns of LULC changes and their implications for water bodies, wetlands, vegetations, bare lands and urban areas in the Densu Delta wetland. Employing advanced techniques such as Markov chain modelling and artificial neural networks (ANNs), the research assesses and predicts LULC alterations. Significantly, the largest loss of LULC is observed in the Densu Delta wetland, where wetlands transition to waterbody cover type (14.02 km²). Model validation for 2023 attests to the accuracy of the model, boasting a correctness percentage of 70% and a kappa value of 0.74. In-depth analyses explore regional variations in the Densu Delta wetlands, revealing distinct patterns in the rates of LULC change before and after 2013. Notably, urbanization emerges as a prominent factor post-2013, with urban areas experiencing remarkable rates of change in the wetland. Transition matrices underscore the intricate interplay of different land cover classes over the years. Simulated LULC predictions for 2033 and 2043 highlight the urban land cover type as having the highest positive change, recording approximately 0.39% for the Densu Delta wetland. The wetland land cover in the Densu Delta wetland exhibit negative changes of about −0.52%. The synthesis of LULC data enhances our understanding of the complex interactions shaping these critical ecosystems. This research offers valuable insights for sustainable environmental conservation, emphasizing the pivotal role of informed urban planning policies. It also unveils potential challenges posed by climate change, advocating for a holistic approach to preserve these vital wetland ecosystems.

GIS-Based Land Use and Land Cover Change Assessment Around Assosa District, Upper Blue Nile Basin, Ethiopia

Land, with its intricate blend of geological, topographical, hydrological, and ecological elements, reflects the interplay of natural processes and human activities. In Ethiopia, traditional agricultural practices often lack systematic planning based on land suitability and physical characteristics, hindering agricultural productivity and economic progress. Understanding shifts in land use and cover is crucial for comprehending broader global transformations. Early studies have unveiled significant alterations in land use and cover between 2009 and 2013. Our investigation extends from 2013 to 2023, employing Landsat 8 imagery at three-year intervals (2013, 2018, and 2023) to analyze five key land cover categories: forests, farmland, built-up areas, barren land, and water bodies. Notably, farmland and built-up areas witnessed substantial expansion over these periods, juxtaposed with a significant decline in forest cover. Forests decreased by 27% from 2013 to 2018, with an additional 7% decrease from 2018 to 2023. Conversely, farmland expanded by 19% from 2013 to 2018 and by 2% from 2018 to 2023, while built-up areas saw respective increases of 4% and 10% over the same periods. These trends underscore potential environmental challenges if unaddressed. Effective land management strategies are crucial to mitigate adverse impacts on ecosystems, agricultural productivity, and overall socio-economic stability. By integrating scientific insights with sustainable practices, we can navigate the intricate dynamics of land use, paving the way for a resilient and prosperous future.

Spatial–Temporal Dynamics of Land Use and Cover in Mata da Pimenteira State Park Based on MapBiomas Brasil Data: Perspectives and Social Impacts

Spatial–Temporal Dynamics of Land Use and Cover in Mata da Pimenteira State Park Based on MapBiomas Brasil Data: Perspectives and Social Impacts

Evolution and Analysis of Water Yield under the Change of Land Use and Climate Change Based on the PLUS-InVEST Model: A Case Study of the Yellow River Basin in Henan Province

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the land use/cover change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario 1), ecological development priority (Scenario 2), and cropland development priority (Scenario 3). Climate change scenarios were constructed using CMIP6 data, representing low-stress (SSP119), medium-stress (SSP245), and high-stress (SSP585) conditions. The results show the following: (1) from 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) by 2030, the water yield of Scenario 1 is highest among different land use scenarios, marginally surpassing Scenario 2 by 1.60 × 108 m3; (4) climate scenarios reveal significant disparities, with SSP126 yielding 54.95 × 108 m3 higher water yield than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

Contribution of Landsat Multispectral Images to the Land Cover Dynamics of the Toéghin-Bangré Watershed for the Period 1998 to 2022 in Burkina Faso

Remote sensing plays an important role in the continuous observation of the earth's surface. It produces large databases that can be used to map and monitor natural resources. To this end, land-use maps are genuine planning and decision-making tools for natural resource management. The aim of this study is to use remote sensing products to show the dynamics of land use, to understand these dynamics and their impact on water resources in general and on hydrology in particular. Specifically, it is a question of identifying the similarities and differences that have occurred, and finally evaluating the rate of change. The methodology adopted is the use of the likelihood algorithm to produce three thematic classifications for the years 1998, 2010 and 2022. Overall accuracy is determined from the confusion matrix, which ranges from 80% to 91%. Supervised classification yielded seven occupancy classes: water bodies, cultivated areas, gallery forests, habitats/outcrops, grassy savannahs, wooded savannahs and bare ground. Quantification of change from 1998 to 2022, assessed by the rate of change (Tc), shows a progressive trend estimated at 31.82% (habitats/outcrops) and 43.40% (grassy savannah), and a regressive trend estimated at -17.92% (cultivated areas); -95.15% (gallery forests); -51.93% (water bodies) and -78.36% (bare ground). At the end of our study, diachronic analysis enabled us to quantify changes between the years 1998-2010, 2010-2022 and overall 1998-2022 with good precision.

Application of a Multi-Layer Perceptron and Markov Chain Analysis-Based Hybrid Approach for Predicting and Monitoring LULCC Patterns Using Random Forest Classification in Jhelum District, Punjab, Pakistan.

Land-use and land-cover change (LULCC) is a critical environmental issue that has significant effects on biodiversity, ecosystem services, and climate change. This study examines the land-use and land-cover (LULC) spatiotemporal dynamics across a three-decade period (1998-2023) in a district area. In order to forecast the LULCC patterns, this study suggests a hybrid strategy that combines the random forest method with multi-layer perceptron (MLP) and Markov chain analysis. To predict the dynamics of LULC changes for the year 2035, a hybrid technique based on multi-layer perceptron and Markov chain model analysis (MLP-MCA) was employed. The area of developed land has increased significantly, while the amount of bare land, vegetation, and forest cover have all decreased. This is because the principal land types have changed due to population growth and economic expansion. This study also discovered that between 1998 and 2023, the built-up area increased by 468 km2 as a result of the replacement of natural resources. It is estimated that 25.04% of the study area's urbanization will increase by 2035. The performance of the model was confirmed with an overall accuracy of 90% and a kappa coefficient of around 0.89. It is important to use advanced predictive models to guide sustainable urban development strategies. The model provides valuable insights for policymakers, land managers, and researchers to support sustainable land-use planning, conservation efforts, and climate change mitigation strategies.

Evolution of Land Use and Land Cover Change in Panjim, Goa: A Comparative Analysis from 1990 to 2024

This research paper investigates the dynamics of land use and land cover (LU/LC) change in Panjim, Goa, India, from 1990 to 2024. Using satellite Data and GIS techniques, the study analyzes the spatial and temporal transformations of land cover categories, including forest, agriculture, urban areas, and water bodies. The primary objective is to understand the drivers of LU/LC change and assess their implications for sustainable urban development and environmental conservation. The findings reveal significant urban expansion, encroachment on agricultural lands, and reduction in forest cover. The study highlights the need for effective land use planning and management strategies to mitigate negative environmental impacts and ensure the city's long-term sustainability. Keywords: Land Use, Land Cover, Supervised Classification, Change Detection, Accuracy Assessment