Land Cover Dynamics Research Articles

Assessing the trends of land cover dynamics and its major driving forces in the Rajparian Wildlife Sanctuary Kashmir, Himalaya

Assessing the trends of land cover dynamics and its major driving forces in the Rajparian Wildlife Sanctuary Kashmir, Himalaya

Impacts of Land use Land cover dynamics on Ecosystem services in maze national park and its environs, southwestern Ethiopia

This study investigated the impacts of LULC changes on selected ecosystem services in Maze National Park (MzNP) and its environs in southwestern Ethiopia. Landsat images from 1985, 2005, and 2020 were used to examine land use land cover (LULC) changes. Images were classified using the Random Forest (RF) classifier, and their accuracy was computed in QGIS. Ecosystem service values (ESVs) were then estimated using the benefit transfer method employing Ecosystem Service Valuation Database (ESVD) coefficients. Additionally, socioeconomic survey was conducted to understand the local community's perceptions regarding the dynamics of ecosystem services. The findings revealed a significant increase in croplands (103.7 %) and built-up areas (31.32 %), while riverine forests, water bodies, and wooded grasslands declined. The overall ESVs decreased by 20 %, from 2038.42 million USD in 1985 to 1628.72 million USD in 2020, mainly driven by reductions in riverine forests and wooded grasslands. As for the individual ESVs for the period 1985 to 2020, only food production increased by 0.7 million USD, while water supply, climate regulation, raw materials, and recreation and tourism declined by 180.35, 2.67, 45.72, and 481.62 million USD, respectively. The coefficient of sensitivity ranged from 0.01 to 0.94, <1, revealed that our estimates are relatively robust. Ecosystem services such as grazing, recreation, wild food, and firewood are highly valued by local residents, but they are declining over time due to environmental degradation and restrictions on access to the park. Thus, understanding LULC changes and their impacts on ESVs can help decision-makers design effective protected area management plans and reduce potential conflicts over resource uses. Further investigations are suggested to more accurately quantify ESVs using high resolution satellite imageries and different valuation methods.

ASSESSING ANTHROPOGENIC PRESSURE ON LAND USE AND LAND COVER IN THE MAFERE SUB-PREFECTURE IN SOUTH-EAST COTE DIVOIRE

Several hectares of forest disappear every year in Cote dIvoire, particularly in the Aboisso Department. Despite the fight to preserve biodiversity and forest cover, numerous pressures (anthropogenic pressures) continue to be exerted on the forests, leading to their destruction and even increased deforestation. This deforestation has many consequences, including reduced biodiversity and climate change. This study was initiated to gain a better understanding of this reduction in forest area. The aim of the study is to assess the level of forest degradation in the Mafere sub-prefecture. The activities involve mapping and analyzing the dynamics of land use and land cover (LULC). To achieve these objectives, the study is based on satellite images (Landsat 5, 7 and 8) from 1986, 2000 and 2020. The results of LULC dynamics show, on the one hand, a decline in closed forest/swamp forest (96.29%), degraded forest (76.75%), crop/fallow (67.03%) and annual crops (38.35%) and, on the other hand, an increase in cocoa (675.25%), oil palm (193.13%), rubber (14.39%) and housing/bare land (101.26%). The extension of agricultural areas in search of resources (natural and financial), demographic pressures, still traditional cultivation techniques and climate change are the main factors causing the degradation of forests in the Mafere sub-prefecture.

Examining Patterns and Trends of Change in Greater Yola, Adamawa State, Nigeria: A 10 Years Spatio-Temporal Investigation

This research paper explores the dynamics of land cover and land use in Greater Yola, Adamawa State, Nigeria, spanning the years 2013 to 2023. Through comprehensive analysis, including remote sensing techniques and spatial modeling, the study examines the temporal trends and implications of urbanization on ecosystem diversity and resilience within the region. The findings reveal a significant trend of rapid urbanization, characterized by the expansion of built-up areas at the expense of natural vegetation, which poses challenges to ecosystem integrity. Furthermore, the analysis highlights the implications of these changes for ecosystem diversity, including the loss of natural habitats and fragmentation of ecosystems, threatening biodiversity and essential ecosystem services. The study underscores the critical need for a balanced approach to urban development and environmental conservation to achieve sustainable development goals. Recommendations are provided to guide policymakers and stakeholders in fostering ecosystem conservation, promoting sustainable land use practices, and mitigating the adverse impacts of urbanization on ecosystem health. By implementing these recommendations, Greater Yola can strive towards achieving a harmonious balance between urban growth and environmental stewardship, ensuring the long-term sustainability of ecosystems and the well-being of its residents.

AI can empower agriculture for global food security: challenges and prospects in developing nations.

Food and nutrition are a steadfast essential to all living organisms. With specific reference to humans, the sufficient and efficient supply of food is a challenge as the world population continues to grow. Artificial Intelligence (AI) could be identified as a plausible technology in this 5th industrial revolution in bringing us closer to achieving zero hunger by 2030-Goal 2 of the United Nations Sustainable Development Goals (UNSDG). This goal cannot be achieved unless the digital divide among developed and underdeveloped countries is addressed. Nevertheless, developing and underdeveloped regions fall behind in economic resources; however, they harbor untapped potential to effectively address the impending demands posed by the soaring world population. Therefore, this study explores the in-depth potential of AI in the agriculture sector for developing and under-developed countries. Similarly, it aims to emphasize the proven efficiency and spin-off applications of AI in the advancement of agriculture. Currently, AI is being utilized in various spheres of agriculture, including but not limited to crop surveillance, irrigation management, disease identification, fertilization practices, task automation, image manipulation, data processing, yield forecasting, supply chain optimization, implementation of decision support system (DSS), weed control, and the enhancement of resource utilization. Whereas AI supports food safety and security by ensuring higher crop yields that are acquired by harnessing the potential of multi-temporal remote sensing (RS) techniques to accurately discern diverse crop phenotypes, monitor land cover dynamics, assess variations in soil organic matter, predict soil moisture levels, conduct plant biomass modeling, and enable comprehensive crop monitoring. The present study identifies various challenges, including financial, infrastructure, experts, data availability, customization, regulatory framework, cultural norms and attitudes, access to market, and interdisciplinary collaboration, in the adoption of AI for developing nations with their subsequent remedies. The identification of challenges and opportunities in the implementation of AI could ignite further research and actions in these regions; thereby supporting sustainable development.

Deforestation detection from spaceborne full-waveform laser altimetry, incorporating terrain effects: A case study in Porto Velho, Brazil

Forest structure, notably the canopy height above the ground, can be obtained from the full-waveform data acquired by satellite laser altimetry. On sloped terrain, the laser pulse can simultaneously reflect from both the vegetation and the ground within their intersecting vertical extents, causing their signals to overlap. This overlap complicates the separation of canopy and ground in the analysis. Consequently, accurately obtaining canopy height and its variation, while considering the influence of terrain factors, poses a significant challenge.In this paper, to counter this, we introduce an innovative approach to remove terrain effects for more reliable identification of canopy and ground. Overlapping footprints from the Ice, Cloud, and land Elevation Satellite (ICESat)/Geoscience Laser Altimeter System (GLAS) and Global Ecosystem Dynamics Investigation (GEDI) campaigns are extracted as coincidental observations. Utilizing the different footprint sizes and by minimizing the center and width of each decomposed Gaussian component pair, optimized ground returns can be obtained. Validation using airborne LiDAR data from the Bartlett Experimental Forest, New Hampshire, USA, indicate that the proposed approach can reduce the ground elevation root-mean-square error (RMSE) by approximately 3 m, compared to the prevalent methods and standard products. While the GEDI product algorithms exhibit a certain accuracy in canopy height retrieval, the proposed approach presents a more constrained error band, signifying less impact from ground elevation discrepancies.While the proposed method enhances canopy height precision, its reliance on overlapping footprints means sacrificing some observations. However, the proposed method is effective in scenarios such as change detection that require repeat observations. As a case study, we applied the proposed method in Porto Velho, Rondônia, Brazil, which is a deforestation hotspot. Using the GlobeLand30 dataset, we investigated the land-cover dynamics from co-located laser footprints, identifying significant forest loss areas and validating our findings with the Hansen Global Forest Change dataset, achieving an accuracy of 83.81 %.In conclusion, this research emphasizes the significance of terrain topography effects in vegetation structure analysis, and positions satellite laser altimetry as instrumental for forest monitoring.

Land Cover Dynamics in the Northwestern Virunga Landscape: An Analysis of the Past Two Decades in a Dynamic Economic and Security Context

The Beni region in the eastern Democratic Republic of Congo is grappling with socioeconomic development and security challenges that have affected its natural ecosystems, especially those located in the northern Virunga National Park. This study aims to document the anthropization of the northwestern Virunga landscape from 1995 to 2021 in the context of insecurity. Using a cartographic approach and ecological-landscape-analysis tools, this study delves into the overall landscape changes through a comparative analysis of protected and unprotected areas. These investigations focus on landscape composition, transitions between land-cover classes, and the spatial transformation process. The northwestern Virunga landscape is undergoing significant land cover changes due to the influence of insecurity on socioeconomic activities, primarily agriculture. Agricultural land encompasses a larger area than other land-cover types. However, its expansion has decelerated since the 2000s. The loss of forested area is discontinuous. During relatively stable periods (1995–2005), forests exhibited a reduction of up to 2.90% in area, while in the period of the return of Iturian refugees to their province, followed by terrorist insecurity in Beni (2005–2021), the forested area increased by 2.07%. Savannah areas, which are mainly located in the graben rift valley and near Butembo, have been more heavily affected by human activity than forests. Ultimately, the apparent stability of the landscape can be attributed to its protected areas, especially Virunga National Park.

Sensitivity analysis on stormwater management response to land cover dynamics and urban expansion of developing City in Lake Hawassa watershed, Ethiopia

AbstractHawassa is a rapidly developing city in Lake Hawassa watershed of Ethiopia. Analyzing the effect of land cover dynamics on surface runoff remains imperative to adaptive urban stormwater management. This study quantified spatial variation of land cover and sensitivity of stormwater management response. Historical 30 years of daily annual rainfall, three satellite imageries, DEM, and hydrological soil group data were analyzed. A statistical‐based combined approach of geospatial techniques and Soil Conservation Service‐Curve Number (SCS‐CN) model was employed. CN and surface runoff depth for the delineated urban watersheds were determined. The result revealed that the built‐up area increased by 30.9 km2, where the rate varies spatially. The variation of impervious land cover explains 58.6% of change in CN with coefficient of 0.352. While CN is inversely correlated with agricultural and vegetation land cover variations. The finding suggests CN explains 96.78% of the change in surface runoff with a significant correlation coefficient of 3.91. The proposed integrated model approach justifies the potential to reorganize the relationship between the spatial effect of land cover variation on surface runoff at the urban watersheds. Thus, suitable local‐specific solutions can be devised for effective management of flood risk and optimize the drainage system of urban areas.

Corrigendum: Evaluating the potential footprints of land use and land cover and climate dynamics on atmospheric pollution in Pakistan

Corrigendum: Evaluating the potential footprints of land use and land cover and climate dynamics on atmospheric pollution in Pakistan

Assessment of land use and land cover dynamics and its impact in direct runoff generation estimation using SCS CN method

Assessment of land use and land cover dynamics and its impact in direct runoff generation estimation using SCS CN method

A comprehensive analysis and future projection of land use and land cover dynamics in a fast-growing city: A case study of Sekondi-Takoradi metropolis, Ghana

Sekondi-Takoradi is the most urbanized metropolis in the Western region of Ghana. Over the past two decades, it has experienced rapid shifts in both population density and land cover. Unfortunately, these transformations have significantly altered the natural landscape and expanded urban coverage. Previous studies have not established enough evidence on land use and land cover changes in the metropolis. For this reason, this study examined the existence, rate, and spatial distribution of land use and land cover as well as the future changes in land cover in the metropolis. Landsat imagery spanning from 1991, 2009, 2016, to 2023 provided the basis for evaluating the spatiotemporal dynamics of land use and cover within the metropolis. The Random Forest Classification algorithm was employed to categorize the images into three distinct classes: water, vegetation, and built-up areas. Analysis revealed a rapid growth in built-up areas by 63.08 km² (32.91 %), accompanied by a decrease in vegetation and water coverage by 60.99 km² (31.82 %) and 2.08 km² (1.09 %) respectively. This highlights the accelerating urbanization trend, emphasizing the critical need for vigilant monitoring and controlled urban expansion to mitigate potential adverse effects. Projections for 2030 indicate further changes with water areas decreasing to 1.21 km² (0.63 %), vegetation diminishing to 95.31 km² (49.73 %), and built-up areas expanding to 95.14 km² (49.64 %). This signifies a significant shift towards built-up areas, altering the metropolis' land cover composition and potentially impeding the achievement of Sustainable Development Goal 11. Understanding these changes in land use and land cover holds significant value for policymakers, enabling them to effectively monitor and safeguard natural resources.

Comprehensive analysis of land use and cover dynamics in djibouti using machine learning technique: A multi-temporal assessment from 1990 to 2023

Understanding land use and land cover (LULC) dynamics in semi-arid regions is vital for unraveling complex environmental processes and resource management. This study delves into the intricate interplay of land patterns and resource dynamics, offering indispensable insights into the environmental repercussions of these changes. The study aims to quantify land use categories in Djibouti's semi-desert region using remote sensing. It analyzes temporal changes and evaluates Random Forest (RF) algorithms for land use classification. Through meticulous quantification and comprehensive temporal analysis, the research contributes significantly to remote sensing and environmental science by enhancing understanding of land use dynamics and informing sustainable land management practices. Leveraging machine learning supervised classification on the Google Earth Engine (GEE) platform using Landsat data spanning four time periods (1990, 2002, 2012, and 2023), alongside spectral indices and Digital Elevation Model (DEM) data, our study achieves unprecedented insights. Our findings reveal a significant landscape transformation, delineating seven major land cover classes: mangroves, bushes, farmland, built-up areas, water bodies, barren land, and salt plains. With overall accuracy ranging from 89 % to 95 %, our assessments demonstrate significant changes in land use types over the studied period. Notably, mangroves, bushes, farmland, and salt areas witnessed declines, while built-up areas, water bodies, and barren lands expanded. This research underscores the pivotal role of remote sensing in monitoring long-term land use changes and their ecological impacts. By harnessing technological advancements, our study empowers stakeholders to make informed decisions for sustainable resource management and environmental conservation in semi-arid landscapes.

Eucalyptus tree expansion and land use and land cover dynamics in Ethiopia- empirical evidence from Gurage Zone, Ethiopia

Gurage Zone is one of the areas of Ethiopia experiencing rapid land use and land cover (LULC) changes over the past few decades. This study assessed the contribution of eucalyptus expansion to LULC changes based on a detailed household level survey, key informant interviews, focus group discussions and a set of Landsat imageries. Both descriptive statistics and econometric model were employed for the analysis of drivers of eucalyptus plantation. LULC change detection technique was used to examine the changes in LULC over time. This study achieved a mean classification accuracy of 89.17% and 0.83 overall Kappa statistics for the tree LULC maps. The result of descriptive analysis revealed that more than 42% of the sampled households have planted eucalyptus trees on crop land. The study indicated that eucalyptus plantation which was fourth in LULC ranking in 2000 has increased by 68.3% in 2021 to become the second largest land cover next to crop land. On the other hand, from 2010 to 2021, the area covered by crops showed a decline. This indicates eucalyptus tree to be the major cause of LULC change in the study area. The econometric estimation identified the micro-level drivers such as land size and access to market to be important factors influencing eucalyptus plantation. This study suggests that the rapid expansion of eucalyptus plantation on fertile soil requires an immediate policy response in order to balance food crop production and eucalyptus plantation in Gurage Zone.

Analysis of Trends in Land Utilization and Land Cover Dynamics in the Kwahu West Municipality, Ghana

The connection between people and the environment has been blamed for changes. Planning for sustainable development requires awareness of land use and land cover (LULC), its severity, causes, and implications. The purpose of this study was to generate maps that illustrate the land use and land cover within the Kwahu West Municipality and detect changes to identify and analyse overall patterns in LULC changes in the area. This research employed remote sensing techniques to assess land use and land cover alterations (LULC). This study used Landsat 7 Enhanced Thematic Mapper (ETM+) images from 2001, Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images from 2015, and Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images from 2023 to detect and quantify LULC changes that occurred over a 22-year study period. Arc GIS 10.8 and ERDAS Envision 13 were used to pre-process the data. According to the study, built-up land and bare land rose in their respective areas and years by 10.675 km2 (or 2%), 18.421 km2 (or 5%), and 29.096 km (or 7%), respectively, between 2001 and 2015 and 2015 and 2023. The overall change from 2001 to 2023 was correspondingly 29.096 km. The results depicted a drastic change in the study area's forest vegetation, which can be attributed to a sharp rise in population, urbanisation, and other infrastructure projects. These changes have been determined to cause biodiversity loss and water contamination. It is recommended that the Municipality's Lands Commission initiate a public education campaign about creating local planning schemes for lands, especially in undeveloped and underdeveloped areas.

Dynamic land cover evapotranspiration model algorithm: DyLEMa

This study presents the “Dynamic Land Cover Evapotranspiration Model Algorithm: DyLEMa” for continuous spatiotemporal evapotranspiration (ET) estimates across diverse land uses. DyLEMa employs a coupled Random Forest model with a novel dynamic recalibrating strategy to improve pre-optimized seasonal hyperparameters following satellite acquisition alongside land cover classes. An analysis of feature importance indicated the significant variability in ET processes across different land cover classes and seasons. Hence, DyLEMa was applied to 20 years of daily 30x30 m pixel resolution Landsat-derived ET data in Illinois to address spatial and temporal discontinuities due to cloud contamination and sensor failures. DyLEMa performance was evaluated on Eddy Covariance measurements to find out that DyLEMa predictions reduced the average PBIAS error from + 31 % to −7% compared to existing US Geological Survey ET products. Spatially, DyLEMa underscores the value of a land cover-aware approach in ET estimation under varied cloud cover rates and their ability to preserve landscape features. However, the performance of DyLEMa was affected by the quality of land cover classification, suggesting the need for a refined region-specific land cover classification. DyLEMa’s flexibility and performance suggest its applicability to other regions and satellite datasets, offering a promising reduction in uncertainty of ET estimates with impacts on environmental and water resources assessments on regional scales.

Terrestrial carbon dynamics and economic valuation of ecosystem service for land use management in the Mediterranean region

Land use change is a significant cause of land degradation, resulting in the removal of carbon and its transfer to the hydrosphere or atmosphere, which can have a detrimental impact on essential ecosystem functions and services. In contrast, rural landscapes offer both ecological and economic benefits to people due to their carbon storage properties. This benefit is often evaluated within the framework of Ecosystem Services (ES). The rural landscapes offer unique structures and ecosystem services. The aim of this study is to provide an economic quantification of carbon storage and sequestration in rural landscapes as an ecosystem service and to introduce carbon-based rural landscape management. The study enables the integration of carbon-based strategies and policies by generating quantitative outputs for landscape plans that have not yet been included in the spatial planning hierarchy. The Upper Seyhan Basin (USB), located on the Eastern Mediterranean coast of Turkiye, was modelled using spatiotemporal and economic valuation approaches to assess terrestrial carbon storage and sequestration in this context. The study modelled and mapped the four main terrestrial carbon storage components in the rural landscape: (i) above-ground biomass, (ii) below-ground biomass, (iii) Soil Organic Carbon (SOC), and (iv) dead organic matter carbon. The future land cover of 2055 was modelled using the Multilayer Perceptron (MLP)-Markov Chain (MC) algorithm based on the “Business-As-Usual” (BAU) scenario to determine future carbon sequestration. The Social Cost of Carbon (SCC) was estimated for Turkiye using the Regional Integrated Model of Climate and Economy (RICE) to economically model carbon services. In the final stage of the study, modelling outputs, including carbon storage pools/sinks, projected land cover dynamics, and SCC components, were used within the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model to map the carbon dynamics of the rural landscape at the regional level. This paper demonstrates the role of geospatial modelling and proposes a new planning and design logic that considers carbon-based inputs and global climate change in an appropriate format for land management by bridging the interface between earth sciences and spatial planning. The study results indicate that, the rural landscape in the study area provided an ecosystem service of 70,266 TgC carbon in 2014 with a storage value of USD 222,853,170. For the 2014–2025 projection, an estimated 1.042 TgC of carbon sequestration was valued at USD 6,234,137. The 2014–2055 projection estimated 3.185 TgC of carbon sequestration valued at USD 58,745,265.

The Relationship between Climate, Agriculture and Land Cover in Matopiba, Brazil (1985–2020)

Climate change has been at the forefront of discussions in the scientific, economic, political, and public spheres. This study aims to analyze the impacts of climate change in the Matopiba region, assessing its relationship with land cover and land use, soybean crop production and yield, and ocean–atmosphere anomalies from 1985 to 2020. The analysis was conducted in four parts: (1) trends in annual and intra-annual climate changes, (2) the spatiotemporal dynamics of land cover and use, (3) the spatiotemporal dynamics of soybean production and yield, and (4) the relationship between climate change, agricultural practices, land cover and use, and ocean–atmosphere anomalies. Statistical analyses, including Mann–Kendall trend tests and Pearson correlation, were applied to understand these relationships comprehensively. The results indicate significant land cover and use changes over 35 years in Matopiba, with municipalities showing increasing soybean production and yield trends. There is a rising trend in annual and intra-annual maximum temperatures, alongside a decreasing trend in annual precipitation in the region. Intra-annual climate trends provide more specific insights for agricultural calendar planning. No correlation was found between the climate change trends and soybean production and yield in the evaluated data attributed to genetic and technological improvements in the region. The North Atlantic Ocean shows a positive correlation with soybean agricultural variables. Evidence suggests soybean production and yield growth under climate change scenarios. This study highlights soybeans’ adaptation and climate resilience in the Matopiba region, providing valuable insights for regional agricultural development and contributing to further research in environmental, water-related, social, and economic areas of global interest.

Sentinel-2 Perspective: Land Use and Land Cover Dynamics in East and West Karbi Anglong

This study delves into the dynamics of land use and land cover (LULC) in East and West Karbi Anglong using Sentinel-2 satellite imagery. In response to escalating environmental changes, remote sensing technologies, particularly Sentinel-2, offer crucial insights for sustainable land management. Through an analysis of LULC patterns, the study aims to inform evidence-based decisions for environmental conservation and resource management in the region. The present study highlights the utility of remote sensing in LULC assessments and acknowledges the necessity for customized approaches in regions with distinctive features such as Karbi Anglong. Methodologically, temporally stratified Sentinel-2 datasets were utilized, with meticulous pre-processing and accuracy assessments ensuring reliability. Results indicate notable shifts in LULC classes, including urban expansion and vegetation decline. Despite limitations, such as potential inaccuracies in data processing, the study emphasizes the importance of monitoring environmental changes and advocating for sustainable land management strategies.

Changes in total and per-capital ecosystem service value in response to land-use land-cover dynamics in north-central Ethiopia

Ecosystems provide a wide range of services crucial for human well-being and decision-making processes at various levels. This study analyzed the major land cover types of north-central Ethiopia and their impact on total and per-capita ecosystem service value (ESV). The ESV was estimated using the benefit-transfer method along the established global and local coefficient values for the periods 1973, 1986, 2001, 2016, and 2024. The findings show that agricultural lands continued to expand at a rate of 563.4 ha year−1, at the expense of forests and grasslands. As a result, the total ESV of the study area declined from $101.4 to $61.03 million and $60.08–$43.69 million, respectively. The ESV per capita was also diminished by $152.4 (37.7%) and $257 (40.6%), respectively. However, land-cover improvement during the period 2001–2016 enhanced the total and per capita ESV in the study area. Therefore, potential future research may be required to develop a valid approach for assessing the robustness and sensitivity of value coefficients for the valuation of the ESV at the landscape level.

The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory

IntroductionThis study delves into the spatiotemporal dynamics of land use and land cover (LULC) in a Metropolitan area over three decades (1991–2021) and extends its scope to forecast future scenarios from 2031 to 2051. The intent is to aid sustainable land management and urban planning by enabling precise predictions of urban growth, leveraging the integration of remote sensing, GIS data, and observations from Landsat satellites 5, 7, and 8.MethodsThe research employed a machine learning-based approach, specifically utilizing the random forest (RF) algorithm, for LULC classification. Advanced modeling techniques, including CA–Markov chains and the Land Change Modeler (LCM), were harnessed to project future LULC alterations, which facilitated the development of transition probability matrices among different LULC classes.ResultsThe investigation uncovered significant shifts in LULC, influenced largely by socio-economic factors. Notably, vegetation cover decreased substantially from 49.21% to 25.81%, while forest cover saw an increase from 31.89% to 40.05%. Urban areas expanded significantly, from 7.55% to 25.59% of the total area, translating into an increase from 76.31 km2 in 1991 to 258.61 km2 in 2021. Forest area also expanded from 322.25 km2 to 409.21 km2. Projections indicate a further decline in vegetation cover and an increase in built-up areas to 371.44 km2 by 2051, with a decrease in forest cover compared to its 2021 levels. The predictive accuracy of the model was confirmed with an overall accuracy exceeding 90% and a kappa coefficient around 0.88.DiscussionThe findings underscore the model’s reliability and provide a significant theoretical framework that integrates socio-economic development with environmental conservation. The results emphasize the need for a balanced approach towards urban growth in the Islamabad metropolitan area, underlining the essential equilibrium between development and conservation for future urban planning and management. This study underscores the importance of using advanced predictive models in guiding sustainable urban development strategies.