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Analysis of Spatio-temporal Dynamics of Urban Sprawl and Growth Pattern Using Geo-spatial Technologies in Pathsala Municipality, Bajali

Jul 2026 · Journal of Geography Environment and Earth Science International · Vol 30, pp. 150-171 · 0 citations

Abstract

Urban expansion in rapidly growing municipalities has intensified land-use transformation, necessitating systematic assessment to support sustainable urban planning. This study investigates the spatio-temporal dynamics of urban sprawl and growth patterns in Pathsala Municipality, Bajali, using geospatial technologies. It aims to quantify urban expansion, analyse land use and land cover (LULC) changes, and identify the spatial characteristics of urban growth. Multi-temporal satellite imagery, remote sensing, and GIS techniques were integrated to map LULC changes, while image classification and change-detection analyses were employed to quantify urban growth and identify emerging spatial patterns. The findings indicate that the built-up area increased by 12.28% during 2005–2011, 21.35% during 2011–2017, and 25.32% during 2017–2023. Conversely, vegetation declined by 4.36%, 1.71%, and 13.62%, while agricultural fields and fallow land declined by 5.56%, 19.95%, and 22.32% over the corresponding periods. The built-up area expanded from 3.42 km2 in 2005 to 5.84 km2 in 2023, while the Largest Patch Index (LPI) increased from 15.42% to 33.05%, Edge Density (ED) declined from 48.86 to 27.26 km/km2, and the Distance Decay Coefficient (β) rose from 0.3437 in 2005 to 0.8255 in 2023. Urban growth was concentrated along major transport corridors and in peripheral areas, reflecting a combination of linear expansion and dispersed sprawl. These changes indicate increasing pressure on land resources and infrastructure. The study applies GIS and remote sensing to analyse long-term urban growth in Pathsala Municipality. It provides localised insights into urbanisation processes and establishes a spatial database to support future monitoring and planning. Despite limitations related to the spatial resolution and temporal availability of satellite data, classification uncertainty, and the exclusion of detailed socio-economic drivers, the findings provide evidence that can support municipal authorities and planners in formulating sustainable land-use policies, infrastructure-development strategies, environmental-conservation measures, and informed decision-making for balanced urban growth.

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