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Volume 3, Issue 4, 2024

Abstract

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Accurate assessment of Global Navigation Satellite System (GNSS) observation data quality is essential for ensuring the reliability of positioning and navigation applications. Traditional evaluation methods, which rely on single-index weighting or simplistic combinations of multiple indicators, have proven insufficient in capturing the multifaceted nature of observation quality. To address these limitations, a comprehensive evaluation framework was developed based on a combined weighting strategy that integrates the information entropy weight method and the coefficient of variation method. This hybrid approach enhances the objectivity and sensitivity of index weighting by leveraging the strengths of both methods. Furthermore, fuzzy mathematics theory was incorporated to model the uncertainty and vagueness inherent in GNSS observations, thereby enabling the systematic identification and exclusion of low-quality and low-confidence data. This integration allows for the robust evaluation of multi-constellation GNSS observation data, accommodating complex and variable observational environments. The proposed method was validated through empirical analysis, demonstrating superior performance in distinguishing high-quality data compared to conventional single-indicator and single-weighting approaches. Experimental results confirm that the proposed framework yields more reliable and scientifically grounded quality assessments, contributing to improved accuracy and stability in downstream GNSS applications.

Abstract

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Urban expansion, driven by rapid population growth, has increasingly encroached upon agricultural land and contributed to the degradation of ecological systems. In this study, the spatiotemporal dynamics of urban growth in Narayanganj District, Bangladesh, were assessed over a 20-year period (2003–2023) using integrated Geographic Information System (GIS) and remote sensing techniques. Land Use and Land Cover (LULC) changes were quantified, and their ecological consequences were evaluated through vegetation indices including the Normalized Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI), alongside the Normalized Difference Built-up Index (NDBI). An LULC classification revealed a net increase of 5.61% in built-up areas, accompanied by reductions of 7.61% and 1.61% in barren land and agricultural land, respectively. The spatial pattern of urban expansion was found to be uneven, with pronounced growth observed from the northern to north-northwestern sectors of the district. A two-phase conversion analysis indicated that 15.68% of agricultural land was transformed into urban areas between 2003 and 2013, followed by a slightly lower conversion rate of 14.74% from 2013 to 2023. Notably, a statistically significant inverse correlation was detected between NDBI and both NDVI and SAVI, suggesting a measurable decline in vegetation health associated with urban intensification. These findings provide empirical and geographically grounded evidence of the adverse ecological impacts of urbanization in a peri-urban context. The integration of multi-temporal satellite images with vegetation and built-up indices enabled a comprehensive evaluation of land transformation processes and their environmental implications. The insights gained from this research may inform sustainable land use planning, urban policy formulation, and conservation strategies aimed at mitigating the loss of agricultural land and safeguarding vegetation health in rapidly urbanizing regions.

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