Analyzing Urban Land Cover and Demographic Shifts through Remote Sensing Technologies

Abstract

This study investigates patterns of urban land cover change in Euless, Texas, using an integrated framework of satellite remote sensing and field-based observation. A 900-hectare area of interest was divided into a 37-point sampling grid, where ground photographs were collected using the GLOBE Observer application to validate and interpret satellite-derived classifications. Multiple remote sensing datasets, including Landsat Time Series Explorer (normalized burn ratio, NBR), ESRI Land Cover (2017/2024), WorldCover 10 m, Dynamic World (2016/2024), Meta/WRI Global Canopy Height, and MRLC NLCD Fractional Impervious Surfaces, were analyzed to quantify changes in built-up area, vegetation cover, canopy height, and surface imperviousness over time. Results indicate a clear increase in built-up land and impervious surfaces across much of the study area, accompanied by an overall reduction in tree canopy; however, localized anomalies in the southwestern portion of the area of interest, especially at sampling points 25 and 26 of the area of interest, demonstrated sustained canopy density and improved vegetation health, with exemplified NBR values increasing over the years. These findings highlight the spatial heterogeneity of urban growth and demonstrate that remote sensing, when validated with field observations, can effectively identify both broad development trends and localized ecological stability within a suburban landscape.