Estimating Urban Growth from Landsat 8 Data Using Post-classification and Albedo Change Analysis in GEE Environment

Urban growth, defined as the increase of artificial surfaces in cities or towns, is now considered critical for the environmental impacts it can generate. Mapping and monitoring the soil sealing phenomenon provides researchers, planners and decision-makers with useful information for sustainable development. Moreover, Earth observation remote sensing images, such as medium-resolution Landsat data, is widely adopted for short to long term urban change analysis. The aim of this paper is to extract information on land consumption occurred on urban areas (period 2015–2023) from Landsat 8 data using GEE cloud platform. The territory of the municipality of Bitritto, in Apulia region (Italy), was chosen as the study site. To achieve the objective of this work, a change detection approach, including both “post-classification comparison” and “image differencing” methods, was applied. To classify Landsat 8 multispectral images into a binary scheme (“urban areas” and “non-urban areas”), a decision tree approach using STRed and SwiRed indices, NIR/SWIR2 ratio and NIR band was developed and implemented in GEE environment. The “image differencing” step was, instead, performed by subtracting two land surface albedo maps, obtained from winter Landsat 8 images, for the two reference years. A neighborhood filter to remove isolated “changed” pixels was also applied to improve the outcome. The results were promising (P.A. 85% and U.A. 77% for the “urban growth” category, with an O.A. of 92%) even though some misclassifications were identified, probably due to “mixed” pixels or to some spectral confusion among certain land cover classes.