Spatial resolution and landscape structure along an urban-rural gradient: Do they relate to remote sensing classification accuracy? – A case study in the megacity of Bengaluru, India

Abstract

Along the urban-rural gradient in megacities, the extent and material composition of impervious surfaces are different. This leads to variations in the frequently mentioned heat-island property, but possibly also to different spectral signatures and, consequently, different accuracies in remote sensing image classification. This, in turn, creates a challenge when it comes to selecting suitable image processing techniques. In this study, we examine how the accuracy of land-cover classification changes along an urban-rural gradient as a function of spatial resolution and the gradient in landscape structure. RapidEye, Sentinel-2A and Landsat 8 images were used. Land-cover classification was performed using a deep learning model and landscape metrics were used to assess landscape structure. A high degree of landscape heterogeneity and lowest classification accuracy was observed in the transition zone between urban and rural domains, within a stretch of 15–20 km from the urban center. As expected, spatial resolution was found to be influential in classification accuracy. A comparison of classifications indicates that within rural landscapes finer resolution images retain more spatial and thematic details in land-cover, e.g., RapidEye and Sentinel-2A imagery better distinguish built-up areas within the agricultural landscape and discriminate more of the mapped land-cover/use classes than Landsat 8. Overall accuracy increased with increasing spatial resolution (30 m, 10 m, 5 m) within the urban and rural areas, however, the 10 m resolution image (Sentinel-2A) produced better results in the transition zone. The findings from this study provide a basis for more focused, consistent and possibly more accurate time-series analyses of land-use dynamics at the urban-rural interface.