Analysis of the association between image resolution and landscape metrics using multi-sensor LULC maps

This study aims to investigate the changes in landscape metrics with varying spatial resolution from Sentinel-2 (10 m), SPOT 7 (1.5 m), Pleaides (0.5 m), and Worldview-4 (0.3 m) images. We implemented Geographic Object-Based Image Analysis (GEOBIA) techniques to all images to identify 21 land use and land cover (LULC) classes, which were then used to calculate several landscape metrics. We performed the Welch hypothesis testing on the class-level landscape metrics and applied Standardized Principal Component Analysis (PCA) with the correlation matrix to reveal the multivariate pattern of landscape metrics. Our results showed that 10 m and even the 1.5 m spatial resolutions cannot guarantee the identification of all LULC classes, and class areas change with varying spatial resolution (sometimes with 200% differences). Sentinel-2 images have some limitations, specifically from the landscape ecological planning perspective; on the other hand, Pleaides and Worldview-4 seem good alternatives to understand habitats’ viability and landscape isolation/connectivity.