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

Abstract

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.

Keywords:

• GEOBIA
• land use/land cover (LULC) mapping
• landscape pattern
• urban habitats
• spatial resolution

Data availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to copyright issues.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

We would like to thank the Young Scientists Award Program of the Turkish Academy of Sciences [TUBA–GEBIP] for supporting Elif Sertel, Istanbul Technical University Department of Scientific Re-search Projects (ITU–BAP) for supporting project [MGA-2017-40936] and Istanbul Technical University—Application and Research Center for Satellite Communications and Remote Sensing (ITU-CSCRS) for providing high-resolution satellite images. S. Szabo was supported by the K 138079 and the KKP 144068 projects. We also thank to Kübra Bahşi for her support.