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

Bibliographic Data

ID21599745
AuthorsBeril Varol (0000-0003-2382-3969, Graduate Program of Geomatics Engineering, Istanbul Technical University Graduate School of Science Engineering and Technology, Istanbul, Turkiye), Szilárd Szabó (0000-0002-2670-7384, University of Debrecen), Raziye Hale Topaloğlu (0000-0001-9706-8068, Yıldız Technical University, corresponding author), Gül Aslı Aksu (0000-0002-6847-6182, Kastamonu University), Elif Sertel (0000-0003-4854-494X, Istanbul Technical University)
Year2024
Volume67
Issue10
Pages2281-2302
Publication date2024-08-23
Open AccessNo
TypeARTICLE
VenueJournal of Environmental Planning and Management (JOURNAL)
Journal identifiersISSN: 0964-0568 • E-ISSN: 1360-0559
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/09640568.2023.2185507
OpenAlexW4323848839
LanguageEN
References cited49

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

Cartography · Geography · Habitat · Land cover · Land use · Landscape ecology · Principal component analysis · Remote sensing · Computer Science · Land Use and Ecosystem Services · Remote Sensing in Agriculture · Wildlife-Road Interactions and Conservation · Artificial Intelligence · Ecology

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