Assessment of temporal aggregation of Sentinel-2 images on seasonal land cover mapping and its impact on landscape metrics

Landscape metrics (LM) play a crucial role in fields such as urban planning, ecology, and environmental research, providing insights into the ecological and functional dynamics of ecosystems. However, in dynamic systems, generating thematic maps for LM analysis poses challenges due to the substantial data volume required and issues such as cloud cover interruptions. The aim of this study was to compare the accuracy of land cover maps produced by three temporal aggregation methods: median reflectance, maximum normalised difference vegetation index (NDVI), and a two-date image stack using Sentinel-2 (S2) and then to analyse their implications for LM calculation. The Google Earth Engine platform facilitated data filtering, image selection, and aggregation. A random forest algorithm was employed to classify five land cover classes across ten sites, with classification accuracy assessed using global measurements and the Kappa index. LM were then quantified. The analysis revealed that S2 data provided a high-quality, cloud-free dataset suitable for analysis, ensuring a minimum of 25 cloud-free pixels over the study period. The two-date and median methods exhibited superior land cover classification accuracy compared to the max NDVI method. In particular, the two-date method resulted in lower fragmentation-heterogeneity and complexity metrics in the resulting maps compared to the median and max NDVI methods. Nevertheless, the median method holds promise for integration into operational land cover mapping programmes, particularly for larger study areas exceeding the width of S2 swath coverage. We find patch density combined with conditional entropy to be particularly useful metrics for assessing fragmentation and configuration complexity.