A1 Vertaisarvioitu data-artikkeli tieteellisessä lehdessä
Seamless Landsat-7 and Landsat-8 data composites covering all Amazonia
Tekijät: Gupta, Rajit; Zuquim, Gabriela; Tuomisto, Hanna
Kustantaja: Elsevier Inc.
Julkaisuvuosi: 2024
Journal: Data in Brief
Tietokannassa oleva lehden nimi: Data in Brief
Artikkelin numero: 111034
Vuosikerta: 57
eISSN: 2352-3409
DOI: https://doi.org/10.1016/j.dib.2024.111034
Verkko-osoite: https://doi.org/10.1016/j.dib.2024.111034
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/459168630
The use of satellite remote sensing has considerably improved scientific understanding of the heterogeneity of Amazonian rainforests. However, the persistent cloud cover and strong Bidirectional Reflectance Distribution Function (BRDF) effects make it difficult to produce up-to-date satellite image composites over the huge extent of Amazonia. Advanced pre-processing and pixel-based compositing over an extended time period are needed to fill the data gaps caused by clouds and to achieve consistency in pixel values across space. Recent studies have found that the multidimensional median, also known as medoid, algorithm is robust to outliers and noise, and thereby provides a useful approach for pixel-based compositing. Here we describe Landsat-7 and Landsat-8 composites covering all Amazonia that were produced using Landsat data from the years 2013–2021 and processed with Google Earth Engine (GEE). These products aggregate reflectance values over a relatively long time, and are, therefore, especially useful for identifying permanent characteristics of the landscape, such as vegetation heterogeneity that is driven by differences in geologically defined edaphic conditions. To make similar compositing possible over other areas and time periods (including shorter time periods for change detection), we make the workflow available in GEE. Visual inspection and comparison with other Landsat products confirmed that the pre-processing workflow was efficient and the composites are seamless and without data gaps, although some artifacts present in the source data remain. Basin-wide Landsat-7 and Landsat-8 composites are expected to facilitate both local and broad-scale ecological and biogeographical studies, species distribution modeling, and conservation planning in Amazonia.
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The work was funded by grants from the Academy of Finland to Hanna Tuomisto (decision numbers 351460 and 344733, the latter through CLAMBIO consortium/BiodivERsA 2019–2020 COFUND Call).
