Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fi

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...

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Published in:Data in Brief
Main Authors: Rajit Gupta, Gabriela Zuquim, Hanna Tuomisto
Format: Article
Language:English
Published: Elsevier 2024-12-01
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S235234092400996X
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author Rajit Gupta
Gabriela Zuquim
Hanna Tuomisto
author_facet Rajit Gupta
Gabriela Zuquim
Hanna Tuomisto
author_sort Rajit Gupta
collection DOAJ
container_title Data in Brief
description 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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spelling doaj-art-e0eee599688b4c1aaa39744d2cc0d9452025-08-20T00:55:29ZengElsevierData in Brief2352-34092024-12-015711103410.1016/j.dib.2024.111034Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fiRajit Gupta0Gabriela Zuquim1Hanna Tuomisto2Department of Biology, University of Turku, 20014, Finland; Corresponding author.Department of Biology, University of Turku, 20014, FinlandDepartment of Biology, University of Turku, 20014, Finland; Department of Biology, Section for Ecoinformatics and Biodiversity, Aarhus University, Ny Munkegade 116, 8000 Aarhus C, DenmarkThe 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.http://www.sciencedirect.com/science/article/pii/S235234092400996XSurface reflectanceAmazon forestGoogle earth engineBRDF correctionPixel-based compositing
spellingShingle Rajit Gupta
Gabriela Zuquim
Hanna Tuomisto
Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fi
Surface reflectance
Amazon forest
Google earth engine
BRDF correction
Pixel-based compositing
title Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fi
title_full Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fi
title_fullStr Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fi
title_full_unstemmed Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fi
title_short Seamless Landsat-7 and Landsat-8 data composites covering all AmazoniaFairdata.fi
title_sort seamless landsat 7 and landsat 8 data composites covering all amazoniafairdata fi
topic Surface reflectance
Amazon forest
Google earth engine
BRDF correction
Pixel-based compositing
url http://www.sciencedirect.com/science/article/pii/S235234092400996X
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AT hannatuomisto seamlesslandsat7andlandsat8datacompositescoveringallamazoniafairdatafi