A survival guide to Landsat preprocessing - PubMed (original) (raw)
. 2017 Apr;98(4):920-932.
doi: 10.1002/ecy.1730. Epub 2017 Mar 20.
Affiliations
- PMID: 28072449
- DOI: 10.1002/ecy.1730
A survival guide to Landsat preprocessing
Nicholas E Young et al. Ecology. 2017 Apr.
Erratum in
- Erratum.
[No authors listed] [No authors listed] Ecology. 2021 Nov;102(11):e03508. doi: 10.1002/ecy.3508. Epub 2021 Oct 6. Ecology. 2021. PMID: 34617274 No abstract available.
Abstract
Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time-consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co-registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.
Keywords: atmospheric correction; change detection; decision tree; ecology; image; normalization; radiometric correction; remote sensing; review; satellite; topographic correction; workflow.
© 2017 The Authors. Ecology, published by Wiley Periodicals, Inc., on behalf of the Ecological Society of America.
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