title: A survival guide to Landsat preprocessing.
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 However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations.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.
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, Colorado, 80523, USA.
authors.forename: Nicholas E
authors.initials: NE
authors.lastname: Young
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, Colorado, 80523, USA.
authors.forename: Ryan S
authors.initials: RS
authors.lastname: Anderson
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, Colorado, 80523, USA.|Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, 80523, USA.
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Chignell
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, Colorado, 80523, USA.|Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, 80523, USA.
authors.forename: Anthony G
authors.initials: AG
authors.lastname: Vorster
authors.affiliation: Land Resources and Environmental Sciences Department, Spatial Sciences Center, Montana State University, Bozeman, Montana, 59717, USA.
authors.forename: Rick
authors.initials: R
authors.lastname: Lawrence
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, 1499 Campus Delivery, Fort Collins, Colorado, 80523, USA.|Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, Colorado, 80523, USA.
authors.forename: Paul H
authors.initials: PH
authors.lastname: Evangelista
mesh_terms: D004463:Ecology / Q000379:methods*; D004784:Environmental Monitoring / Q000379:methods*; D063809:Satellite Imagery
keywords: atmospheric correction; change detection; decision tree; ecology; image; normalization; radiometric correction; remote sensing; review; satellite; topographic correction; workflow