Show simple item record

dc.contributor.authorYoung, Nicholas E.
dc.contributor.authorAnderson, Ryan S.
dc.contributor.authorChignell, Stephen M.
dc.contributor.authorVorster, Anthony G.
dc.contributor.authorLawrence, Rick L.
dc.contributor.authorEvangelista, Paul H.
dc.date.accessioned2017-08-16T19:16:16Z
dc.date.available2017-08-16T19:16:16Z
dc.date.issued2017-04
dc.identifier.citationYoung, Nicholas E. , Ryan S. Anderson, Stephen M. Chignell, Anthony G. Vorster, Rick Lawrence, and Paul H. Evangelista. "A survival guide to Landsat preprocessing." Ecology 98, no. 4 (April 2017): 920-932. https://dx.doi.org/10.1002/ecy.1730.en_US
dc.identifier.issn0012-9658
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/13529
dc.description.abstractLandsat 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 However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations.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.en_US
dc.description.sponsorshipAgriculture and Food Research Initiative Competitive Grant (2013-68005-21298)en_US
dc.titleA survival guide to Landsat preprocessingen_US
dc.typeArticleen_US
mus.citation.extentfirstpage920en_US
mus.citation.extentlastpage932en_US
mus.citation.issue4en_US
mus.citation.journaltitleEcologyen_US
mus.citation.volume98en_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.1002/ecy.1730en_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.data.thumbpage2en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record