A survival guide to Landsat preprocessing.
By N. Young, Ryan Anderson, Stephen M. Chignell, Anthony G. Vorster, Rick L. Lawrence, Paul H. Evangelista. Ecology, 2017
372 citations recorded by the source corpus.
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 However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations.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.
Subfields: Ecological Modeling, Ecology, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/9562db37f150bc1b142a4ba16fea9576b88ab990
How researchers see this paper
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In Ecological Modeling
In Ecology
- Stefania Bonafoni
- A. Suarez
- Matamyo Simwanda
- Darius Phiri
- Manjula Ranagalage
- J. Xiao
- Y. Murayama
- E. Larson
In Global and Planetary Change
Citations in the corpus
- Developments in Landsat Land Cover Classification Methods: A Review (2017, background): “The major sources of geometric errors are insufficient ground control points for some scenes, errors in the geo-registration procedures and the level of calibration of a particular Landsat satellite sensor [149].”
- Sentinel-2 Data for Land Cover/Use Mapping: A Review (2020, background): “The geometric correction adjusts the position of the images in line with the ground position [9,13,28,51].”
- Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives (2018, background): “Since the launch of Landsat in 1972, the satellite constellation has carried four generations of sensors including: (1) Multispectral Scanner (MSS) sensors, optical instruments, and video cameras aboard Landsat 1, 2, and 3, (2) MSS and Thematic Mapper (TM) sensors aboard Landsat 4 and 5, (3) Enhance”
- From eDNA to citizen science: emerging tools for the early detection of invasive species (2020, methodology): “However, scientists in the RS community have worked to make this tool more accessible by publishing useful guides (Young et al. 2017), and by providing surface reflectance data that have been preprocessed and are ready to use in ecological analyses (eg Landsat Level2 and Analysis Ready Data [ARD] im”