Peer Community Journal (Jan 2023)
Ten simple rules for working with high resolution remote sensing data
- Mahood, Adam L.,
- Joseph, Maxwell B.,
- Spiers, Anna I.,
- Koontz, Michael J.,
- Ilangakoon, Nayani,
- Solvik, Kylen K.,
- Quarderer, Nathan,
- McGlinchy, Joe,
- Scholl, Victoria M.,
- St. Denis, Lise A.,
- Nagy, Chelsea,
- Braswell, Anna,
- Rossi, Matthew W.,
- Herwehe, Lauren,
- Wasser, Leah,
- Cattau, Megan E.,
- Iglesias, Virginia,
- Yao, Fangfang,
- Leyk, Stefan,
- Balch, Jennifer K.
Affiliations
- Mahood, Adam L.
- ORCiD
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA; Water Resources, USDA-ARS, Fort Collins, CO, USA
- Joseph, Maxwell B.
- ORCiD
- Earth Lab, University of Colorado, Boulder - CO, USA
- Spiers, Anna I.
- ORCiD
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder - CO, USA
- Koontz, Michael J.
- ORCiD
- Earth Lab, University of Colorado, Boulder - CO, USA
- Ilangakoon, Nayani
- Earth Lab, University of Colorado, Boulder - CO, USA
- Solvik, Kylen K.
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA
- Quarderer, Nathan
- Earth Lab, University of Colorado, Boulder - CO, USA
- McGlinchy, Joe
- Earth Lab, University of Colorado, Boulder - CO, USA; Hydrostat, Inc. - Washington, DC, USA
- Scholl, Victoria M.
- ORCiD
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA
- St. Denis, Lise A.
- Earth Lab, University of Colorado, Boulder - CO, USA
- Nagy, Chelsea
- Earth Lab, University of Colorado, Boulder - CO, USA; Environmental Data Science Innovation and Inclusion Lab, University of Colorado, Boulder - CO, USA
- Braswell, Anna
- ORCiD
- School of Forest, Fisheries, and Geomatic Sciences, Institute of Food and Agricultural Sciences, University of Florida, Gainesville - FL, USA; Florida Sea Grant, Institute of Food and Agricultural Sciences, University of Florida, Gainesville - FL, USA
- Rossi, Matthew W.
- Earth Lab, University of Colorado, Boulder - CO, USA
- Herwehe, Lauren
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA
- Wasser, Leah
- ORCiD
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA
- Cattau, Megan E.
- ORCiD
- Department of Human-Environment Systems, Boise State University, Boise - ID, USA
- Iglesias, Virginia
- Earth Lab, University of Colorado, Boulder - CO, USA
- Yao, Fangfang
- Earth Lab, University of Colorado, Boulder - CO, USA
- Leyk, Stefan
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA; Institute of Behavioral Science, University of Colorado, Boulder - CO, USA
- Balch, Jennifer K.
- Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA; Environmental Data Science Innovation and Inclusion Lab, University of Colorado, Boulder - CO, USA
- DOI
- https://doi.org/10.24072/pcjournal.223
- Journal volume & issue
-
Vol. 3
Abstract
Researchers in Earth and environmental science can extract incredible value from high- resolution (sub-meter, sub-hourly or hyper-spectral) remote sensing data, but these data can be difficult to use. Correct, appropriate and competent use of such data requires skills from remote sensing and the data sciences that are rarely taught together. In practice, many researchers teach themselves how to use high-resolution remote sensing data with ad hoc trial and error processes, often resulting in wasted effort and resources. In order to implement a consistent strategy, we outline ten rules with examples from Earth and environmental science to help academic researchers and professionals in industry work more effectively and competently with high-resolution data.
