Ten simple rules for working with high resolution remote sensing data
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 dat...
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2023-01-01
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| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.223/ |
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| author | 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. |
| author_facet | 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. |
| author_sort | Mahood, Adam L. |
| collection | DOAJ |
| description | 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.
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| format | Article |
| id | doaj-art-22c9e8e958a94e6dbf163ff8e33b8d6b |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-22c9e8e958a94e6dbf163ff8e33b8d6b2025-02-07T10:16:50ZengPeer Community InPeer Community Journal2804-38712023-01-01310.24072/pcjournal.22310.24072/pcjournal.223Ten simple rules for working with high resolution remote sensing dataMahood, Adam L.0https://orcid.org/0000-0003-3791-9654Joseph, Maxwell B.1https://orcid.org/0000-0002-7745-9990Spiers, Anna I.2https://orcid.org/0000-0003-3517-1072Koontz, Michael J.3https://orcid.org/0000-0002-8276-210XIlangakoon, Nayani4Solvik, Kylen K.5Quarderer, Nathan6McGlinchy, Joe7Scholl, Victoria M.8https://orcid.org/0000-0002-2085-1449St. Denis, Lise A.9Nagy, Chelsea10Braswell, Anna11https://orcid.org/0000-0002-3677-0635Rossi, Matthew W.12Herwehe, Lauren13Wasser, Leah14https://orcid.org/0000-0002-8177-6550Cattau, Megan E.15https://orcid.org/0000-0003-2164-3809Iglesias, Virginia16Yao, Fangfang17Leyk, Stefan18Balch, Jennifer K.19Earth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USA; Water Resources, USDA-ARS, Fort Collins, CO, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USA; Department of Ecology and Evolutionary Biology, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USA; Hydrostat, Inc. - Washington, DC, USAEarth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USA; Environmental Data Science Innovation and Inclusion Lab, University of Colorado, Boulder - CO, USASchool 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, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USA; Department of Geography, University of Colorado, Boulder - CO, USADepartment of Human-Environment Systems, Boise State University, Boise - ID, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth Lab, University of Colorado, Boulder - CO, USAEarth 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, USAEarth 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, USAResearchers 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. https://peercommunityjournal.org/articles/10.24072/pcjournal.223/ |
| spellingShingle | 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. Ten simple rules for working with high resolution remote sensing data Peer Community Journal |
| title | Ten simple rules for working with high resolution remote sensing data |
| title_full | Ten simple rules for working with high resolution remote sensing data |
| title_fullStr | Ten simple rules for working with high resolution remote sensing data |
| title_full_unstemmed | Ten simple rules for working with high resolution remote sensing data |
| title_short | Ten simple rules for working with high resolution remote sensing data |
| title_sort | ten simple rules for working with high resolution remote sensing data |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.223/ |
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