SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains

Handle URI:
http://hdl.handle.net/10754/626083
Title:
SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains
Authors:
Rodríguez-Jeangros, Nicolás; Hering, Amanda S.; Kaiser, Timothy; McCray, John E.
Abstract:
Land cover (LC) products, derived primarily from satellite spectral imagery, are essential inputs for environmental studies because LC is a critical driver of processes involved in hydrology, ecology, and climatology, among others. However, existing LC products each have different temporal and spatial resolutions and different LC classes that rarely provide the detail required by these studies. Using multiple existing LC products, we implement our Spatiotemporal Categorical Map Fusion (SCaMF) methodology over a large region of the Rocky Mountains (RM), encompassing sections of six states, to create a new LC product, SCaMF–RM. To do this, we must adapt SCaMF to address the prediction of LC in large space–time regions that present nonstationarities, and we add more flexibility in the LC classifications of the predicted product. SCaMF–RM is produced at two high spatial resolutions, 30 and 50 m, and a yearly frequency for the 30-year period 1983–2012. When multiple products are available in time, we illustrate how SCaMF–RM captures relevant information from the different LC products and improves upon flaws observed in other products. Future work needed includes an exhaustive validation not only of SCaMF–RM but also of all input LC products.
Citation:
Rodríguez-Jeangros N, Hering AS, Kaiser T, McCray JE (2017) SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains. Remote Sensing 9: 1015. Available: http://dx.doi.org/10.3390/rs9101015.
Publisher:
MDPI AG
Journal:
Remote Sensing
KAUST Grant Number:
OSR-2015-CRG4-2582
Issue Date:
2-Oct-2017
DOI:
10.3390/rs9101015
Type:
Article
ISSN:
2072-4292
Sponsors:
The authors would like to thank the Colorado Higher Education Competitive Research Authority (CHECRA), state-provided matching funds for a National Science Foundation WSC program (grant no. WSC-1204787), for funding the project, and the high-performance computing support from Yellowstone provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. Specifically, we would like to thank Richard Valent from NCAR for his crucial support in the management of computational allocations and hurdles, and Laura Guy from the Arthur Lakes Library at Colorado School of Mines for her valuable assistance in the preparation of the online repository of SCaMF–RM. Amanda S. Hering has received support from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR), Grant/Award Number: OSR-2015-CRG4-2582.
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Full metadata record

DC FieldValue Language
dc.contributor.authorRodríguez-Jeangros, Nicolásen
dc.contributor.authorHering, Amanda S.en
dc.contributor.authorKaiser, Timothyen
dc.contributor.authorMcCray, John E.en
dc.date.accessioned2017-11-01T08:19:12Z-
dc.date.available2017-11-01T08:19:12Z-
dc.date.issued2017-10-02en
dc.identifier.citationRodríguez-Jeangros N, Hering AS, Kaiser T, McCray JE (2017) SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains. Remote Sensing 9: 1015. Available: http://dx.doi.org/10.3390/rs9101015.en
dc.identifier.issn2072-4292en
dc.identifier.doi10.3390/rs9101015en
dc.identifier.urihttp://hdl.handle.net/10754/626083-
dc.description.abstractLand cover (LC) products, derived primarily from satellite spectral imagery, are essential inputs for environmental studies because LC is a critical driver of processes involved in hydrology, ecology, and climatology, among others. However, existing LC products each have different temporal and spatial resolutions and different LC classes that rarely provide the detail required by these studies. Using multiple existing LC products, we implement our Spatiotemporal Categorical Map Fusion (SCaMF) methodology over a large region of the Rocky Mountains (RM), encompassing sections of six states, to create a new LC product, SCaMF–RM. To do this, we must adapt SCaMF to address the prediction of LC in large space–time regions that present nonstationarities, and we add more flexibility in the LC classifications of the predicted product. SCaMF–RM is produced at two high spatial resolutions, 30 and 50 m, and a yearly frequency for the 30-year period 1983–2012. When multiple products are available in time, we illustrate how SCaMF–RM captures relevant information from the different LC products and improves upon flaws observed in other products. Future work needed includes an exhaustive validation not only of SCaMF–RM but also of all input LC products.en
dc.description.sponsorshipThe authors would like to thank the Colorado Higher Education Competitive Research Authority (CHECRA), state-provided matching funds for a National Science Foundation WSC program (grant no. WSC-1204787), for funding the project, and the high-performance computing support from Yellowstone provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. Specifically, we would like to thank Richard Valent from NCAR for his crucial support in the management of computational allocations and hurdles, and Laura Guy from the Arthur Lakes Library at Colorado School of Mines for her valuable assistance in the preparation of the online repository of SCaMF–RM. Amanda S. Hering has received support from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR), Grant/Award Number: OSR-2015-CRG4-2582.en
dc.publisherMDPI AGen
dc.subjectbig dataen
dc.subjectland cover producten
dc.subjectparallel computingen
dc.subjectspatiotemporal categorical dataen
dc.titleSCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountainsen
dc.typeArticleen
dc.identifier.journalRemote Sensingen
dc.contributor.institutionCivil and Environmental Engineering Department, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USAen
dc.contributor.institutionDepartment of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX 76798, USAen
dc.contributor.institutionResearch and High Performance Computing, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USAen
dc.contributor.institutionHydrologic Science and Engineering Program, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USAen
kaust.grant.numberOSR-2015-CRG4-2582en
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