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Remote Sens. 2017, 9(10), 1015; https://doi.org/10.3390/rs9101015

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

1
Civil and Environmental Engineering Department, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA
2
Department of Statistical Science, Baylor University, One Bear Place #97140, Waco, TX 76798, USA
3
Research and High Performance Computing, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA
4
Hydrologic Science and Engineering Program, Colorado School of Mines, 1500 Illinois St., Golden, CO 80401, USA
*
Author to whom correspondence should be addressed.
Received: 6 July 2017 / Revised: 15 September 2017 / Accepted: 25 September 2017 / Published: 30 September 2017
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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. View Full-Text
Keywords: big data; land cover product; parallel computing; spatiotemporal categorical data big data; land cover product; parallel computing; spatiotemporal categorical data
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rodríguez-Jeangros, N.; Hering, A.S.; Kaiser, T.; McCray, J.E. SCaMF–RM: A Fused High-Resolution Land Cover Product of the Rocky Mountains. Remote Sens. 2017, 9, 1015.

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