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

Type
Article

Authors
Rodríguez-Jeangros, Nicolás
Hering, Amanda S.
Kaiser, Timothy
McCray, John E.

KAUST Grant Number
OSR-2015-CRG4-2582

Date
2017-09-30

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.

Acknowledgements
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.

Publisher
MDPI AG

Journal
Remote Sensing

DOI
10.3390/rs9101015

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