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Remote Sens. 2018, 10(9), 1455; https://doi.org/10.3390/rs10091455

BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine

1
Department of Natural Resource Sciences, McGill School of Environment, Montreal, QC H9X 3V9, Canada
2
The Woods Hole Research Center, Falmouth, MA 02540, USA
*
Author to whom correspondence should be addressed.
Received: 30 June 2018 / Revised: 4 September 2018 / Accepted: 6 September 2018 / Published: 12 September 2018
(This article belongs to the Special Issue Google Earth Engine Applications)
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Abstract

Remote sensing is undergoing a fundamental paradigm shift, in which approaches interpreting one or two images are giving way to a wide array of data-rich applications. These include assessing global forest loss, tracking water resources across Earth’s surface, determining disturbance frequency across decades, and many more. These advances have been greatly facilitated by Google Earth Engine, which provides both image access and a platform for advanced analysis techniques. Within the realm of land-use/land-cover (LULC) classifications, Earth Engine provides the ability to create new classifications and to access major existing data sets that have already been created, particularly at global extents. By overlaying global LULC classifications—the 300-m GlobCover 2009 LULC data set for example—with sharper images like those from Landsat, one can see the promise and limits of these global data sets and platforms to fuse them. Despite the promise in a global classification covering all of the terrestrial surface, GlobCover 2009 may be too coarse for some applications. We asked whether the LULC labeling provided by GlobCover 2009 could be combined with the spatial granularity of the Landsat platform to produce a hybrid classification having the best features of both resources with high accuracy. Here we apply an improvement of the Bayesian Updating of Land Cover (BULC) algorithm that fused unsupervised Landsat classifications to GlobCover 2009, sharpening the result from a 300-m to a 30-m classification. Working with four clear categories in Mato Grosso, Brazil, we refined the resolution of the LULC classification by an order of magnitude while improving the overall accuracy from 69.1 to 97.5%. This “BULC-U” mode, because it uses unsupervised classifications as inputs, demands less region-specific knowledge from analysts and may be significantly easier for non-specialists to use. This technique can provide new information to land managers and others interested in highly accurate classifications at finer scales.
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Keywords: land cover; deforestation; Brazilian Amazon; Bayesian statistics; BULC-U; Mato Grosso; spatial resolution; Landsat; GlobCover land cover; deforestation; Brazilian Amazon; Bayesian statistics; BULC-U; Mato Grosso; spatial resolution; Landsat; GlobCover
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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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Lee, J.; Cardille, J.A.; Coe, M.T. BULC-U: Sharpening Resolution and Improving Accuracy of Land-Use/Land-Cover Classifications in Google Earth Engine. Remote Sens. 2018, 10, 1455.

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