Implementation of the LandTrendr Algorithm on Google Earth Engine
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College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA
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College of Forestry, Oregon State University, Corvallis, OR 97331, USA
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Google Switzerland, Zurich 8002, Switzerland
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Google, Mountain View, Mountain View, CA 94043, USA
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US Forest Service Pacific Northwest Research Station, Corvallis, OR 97331, USA
6
US Forest Service Rocky Mountain Research Station Ogden, UT 84401, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 691; https://doi.org/10.3390/rs10050691
Received: 10 March 2018
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Revised: 21 April 2018
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Accepted: 26 April 2018
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Published: 1 May 2018
(This article belongs to the Section Remote Sensing Image Processing)
The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the U.S., we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.
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Keywords:
change detection; time-series; Landsat; Google Earth Engine; cloud-computing; LandTrendr
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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.
MDPI and ACS Style
Kennedy, R.E.; Yang, Z.; Gorelick, N.; Braaten, J.; Cavalcante, L.; Cohen, W.B.; Healey, S. Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sens. 2018, 10, 691. https://doi.org/10.3390/rs10050691
AMA Style
Kennedy RE, Yang Z, Gorelick N, Braaten J, Cavalcante L, Cohen WB, Healey S. Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote Sensing. 2018; 10(5):691. https://doi.org/10.3390/rs10050691
Chicago/Turabian StyleKennedy, Robert E, Zhiqiang Yang, Noel Gorelick, Justin Braaten, Lucas Cavalcante, Warren B. Cohen, and Sean Healey. 2018. "Implementation of the LandTrendr Algorithm on Google Earth Engine" Remote Sensing 10, no. 5: 691. https://doi.org/10.3390/rs10050691
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