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Implementation of the LandTrendr algorithm on Google Earth EngineAuthor(s): Robert E. Kennedy; Zhiqiang Yang; Noel Gorelick; Justin Braaten; Lucas Cavalcante; Warren B. Cohen; Sean Healey
Source: Remote Sensing. 10: 691.
Publication Series: Scientific Journal (JRNL)
Station: Pacific Northwest Research Station
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DescriptionThe 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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CitationGoeking, Sara A.; Menlove, Jim. 2017. New Mexico's forest resources, 2008-2014. Resour. Bull. RMRS-RB-24. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 68 p.
Keywordschange detection, time-series, Landsat, Google Earth Engine, cloud-computing, LandTrendr
- Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms
- Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync — Tools for calibration and validation
- Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales
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