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Global Positioning System (GPS) measurements in China indicate that crustal shortening accommodates most of India's penetration into Eurasia. Deformation within the Tibetan Plateau and its margins, the Himalaya, the Altyn Tagh, and the Qilian Shan, absorbs more than 90% of the relative motion between the Indian and Eurasian plates. Internal shortening of the Tibetan plateau itself accounts for more than one-third of the total convergence. However, the Tibetan plateau south of the Kunlun and Ganzi-Mani faults is moving eastward relative to both India and Eurasia. This movement is accommodated through rotation of material around the eastern Syntaxis. The North China and South China blocks, east of the Tibetan Plateau, move coherently east-southeastward at rates of 2 to 8 millimeters per year and 6 to 11 millimeters per year, respectively, with respect to the stable Eurasia.

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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.