Implementation of the LandTrendr Algorithm on Google Earth Engine

Remote Sensing - Tập 10 Số 5 - Trang 691
Robert E. Kennedy1, Zhiqiang Yang2, Noel Gorelick3, Justin Braaten1, Lucas Costa Pereira Cavalcante4, Warren B. Cohen5, Sean P. Healey6
1College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR 97331, USA.
2College of Forestry, Oregon State University, Corvallis, OR 97331, USA
3Google Switzerland, Zurich 8002, Switzerland
4Google, Mountain View, Mountain View, CA 94043, USA
5US Forest Service Pacific Northwest Research Station, Corvallis, OR 97331, USA
6US Forest Service Rocky Mountain Research Station, Ogden, UT 84401, USA

Tóm tắt

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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Tài liệu tham khảo

Wulder, 2012, Opening the archive: How free data has enabled the science and monitoring promise of Landsat, Remote Sens. Environ., 122, 2, 10.1016/j.rse.2012.01.010

Zhu, 2017, Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS J. Photogramm. Remote Sens., 130, 370, 10.1016/j.isprsjprs.2017.06.013

Kennedy, 2010, Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms, Remote Sens. Environ., 114, 2897, 10.1016/j.rse.2010.07.008

Kennedy, 2007, Trajectory-based change detection for automated characterization of forest disturbance dynamics, Remote Sens. Environ., 110, 370, 10.1016/j.rse.2007.03.010

Griffiths, 2012, Using annual time-series of Landsat images to assess the effects of forest restitution in post-socialist Romania, Remote Sens. Environ., 118, 199, 10.1016/j.rse.2011.11.006

Bartz, K.K., Ford, M.J., Beechie, T.J., Fresh, K.L., Pess, G.R., Kennedy, R.E., Rowse, M.L., and Sheer, M. (2015). Trends in Developed Land Cover Adjacent to Habitat for Threatened Salmon in Puget Sound, Washington, USA. PLoS ONE, 10.

Kennedy, 2015, Attribution of disturbance change agent from Landsat time-series in support of habitat monitoring in the Puget Sound region, USA, Remote Sens. Environ., 166, 271, 10.1016/j.rse.2015.05.005

Kennedy, 2018, An empirical, integrated forest carbon monitoring system, Environ. Res. Lett., 13, 041001, 10.1088/1748-9326/aa9d9e

Schwantes, 2016, Quantifying drought-induced tree mortality in the open canopy woodlands of central Texas, Remote Sens. Environ., 181, 54, 10.1016/j.rse.2016.03.027

Wang, X., Huang, H., Gong, P., Biging, G.S., Xin, Q., Chen, Y., Yang, J., and Liu, C. (2016). Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery. Remote Sens., 8.

Schneibel, 2017, Assessment of spatio-temporal changes of smallholder cultivation patterns in the Angolan Miombo belt using segmentation of Landsat time series, Remote Sens. Environ., 195, 118, 10.1016/j.rse.2017.04.012

Shen, 2017, Spatio-temporal variations in plantation forests’ disturbance and recovery of northern Guangdong Province using yearly Landsat time series observations (1986–2015), Chin. Geogr. Sci., 27, 600, 10.1007/s11769-017-0880-z

Gorelick, 2017, Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sens. Environ., 202, 18, 10.1016/j.rse.2017.06.031

Zhu, 2012, Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sens. Environ., 118, 83, 10.1016/j.rse.2011.10.028

Homer, 2007, Completion of the 2001 National Land Cover Database for the conterminous United States, Photogramm. Eng. Remote Sens., 73, 337

Lutes, D.C. (2005). Landscape Assessment: Remote Sensing of Severity, the Normalized Burn Ratio, in FIREMON: Fire Effects Monitoring and Inventory System.

Cohen, 2018, A LandTrendr multispectral ensemble for forest disturbance detection, Remote Sens. Environ., 205, 131, 10.1016/j.rse.2017.11.015

Kennedy, 2012, Spatial and temporal patterns of forest disturbance and regrowth within the area of the Northwest Forest Plan, Remote Sens. Environ., 122, 117, 10.1016/j.rse.2011.09.024

Healey, 2018, Mapping forest change using stacked generalization: An ensemble approach, Remote Sens. Environ., 204, 717, 10.1016/j.rse.2017.09.029

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Remote Sensing

Tập 10 Số 5

691

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