geeLandsat: Landsat reflectance and NDVI time series from Google Earth...

In Rbeast: Bayesian Change-Point Detection and Time Series Decomposition

Landsat reflectance and NDVI time series from Google Earth Engine

Description

Get Landsat reflectance and NDVI time series from Google Earth Engine given longitude and latitude

Usage

   geeLandsat(lon=NA, lat=NA, radius=100, stat='mean',timeout=700)

Arguments

lon	numeric within [-180,180]
lat	numeric within [-90, 90]
radius	a positive number ( <=500 meters ); the radius of a buffer around the given latitude and longitude for aggregation. If radius=0, the single pixel at the lat and lon will be retrieved
stat	character; if radius>0, used to specify the spatial aggregation method for pixels in the buffer. Possible values are 'mean','min','max', or 'median'.
timeout	integer; the seconds elapsed to wait for connection timeout. See the note for an explanation.

Value

a data.frame object consisting of dates, sensor type, reflectances, and NDVI for the requested location. It contains only valid and clear-sky values as obtained by referring to the standard clouds flags.

Note

As a poor man's scheme to interact with Google Earth Engine, geeLandsat should be used only for occasional retrieval of Landsat time series at a few sites, NOT for batch downloading for thousands of sites in a R loop. This procedure is provided to get example time series for testing BEAST. Behind the scene, this function calls to a free Python-based server using my own GEE credential. Normally it takes several seconds to retrieve one time series, but as a free cloud service, the Python server only offers 100 seconds of free CPU time per day, with throttling applied. So it may take up to a few mins to get a time series on your end. It may fail due to connection timeout; if so, give it a few tries. If you need to retrieve data for thousands or millions of sites, please contact the author.

References

1. Zhao, K., Wulder, M.A., Hu, T., Bright, R., Wu, Q., Qin, H., Li, Y., Toman, E., Mallick, B., Zhang, X. and Brown, M., 2019. Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm. Remote Sensing of Environment, 232, p.111181 (the beast algorithm paper).

2. Zhao, K., Valle, D., Popescu, S., Zhang, X. and Mallick, B., 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment, 132, pp.102-119 (the Bayesian MCMC scheme used in beast).

3. Hu, T., Toman, E.M., Chen, G., Shao, G., Zhou, Y., Li, Y., Zhao, K. and Feng, Y., 2021. Mapping fine-scale human disturbances in a working landscape with Landsat time series on Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 176, pp.250-261(a beast application paper).

See Also

beast, beast.irreg, beast123, minesweeper, tetris

Examples

 library(Rbeast)
## Not run:  
 df = geeLandsat(lon=-80.983877,lat= 40.476882) #if it fails, try a few more times before giving up
 print(df)

## End(Not run)

 

Rbeast documentation built on May 31, 2023, 9:23 p.m.