geeLandsat: 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)

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.

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.

References

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).
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).
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).

Examples

Run this code



 library(Rbeast)
if (FALSE) { 
 df = geeLandsat(lon=-80.983877,lat= 40.476882) #if it fails, try a few more times before giving up
 print(df)
}