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loicdtx / bfastSpatial

Set of utilities and wrappers to perform change detection on satellite image time-series (Landsat and MODIS). Includes pre-processing steps and functions for spatial implementation of bfastmonitor change detection and post processing of the results.

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running processLandsatBatch

harisonkipkulei opened this issue · comments

commented

I have an issue when running the processLandsatBatch. How can I solve the error. I have attached part of the code

Error in lfn[f]:2 : NA/NaN argument

Loading packages

library(raster)
library(devtools)
library(bfastSpatial)
library(readxl)

Set the working directory

projectPath <- "D:/baringo/data/"
inputdata <- "D:/baringo/data/"
setwd(projectPath)

srdir <- dirout <- file.path(dirname(rasterTmpFile()), 'bfmspatial')
dir.create(dirout, showWarning=FALSE)

Create an extent variable

newExtent <- extent(c(122659, 219716, 77689, 185491))

List the Landsat scenes

inputList <- list.files(inputdata, full.names=TRUE)
inputList

Process new landsat scenes

processLandsatBatch(x = inputList, outdir = dirout, srdir = srdir, delete = TRUE, vi = 'ndvi', mask = 'cfmask', keep = 0, e = newExtent, overwrite = TRUE)

commented

@harisonkipkulei as a general comment, the bfastSpatial project is old and unmaintained; I would therefore recommend not to use it. In particular these pre-processing functions you're trying to use are tailored to old Landsat collections that no longer exist; they probably have no chance of working at all. I don't really have a replacement in mind for that, but the geospatial data delivery ecosystem has evolved a lot and it is now a lot easier to access and assemble time-series stacks than it was 9 years ago when we created the bfastSpatial project.
Regarding the actual monitoring, I'd suggest you have a look at the nrt python package. The bfastmonitor algorithm is fully implemented there (see MoSum and CuSum) and it is more stable and much faster than bfastSpatial ever was.