Description Usage Arguments Details Value References
Uses the GNSPI algorithm from Zhu et al. See fill_gaps
for
details. In hilly areas, gap fill should be done after topographic
correction.
1 2 3 |
data_dir |
folder where input images are located, with filenames as
output by the |
wrspath |
World Reference System (WRS) path |
wrsrow |
World Reference System (WRS) row |
start_date |
start date of period from which images will be chosen to
fill cloudy areas in the base image (as |
end_date |
end date of period from which images will be chosen to fill
cloudy areas in the the base image (as |
base_date |
ideal date for base image (base image will be chosen as the image among the available images that is closest to this date). If NULL, then the base image will be the image with the lowest cloud cover. |
tc |
if |
threshold |
maximum percent gap allowable in base image. Gap fill will not occur unless percent gap in base image is greater than this value. |
n_cpus |
the number of CPUs to use for processes that can run in parallel |
notify |
notifier to use (defaults to |
verbose |
whether to print detailed status messages |
... |
additional arguments passed to |
The auto_gap_fill
function allows an analyst to automatically
construct a gap-filled image after specifying: data_dir
(a folder of
Landsat images), wrspath
and wrsrow
(the WRS-2 path/row to
use), and start_date
and end_date
(a start and end date
limiting the images to use in the algorithm). The analyst can also
optionally specify a base_date
, and the auto_gap_fill
function
will automatically pick the image closest to that date to use as the base
image.
As the auto_gap_fill
function automatically chooses images for
inclusion in the gap fill process, it relies on having images stored on disk
in a particular way. To ensure that images are correctly stored on your hard
disk, use the auto_preprocess_landsat
function to extract the
original Landsat CDR hdf files from the USGS archive. The
auto_preprocess_landsat
function will ensure that images are
extracted and renamed properly so that they can be used with the
auto_gap_fill
script.
Raster*
object with gap filled image.
Zhu, X., Liu, D., Chen, J., 2012. A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment 124, 49–60.
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