README.md
In DavidDHofmann/floodmapr: Download and Classify Watermaps for the Okavango Delta
floodmapr 
floodmapr is an R-package that allows you to download and classify
MODIS MCD43A4 satellite
imagery into binary maps of dryland and water cover. To be able to
download data, you need to have an
EarthData account (free). The
classification algorithm is based on the publication of Wolski et al.,
2017 and currently only applicable for the extent of the Okavango Delta
(see Okavango Research
Institute).
Installation
You can install the development version of floodmapr from
GitHub with:
# install.packages("devtools")
devtools::install_github("DavidDHofmann/floodmapr")
Workflow
The floodmapr package follows a three step process using the
functions, modis_download(), modis_load(), and modis_classify().
-
modis_download() Download MCD43A4 satellite imagery for desired
dates for the extent of the Okavango Delta. The function
automatically downloads, extracts, stitches and reprojects (EPSG
4326) corresponding modis tiles and stores them into a multiband
raster. The output files are named according to the input date
(e.g. 2020-01-01.tif). Note: while only band 7 will be used in
the later process, bands 1-7 are stored for completeness.
-
modis_load() Once you have downloaded the satellite imagery, load
it into R using modis_load(). This will only load band 7 into your
current R session.
-
modis_classify() Classify the satellite image into a binary map of
water (valued 1) and dryland (valued 0). You can then use the
resulting raster and e.g. plot() it.
In addition to the above described main functions there are some
additional helper functions that allow you to do some preliminary
analysis on MODIS MCD43A4 band 7.
-
modis_bimodal() allows you to check if modis band 7 is bimodal.
This is required to be able to classify the image. Bimodality can
fail during some months of the year, when surface reflectance of
water and dryland become more alike.
-
modis_specs() plots the densities of water- and
dryland-reflectance values. Again, this can be used to visualize how
bimodal the distribution of these values is.
-
modis_precentiles() percentile values of water- and
dryland-reflecatances.
Finally, there is a function called modis_watermask(). This function
can be used to create a dynamic watermask that can ultimately be fed
into modis_classify(). In contrast to the static watermask used by
floodmapr in the background, the dynamic watermask uses previously
classified foodmaps to determine areas that were frequently flooded in
the past. This way, the algorithm is able to learn about potential
long-term changes in the system.
Example
Here is a complete example of the above outlined workflow
library(floodmapr)
# Download modis data (you need to provide your own username and password)
downloaded <- modis_download(
dates = c("2017-08-21", "2017-08-22")
, outdir = tempdir()
, tmpdir = tempdir()
, username = username
, password = password
, overwrite = F
)
#> Downloading tiles: 1 out of 4
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#> All tiles downloaded. Extracting tiffs from hdf files now...
#> All hdf files extracted. Stitching tiles now...
#> All tiles stitched. Reprojecting and cropping final tiles now...
#> Finished!
# Load data
loaded <- modis_load(downloaded[1])
# Do some checks
modis_specs(loaded)
modis_bimodal(loaded)
#> [1] TRUE
modis_percentiles(loaded)
#> WaterPercentiles DrylandPercentiles
#> 1% 0.061975 0.185900
#> 99% 0.142550 0.333344
# Classify data
classified <- modis_classify(loaded)
# Visualize classified image (0 = dryland, 1 = water, 2 = clouds)
plot(classified, col = c("white", "cornflowerblue"))
References
Wolski, Piotr, Mike Murray-Hudson, Kgalalelo Thito, und Lin Cassidy.
„Keeping It Simple: Monitoring Flood Extent in Large Data-Poor
Wetlands Using MODIS SWIR Data“. International Journal of Applied Earth
Observation and Geoinformation 57 (May 2017): 224–34.
https://doi.org/10.1016/j.jag.2017.01.005.
DavidDHofmann/floodmapr documentation built on Jan. 15, 2024, 9:32 p.m.
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floodmapr
floodmapr is an R-package that allows you to download and classify
MODIS MCD43A4 satellite
imagery into binary maps of dryland and water cover. To be able to
download data, you need to have an
EarthData account (free). The
classification algorithm is based on the publication of Wolski et al.,
2017 and currently only applicable for the extent of the Okavango Delta
(see Okavango Research
Institute).
Installation
You can install the development version of floodmapr from GitHub with:
# install.packages("devtools")
devtools::install_github("DavidDHofmann/floodmapr")
Workflow
The floodmapr package follows a three step process using the
functions, modis_download(), modis_load(), and modis_classify().
-
modis_download()Download MCD43A4 satellite imagery for desired dates for the extent of the Okavango Delta. The function automatically downloads, extracts, stitches and reprojects (EPSG 4326) corresponding modis tiles and stores them into a multiband raster. The output files are named according to the input date (e.g. 2020-01-01.tif). Note: while only band 7 will be used in the later process, bands 1-7 are stored for completeness. -
modis_load()Once you have downloaded the satellite imagery, load it into R usingmodis_load(). This will only load band 7 into your current R session. -
modis_classify()Classify the satellite image into a binary map of water (valued 1) and dryland (valued 0). You can then use the resulting raster and e.g.plot()it.
In addition to the above described main functions there are some additional helper functions that allow you to do some preliminary analysis on MODIS MCD43A4 band 7.
-
modis_bimodal()allows you to check if modis band 7 is bimodal. This is required to be able to classify the image. Bimodality can fail during some months of the year, when surface reflectance of water and dryland become more alike. -
modis_specs()plots the densities of water- and dryland-reflectance values. Again, this can be used to visualize how bimodal the distribution of these values is. -
modis_precentiles()percentile values of water- and dryland-reflecatances.
Finally, there is a function called modis_watermask(). This function
can be used to create a dynamic watermask that can ultimately be fed
into modis_classify(). In contrast to the static watermask used by
floodmapr in the background, the dynamic watermask uses previously
classified foodmaps to determine areas that were frequently flooded in
the past. This way, the algorithm is able to learn about potential
long-term changes in the system.
Example
Here is a complete example of the above outlined workflow
library(floodmapr)
# Download modis data (you need to provide your own username and password)
downloaded <- modis_download(
dates = c("2017-08-21", "2017-08-22")
, outdir = tempdir()
, tmpdir = tempdir()
, username = username
, password = password
, overwrite = F
)
#> Downloading tiles: 1 out of 4
#> Downloading: 27 B Downloading: 27 B Downloading: 27 B Downloading: 320 B Downloading: 320 B Downloading: 320 B | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | 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#> All tiles downloaded. Extracting tiffs from hdf files now...
#> All hdf files extracted. Stitching tiles now...
#> All tiles stitched. Reprojecting and cropping final tiles now...
#> Finished!
# Load data
loaded <- modis_load(downloaded[1])
# Do some checks
modis_specs(loaded)
modis_bimodal(loaded)
#> [1] TRUE
modis_percentiles(loaded)
#> WaterPercentiles DrylandPercentiles
#> 1% 0.061975 0.185900
#> 99% 0.142550 0.333344
# Classify data
classified <- modis_classify(loaded)
# Visualize classified image (0 = dryland, 1 = water, 2 = clouds)
plot(classified, col = c("white", "cornflowerblue"))
References
Wolski, Piotr, Mike Murray-Hudson, Kgalalelo Thito, und Lin Cassidy. „Keeping It Simple: Monitoring Flood Extent in Large Data-Poor Wetlands Using MODIS SWIR Data“. International Journal of Applied Earth Observation and Geoinformation 57 (May 2017): 224–34. https://doi.org/10.1016/j.jag.2017.01.005.
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