introduction.Rmdmapme.vegetation facilitates two important tasks: first, it can be
used to create the so called „input data“ (or „predictor variables“)
that are needed to perform supervised image classification and to create
land use / land cover (LULC) maps, based on remote sensing images. This
task will be performed in the {mapme.classification}
package. Second, {mapme.vegetation} can be used to perform
a spatial assessment of vegetation cover change in your study area. To
this end, it provides comprehensive functionality to perform vegetation
trend and before-after change assessments, which can be the basis for
diff-in-diff analysis, to give one example.
{mapme.vegetation} is meant for users with at least some
basic GIS and Remote Sensing background. It supports the creation of a
harmonized timeseries based on optical Earth Observation (EO) satellite
imagery. Users will need at least basic knowledge regarding the concepts
behind operating, (pre-) processing and analyzing EO data. Currently,
only Sentinel-2 L2A (top of canopy reflectance values) data are
supported. These data are retrieved from an open access AWS bucket via the
STAC API that we can communicate
with through the {rstac}.
By using the aria2 downloader we
can substantially speed up download time. Later on, images are processed
locally to create a harmonized time series of surface reflectance and
vegetation indices. In order to efficiently mask cloudy pixels we rely
on GRASS GIS which we can use via
the {rgrass7}.
mapme.vegetation eases the process of establishing a
harmonized timeseries from multiple satellite observations. Single
observations of a certain region on the Earth’s surface from optical
sensors might be obstructed by e.g. extensive cloud cover.
Polar-orbiting satellites like Sentinel-2 nowadays have high revisit
frequency allowing us to obtain single images every 4 to 10 days for the
same location. In order to harness this information richness
mapme.vegetation provides several routines to consistently
process satellite images to derive a dataset which is ready for
analysis. These routines include the masking of cloud pixels,
calculation of vegetation indices, filling the gaps for missing
information and smoothing of the resulting signal. With
mapme.vegetation these processes are easy to implement and
highly customizable for specific user needs. In most cases, however, the
default settings allows users to seamlessly create an analysis-ready
timeseries based on Sentinel-2 for any location on the Earth.
Currently, the package offers several functionalities, which should ideally be used in a consecutive manner in order to realize the image preparation workflow:
{sf}
package. Additionally, the temporal extent and the relevant bands of
Sentinel-2 needs to be specified. User’s can chosse between a high
number of vegetation indices (VI) to be calculated based on the input
data.potential limitations arise from the fact that at the time being,
{mapme.vegetation} uses data from the AWS bucket.
Currently, only processed Sentinel-2 data are available, though it is
planned to support more satellite missions that will be made available
via the STAC API by different data provides. That means that globally
data is only available starting from January, 2017
Sentinel-2 data at AWS are processed COGs, that means the data can not be used as standard input to tools such as ESA’s SNAP toolbox.
most of the implemented functionality is pixel-based by design.
Focal operations currently are not supported, mainly because {gdalcubes}
is missing such functionality. That means that operations that
consider the spatial neighborhood of a pixel currently are not
supported.
We are planning to add new features and to extend the functionality
of {mapme.vegetation}, and to address these limitations
best possible.