In mapme-initiative/mapme.vegetation: Tool chain for Sentinel-2 L2A processing

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Objectives

mapme.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 package

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.

Functionalities

Currently, the package offers several functionalities, which should ideally be used in a consecutive manner in order to realize the image preparation workflow:

• download Sentinel-2 images for a space-time location
• mask out cloudy pixels
• calculate vegetation indices and/or retrieve the raw surface reflectance values
• fill missing observations through linear interpolation or more sophisticated interpolation stratgies on a pixel basis
• smooth the pixel's signal by applying a Savitzkiy-Golay filter or other smoothing functions
• optionally extract zonal statistics for areas of interest
• optionally conduct a trend analysis or pre-post-comparisons

Inputs, Outputs

• an area of interest, usually in form of an ESRI Shapefile, read into the R session as an sf object with the {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.
• the spatial and temporal resoultion of the target data set. User's can specify these values so that the output data best fits the project's needs.
• the processed raster files are outputted as multi-bands GeoTIFFs
• additionally, aggregates, zonal-statistics and pre-post comparisons can be made

Limitations

• 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.

mapme-initiative/mapme.vegetation documentation built on Feb. 10, 2023, 10:51 p.m.