Creating such maps of Prosopis juliflora usually requires calibrating machine learning algorithms, which classify satellite images into different thematic categories such as Prosopis juliflora and other, non-invasive vegetation species. The calibration data should be collected on the ground, e.g. with GPS devices and photo cameras. This in turn requires some essential planning steps to prepare the field campaigns for reference data collection, and the provision of financial and personnel resources. The MAPME Open Source Guide provides more information about this topic.
The classification of satellite images can be done with the mapme.vegetation and the mapme.classification software packages. These are based on the R-programming language and offer a wide variety of tools to download and pre-process satellite images and to calibrate machine learning algorithms for creating maps of land use / land cover.
Without in-situ calibration data, proxies for changes in woody vegetation could be assessed instead. By using high-resolution satellite data freely available through the NICFI program and an unsupervised classification procedure proposed by Ludwig et al (2019) the extent of woody vegetation was mapped in five distinct study areas in Ethiopia and to identify areas with strong Prosopis Juliflora encroachment. This method utilizes the contrast between green woody vegetation against the non-vegetated soil background during the dry season and thanks to the NIFCI program can be applied to images from 2016 and onward until 2021.