  Random Forest (RF) is a well-known data-driven algorithm applied in several
fields thanks to its flexibility in modeling the relationship between the
response variable and the predictors, also in case of strong non-linearities.
In environmental applications, it often occurs that the phenomenon of interest
may present spatial and/or temporal dependence that is not taken explicitly
into account by RF in its standard version. In this work, we propose a taxonomy
to classify strategies according to when (Pre-, In- and/or Post-processing)
they try to include the spatial information into regression RF. Moreover, we
provide a systematic review and classify the most recent strategies adopted to
"adjust" regression RF to spatially dependent data, based on the criteria
provided by the Preferred Reporting Items for Systematic reviews and
Meta-Analysis (PRISMA). The latter consists of a reproducible methodology for
collecting and processing existing literature on a specified topic from
different sources. PRISMA starts with a query and ends with a set of scientific
documents to review: we performed an online query on the 25$^{th}$ October 2022
and, in the end, 32 documents were considered for review. The employed
methodological strategies and the application fields considered in the 32
scientific documents are described and discussed. This work falls inside the
Agriculture Impact On Italian Air (AgrImOnIA) project.
