fix urls in c16

16-synthesis.Rmd

@@ -118,7 +118,7 @@ We deliberately omitted some topics that are covered in-depth elsewhere.
 Statistical modeling of spatial data such as point pattern analysis, spatial interpolation (kriging) and spatial regression, for example, are mentioned in the context of machine learning in Chapter \@ref(spatial-cv) but not covered in detail.
 There are already excellent resources on these methods, including statistically orientated chapters in @pebesma_spatial_2023 and books on point pattern analysis [@baddeley_spatial_2015], Bayesian techniques applied to spatial data [@gomez-rubio_bayesian_2020], and books focused on particular applications such as health [@moraga_geospatial_2019] and [wildfire severity analysis](https://bookdown.org/mcwimberly/gdswr-book/application---wildfire-severity-analysis.html) [@wimberly_geographic_2023].
 Other topics which received limited attention were remote sensing and using R alongside (rather than as a bridge to) dedicated GIS software.
-There are many resources on these topics, including @wegmann_remote_2016 and the GIS-related teaching materials available from [Marburg University](https://moc.online.uni-marburg.de/doku.php).
+There are many resources on these topics, including a [discussion on remote sensing in R](https://github.com/r-spatial/discuss/issues/56), @wegmann_remote_2016 and the GIS-related teaching materials available from [Marburg University](https://geomoer.github.io/moer-info-page/courses.html).
 
 We focused on machine learning rather than spatial statistical inference in chapters \@ref(spatial-cv) and \@ref(eco) because of the abundance of quality resources on the topic.
 These resources include @zuur_mixed_2009, @zuur_beginners_2017 which focus on ecological use cases, and freely available teaching material and code on *Geostatistics & Open-source Statistical Computing* hosted at [css.cornell.edu/faculty/dgr2](http://www.css.cornell.edu/faculty/dgr2/teach/).