Unfortunately, rgeoboundaries was removed from CRAN at the end of November. Please run this chunk to install it from the GitHub repo instead: install.packages("remotes") remotes::install_github("wmgeolab/rgeoboundaries")
Great job!! And useful! About the data source, I am working on municipalities of Catalonia (Spain) and, by empirical observation, I have the impression that forest data coming from Landsat are more accurate than those from Sentinel 2 (you made a tutorial on it one year ago)... May be?
Thank you so much for the amazing video! I was wondering what modifications I would need to make to the code in order to apply it to all of Spain, rather than just Galicia.
great videos as always Milos, anyway I have a question, what if I had a very big area of scope (e.g.: Indonesia) with multiple tiles, is there a way to save/render/process such a big map? I always encounter the memory allocation problem :(
Great work Milos. I am actually interested in this tutorial. i want to ask however that if the roi falls in about two or more tiles in the GLAD data. how will the script look like?
yes there is. You can use the terra::mosaic() function to merge contiguous spatRaster (urls needs to be updated and it is easier to just copy and paste and not use the fancy function) objects. Just merge together the forest covers and forest losses. You then just need to replace the references to the new objects in the forest_loss_region and forest_cover_region. I just made a similar map for another country so it works.
Unfortunately, rgeoboundaries was removed from CRAN at the end of November. Please run this chunk to install it from the GitHub repo instead: install.packages("remotes") remotes::install_github("wmgeolab/rgeoboundaries")
Thank you for an amazing video.However I have a simple error when replicating the given code.I get resampled to 3688776 cells instead of the plot.If you know how to resolve this please reply.
Unfortunately, rgeoboundaries was removed from CRAN at the end of November. Please run this chunk to install it from the GitHub repo instead:
install.packages("remotes")
remotes::install_github("wmgeolab/rgeoboundaries")
Great one!
Great tutorial. Worth mentioning forest gain capture stopped at 2012. Caught me out a while ago....
Excellent point, thanks for flagging it!
Amazing, I'm definitely gonna try this. Thanks.
Yaay! Let me know how it goes
Maan! you the Best. I am working on the same topic. Thank you again for this amazing tutorial.
Great tutorial as always.💯
Thank you, Luis! Please let me know how it goes for you
Always Amazing. Great
Great job!! And useful!
About the data source, I am working on municipalities of Catalonia (Spain) and, by empirical observation, I have the impression that forest data coming from Landsat are more accurate than those from Sentinel 2 (you made a tutorial on it one year ago)... May be?
Super Sir!
Thank you, much appreciated 🙏🏼
Best videos.
Thank you so much, Mike!
Thank you so much for the amazing video! I was wondering what modifications I would need to make to the code in order to apply it to all of Spain, rather than just Galicia.
great videos as always Milos, anyway I have a question, what if I had a very big area of scope (e.g.: Indonesia) with multiple tiles, is there a way to save/render/process such a big map? I always encounter the memory allocation problem :(
Thanks! I have a short blob that could help you get started. Check it out here: github.com/milos-agathon/deforestation-maps/blob/main/R/multiple-files
Great work Milos. I am actually interested in this tutorial. i want to ask however that if the roi falls in about two or more tiles in the GLAD data. how will the script look like?
yes there is. You can use the terra::mosaic() function to merge contiguous spatRaster (urls needs to be updated and it is easier to just copy and paste and not use the fancy function) objects. Just merge together the forest covers and forest losses. You then just need to replace the references to the new objects in the forest_loss_region and forest_cover_region.
I just made a similar map for another country so it works.
Hi guys, does anyone here had problems to install rgeoboundaries? It get the "ERROR: lazy loading failed". I tried different ways and couldn't.
Unfortunately, rgeoboundaries was removed from CRAN at the end of November. Please run this chunk to install it from the GitHub repo instead:
install.packages("remotes")
remotes::install_github("wmgeolab/rgeoboundaries")
Thank you for an amazing video.However I have a simple error when replicating the given code.I get resampled to 3688776 cells instead of the plot.If you know how to resolve this please reply.
Thank you for the tutorial. Any idea why I am getting this error?
> forest_loss_region
> forest_loss_region
Hi Mircea, I followed up on your issue on GitHub. Let's continue our conversation there
try rerunning it after restarting R