studio: index and intro almost done, updates in the grass part

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diff --git a/.ipynb_checkpoints/notebook_ex_rs_grass-checkpoint.ipynb b/.ipynb_checkpoints/notebook_ex_rs_grass-checkpoint.ipynb
diff --git a/_freeze/index/execute-results/html.json b/_freeze/index/execute-results/html.json
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-    "markdown": "---\ntitle: \"Leveraging remote sensing for Public Health\"\nsubtitle: \"Visiting the Geospatial Analytics Center at NCSU\"\ndate: '2023-03-30'\nauthor: \"Verónica Andreo\"\ntoc: true\n---\n\n\n## Overview\n\nVector-borne and zoonotic diseases are responsible for one-sixth of disease \nand disability worldwide. Their distribution and spread is highly dependent \non the environment. In the face of the environmental changes brought about \nby the Earth system crisis, remote sensing has gained renewed relevance for\npublic health applications. For example, time series of remotely sensed \nvariables can be used to understand the spatio-temporal conditions that \nfavor mosquito populations and may pose a high risk of West Nile Fever \noutbreaks. In this talk I'll show how we use remote sensing data of different\nspatial and temporal resolutions to predict the risk of diseases such as \nhantavirus, dengue and leishmaniasis, to allocate sensors for mosquito \nsampling, and to quantify access to health care, among others. I will also \ndiscuss various limitations, challenges, and future directions in the use of\nremote sensing for operational early warning systems and applications to \nsupport timely decision making in the field of Public Health. \nSpoiler alert! GRASS GIS is one of the main characters in this journey.\n\n## Contents\n\n1.  Lecture: \"**Environmental drivers of vector-borne and zoonotic diseases: Leveraging remote sensing for Public Health**\"\n    1. Motivation\n    2. Health Geography\n    3. Disease Ecology\n    4. Leveraging remote sensing for Disease Ecology\n       - Resolution vs scale\n       - How can we use RS?\n       - Examples\n    5. Gaps, challenges and opportunities\n    6. Conclusion\n2.  Studio: \"**Using satellite data for species distribution modeling with GRASS GIS and R**\"\n    1. GRASS GIS\n    2. Interface GRASS-R\n    3. Demo session\n\n\n",
+    "markdown": "---\ntitle: \"Leveraging remote sensing for Public Health\"\nsubtitle: \"Visiting the Geospatial Analytics Center at NCSU\"\ndate: '2023-03-31'\nauthor: \"Verónica Andreo\"\ntoc: true\n---\n\n\n## Overview\n\nVector-borne and zoonotic diseases are responsible for one-sixth of disease \nand disability worldwide. Their distribution and spread is highly dependent \non the environment. In the face of the environmental changes brought about \nby the Earth system crisis, remote sensing has gained renewed relevance for\npublic health applications. For example, time series of remotely sensed \nvariables can be used to understand the spatio-temporal conditions that \nfavor mosquito populations and may pose a high risk of West Nile Fever \noutbreaks. In this talk I'll show how we use remote sensing data of different\nspatial and temporal resolutions to predict the risk of diseases such as \nhantavirus, dengue and leishmaniasis, to allocate sensors for mosquito \nsampling, and to quantify access to health care, among others. I will also \ndiscuss various limitations, challenges, and future directions in the use of\nremote sensing for operational early warning systems and applications to \nsupport timely decision making in the field of Public Health. \nSpoiler alert! GRASS GIS is one of the main characters in this journey.\n\n## Contents\n\n1.  Lecture: \"**Environmental drivers of vector-borne and zoonotic diseases: Leveraging remote sensing for Public Health**\"\n    1. Motivation\n    2. Health Geography\n    3. Disease Ecology\n    4. Leveraging remote sensing for Disease Ecology\n       - Resolution vs scale\n       - How can we use RS?\n       - Examples\n    5. Gaps, challenges and opportunities\n    6. Conclusion\n2.  Studio: \"**Using satellite data for species distribution modeling with GRASS GIS and R**\"\n    1. Intro to GRASS GIS\n    2. Processing data in GRASS\n    3. Modeling with R\n\n\n",
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diff --git a/_freeze/notebook_ex_rs_grass/execute-results/html.json b/_freeze/notebook_ex_rs_grass/execute-results/html.json
diff --git a/_freeze/notebook_ex_sdm_r/execute-results/html.json b/_freeze/notebook_ex_sdm_r/execute-results/html.json
diff --git a/_freeze/notebook_intro/execute-results/html.json b/_freeze/notebook_intro/execute-results/html.json
diff --git a/_freeze/studio_index/execute-results/html.json b/_freeze/studio_index/execute-results/html.json
@@ -1,8 +1,10 @@
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-    "markdown": "---\ntitle: \"Using Satellite Data for Species Distribution Modeling with GRASS GIS and R\"\nauthor: \"Verónica Andreo\"\ndate: '2023-03-30'\n---\n\n\nTraditionally, species distribution models (SDM) use climatic data as predictors of habitat suitability for the target species. In this studio, we will explore the use of satellite data to derive relevant predictors. The satellite data processing, from download to analysis, will be performed using GRASS GIS software functionality. Then, we'll read our predictors within R and perform SDM, visualize and analyze results there, to then exemplify how to write the output distribution maps back into GRASS.\n\n# Getting ready\n\n## Software \n\n### GRASS GIS\n\nWe will use **GRASS GIS 8.2+**. It can be installed either \nthrough standalone installers/binaries or through\n[OSGeo-Live](https://live.osgeo.org/en/index.html) \n(a linux based virtual machine which includes all OSGeo software and packages).\n\n##### MS Windows\n\nThere are two different options:\n1. [Standalone installer 64-bit](https://grass.osgeo.org/grass78/binary/mswindows/native/x86_64/WinGRASS-7.8.5-2-Setup-x86_64.exe) \n2. [OSGeo4W 64-bit](http://download.osgeo.org/osgeo4w/v2/osgeo4w-setup.exe) \n\nFor Windows users, **we strongly recommend installing GRASS GIS through the OSGeo4W package** (second option), \nsince it allows to install all OSGeo software. See this \n[**installation guide**](https://gitlab.com/veroandreo/grass-gis-conae/-/blob/master/pdf/00_installation.pdf) \nfor details (Follow only the GRASS GIS part).\n\n##### Ubuntu Linux\n\nInstall GRASS GIS 8.2+ from the \"unstable\" package repository:\n\n```\nsudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable\nsudo apt-get update\nsudo apt-get install grass grass-gui grass-dev\n```\n\n##### Fedora, openSuSe Linux\n\nFor other Linux distributions including **Fedora** and **openSuSe**, simply install GRASS GIS with the respective package manager. See also [here](https://grass.osgeo.org/download/)\n\n##### Mac OS\n\nHave a look at: http://grassmac.wikidot.com/downloads\n\n#### GRASS GIS Add-on that will be used during the demo\n\n* [r.bioclim](https://grass.osgeo.org/grass7/manuals/addons/r.bioclim.html): Calculates bioclimatic indices as those in [WorldClim](https://www.worldclim.org/bioclim).\n\nInstall with `g.extension extension=name_of_addon`\n\n### R and R-Studio\n\nThe following packages should be installed beforehand:\n\n```r\n  install.packages(c(\"rgrass\",\"terra\",\"raster\",\"sf\",\"mapview\",\"biomod2\"))\n```\n### Python\n\n## Other software\n\nWe will use the software **MaxEnt** to model habitat suitability. The software can be downloaded from: https://biodiversityinformatics.amnh.org/open_source/maxent/\n\n## Data\n\nPlease, create a folder in your `$HOME` directory, or under `Documents` if in Windows, and name it **grassdata_ogh**. Then, download the following ready to use *location* and unzip  within `grassdata_ogh`:\n\n* [Northern Italy (1.7 Gb)](https://drive.google.com/file/d/1z1b2NLC4Z6yzz_57RddTdRRK_gUkd7fU/view?usp=sharing)\n\nIn the end, your `grassdata` folder should look like this:\n\n```\n  grassdata/\n  └── eu_laea\n      ├── italy_LST_daily\n      └── PERMANENT\n```\n\n\n## References\n\n-   https://github.com/veroandreo/foss4g2022_grass4rs\n-   https://github.com/veroandreo/grass_opengeohub2021\n\n<!-- - Neteler, M. and Mitasova, H. (2008): *Open Source GIS: A GRASS GIS Approach*. Third edition. ed. Springer, New York. [Book site](https://grassbook.org/) -->\n<!-- - Neteler, M., Bowman, M.H., Landa, M. and Metz, M. (2012): *GRASS GIS: a multi-purpose Open Source GIS*. Environmental Modelling & Software, 31: 124-130 [DOI](http://dx.doi.org/10.1016/j.envsoft.2011.11.014) -->\n<!-- - Gebbert, S. and Pebesma, E. (2014). *A temporal GIS for field based environmental modeling*. Environmental Modelling & Software, 53, 1-12. [DOI](https://doi.org/10.1016/j.envsoft.2013.11.001) -->\n<!-- - Gebbert, S. and Pebesma, E. (2017). *The GRASS GIS temporal framework*. International Journal of Geographical Information Science, 31, 1273-1292. [DOI](http://dx.doi.org/10.1080/13658816.2017.1306862) -->\n<!-- - Gebbert, S., Leppelt, T. and Pebesma, E. (2019). *A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis*. Data, 4, 86. [DOI](https://doi.org/10.3390/data4020086) -->",
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+    "markdown": "---\ntitle: \"Using Satellite Data for Species Distribution Modeling with GRASS GIS and R\"\nauthor: \"Verónica Andreo\"\ndate: '2023-03-31'\n---\n\n\nTraditionally, species distribution models (SDM) use climatic data as predictors of habitat suitability for the target species. In this studio, we will explore the use of satellite data to derive relevant predictors. The satellite data processing, from download to analysis, will be performed using GRASS GIS software functionality. Then, we'll read our predictors within R and perform SDM, visualize and analyze results there, to then exemplify how to write the output distribution maps back into GRASS.\n\n# Getting ready\n\nWe'll run this session online, however, if you want to run it locally \nafterwards, here are the requirements.\n\n## Software \n\n### GRASS GIS\n\nWe will use **GRASS GIS 8.2+**. It can be installed either \nthrough standalone installers/binaries or through\n[OSGeo-Live](https://live.osgeo.org/en/index.html) \n(a linux based virtual machine which includes all OSGeo software and packages).\n\n##### MS Windows\n\nThere are two different options to install GRASS GIS in MS Windows:\n\n1. [Standalone installer 64-bit](https://grass.osgeo.org/grass82/binary/mswindows/native/WinGRASS-8.2.1-1-Setup.exe) \n2. [OSGeo4W 64-bit](http://download.osgeo.org/osgeo4w/v2/osgeo4w-setup.exe) \n\nFor Windows users, **we strongly recommend installing GRASS GIS through the OSGeo4W package** (second option), \nsince it allows to install all OSGeo software and resolves dependencies. \n\n##### Ubuntu Linux\n\nInstall GRASS GIS 8.2+ from the \"unstable\" package repository:\n\n```bash\n  sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable\n  sudo apt-get update\n  sudo apt-get install grass grass-gui grass-dev\n```\n\n##### Fedora, openSuSe Linux\n\nFor other Linux distributions including **Fedora** and **openSuSe**, simply \ninstall GRASS GIS with the respective package manager. See also [here](https://grass.osgeo.org/download/linux/)\n\n##### Mac OS\n\nFind GRASS GIS binaries on <http://grassmac.wikidot.com/> or install the \nlatest available version from [MacPorts](https://ports.macports.org/port/grass/).\n\n#### GRASS GIS Add-ons \n\n* [r.bioclim](https://grass.osgeo.org/grass-stable/manuals/addons/r.bioclim.html): Calculates bioclimatic indices as those in [WorldClim](https://www.worldclim.org/bioclim).\n\nInstall with `g.extension extension=name_of_addon`\n\n### R packages\n\nThe following R packages should be installed beforehand:\n\n```r\n  install.packages(c(\"rgrass\",\"terra\",\"raster\",\"sf\",\"mapview\",\"biomod2\"))\n```\n\n### Python libraries\n\nThe following Python libraries should be installed beforehand:\n\n```bash\n  pip install folium \n```\n\n## Other software\n\nWe will use the software **MaxEnt** to model habitat suitability. It can be \ndownloaded from: <https://biodiversityinformatics.amnh.org/open_source/maxent/>\n\n## Data\n\nDownload the following ready to use *location* with reconstructed daily LST \naverages (@metz_new_2017) for Northern Italy. This dataset is courtesy of\n[mundialis GmbH & Co. KG](mundialis.de/en/).\n\n* [Northern Italy (1.7 Gb)](https://drive.google.com/file/d/1z1b2NLC4Z6yzz_57RddTdRRK_gUkd7fU/view?usp=sharing)\n\n\n## References\n\n:::{#refs}\n:::\n\n<!-- - https://github.com/veroandreo/foss4g2022_grass4rs -->\n<!-- - https://github.com/veroandreo/grass_opengeohub2021 -->\n<!-- - Neteler, M. and Mitasova, H. (2008): *Open Source GIS: A GRASS GIS Approach*. Third edition. ed. Springer, New York. [Book site](https://grassbook.org/) -->\n<!-- - Neteler, M., Bowman, M.H., Landa, M. and Metz, M. (2012): *GRASS GIS: a multi-purpose Open Source GIS*. Environmental Modelling & Software, 31: 124-130 [DOI](http://dx.doi.org/10.1016/j.envsoft.2011.11.014) -->\n<!-- - Gebbert, S. and Pebesma, E. (2014). *A temporal GIS for field based environmental modeling*. Environmental Modelling & Software, 53, 1-12. [DOI](https://doi.org/10.1016/j.envsoft.2013.11.001) -->\n<!-- - Gebbert, S. and Pebesma, E. (2017). *The GRASS GIS temporal framework*. International Journal of Geographical Information Science, 31, 1273-1292. [DOI](http://dx.doi.org/10.1080/13658816.2017.1306862) -->\n<!-- - Gebbert, S., Leppelt, T. and Pebesma, E. (2019). *A Topology Based Spatio-Temporal Map Algebra for Big Data Analysis*. Data, 4, 86. [DOI](https://doi.org/10.3390/data4020086) -->",
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diff --git a/_quarto.yml b/_quarto.yml
@@ -25,7 +25,6 @@ website:
           - href: notebook_intro.qmd
           - href: notebook_ex_rs_grass.qmd
           - href: notebook_ex_sdm_r.qmd
-          - href: grassgis4rs.ipynb
   page-footer:
     center: 
        - text: "&#169; 2023 Verónica Andreo, <span xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dct=\"http://purl.org/dc/terms/\">licensed under <a href=\"http://creativecommons.org/licenses/by/4.0/?ref=chooser-v1\" target=\"_blank\" rel=\"license noopener noreferrer\" style=\"display:inline-block;\">CC BY 4.0<img style=\"height:18px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/cc.svg?ref=chooser-v1\"><img style=\"height:18px!important;margin-left:3px;vertical-align:text-bottom;\" src=\"https://mirrors.creativecommons.org/presskit/icons/by.svg?ref=chooser-v1\"></a></p>"
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diff --git a/assets/img/GUI_description.png b/assets/img/GUI_description.png
diff --git a/assets/img/studio/Single_Window_GUI.png b/assets/img/studio/Single_Window_GUI.png
diff --git a/assets/img/grass_single_window.png → assets/img/studio/grass_single_window.png b/assets/img/grass_single_window.png → assets/img/studio/grass_single_window.png
diff --git a/assets/img/grass_start.png → assets/img/studio/grass_start.png b/assets/img/grass_start.png → assets/img/studio/grass_start.png
diff --git a/assets/img/grass_within_rstudio_session.png → ...g/studio/grass_within_rstudio_session.png b/assets/img/grass_within_rstudio_session.png → ...g/studio/grass_within_rstudio_session.png
diff --git a/assets/img/studio/lst_north_italy_2014.png b/assets/img/studio/lst_north_italy_2014.png
diff --git a/docs/about.html b/docs/about.html
@@ -9954,19 +9954,15 @@
     <ul class="dropdown-menu" aria-labelledby="nav-menu-">    
         <li>
     <a class="dropdown-item" href="./notebook_intro.html">
- <span class="dropdown-text">Introduction to GRASS GIS</span></a>
+ <span class="dropdown-text">Part 1: Intro to GRASS GIS</span></a>
   </li>  
         <li>
     <a class="dropdown-item" href="./notebook_ex_rs_grass.html">
- <span class="dropdown-text">Part 1: Processing data in GRASS</span></a>
+ <span class="dropdown-text">Part 2: Processing data in GRASS</span></a>
   </li>  
         <li>
     <a class="dropdown-item" href="./notebook_ex_sdm_r.html">
- <span class="dropdown-text">Part 2: Modelling with R</span></a>
-  </li>  
-        <li>
-    <a class="dropdown-item" href="./grassgis4rs.html">
- <span class="dropdown-text"><strong><span>GRASS GIS for remote sensing</span></strong></span></a>
+ <span class="dropdown-text">Part 3: Modelling with R</span></a>
   </li>  
     </ul>
   </li>