Add materials from Geocomputation courses

content/tutorials/external/spatial_ecology_geocomp.qmd

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+---
+title: "Spatial Ecology - Geocomputation with GRASS"
+author: "Giuseppe Amatulli"
+date: 06/19/2025
+format:
+  html:
+    toc: true
+    code-tools: true
+    code-copy: true
+    code-fold: false
+categories: [advanced, Bash, HPC, hydrology, external, course]
+description: GRASS tutorials for geocomputation, hydrology, species distribution modeling, and HPC.
+execute:
+  eval: false
+---
+
+# GRASS Introduction
+
+Learn the basics of GRASS including the data structure, command syntax, working environment, mapsets, and region settings using the command line.
+
+<https://spatial-ecology.net/docs/build/html/GRASS/grass_intro.html>
+
+# Start a New GRASS Project
+
+Create a new GRASS project from scratch, import data, and configure the working environment.
+
+<https://spatial-ecology.net/docs/build/html/GRASS/grass_newproject.html>
+
+# Stream-Network Extraction and Basin Delineation
+
+Use GRASS for hydrographic feature extraction at large scales, including tiled flow accumulation, stream network extraction, and basin delineation. Also available as a [Google Colab version](https://spatial-ecology.net/docs/build/html/GRASS/grass_hydro_colab.html).
+
+<https://spatial-ecology.net/docs/build/html/GRASS/grass_hydro.html>
+
+# Species Distribution Modeling with GRASS
+
+Geocomputation for a Random Forest species distribution model using GRASS to prepare environmental predictors and generate predictions.
+
+<https://spatial-ecology.net/docs/build/html/GRASS/SDM1_MWood_gecomp4GRASS.html>
+
+<https://spatial-ecology.net/docs/build/html/GRASS/SDM1_MWood_GRASSmodel.html>
+
+# Use of GRASS in HPC
+
+Set up and run GRASS in a High Performance Computing cluster environment using Bash scripting and the Slurm queuing system.
+
+<https://spatial-ecology.net/docs/build/html/HPC/hpc_setting_grass.html>
+
+Video recording (29 min): <https://youtu.be/4i1Fs29D0-A>
+
+Author: Giuseppe Amatulli, Yale University / Spatial Ecology
+
+These materials were developed as part of the US NSF-funded POSE project (Award 2303651).
+
+![](images/spatial_ecology.png){.preview-image}

content/tutorials/external/spatial_ecology_geocomp_es.qmd

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+---
+title: "Spatial Ecology - Geocomputación con GRASS"
+lang: es
+author: "Giuseppe Amatulli"
+date: 12/19/2024
+format:
+  html:
+    toc: true
+    code-tools: true
+    code-copy: true
+    code-fold: false
+categories: [advanced, Bash, HPC, hydrology, external, course, Spanish]
+description: Curso de geocomputación con GRASS en español, incluyendo introducción, hidrología y machine learning.
+execute:
+  eval: false
+---
+
+# Geocomputación para aplicaciones ambientales: uso de GDAL y GRASS
+
+Curso online de 5 semanas con explicaciones en español y material en inglés. El curso incluye tres bloques dedicados a GRASS que cubren introducción, hidrología y machine learning.
+
+Programa del curso: <https://spatial-ecology.net/docs/build/html/COURSESAROUNDTHEWORLD/course_geocomp_11-12_2024.html>
+
+## Clase 6: Introducción a GRASS
+
+Configuración del entorno de trabajo, estructura de datos, comandos básicos y scripting en Bash.
+
+Video: <https://youtu.be/XlYLkgCm55A>
+
+Material: <https://spatial-ecology.net/docs/build/html/GRASS/grass_intro.html>
+
+## Clase 7: Análisis hidrológico con GRASS
+
+Extracción de redes de drenaje y delimitación de cuencas a gran escala.
+
+Video: <https://youtu.be/230t0J_H9TY>
+
+Material: <https://spatial-ecology.net/docs/build/html/GRASS/grass_hydro.html>
+
+## Clase 8: Machine Learning con GRASS
+
+Modelado de distribución de especies usando Random Forest con GRASS para preparar predictores ambientales.
+
+Video: <https://youtu.be/P7bJn_Lbj4A>
+
+Material: <https://spatial-ecology.net/docs/build/html/GRASS/SDM1_MWood_gecomp4GRASS.html>
+
+---
+
+Lista completa de videos: <https://www.youtube.com/playlist?list=PL2h1_A_4X7LDPLri1UUA1_5GlRaNfIpE4>
+
+Autor: Giuseppe Amatulli, Yale University / Spatial Ecology
+
+Estos materiales fueron desarrollados como parte del proyecto POSE financiado por la US National Science Foundation (NSF Award 2303651).
+
+![](images/spatial_ecology.png){.preview-image}