diff --git a/content/tutorials/external/images/spatial_ecology.png b/content/tutorials/external/images/spatial_ecology.png new file mode 100644 index 0000000..0482ca6 Binary files /dev/null and b/content/tutorials/external/images/spatial_ecology.png differ diff --git a/content/tutorials/external/spatial_ecology_geocomp.qmd b/content/tutorials/external/spatial_ecology_geocomp.qmd new file mode 100644 index 0000000..7872bd4 --- /dev/null +++ b/content/tutorials/external/spatial_ecology_geocomp.qmd @@ -0,0 +1,63 @@ +--- +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 +--- + +![](images/spatial_ecology.png){.preview-image} + +The following tutorials were developed as part of the +[Spatial Ecology courses](https://spatial-ecology.net/training/), +where students are introduced to an array of powerful open-source geocomputation tools and machine learning methodologies under the Linux environment. + +## GRASS Introduction + +Learn the basics of GRASS including the data structure, command syntax, working environment, mapsets, and region settings using the command line. + + + +## Start a New GRASS Project + +Create a new GRASS project from scratch, import data, and configure the working environment. + + + +## 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). + + + +## Species Distribution Modeling with GRASS + +Geocomputation for a Random Forest species distribution model using GRASS to prepare environmental predictors and generate predictions. + + + + + +## 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. + + + +Video recording (29 min): + +--- + +Author: Giuseppe Amatulli, Yale University / Spatial Ecology + +These materials were developed as part of the US NSF-funded POSE project (Award 2303651). + + diff --git a/content/tutorials/external/spatial_ecology_geocomp_es.qmd b/content/tutorials/external/spatial_ecology_geocomp_es.qmd new file mode 100644 index 0000000..75a2c0d --- /dev/null +++ b/content/tutorials/external/spatial_ecology_geocomp_es.qmd @@ -0,0 +1,56 @@ +--- +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 +--- + +![](images/spatial_ecology.png){.preview-image} + +# 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: + +## Clase 6: Introducción a GRASS + +Configuración del entorno de trabajo, estructura de datos, comandos básicos y scripting en Bash. + +Video: + +Material: + +## Clase 7: Análisis hidrológico con GRASS + +Extracción de redes de drenaje y delimitación de cuencas a gran escala. + +Video: + +Material: + +## Clase 8: Machine Learning con GRASS + +Modelado de distribución de especies usando Random Forest con GRASS para preparar predictores ambientales. + +Video: + +Material: + +Lista completa de videos: + +--- + +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).