prepared by Felipe Bravo iuFOR - University of Valladolid with support from Dr. Dang Kinh Bac, Vietnam National University-University of Science, to import satellite images and from Andrés Bravo-Núñez for ML algorithms developent and Aitor Vázquez, iuFOR - University of Valladolid, to import the raw NFI dataset.
This case study has been prepared through the Erasmus+ project VirtualForests (https://virtualforests.eu/) and was tested with the participants in the Blended Intensive Program Nature Conservation and Artificial Intelligence (https://portal3.ipb.pt/index.php/en/gri/blended-intensive-programmes) at IPB-Instituto Politécnico de Bragança, Portugal (https://portal3.ipb.pt/index.php/pt/ipb) in June 2023
This repository includes the lab instructions and the datasets to complete the assignments. Users have to install R, RStudio and QGIS. Additionally must have an account to use https://earthexplorer.usgs.gov/.
These materials have used by the author during regular master quantitative forestry courses at University of Valladolid, Spain and the mobility week at IPB-Instituto Politecnico de Bragança in 2023
Before we start with the hands-on exercice we could watch this video on the opportunity of artificial intelligence in forest management and this other about a project to use AI to insight on ingrowth in complex forests
After you will complete the hands-on exercice your will realize that normally in forest science ground data are scarce while machine learning algorithms tend to work better when more data are available. This video on data augmentation can provide ideas about how to get synthetic data to expand our datasets.
If interested in get more information you can watch whole Conference Artificial Intelligence and Ecosystems Management organized by SMART Global Ecosystems and iuFOR at Palencia (Spain), April 17th-21st 2023
- Bravo et al, 2015 Análisis de datos selvícolas con R in Spanish
- Hastie, T., Tibshirani, R., Friedman, J. 2017 The Elements of Statistical Learning Data Mining, Inference, and Prediction
- James, G., Witten, D, Hastie, T., Tibshirani, R. 2017 An introduction to statistical learning with applications in R
- James, G., Witten, D, Hastie, T., Tibshirani, R. 2021 An introduction to statistical learning with applications in R. 2nd Edition
- Lovelace, R., Cheshire, J., Oldroyd, R. et al. 2016 Introduction to visualising spatial data in R
- Robinson, A. 2016 icebreakeR and its data and other materials