Commonly used indicator which applies vegetation cover information to conduct trend analysis of vegetation index data (Higginbottom and Symeonakis, 2004 AU10: The in-text citation "Higginbottom and Symeonakis, 2004" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Published in Chapter:
Promises and Challenges Relating to Machine Learning Techniques to Predict Areas at Risk of Desertification: A State-of-the-Art Review
Marie-Isabelle von Schoenborn (ESCP Business School, Berlin, Germany) and Markus Bick (ESCP Business School, Berlin, Germany)
Copyright: © 2023
|Pages: 32
DOI: 10.4018/978-1-6684-5959-1.ch003
Abstract
Land degradation and desertification are considered substantial issues in ecological and social research; hence the need for a quantitative, reliable, and repeatable methodology to evaluate desertification processes is urgent. This chapter aims to review the advances and limitations of existing work seeking to predict desertification through the use of automated, data-driven methods (i.e. machine learning). Using the CRISP-DM framework, existing research was classified into classic (supervised) ML models using field data, classic ML models using remote sensing data, and deep-learning models using remote sensing data. However, more research is needed to incorporate feedback effects and human intervention, as well as to make the distinction between desertification risk and desertification in ML models. Finally, the chapter suggests that existing research should be collated and formed into a “desertification warning system,” addressing the need to harmonize data, better understand desertification, and aim for the greater inclusion of local end-stakeholders.