AI as a tool to reduce desertification

The problem of desertification

Desertification threatens the livelihoods of many communities, especially people with fewer resources in vulnerable areas. A UNCCD briefing published in 2021 reported that this process threatened the livelihoods of more than one billion people.

Source: UNCCD, land and human health, 2021.

Editorial note, 25 October 2026: the population figure in the original version has been corrected and a source added. The article retains its original 2022 publication context.

Desertification is land degradation in arid, semi-arid and dry sub-humid areas. It can result from climate variations and human activities such as deforestation or overexploitation of resources. It reduces land productivity; preventing and reducing it, and restoring affected areas, requires measures adapted to each context.

Some of the territories at risk are part of the African continent, the USA, South America, Australia or Spain. To reduce the impact that desertification produces in these areas, the political, socioeconomic, scientific and/or social factors of the moment must be treated with the aim of achieving appropriate measures to reduce the effects that this event unfortunately entails.

AI and desertification

For the different studies of desertification in the different areas of the planet with irrigation, various artificial intelligence techniques are used in order to be able to analyze all the data obtained in a more exhaustive way. For machine learning, the most common methods are the use of: Nearest Neighbor (K-NN), Randomforest (RF) or Support vector machine (SVM). On the other hand, we also find deep learning, among which the use of: Multilayer perceptron (MLP) or Convolutional Neural Networks (CNN).

Leaving aside the methodologies used for data analysis, we must also consider two types of variables to analyze each of the different situations. Thus, on the one hand we find the biophysical variables and on the other, the socioeconomic variables.

BIOPHYSICAL VARIABLES

–Aridity index

It is calculated by the ratio of annual precipitation and annual potential evapotranspiration (variable between the range of 0.05 and 0.65). Thanks to the use of RandomForest, XGBoostasí As artificial neural networks, we can predict evapotranspiration and precipitation effectively.

Water stress of vegetation and state of groundwater reserves:

Convolutional neural networks have been used to complete anomaly data in terrestrial water storage estimation. On the other hand, the use of deep learning with infrared thermal images has also been identified as a very useful technique for the evaluation of water stress of crops and natural vegetation.

–Accurate identification of grasslands and soil organic carbon:

Through the combination of machine and deep learning we manage to detect the vegetation and soil type from the precise results generated from the algorithms.

–Satellite images:

Based on abrupt signals due to changes in vegetation cover, precise maps of deforested areas can subsequently be created using deep learning. In comparison to machine learning, which will allow us, on the other hand, to predict potential areas of deforestation in order to achieve better management of the affected territory.

SOCIO-ECONOMIC VARIABLES

–Population density:

Artificial intelligence can help us quantify producer-consumer commercial networks at a global level and consequently know the impact of the city on the countryside. In addition, images from drones, airplanes or satellites allow us to create precise urban maps that help us predict the expansion of urban territory.

–Association of middle income and desertification:

Using satellite images and deep learning we can understand the economic well-being of the population in different countries.

–Large-scale, irrigated agriculture and overgrazing:

Artificial intelligence is widely applied in different agricultural studies in order to achieve sustainable management of resources to combat desertification.

In this figure we can see: A) Conceptual map in relation to “artificial intelligence” + “desertification” in which you can see the links between the terms that currently receive the most attention (until 2020) in the identified scientific publications. B) Connections with “landdegradation”. C) Connections with methods based on artificial intelligence, where “neural networks” can be observed as a term with great importance.

In summary, artificial intelligence applied in the field of desertification allows us to respond to where and when it could occur. However, it should be noted that the study is complex due to the number of factors to be taken into account. We must work synchronously with the different spatial and temporal scales, being aware of the difficulties this entails and also taking into account the biophysical and socioeconomic variables to treat desertification and address the problem it causes. Once we know the impact that these conditions have, we will be able to influence the most appropriate solutions in each of the areas.

Bibliography:

1.-Guirado, E., Martínez-Valderrama, J. 2021. Potential of artificial intelligence to advance the study of desertification. Ecosystems 30(3): 2250. https://doi.org/10.7818/ECOS.2250

WRITTEN BY

Eva Arnall

Saturdays.AI

Saturdays.AI is an impact-focused organization on a mission to empower diverse individuals to learn Artificial Intelligence in a collaborative and project-based way, beyond the conventional path of traditional education.