Detecting the signs of desertification with Landsat imagery: A semi-supervised anomaly detection approach

Abstract

Desertification detection is a crucial step to improve the management of affected areas and aid in mitigating the negative impacts of desertification. This study proposes a semi-supervised approach that uses Landsat imagery and radiometric data to detect desertification. The approach involves extracting radiometric data, which is used as an indicator to identify the thematic type and desertification evolution over time. Four anomaly detection techniques, including One-Class Support Vector Machine (OCSVM), Isolation Forest, Elliptic Envelope, and Local Outlier Factor, are trained on radiometric data from non-desertified regions to detect abnormal events related to desertification. These semi-supervised techniques use unlabeled data during training and only require desertification-free data, making them practical. The study was conducted in the arid region around Biskra, Algeria, which is a well-known area strongly affected by desertification. The OCSVM method achieved the highest detection accuracy of 95.40% in comparison to other methods and studies. Furthermore, to enhance result reliability, the Bootstrap technique was employed to generate 95% confidence intervals for five evaluation metrics.

Keywords

• Desertification detection
• Semi-supervised anomaly detection
• Radiometric data
• Landsat imagery
• Machine learning