Detection of Agricultural Terraces Platforms Using Machine Learning from Orthophotos and LiDAR-Based Digital Terrain Model: A Case Study in Roya Valley of Southeast France

Michael Vincent Tubog, Karine Emsellem and Stephane Bouissou ()
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Michael Vincent Tubog: Physics and Geology Department, Negros Oriental State University, Kagawasan Avenue, Dumaguete City 6200, Philippines
Karine Emsellem: ESPACE Laboratory UMR7300 98, Université Côte d’Azur, bd Edouard Herriot, 06200 Nice, France
Stephane Bouissou: ESPACE Laboratory UMR7300 98, Université Côte d’Azur, bd Edouard Herriot, 06200 Nice, France

Land, 2025, vol. 14, issue 5, 1-28

Abstract: Terraces have long transformed steep slopes into gradual steps, reducing erosion and enabling agriculture on marginal land. In France’s Roya Valley, these dry stone structures, neglected for decades, demonstrated remarkable resilience during storm Alex in October 2020. This prompted civil society and researchers to identify terraces that could support food security and agri-tourism initiatives. This study aimed to develop a semi-automatic method for detecting and mapping terraced areas using LiDAR and orthophoto data from French repositories, processed with GIS and analyzed through a Support Vector Machine (SVM) classification algorithm. The model identified 18 terraces larger than 1 hectare in Saorge and 35 in La Brigue. Field visits confirmed evidence of abandonment in several areas. Accuracy tests showed a user accuracy (UA) of 97% in Saorge and 72% in La Brigue. This disparity reflects site-specific differences, including terrain steepness, vegetation density, and data resolution. These results highlight the value of machine learning for terrace mapping while emphasizing the need to account for local geomorphological and data-quality factors to improve model performance. Enhanced terrace detection supports sustainable land management, agricultural revitalization, and risk mitigation in mountainous regions, offering practical tools for future landscape restoration and food resilience planning.

Keywords: terraces; LiDAR; SVM (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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