Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas

Khouloud Abida (), Meriem Barbouchi, Khaoula Boudabbous, Wael Toukabri, Karem Saad, Habib Bousnina and Thouraya Sahli Chahed
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Khouloud Abida: National Institute of Agronomy of Tunisia (INAT), Carthage University, Avenue Charles Nicolle, Tunis 1082, Tunisia
Meriem Barbouchi: Laboratoire Sciences et Techniques Agronomiques (LR16INRAT05), National Institute of Agricultural Research of Tunisia (INRAT), Carthage University, Tunis 1004, Tunisia
Khaoula Boudabbous: National Institute of Agronomy of Tunisia (INAT), Carthage University, Avenue Charles Nicolle, Tunis 1082, Tunisia
Wael Toukabri: Laboratoire Sciences et Techniques Agronomiques (LR16INRAT05), National Institute of Agricultural Research of Tunisia (INRAT), Carthage University, Tunis 1004, Tunisia
Karem Saad: Ecole National des Ingénieurs de Sfax (ENIS), Carthage University, Sfax 3038, Tunisia
Habib Bousnina: National Institute of Agronomy of Tunisia (INAT), Carthage University, Avenue Charles Nicolle, Tunis 1082, Tunisia
Thouraya Sahli Chahed: National Centre for Mapping and Remote Sensing, Ministry of National Defense (CNCT), Tunis 1080, Tunisia

Agriculture, 2022, vol. 12, issue 9, 1-13

Abstract: Mapping and monitoring land use (LU) changes is one of the most effective ways to understand and manage land transformation. The main objectives of this study were to classify LU using supervised classification methods and to assess the effectiveness of various machine learning methods. The current investigation was conducted in the Nord-Est area of Tunisia, and an optical satellite image covering the study area was acquired from Sentinel-2. For LU mapping, we tested three machine learning models algorithms: Random Forest (RF), K-Dimensional Trees K-Nearest Neighbors (KDTree-KNN) and Minimum Distance Classification (MDC). According to our research, the RF classification provided a better result than other classification models. RF classification exhibited the best values of overall accuracy, kappa, recall, precision and RMSE, with 99.54%, 0.98%, 0.98%, 0.98% and 0.23%, respectively. However, low precision was observed for the MDC method (RMSE = 1.15). The results were more intriguing since they highlighted the value of the bare soil index as a covariate for LU mapping. Our results suggest that Sentinel-2 combined with RF classification is efficient for creating a LU map.

Keywords: sentinel-2; land use mapping; supervised classification; spectral index; machine learning (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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