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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Khouloud Abida & Meriem Barbouchi & Khaoula Boudabbous & Wael Toukabri & Karem Saad & Habib Bousnina & Thouraya Sahli Chahed, 2022. "Sentinel-2 Data for Land Use Mapping: Comparing Different Supervised Classifications in Semi-Arid Areas," Agriculture, MDPI, vol. 12(9), pages 1-13, September.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1429-:d:911010
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    References listed on IDEAS

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    1. Can Trong Nguyen & Amnat Chidthaisong & Phan Kieu Diem & Lian-Zhi Huo, 2021. "A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8," Land, MDPI, vol. 10(3), pages 1-18, February.
    2. Nij Tontisirin & Sutee Anantsuksomsri, 2021. "Economic Development Policies and Land Use Changes in Thailand: From the Eastern Seaboard to the Eastern Economic Corridor," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    3. Ge Shi & Nan Jiang & Lianqiu Yao, 2018. "Land Use and Cover Change during the Rapid Economic Growth Period from 1990 to 2010: A Case Study of Shanghai," Sustainability, MDPI, vol. 10(2), pages 1-15, February.
    4. G. Banko, 1998. "A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data and of Methods Including Remote Sensing Data in Forest Inventory," Working Papers ir98081, International Institute for Applied Systems Analysis.
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    Cited by:

    1. Manel Khlif & Maria José Escorihuela & Aicha Chahbi Bellakanji & Giovanni Paolini & Zeineb Kassouk & Zohra Lili Chabaane, 2023. "Multi-Year Cereal Crop Classification Model in a Semi-Arid Region Using Sentinel-2 and Landsat 7–8 Data," Agriculture, MDPI, vol. 13(8), pages 1-21, August.
    2. Wenfei Luan & Ge Li & Bo Zhong & Jianwei Geng & Xin Li & Hui Li & Shi He, 2023. "Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network," Land, MDPI, vol. 12(8), pages 1-20, August.
    3. Sa’ad Ibrahim, 2022. "Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Lands," Agriculture, MDPI, vol. 13(1), pages 1-22, December.

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