IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v12y2022i9p1429-d911010.html

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/12/9/1429/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/12/9/1429/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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 Landscape," Agriculture, MDPI, vol. 13(1), pages 1-22, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Selamawit Haftu Gebresellase & Zhiyong Wu & Huating Xu & Wada Idris Muhammad, 2023. "Scenario-Based LULC Dynamics Projection Using the CA–Markov Model on Upper Awash Basin (UAB), Ethiopia," Sustainability, MDPI, vol. 15(2), pages 1-27, January.
    2. Yunfeng Hu & Batu Nacun, 2018. "An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015," Sustainability, MDPI, vol. 10(11), pages 1-22, November.
    3. Yanzhuo Liu & Shanshan Song & Chunjuan Bi & Junli Zhao & Di Xi & Ziqi Su, 2019. "Occurrence, Distribution and Risk Assessment of Mercury in Multimedia of Soil-Dust-Plants in Shanghai, China," IJERPH, MDPI, vol. 16(17), pages 1-19, August.
    4. Somnath DAS & Abhay Sankar SAHU, 2020. "Monitoring Land Use / Land Cover Changes Using Remote Sensing And Gis: A Case Study On Kanchrapara Municipality And Its Adjoining Area, West Bengal, India," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 43-54, June.
    5. Nehir Uyar, 2025. "Index-Driven Soil Loss Mapping Across Environmental Scenarios: Insights from a Remote Sensing Approach," Sustainability, MDPI, vol. 17(17), pages 1-23, September.
    6. Chunying Ning & Rajan Subedi & Lu Hao, 2023. "Land Use/Cover Change, Fragmentation, and Driving Factors in Nepal in the Last 25 Years," Sustainability, MDPI, vol. 15(8), pages 1-19, April.
    7. Roestamy, Martin & Martin, Abraham Yazdi & Rusli, Radif Khotamir & Fulazzaky, Mohamad Ali, 2022. "A review of the reliability of land bank institution in Indonesia for effective land management of public interest," Land Use Policy, Elsevier, vol. 120(C).
    8. Akhtar Ebrahimi & Farhad Zolfaghari & Marzieh Ghodsi & Fatemeh Narmashiri, 2024. "Assessing the accuracy of spectral indices obtained from Sentinel images using field research to estimate land degradation," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-15, July.
    9. Xiuyan Zhao & Changhong Miao, 2022. "Spatial-Temporal Changes and Simulation of Land Use in Metropolitan Areas: A Case of the Zhengzhou Metropolitan Area, China," IJERPH, MDPI, vol. 19(21), pages 1-27, October.
    10. Bo Wen & Yunhua Pan & Yanyuan Zhang & Jingjie Liu & Min Xia, 2018. "Does the Exhaustion of Resources Drive Land Use Changes? Evidence from the Influence of Coal Resources-Exhaustion on Coal Resources–Based Industry Land Use Changes," Sustainability, MDPI, vol. 10(8), pages 1-13, August.
    11. Karthik Karunakaran & Karuppasamy Sudalaimuthu, 2025. "GWR–ANN modelling for spatiotemporal prediction of land surface temperature in Thanjavur delta: evaluating environmental impacts on climate action and SDGs," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(8), pages 20181-20228, August.
    12. Liu, Dan & Yu, Chenglong & Feng, Rui & Yin, Shiping, 2025. "An ensemble learning-based approach for large-scale cultivated land quality monitoring," Ecological Modelling, Elsevier, vol. 508(C).
    13. Anna Roccati & Fabio Luino & Laura Turconi & Pietro Piana & Charles Watkins & Francesco Faccini, 2018. "Historical Geomorphological Research of a Ligurian Coastal Floodplain (Italy) and Its Value for Management of Flood Risk and Environmental Sustainability," Sustainability, MDPI, vol. 10(10), pages 1-24, October.
    14. Nehir Uyar & Azize Uyar, 2026. "Land surface temperature prediction in frozen ground regions using machine learning and ensemble models with remote sensing data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 122(1), pages 1-27, January.
    15. Pankaj Bajracharya & Selima Sultana, 2020. "Rank-size Distribution of Cities and Municipalities in Bangladesh," Sustainability, MDPI, vol. 12(11), pages 1-26, June.
    16. Zongpan Bian & Dongdong Zhang & Jun Xu & Hongtao Tang & Zhuoli Bai & Yan Li, 2022. "Study on the Evolution Law of Surface Landscape Pattern in Earthquake-Stricken Areas by Remote Sensing: A Case Study of Jiuzhaigou County, Sichuan Province," Sustainability, MDPI, vol. 14(20), pages 1-23, October.
    17. Hossain Mohammad Arifeen & Md. Shahariar Chowdhury & Haoran Zhang & Tanita Suepa & Nowshad Amin & Kuaanan Techato & Warangkana Jutidamrongphan, 2021. "Role of a Mine in Changing Its Surroundings—Land Use and Land Cover and Impact on the Natural Environment in Barapukuria, Bangladesh," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    18. Nangware Kajia Msofe & Lianxi Sheng & James Lyimo, 2019. "Land Use Change Trends and Their Driving Forces in the Kilombero Valley Floodplain, Southeastern Tanzania," Sustainability, MDPI, vol. 11(2), pages 1-25, January.
    19. Saurabh Singh & Ram Avtar & Ankush Kumar Jain & Wafa Saleh Alkhuraiji & Mohamed Zhran, 2025. "Influence of Land Use/Land Cover Dynamics on Urban Surface Metrics in Semi-Arid Heritage Cities," Land, MDPI, vol. 14(9), pages 1-20, September.
    20. Dr. Thanakit Ouanhlee, 2026. "AI-Driven Compensation Transparency and Human Capital Accounting Disclosure: A Framework for Manufacturing Organizations in Emerging Economies," International Journal of Research and Innovation in Applied Science, RSIS International, vol. 11(4), pages 2310-2384, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:12:y:2022:i:9:p:1429-:d:911010. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager The email address of this maintainer does not seem to be valid anymore. Please ask MDPI Indexing Manager to update the entry or send us the correct address (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.