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Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India

Author

Listed:
  • Kotapati Narayana Loukika

    (Department of Civil Engineering, National Institute of Technology Warangal, Warangal 506004, India)

  • Venkata Reddy Keesara

    (Department of Civil Engineering, National Institute of Technology Warangal, Warangal 506004, India)

  • Venkataramana Sridhar

    (Department of Biological Systems Engineering, Virginia Polytechnic Institute, State University, Blacksburg, VA 24061, USA)

Abstract

The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Monitoring and assessing LULC change over large areas is critical in a variety of fields, including natural resource management and climate change research. LULC change has emerged as a critical concern for policymakers and environmentalists. As the need for the reliable estimation of LULC maps from remote sensing data grows, it is critical to comprehend how different machine learning classifiers perform. The primary goal of the present study was to classify LULC on the Google Earth Engine platform using three different machine learning algorithms—namely, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and to compare their performance using accuracy assessments. The LULC of the study area was classified via supervised classification. For improved classification accuracy, NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) indices were also derived and included. For the years 2016, 2018, and 2020, multitemporal Sentinel-2 and Landsat-8 data with spatial resolutions of 10 m and 30 m were used for the LULC classification. ‘Water bodies’, ‘forest’, ‘barren land’, ‘vegetation’, and ‘built-up’ were the major land use classes. The average overall accuracy of SVM, RF, and CART classifiers for Landsat-8 images was 90.88%, 94.85%, and 82.88%, respectively, and 93.8%, 95.8%, and 86.4% for Sentinel-2 images. These results indicate that RF classifiers outperform both SVM and CART classifiers in terms of accuracy.

Suggested Citation

  • Kotapati Narayana Loukika & Venkata Reddy Keesara & Venkataramana Sridhar, 2021. "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13758-:d:701460
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    References listed on IDEAS

    as
    1. Kang, Hyunwoo & Sridhar, Venkataramana & Mills, Bradford F. & Hession, W. Cully & Ogejo, Jactone A., 2019. "Economy-wide climate change impacts on green water droughts based on the hydrologic simulations," Agricultural Systems, Elsevier, vol. 171(C), pages 76-88.
    2. Alemayehu Midekisa & Felix Holl & David J Savory & Ricardo Andrade-Pacheco & Peter W Gething & Adam Bennett & Hugh J W Sturrock, 2017. "Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-15, September.
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    Cited by:

    1. Alireza Taheri Dehkordi & Mohammad Javad Valadan Zoej & Hani Ghasemi & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "A New Clustering Method to Generate Training Samples for Supervised Monitoring of Long-Term Water Surface Dynamics Using Landsat Data through Google Earth Engine," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    2. Chunyu Li & Rong Cai & Wei Tian & Junna Yuan & Xiaofei Mi, 2023. "Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    3. Zhiqi Jiang & Yijun Wen & Gui Zhang & Xin Wu, 2022. "Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data," Sustainability, MDPI, vol. 14(7), pages 1-19, March.
    4. Gladys Maria Villegas Rugel & Daniel Ochoa & Jose Miguel Menendez & Frieke Van Coillie, 2023. "Evaluating the Applicability of Global LULC Products and an Author-Generated Phenology-Based Map for Regional Analysis: A Case Study in Ecuador’s Ecoregions," Land, MDPI, vol. 12(5), pages 1-32, May.
    5. Kotapati Narayana Loukika & Venkata Reddy Keesara & Eswar Sai Buri & Venkataramana Sridhar, 2022. "Predicting the Effects of Land Use Land Cover and Climate Change on Munneru River Basin Using CA-Markov and Soil and Water Assessment Tool," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    6. Azher Ibrahim Al-Taei & Ali Asghar Alesheikh & Ali Darvishi Boloorani, 2023. "Land Use/Land Cover Change Analysis Using Multi-Temporal Remote Sensing Data: A Case Study of Tigris and Euphrates Rivers Basin," Land, MDPI, vol. 12(5), pages 1-14, May.
    7. Kumari Priya & Talukdar Sasanka & Krishna K. Osuri, 2023. "Land use land cover representation through supervised machine learning methods: sensitivity on simulation of urban thunderstorms in the east coast of India," 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. 116(1), pages 295-317, March.
    8. Yiqing Shao & Zengchuan Dong & Jinyu Meng & Shujun Wu & Yao Li & Shengnan Zhu & Qiang Zhang & Ziqin Zheng, 2023. "Analysis of Runoff Variation and Future Trends in a Changing Environment: Case Study for Shiyanghe River Basin, Northwest China," Sustainability, MDPI, vol. 15(3), pages 1-23, January.

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