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Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding

Author

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  • Clement E. Akumu

    (Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, Nashville, TN 37209, USA)

  • Eze O. Amadi

    (Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, Nashville, TN 37209, USA)

  • Samuel Dennis

    (Department of Agricultural and Environmental Sciences, College of Agriculture, Tennessee State University, Nashville, TN 37209, USA)

Abstract

Frequent flooding worldwide, especially in grazing environments, requires mapping and monitoring grazing land cover and pasture quality to support land management. Although drones, satellite, and machine learning technologies can be used to map land cover and pasture quality, there have been limited applications in grazing land environments, especially monitoring land cover change and pasture quality pre- and post-flood events. The use of high spatial resolution drone and satellite data such as WorldView-4 can provide effective mapping and monitoring in grazing land environments. The aim of this study was to utilize high spatial resolution drone and WorldView-4 satellite data to map and monitor grazing land cover change and pasture quality pre-and post-flooding. The grazing land cover was mapped pre-flooding using WorldView-4 satellite data and post-flooding using real-time drone data. The machine learning Random Forest classification algorithm was used to delineate land cover types and the normalized difference vegetation index (NDVI) was used to monitor pasture quality. This study found a seven percent (7%) increase in pasture cover and a one hundred percent (100%) increase in pasture quality post-flooding. The drone and WorldView-4 satellite data were useful to detect grazing land cover change at a finer scale.

Suggested Citation

  • Clement E. Akumu & Eze O. Amadi & Samuel Dennis, 2021. "Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding," Land, MDPI, vol. 10(3), pages 1-13, March.
    • Handle: RePEc:gam:jlands:v:10:y:2021:i:3:p:321-:d:520971
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    References listed on IDEAS

    as
    1. Abel Ramoelo & Moses Azong Cho & Renaud Mathieu & S. Madonsela & Ruben Van De Kerchove & Zaneta Kaszta & Eléonore Wolff, 2015. "Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data," ULB Institutional Repository 2013/226509, ULB -- Universite Libre de Bruxelles.
    2. Jérôme Théau & Étienne Lauzier-Hudon & Lydiane Aubé & Nicolas Devillers, 2021. "Estimation of forage biomass and vegetation cover in grasslands using UAV imagery," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-18, January.
    3. Xiaohua Zhang & Xiuli Chen & Meirong Tian & Yongjun Fan & Jianjun Ma & Danlu Xing, 2020. "An evaluation model for aboveground biomass based on hyperspectral data from field and TM8 in Khorchin grassland, China," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-12, February.
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    Cited by:

    1. Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.
    2. McCarthy, Nancy & Cavatassi, Romina & Maggio, Giuseppe, 2023. "IFAD RESEARCH SERIES 88: The Impact of Climate Change on Livestock Production in Mozambique," IFAD Research Series 330875, International Fund for Agricultural Development (IFAD).

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    1. Benjamin T. Fraser & Christine L. Bunyon & Sarah Reny & Isabelle Sophia Lopez & Russell G. Congalton, 2022. "Analysis of Unmanned Aerial System (UAS) Sensor Data for Natural Resource Applications: A Review," Geographies, MDPI, vol. 2(2), pages 1-38, June.

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