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Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine

Author

Listed:
  • Zhengrong Liu

    (School of Pubilc Adminstration and Law, Northeast Agricultural University, Harbin 150030, China)

  • Huanjun Liu

    (School of Pubilc Adminstration and Law, Northeast Agricultural University, Harbin 150030, China
    Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China)

  • Chong Luo

    (Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China)

  • Haoxuan Yang

    (College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, China)

  • Xiangtian Meng

    (School of Pubilc Adminstration and Law, Northeast Agricultural University, Harbin 150030, China)

  • Yongchol Ju

    (Wonsan University of Agriculture, Won San City, Kangwon Province, DPRK)

  • Dong Guo

    (Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, China)

Abstract

Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of spatial and temporal disaster patterns of large-scale and long-duration series. Google Earth Engine provides the possibility of quickly extracting the disaster range over a large area. Based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products with 250-m spatial resolution synthesized over 16 days from the period 2005–2019 to develop a rapid and effective method for monitoring disasters across a wide spatiotemporal range. Three types of disaster monitoring and scope extraction models are proposed: the normalized difference vegetation index (NDVI) median time standardization model ( R NDVI_TM(i) ), the NDVI median phenology standardization model ( R NDVI_AM(i)(j) ), and the NDVI median spatiotemporal standardization model ( R NDVI_ZM(i)(j) ). The optimal disaster extraction threshold for each model in different time phases was determined using Otsu’s method, and the extraction results were verified by medium-resolution images and ground-measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang Province from 2010–2019 was extracted, and the spatial and temporal patterns of the disasters were analyzed based on meteorological data. This analysis revealed that the three aforementioned models exhibited high disaster monitoring and range extraction capabilities, with verification accuracies of 97.46%, 96.90%, and 96.67% for R NDVI_TM(i) , R NDVI_AM(i) , and (j) R NDVI_ZM(i)(j) , respectively. The spatial and temporal disaster distributions were found to be consistent with the disasters of the insured plots and the meteorological data across the entire province. Moreover, different monitoring and extraction methods were used for different disasters, among which wind hazard and insect disasters often required a delay of 16 days prior to observation. Each model also displayed various sensitivities and was applicable to different disasters. Compared with other techniques, the proposed method is fast and easy to implement. This new approach can be applied to numerous types of disaster monitoring as well as large-scale agricultural disaster monitoring and can easily be applied to other research areas. This study presents a novel method for large-scale agricultural disaster monitoring.

Suggested Citation

  • Zhengrong Liu & Huanjun Liu & Chong Luo & Haoxuan Yang & Xiangtian Meng & Yongchol Ju & Dong Guo, 2020. "Rapid Extraction of Regional-scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine," Sustainability, MDPI, vol. 12(16), pages 1-27, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6497-:d:397890
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    References listed on IDEAS

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    1. García-Tejero, I.F. & Hernández, A. & Padilla-Díaz, C.M. & Diaz-Espejo, A. & Fernández, J.E, 2017. "Assessing plant water status in a hedgerow olive orchard from thermography at plant level," Agricultural Water Management, Elsevier, vol. 188(C), pages 50-60.
    2. Md Shahinoor Rahman & Liping Di, 2020. "A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment," Agriculture, MDPI, vol. 10(4), pages 1-30, April.
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    1. Moisei Zakharov & Sébastien Gadal & Jūratė Kamičaitytė & Mikhail Cherosov & Elena Troeva, 2022. "Distribution and Structure Analysis of Mountain Permafrost Landscape in Orulgan Ridge (Northeast Siberia) Using Google Earth Engine," Land, MDPI, vol. 11(8), pages 1-21, July.

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