Working Paper Article Version 1 This version is not peer-reviewed

A Rapid Extraction Method for Regional-Scale Agricultural Disasters Based on Google Earth Engine

Version 1 : Received: 15 July 2020 / Approved: 21 July 2020 / Online: 21 July 2020 (03:12:22 CEST)

A peer-reviewed article of this Preprint also exists.

Liu, Z.; Liu, H.; Luo, C.; Yang, H.; Meng, X.; Ju, Y.; Guo, D. Rapid Extraction of Regional-Scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine. Sustainability 2020, 12, 6497. Liu, Z.; Liu, H.; Luo, C.; Yang, H.; Meng, X.; Ju, Y.; Guo, D. Rapid Extraction of Regional-Scale Agricultural Disasters by the Standardized Monitoring Model Based on Google Earth Engine. Sustainability 2020, 12, 6497.

Journal reference: Sustainability 2020, 12, 6497
DOI: 10.3390/su12166497

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. 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 (RNDVI_TM(i)), the NDVI median phenology standardization model (RNDVI_AM(i)(j)), and the NDVI median spatiotemporal standardization model (RNDVI_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 RNDVI_TM(i), RNDVI_AM(i), and (j)RNDVI_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 were 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.

Subject Areas

Google Earth Engine; MODIS; disaster monitoring; remote sensing index

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