Version 1
: Received: 3 July 2020 / Approved: 5 July 2020 / Online: 5 July 2020 (12:32:00 CEST)
How to cite:
Liu, Z.; Liu, H.; Luo, C.; Yang, H.; Ju, Y.; Guo, D. A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine. Preprints2020, 2020070072. https://doi.org/10.20944/preprints202007.0072.v1
Liu, Z.; Liu, H.; Luo, C.; Yang, H.; Ju, Y.; Guo, D. A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine. Preprints 2020, 2020070072. https://doi.org/10.20944/preprints202007.0072.v1
Liu, Z.; Liu, H.; Luo, C.; Yang, H.; Ju, Y.; Guo, D. A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine. Preprints2020, 2020070072. https://doi.org/10.20944/preprints202007.0072.v1
APA Style
Liu, Z., Liu, H., Luo, C., Yang, H., Ju, Y., & Guo, D. (2020). A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine. Preprints. https://doi.org/10.20944/preprints202007.0072.v1
Chicago/Turabian Style
Liu, Z., Yongchol Ju and Dong Guo. 2020 "A Rapid Extraction Method of Regional Scale Agricultural Disasters Based on Google Earth Engine" Preprints. https://doi.org/10.20944/preprints202007.0072.v1
Abstract
Remote sensing has been used as an important tool for disaster monitoring and disaster scope extraction, especially for the analysis of temporal and spatial disasters patterns of large-scale long time series. In order to find out a rapid and effective method to monitor disaster in a wide range, based on the Google Earth Engine cloud platform, this study used MODIS vegetation index products of 250 meter spatial resolution synthesized in 16 days during the year 2005-2019 and three kinds of disaster monitoring and scope extraction models are proposed: normalized vegetation index median time standardization (RNDVI_TM(i)) model, the normalized vegetation index median phenology Standardization(RNDVI_AM(i)(j)) model, normalized vegetation index median spatiotemporal Standardization (RNDVI_ZM(i)(j)) model. The optimal threshold of disaster extraction for each model in different time phases was determined by Otsu method, and the extraction results were verified by Medium resolution image and ground measured data of the same or quasi-same period. Finally, the disaster scope of cultivated land in Heilongjiang province from 2010 to 2019 was extracted and the temporal and spatial pattern of disasters was analyzed based on the meteorological data. It shows that the three above-mentioned models have high disaster monitoring and range extraction capabilities with the verification accuracy of RNDVI_TM(i) 97.46%, RNDVI_AM(i)(j) 96.90%, and RNDVI_ZM(i)(j) 96.67% respectively. The spatial and temporal distribution of disasters is consistent with the disaster of the insured plots and meteorological data in the whole province. Meanwhile, it turns out that different monitoring and extraction methods are used in different disasters, among which wind hazard and insect disasters often need to be delayed for 16 days to observe. Each model also has various sensitivity and applicability to different disasters. Compared with other methods, this method is fast, and convenient, which allows it to be used for large-scale agricultural disaster monitoring and is easy to be applied into other research areas. The research provides a new idea for large-scale agricultural disaster monitoring.
Keywords
Google Earth Engine; MODIS; Disaster monitoring
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
