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 (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 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.
Highlights
Climate impact and environmental change are two important factors that restrict the development of agricultural production
After the cultivated land was categorized into regions according to its phenological values, the median values of the different phenological regions were extracted from the processed images in Google Earth Engine (GEE) as NDVIAMED(i)(j) and NDVIZMED(i)(j), and RNDVI_AM(i)(j) and RNDVI_ZM(i)(j) were calculated
Based on the thresholds of the different time phases listed in Table 6, the typical disasters verified by the HJ-1A/B monitoring range and the disaster scope of Heilongjiang Province from 2010–2019 were extracted
Summary
Climate impact and environmental change are two important factors that restrict the development of agricultural production. Compared with the traditional methods, the use of remote sensing to monitor agricultural disasters has the advantages of continuous spatiotemporal access to high-resolution surface information, fast data acquisition, and a wide range. For these reasons, remote sensing has been widely used in agricultural disaster and vegetation dynamic monitoring, and numerous remote sensing measurement methods have been developed to monitor global vegetation and extreme climate events [2,3]. There is an urgent necessity to establish a rapid and large-scale agricultural disaster monitoring method with remote sensing as its technical basis
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