Disaster monitoring and loss assessment method based on fusion of meteorological and remote sensing data

Abstract

The invention belongs to the technical field of agricultural insurance and particularly relates to a disaster monitoring and loss assessment method based on fusion of meteorological and remote sensingdata. Agro meteorological hazard risk assessment, affected area remote sensing monitoring, and post-disaster loss assessment are combined with a spatial analysis and statistics function of a GIS, with natural disaster risk analysis as a basic principle, a disaster-caused factor model is built from the risk of a disaster-caused factor, the vulnerability of a hazard-affected body and the disaster resistance and reduction ability, monthly precipitation anomaly data are calculated and accumulated through TRMM satellite data as the disaster-caused factor, an agricultural disaster is monitored froma meteorological disaster, and a basis is made for adjustment of the agricultural structure and formulation of disaster resistance and reduction measures. The method of the invention has the advantages that the influence of meteorological conditions on the appearance and the yield of crops can be grasped in a wide-range, all-weather and real-time mode, and a basis is provided for agricultural production decision making, post disaster rescue and agricultural insurance and other work.

Description

technical field [0001] The invention belongs to the technical field of agricultural insurance, and in particular relates to a disaster monitoring and loss determination method based on the fusion of meteorological and remote sensing data, which is applied in natural disaster monitoring and loss determination by using remote sensing technology, GIS technology and meteorological data. Background technique [0002] At present, the use of remote sensing data for disaster monitoring and damage assessment is mainly based on the band data included in the image (red band / infrared band / thermal infrared band) to perform vegetation index / water stress index / temperature inversion calculations, combined with precipitation / temperature at meteorological stations / wind speed and other related data indicators, select appropriate indicators to establish a disaster damage assessment model; because the data integrated into the model is relatively single, the representativeness is poor, and the d...

AI Technical Summary

Problems solved by technology

Specifically, it provides a complete and effective method for disaster monitoring and damage determination, which effectively solves the problem that traditional crop disaster monitoring obtains some weather related parameters through weather stations. The spatial distribution of weather stations is uneven and the timeliness is poor. The problems of inaccurate accuracy and unreliable experimental data caused by the constraints of external factors such as , material resources, etc.

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