A Judgment Method of Grassland Burning Fire Point Data Based on Remote Sensing Image

Abstract

The invention provides a method for judging grassland burning fire point data based on remote sensing images. The grassland fire point prediction model of the method is obtained by training a large amount of historical fire point remote sensing data, so judging grassland burning fire point by this model will not Affected by data changes caused by changes in hardware and weather, further combining with the grassland fire monitoring model established through the current remote sensing data, it can be judged whether the current environment of the area meets the conditions for fire occurrence, and comprehensively judge the fire point of grassland combustion, which greatly improves the The instability of the original judging method is reduced, the accuracy of judging fire point of pasture combustion is improved, and a reliable basis is provided for strengthening fire point monitoring of pasture.

Description

technical field [0001] The invention relates to the technical field of grassland fire monitoring and remote sensing, in particular to a method for judging fire point data of grassland combustion based on remote sensing images. Background technique [0002] The occurrence of grassland fires is generally accidental, and the place of occurrence is generally a relatively remote grassland, and there are many influencing factors, and it is difficult to prevent. In recent years, with global warming, extreme weather has occurred frequently, and the northwestern region of my country has become more and more prone to continuous high temperature and sunny weather. At the same time, due to the economic development and the further growth of animal husbandry, the grassland vegetation remains more and more intact. Therefore, grassland fires occur frequently. [0003] With the advancement of science and technology, especially since the early 1980s, the rapid development of information tech...

AI Technical Summary

Problems solved by technology

At present, the remote sensing monitoring of pasture fires is developing rapidly and the models are relatively complete. However, some sensors for obtaining remote sensing data are gradually aging, and the data sources are not stable. When in use, due to inaccurate satellite hardware and large changes in weather conditions during transit, it is easy to produce multiple judgments and misjudgments; at present, the defects of the usual fire point judgment algorithm itself will also lead to misjudgment of grassland burning fire point data

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