English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The GF-4 satellite has a moderate spatial resolution and is capable of high temporal frequency observation. Thus, it can play an important role in the acquisition of information regarding fires. In this article, the characteristics of brightness temperature difference between fire point area and nonfire point area are comprehensively analyzed. Employing the advantages of high-frequency continuous observation of these data, an improved algorithm for joint spatial attribute detection using multitemporal data is proposed. This method is based on the traditional contextual method and incorporates the concept of time sequence, and uses the changes in time phase before and after the fire to reduce the false alarm caused by the high heterogeneity of pixels in a single spatial scene in the traditional method. In order to evaluate the accuracy of the algorithm, the perfect moderate resolution imaging spectrometer thermal anomaly product is used as the verification data to evaluate the feasibility of the algorithm. At the same time, it is necessary to compare the algorithm in this article with the fire algorithm currently applied to GF-4 with the four typical regions. The improved algorithm shows better drawing ability, and the consistency of fire description of biomass combustion for many days is improved. In the case of the Heilongjiang region on October 30, 2017, the accuracy rate is improved by 62&#x0025;, compared with the contextual method.

GF-4 Satellite Fire Detection With an Improved Contextual Algorithm