Satellite Remote Sensing False Forest Fire Hotspot Excavating Based on Time-Series Features

Abstract

Satellite remote sensing has become an important means of forest fire monitoring because it has the advantages of wide coverage, few ground constraints and high dynamics. When utilizing satellites for forest fire hotspot monitoring, two types of ground hotspots, agricultural and other fire hotspots can be ruled out through ground object features. False forest fire hotspots within forested areas must be excluded for a more accurate distinction between forest fires and non-forest fires. This study utilizes spatio-temporal data along with time-series classification to excavate false forest fire hotspots exhibiting temporal characteristics within forested areas and construct a dataset of such false forest fire hotspots, thereby achieving a more realistic forest fire dataset. Taking Hunan Province as the research object, this study takes the satellite ground hotspots in the forests of Hunan Province as the suspected forest fire hotspot dataset and excludes the satellite ground hotspots in the forests such as fixed heat sources, periodic heat sources and recurring heat sources which are excavated. The validity of these methods and results was then analyzed. False forest fire hotspots, from satellite ground hotspots extracted from 2019 to 2023 Himawari-8/9 satellite images, closely resemble the official release of actual forest fires data and the accuracy rate in the actual forest fire monitoring is 95.12%. This validates that the method employed in this study can improve the accuracy of satellite-based forest fire monitoring.

Keywords

• ground object features
• forest fire monitoring
• false forest fire hotspots
• time-series classification scheme
• Hunan province