A Contextual and Multitemporal Active-Fire Detection Algorithm Based on FengYun-2G S-VISSR Data

Abstract

Wildfires are one of the most destructive disasters on the planet. They also significantly impact the land surface. Satellite data have been widely used to detect the outbreak and monitor the expansion of fire incidents for damage assessment and disaster management. Polar-orbiting satellite data have been used for several decades but data from geostationary satellites, which can provide observations with a high temporal resolution, have received much less attention. This paper utilizes data from FengYun-2G, a Chinese geostationary satellite, to detect wildfires in two selected research regions in January 2016. The detection algorithm systemizes image-based analysis to filter out obvious nonfire pixels and temporal analysis to confirm the true detections. Fire detection is based on comparisons between predicted and observed values. The results show that the proposed method has some advantages compared with the use of polar-orbiting satellite data, including early detection and continuous observation. The validation work is conducted based on the collection 6.1 Global Monthly Fire Location Product generated from fire detections by Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The average accuracy within the target time is 56%, while the omission error rate is over 78%. In detail, the algorithm has a lower omission error rate in Australia while it fails in detecting most of the fire pixels in India. The dominance of small fire incidents, as well as low spatial resolution greatly limit the detection ability. Many small fires were beyond the ability of Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) data when no significant fire characteristics could be captured. Future development of the algorithm will focus on improving the results by enhancing the adaption to different regions, as well as, including multisource data sets.

Building scientific capacity in disaster risk reduction for sustainable development

“…For example, the approach to large-scale floods at each stage of the disaster management cycle has benefited greatly from Earth observation data, through improving numerical weather predictions, addressing data gaps and detecting surface water extent and heights (Alfieri et al, 2018). Similarly, several drought indices have been developed using Earth observation data (Aitekeyeva et al, 2020), while fire-risk estimation has made extensive use of spatial and temporal Earth observation data, including by deriving information on meteorological parameters and developing new techniques to detect burned areas (Shan et al, 2017) and active fires (Lin et al, 2017, 2018, 2019). Earth observation data has also been used to develop proxies for monitoring aspects of built-up, economic, social and natural environments in urban settlements to inform disaster risk management (Ghaffarian et al, 2018).…”

“…Similarly, several drought indices have been developed using Earth observation data (Aitekeyeva et al, 2020), while fire-risk estimation has made extensive use of spatial and temporal Earth observation data, including by deriving information on meteorological parameters and developing new techniques to detect burned areas (Shan et al, 2017) and active fires (Lin et al, 2017(Lin et al, , 2018(Lin et al, , 2019. Earth observation data has also been used to develop proxies for monitoring aspects of built-up, economic, social and natural environments in urban settlements to inform disaster risk management (Ghaffarian et al, 2018).…”

Accuracy Assessment of Fy-4a Fire/Hotspot (Fhs) Product in Wildfire Detection

“…Previous fire detection algorithm studies of Z. Lin et al (2019) used a midinfrared band of 3.4-4.0μm from the FY-2G data. This band range is derived from both the incident radiation information of the sun and the emission information of the surface and clouds where radiation energy varies greatly (S. S. Chu et al, 2019).…”

“…A few authors have developed fire or hotspot algorithms for the Fengyun satellites Lin, et al (2019),. compared the fire algorithm developed for the FY-2G Stretched Visible and Infrared Spin Scan Radiometer (S-VISSR) with the fire detection from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA Earth Observing System satellites Terra and Aqua.…”

Modeling Conditions Appropriate for Wildfire in South East China – A Machine Learning Approach

“…Most of the forested ecosystem of the world face fire risk as a critical natural disturbance (Chowdhury and Hassan, 2015;Lin et al, 2018Lin et al, , 2019Hansen et al, 2020). Wildfires are a formidable force that may cover thousands of acres and burn for many days (Coen and Schroeder, 2013).…”