Evaluation of Four Satellite-Derived Fire Products in the Fire-Prone, Cloudy, and Mountainous Area Over Subtropical China

In the absence of historical fire records, end users intend to adopt free satellite-derived fire products, including global burn area (BA) and active fire (AF) products, to understand the historical fire dynamics for better forest management. Previous literature evaluated the accuracy of these fire...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Jiao, Miao, Quan, Xingwen, Yao, Jinsong
Format: Article
Language:English
Subjects:
Algorithms
Artificial satellites
Climate change
Cloudy and mountainous area
Clustering
Detection
Earth
End users
fire
Fire detection
Fire Disturbance Climate Change Initiative (Fire_CCI51)
Fires
Forest management
Forestry
Imaging radiometers
Imaging techniques
Infrared imaging
Infrared radiometers
Landsat
Landsat satellites
MCD14ML
MCD64A1
Measurement
MODIS
Mountain regions
Mountainous areas
Mountains
Performance evaluation
Radiometers
Radiometry
Random sampling
Records
Remote sensing
Satellites
Spectroradiometers
Statistical sampling
VNP14DL
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Summary:In the absence of historical fire records, end users intend to adopt free satellite-derived fire products, including global burn area (BA) and active fire (AF) products, to understand the historical fire dynamics for better forest management. Previous literature evaluated the accuracy of these fire products in regions with different environments, but no study evaluated the performance of these fire products in fire-prone, cloudy, and mountainous areas. This study contributed to filling this gap, through the first evaluation of four broadly used fire products: Moderate Resolution Imaging Spectroradiometer (MODIS)-based MCD64A1, MCD14ML, Visible Infrared Imaging Radiometer Suite (VIIRS)-based VNP14DLIMGTDL_NRT (hereafter simplified as VNP14DL), and European Space Agency (ESA) Fire Disturbance Climate Change Initiative (Fire_CCI51) over subtropical China. Two methods were applied to this end: the spatiotemporal clustering algorithm based on official historical fire records and the density-based random sampling and estimation method from the Landsat 8 fire scenes. The results show that both the AF and BA products show poor fire detection ability in this area (with all the F1 -Score < 0.5). Among them, the VNP14DL performed best, followed by MCD14ML, Fire_CCI51, and MCD64A1. The MCD14ML had the best detection capability for small fires (
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3188259