NDLI: Agricultural Burning Detection Using Remote Sensing: A Case Study in Zhejiang Province, China

Content Provider	IEEE Xplore Digital Library
Author	Zili Zhang Haiyan Yu Zunying Hu Bin Zhou Mingchun Han
Copyright Year	2010
Abstract	Random agricultural burning such as crop straw fire, especial during the harvest seasons of summer and autumn, is a very common phenomenon in rural or suburban cities in China. However, several pollutions may caused by this kind of biomass burning including climate-destructive aerosols and carbon fluxes which decrease seriously regional air quality and may have an negative impact on public human health leading to respiratory illness. Using remote sensing is an available method to detect and monitor agricultural burning arbitrarily with its better time and spatial effectiveness. As a part of EOS mission, MODIS was suitable for fire detection as some high-temperature-sensitive bands of the sensor had been improved. A hybrid algorithm presented by the paper had archived two major advancements on adjusting the potential thresholds to suit lower temperature of crop straw burning than forest fires and introducing latest land cover data to exclude fake agriculture burning fire points which not located in croplands. The results had been validated by FIRMS's active fire production suggesting that the modified algorithm works well for agriculture burning under complex land characteristic of eastern China, especially in Zhejiang province. A very large potential for satellite data as an efficient method to detect agriculture burning was also demonstrated in the study.
Starting Page	1
Ending Page	4
File Size	2873169
Page Count	4
File Format	PDF
ISBN	9781424447121
ISSN	21517622
DOI	10.1109/ICBBE.2010.5516543
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fires Crops Carbon dioxide Cities and towns Aerosols Air pollution Agriculture Biomass Remote sensing Remote monitoring
Content Type	Text
Resource Type	Article

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