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

Zhang, Zili; Yu, Hongbin; Hu, Zunying; Zhou, Bin; Han, Meng

doi:10.1109/icbbe.2010.5516543

Cited by 1 publication

(1 citation statement)

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“…With respect to human factors, the distance between the power line and cropland, as well as the distance between the power line and man-made structures, were selected as input variables. These variables were selected because sometimes crop straws are burned after harvest [ 27 ], and many sacrificial paper and firecrackers are usually burned around man-made structures during some traditional Chinese festivals, which can lead to wildfire occurrence in high-voltage PLCs. Since the Chinese government has banned the field burning of crop straws and firecrackers in recent years, contributions of the above variables to wildfire occurrence have been reduced slightly.…”

Section: Description Of Developed Modelmentioning

confidence: 99%

A Model Design for Risk Assessment of Line Tripping Caused by Wildfires

Shi

Yao

Wang

et al. 2018

Sensors

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A power line is particularly vulnerable to wildfires in its vicinity, and various damage including line tripping can be caused by wildfires. Using remote sensing techniques, a novel model developed to assess the risk of line tripping caused by the wildfire occurrence in high-voltage power line corridors is presented. This model mainly contains the wildfire risk assessment for power line corridors and the estimation of the probability of line tripping when a wildfire occurs in power line corridors. For the wildfire risk assessment, high-resolution satellite data, Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological data, and digital elevation model (DEM) data were employed to infer the natural factors. Human factors were also included to achieve good reliability. In the estimation of the probability of line tripping, vegetation characteristics, meteorological status, topographic conditions, and transmission line parameters were chosen as influencing factors. According to the above input variables and observed historical datasets, the risk levels for wildfire occurrence and line tripping were obtained with a logic regression approach. The experimental results demonstrate that the developed model can provide good results in predicting wildfire occurrence and line tripping for high-voltage power line corridors.

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Section: Description Of Developed Modelmentioning

confidence: 99%