Spatially Refined Biomass and Combustion Efficiency Estimations in Support of Forest Fires Emissions Quantification

Oliva, Patricia; Duran, Leonardo; Venegas, Alejandro; Vidal, P.; Montoya, Claudia

doi:10.1109/igarss.2019.8899806

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…The binary code is generated through various voltage changes in the alarm device. The addressing fire alarm system uses a mini computer to convert the transmitted binary code [3][4][5]. Addressable fire alarm systems provide more functionality and flexibility than traditional fire alarm systems.…”

Section: Introductionmentioning

confidence: 99%

Smart Fire Detector utilizing IoT-based Zigbee Sensor

Park

Kim

2022

IJEECS

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<pre>There are several differences between the two types of alarm systems, conventional systems and addressable systems. It is important to carefully determine the introduction of a fire alarm system according to the installation environment. Talking about the main difference relates to how the connected device communicates with the main control panel by sending a signal. Cost is another factor that can be a determinant of your chosen fire alarm system. In this paper, we proposed smart addressable fire detection system. In the proposed system, IoT was used and the network was constructed using ZigBee module. In the configured network, it consists of a local server and a control server. The local server controls the addressing sensor and sends the information obtained from the sensor to the control server. The control server receives data transmitted from the local server and enables quick fire action. In the actual implementation, the local server used the Lycra controller and ZigBee module. In addition, the control server used the Raspberry Pi and ZigBee modules and connected to the Ethernet so that the administrator could monitor or control the local server.</pre>

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Section: Introductionmentioning

confidence: 99%

Smart Fire Detector utilizing IoT-based Zigbee Sensor

Park

Kim

2022

IJEECS

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“…These estimates have been obtained from two methods. The first, proposed by Seiler and Crutzen (1980), estimates the emissions by combining the burned area, the biomass available for burning, the consumption efficiency factors, and the emission factors by vegetation type (Oliva et al 2019;Reid et al 2009;Van Der Werf et al 2017). The second method quantifies the biomass burned considering the integration over time of Fire Radiative Power (FRP) to obtain Fire Radiative Energy (FRE) (Vadrevu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Estimation of biomass consumption coefficients for FRP-based forest fires emission calculations

Oliva¹,

Espinosa²,

Briceño³

2022

Advances in Forest Fire Research 2022

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The lengthening of the fire season in Portugal has been understudied, although there is evidence of changes in recent decades. In this brief study, we focused on the annual cycles of fire activity and related meteorological fire danger, thus bypassing the problem of subjective definition of what should mark the beginning and end of the fire season. Based on the daily time series of burned areas and occurrences collected in mainland Portugal by state services from 1980 to 2018, we searched for significant changes in the monthly and daily relative in-year distributions. In particular, an exceedance date method was used to determine day-scale trends in the anticipation/prolongation of fire activity in the year. We found an unequivocal diminishing trend in the proportion of annual burned area that occurs in the summer months (July to September), which was offset by an increase in the fraction from January to June. Apparently, the month of March plays an important role in these changes that should be clarified in future analyses. The daily analysis shows a clear shift of the cumulative curves of burned area to the left, which suggests an in-year anticipation of fire activity. Considering the dates of exceedance of the 15% of annual burned area, the found linear trend implies an anticipation rate of 1.34 days per year. Next, we evaluated seasonal changes in fire weather through the annual cycle of cumulative DSR, computed from daily FWI values. The results show, on average, an earlier increase in DSR when comparing the latter second half of the period (2000-2018) with the first one former (1980-1999). This difference develops from April to the end of September. Interestingly, a closer look shows a first increase in the gap as early as March, which disappears in April before steadily increasing again. Finally, we suggested that up-to-date knowledge of the interannual and interdecadal changes in the seasonality of both wildfires and fire weather is a decisive component, but not the only one, of a comprehensive and complex management of the fire season. A good illustration of this complexity is the fact that the annual definition of the fire season window necessarily affects the annual fire cycle by conditioning the ignition calendar.

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Spatially Refined Biomass and Combustion Efficiency Estimations in Support of Forest Fires Emissions Quantification

Cited by 2 publications

References 13 publications

Smart Fire Detector utilizing IoT-based Zigbee Sensor

Smart Fire Detector utilizing IoT-based Zigbee Sensor

Estimation of biomass consumption coefficients for FRP-based forest fires emission calculations

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