Single bands leaf reflectance prediction based on fuel moisture content for forestry applications

Arevalo-Ramirez, Tito; Fuentes, A.; Cabello, Pedro Sebastián Reszka; Cheein, Fernando Auat

doi:10.1016/j.biosystemseng.2020.12.003

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Fuel mapping of the study area is another challenging aspect, as it needs territorial division in terms of plant species, forest type (native or plantation), canopy cover, stand height, fuel load, etc. In Chile, CONAF has formulated a land classification in terms of these variables, but if this information is unavailable, satellite imagery can help to match actual vegetation with standard fuel models (Aragoneses and Chuvieco 2021), a procedure that can be assisted by relating vegetation spectral indices to wildland fuel properties (Villacrés et al 2019;Arevalo-Ramirez et al 2021). Simplifying the fuel distribution as done in this case study is also recommended, as it does not produce significant variation in the results.…”

Section: Considerations On Data and Modellingmentioning

confidence: 99%

Assessing wildfire risk to critical infrastructure in central Chile: application to an electrical substation

Severino,

Fuentes,

Valdivia

et al. 2024

Int. J. Wildland Fire

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Background Wildfires have caused significant damage in Chile, with critical infrastructure being vulnerable to extreme wildfires. Aim This work describes a methodology for estimating wildfire risk that was applied to an electrical substation in the wildland–urban interface (WUI) of Valparaíso, Chile. Methods Wildfire risk is defined as the product between the probability of a wildfire reaching infrastructure at the WUI and its consequences or impacts. The former is determined with event trees combined with modelled burn probability. Wildfire consequence is considered as the ignition probability of a proxy fuel within the substation, as a function of the incident heat flux using a probit expression derived from experimental data. The heat flux is estimated using modelled fire intensity and geometry and a corresponding view factor from an assumed solid flame. Key results The probability of normal and extreme fires reaching the WUI is of the order of 10−4 and 10−6 events/year, respectively. Total wildfire risk is of the order of 10−5 to 10−4 events/year Conclusions This methodology offers a comprehensive interpretation of wildfire risk that considers both wildfire likelihood and consequences. Implications The methodology is an interesting tool for quantitatively assessing wildfire risk of critical infrastructure and risk mitigation measures.

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Section: Considerations On Data and Modellingmentioning

confidence: 99%

Assessing wildfire risk to critical infrastructure in central Chile: application to an electrical substation

Severino,

Fuentes,

Valdivia

et al. 2024

Int. J. Wildland Fire

Self Cite

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Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies

Chakhvashvili,

Machwitz,

Antala

et al. 2024

Precision Agric

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Introduction Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.

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Construction of 3D maps of vegetation indices retrieved from UAV multispectral imagery in forested areas

Villacrés

Cheein

2022

Biosystems Engineering

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Single bands leaf reflectance prediction based on fuel moisture content for forestry applications

Cited by 5 publications

References 39 publications

Assessing wildfire risk to critical infrastructure in central Chile: application to an electrical substation

Assessing wildfire risk to critical infrastructure in central Chile: application to an electrical substation

Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies

Construction of 3D maps of vegetation indices retrieved from UAV multispectral imagery in forested areas

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