Short-term fire risk: foliage moisture content estimation from satellite data

1999 ◽  
pp. 17-38 ◽  
Author(s):  
Emilio Chuvieco ◽  
Michel Deshayes ◽  
Nicholas Stach ◽  
David Cocero ◽  
David Riaño
Keyword(s):  
Moisture Content ◽  
Satellite Data ◽  
Fire Risk ◽  
Short Term

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Biodegradable Flame Retardants for Biodegradable Polymer

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1038
Author(s):  
Muhammad Maqsood ◽  
Gunnar Seide
Keyword(s):  
Renewable Resources ◽  
Flame Retardants ◽  
Fire Risk ◽  
Polymeric Materials ◽  
Good Choice ◽  
Airborne Particle ◽  
Short Term ◽  
Renewable Materials ◽  
Long Time ◽  
Significant Attention

To improve sustainability of polymers and to reduce carbon footprint, polymers from renewable resources are given significant attention due to the developing concern over environmental protection. The renewable materials are progressively used in many technical applications instead of short-term-use products. However, among other applications, the flame retardancy of such polymers needs to be improved for technical applications due to potential fire risk and their involvement in our daily life. To overcome this potential risk, various flame retardants (FRs) compounds based on conventional and non-conventional approaches such as inorganic FRs, nitrogen-based FRs, halogenated FRs and nanofillers were synthesized. However, most of the conventional FRs are non-biodegradable and if disposed in the landfill, microorganisms in the soil or water cannot degrade them. Hence, they remain in the environment for long time and may find their way not only in the food chain but can also easily attach to any airborne particle and can travel distances and may end up in freshwater, food products, ecosystems, or even can be inhaled if they are present in the air. Furthermore, it is not a good choice to use non-biodegradable FRs in biodegradable polymers such as polylactic acid (PLA). Therefore, the goal of this review paper is to promote the use of biodegradable and bio-based compounds for flame retardants used in polymeric materials.

scholarly journals Post-Wildfire Logging Hinders Regeneration and Increases Fire Risk

Science ◽  
2006 ◽  
Vol 311 (5759) ◽  
pp. 352-352 ◽  
Author(s):  
D. C. Donato ◽  
J. B. Fontaine ◽  
J. L. Campbell ◽  
W. D. Robinson ◽  
J. B. Kauffman ◽  
...  
Keyword(s):  
Fire Risk ◽  
Fuel Reduction ◽  
Short Term ◽  
High Severity ◽  
Fuel Loads ◽  
Conifer Regeneration ◽  
Biscuit Fire ◽  
Additional Reduction

We present data from a study of early conifer regeneration and fuel loads after the 2002 Biscuit Fire, Oregon, USA, with and without postfire logging. Natural conifer regeneration was abundant after the high-severity fire. Postfire logging reduced median regeneration density by 71%, significantly increased downed woody fuels, and thus increased short-term fire risk. Additional reduction of fuels is necessary for effective mitigation of fire risk. Postfire logging can be counterproductive to the goals of forest regenration and fuel reduction.

scholarly journals Prediction of Solar Power Using Near-Real Time Satellite Data

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5865
Author(s):  
Abhnil Amtesh Prasad ◽  
Merlinde Kay
Keyword(s):  
Real Time ◽  
Satellite Data ◽  
Solar Power ◽  
Short Term ◽  
Motion Vectors ◽  
Clear Sky ◽  
Cloud Motion ◽  
Visible Images ◽  
Grid Operators ◽  
Short Term Prediction

Solar energy production is affected by the attenuation of incoming irradiance from underlying clouds. Often, improvements in the short-term predictability of irradiance using satellite irradiance models can assist grid operators in managing intermittent solar-generated electricity. In this paper, we develop and test a satellite irradiance model with short-term prediction capabilities using cloud motion vectors. Near-real time visible images from Himawari-8 satellite are used to derive cloud motion vectors using optical flow estimation techniques. The cloud motion vectors are used for the advection of pixels at future time horizons for predictions of irradiance at the surface. Firstly, the pixels are converted to cloud index using the historical satellite data accounting for clear, cloudy and cloud shadow pixels. Secondly, the cloud index is mapped to the clear sky index using a historical fitting function from the respective sites. Thirdly, the predicated all-sky irradiance is derived by scaling the clear sky irradiance with a clear sky index. Finally, a power conversion model trained at each site converts irradiance to power. The prediction of solar power tested at four sites in Australia using a one-month benchmark period with 5 min ahead prediction showed that errors were less than 10% at almost 34–60% of predicted times, decreasing to 18–26% of times under live predictions, but it outperformed persistence by >50% of the days with errors <10% for all sites. Results show that increased latency in satellite images and errors resulting from the conversion of cloud index to irradiance and power can significantly affect the forecasts.

Techniques to Predict Agricultural Droughts

2005 ◽  
Author(s):  
Zekai Şen
Keyword(s):  
Crop Yield ◽  
Satellite Data ◽  
Wheat Yield ◽  
Weather Variables ◽  
Short Term ◽  
Yield Model ◽  
Regression Techniques ◽  
The Difference ◽  
Effects Of Drought

In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, severe, or disastrous, based on the difference between the current yield and the average yield. Regression techniques estimate crop yields using yield-affecting variables. A comprehensive list of possible variables that affect yield is provided in chapter 1. Usually, the weather variables routinely available for a historical period that significantly affect the yield are included in a regression analysis. Regression techniques using weather data during a growing season produce short-term estimates (e.g., Sakamoto, 1978; Idso et al., 1979; Slabbers and Dunin, 1981; Diaz et al., 1983; Cordery and Graham, 1989; Walker, 1989; Toure et al., 1995; Kumar, 1998). Various researchers in different parts of the world (see other chapters) have developed drought indices that can also be included along with the weather variables to estimate crop yield. For example, Boken and Shaykewich (2002) modifed the Western Canada Wheat Yield Model (Walker, 1989) drought index using daily temperature and precipitation data and advanced very high resolution radiometer (AVHRR) satellite data. The modified model improved the predictive power of the wheat yield model significantly. Some satellite data-based variables that can be used to predict crop yield are described in chapters 5, 6, 9, 13, 19, and 28. The short-term estimates are available just before or around harvest time. But many times long-term estimates are required to predict drought for next year, so that long-term planning for dealing with the effects of drought can be initiated in time.

Properties of thermally modified medium-density fibreboards

Holzforschung ◽  
2012 ◽  
Vol 66 (1) ◽  
Author(s):  
Jürgen Bonigut ◽  
Detlef Krug ◽  
Beate Stephani
Keyword(s):  
Thermal Treatment ◽  
Moisture Content ◽  
Equilibrium Moisture Content ◽  
Phenol Formaldehyde ◽  
Creep Behaviour ◽  
Modulus Of Rupture ◽  
Medium Density ◽  
Thickness Swelling ◽  
Short Term ◽  
European Standards

Abstract Thermal treatment of solid timber and oriented strandboards (OSB) improves durability against fungal decay and dimensional stability (swelling and shrinking). It is not clear whether thermal treatment of medium-density fibreboards (MDF) has the same effects. In this work, four variants of phenol-formaldehyde (PF)-bonded MDF with varying contents of resin and hydrophobing agent were thermally post-treated according to the Mühlböck procedure at three different maxi-mum temperatures. The short-term properties internal bond, modulus of rupture, modulus of elasticity, thickness swelling and equilibrium moisture content and the long-term property creep behaviour of treated variants and of one untreated variant have been tested. The results are presented and discussed in comparison with the respective European standards. Altogether, the thermal treatment had a positive effect on most of the tested mechanical short-term properties. The moisture-related properties, i.e., thickness swelling and equilibrium moisture content, were also positively influenced. The creep behaviour of heat-treated MDF could also be improved by thermal modification.

Hétérogénéité d'une lande haute à Ulex europaeus en relation avec la propagation du feu (Bretagne, France)

1998 ◽  
Vol 76 (5) ◽  
pp. 804-817 ◽  
Author(s):  
Christelle Hely ◽  
Françoise Forgeard
Keyword(s):  
Moisture Content ◽  
Fire Risk ◽  
Soil Surface ◽  
Propagation Models ◽  
Heterogeneous Distribution ◽  
Fuel Distribution ◽  
Ulex Europaeus ◽  
Fire Propagation ◽  
Organic Horizons ◽  
Layered Pattern

This study analyzes plant material in a high Ulex europaeus heath to provide information on the partitioning of this ecosystem for fire propagation models. The aboveground biomass, followed for 15 months, has a spatially heterogeneous distribution that is a result of the layered pattern of the various branches. This pattern creates an internal moisture gradient that decreases from the apex to the base of the plant. This gradient also varies according to the species phenology. New, green branches with a high moisture content are at the top of the plant (upper strata), whereas woody branches with a lower moisture content are found near the ground (lower strata). Dry branches and spines, which produce most of the litter, are homogeneously distributed throughout the plant. Temporally, the layered pattern is homogeneous through the year and thus creates a constant fire risk. Soil organic horizons are temporally, spatially, and compositionally heterogeneous. The L layer is always two to three times thinner and drier than the duff layer (F+H). The total depth, weight, and moisture content of the organic horizons vary considerably across both the plot and square metre scales. The distance from a plant has a significant influence on the depth distribution of the soil organic horizons. Fuel distribution on both the soil surface and the plant must be considered to understand fire behaviour in this ecosystem.Key words: architecture, biomass, fire, fuel, humus, moisture content.

scholarly journals Modeling Indoor Relative Humidity and Wood Moisture Content as a Proxy for Wooden Home Fire Risk

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5050 ◽  
Author(s):  
Torgrim Log
Keyword(s):  
Relative Humidity ◽  
Moisture Content ◽  
Moisture Sorption ◽  
Fire Risk ◽  
Cold Weather ◽  
Indoor Climate ◽  
Ambient Conditions ◽  
Wood Panel ◽  
Fire Disaster ◽  
Strong Winds

Severe wooden home conflagrations have previously been linked to the combination of very dry indoor climate in inhabited buildings during winter time, resulting in rapid fire development and strong winds spreading the fire to neighboring structures. Knowledge about how ambient conditions increase the fire risk associated with dry indoor conditions is, however, lacking. In the present work, the moisture content of indoor wooden home wall panels was modeled based on ambient temperature and relative humidity recorded at meteorological stations as the climatic boundary conditions. The model comprises an air change rate based on ambient and indoor (22 °C) temperatures, indoor moisture sources and wood panel moisture sorption processes; it was tested on four selected homes in Norway during the winter of 2015/2016. The results were compared to values recorded by indoor relative humidity sensors in the homes, which ranged from naturally ventilated early 1900s homes to a modern home with balanced ventilation. The modeled indoor relative humidity levels during cold weather agreed well with recorded values to within 3% relative humidity (RH) root mean square deviation, and thus provided reliable information about expected wood panel moisture content. This information was used to assess historic single home fire risk represented by an estimated time to flashover during the studied period. Based on the modelling, it can be concluded that three days in Haugesund, Norway, in January 2016 were associated with very high conflagration risk due to dry indoor wooden materials and strong winds. In the future, the presented methodology may possibly be based on weather forecasts to predict increased conflagration risk a few days ahead. This could then enable proactive emergency responses for improved fire disaster risk management.

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