Estimation of Crown Fuel Load of Suppressed Trees in  Non-treated Young Calabrian Pine (Pinus brutia Ten.) Plantation Areas

Baysal, İsmail; Yurtgan, Mehmet; Küçük, Ömer; Öztürk, Nuray

doi:10.17475/kastorman.662733

Cited by 6 publications

(4 citation statements)

References 37 publications

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“…Information on characteristics and the vertical distribution of crown fuels is essential in fire behavior and fuel management studies. Some investigations have already been carried out to determine crown fuels or biomass for coniferous natural forest (Stocks 1980, Ter-Mikaelian & Korzukhin 1997 and plantations (Forrest & Ovington 1970, Ritson & Sochacki 2003, Koukoulomatis & Mitsopoulos 2007, Baysal et al 2019). However, dead fuels or ladder fuels on trees have been rarely investigated (Mitsopoulos & Dimitrakopoulos 2007, Ruiz-González & Alvarez-González 2011, Wang & Niu 2016 especially in planted and nontreated (unthinned) conifer stands (Williams 1976, Chen & Li 2010.…”

Section: Introductionmentioning

confidence: 99%

“…However, dead fuels or ladder fuels on trees have been rarely investigated (Mitsopoulos & Dimitrakopoulos 2007, Ruiz-González & Alvarez-González 2011, Wang & Niu 2016 especially in planted and nontreated (unthinned) conifer stands (Williams 1976, Chen & Li 2010. Other studies assessed the amount and properties of crown fuels of the broadest fuel types composed of Calabrian pine , Bilgili & Küçük 2009, Güngöroglu et al 2018, Baysal et al 2019, Baysal 2021 and Anatolian Black pine (Pinus nigra J.F. Arnold - Küçük et al 2007a in Turkey.…”

Section: Introductionmentioning

confidence: 99%

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Fuel characterization and crown fuel load prediction in non-treated Calabrian pine (Pinus brutia Ten.) plantation areas

Yurtgan¹,

Baysal²,

Küçük³

2022

iForest

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Successful management of young, fire-prone Calabrian pine forests requires an accurate characterization of surface and canopy fuel loads at stand level. This study characterizes the surface and canopy fuel characteristics in unthinned Calabrian pine plantations in Turkey. Fifteen sample plots were measured to determine the surface and crown fuel characteristics of very young, young and middle aged Calabrian pine stands (10 to 28 years old). Thirty-six trees were destructively sampled to quantify the crown fuel loads and canopy fuel characteristics of the stands. Surface fuel load ranged from 11.38 t ha -1 in the young stands to 35.27 t ha -1 in the middle aged stands. Dead fuel load as ladder fuels on the trees ranged from 0.77 kg in very young stands to 13.56 kg in the young stands. Live fuel loads on the trees ranged from 0.77 kg to 23.29 kg in the young aged stands. Total active crown fuel load was 58.7%, 52.1% and 49.5% of total crown fuel load in very young, young and middle aged stands, respectively. Our results improve the current crown fuel model predictions and showed the importance of dead fuel load in fire management studies both for the determination of crown fuel loads and the calculation of carbon stocks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuel characterization and crown fuel load prediction in non-treated Calabrian pine (Pinus brutia Ten.) plantation areas

Yurtgan¹,

Baysal²,

Küçük³

2022

iForest

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“…The canopy fuel load commonly includes foliage (including needles and leaves), branch wood, and other suspended biomass such as lichens and mosses [11]. The foliage fuel load (FFL, referring to the dry weight of needles and leaves per unit area) and branch fuel load (BFL, referring to the dry weight of branches per unit area) are the main energy source supporting the spread of crown fire [12][13][14][15][16][17]. Stem fuel load (SFL, referring to the dry weight of stems per unit area) can interact with fires and help carry fires although it is not a substantial loss of biomass [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data

2022

Remote Sensing

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Forest fuel load is the key factor for fire risk assessment, firefighting, and carbon emissions estimation. Remote sensing technology has distinct advantages in fuel load estimation due to its sensitivity to biomass and adequate spatiotemporal observations for large scales. Many related works applied empirical methods with individual satellite observation data to estimate fuel load, which is highly conditioned on local data and limited by saturation problems. Here, we combined optical data (i.e., Landsat 7 ETM+) and spaceborne Synthetic Aperture Radar (SAR) data (i.e., ALOS PALSAR) in a proposed semi-empirical retrieval model to estimate above-ground live forest fuel loads (FLAGL). Specifically, optical data was introduced into water cloud model (WCM) to compensate for vegetation coverage information. For comparison, we also evaluated the performance of single spaceborne L-band SAR data (i.e., ALOS PALSAR) in fuel load estimation with common WCM. The above two comparison experiments were both validated by field measurements (i.e., BioSAR-2008) and leave-one-out cross-validation (LOOCV) method. WCM with single SAR data could achieve reasonable performance (R2 = 0.64 or higher and RMSEr = 35.3% or lower) but occurred an underestimation problem especially in dense forests. The proposed method performed better with R2 increased by 0.05–0.13 and RMSEr decreased by 5.8–12.9%. We also found that the underestimation problem (i.e., saturation problem) was alleviated even when vegetation coverage reached 65% or the total FLAGL reached about 183 Tons/ha. We demonstrated our FLAGL estimation method by validation in an open-access dataset in Sweden.

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“…Fuel consumption indicates the amount of vegetative biomass consumed during wildland fire (Ottmar, 2014). Fine fuels (needles and branches <0.6 cm in diameter) are considered available fuels (Fernández-Alonso et al, 2013;Küçük & Bilgili, 2007;Küçük et al, 2009;Mitsopoulos & Dimitrakopoulos, 2007) that are consumed in a crown fire (Baysal et al, 2019;Scott & Reinhardt, 2002;Stocks et al, 2004). Fuel consumption can be directly estimated in the field or predicted using models (de Groot et al, 2007;Ottmar, 2014) or satellite data (Andela et al, 2016;Ruecker et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Yangın Şiddetinin Uzaktan Algılama ve Coğrafi Bilgi Sistemleri ile Hesaplanması: 2021 Yılı Milas-Karacahisar Yangını

Coşkuner

Bılgılı

2022

Kastamonu Üniversitesi Orman Fakültesi Dergisi

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Aim of the study: The objective of this study is to calculate fireline intensity using remote sensing and geographic information systems, to investigate relationship between fireline intensity and VIIRS active fire data, and to develop a practical fireline intensity estimation model. Material and methods: The Visible Infrared Imaging Radiometer Suite (VIIRS) active fire/hotspot data provided by Suomi National Polar Orbiting Partnership (S-NPP) and National Oceanic and Atmospheric Administration (NOAA-20) satellites were used to estimate the rate of fire spread. Fuel consumption was estimated using Sentinel-2 images, stand type maps and surface and available crown fuel loading models for Turkish red pine (Pinus brutia Ten.). The fireline intensity was then calculated using Byram’s (1959) fireline intensity equation. Main results: The results indicated that the number of VIIRS active fire data was well correlated with fireline intensity, rate of fire spread and fuel consumption. The calculated fireline intensity ranged between 175.0 and 47597.2 kW/m with an average value of 9280.4 kW/m. The number of VIIRS active fire data alone explained 72% of the variation in fireline intensity. Highlights: Satellite based products can be effectively used to calculate fireline intensity through estimating rate of fire spread and fuel consumption easily and effectively in burned areas.

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Estimation of Crown Fuel Load of Suppressed Trees in Non-treated Young Calabrian Pine (Pinus brutia Ten.) Plantation Areas

Cited by 6 publications

References 37 publications

Fuel characterization and crown fuel load prediction in non-treated Calabrian pine (Pinus brutia Ten.) plantation areas

Fuel characterization and crown fuel load prediction in non-treated Calabrian pine (Pinus brutia Ten.) plantation areas

A Semi-Empirical Retrieval Method of Above-Ground Live Forest Fuel Loads by Combining SAR and Optical Data

Yangın Şiddetinin Uzaktan Algılama ve Coğrafi Bilgi Sistemleri ile Hesaplanması: 2021 Yılı Milas-Karacahisar Yangını

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