PREDICTING CANOPY FUEL CHARACTERISTICS IN Pinus brutia Ten., Pinus nigra Arnold AND Pinus pinaster Ait. FORESTS FROM STAND VARIABLES IN NORTH-WESTERN TURKEY

Küçük, Ömer; Goltas, Merih; Demirel, Tufan; Mitsopoulos, Ioannis; Bılgılı, Ertuğrul

doi:10.30638/eemj.2021.031

Cited by 5 publications

(1 citation statement)

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“…In 2021, 2.793 wildfires occurred in Turkiye, and approximately 139.503 ha of forest area was burnt (GDF, 2021). Although the Mediterranean regions of Turkiye are more sensitive to forest fires, the West Black Sea region of Turkiye has also become one of the important fire-prone regions in recent years (Küçük & Ünal, 2005;Küçük et al, 2017;Mitsopoulos et al, 2017;Kucuk et al, 2018;Yavuz et al 2018;Kucuk et al, 2021). In 2020, a big forest fire occurred in the Western Black Sea region of Turkiye and burned almost 1300 ha of forest area in Kastamonu -Taşköprü Sub-District Forest Directorate.…”

Section: Introductionmentioning

confidence: 99%

Burn severity evaluation in black pine forests with topographical factors using Sentinel-2 in Kastamonu, Turkiye

Genç,

Küçük,

Keleş

et al. 2023

CERNE

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Background: Forest fires are one of the most important natural disasters all over the world in terms of the damage they cause to the ecosystem. Turkey is also exposed to wildfires damaging large areas of forests every year. Wildfires endanger the sustainability of forest resources and cause significant biological and ecological damage to the forests. It is crucial to estimate fire behaviour characteristics to take effective interventions in the fire events. Obtaining data based on terrestrial measurements is very expensive and very time-consuming to predict fire behaviour characteristics. The use of remote sensing technologies is therefore very useful since the satellite images will be faster, more sensitive, and economical to determine the burning severity and burning areas.Results: In this study, the forest fire that occurred in the Kastamonu-Taşköprü district was analyzed with remote sensing techniques. The fire occurred in 2020 (06.09.2020). The fire satellite image was thus taken from the pre-fire period (26.08.2020) and post-fire period (13.10.2020). First of all, prefire and post-fire Sentinel-2 images of fire areas were used to determine the burned area using NBR (Normalized Burn Ratio) and dNBR (Differenced Normalized Burn Ratio) indices. Also, burned area rate and burn severity were evaluated depending on the altitude, aspect, and slope factors. Conclusion:The total burned area was 1504.9 ha. The maps showed that the burned areas were covered by moderate-and high-severity classes. The forest fire was more severe in the altitude range from 1170 to 1370m, at 20-33% slope and northerly aspects in the burned area. The existence of extreme meteorological factors and the horizontal and vertical continuity of the forest fuels have been the main factors in the effect of the fire on large areas. Estimation of forest fire risk, taking into account extreme meteorological conditions and fuel properties, will have an important place in forest ecology and management.

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Section: Introductionmentioning

confidence: 99%

Burn severity evaluation in black pine forests with topographical factors using Sentinel-2 in Kastamonu, Turkiye

Genç,

Küçük,

Keleş

et al. 2023

CERNE

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Fire behavior prediction with artificial intelligence in thinned black pine (Pinus nigra Arnold) stand

Küçük

Sevinç

2023

Forest Ecology and Management

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Estimating Mediterranean stand fuel characteristics using handheld mobile laser scanning technology

Coskuner,

Vatandaslar,

Ozturk

et al. 2023

Int. J. Wildland Fire

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Background Accurate, timely and easily obtainable information on stand fuel is of great importance in the prediction of fire behaviour. Aims The objective of this study is to measure several stand fuel characteristics with handheld mobile laser scanning (HMLS) in six fuel types for Mediterranean region, and compare the results with traditional field fuel measurements (FFM) in 35 different sampling plots. Methods The measurements involved overstorey (the number of trees, diameter at breast height, crown base height, tree height, maximum tree height, stand crown closure) and understorey (understorey closure, understorey height) fuel characteristics, and ground slope. Correlation analysis and t-test were performed to examine the relationship between FFM and HMLS datasets. In addition, cross-validation statistics (RMSE, rRMSE and R2) were employed to evaluate the accuracy of the HMLS method. Key results The results indicated strong correlations among all fuel characteristics. However, overstorey fuel characteristics were more favourable (r-values between 0.804 and 0.996, P < 0.01) than understorey (r-values between 0.483 and 0.612, P < 0.01). There was no significant difference between FFM and HMLS datasets in all fuel characteristics (P > 0.05). Conclusions The results indicated that the HMLS was practical, cost-effective, time-efficient and required less labour as compared to traditional FFM in plot-level (i.e. 0.1 ha) inventories.

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PREDICTING CANOPY FUEL CHARACTERISTICS IN Pinus brutia Ten., Pinus nigra Arnold AND Pinus pinaster Ait. FORESTS FROM STAND VARIABLES IN NORTH-WESTERN TURKEY

Cited by 5 publications

References 0 publications

Burn severity evaluation in black pine forests with topographical factors using Sentinel-2 in Kastamonu, Turkiye

Burn severity evaluation in black pine forests with topographical factors using Sentinel-2 in Kastamonu, Turkiye

Fire behavior prediction with artificial intelligence in thinned black pine (Pinus nigra Arnold) stand

Estimating Mediterranean stand fuel characteristics using handheld mobile laser scanning technology

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