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  • Küçük Resim
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    Diurnal surface fuel moisture prediction model for Calabrian pine stands in Turkey
    (SISEF - Italian Society of Silviculture and Forest Ecology, 2019) Bilgili, Ertuğrul; Çoşkuner, Kadir Alperen; Usta, Yetkin; Sağlam, Bülent; Küçük, Ömer; Berber, Tolga; Göltaş, Merih
    This study presents a dynamic model for the prediction of diurnal changes in the moisture content of dead surface fuels in normally stocked Calabrian pine stands under varying weather conditions. The model was developed based on several empirical relationships between moisture contents of dead surface fuels and weather variables, and calibrated using field data collected from three Calabrian stands from three different regions of Turkey (Mugla, southwest; Antalya, south; Trabzon, north-east). The model was tested and validated with independent measurements of fuel moisture from two sets of field observations made during dry and rainy periods. Model predictions showed a mean absolute error (MAE) of 1.19% for litter and 0.90% for duff at Mugla, and 3.62% for litter and 14.38% for duff at Antalya. When two rainy periods were excluded from the analysis at Antalya site, the MAE decreased from 14.38% to 4.29% and R-2 increased from 0.25 to 0.83 for duff fuels. Graphical inspection and statistical validation of the model indicated that the diurnal litter and duff moisture dynamics could be predicted reasonably. The model can easily be adapted for other similar fuel types in the Mediterranean region.
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    Estimating Mediterranean stand fuel characteristics using handheld mobile laser scanning technology
    (CSIRO, 2023) Coşkuner, Kadir Alperen; Vatandaşlar, Can; Öztürk, Murat; Harman, İsmet; Bilgili, Ertuğrul; Karahalil, Uzay; Berber, Tolga; Tunç Görmüs, Esra
    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.
  • Küçük Resim
    Öğe
    Preliminary results of canopy fuel load estimation using mobile laser scanning in Turkish red pine stands
    (Springer Science and Business Media Deutschland GmbH, 2025) Coşkuner, Kadir Alperen; Vatandaşlar, Can; Özturk, Murat; Harman, İsmet; Karahalil, Uzay; Berber, Tolga; Görmüş, Esra Tunç; Bilgili, Ertugrul
    There is a growing interest in stand fuel inventories through the use of light detection and ranging (LiDAR) technology among fire researchers. In this study, canopy fuel load of Turkish red pine (Pinus brutia Ten.) stands was estimated based on point-cloud data collected from five sample plots using a mobile laser scanner (MLS). For this, stand fuel characteristics (i.e., dbh and number of trees) and fuel load models were used in combination. The estimation results were stand fuel characteristics were compared against the ground truth obtained through traditional field measurements. Preliminary results indicated that MLS was able to capture dbh and the number of trees information at the plot level. Thus, it is possible to estimate canopy fuel loads in Turkish red pine relying on species-specific models that utilize MLS data as input. Further research is needed for automating the point-cloud data analysis workflow and more accurate characterization of surface fuel in Mediterranean forests.