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

被引:0
|
作者
Yurtgan, Mehmet [1 ]
Baysal, Ismail [2 ]
Kucuk, Omer [3 ]
机构
[1] Turkish Republ Minist Agr & Forestry Affairs, Gen Directorate Nat Conservat & Natl Pk, TR-54000 Sakarya, Turkey
[2] Izmir Katip Celebi Univ, Fac Forestry, TR-35620 Izmir, Turkey
[3] Kastamonu Univ, Fac Forestry, TR-61080 Kastamonu, Turkey
关键词
Surface Fuel; Dead Crown Fuel; Live Crown Fuel; Non-treated; Pi-nus brutia; T?rkiye; BIOMASS EQUATIONS; FIRE MANAGEMENT; FOREST; STANDS; TREES;
D O I
10.3832/ifor4048-015[online2022-11-03]
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
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.
引用
收藏
页码:458 / 464
页数:7
相关论文
共 39 条
  • [31] Deadwood volume assessment in Calabrian pine (Pinus brutia Ten.) peri-urban forests: Comparison between two sampling methods
    De Meo, Isabella
    Agnelli, Alessandro Elio
    Graziani, Anna
    Kitikidou, Kyriaki
    Lagomarsino, Alessandra
    Milios, Elias
    Radoglou, Kalliopi
    Paletto, Alessandro
    JOURNAL OF SUSTAINABLE FORESTRY, 2017, 36 (07) : 666 - 686
  • [32] TECHNICAL AND ECONOMICAL EVALUATIONS OF CALABRIAN PINE (PINUS BRUTIA TEN.) SEMI-ARID PLANTATIONS IN THE SANLIURFA-HARRAN PLAIN OF TURKEY
    Dasdemir, I
    Ozel, H-B
    Kaya, H.
    APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2019, 17 (02): : 1757 - 1772
  • [33] Prediction of Bending Properties for Turkish Red Pine (Pinus brutia Ten.) Lumber using Stress Wave Method
    Guntekin, Ergun
    Emiroglu, Zeynep Gozde
    Yilmaz, Tugba
    BIORESOURCES, 2013, 8 (01): : 231 - 237
  • [34] Analyzing deadwood volume of Calabrian pine (Pinus brutia Ten.) in relation to stand and site parameters: a case study in Koprulu Canyon National Park
    Karahalil, Uzay
    Baskent, Emin Z.
    Sivrikaya, Fatih
    Kilic, Burak
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2017, 189 (03)
  • [35] Analyzing deadwood volume of Calabrian pine (Pinus brutia Ten.) in relation to stand and site parameters: a case study in Köprülü Canyon National Park
    Uzay Karahalil
    Emin Z. Başkent
    Fatih Sivrikaya
    Burak Kılıç
    Environmental Monitoring and Assessment, 2017, 189
  • [36] PREDICTING CANOPY FUEL CHARACTERISTICS IN Pinus brutia Ten., Pinus nigra Arnold AND Pinus pinaster Ait. FORESTS FROM STAND VARIABLES IN NORTH-WESTERN TURKEY
    Kucuk, Omer
    Goltas, Merih
    Demirel, Tufan
    Mitsopoulos, Ioannis
    Bilgili, Ertugrul
    ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, 2021, 20 (02): : 309 - 318
  • [37] Nonlinear Quantile Mixed-Effects Models for Prediction of the Maximum Crown Width of Fagus sylvatica L., Pinus nigra Arn. and Pinus brutia Ten.
    Raptis, Dimitrios, I
    Kazana, Vassiliki
    Kechagioglou, Stavros
    Kazaklis, Angelos
    Stamatiou, Christos
    Papadopoulou, Dimitra
    Tsitsoni, Thekla
    FORESTS, 2022, 13 (04):
  • [38] COMPARISON OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM, ARTIFICIAL NEURAL NETWORKS AND NON-LINEAR REGRESSION FOR BARK VOLUME ESTIMATION IN BRUTIAN PINE (PINUS BRUTIA TEN.)
    Catal, Y.
    Saplioglu, K.
    APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, 2018, 16 (02): : 2015 - 2027
  • [39] Crown-Level Structure and Fuel Load Characterization from Airborne and Terrestrial Laser Scanning in a Longleaf Pine (Pinus palustris Mill.) Forest Ecosystem
    Rocha, Kleydson Diego
    Silva, Carlos Alberto
    Cosenza, Diogo N.
    Mohan, Midhun
    Klauberg, Carine
    Schlickmann, Monique Bohora
    Xia, Jinyi
    Leite, Rodrigo, V
    Alves de Almeida, Danilo Roberti
    Atkins, Jeff W.
    Cardil, Adrian
    Rowell, Eric
    Parsons, Russ
    Sanchez-Lopez, Nuria
    Prichard, Susan J.
    Hudak, Andrew T.
    REMOTE SENSING, 2023, 15 (04)