Količine goriva u šumskoj prostirci sastojina hrasta crnike (Quercus ilex L.) i hrasta medunca (Quercus pubescens Willd.)

Nera Bakšić, Darko Bakšić
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Abstract

The application of newer remote sensing methods, such as aerial and terrestrial lidar scanning and the use of "Structure-from-motion" (SfM) photogrammetry, complemented field data collection and enabled 3D mapping of forest fuel layers, greatly simplifying and improving their characterization. However, these methods are not suitable for quantifying forest floor characteristics. For this purpose, it is still necessary to collect data using classical field methods, determining the presence of subhorizons and their depth, while the characteristics of the forest floor: bulk density, load, carbon concentration and carbon stock are determined in the laboratory. Therefore, it is still common practice to create regression equations that allow operatives to determine the amount of available forest floor fuel and the carbon stock it contains based on the depth of the forest floor, which is an easily measurable variable, or to determine forest floor loading by subhorizon and overall. Forest floor information is used in models for predicting forest fire behavior and spread, in fire effects models, in planning and monitoring mechanical fuel reduction, in quantifying fuel consumption and smoke emissions, in quantifying carbon stocks, in describing habitat and its productivity, and in planning for preparedness. As stands of holm oak (Quercus ilex L.) and pubescent oak (Quercus pubescens Willd.) are located in the Mediterranean part of Croatia, where the risk of forest fires is the highest, and the previously published data on the forest floor are not suitable for the models, the main objectives of the research were to determine the depth, bulk density and load of individual subhorizons of the forest floor and to create regression equations that allow estimating the amount of available fuel in the forest floor based on its depth. Sampling of forest floor in holm oak stands (60, 90 and 100 years old) was conducted on the islands of Lastovo and Mljet, while sampling in pubescent oak stands (53 and 90 years old) was conducted in Dugopolje and Biograd na Moru. The amount of forest floor and OC stock in old stands of holm oak and pubescent oak is twice as small as in old stands of Aleppo pine and Dalmatian black pine. This suggests that pine stands have a significantly greater amount of potentially available fuel in the forest floor and are therefore potentially more at risk from fire, but also that they store twice as much carbon in the forest floor. In this study, for the first time in Croatia, bulk densities of individual subhorizons of the forest floor were determined for holm oak and pubescent oak and regression equations were created that allow estimating the amount of available fuel in the forest floor based on its depth per subhorizon and overall. The results of this research have practical value in simpler quantification of forest floor fuel loading, which is important for operational use of models to predict forest fire behavior and spread, but they can also be used in other models mentioned earlier.
苏门答腊栎(Quercus ilex L.)和medunca oak(Quercus pubescens Willd.)群落的燃料数量
航空和陆地激光雷达扫描以及 "运动结构"(SfM)摄影测量等新型遥感方法的应用,补充了实地数据收集工作,实现了森林燃料层的三维制图,大大简化和改善了森林燃料层的特征描述。然而,这些方法并不适合量化林地特征。为此,仍有必要使用传统的野外方法收集数据,确定是否存在子地层及其深度,同时在实验室中确定林地的特征:容积密度、负荷、碳浓度和碳储量。因此,通常的做法仍然是建立回归方程,使操作人员能够根据林地深度(这是一个易于测量的变量)确定可用的林地燃料量及其所含的碳储量,或根据子地层和整体确定林地负荷。林地信息可用于预测森林火灾行为和蔓延的模型、火灾影响模型、规划和监测机械燃料削减、量化燃料消耗和烟雾排放、量化碳储量、描述栖息地及其生产力以及规划备灾。由于冬青栎(Quercus ilex L.)和短叶栎(Quercus pubescens Willd.)林分位于克罗地亚的地中海地区,那里森林火灾的风险最高,而之前公布的林地数据并不适合模型,因此研究的主要目标是确定林地各个子区域的深度、体积密度和负荷,并建立回归方程,以便根据林地深度估算林地中的可用燃料量。在 Lastovo 岛和 Mljet 岛对冬青栎林(树龄分别为 60 年、90 年和 100 年)的林地进行了采样,而在 Dugopolje 和 Biograd na Moru 则对短叶栎林(树龄分别为 53 年和 90 年)进行了采样。冬青栎和短叶栎古老林分的林地和 OC 储量是阿勒颇松和达尔马提亚黑松古老林分的两倍。这表明,松林林地中潜在的可利用燃料量要大得多,因此潜在的火灾风险也更大,但它们在林地中储存的碳量也要多一倍。在这项研究中,克罗地亚首次测定了冬青栎和短叶栎林地各个子层的体积密度,并建立了回归方程,可以根据每个子层和整体的深度估算林地中的可用燃料量。这项研究的结果在简化林地燃料负荷的量化方面具有实用价值,这对预测林火行为和蔓延的模型的实际使用非常重要,但它们也可用于前面提到的其他模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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