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气候、植被和地形对大兴安岭林火烈度空间格局的影响
引用本文:付婧婧,吴志伟,闫赛佳,张宇婧,顾先丽,杜林翰.气候、植被和地形对大兴安岭林火烈度空间格局的影响[J].生态学报,2020,40(5):1672-1682.
作者姓名:付婧婧  吴志伟  闫赛佳  张宇婧  顾先丽  杜林翰
作者单位:江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;江西师范大学地理与环境学院, 南昌 330022,江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;江西师范大学地理与环境学院, 南昌 330022;中国科学院沈阳应用生态研究所, 沈阳 110016,江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;江西师范大学地理与环境学院, 南昌 330022,江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;江西师范大学地理与环境学院, 南昌 330022,江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;江西师范大学地理与环境学院, 南昌 330022,江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;江西师范大学地理与环境学院, 南昌 330022
基金项目:国家自然科学基金项目(31570462);江西省教育厅科学技术研究项目(GJJ160275)
摘    要:在北方森林中火干扰是森林景观变化的主导因素。林火烈度作为衡量林火动态的重要指标,较为直观地反映了火干扰对森林生态系统的破坏程度,其空间格局深刻地影响着森林景观中的多种生态过程(如树种组成、种子扩散以及植被的恢复)。解释林火烈度空间格局有助于揭示林火干扰后森林景观格局的形成机制,对预测未来林火烈度空间格局以及制定科学合理林火管理策略均有重要意义。基于LandsatTM/ETM遥感影像,将2000—2016年大兴安岭呼中林区的36场火的林火烈度划分为未过火、轻度、中度、重度4个等级。采用FRAGSTAT景观格局分析软件从类型水平上计算了斑块所占景观面积比、面积加权平均斑块面积、面积加权平均斑块分维数、面积加权边缘面积比、斑块密度5个景观指数,以对林火烈度空间格局进行了定量化描述。并且采用随机森林模型,分析了气候、地形、植被对林火烈度空间格局的影响及其边际效应。通过研究得出以下结果:(1)相对于未过火、轻度、以及中度火烧斑块,重度火烧斑块的面积更大、形状更简单;(2)海拔对重度火烧斑块的空间格局起着至关重要的作用,其次是坡向、坡度、植被覆盖度、相对湿度、温度等;(3)随着海拔的升高,面积加权平均斑块面积和面积加权平均斑块分维数的边际效应曲线呈上升趋势,而面积加权边缘面积比和斑块密度呈下降趋势;除了面积加权平均斑块面积外,都受到火前植被覆盖度的影响,且植被覆盖度为0.2—0.3范围内,重度火烧斑块在景观中所占比例最大。总的来看,2000—2016年大兴安岭呼中森林景观中重度火烧斑块与未过火、轻度以及中度火烧斑块存在显著差异性。相对于气候,地形和植被对于塑造重度火烧斑块空间格局具有重要作用。因此,应针对重度火烧区域进行可燃物处理,从景观层面上合理配置森林斑块,从而降低高烈度森林大火发生的风险。

关 键 词:林火烈度  空间格局  景观指数  随机森林模型
收稿时间:2019/2/14 0:00:00
修稿时间:2019/11/5 0:00:00

Effects of climate, vegetation, and topography on spatial patterns of burn severity in the Great Xing'an Mountains
FU Jingjing,WU Zhiwei,YAN Saiji,ZHANG Yujing,GU Xianli and DU Linhan.Effects of climate, vegetation, and topography on spatial patterns of burn severity in the Great Xing''an Mountains[J].Acta Ecologica Sinica,2020,40(5):1672-1682.
Authors:FU Jingjing  WU Zhiwei  YAN Saiji  ZHANG Yujing  GU Xianli and DU Linhan
Institution:Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China,Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China;Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China,Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China,Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China,Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China and Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
Abstract:Fire is a major driver of forest landscape change in boreal forests. Burn severity is one of the main indexes for measuring the damage degree of fire on forest ecosystems. Spatial patterns of burn severity affect numerous ecological processes (e.g., species composition, seed dispersal, and vegetation restoration). Explaining spatial patterns of burn severity is conducive to reveal the formation mechanism of forest landscape patterns after fire, which is of great significance for predicting spatial patterns of burn severity in the future and formulating scientific fire management strategies. Based on Landsat TM/ETM remote sensing images, we mapped the burn severity of 36 fires that occurred between 2000 and 2016 in Huzhong forest region of the Great Xing''an Mountains by calculating the post-fire Normalized Burn Ratio index (NBR) and classified the fires into unburned, low, moderate and high severity classes. For each fire, we calculated five landscape metrics to quantitatively describe spatial patterns of burn severity at the class level using the FRAGSTATS program. The landscape pattern metrics were percentage of landscape (PLAND), area-weighted mean patch size (AREA_AM), area-weighted mean fractal dimension index (FRAC_AM), perimeter-area ratio (PARA_AM), and patch density (PD). Using Random Forest models, we analyzed the relative importance and marginal effects of weather, topography, and vegetation variables on determining spatial patterns of burn severity. The results showed that:1) compared with unburned, low-, and moderate-severity patches, the high-severity patches were more larger and simpler in shape. 2) Elevation played an important role in shaping spatial patterns of burn severity, followed by aspect, slope, vegetation coverage, relative humidity, and temperature. 3) With the increase in elevation, the marginal effect curve of area-weighted mean patch area and area-weighted mean patch fractal dimension showed an obvious increasing trend, whereas area-weighted perimeter-area ratio and patch density exhibited a decreasing trend. In addition to area-weighted mean patch area, all of them were affected by pre-fire vegetation coverage. When pre-fire vegetation coverage ranged fom 0.2 to 0.3, the proportion of high-severity patches in the landscape were the largest. In general, the high-severity patches differed significantly from unburned, low- and moderate-severity patches for five spatial pattern metrics. Topography and vegetation were more important in shaping the spatial pattern of high-severity patches than climate. Therefore, it would be urgent to implement forest fuel treatment in high-severity areas. It is necessary to allocate different forest patches reasonably from the landscape level, then to reduce the risk of high-severity forest large fires.
Keywords:burn severity  spatial pattern  the landscape metric  Random Forest model
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