黑龙江大兴安岭呼中林区火烧点格局分析及影响因素

林火是森林生态系统景观格局、动态和生态过程的重要自然驱动力,理解林火发生空问格局与影响因素对于林火安全管理具有重要的作用.采用点格局分析方法,以黑龙江大兴安岭呼中林区1990-2005年火烧数据为研究案例,分析了火烧点空间格局及其影响因素.结果表明,火烧点在空间上的分布是不均匀的,呈现聚集分布,存在一些火烧高发区和低发区.呼中林区火烧概率是0.004--0.012次/(km2.a),平均火烧概率为0.0077次/(km2.a).人类活动因子、地形因子和植被凶子对林火的发生均具有重要作用.应用空间点格局分析方法表明,距离居民点和道路的距离、高程、坡度和林型是影响林火发生的显著因子.因此在进行森林防火管理时,仅仅通过控制人类活动对于降低林火火险的效果是有限的,地形和林型也是林火防控时重点要考虑的因素.     

大兴安岭人为火发生影响因素及气候变化下的趋势

我国重要的北方针叶林地区大兴安岭是林火高发地区.受气候变暖影响,该地区林火发生频率将会发生显著变化.模拟人为火的发生分布与影响因素之间的关系、加强气候变化下人为火的发生分布预测,对于林火管理和减少森林碳损失具有重要作用.本文采用点格局分析方法,基于大兴安岭1967—2006年的火烧数据,建立人为火空间分布与影响因素之间的关系模型,该模型以林火发生次数为因变量,选取非生物因子(年均温和降水量、坡度、坡向和海拔)、生物因子(植被类型)和人为活动因子(距离道路距离、距离居民点距离、道路密度)共9个因子为自变量.并采用RCP 2.6和RCP 8.5气候情景数据代替当前气候情景预测2050年大兴安岭人为火的空间分布状况.结果表明: 点格局模型能够较好地模拟人为火发生分布与空间变量的关系,可以预测未来气候下人为火的发生概率.其中,气候因子对人为火的发生具有明显的控制作用,植被类型、海拔和人为活动等因子对人为火的发生也具有重要影响.林火发生预测结果表明,未来气候变化下,南部地区的林火发生概率将进一步增加,北部和沿主要道路干线附近将成为新的人为火高发区.与当前相比,2050年大兴安岭人为火的发生概率将增加72.2%～166.7%.在未来气候情景下,人为火的发生更多受气候和人为活动因素的控制.     

气候、植被和地形对大兴安岭林火烈度空间格局的影响

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。     

贵州省林火发生驱动因子及预测模型

中国西南林区火源复杂,人为干扰大,多为喀斯特地貌和农林交错区,山形复杂,是中国林火发生的重灾区。分析该区域林火发生驱动因子,并进行火险区划,对于该地区合理的林火管理工作具有重要意义。本研究以西南地区的贵州省为对象,基于2011—2020年的森林火点数据、地理空间数据、气象数据、植被数据和人类活动数据等,利用ArcGIS 10.7的空间分析和R Studio等软件分析贵州省近10年林火分布时空格局,得到林火发生的驱动因子和概率预测模型,分别绘制春夏秋冬4个季节的贵州省林火发生概率和森林火险区划图。结果表明：近10年,贵州省火点数量逐年呈下降趋势,每年主要集中在1—3月,占全年火点数量的61%;距居民点距离、距铁路距离、人口密度、逐月平均空气温度、逐月平均相对湿度和逐月累计降雨量对贵州省林火发生概率有显著影响,得到模型的预测准确率为81.9%,曲线下面积为0.904;贵州省春季林火发生概率高于其他季节,且春、秋和冬季的森林火灾高火险区主要集中在贵州省西部,而夏季则主要是贵州东部的林火发生概率较高。研究得到贵州省林火发生驱动因子和基于季节火险区划图,对于该地区科学林火管理具有重要意义,贵州西...     

大兴安岭呼中林区粗木质残体贮量及其环境梯度

对大兴安岭呼中林区主要植被类型、兴安落叶松不同林型内粗木质残体贮量进行对比研究,并利用除趋势典范对应分析对其环境梯度进行定量分析.结果表明：云杉林粗木质残体贮量较高,为0.20 m³·hm^-2,且不同植被类型之间呈显著性差异;兴安落叶松不同林型粗木质残体贮量在0～0.28 m³·hm^-2,其中偃松群落最高,为0.28 m³·hm^-2,泥炭藓-杜香-落叶松林最低（0）,且各林型之间差异不显著.粗木质残体贮量分布格局较复杂,受多因素交叉影响;海拔、坡位等地形因子和林分年龄、郁闭度等林分条件是影响森林粗木质残体贮量的主要环境因子,二者综合作用表达了该地区森林粗木质残体贮量的空间生态梯度.     

2005-2007年大兴安岭林火释放碳量

根据野外火烧迹地调查,比较过火前后归一化植被指数的差异,计算2005—2007年大兴安岭林区各种可燃物类型的过火面积、火烧消耗的可燃物量,对森林火烧程度进行分级,并利用植物平均含碳率估算林火释放碳量.结果表明：2005—2007年大兴安岭林区总过火面积为436512.5 hm²,其中轻度、中度和重度火烧面积分别为207178.4、150159.2和79159.4 hm².这些火烧消耗可燃物量为3.9×10⁶ t,释放碳1.76×10⁶ t,其中落叶松林、针阔混交林、阔叶林和草地燃烧释放的碳量分别为0.34×10⁶、0.83×10⁶、0.27×10⁶和0.32×10⁶ t.     

流域"源-汇"景观格局变化及其对磷污染负荷的影响——以天津于桥水库流域为例

基于1999年和2009年天津于桥水库流域两期TM遥感影像,应用遥感解译和空间分析的方法,分析了于桥水库流域"源-汇"景观格局变化特征,通过磷污染过程模型对流域不同景观格局下的磷污染负荷进行空间模拟,并采用情景模拟方法对磷污染空间变化进行了研究。结果表明:(1)10a来,于桥水库流域"汇"型景观格局(林地和灌草地)面积比例减少了18.44%,"源"型景观格局(耕地、园地、村镇及建筑)面积有不同程度的增加,面积比例上升了12.34%。(2)流域不同的"源-汇"景观格局总磷污染负荷模拟值有明显差异。从1999年的1.00(kg/km²)上升至2009年的1.12(kg/km²),上升比例达11％。"源"型景观格局总磷污染量,由1999年的0.98(kg/km²)上升至2009年的1.49(kg/km²),上升幅度达51.5%。(3)3个子流域对磷污染影响存在较大的空间异质性。其中淋河流域总磷负荷最高为1.26(kg/km²),沙河流域总磷负荷为1.14(kg/km²),黎河流域的总磷负荷量最低为1.10(kg/km²)。"源"型景观格局中淋河、沙河和黎河流域中农田景观的总磷量分别为1.93(kg/km²)、1.85(kg/km²)和1.65(kg/km²)。     

基于广义线性模型和最大熵模型的黑龙江省林火空间分布模拟

林火分布模型是在较大区域上描述林火空间分布的强有力工具,并可以确定影响林火分布的控制因子.本研究基于黑龙江省1996-2006年的历史火烧记录数据,分别采用广义线性模型和最大熵模型分析了地形、人类活动和土地覆被类型等环境控制因子对黑龙江省林火空间分布的影响,并比较了模型预测精度、评价环境变量重要性及预测火点概率分布图等.结果表明:两个模型的预测精度达中等水平,而最大熵模型的预测精度要略高于广义线性模型.总体而言,与人类活动相关的变量是林火分布模型最佳的环境变量,地形变量次之.尽管两个模型在预测精度和环境变量重要性方面都有很大的相似性,但最大熵模型产生的火点概率图空间格局与广义线性模型产生的明显不同.本研究说明,为了更加精确地确定森林火灾发生的热点地区,应该采用不同模型进行比较,或者有选择性地进行组合以产生综合的预测结果,从而为森林防火工作提供更加合理高效的建议.     

不同环境类型对华北落叶松分布的潜在影响

植物分布-环境因子之间的关系是生态学、生物地理学研究中一个核心问题。采用最大熵物种分布模型,选取气候、土壤和地形3类环境类型中33个因子,对气候、气候-土壤、气候-地形、气候-土壤-地形4种环境组合对华北落叶松在河北省、山西省和内蒙古自治区的分布预测进行建模和检验。利用ArcGIS空间统计,划分华北落叶松分布适宜区,并制作分布适宜性等级图。结果表明,不同环境类型组合对华北落叶松分布影响的训练集和检验集的AUC值在0.965-0.983之间,均达到极准确的精度水平。在影响华北落叶松分布的主导因子上,气候、气候-土壤两种环境类型组合均为最热月的最高温度、温度的年较差和季节性温度变异,累计贡献率均达到74%以上,而气候-地形、气候-土壤-地形两种环境类型组合中海拔和坡度的影响最大,分别为48.8%和51.8%。在影响华北落叶松适生区（中、高、极高适宜区）面积上,气候、气候-地形两种环境类型组合差异不大,分别为102583km²和100698 km²,而气候-土壤、气候-土壤-地形两种环境类型组合影响下的华北落叶松适生区面积出现显著下降分别为57134 km²和66754 km²。最大熵模型能够很好的预测华北落叶松分布,地形因子能明显改变单一气候因子对华北落叶松分布的预测结果,虽然土壤因子对落叶松分布格局的影响不大,但在适宜性、尤其是中等以上适宜区分布上,其影响作用显著。以上结论可为华北地区的生态修复和落叶松可持续经营提供依据。     

基于3PGS-MTCLIM模型模拟根河林区火后植被净初级生产力恢复及其影响因子

林火是大兴安岭林区主要的干扰因子,且对森林生态系统的碳平衡有着重要影响.火干扰强度以及不同地形条件所导致的山地气候差异是影响火后植被净初级生产力恢复过程的主导因素.本研究以内蒙古根河林区为例,使用多时相的Landsat TM遥感数据(2008—2012年)和1980—2010年间的气象资料,结合山地小气候模型MTCLIM与光能利用效率模型3PGS,模拟森林火后植被净初级生产力(NPP)的时空恢复过程,并探讨不同火烧强度和地形因子对NPP恢复进程的影响.结果表明: 3PGS-MTCLIM模型能够较准确地在小尺度范围内模拟NPP的空间分布格局,模拟结果与样地具有较好的对应关系,R²=0.828;3PGS-MTCLIM模型模拟火后NPP下降百分比在43%~80%,相对于火前NPP水平该区域的平均恢复周期大约为10年;火烧强度对火后恢复具有显著影响,火烧强度越强,NPP恢复所需要的周期越长,火后NPP恢复速度呈现先快后慢的增长趋势;地形因子中,海拔对火后NPP恢复程度的影响最明显,其次为坡度,而坡向的影响最小.     

Firescape ecology: how topography determines the contrasting distribution of fire and rain forest in the south‐west of the Tasmanian Wilderness World Heritage Area

Aim To test the hypothesis that ‘islands’ of fire‐sensitive rain forest are restricted to topographic fire refugia and investigate the role of topography–fire interactions in fire‐mediated alternative stable state models. Location A vegetation mosaic of moorland, sclerophyll scrub, wet sclerophyll eucalypt forest and rain forest in the rugged, fire‐prone landscapes of south‐west Tasmania, Australia. Methods We used geospatial statistics to: (1) identify the topographic determinants of rain forest distribution on nutrient‐poor substrates, and (2) identify the vegetation and topographic variables that are important in controlling the spatial pattern of a series of very large fires (> 40,000 ha) that were mapped using Landsat Thematic Mapper (TM) satellite imagery. Results Rain forest was more likely to be found in valleys and on steep south‐facing slopes. Fires typically burned within highly flammable treeless moorland and stopped on boundaries with less flammable surrounding vegetation types such as wet sclerophyll forest and rain forest. Controlling for the effect of vegetation, fires were most likely to burn on flats, ridges and steep north‐facing slopes and least likely to burn in valleys and on steep south‐facing slopes. These results suggest an antagonism between fire and rain forest, in which rain forest preferentially occupies parts of the landscape where fire is least likely to burn. Main conclusions The distribution of rain forest on nutrient‐poor substrates was clearly related to parts of the landscape that are protected from fire (i.e. topographic fire refugia). The relative flammability of vegetation types at the landscape scale offers support to the proposed hierarchy of fire frequencies (moorland > scrub > wet sclerophyll > rain forest) that underpins the ecological models proposed for the region. The interaction between fire occurrence and a range of topographic variables suggests that topography plays an important role in mediating the fire–vegetation feedbacks thought to maintain vegetation mosaics in south‐west Tasmania. We suggest that these fire–topography interactions should be included in models of fire‐mediated alternative stable vegetation states in other fire‐prone landscapes.     

Historic distribution and driving factors of human-caused fires in the Chinese boreal forest between 1972 and 2005

Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management. The aims of the study were: (i) to describe the history of human-caused fires by season and size of burned area over time; (ii) to identify the spatial patterns of human-caused fires and test for the existence of 'hotspots' to determine their exact locations in the Daxing'an Mountains; (iii) to determine the driving factors that determine the spatial distribution and the possibility of human-caused fire occurrence.Methods In this study, K -function and Kernel density estimation were used to analyze the spatial pattern of human-caused fires. The analysis was conducted in S-plus and ArcGIS environments, respectively. The analysis of driving factors was performed in SPSS 19.0 based on a logistic regression model. The variables used to identify factors that influence fire occurrence included vegetation types, meteorological conditions, socioeconomic factors, topography and infrastructure factors, which were extracted and collected through the spatial analysis mode of ArcGIS and from official statistics, respectively.Important findings The annual number of human-caused fires and the area burnt have declined since 1987 due to the implementation of a forest fire protection act. There were significant spatial heterogeneity and seasonal variations in the distribution of human-caused fires in the Daxing'an Mountains. The heterogeneity was caused by elevation, distance to the nearest railway, forest type and temperature. A logistic regression model was developed to predict the likelihood of human-caused fire occurrence in the Daxing'an Mountains; its global accuracy attained 64.8%. The model was thus comparable to other relevant studies.     

Wildfire refugia in forests: Severe fire weather and drought mute the influence of topography and fuel age

Wildfire refugia (unburnt patches within large wildfires) are important for the persistence of fire‐sensitive species across forested landscapes globally. A key challenge is to identify the factors that determine the distribution of fire refugia across space and time. In particular, determining the relative influence of climatic and landscape factors is important in order to understand likely changes in the distribution of wildfire refugia under future climates. Here, we examine the relative effect of weather (i.e. fire weather, drought severity) and landscape features (i.e. topography, fuel age, vegetation type) on the occurrence of fire refugia across 26 large wildfires in south‐eastern Australia. Fire weather and drought severity were the primary drivers of the occurrence of fire refugia, moderating the effect of landscape attributes. Unburnt patches rarely occurred under ‘severe’ fire weather, irrespective of drought severity, topography, fuels or vegetation community. The influence of drought severity and landscape factors played out most strongly under ‘moderate’ fire weather. In mesic forests, fire refugia were linked to variables that affect fuel moisture, whereby the occurrence of unburnt patches decreased with increasing drought conditions and were associated with more mesic topographic locations (i.e. gullies, pole‐facing aspects) and vegetation communities (i.e. closed‐forest). In dry forest, the occurrence of refugia was responsive to fuel age, being associated with recently burnt areas (<5 years since fire). Overall, these results show that increased severity of fire weather and increased drought conditions, both predicted under future climate scenarios, are likely to lead to a reduction of wildfire refugia across forests of southern Australia. Protection of topographic areas able to provide long‐term fire refugia will be an important step towards maintaining the ecological integrity of forests under future climate change.     

Scale matters: fire–vegetation feedbacks are needed to explain tropical tree cover at the local scale

At a broad (regional to global) spatial scale, tropical vegetation is controlled by climate; at the local scale, it is believed to be determined by interactions between disturbance, vegetation and local conditions (soil and topography) through feedback processes. It has recently been suggested that strong fire–vegetation feedback processes may not be needed to explain tree‐cover patterns in tropical ecosystems and that climate–fire determinism is an alternative possibility. This conclusion was based on the fact that it is possible to reproduce observed patterns in tropical regions (e.g. a trimodal frequency distribution of tree cover) using a simple model that does not explicitly incorporate fire–vegetation feedback processes. We argue that these two mechanisms (feedbacks versus fire–climate control) operate at different spatial and temporal scales; it is not possible to evaluate the role of a process acting at fine scales (e.g. fire–vegetation feedbacks) using a model designed to reproduce regional‐scale pattern (scale mismatch). While the distributions of forest and savannas are partially determined by climate, many studies are providing evidence that the most parsimonious explanation for their environmental overlaps is the existence of feedback processes. Climate is unlikely to be an alternative to feedback processes; rather, climate and fire–vegetation feedbacks are complementary processes at different spatial and temporal scales.     

Climatic and Landscape Influences on Fire Regimes from 1984 to 2010 in the Western United States

An improved understanding of the relative influences of climatic and landscape controls on multiple fire regime components is needed to enhance our understanding of modern fire regimes and how they will respond to future environmental change. To address this need, we analyzed the spatio-temporal patterns of fire occurrence, size, and severity of large fires (> 405 ha) in the western United States from 1984–2010. We assessed the associations of these fire regime components with environmental variables, including short-term climate anomalies, vegetation type, topography, and human influences, using boosted regression tree analysis. Results showed that large fire occurrence, size, and severity each exhibited distinctive spatial and spatio-temporal patterns, which were controlled by different sets of climate and landscape factors. Antecedent climate anomalies had the strongest influences on fire occurrence, resulting in the highest spatial synchrony. In contrast, climatic variability had weaker influences on fire size and severity and vegetation types were the most important environmental determinants of these fire regime components. Topography had moderately strong effects on both fire occurrence and severity, and human influence variables were most strongly associated with fire size. These results suggest a potential for the emergence of novel fire regimes due to the responses of fire regime components to multiple drivers at different spatial and temporal scales. Next-generation approaches for projecting future fire regimes should incorporate indirect climate effects on vegetation type changes as well as other landscape effects on multiple components of fire regimes.     

气候变化背景下江西省林火空间预测

林火是森林生态系统中重要的干扰因子之一,深刻地影响森林景观结构和功能。在全球气候化背景下,揭示气候变化对林火空间分布格局的影响,可为林火管理和防火资源分配提供科学指导。因此,基于江西省2001—2015年MODIS火影像数据(MCD14ML)和年均气温、年均降水量、植被、地形、人口密度、距道路距离、距居民点距离7个因子数据,利用增强回归树模型:(1)分析林火发生影响因子的相对重要性及其边际效应;(2)将GFDL-CM3和GISS-E2-R气候变化模式中的年均气温和年均降水量作为未来的气象数据,在3个温室气体排放量情景(RCP2.6、RCP4.5、RCP8.5)下,对2050年(2041—2060的平均值)和2070年(2061—2080的平均值)江西省林火分布进行预测,生成林火发生概率图。并采用受试者工作特征(ROC曲线)和混淆矩阵评估模型预测的精度。研究结果表明:(1)年均气温和海拔与江西省林火发生的相关性较强,年均降水量、居民点距离、人口密度、道路距离与林火发生的相关性较弱,但是与林火发生密切相关的如降水、风速等也应重点关注;(2)训练数据(70%)和验证数据(30%)的AUC值(ROC曲线下面积值)均为0.736,混淆矩阵对火点预测的正确率为67.8%,表明模型能够较好地预测研究区林火的发生;(3)在RCP8.5排放情景中林火发生的增幅最明显,其增幅较大的区域由赣南向赣北移动;(4)未来2050年和2070年林火发生与当前气候(2001—2015年)下相比,赣州市、鹰潭市的增幅较为明显,其他区域不明显。江西省各林业管理部门要加强林火高发区及潜在发生区的森林监测和管理,加大防火宣传力度,提升民众的森林防火意识。     

松材线虫病发生点格局及影响因素

松材线虫病在中国大陆造成了巨大的生态与经济价值损失,南方地区尤为严重,分析松材线虫病空间分布、量化环境因素对其发生的影响对于松材线虫病的防控整治具有重要意义。本研究以江西省赣州市南康区松材线虫病为研究对象,采用核平滑密度、Ripley’s K函数、点过程模拟等空间点格局分析方法,探讨了区域松材线虫病发生的空间格局及其对环境变量的响应。结果表明: 研究区松材线虫病的发生不是随机分布,而是存在显著的空间聚集区。地形因子、植被因子和人类活动因子是影响松材线虫病空间异质性分布的主要因素。空间点格局分析表明,海拔、坡度、距最近道路距离、道路密度、距最近居民点距离、郁闭度和植被类型对松材线虫病的发生具有重要影响。在森林病害管理中,除了加强因人类活动引起病害传播源的管控外,还应该考虑地形、植被类型等特征进行松材线虫病害的综合预警监测。     

Factors Controlling Vegetation Fires in Protected and Non-Protected Areas of Myanmar

Fire is an important disturbance agent in Myanmar impacting several ecosystems. In this study, we quantify the factors impacting vegetation fires in protected and non-protected areas of Myanmar. Satellite datasets in conjunction with biophysical and anthropogenic factors were used in a spatial framework to map the causative factors of fires. Specifically, we used the frequency ratio method to assess the contribution of each causative factor to overall fire susceptibility at a 1km scale. Results suggested the mean fire density in non-protected areas was two times higher than the protected areas. Fire-land cover partition analysis suggested dominant fire occurrences in the savannas (protected areas) and woody savannas (non-protected areas). The five major fire causative factors in protected areas in descending order include population density, land cover, tree cover percent, travel time from nearest city and temperature. In contrast, the causative factors in non-protected areas were population density, tree cover percent, travel time from nearest city, temperature and elevation. The fire susceptibility analysis showed distinct spatial patterns with central Myanmar as a hot spot of vegetation fires. Results from propensity score matching suggested that forests within protected areas have 11% less fires than non-protected areas. Overall, our results identify important causative factors of fire useful to address broad scale fire risk concerns at a landscape scale in Myanmar.