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.     

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。     

基于RS与GIS的农村居民点空间变化特征与景观格局影响研究

农村居民点作为乡村地域空间人口聚居形态,是乡村聚落景观重要组成部分,其空间布局、演变特征受自然、社会、经济多重因素的影响。利用都江堰市2005年和2010年两期遥感影像提取农村居民点、坡度、道路、河流等矢量数据,借助RS、GIS空间分析技术,定量研究都江堰市农村居民点的空间变化过程、格局和趋势,并选取景观格局指数对影响农村居民点布局特征的因素进行深入分析。结果表明:(1)2005年和2010年都江堰市农村居民点的空间分布总体上均表现出显著的聚集趋势,2010年农村居民点的聚集程度要比2005年高,但居民点集聚的空间态势没有发生明显变化,仍然集中在都江堰市的东南部;(2)坡度、道路和河流对都江堰市农村居民点的布局有显著影响,其中超过80%的居民点分布在0—10°坡度范围内,超过50%的居民点分布在道路500 m范围内,近60%居民点分布在河流1000 m范围内;(3)农村居民点空间布局除了受地形因素影响外,还与国家级风景名胜区、世界文化遗产区等保护政策,农村土地综合整治和灾后重建等规划因素密切相关。该研究以期为农村居民点动态变化监测、农村土地整理效果评价、新农村规划等理论和实践提供重要决策参考和技术支撑。     

西藏城乡居民点空间分异特征与影响因素

西藏地处青藏高原腹地,区内适宜人类生活的空间十分有限,居民点空间分布受多因素制约.为揭示西藏居民点的空间分布特征和主要影响因素,采用高分影像,基于目视与机器解译结合提取西藏城乡居民点斑块数据,运用核密度、空间热点探测、GeoDetector等方法探索其空间分异特征与影响因素,以期为今后高原地区宜居地选址、制定区域经济发展等相关政策提供参考.结果表明: 西藏城乡居民点呈聚集分布模式,整体密度分布差异较大;呈以拉萨市为高密度核心,昌都市、林芝市、阿里地区为低密度边缘的“核心-边缘”结构;高密度核心区拉萨市斑块密度多达220 ind·km^-2,而低密度边缘区密度最大仅为5.7 ind·km^-2,两个区域相差43倍.西藏城乡居民点规模高低值聚簇现象显著,大斑块高值数量稀少,小斑块低值数量绝对优势突出;大斑块高值占比仅为3.7%,集中分布于拉萨市和山南市,低值聚簇区占比达到67.2%,主要分布于羌塘高原的阿里地区和那曲市.西藏城乡居民点被划分成6种类型,形成了以“一江两河”地区和“三江流域”为中心的两个圈层结构,由内到外依次为大斑块主导型、中密度团簇型、高密度散点型、低密度散点型和高寒地无人型;拉萨市以中密度团簇型为主,占比为31%,山南市以低密度散点型为主,占比为38%,昌都市以低密度散点型为主,占比高达51%,阿里地区、那曲市和日喀则市均以高寒地无人型为主,其中,在阿里地区占比高达64%.不同影响因子对西藏城乡居民点空间分布的影响程度差异性显著;人口和GDP对城乡居民点的分布均具有很强的决定性;此外,城镇居民点表现出强烈的道路指向性,农村居民点更多地表现为河流指向性.     

基于FARSITE模型的丰林自然保护区潜在林火行为空间分布特征

将FARSITE火行为模型应用于丰林自然保护区林火行为预测,并根据Rothermel和Rinehart制定的林火行为等级标准,编制潜在林火行为空间区划图,以探讨林火行为的空间分布特征。研究结果表明:在有可能、易发生和极易发生3个森林火险等级下,丰林自然保护区林火行为主要分布在Class I和ClassⅡ等级上,使用手工工具可能从火头控制火势蔓延;林火行为等级空间分布特征较为明显,ClassⅢ等级的火行为主要分布在区域中东部,ClassⅡ主要分布在区域中部,而Class I则主要分布在区域四周;高强度林火行为主要分布在可燃物模型为FL-Ⅰ和FL-Ⅱ,海拔为300—400 m、坡度为平坡和缓坡、坡向为阳坡上。高强度林火发生在接近道路与居民点500 m的区域内分布面积明显高于分布在距离道路居民点500—1000 m和1000—1500 m区域。     

我国北方针叶林人为火发生的预测模型

我国北方针叶林带是重要的森林资源储藏地,也是林火发生的重灾区,其自然火和人为火所占比例相当. 气象因子、地形特征、植被条件、人为基础设施等因素对人为火发生具有显著影响,国内目前应用空间分析技术对北方针叶林带人为火影响因子的研究还存在一定不确定性. 本文基于1974—2009年间人为火的空间地理坐标,结合研究地的气象因子、基础地理信息及矢量化林相图,应用ArcGIS 10.0中的空间分析工具和SPSS 19.0的逻辑斯蒂回归模型对影响人为火发生的主要驱动因子进行分析,并建立人为火发生的概率模型. 利用HADCM2模式下研究区域未来气象数据对塔河地区2015年人为火发生情况进行计算.结果表明： 距离铁路距离（x₁）和平均相对湿度（x₂）对研究区域人为火发生具有显著影响,并得到火险概率模型P=1/［1+e^{-(3.026-0.00011x1-0.047x₂)}］. 模型校验结果显示,模型的准确度可达到80%.林火发生预测结果表明,塔河地区2015年 4—6月、8月为人为火高发期,其中,4—5月的林火发生概率最高.从火险空间分布来看,高火险主要集中在塔河西部和西南部,铁路线路主要包含在此区域.     

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,理解林火发生空间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、地形因子和植被因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。     

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.     

黑河下游胡杨种群的点格局分析

采用空间点格局分析方法中的Ripley K函数,将宏观的地理环境与微观的点格局相结合,分析了不同尺度下黑河下游胡杨种群的空间分布格局.结果表明: 胡杨种群密度随离河距离增大而减小,且东河胡杨种群密度整体上高于西河;胡杨种群分布格局随空间尺度的变化而变化,主要在中尺度5～12 m呈聚集分布,在小尺度0～2 m和大尺度38～40 m呈随机分布,极少呈均匀分布;胡杨种群在不同离河距离主要呈聚集分布,且聚集强度随离河距离的增大呈增大趋势,而胡杨种群在离河距离较远的地段有扩散趋势,转为随机分布.表明胡杨种群内部的生态联系对空间尺度具有很强的依赖性,胡杨种群空间分布主要受表层土壤含水量和地下水埋深的影响.     

基于网络K函数的西双版纳人工林空间格局及动态

区域植被格局的分布特征受诸多要素影响,但其空间格局和动态具有一定规律或自相关性,道路网络作为景观中显著的人工线性要素,在很大程度上影响着区域的植被格局特征,特别是人工植被的分布特征.运用网络K函数,分析了道路网络和人工林空间格局分布的相互关系,并且用二元网络K函数研究了人工林扩展对针叶林和阔叶林的影响.结果表明:人工林在1970-2000年间种群分布格局有非常明显的变化,特别是从1990到2000年,种群面积不断扩大,主要从北部地区扩展到西北和东南地区.1970-1990年人工林扩展主要集中在低海拔的道路网络附近,沿道路网络呈现明显的集聚分布,公路效应明显.但后期逐渐向距公路较远、海拔较高的地区扩展,到2000年在大尺度下人工林斑块呈显著随机分布.同时,人工林面积的增长对针叶林影响显著,对阔叶林有影响但是并不显著.二元网络K函数表明,在1970到1990年人工林与针叶林沿道路网络在小尺度为负关联,在局部地区存在着竞争,但在大尺度上对环境条件的要求具有一致性为正关联.到2000年,在大尺度上人工林与针叶林的种群分布格局呈显著负相关,人工林面积的不断扩展导致了针叶林面积的下降.     

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.     

Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China

Understanding the spatial patterns of fire occurrence and its response to climate change is vital to fire risk mitigation and vegetation management. Focusing on boreal forests in Northeast China, we used spatial point pattern analysis to model fire occurrence reported from 1965 to 2009. Our objectives were to quantitate the relative importance of biotic, abiotic, and human influences on patterns of fire occurrence and to map the spatial distribution of fire occurrence density (number of fires occurring over a given area and time period) under current and future climate conditions. Our results showed human‐caused fires were strongly related to human activities (e.g. landscape accessibility), including proximity to settlements and roads. In contrast, fuel moisture and vegetation type were the most important controlling factors on the spatial pattern of lightning fires. Both current and future projected spatial distributions of the overall (human‐ + lightning‐caused) fire occurrence density were strongly clustered along linear components of human infrastructure. Our results demonstrated that the predicted change in overall fire occurrence density is positively related to the degree of temperature and precipitation change, although the spatial pattern of change is expected to vary spatially according to proximity to human ignition sources, and in a manner inconsistent with predicted climate change. Compared to the current overall fire occurrence density (median value: 0.36 fires per 1000 km² per year), the overall fire occurrence density is projected to increase by 30% under the CGCM3 B1 scenario and by 230% under HadCM3 A2 scenario in 2081–2100, respectively. Our results suggest that climate change effects may not outweigh the effects of human influence on overall fire occurrence over the next century in this cultural landscape. Accurate forecasts of future fire‐climate relationships should account for anthropogenic influences on fire ignition density, such as roads and proximity to settlements.     

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。     

Substrate controls growth rates of the woody pioneer Leptospermum lanigerum colonizing montane grasslands in northern Tasmania

Woody encroachment into grasslands is occurring across the world and is of concern to land managers. Studies of forest–grassland boundaries have informed models describing factors that govern tree establishment and the maintenance and origin of grassland ecosystems. Central to these models is the role of fire relative to ‘bottom up’ resources such as soil and the geological substrate in determining the extent of grassland and forest in the landscape. The view that human lit fires have shaped vegetation across the Australian continent has been bolstered by early 19th century observations of Aboriginal‐set fires in Tasmanian montane grasslands and the documented encroachment of trees into these grasslands in the 20th century. We examined the pattern of lateral encroachment of woolly tea‐tree (Leptospermum lanigerum (Sol. ex Aiton) Sm.) into these grasslands and used tree ring chronologies to investigate (i) past fire activity and (ii) how the geological substrate mediates growth rates of L. lanigerum. Changes in fire regimes inferred from L. lanigerum recruitment were corroborated by historical records. Encroachment (and increases in woody cover) of trees into grasslands was highest on granitic substances, although L. lanigerum growth rates were highest on basalt substrates, followed by conglomerate, granite and Mathinna sediments. Frequent burning up to the 1980s may have stymied the encroachment of trees in grasslands underlain by basalt. Growth rates decreased with increasing distance from the forest edge. This may be due to incremental changes in soil resources, grass competition and/or microclimate. The dynamics between grasslands and forests in montane Tasmania are consistent with tree growth–fire interaction models that highlight the interplay of edaphic factors, growth rates and fire history. Such complexity cautions against generalizations concerning the direct effects of landscape fire in shaping vegetation distribution across Australia.     

土地利用变化对沙地土壤全氮空间分布格局的影响

利用经典统计学和地统计学相结合的方法,分析了科尔沁沙地东南缘草地和8年前开垦的耕地土壤全氮含量和空间分布格局．结果表明,草地与耕地表层(O～10cm)土壤全氮含量差异不显著,亚表层(10～20cm)含量差异显著(P     

Human Impacts on the Fire Regime of Interior Alaska: Interactions among Fuels, Ignition Sources, and Fire Suppression

Wildfire is the major natural agent of disturbance in interior Alaska. We examined the magnitude of human impact on fire by comparing fire regime between individual 1-km² grid cells designated for fire suppression with lands where fires are allowed to burn naturally. Two-thirds of interior Alaska has an essentially natural fire regime, with few human ignitions, negligible suppression activity, and many large lightning-caused fires. In the 17% of land that is designated for fire suppression due to its proximity to communities and roads, there was a 50% reduction in the proportion of area burned from 1992–2001, relative to areas without suppression. The remaining 16% of land serves as a buffer, receives some suppression, and has an intermediate fire regime. Even though there were 50 times more fires and the fire season began two months earlier in lands designated for suppression, most of these fires were lit by people and remained small because fires tended to occur at times and places less favorable for fire spread and were more accessible to fire fighters compared to lands not designated for suppression. Even in the absence of fire suppression, human-caused fires were less likely to exceed 400 ha compared to lightning-caused fires. Fire suppression reduced area burned in all fuel types but was somewhat more effective in less flammable (non-forest) vegetation. Alaska’s fire policy of focusing suppression efforts on a small proportion of the fire-prone region maximizes the ecological and social benefits associated with fire-dependent ecosystem services, while minimizing the social and ecological costs of suppression. Application of this policy to other areas would require well-informed managers and stakeholders to make difficult decisions about the relative costs and benefits of fire across ecologically and culturally variable landscapes.     

应用空间点模式方法研究大兴安岭雷击火空间分布格局

林火空间分布格局的研究对于林火发生预测以及理解林火在森林景观变化过程中的作用具有重要的意义.运用空间点模式方法对大兴安岭地区1988～2005年间雷击火发生情况进行了统计分析进而确定雷击火空间分布情况,研究结果表明大兴安岭地区雷击火成聚集分布,并存在雷击火热点地区,其核心区地理坐标分别为123°06′E,52°20′N;123°41′E,51°34′N;124°08′E,50°48′N.     

The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls

Aims Grassland is the most widely distributed vegetation type on the Xizang Plateau. Accurate remote sensing estimation of the grassland aboveground biomass (AGB) in this region is influenced by the types of vegetation indexes (VIs) used, the grain size (resolution) of the remote sensing data and the targeted ecosystem features. This study attempts to answer the following questions: (i) Which VI can most accurately reflect the grassland AGB distribution on the Xizang Plateau? (ii) How does the grain size of remote sensing imagery affect AGB reflection? (iii) What is the spatial distribution pattern of the grassland AGB on the plateau and its relationship with the climate?Methods We investigated 90 sample sites and measured site-specific AGBs using the harvest method for three grassland types (alpine meadow, alpine steppe and desert steppe). For each sample site, four VIs, namely, Normalized Difference VI (NDVI), Enhanced VI, Normalized Difference Water Index (NDWI) and Modified Soil-Adjusted VI (MSAVI) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) products with grain sizes of 250 m and 1 km. Linear regression models were employed to identify the best estimator of the AGB for the entire grassland and the three individual grassland types. Paired Wilcoxon tests were applied to assess the grain size effect on the AGB estimation. General linear models were used to quantify the relationships between the spatial distribution of the grassland AGB and climatic factors.Important findings The results showed that the best estimator for the entire grassland AGB on the Xizang Plateau was MSAVI at a 250 m grain size (MSAVI 250 m). For each individual grassland type, the best estimator was MSAVI at a grain size of 250 m for alpine meadow, NDWI at a grain size of 1 km for alpine steppe and NDVI at a grain size of 1 km for desert steppe. The explanation ability of each VI for the grassland AGB did not significantly differ for the two grain sizes. Based on the best fit model (AGB =^-10.80 + 139.13 MSAVI 250 m), the spatial pattern of the grassland AGB on the plateau was characterized. The AGB varied from 1 to 136g m ?2. Approximately 59% of total spatial variation in the AGB for the entire grassland was explained by the combination of the mean annual precipitation (MAP) and mean annual temperature. The explanatory power of MAP was weaker for each individual grassland type than that for the entire grassland. This study illustrated the high efficiency of the VIs derived from MODIS data in the grassland AGB estimation on the Xizang Plateau due to the vegetation homogeneity within a 1×1 km pixel in this region. Furthermore, MAP is a primary driver on the spatial variation of AGB at a regional scale.