地形因子对FAST周边喀斯特植物多样性及空间分布的影响

为探究地形因子对500米口径球面射电望远镜(Five-hundred-meter Aperture Spherical radio Telescope, FAST)周边植物物种多样性及空间分布的影响，该文选取FAST周边喀斯特峰丛洼地3种典型植物群落(乔木层、灌木层、藤本层)作为研究对象，采用方差分析及典范对应分析(CCA)研究不同地形因子(海拔、坡度、坡向、坡位)梯度下植物群落物种多样性及空间分布特征。结果表明：(1)FAST周边植物群落α多样性指数呈现灌木层>乔木层>藤本层的趋势，乔木层、藤本层植物α多样性指数随海拔升高而增加(P<0.05),地形因子对灌木层植物α多样性无显著性影响。(2)FAST周边植物群落物种的空间分布受海拔的影响最大，其次为坡度(P<0.05)。(3)FAST周边3种植物群落的Jaccard相似性指数随海拔的升高呈现增加的趋势，沿坡度的增加呈现先升高后降低的趋势。综上所述，物种对生境的选择具有差异性，海拔和坡度是影响FAST周边喀斯特峰丛洼地植物群落空间分布的关键因子。     

沁河流域植被覆盖时空分异特征

以4期Landsat TM/OLI为遥感数据源,采用像元二分模型估算植被覆盖度,运用转移矩阵、地学信息图谱和重心迁移模型分析1993—2016年沁河流域植被覆盖的时空演变特征;并结合地形数据分析海拔、坡度和坡向上植被覆盖度的空间响应规律。结果表明:沁河流域植被覆盖度呈北高南低的空间分布特征,且高等级覆盖的植被主要由低等级覆盖的植被转化而来; 1993—2016年,沁河流域植被覆盖度呈不显著波动上升趋势,显著改善(55.99%)和轻微改善(10.13%)之和远大于显著退化(7.31%)和轻微退化(4.59%)之和,反映了良好的植被状况;沁河流域植被覆盖度重心整体表现为向南偏东迁移2.05 km,其中较高和高植被覆盖迁移最为明显;沁河流域植被覆盖度与海拔、坡度呈显著正相关,显著改善面积占比随高程、坡度的增加呈先增加后减小趋势,坡向在东北、西北和西南方向为优势地形位。研究结果有助于为该流域水土流失治理和生态环境的恢复提供决策依据。     

高寒退化草地狼毒种群地上生物量空间格局对地形的响应

地上生物量的空间格局是物种分布格局的重要内容,在小尺度范围内因地形差异导致的环境异质性是这种格局形成和演变的环境基础.在2011年8月野外调查的基础上,借助ArcGIS和S-PLUS软件,利用广义相加模型(GAM)对退化高寒草地狼毒(Stellera chamaejasme)种群的空间分布格局进行了研究,定量分析了狼毒地上生物量空间格局对主要地形因子的响应机制.结果表明:狼毒种群地上生物量受地形因子影响的顺序为坡向＞坡度＞海拔＞平面曲率＞剖面曲率;坡向和坡度两种地形因子对狼毒的地上生物量空间格局的贡献率分别为3.75和1.48,其余因子则相对较小;狼毒地上生物量在海拔、平面曲率和剖面曲率梯度上的分布比较均衡,在坡向梯度上呈开口向下的抛物线趋势,在坡度梯度上则呈现开口向上的抛物线趋势.狼毒地上生物量空间异质性及其与地形因子之间的关系,反映了狼毒在地形因子对水热条件重分配影响下的响应机制及生长策略.     

地形对阔叶红松林物种多样性的影响

为了更好地了解阔叶红松林(Pinus koraiensis)物种多样性的维持机制,对不同地形下的阔叶红松林乔木和灌木种,进行了物种多样性指数比较、物种多度分布模型拟合及物种多样性指数与环境因子的排序分析。结果表明,乔木在阴坡、半阴坡的丰富度显著高于半阳坡;灌木在坡度25°处的均匀度显著高于坡度6°处,在谷地处的4个多样性指数显著低于其他坡位,在阴坡、半阴坡处的4个多样性指数显著高于平地。统计模型、生态位模型、中性模型在不同地形下均能够通过拟合优度检验,物种多度分布在不同地形间差异不明显;地形对乔木的多样性指数的影响:海拔凹凸度坡度坡向坡位,对灌木的多样性指数的影响:坡位海拔坡向坡度凹凸度,土壤对乔木的物种多样性指数的影响:土壤pH土壤容重土壤有效N含量土壤速效K含量,对灌木的物种多样性指数的影响:土壤pH土壤有机质含量土壤速效P含量土壤全N含量土壤全P含量。总体而言,物种多样性随着坡度的增加而增加,阴坡处最高,谷地处最低。     

地形对七姊妹山自然保护区植物丰富度及分布格局的影响

该研究以七姊妹山自然保护区40个(20×20m2)植物群落调查样方为基础,并采用回归分析和典型对应分析(CCA)的方法研究该区地形对植物物种丰富度及植物分布格局的影响,以明确海拔、坡度、坡向、坡位等地形因子的相对重要性,为该区植物多样性的保护和管理提供理论依据。结果表明:(1)七姊妹山自然保护区40个调查样地共有植物633种,隶属133科,316属,其中乔木118种,灌木150种,草本365种。(2)曲线回归方程拟合结果显示,七姊妹山自然保护区植物物种丰富度分别与海拔、坡度具有显著相关性,物种丰富度沿海拔梯度升高而增大,沿坡度梯度先减少后增大之后又减小。(3)从植物的生活型来看,在所有海拔段,乔木物种丰富度始终低于灌木和草本植物;在低、中低海拔地带,灌木物种丰富度均高于乔木和草本植物;而在中、高海拔地带草本植物物种丰富度较大且高于乔木和灌木。(4)CCA排序结果表明,地形因子对植物物种的分布具有显著影响按其影响强度排序为海拔坡度坡位坡向,说明海拔是影响该区植物物种分布最重要的地形因子。     

地形对新疆昌吉州草地净初级生产力分布格局的影响

新疆草地资源丰富且地形多变,地形作为影响植被生产力最主要的环境因素之一却未被充分考虑。以Landsat和DEM为数据源,以新疆昌吉州草地为研究对象,应用CASA模型计算得到连年的净初级生产力,采用Arc GIS的空间分析方法对新疆昌吉州草地2000—2016年的净初级生产力分布进行了分析。研究结果表明,地形对生产力的分布有着显著影响,在海拔、坡度和坡向3个地形因子对整体趋势变化的影响分析中发现,坡度引起的NPP变化最大,坡向次之,海拔较小。在整体特征上,海拔每升高30 m,生产力增加4.11 g/m~2;坡度每增加1°生产力增加-0.225 g/m~2;N坡向生产力水平最高(23.23 g/m~2),SW坡向最低(3.54 g/m~2)。不同生产力年份生产力在地形因子作用下变化趋势相同但变化幅度不同,较高生产力年份中3个地形因子的变化幅度都是最大的。     

中卫山羊核心产地植物群落的数量分类与排序

应用数量分类(TWINSPAN)和DCA排序方法,对我国特有裘皮用山羊(中卫山羊)核心产地——宁夏香山地区植物群落进行多元分析,根据TWINSPAN分类结果,并结合生态特征将所调查的28个样地植被分为6组,代表了该山地在海拔、地形、土壤、山坡坡度等环境作用下植被空间的分布格局。TWINSPAN分类结果与DCA排序结果较一致,DCA排序第一轴结果主要体现出海拔和山坡坡度对植被分布的作用,第二轴结果主要体现了地形(坡向)和土壤基质(沙地、石质山坡、土质山坡)对植被分布的作用。影响该干旱山地植被分布的主要环境因子有海拔和山坡的坡度,另外长期过度放牧对植被的空间分布影响较大,导致了该山地系统植被空间结构紊乱,并干扰了群落数量分析结果的准确性。     

地形对贺兰山步甲群落物种多样性分布格局的影响

【目的】步甲是主要栖息于地表的种类最丰富的昆虫类群之一,它们对生境的变化更为敏感。分析地形因子对贺兰山步甲昆虫群落物种多样性分布格局的影响,以期揭示步甲昆虫物种多样性分布格局形成和稳定的机制。【方法】2015年7-8月选取贺兰山山地针叶林、山地疏林、山地灌丛、山地草原和浅山荒漠5种生境98个样地,用杯诱法对步甲群落物种组成和多样性进行调查,并采用典范对应分析(CCA)分析物种多样性指数和物种分布与地形因子之间的关系,运用广义可加模型(GAM)拟合不同生境步甲群落多样性指数对海拔梯度的响应曲线,探讨贺兰山步甲群落物种多样性的垂直分布格局。【结果】共采集步甲昆虫21属65种10 989头,其中,直角通缘步甲Pterostichus gebleri和径婪步甲Harpalus salinus为优势种,其个体数量分别占总捕获个体数的44.93%和11.33%。山地疏林生境步甲物种丰富度最高,山地针叶林的步甲Shannon-Wiener多样性指数最高,浅山荒漠的步甲均匀度最高。海拔、坡向、坡度、剖面曲率和地形湿度指数的综合作用对步甲物种多样性分布格局有显著影响。其中,海拔对5种生境的步甲分布影响均显著,且解释力度最高;坡向对山地针叶林和浅山荒漠步甲分布影响显著。步甲总体丰富度和个体数量与海拔呈不对称的单峰曲线关系,Shannon-Wiener多样性指数随海拔呈先递增后保持稳定的变化,均匀度指数与海拔呈"V"型变化趋势。【结论】贺兰山山地步甲物种多样性的分布格局受海拔为主的多种地形因子综合作用的影响。     

四川小河沟自然保护区植被类型在地形上的分异研究

以四川小河沟自然保护区为研究对象,在遥感影像和DEM数据的基础上,对植被类型在地形上的分布差异进行研究。结果表明:(1)随着海拔高度的逐渐上升,各种植被类型依次出现和消失,呈顺序分布,并且每种植被类型在相应的海拔段上拥有优势地位。(2)植被类型的分布与坡向之间存在相关性,大面积的常绿落叶阔叶林和次生落叶阔叶林常常在西南坡向上出现,高山灌丛和高山草甸在南坡上的分布面积最大,而在平地、北坡和西北坡上则少有亚高山草甸分布。(3)随着坡度由缓变陡,各植被类型的面积比例在不同的坡度段内发生相应变化,但各坡度段上的植被类型均以次生落叶阔叶林和亚高山针叶林为主,除亚高山草甸外,其余植被类型均主要分布在坡度20~°50°的区域内。     

基于RBFN的桂西北喀斯特区植被碳密度空间分布影响因素分析

在2010年植被碳密度空间分布结果的基础上,通过13个环境因子的1377个样点数据,建立径向基函数网络(Radial Basis Function Network,RBFN)模型,对桂西北喀斯特区植被碳密度空间分布的影响因素进行了初步探讨。研究结果显示:对该区植被碳密度空间分布影响最为重要的前4位为地类、森林类型、林种和植被类型4个因子,其标准化重要性分别在50%以上;其次为石漠化程度、腐殖层厚度、面积等级、植被总覆盖度和土层厚度5个因子,其标准化的重要性分别在15%—30%;影响最小的是坡位、坡度、坡向和海拔4个地形因子,其标准化重要性仅2%—11%。研究表明地形因子对植被碳密度空间分布影响有限,更为重要的是土地类型、森林类型、林种和植被类型等可通过人为活动改变的因素,因此生态环境移民、退耕还林等石漠化治理措施对植被碳密度空间分布具有重要影响。     

Application of geographically weighted regression in estimating the effect of climate and site conditions on vegetation distribution in Haihe Catchment,China

Climate and topography are the two key factors influencing vegetation pattern, distribution, and plant growth. Traditionally, studies on the relationship between vegetation and climate rely largely on field data from limited samples. Now, digital elevation model (DEM) and remote sensing data readily provide huge amounts of spatial data on site-specific conditions like elevation, aspect, and climate, while recent development of geographically weighted regression (GWR) analysis facilitates efficient spatial evaluation of interactions among vegetation and site conditions. Using Haihe Catchment as a case study, GWR is applied in establishing spatial relations among leaf area index (LAI; a critical vegetation index from Moderate Resolution Imaging Spectroradiometer (MODIS)) and interpolated climate variables and site conditions including elevation, aspect, and Topographic Wetness Index (TWI). This study suggests that the GWR solution to spatial effect of climate and site conditions on vegetation is much better than ordinary least squares (OLS). In most of the study area, effects of elevation, aspect change from south to north, and precipitation on LAI are positive, while temperature, TWI, and potential evapotranspiration have a negative influence. Spatially, models perform better in places with large spatial variations in LAI—primarily driven by strong spatial variations in temperature and precipitation. On the contrary, the effect of topographic and climatic factors on vegetation is weak in regions with small spatial variations in LAI. This study shows that overall water availability is a determining factor for spatial variations in vegetation.     

喀斯特季节性雨林木本植物胸高断面积分布格局及其对地形因子的响应

森林生态系统地上生物量的分布格局是物种分布格局的重要内容。局域尺度内因地形差异引起的生境异质性是地上生物量分布格局形成的环境基础。本文以弄岗北热带喀斯特季节性雨林15 ha动态监测样地2011年第一次普查数据中每木个体为研究对象, 尝试以每20 m × 20 m样方内所有个体及不同径级类群的胸高断面积之和为木本植物地上生物量的衡量指标, 利用广义可加模型对喀斯特季节性雨林中胸高断面积之和的空间分布格局进行研究, 定量分析了其对7个地形因子的响应。结果表明, 洼地、山坡和山脊3种生境类型中, 所有个体的胸高断面积之和山坡最高、山脊最低, 且山脊与山坡、洼地的差异均显著; 广义可加模型结果显示, 不同地形因子对胸高断面积之和的解释偏差差异明显, 其中海拔、坡向、凹凸度、岩石裸露率对胸高断面积之和的解释偏差依次降低, 而干旱度指数、坡度和地形湿润指数解释偏差相对较小。喀斯特季节性雨林木本植物胸高断面积之和空间分布的异质性及其与地形因子之间的关系, 反映了胸高断面积之和在地形因子对土壤、水分和光照等条件重分配影响下的多重响应机制及生长策略。     

Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques

Forests are living dynamic systems and these unique ecosystems are essential for life on earth. Forest fires are one of the major environmental concerns, economic, and social in the worldwide. The aim of current research is to identify general indicators influencing on forest fire and compare forest fire susceptibility maps based on the boosted regression tree (BRT), generalized additive model (GAM), and random forest (RF) data mining models in the Minudasht Township, Golestan Province, Iran. According to expert opinion and literature review, fifteen condition factors on forest fire have been selected in the study area. These are slope degree, slope aspect, elevation, topographic wetness index (TWI), topographic position index (TPI), plan curvature, wind effect, annual temperature and rainfall, soil texture, distance to roads, rivers, and villages, normalized difference vegetation index (NDVI), and land use. Forest fire locations were identified using MODIS images, historical records, and extensive field checking. 106 (≈70%) locations, out of 151 forest fires identified, were used for models building/training, while the remaining 45 (≈30%) cases were used for the models validation.BRT, GAM, and RF data mining models were used to distinguish between presence and absence of forest fires and its mapping. These algorithms were used to perform feature selection in order to reveal the variables that contribute more to forest fire occurrence. Finally, for validation of models, the area under the curve (AUC) for forest fire susceptibility maps was calculated. The validation of results showed that AUC for three mentioned models varies from 0.7279 to 0.8770 (AUC_BRT = 80.84%, AUC_GAM = 87.70%, and AUC_RF = 72.79%,). Results indicated that the main drivers of forest fire occurrence were annual rainfall, distance to roads, and land use factors. The results can be applied to primary warning, fire suppression resource planning, and allocation work.     

Flood susceptibility mapping using extremely randomized trees for Assam 2020 floods

The year 2020 proved disastrous for the north eastern state of India, Assam. The state witnessed terrible floods in the midst of the pandemic. The current study aims to better understand the role played by various factors that contributed to the deluge. To this end, the current study undertakes a flood susceptibility mapping using a seldom employed decision tree based ensemble machine learning technique of extremely randomized trees (ERT). The model was trained and tested on a flood inventory superimposed with 14 flood influencing factors, namely slope, elevation, aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), slope length, land use, geology, soil type, topographic roughness index (TRI), rainfall, distance from rivers, plan and profile curvature. The model was compared against other mapping techniques and produced an area under the receiver operating characteristic curve (AUC) of 0.901 outperforming others. The generated susceptibility map deduced the presence of low elevation, high rainfall and close proximity to rivers as major factors leading up to the disaster. It prophesizes a very high flood risk for approximately 18.32% of the study area concentrated in the northern and western part of the study region.     

基于RS、GIS的天目山自然保护区植被空间分布规律研究

以RS、GIS技术为工具。将天目山自然保护区的TM遥感影像进行处理与分类得到该区的植被分布图。利用GIS空间分析功能。研究了天目山自然保护区内各植被类型空间分布规律。重点研究了其中的一种植被类型。研究结果定量描述了自然保护区内各植被类型的高程、坡向、坡度分布,给出科学的统计结果。总结了研究区内植被类型的空间分布规律,把对植被类型的空间分布规律的定性理解转为定量描述,结合实际给出了理论解释。为自然保护区的规划、管理和保护提供了科学依据。     

Vascular plant species richness along environmental gradients in a cool temperate to sub-alpine mountainous zone in central Japan

In order to clarify how vegetation types change along the environmental gradients in a cool temperate to sub-alpine mountainous zone and the determinant factors that define plant species richness, we established 360 plots (each 4 × 10 m) within which the vegetation type, species richness, elevation, topographic position index (TPI), slope inclination, and ground light index (GLI) of the natural vegetation were surveyed. Mean elevation, TPI, slope inclination, and GLI differed across vegetation types. Tree species richness was negatively correlated with elevation, whereas fern and herb species richness were positively correlated. Tree species richness was greater in the upper slope area than the lower slope area, whereas fern and herb species richness were greater in the lower slope area. Ferns and trees species richness were smaller in the open canopy, whereas herb species richness was greater in the open canopy. Vegetation types were determined firstly by elevation and secondary by topographic configurations, such as topographic position, and slope inclination. Elevation and topography were the most important factors affecting plant richness, but the most influential variables differed among plant life-form groups. Moreover, the species richness responses to these environmental gradients greatly differed among ferns, herbs, and trees.     

Assessment of flora diversity in a minor river valley using ecological indicator values, Geographical Information Systems and Digital Elevation Models

Ellenberg indicator values (EIV) have been widely used to estimate habitat variables from floristic data and to predict vegetation composition based on habitat properties. Geographical Information Systems (GIS) and Digital Elevation Models (DEM) are valuable tools for studying the relationships between topographic and ecological characters of river systems. A 3-meter resolution DEM was derived for a. 3-km-long break section of the Szum River (SE Poland) from a 1:10,000 topographic map. Data on the diversity and ecological requirements of the local vascular flora were obtained while making floristic charts for 32 sections of the river valley (each 200 m long) and physical and chemical soil measurements; next, the data were translated into EIV. The correlations of the primary and secondary topographic attributes of the valley, species richness, and EIV (adapted for the Polish vascular flora) were assessed for all species recognized in each valley section. The total area and proportion of a flat area, mean slope, slope curvature, solar radiation (SRAD), and topographic wetness index (TWI) are the most important factors influencing local flora richness and diversity. The highest correlations were found for three ecological indicators, namely light, soil moisture, and soil organic content. The DEM seems to be useful in determination of correlations between topographic and ecological attributes along a minor river valley.     

Effect of error in the DEM on environmental variables for predictive vegetation modelling

Question: Predictive vegetation modelling relies on the use of environmental variables, which are usually derived from abase data set with some level of error, and this error is propagated to any subsequently derived environmental variables. The question for this study is: What is the level of error and uncertainty in environmental variables based on the error propagated from a Digital Elevation Model (DEM) and how does it vary for both direct and indirect variables? Location: Kioloa region, New South Wales, Australia Methods: The level of error in a DEM is assessed and used to develop an error model for analysing error propagation to derived environmental variables. We tested both indirect (elevation, slope, aspect, topographic position) and direct (average air temperature, net solar radiation, and topographic wetness index) variables for their robustness to propagated error from the DEM. Results: It is shown that the direct environmental variable net solar radiation is less affected by error in the DEM than the indirect variables aspect and slope, but that regional conditions such as slope steepness and cloudiness can influence this outcome. However, the indirect environmental variable topographic position was less affected by error in the DEM than topographic wetness index. Interestingly, the results disagreed with the current assumption that indirect variables are necessarily less sensitive to propagated error because they are less derived. Conclusions: The results indicate that variables exhibit both systematic bias and instability under uncertainty. There is a clear need to consider the sensitivity of variables to error in their base data sets in addition to the question of whether to use direct or indirect variables.     

Using topographic wetness index in vegetation ecology: does the algorithm matter?

Questions: How important is the choice of flow routing algorithm with respect to application of topographic wetness index (TWI) in vegetation ecology? Which flow routing algorithms are preferable for application in vegetation ecology? Location: Forests in three different regions of the Czech Republic. Methods: We used vegetation data from 521 georeferenced plots, recently sampled in a wide range of forest communities. From a digital elevation model, we calculated 11 variations of TWI for each plot with 11 different flow routing algorithms. We evaluated the performance of differently calculated TWI by (1) Spearman rank correlation with average Ellenberg indicator values for soil moisture, (2) Mantel correlation coefficient between dissimilarities of species composition and dissimilarities of TWI and (3) the amount of variation in species composition explained by canonical correspondence analysis. Results: The choice of flow routing algorithm had a considerable effect on the performance of TWI. Correlation with Ellenberg indicator values for soil moisture, Mantel correlation coefficient and explained variation doubled when the appropriate algorithm was used. In all regions, multiple flow routing algorithms performed best, while single flow routing algorithms performed worst. Conclusions: We recommend the multiple flow routing algorithms of Quinn et al. and Freeman for application in vegetation ecology.     

小流域土壤水分空间异质性及其与环境因子的关系

以丹江口库区五龙池小流域为研究区域,利用双向指示种法将观测期(2008年4-10月)划分为不同干湿时段;运用前向选择法和Monte Carlo检验法对显著影响不同时段土壤水分空间格局的环境因子进行筛选;利用冗余分析排序法分析不同时段土壤水分格局与环境因子的关系;运用偏冗余分析定量分离环境、空间及其交互作用对土壤水分变异的影响.结果表明:观测期土壤水分被划分为7个子类,分别属于干旱、半干旱、半湿润和湿润4个时段;在干旱期,土地利用类型是影响土壤水分空间格局的主控因子,土层厚度、相对高程、剖面曲率、土壤容重和土壤有机质的影响也达到显著水平;在半干旱期,土层厚度起主导作用,土地利用类型、地形湿度指数、土壤容重和剖面曲率显著影响土壤水分;在半湿润期,地形湿度指数的影响最大,土地利用、坡向正弦值的作用显著;在湿润期,地形湿度指数和坡向正弦值是主要影响因子,相对高程、汇水面积的影响显著.不同干湿期土壤水分的空间分布与环境空间的生态梯度格局吻合较好.从干旱期到湿润期,环境因子独立作用不断减小,但始终处于主导地位,空间位置独立作用总体变化不大且一直维持在较低水平,环境-空间位置交互作用的贡献逐渐增大.