神农架植被及其生物多样性与垂直分布格局-兼论中国中、北亚热带植被带的划分

本文主要通过样线法和样方法相结合，进行了大量的群落学调查和分析，分别从植物区系、物种多样性的垂直分布格局和森林群落类型三个方面分析了神农架植被的基本特征及其物种多样性，结果表明： 1．神农架地区具有很高的物种丰富度，有高等植物3，479种，隶属于1，010属，202科。 其中，蕨类植物305种，80属，32科;种子植物3，174种，930属，170科，其中裸子植物32种，19属，6科，被子植物3，142种，911属，164科;单子叶植物501种，175属，21科，双子叶植物2，641种，736属，143科。植物区系属的分布区类型中北温带分布型最多，其次为东亚分布、泛热带分布、东亚北美间断分布、旧世界温带分布以及热带亚洲分布。中国特有成分占5.65%，较全国的8.12%低。温热比(温带分布型(8-11)属数与热带分布型(2-7)属数的比值）为1.200，比全国(0.385)高。 调查样方中共出现高等植物784种，隶属于454属，144科，其中蕨类植物41种，32属，16科;种子植物743种，422属，128科，其中裸子植物20种，14属，5科，被子植物723种，408属，123科;单子叶植物86种，58属，11科，双子叶植物637种，350属，112科。属的分布区类型中北温带分布型最多，其次为东亚分布、泛热带分布、东亚北美间断分布、旧世界温带分布以及热带亚洲分布。温热比为1.52，草本层>乔木层>灌木层分别为2.18、1.76和1.14。 2．神农架植被类型多样，具有常绿阔叶林、常绿落叶阔叶混交林、落叶阔叶林、针阔混交林、亚高山针叶林、硬叶常绿阔叶林和亚高山灌丛草甸等自然植被类型。本文，依据乔木物种的重要值将神农架地区的森林植被划分出了69个类型。用Twinspan将调查的森林群落划分为32组，能基本上反映群落间相似的关系。 3．神农架地区具有完整的植被垂直带谱：海拔900 (1300) m以下为常绿阔叶林带;海拔900 (1300) m～1500 (1800)ⅡI为常绿落叶阔叶混交林带;海拔1500 (1800) m-2000 (2200)m为落叶阔叶林带;海拔2000 (2200) m～2400 (2600)m为针阔混交林带：海拔2400 (2600)m以上为亚高山针叶林带。神农架地区植被的垂直带的分化从总体上比较显著，但由于小生境的异质性和人为干扰，垂直带谱又具有一定的模糊性和次生性。南北坡具有一定的差异，但不十分明显，也说明神农架植被的过渡性。 4．神农架物种多样性的垂直分布格局。神农架的物种多样性与海拔的关系，类似于“中间膨胀”规律(mid-altitude bulge)，在中低海拔处生物多样性最高。通过二次多项式回归拟合，得到如下拟合曲线： 1)海拔与总体物种数：y= _14.445x2+ 34.74lx+42.07，Xd=1.203km; 2)海拔与乔木层物种数：y=-6.9707x2+ 21.334x+0.2004，Xdrl.530km; 3)海拔与灌木层物种数：y=-6.1599x2+ 9.9747x+30.991，Xd=0.8 lOkm: 4)海拔与草本层物种数：y= _3.9907x2+ 10.455x+15.35，Xd-1.308km; 5）海拔与乔木层Shannon-Wiener指数：y=_0.3337x2+ 0.9877x+0.2537，Xd' 1.480km; 6)海拔与灌木层Shannon-Wiener指数：y=-0.1938xz+ 0.422lx+1.2103，Xd=1.089km： 7)海拔与草本层Shannon-Wiener指数：y=_0.1072x2+ 0.294lx+0.9954，Xd=1.372km; x为海拔( km)，y为各物种多样性指标，Xd为物种多样性的最大时的海拔。 从这些拟合曲线中可以看出：总体物种多样性在海拔1200m左右的常绿落叶阔叶混交林带最高：乔木层物种多样性在海拔1500m左右的常绿落叶阔叶混交林带与落叶阔叶林的过渡带最高;灌木层物种多样性在海拔800-llOOm左右的常绿阔叶林与常绿落叶阔叶混交林带的过渡带最高;草本层物种多样性在海拔1300-1400m左右的常绿落叶阔叶混交林带最高。 但物种多样性随海拔变化有许多的起伏和波动。这些波动有些反映了群落的垂直带谱随海拔梯度变化的特点，在垂直带谱的过渡区物种多样性往往较高;有些波动反映了一些特殊的生境，有些反映了人为活动的影响，造成了神农架植被的次生性。因此，影响神农架物种多样性垂直分布的因素有：植被本身的性质和特点、过渡带的特点、生境的异质性和人为活动。 5．神农架植被水平地带性的过渡性。海拔1300m以下的植物属的分布区类型的温热比南坡总是比北坡小，而且相差十分显著，反映了神农架作为植被分界线的价值。神农架南坡的基带植被是常绿阔叶林，因此南坡属于中亚热带。北坡的基带植被，虽然也有常绿树种的零星分布，甚至有小块的常绿阔叶林，完全由于小生境所至，分布的主要类型是常绿落叶阔叶混交林，应属于北亚热带。因此，神农架是中、北亚热带重要的过渡地带。神农架地区中北亚热带的具体分界线宜按照分长江干流和汉水的水岭来划界，即猴子石、大窝坑、神农架、神农顶、老君山一线，南坡属于中亚热带，北坡属于北亚热带。 总之，神农架处于我国中、北亚热带的过渡带，具有过渡带的性质，具有很高的物种多样性，拥有完整的植被垂直带谱，具有多种多样的植物群落及其组成的生态系统。而且，具有我国许多特有植物和珍稀濒危保护植物和许多资源植物。因此，神农架植被在我国植被体系中具有重要的地位，是我国生物多样性最丰富的地区之一，是生物多样性保护的关键地区，也应是生物多样性研究的热点地区。 另外，调查分析了黄山和万朝山植被及其物种多样性与垂直分布格局，结果表明： 6．黄山样方中共出现高等植物259种，隶属于263属，110科，其中蕨类植物14种，II属，8科，种子植物345种，152属，105科，其中裸子植物9种，8属，6科，被子植物336种，144属，99科，其中单子叶植物37种，27属，6科，双子叶植物299种，117属，90科。属的分布区类型中北温带分布最多，其次为东亚分布和泛热带分布，再次为东亚北美间断分布、热带亚洲分布以及旧世界温带分布，与神农架和万朝山也较相似，但热带分布的属更多一些。温热比为1.1875，灌木层>草本层>乔木层，分别为1.3818、1.2609和1.2143。 黄山的森林植被类型有针叶林、常绿阔叶林、常绿落叶阔叶混交林、针阔混交林、落叶阔叶林和竹林。Twinspan将调查的森林群落划分为22组，反映群落间相似的关系，比较清楚和适用。依据乔木物种的重要值将森林植被划分出了34个类型。黄山物种多样性的与海拔的关系不十分明显。黄山植被的垂直带谱不是十分明显，将其垂直带谱划分为：海拔1300m(1500m)以下为常绿阔叶林带;海拔1300m(1500m)-1500m(1600m)常绿落叶阔叶混交林 带;1500m(1600m)以上为落叶阔叶林、黄山松林、山地灌木草丛带。垂直带谱在不同坡向上有差别，东、南、西坡的相似性较大，而北坡与其差别较大。 7．万朝山样方中共出现高等植物490种，隶属于339属，124科，其中蕨类植物21种，18属，11科，种子植物469种，321属，113科，其中裸子植物9种，7属，4科，被子植物460种，314属，109科，其中单子叶植物47种，37属，11科，双子叶植物413种，277属，98科。植物属的分布区类型中，北温带分布所占最多，其次为泛热带分布、东亚分布、东亚北美间断分布、旧世界温带分布以及热带亚洲分布，。温热比为1.3366，草本层>乔木层>灌木层，分别为1.5429、1.4063和1.0645。 万朝山的植被类型包括针叶林、落叶阔叶林、针阔混交林和常绿落时阔叶混交林，但没有典型的常绿阔叶林。依据乔木物种的重要值将森林植被划分出了20个类型。万朝山物种多样性与海拔的关系则不十分明显。万朝山的人为干扰比较强，植被的次生性很大，南、北坡物种多样性随海拔升高的起伏较大。

Landscape and soil regionalization in southern Brazilian Amazon and contiguous areas: methodology and relevance for ecological studies

Soils of a large tropical area with differentiated landscapes cannot be treated uniformly for ecological applications. We intend to develop a framework based on physiography that can be used in regional applications. The study region occupies more than 1.1 million km² and is located at the junction of the savanna region of Central Brazil and the Amazon forest. It includes a portion of the high sedimentary Central Brazil plateau and large areas of mostly peneplained crystalline shield on the border of the wide inner-Amazon low sedimentary plain. A first broad subdivision was made into landscape regions followed by a more detailed subdivision into soil regions. Mapping information was extracted from soil survey maps at scales of 1:250000-1:500000. Soil units were integrated within a homogenized legend using a set of selected attributes such as taxonomic term, the texture of the B horizon and the associated vegetation. For each region, a detailed inventory of the soil units with their area distribution was elaborated. Ten landscape regions and twenty-four soil regions were recognized and delineated. Soil cover of a region is normally characterized by a cluster composed of many soil units. Soil diversity is comparable in the landscape and the soil regions. Composition of the soil cover is quantitatively expressed in terms of area extension of the soil units. Such geographic divisions characterized by grouping soil units and their spatial estimates must be used for regional ecological applications.

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Advances in vegetation management for power line corridor monitoring using aerial remote sensing techniques

This paper presents a comprehensive discussion of vegetation management approaches in power line corridors based on aerial remote sensing techniques. We address three issues 1) strategies for risk management in power line corridors, 2) selection of suitable platforms and sensor suite for data collection and 3) the progress in automated data processing techniques for vegetation management. We present initial results from a series of experiments and, challenges and lessons learnt from our project.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

The role of vegetation in enhancing radon concentration and ion production in the atmosphere

The role of ions in the production of atmospheric particles has gained wide interest due to their profound impact on climate. Away from anthropogenic sources, molecules are ionized by alpha radiation from radon exhaled from the ground and cosmic gamma radiation from space. These molecular ions quickly form into ‘cluster ions’, typically smaller than about 1.5 nm. Using our measurements and the published literature, we present evidence to show that cluster ion concentrations in forest areas are consistently higher than outside. Since alpha radiation cannot penetrate more than a few centimetres of soil, radon present deep in the ground cannot directly contribute to the measured cluster ion concentrations. We propose an additional mechanism whereby radon, which is water soluble, is brought up by trees and plants through the uptake of groundwater and released into the atmosphere by transpiration. We estimate that, in a forest comprising eucalyptus trees spaced 4m apart, approximately 28% of the radon in the air may be released by transpiration. Considering that 24% of the earth’s land area is still covered in forests; these findings have potentially important implications for atmospheric aerosol formation and climate.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Importance of soil ecology in vegetation rehabilitation on a North Stradbroke Island sand mine

Sibelco Australia Limited (SAL), a mineral sand mining operation on North Stradbroke Island, undertakes progressive rehabilitation of mined areas. Initial investigations have found that some areas at SAL’s Yarraman Mine have failed to redevelop towards approved criteria. This study, undertaken in 2010, examined ground cover rehabilitation of different aged plots at the Yarraman Mine to determine if there was a relationship between key soil and vegetation attributes. Vegetation and soil data were collected from five plots rehabilitated in 2003, 2006, 2008, 2009 and 2010, and one unmined plot. Cluster (PATN) analysis revealed that vegetation species composition, species richness and ground cover differed between plots. Principal component analysis (PCA) extracted ten soil attributes that were then correlated with vegetation data. The attributes extracted by PCA, in order of most common variance, were: water content, pH, terrolas depth, elevation, slope angle, leaf litter depth, total organic carbon, and counts of macrofauna, fungi and bacteria. All extracted attributes differed between plots, and all except bacteria correlated with at least one vegetation attribute. Water content and pH correlated most strongly with vegetation cover suggesting an increase in soil moisture and a reduction in pH are required in order to improve vegetation rehabilitation at Yarraman Mine. Further study is recommended to confirm these results using controlled experiments and to test potential solutions, such as organic amendments.

The influence of vegetation and soil type on the speciation of iron in soil water

Soluble organic matter derived from exotic Pinus species has been shown to form stronger complexes with iron (Fe) than that derived from most native Australian species. It has also been proposed that the establishment of exotic Pinus plantations in coastal southeast Queensland may have enhanced the solubility of Fe in soils by increasing the amount of organically complexed Fe, but this remains inconclusive. In this study we test whether the concentration and speciation of Fe in soil water from Pinus plantations differs significantly from soil water from native vegetation areas. Both Fe redox speciation and the interaction between Fe and dissolved organic matter (DOM) were considered; Fe - DOM interaction was assessed using the Stockholm Humic Model. Iron concentrations (mainly Fe 2+) were greatest in the soil waters with the greatest DOM content collected from sandy podosols (Podzols), where they are largely controlled by redox potential. Iron concentrations were small in soil waters from clay and iron oxide-rich soils, in spite of similar redox potentials. This condition is related to stronger sorption on to the reactive clay and iron oxide mineral surfaces in these soils, which reduces the amount of DOM available for electron shuttling and microbial metabolism, restricting reductive dissolution of Fe. Vegetation type had no significant influence on the concentration and speciation of iron in soil waters, although DOM from Pinus sites had greater acidic functional group site densities than DOM from native vegetation sites. This is because Fe is mainly in the ferrous form, even in samples from the relatively well-drained podosols. However, modelling suggests that Pinus DOM can significantly increase the amount of truly dissolved ferric iron remaining in solution in oxic conditions. Therefore, the input of ferrous iron together with Pinus DOM to surface waters may reduce precipitation of hydrous ferric oxides (ferrihydrite) and increase the flux of dissolved Fe out of the catchment. Such inputs of iron are most probably derived from podosols planted with Pinus.