Classification and ordination analysis of herbaceous flora in district Tor Ghar,western Himalaya

District Tor Ghar is phytosocilogically unexplored region in the western Himalaya. Present study was conducted in district Tor Ghar during the summers of 2012 and 2013 to study floristic diversity of herbaceous flora in the region. The district Tor Ghar is located on 34° 32′ - 34° 50′ N, and 72° 48′ - 72°58′ E. A total of 229 herb species belonging to 168 genera and 66 families were identified. Herbaceous vegetation was sampled from 1 × 1 m quadrats. For vegetation classification into plant communities, presence, absence (1,0) data on 229 herbaceous species and 64 locations were analyzed in PCORD version 5 via Cluster and Two Way Cluster Analyses. Six plant communities were established. Plant abundance data were treated in CANOCO version 4.5 to measure the ecological gradient of plant species and communities of the region. Detrended Correspondence Analysis (DCA) a type of indirect gradient analysis reconfirmed the results of Cluster and Two way Cluster Analysis. Moreover the study furnishes useful information for improvement, protection and management of present vegetation status for future generation. A comprehensive conservation measure with practical implementation is utmost necessary for conservation of rapidly depleting plant resources of the area.     

甘肃兴隆山国家自然保护区表土花粉组合及数量分析

提要通过对兴隆山国家自然保护区不同植物群落40个样点的表土花粉组合特征、数量分析[分层聚类分析（HCA）和降趋势对应分析（DCA）],研究了花粉组合与植物群落的关系。结果显示：1）大部分森林和部分灌丛的花粉组合与植物群落之间对应关系很好,如青杆林（云杉属组合）、辽东栎林（栎属-桦属-蒿属组合）、桦林（桦属-蒿属-沙棘属组合）和沙棘灌丛（沙棘属组合）;2）蔷薇灌丛（蒿属-蔷薇科组合）和草原（蒿属-菊科组合）的花粉组合基本上能反映群落特征;3）山杨林、糙皮桦灌丛和高山灌丛草甸的花粉组合与植物群落的对应性较差;4）云杉属花粉的长距离传播能力有限;5）栎属花粉仅在辽东栎林中含量较高;6）桦属花粉含量较高时（〉30％）能够反映桦林的存在,含量较低时（〈10％）可能当地无桦树生长;7）沙棘属和蒿属花粉具超代表性;8）豆科、禾本科和蔷薇科花粉具低代表性;9）HCA和DCA分析对于区分主要花粉类型和揭示环境梯度是有效的,但反映植被细节特征的能力有限。     

Patterns of ordination and classification instability resulting from changes in input data order

Abstract. Random rearrangement of entry order in three data sets often changed ordination and classification results based on Reciprocal Averaging. Results varied with the data set and method used. Eliminating infrequently occurring species largely reduced, but did not always eliminate, the variability. Overall, results appeared related to data set complexity, the type of data or transformation, and the analysis method used. Detrended Correspondence Analysis had the greatest variability of the ordination methods tested. Results from quantitative data were usually more variable than presence/absence data. Variation in cluster analysis was related to the number of tie values in the similarity matrix. Detailed tests using randomization of entry order of individual data sets with each of the programs to be used are needed to individually assess the effects on the results.; Keywords :; Cluster analysis; DECORANA; Ecological group; Entry order; Environmental gradient; TWINSPAN     

Numerical reproduction of traditional classifications and automatic vegetation identification

Questions: Is it possible to develop an expert system to provide reliable automatic identifications of plant communities at the precision level of phytosociological associations? How can unreliable expert‐based knowledge be discarded before applying supervised classification methods? Material: We used 3677 relevés from Catalonia (Spain), belonging to eight orders of terrestrial vegetation. These relevés were classified by experts into 222 low‐level units (associations or sub‐associations). Methods: We reproduced low‐level, expert‐defined vegetation units as independent fuzzy clusters using the Possibilistic C‐means algorithm. Those relevés detected as transitional between vegetation types were excluded in order to maximize the number of units numerically reproduced. Cluster centroids were then considered static and used to perform supervised classifications of vegetation data. Finally, we evaluated the classifier's ability to correctly identify the unit of both typical (i.e. training) and transitional relevés. Results: Only 166 out of 222 (75%) of the original units could be numerically reproduced. Almost all the unrecognized units were sub‐associations. Among the original relevés, 61% were deemed transitional or untypical. Typical relevés were correctly identified 95% of the time, while the efficiency of the classifier for transitional data was only 64%. However, if the second classifier's choice was also considered, the rate of correct classification for transitional relevés was 80%. Conclusions: Our approach stresses the transitional nature of relevé data obtained from vegetation databases. Relevé selection is justified in order to adequately represent the vegetation concepts associated with expert‐defined units.     

Mapping vegetation types in semi-arid riparian regions using random forest and object-based image approach: A case study of the Colorado River Ecosystem,Grand Canyon,Arizona

Riparian regions are essential habitats for wildlife and play a vital role in agricultural production, but they are highly dynamic environments impacted by fluctuations of water levels. Monitoring vegetation types along narrow river corridors is complicated and requires high-resolution imagery and advanced remote sensing techniques due to the mixture of vegetation and other types of land covers. The primary aim of this paper is to develop a framework using airborne imagery, object-based image approach (OBIA), hyper-spectral analysis and Random Forest to classify vegetation along narrow, semi-arid riparian corridors via a case study of the Grand Canyon, the Colorado River. By analyzing hyper-spectral and field data with Random Forest, we found that the bandwidths from 642 to 682 nm and 750 to 870 nm were useful for vegetation classification in this case study. As a result, the red and near-infrared bands of aerial photos were used with ancillary data for species classification, and the overall accuracy (OA) of classification with these images reached up to 94.8% with a Kappa's coefficient of 0.93. The similarity of vegetation phenology caused most of the misclassified cases. Low cost unmanned aerial vehicles should be used to acquire more frequent data, which is essential to understand how vegetation patterns change over time.     

Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests

Floodplain forests play a crucial role in the storage of organic carbon (C_org). However, modeling of carbon stocks in these dynamic ecosystems remains inherently difficult. Here, we present the spatial estimation of C_org stocks in riparian woody vegetation and soils (to a depth of 1 m) in a Central European floodplain using very high spatial resolution remote sensing data and auxiliary geodata. The research area is the Danube Floodplain National Park in Austria, one of the last remaining wetlands with near-natural vegetation in Central Europe. Different vegetation types within the floodplain show distinct capacities to store C_org. We used remote sensing to distinguish the following vegetation types: meadow, reed bed and hardwood, softwood, and cottonwood forests. Spectral and knowledge-based classification was performed with object-based image analysis. Additional knowledge rules included distances to the river, object area, and slope information. Five different classification schemes based on spectral values and additional knowledge rules were compared and validated. Validation data for the classification accuracy were derived from forest inventories and topographical maps. Overall accuracy for vegetation types was higher for a combination of spectral- and knowledge-based classification than for spectral values alone. While water, reed beds and meadows were clearly detectable, it remained challenging to distinguish the different forest types. The total carbon storage of soils and vegetation was quantified using a Monte Carlo simulation for all classified vegetation types, and the spatial distribution was mapped. The average storage of the study site is 428.9 Mg C ha⁻¹. Despite certain difficulties in vegetation classification this method allows an indirect estimation of C_org stocks in Central European floodplains.     

Eco-Floristic studies of native plants of the Beer Hills along the Indus River in the districts Haripur and Abbottabad,Pakistan

The present study was conducted to elaborate vegetation composition structure to analyze role of edaphic and topographic factors on plant species distribution and community formation during 2013–14. A mixture of quadrat and transect methods were used. The size of quadrat for trees shrubs and herbs were 10 × 5, 5 × 2, 1 × 1 meter square respectively. Different phytosociological attribute were measured at each station. Primary results reported 123 plant species belong to 46 families. Asteraceae and Lamiaceae were dominant families with 8 species each. PCORD version 5 were used for Cluster and Two Way Cluster Analyses that initiated 4 plant communities within elevation range of 529–700 m from sea level. Indicator species analyses (ISA) were used to identify indicator species of each community. CANOCO Software (version 4.5) was used to measure the influence of edaphic and topographic variables on species composition, diversity and community formation. Whereas Canonical Correspondence Analysis (CCA) was used to measure the effect of environmental variables which showed elevation and aspect were the stronger environmental variable among topographic and CaCO₃ contents, electric conductivity, soil pH were the stronger edaphic factors in determination of vegetation and communities of the Bheer Hills. Grazing pressure was one of the main anthropogenic factors in this regard.     

Classification of vegetation in the Western Australian wheatbelt using Landsat MSS data

We examined the use of Landsat multispectral scanner (MSS) data to provide preliminary information on broad vegetation types present within nature reserves in the wheatbelt region of Western Australia. We analysed Landsat data for an area of natural vegetation for which ground survey and aerial photographic data are available. We used canonical variate analysis to examine the degree of spectral separation between training sites selected in the main structural vegetation types. The training classes were then grouped into spectral classes and an allocation procedure used to map the pixels in the reserve into these classes. The analysis provided a good correspondence between spectral classes and broad vegetation types recognised from aerial photography, but did not discriminate between differences in dominant species (e.g. between different types of Eucalypt woodland). The classification derived from the study reserve was then applied successfully to two nearby reserves, indicating that the data can be used to provide initial information on the broad vegetation types present in wheatbelt reserves, although it is not suitable for finer resolution studies.Abbreviation MSS = Multi-spectral scanner     

植被生化组分光谱模型抗土壤背景能力分析

应用LOPEX'93(Leaf Optical Properties Experiment)数据,分析了统计回归模型在进行植被叶绿素和水分反演中抗土壤背景影响的能力,模型参数分别使用了:反射率及其变化形式、光谱位置变量、植被指数。在LOPEX'93数据库的植被波谱中分别加入10%-90%的实测土壤光谱信息,得到植被与土壤的混合光谱,并分析混合光谱对植被生化组分的响应。结果表明:应用反射率及其变化形式进行植被叶绿素反演时,以730nm和400nm组合的反射率和反射率倒数的对数为参数的模型具有最高的抗土壤背景能力,在土壤背景所占比例从低到高的变化过程中,以二者反射率组合为参数的模型与叶绿素的相关系数,始终保持在0.645附近,以二者反射率倒数的对数为参数的模型与植被叶绿素的相关系数保持在0.650附近;应用反射率及其变化形式进行植被含水量反演时,以1100,1170,1000,1040,1080nm组合的反射率为参数的模型以及以1170,960,1210,1090,1080,950,1220,1210nm反射率倒数的对数组合为参数的模型具有较高的稳定性,在土壤组分变化的过程中,以上模型与植被含水量的相关系数均稳定的高于0.99;对于光谱位置变量的分析中,以红边-绿峰-红谷组合的模型与植被叶绿素含量具有较高、而且稳定的相关系数,在土壤背景所占比例变化的情况下,相关系数稳定在0.53附近;在应用植被指数进行叶绿素的反演过程中,植被指数与叶绿素的相关系数在土壤背景所占比例变化的情况下变化较大,抗土壤背景的能力均较差;在应用植被指数进行植被水分含量的反演时,以水分指数Ratio975和Ratio1200相关系数最高,且在不同比例土壤背景变化下稳定,相关系数分别分布在0.980附近和0.960附近。该结果可用于指导不同植被覆盖条件下植被冠层参数的反演,提高反演的稳定性和准确性。     

Identifying molecular markers associated with classification of genotypes by External Logistic Biplots

For characterization of genetic diversity in genotypes several molecular techniques, usually resulting in a binary data matrix, have been used. Despite the fact that in Cluster Analysis (CA) and Principal Coordinates Analysis (PCoA) the interpretation of the variables responsible for grouping is not straightforward, these methods are commonly used to classify genotypes using DNA molecular markers. In this article, we present a novel algorithm that uses a combination of PCoA, CA and Logistic Regression (LR), as a better way to interpret the variables (alleles or bands) associated to the classification of genotypes. The combination of three standard techniques with some new ideas about the geometry of the procedures, allows constructing an External Logistic Biplot (ELB) that helps in the interpretation of the variables responsible for the classification or ordination. An application of the method to study the genetic diversity of four populations from Africa, Asia and Europe, using the HapMap data is included. AVAILABILITY: The Matlab code for implementing the methods may be obtained from the web site: http://biplot.usal.es.     

Plant species and communities assessment in interaction with edaphic and topographic factors; an ecological study of the mount Eelum District Swat,Pakistan

The current analyses of vegetation were aimed to study the different effects of environmental variables and plant species and communities interaction to these variables, identified threats to local vegetation and suggestion for remedial measures in the Mount Eelum, Swat, Pakistan. For assessment of environmental variability quantitative ecological techniques were used through quadrats having sizes of 2 × 2, 5 × 5 and 10 × 10 m² for herbs, shrubs and trees respectively. Result of the present study revealed 124 plant species in the study area. Canonical Correspondence Analysis (CCA) was used to analyze the ecological gradient of vegetation. The environmental data and species abundance were used in CANOCO software version 4.5. The presence absence data of plant species were elaborated with Cluster and Two Way Cluster Analysis techniques using PC-ORD version 5 to show different species composition that resulted in five plant communities. Findings indicate that elevation, aspect and soil texture are the strongest variables that have significant effect on species composition and distribution of various communities shown with P value 0.0500. It is recommended to protect and use sensibly whole of the Flora normally and rare species particularly in the region.     

Cluster analysis,ordination and dominance-structural classification applied to diverse tropical vegetation at Jabiluka,Northern Territory

One hundred and seven sample plots were established on a study area at Jabiluka, Northern Territory, and detailed quantitative floristic and structural data were collected. Data collection was by sampling both on aerial photographs and in the field, and both sets of data were used to describe the primary floristic types and structural sub-types. Cluster analysis (Orloci 1967, 1969), polar ordination (Mathews 1977) and Specht's (1970, 1977) approach to vegetation classification were used to analyse the data. Two independent clustering techniques, one based on art information measure and the other on a measure of within group dispersion, produced very similar dendrograms. The analyses consistently separated the plots into three major groups - floodplain, dryland and sandstone landscapes; within these groups 15 floristic associations and eight structural formations were identified. The environmental parameters associated with the various groups were substrate type, and seasonal inundation from the Magela Creek system. The results of ordination did not highlight any environmental parameters not already made evident by cluster analysis.     

六盘山森林植被的数量分类

 六盘山位于我国黄土高原西部,秦岭西段以北,是一座南北走向的狭长石质山地,具明显 的温带大陆性季风气候。地处温带森林草原地带,地带性植被类型为落叶阔叶林和草甸草原。 本文运用相似系数法和组平均法等数学方法进行森林植被的分类。通过比较和分析,划 分出9个群系,初步建立六盘山森林植被的分类系统。这一结果与定性分类(依据植物群落学——生态学原则)所得出的结果基本相同,说明本文所采用的数量分类方法和分类指标是适用于温带地区森林植被的分类。     

Quantitative modeling of viable cell density,cell size,intracellular conductivity,and membrane capacitance in batch and fed‐batch CHO processes using dielectric spectroscopy

Dielectric spectroscopy was used to analyze typical batch and fed‐batch CHO cell culture processes. Three methods of analysis (linear modeling, Cole–Cole modeling, and partial least squares regression), were used to correlate the spectroscopic data with routine biomass measurements [viable packed cell volume, viable cell concentration (VCC), cell size, and oxygen uptake rate (OUR)]. All three models predicted offline biomass measurements accurately during the growth phase of the cultures. However, during the stationary and decline phases of the cultures, the models decreased in accuracy to varying degrees. Offline cell radius measurements were unsuccessfully used to correct for the deviations from the linear model, indicating that physiological changes affecting permittivity were occurring. The β‐dispersion was analyzed using the Cole–Cole distribution parameters Δε (magnitude of the permittivity drop), f_c (critical frequency), and α (Cole–Cole parameter). Furthermore, the dielectric parameters static internal conductivity (σ_i) and membrane capacitance per area (C_m) were calculated for the cultures. Finally, the relationship between permittivity, OUR, and VCC was examined, demonstrating how the definition of viability is critical when analyzing biomass online. The results indicate that the common assumptions of constant size and dielectric properties used in dielectric analysis are not always valid during later phases of cell culture processes. The findings also demonstrate that dielectric spectroscopy, while not a substitute for VCC, is a complementary measurement of viable biomass, providing useful auxiliary information about the physiological state of a culture. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

基于无人机多源遥感数据的亚热带森林树种分类

树种多样性是生态学研究的重要内容,树木的种类和空间分布信息可有效服务于可持续森林管理。但在复杂林分条件下,获取高精度分类结果的难度大。而无人机遥感可获取局域超精细数据,为树种分类精度的提高提供了可能。基于可见光、高光谱、激光雷达等多源无人机遥感数据,探究其在亚热带林分条件下的树种分类潜力。研究发现:(1)随机森林分类器总体精度和各树种的F1分数最高,适合亚热带多树种的分类制图,其区分13种类别(8乔木,4草本)的总体精度为95.63%,Kappa系数为0.948;(2)多源数据的使用可以显著提高分类精度,全特征模型精度最高,且高光谱和激光雷达数据显著影响全特征模型分类精度,可见光纹理数据作用较小;(3)分类特征重要性从大到小排序为结构信息,植被指数,纹理信息,最小噪声变换分量。     

Pollen assemblages as paleoenvironmental proxies in the Florida Everglades

Analysis of 170 pollen assemblages from surface samples in eight vegetation types in the Florida Everglades indicates that these wetland sub-environments are distinguishable from the pollen record and that they are useful proxies for hydrologic and edaphic parameters. Vegetation types sampled include sawgrass marshes, cattail marshes, sloughs with floating aquatics, wet prairies, brackish marshes, tree islands, cypress swamps, and mangrove forests. The distribution of these vegetation types is controlled by specific environmental parameters, such as hydrologic regime, nutrient availability, disturbance level, substrate type, and salinity; ecotones between vegetation types may be sharp. Using R-mode cluster analysis of pollen data, we identified diagnostic species groupings; Q-mode cluster analysis was used to differentiate pollen signatures of each vegetation type. Cluster analysis and the modern analog technique were applied to interpret vegetational and environmental trends over the last two millennia at a site in Water Conservation Area 3A. The results show that close modern analogs exist for assemblages in the core and indicate past hydrologic changes at the site, correlated with both climatic and land-use changes. The ability to differentiate marshes with different hydrologic and edaphic requirements using the pollen record facilitates assessment of relative impacts of climatic and anthropogenic changes on this wetland ecosystem on smaller spatial and temporal scales than previously were possible.     

Regression Tree Analysis of satellite and terrain data to guide vegetation sampling and surveys

Abstract. Monitoring of regional vegetation and surface biophysical properties is tightly constrained by both the quantity and quality of ground data. Stratified sampling is often used to increase sampling efficiency, but its effectiveness hinges on appropriate classification of the land surface. A good classification must be sufficiently detailed to include the important sources of spatial variability, but at the same time it should be as parsimonious as possible to conserve scarce and expensive degrees of freedom in ground data. As part of the First ISLSCP (International Satellite Land Surface Climatology Program) Field Experiment (FIFE), we used Regression Tree Analysis to derive an ecological classification of a tall grass prairie landscape. The classification is derived from digital terrain, land use, and land cover data and is based on their association with spectral vegetation indices calculated from single-date and multi-temporal satellite imagery. The regression tree analysis produced a site stratification that is similar to the a priori scheme actually used in FIFE, but is simpler and considerably more effective in reducing sample variance in surface measurements of variables such as biomass, soil moisture and Bowen Ratio. More generally, regression tree analysis is a useful technique for identifying and estimating complex hierarchical relationships in multivariate data sets.     

基于NDVI_Ts特征空间的中国土地覆盖分类研究

 归一化植被指数（NDVI）与地表温度（Ts）是描述地表覆盖特征的两个重要参数, 其构成的NDVI_Ts特征空间具有丰富的地学和生态学内涵。该文在NOAA/AVHRR连续时间序列数据反演Ts的基础上,通过主成分分析、非监督分类和基于DEM的分类后处理等方法,以Ts/NDVI为指标对中国土地覆盖进行分类。结果表明,Ts/NDVI对中国较大尺度上不同土地覆盖类型的差异具有较强的敏感性,其对中国土地覆盖分类结果的野外抽样检验精度比传统的单独利用NDVI时间序列进行非监督分类提高了3.3%,Kappa系数提高了0.020 2;在综合其它反映植被特征及其环境的指标（如气候、地形等）的基础上,利用Ts/NDVI将有可能较为准确 地提取中国植被或土地覆盖的信息,有利于对其进行分类和变化监测,具有深远的研究潜力 和应用价值。