Vegetation mapping using hierarchical object‐based image analysis applied to aerial imagery and lidar data

Aims: The primary objective of this study is to map the distribution and quantify the cover of vegetation alliances over the entirety of San Clemente Island (SCI). To this end, we develop and evaluate the mapping method of hierarchical object‐based classification with a rule‐based expert system. Location: San Clemente Island, California, USA. Methods: We developed and tested an approach based on hierarchical object‐based classification with a rule‐based expert system to effectively map vegetation communities on SCI following the Manual of California Vegetation classification system. In this mapping approach, the shrub species defining each vegetation community and non‐shrub growth forms were first mapped using aerial imagery and lidar data, then used as input in an automated mapping rule set that incorporates the percent cover rules of a field‐based mapping rule set. Results: The final vegetation map portrays the distribution of 19 vegetation communities across SCI, with the largest areas comprised of California Annual and Perennial Grassland (35%) and three types of coastal sage scrub and maritime succulent scrub, comprising a combined 53% of the area. Map accuracy was assessed to be 79% based on fuzzy methods and 61% with a traditional accuracy assessment. The accuracy of tree identification was assessed to be 81%, but species‐level tree accuracy was 45%. Conclusions: Semi‐automated approaches to vegetation community mapping can produce repeatable maps over large spatial extents that facilitate ecological management efforts. However, some low‐statured shrub community types were difficult to differentiate due to patchy canopies of co‐occurring species including abundant non‐native grasses characteristic of complex disturbance histories. Species‐level tree mapping accuracy was low due to the difficulty of identifying species within poorly illuminated canyons, resulting from sub‐optimal image acquisition timing.     

Standard survey designs drive bias in the mapping of upland swamp communities

Vegetation maps are critical biodiversity planning instruments, but the classification of vegetation for mapping can be strongly biased by survey design. Standardization of survey design across different vegetation types is therefore increasingly recommended for vegetation mapping programs. However, some vegetation types have complex small‐scale vegetation patterns that are important in characterizing these vegetation types, and standard designs will often not capture these patterns. The objective of this paper was to investigate the magnitude of potential map bias that results from survey design standardization and recommend approaches to deal with this bias. We surveyed upland swamps of the Greater Blue Mountains World Heritage Area Australia using two contrasting survey designs, including the standard 400 m² single quadrat design recommended and used by authorities. We then derived a classification for these swamps and tested the effect of survey design on this classification, species richness and the type of species detected (obligate or facultative swamp species). Species richness and species type were not significantly different among survey techniques. However, more than 40% of swamps clustered differently among survey designs. Thus, one of the 10 derived communities (which is floristically consistent with a previously mapped endangered community) was indistinct, and some individual swamps misclassified using the standard survey design. An effect of landscape position on swamp floristic patterns and a significant trend for high similarity scores among swamps surveyed with multiple small quadrats compared to the standard survey design was also determined. Australian upland swamps are classified at the global scale as shrub‐dominated wetlands, and complex floristic patterns have been recorded in shrub‐dominated wetlands in both northern and southern hemispheres. We therefore advocate either multiple survey designs or different survey standards for upland swamp communities and other vegetation types that have complex floristic patterns at small scales.     

Reconstructing pre-impact vegetation cover in modified landscapes using environmental modelling, historical surveys and remnant vegetation data: a case study in the Fleurieu Peninsula, South Australia

Aim To investigate the application of environmental modelling to reconstructive mapping of pre‐impact vegetation using historical survey records and remnant vegetation data. Location The higher elevation regions of the Fleurieu Peninsula region in South Australia were selected as a case study. The Fleurieu Peninsula is an area typical of many agricultural regions in temperate Australia that have undergone massive environmental transformation since European settlement. Around 9% of the present land cover is remnant vegetation and historical survey records from the ad 1880s exist. It is a region with strong gradients in climate and topography. Methods Records of pre‐impact vegetation distribution made in surveyors’ field notebooks were transcribed into a geographical information system and the spatial and classificatory accuracy of these records was assessed. Maps of remnant vegetation distribution were obtained. Analysis was undertaken to quantify the environmental domains of historical survey record and remnant vegetation data to selected meso‐scaled climatic parameters and topo‐scaled terrain‐related indices at a 20 m resolution. An exploratory analytical procedure was used to quantify the probability of occurrence of vegetation types in environmental domains. Probability models spatially extended to geographical space produce maps of the probability of occurrence of vegetation types. Individual probability maps were combined to produce a pre‐impact vegetation map of the region. Results Surveyors’ field notebook records provide reliable information that is accurately locatable to levels of resolution such that the vegetation data can be spatially correlated with environmental variables generated on 20 m resolution environmental data sets. Historical survey records of vegetation were weakly correlated with the topo‐scaled environmental variables but were correlated with meso‐scaled climate. Remnant vegetation records similarly not only correlated to climate but also displayed stronger relationships with the topo‐scaled environmental variables, particularly slope. Main conclusions A major conclusion of this study is that multiple sources of evidence are required to reconstruct past vegetation patterns in heavily transformed region. Neither the remnant vegetation data nor historical survey records provided adequate data sets on their own to reconstruct the pre‐impact vegetation of the Fleurieu Peninsula. Multiple sources of evidence provide the only means of assessing the environmental and historical representativeness of data sets. The spatial distribution of historical survey records was more environmentally representative than remnant vegetation data, which reflect biases due to land clearance. Historical survey records were also shown to be classificatory and spatially accurate, thus are suitable for quantitative spatial analyses. Analysis of different spatial vegetation data sets in an environmental modelling framework provided a rigorous means of assessing and comparing respective data sets as well as mapping their predicted distributions based on quantitative correlations. The method could be usefully applied to other regions where predictions of pre‐impact vegetation cover are required.     

《中国植被志》研编内容与规范

植被志是基于植被(或植物群落)调查资料, 全面记叙植被的外貌、物种组成、结构和功能, 以及地理分布和生境条件等特征, 并对同类植被进行归纳和总结的志书。《中国植被志》是第一部对中国植被进行全面记述的志书, 预计完成约48卷110册。在借鉴《中国植被》(1980)(简称“80方案”)植被分类基本原则的基础上,《中国植被志》将对中国植被分类系统的高级分类单位(植被型组、植被型和植被亚型)进行归纳和总结, 对中级和低级分类单位(群系组、群系、亚群系, 群丛组、群丛)进行详细描述。植被高级分类单位的描述具有概括性质, 是在中国植被分类系统中级和低级分类单位描述的基础上, 对其在全球和中国境内的地理分布、自然环境、群落外貌、植被类型及多样性、优势种或共优势种、生物多样性保育价值以及资源现状等进行概述, 并对“80方案”相关内容进行修订和拓展, 将提供对中国植被基本特征客观、准确的记述。在植被中级和低级分类单位中, 群系组描述的内容包括地理分布、自然环境、群落外貌、植被类型以及价值与保育等内容; 群系描述的主要内容包括地理分布、自然环境、生态特征、物种组成、群落结构、群丛组和群丛的分类与描述、优势种的生物学特性、生物量与生产力、植被动态与演替以及价值与保育等方面。作为植被志研编的核心内容, 群丛组和群丛的分类与描述主要基于植被调查资料, 采用数量分类方法, 根据群落结构和物种组成的差异划分出不同的植被类型, 并对其基本特征进行定量描述和归纳。其中, 群落的层片结构、特定植被分类单元的特征种或特征种组的筛选与甄别是植被类型划分的关键环节; 而群落外貌, 群落结构, 物种组成, 各类物种的生长习性、生境的偏适性等是群丛组和群丛描述与归纳的重点内容。该文提出了中国植被中级和低级分类单位的命名方案, 其特点在于植被类型的科学名称中同时体现了植被分类单元特征种或优势种的名称及其所属的高级植被分类单位(植被型组或植被型)的名称, 兼顾了植被名称的规范性与实用性。《中国植被志》的研编工作由文献整编、群落调查、数据分析与整理、文本撰写等环节组成。该文对植被样方的调查与收集, 文献收集与整编, 气候、土壤、地形等相关数据的来源及其整理方法, 植被分类方法, 植被命名, 植被分类单元描述的内容, 植被志章节编写大纲、体裁及撰写等多个规范进行了详细的阐述或示例。     

Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values

Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification? Location: The Netherlands. Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three‐dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations. Results and Conclusions: With indicator values, most reléves were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models.     

Formalized reproduction of an expert‐based phytosociological classification: A case study of subalpine tall‐forb vegetation

Abstract. Delimitation of vegetation units in phytosociology is traditionally based on expert knowledge. Applications of expert‐based classifications are often inconsistent because criteria for assigning relevés to vegetation units are seldom given explicitly. Still, there is, e.g. in nature conservation, an increasing need for a consistent application of vegetation classification using computer expert systems for unit identification. We propose a procedure for formalized reproduction of an expert‐based vegetation classification, which is applicable to large phytosociological data sets. This procedure combines Bruelheide's Cocktail method with a similarity‐based assignment of relevés to constancy columns of a vegetation table. As a test of this method we attempt to reproduce the expert‐based phytosociological classification of subalpine tall‐forb vegetation of the Czech Republic which has been made by combination of expert judgement and stepwise numerical classification of 718 relevés by TWINSPAN. Applying the Cocktail method to a geographically stratified data set of 21794 relevés of all Czech vegetation types, we defined groups of species with the statistical tendency of joint occurrences in vegetation. Combinations of 12 of these species groups by logical operators AND, OR and AND NOT yielded formal definitions of 14 of 16 associations which had been accepted in the expert‐based classification. Application of these formal definitions to the original data set of 718 relevés resulted in an assignment of 376 relevés to the associations. This assignment agreed well with the original expert‐based classification. Relevés that remained un‐assigned because they had not met the requirements of any of the formal definitions, were subsequently assigned to the associations by calculating similarity to relevé groups that had already been assigned to the associations. A new index, based on frequency and fidelity, was proposed for calculating similarity. The agreement with the expert‐based classification achieved by the formal definitions was still improved after applying the similarity‐based assignment. Results indicate that the expert‐based classification can be successfully formalized and converted into a computer expert system.     

上海大金山岛植被分类与制图——基于网格化清查方法

网格化清查方法有助于准确诊断一个地区的植被性质,并为探索植被分类方法提供支持。该研究以上海大金山岛为对象,借助航拍影像等间距地将其划分为140个清查网格(40 m × 40 m),按照统计样方法逐网格调查植物群落特征,综合运用列表法和双向指示种法,进行植被分类并绘制现状植被图。按照新修订的植被三级分类系统进行分类:一级单位根据植被型,大金山岛植被划分为落叶阔叶林、常绿落叶阔叶混交林、常绿阔叶林、落叶阔叶灌丛、常绿落叶阔叶混交灌丛、常绿阔叶灌丛和草丛; 二级单位根据优势种和植物区系特征,可划分出15种群丛或群落类型; 三级单位根据群落年龄和外貌可划分为22种群落类型。以上结果表明,大金山岛不仅是上海市物种多样性最高的区域,也拥有华东海岛最典型、最多样的自然半自然森林群落。就植被状态而言,地带性森林群落处于演替中后期,但少数次生植被处于演替前期,且面临着猴群干扰导致的植被发育停滞不前等生态问题。关于植被分类方法,网格化清查方法可充分揭示植物群丛连续性中包含间断性和过渡性群落的现象。     

Local ranges of phytosociological associations: are they reflected in numerical classification?

In the tradition of European phytosociology, delimitations of vegetation units such as associations are mostly based on data from small areas where more detailed vegetation sampling has been carried out. Such locally delimited vegetation units are often accepted in large-scale synthetic classifications, e.g. national vegetation monographs, and tentatively assigned to a small geographical range, forming groups of similar (vicarious) vegetation units in different small areas. These vicarious units, however, often overlap in species composition and are difficult to recognize from each other. We demonstrate this issue using an example of the classification of dry grasslands (Festuco-Brometea) in the Czech Republic. The standard vegetation classification of the Czech Republic supposes that the majority of accepted associations (66 out of 68) have a restricted distribution in one of the two major regions, Bohemia or Moravia. We compared the classification into traditional associations with the numerical classification of 1440 phytosociological relevés from the Czech Republic, in order to test whether the traditionally recognized associations with small geographical ranges are reflected in numerical classification. In various comparisons, the groups of relevés identified by numerical analysis occupied larger areas than the traditional associations. This suggests that with consistent use of total species composition as the vegetation classification criterion, the resulting classification will usually include more vegetation units with larger geographical ranges, while many of the traditional local associations will disappear.     

遥感和GIS支持下的城市植被制图及其特征分析

 以上海城市植被为研究对象,探索城市植被的分类理论和方法,提出一套适用于城市植被分类与制图的方法。根据城市植被信息获取的方式以及生态-外貌分类原则,建立了基于植物生活型的、可用图形表示的城市植被分类体系,分天然与半天然植被和人工植被两个大类,共有9个植被型和34个植被亚型。在遥感和地理信息系统(GIS)技术的支持下,建立研究区的城市植被信息数据库,并在此基础上对城市植被类型的特征以及与土地利用的关系进行分析比较,以期掌握城市植被类型的数量特征及其空间分布,同时按不同分类级别用计算机进行城市植被制图。城市植被     

Spatial analysis of oblique photo‐point images for quantifying spatio‐temporal changes in plant communities

Question: Can spatial analytical techniques be used to extract quantitative measurements of vegetation communities from ground‐based permanent photo‐point images? Location: Mount Aspiring National Park, south‐western South Island, New Zealand. Methods: Sets of ground‐based photographs representing two contrasting vegetation types were selected to test two spatial analytical techniques. In the grid technique, a grid was superimposed onto the photographs and the frequency of species presence in each grid‐square was calculated to estimate species abundance/cover over the defined area. In the object‐oriented technique, the photographs were segmented into meaningful objects, based on the colour of the pixels and the textural patterns of the images, and the area occupied by an object in the image was used to derive species abundance/cover over the area. Results: Both techniques allow quick and easy classification of digital elements into ecologically relevant categories of vegetation components. The grid technique appeared more robust, being quick and efficient, accommodating all image types and providing presence/absence matrices for multivariate analysis. Fewer classes were identified using the object‐oriented technique, in particular for the forest interior site and for small individual plants such as Astelia spp. Conclusions: Both techniques showed potential for the objective quantitative analysis of long‐term vegetation monitoring of cover and changes of several component species, using repeat ground‐based photographs more specifically for grassland habitats. However, both rely to various degrees on manual classification. Corrective factors and strict protocols for taking the photographs are necessary to account for variation in view angles and to compute values more representative of absolute species abundance.     

High‐resolution riparian vegetation mapping to prioritize conservation and restoration in an impaired desert river

In highly impaired watersheds, it is critical to identify both areas with desirable habitat as conservation zones and impaired areas with the highest likelihood of improvement as restoration zones. We present how detailed riparian vegetation mapping can be used to prioritize conservation and restoration sites within a riparian and instream habitat restoration program targeting 3 native fish species on the San Rafael River, a desert river in southeastern Utah, United States. We classified vegetation using a combination of object‐based image analysis (OBIA) on high‐resolution (0.5 m), multispectral, satellite imagery with oblique aerial photography and field‐based data collection. The OBIA approach is objective, repeatable, and applicable to large areas. The overall accuracy of the classification was 80% (Cohen's κ = 0.77). We used this high‐resolution vegetation classification alongside existing data on habitat condition and aquatic species' distributions to identify reaches' conservation value and restoration potential to guide management actions. Specifically, cottonwood (Populus fremontii) and tamarisk (Tamarix ramosissima) density layers helped to establish broad restoration and conservation reach classes. The high‐resolution vegetation mapping precisely identified individual cottonwood trees and tamarisk thickets, which were used to determine specific locations for restoration activities such as beaver dam analogue structures in cottonwood restoration areas, or strategic tamarisk removal in high‐density tamarisk sites. The site prioritization method presented here is effective for planning large‐scale river restoration and is transferable to other desert river systems elsewhere in the world.     

优势种植被分类系统的逻辑分析与示例方案化

逻辑学原理是各种分类系统科学性及规范性的必要检验工具。本文采用逻辑学原理检验基于优势种的《中国植被》的植被分类系统, 结果发现目前常用的植被分类系统存在较多逻辑错误, 需要予以纠正。于是, 在强调植物生活型分类系统和植被分类系统一致性的基础上, 依据逻辑学原理给出建立植被分类系统的步骤和方法, 提出规范的植物生活型分类系统和植被分类系统示例方案。鉴于多建群种植被的客观存在及其存在形式多样, 在分类系统中给出相应的位置——多建群种植被纲。同时, 针对国内植被分类学界从未形成统一的植被命名规则, 且又有多种命名方式并存的现状, 提出了函数命名法。     

广西常绿阔叶林分类系统及特点

常绿阔叶林是广西分布最广泛、最为复杂多样的植被类型.遵循《中国植被》一书的植被分类原则,并参考宋永昌先生的《中国常绿阔叶林分类试行方案》.根据高级单位以生态外貌、中级单位以优势度类型、低级单位以特征种组的分类原则,将广西常绿阔叶林划分出5个植被亚型、11个群系组和102个群系.在5个植被亚型中,典型常绿阔叶林和季风常绿...     

New pollen classification of Chenopodiaceae for exploring and tracing desert vegetation evolution in eastern arid central Asia

Members of the Chenopodiaceae are the most dominant elements in the central Asian desert. The different genera and species within this family are common in desert vegetation types. Should it prove possible to link pollen types in this family to specific desert vegetation, it would be feasible to trace vegetation successions in the geological past. Nevertheless, the morphological similarity of pollen grains in the Chenopodiaceae rarely permits identification at the generic level. Although some pollen classifications of Chenopodiaceae have been proposed, none of them tried to link pollen types to specific desert vegetation types in order to explore their ecological significance. Based on the pollen morphological characters of 13 genera and 24 species within the Chenopodiaceae of eastern central Asia, we provide a new pollen classification of this family with six pollen types and link them to those plant communities dominated by Chenopodiaceae, for example, temperate dwarf semi‐arboreal desert (Haloxylon type), temperate succulent halophytic dwarf semi‐shrubby desert (Suaeda, Kalidium, and Atriplex types), temperate annual graminoid desert (Kalidium type), temperate semi‐shrubby and dwarf semi‐shrubby desert (Kalidium, Iljini, and Haloxylon types), and alpine cushion dwarf semi‐shrubby desert (Krascheninnikovia type). These findings represent a new approach for detecting specific desert vegetation types and deciphering ecosystem evolution in eastern central Asia.     

中国喀斯特植被分类系统

我国喀斯特地貌分布广泛,是全球喀斯特集中分布区面积最大且岩溶发育最强烈的典型区域。喀斯特植被物种组成独特,生物多样性丰富,群落结构复杂,植被类型极其丰富。该文对已发表的喀斯特植被类型和近期的调查资料进行系统整理,依据《中国植被志》的分类原则,在群系尺度上编制了中国喀斯特植被分类系统,包括3个植被型组、13个植被型和554个群系。探讨了喀斯特植被的分类系统与原有分类系统的衔接,并依据相关资料,对喀斯特生境专性群系进行了识别和判定,以期为喀斯特区域的石漠化治理、植被修复提供科学参考。     

Characterization of moorland vegetation and the prediction of bird abundance using remote sensing

Aims To characterize and identify upland vegetation composition and height from a satellite image, and assess whether the resulting vegetation maps are accurate enough for predictions of bird abundance. Location South‐east Scotland, UK. Methods Fine‐taxa vegetation data collected using point samples were used for a supervised classification of a Landsat 7 image, while linear regression was used to model vegetation height over the same image. Generalized linear models describing bird abundance were developed using field‐collected bird and vegetation data. The satellite‐derived vegetation data were substituted into these models and efficacy was examined. Results The accuracy of the classification was tested over both the training and a set of test plots, and showed that more common vegetation types could be predicted accurately. Attempts to estimate the heights of both dwarf shrub and graminoid vegetation from satellite data produced significant, but weak, correlations between observed and predicted height. When these outputs were used in bird abundance–habitat models, bird abundance predicted using satellite‐derived vegetation data was very similar to that obtained when the field‐collected data were used for one bird species, but poor estimates of vegetation height produced from the satellite data resulted in a poor abundance prediction for another. Conclusions This pilot study suggests that it is possible to identify moorland vegetation to a fine‐taxa level using point samples, and that it may be possible to derive information on vegetation height, although more appropriate field‐collected data are needed to examine this further. While remote sensing may have limitations compared with relatively fine‐scale fieldwork, when used at relatively large scales and in conjunction with robust bird abundance–habitat association models, it may facilitate the mapping of moorland bird abundance across large areas.     

Assignment of relevés to pre‐defined classes by supervised clustering of plant communities using a new composite index

Question: How does a newly designed method of supervised clustering perform in the assignment of relevé (species composition) data to a previously established classification. How do the results compare to the assignment by experts and to the assignment using a completely different numerical method? Material: Relevés analysed represent 4186 Czech grassland plots and 4990 plots from a wide variety of vegetation types (359 different associations or basal communities) in The Netherlands. For both data sets we had at our disposal an expert classification, and for the Czech data we also had available a numerical classification as well as a classification based on a neural network method (multi‐layer perceptron). Methods: Two distance indices, one qualitative and one quantitative, are combined into a single index by weighted multiplication. The composite index is a distance index for the dissimilarity between relevés and vegetation types. For both data sets the classifications by the new method were compared with the existing classifications. Results: For the Czech grasslands we correctly classified 81% of the plots to the classes of an expert classification at the alliance level and 71% to the classes of the numerical classification. Correct classification rates for the Dutch relevés were 64, 78 and 83 % for the lowest (subassociation or association), association, and alliance level, respectively. Conclusion: Our method performs well in assigning community composition records to previously established classes. Its performance is comparable to the performance of other methods of supervised clustering. Compared with a multi‐layer perceptron (a type of artificial neural network), fewer parameters have to be estimated. Our method does not need the original relevé data for the types, but uses synoptic tables. Another practical advantage is the provision of directly interpretable information on the contributions of separate species to the result.     

Macrophyte‐based assessment of lakes – a contribution to the implementation of the European Water Framework Directive in Germany

The European Water Framework Directive requires ecological status classification and monitoring of surface and ground water bodies using biological indicators. To fulfill the demands of the Directive, a macrophyte‐based assessment system was developed for application on four lake site types in Germany. Biological lake site types were established using differences in characteristic macrophyte communities, reflecting ecoregion, Ca²⁺ content, mixis and morphology. Ecological status classification of lake sites is based on macrophyte abundance along 275 transects in 95 natural German lakes and the calculation of a reference index value, in some cases supplemented by submerged vegetation data. The reference index quantifies the deviation of species composition and abundance from reference conditions and classifies sites to one of the five ecological quality classes specified in the Directive. Based on an example of Lake Chiemsee, Germany, the possibilities for a wholelake assessment are discussed. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Phytosociological data give biased estimates of species richness

Abstract. Large phytosociological data sets of three types of grassland and three types of forest vegetation from the Czech Republic were analysed with a focus on plot size used in phytosociological sampling and on the species‐area relationship. The data sets included 12975 relevés, sampled by different authors in different parts of the country between 1922 and 1999. It was shown that in the grassland data sets, the relevés sampled before the 1960s tended to have a larger plot size than the relevés made later on. No temporal variation in plot sizes used was detected in forest relevés. Species‐area curves fitted to the data showed unnatural shapes, with levelling‐off or even decrease in plot sizes higher than average. This distortion is explained by the subjective, preferential method of field sampling used in phytosociology. When making relevés in species‐poor vegetation, researchers probably tend to use larger plots in order to include more species. The reason for this may be that a higher number of species gives a higher probability of including presumed diagnostic species, so that the relevé can be more easily classified in the Braun‐Blanquet classification system. This attitude of phytosociologists has at least two consequences: (1) in phytosociological data bases species‐poor vegetation types are underrepresented or relevés are artificially biased towards higher species richness; (2) the suitability of phytosociological data for species richness estimation is severely limited.