Vegetation Classification

This book reviews the flora and vegetation cover of unique examples of the natural ecosystems known as raised bogs that are to be found in Belarus. It presents detailed information about the country’s raised bog flora based on original data arising from long-term field studies of Belarussian mires. Taxonomical, phytogeographical, biomorphological and ecological-coenotic analyses are
included. Lists of phytocoenotic synataxa have been prepared using both the Braun-Blanquet (floristic) and the dominance approaches, and the syntaxomonical units defined according to these two vegetation classification systems are compared. The results are used in developing action plans for mire conservation and a national strategy for the rational use of raised bogs in Belarus. The volume is copiously illustrated with colour maps and the authors’ own photographs.
This book will be of interest to scientists (biologists, ecologists, geographers, etc.), university lecturers and their students, schoolteachers, environmental protection and nature conservation agency personnel, and all nature lovers.

A historical overview of the phytosociological method in Europe is presented. Some methodological and procedural differences in the application of the Braun-Blanquet approach, from the selection of the sampling plots to the assignment of relevés to existing or newly described units, are briefly compared. The main advantages and limitations of the phytosociological vegetation classification are reviewed and discussed, also in light of their applications for vegetation mapping and monitoring.

The vegetation of pine forests of Shangla District of Khyber Pakhtunkhwa Province of Pakistan is
described. Thirty stands at different locations were selected in this study. In each stand gymnospermic species
were widely distributed and dominated. Sampling was performed by Point Centered Quarter Method (PCQ).
Pinus wallichiana exhibited higher density 409 ha-1 with 132.1 m2 ha-1 basal area occurring in 26 stands. Abies
pindrow occurred in 7 stands with density of 384 ha-1 and 145.3 m2 ha-1 basal area, while Picea smithiana
attained low frequency, recorded from 2 sites. It is shown that population of young stands exhibited higher
density.

Aims: Most vegetation classification systems developed for large areas include
various inconsistencies. Therefore, we (1) propose a new consistent Cocktailbased
approach to redefine the traditional phytosociological classification of species-
poor vegetation; (2) apply it to create a classification protocol for aquatic
vegetation; (3) implement this protocol in a computer expert system; and (4) test
it with a data set previously classified using an older version of the Cocktail
method.
Methods: The new approach uses formal logic to provide formal definitions of
vegetation units. In the classification protocol for aquatic vegetation we defined
consistent criteria for delimitation of associations according to the concepts that
are predominantly used in phytosociology, based on species cover, dominance
patterns and functional species groups. We applied these criteria in a computer
expert systemrunning in the JUICE 7.0 program, and applied themto a test data
set of 12 171 vegetation plots from the Czech Republic containing at least one
aquatic species. The new classification was compared with (1) the previous
national Cocktail classification based on species cover values and in few cases on
sociological species groups, and (2) a non-formalized expert-based classification.
Results: Thirteen functional species groups were created to build logical formulas
of 64 aquatic associations and 5297 (44% of the total data set) vegetation
plots were assigned to these associations, i.e. by 4% and 12% more than in the
previous Cocktail and expert-based classifications, respectively. There was 94%
and 83% classification agreement with the previous Cocktail and expert-based
classification.
Conclusions: The new approach produces a formal, consistent and unequivocal
classification of species-poor vegetation with several advantages over similar
approaches. It provides not only a set of formal definitions of vegetation units,
but also a set of rules for building such definitions. All associationswith common
characteristics are defined by structurally identical formulas, ensuring consistency
of the classification. While similar approaches for species-rich vegetation
use sociological species groups, which are not applicable to species-poor vegetation,
the new approach introduces the use of functional species groups, which
reflect vegetation physiognomy and spatial structure and, in combination with
species dominance, enable the classification of species-poor vegetation in a similarmanner
as in traditional phytosociology.

Identification of crop species is an important issue in agricultural management. In recent years, many studies have explored this topic using multi-spectral and hyperspectral remote sensing data. In this study, we perform dedicated research to propose a framework for mapping crop species by combining hyperspectral and Light Detection and Ranging (LiDAR) data in an object-based image analysis (OBIA) paradigm. The aims of this work were the following: (i) to understand the performances of different spectral dimension-reduced features from hyperspectral data and their combination with LiDAR derived height information in image segmentation; (ii) to understand what classification accuracies of crop species can be achieved by combining hyperspectral and LiDAR data in an OBIA paradigm, especially in regions that have fragmented agricultural landscape and complicated crop planting structure; and (iii) to understand the contributions of the crop height that is derived from LiDAR data, as well as the geometric and textural features of image objects, to the crop species' separabilities. The study region was an irrigated agricultural area in the central Heihe river basin, which is characterized by many crop species, complicated crop planting structures, and fragmented landscape. The airborne hyperspectral data acquired by the Compact Airborne Spectrographic Imager (CASI) with a 1 m spatial resolution and the Canopy Height Model (CHM) data derived from the LiDAR data acquired by the airborne OPEN ACCESS Remote Sens. 2015, 7 923 Leica ALS70 LiDAR system were used for this study. The image segmentation accuracies of different feature combination schemes (very high-resolution imagery (VHR), VHR/CHM, and minimum noise fractional transformed data (MNF)/CHM) were evaluated and analyzed. The results showed that VHR/CHM outperformed the other two combination schemes with a segmentation accuracy of 84.8%. The object-based crop species classification results of different feature integrations indicated that incorporating the crop height information into the hyperspectral extracted features provided a substantial increase in the classification accuracy. The combination of MNF and CHM produced higher classification accuracy than the combination of VHR and CHM, and the solely MNF-based classification results. The textural and geometric features in the object-based classification could significantly improve the accuracy of the crop species classification. By using the proposed object-based classification framework, a crop species classification result with an overall accuracy of 90.33% and a kappa of 0.89 was achieved in our study area.

Aims: Classification of vegetation is an essential tool to describe, understand, predict and manage biodiversity. Given the multiplicity of approaches to classify vegetation, it is important to develop international consensus around a set of general guidelines and purpose-specific standard protocols. Before these goals can be achieved, however, it is necessary to identify and understand the different
choices that aremade during the process of classifying vegetation. This paper presents a framework to facilitate comparisons between broad-scale plot-based classification approaches.
Results: Our framework is based on the distinction of four structural elements (plot record, vegetation type, consistent classification section and classification system) and two procedural elements (classification protocol and classification approach). For each element we describe essential properties that can be used for comparisons. We also review alternative choices regarding critical decisions of classification approaches; with a special focus on the procedures used to define vegetation types from plot records. We illustrate our comparative framework by applying it to different broad-scale classification approaches.
Conclusions: Our framework will be useful for understanding and comparing plot-based vegetation classification approaches, as well as for integrating classification systems and their sections.

Acidophilous grasslands are one of the most characteristic elements of the mountain landscape of the cantabrian regions, particularly in areas traditionally used for extensive grazing. They occupy large areas, frequently in mosaic with heath-lands, spiny communities and beech forests. They develop mostly over oligotrophic substrata as well as strongly leached deep soils on limestones. The analysis of these grasslands carried out in the present study, has permitted us to recognise a new association: Carici piluliferae-Agrostietum curtisii ass. nova.

The European Vegetation Archive (EVA) is a centralized database of European vegetation plots developed by the IAVS Working Group European Vegetation Survey. It has been in development since 2012 and first made available for use in research projects in 2014. It stores copies of national and regional vegetation-plot databases on a single software platform. Data storage in EVA does not affect on-going independent development of the contributing databases, which remain the property of the data contributors. EVA uses a prototype of the data-base management software TURBOVEG 3 developed for joint management of multiple databases that use different species lists. This is facilitated by the SynBioSys Taxon Database, a system of taxon names and concepts used in the individual European databases and their corresponding names on a unified list of European flora. TURBOVEG 3 also includes procedures for handling data requests, selections and provisions according to the approved EVA Data Property and Governance Rules. By 30 June 2015, 61 databases from all European regions have joined EVA, contributing in total 1 027 376 vegetation plots, 82% of them with geographic coordinates, from 57 countries. EVA provides a unique data source for large-scale analyses of European vegetation diversity both for fundamental research and nature conservation applications. Updated information on EVA is available online at http://euroveg.org/eva-database.