Vegetation Classification System Research Articles

CERN生物长期样地背景和植被分类特征本底数据集

This dataset is a comprehensive integration of 95 long-term plots from 22 natural ecosystem stations. We first sorted out the background information such as the geographical location, establishment time, area and representativeness of the long-term plot of ecological station. Drawing from the survey data of plant communities at the beginning of the plot establishment and adhering to the latest Chinese vegetation classification system, we then meticulously delineated the vegetation formation group, vegetation formation and vegetation subformation of each sample plot’s plant community. Based on the list of dominant species found in each sample plot, we identified and named the alliance and association of the vegetation in the plot. The construction of this dataset went through multiple rounds of expert review-station verification-expert review-station acceptance, etc. The dataset comprises station code, station name, plot code, plot name, plot type, plot representativeness, geographical location, altitude, plot area and shape, plot establishment time and design service life, vegetation formation group, vegetation formation, vegetation subformation, alliance, association, dominant species and remarks. It is expected to provide data support for the studies on flora, vegetation resource distribution, and biodiversity conservation.

Proposal (36) to conserve the name Philonotidion seriatae Hinterlang 1992 for the species-poor, bryophyte-dominated, non-calcareous arctic-alpine spring vegetation of Europe

According to the International Code of Phytosociological Nomenclature, a younger name of a syntaxon may be conserved against its older name to improve the stability of the nomenclature and avoid misunderstandings in scientific communication. Here, we propose conserving the name Philonotidion seriataeHinterlang 1992 for arctic-alpine, bryophyte-dominated, non-calcareous spring vegetation against the names Cardamino-MontionBraun-Blanquet 1925, Cardamino-MontionBraun-Blanquet 1926, and MontionMaas 1959. In current vegetation classification systems, the two name-giving taxa of Cardamino-Montion no longer indicate the character of the vegetation corresponding to the nomenclatural type of this alliance and are instead characteristic of other currently distinguished alliances. Maintaining the oldest name Cardamino-Montion in strict adherence to the Code would be a source of errors. In the current vegetation classification systems, two similar but counter-intuitive names would then have to be used: Cardamino-Montion for arctic-alpine springs (although the name-giving taxa are more indicative of montane springs) and Epilobio nutantis-Montion for montane springs (although the name-giving taxon Epilobium nutans is indicative of arctic-alpine vegetation). Hence, there is a risk that the name Cardamino-Montion may gradually become ambiguous. We also propose conserving the name Philonotidion seriatae against Mniobryo-Epilobion hornemanniiNordhagen 1943 to prevent confusion in case of a merger of these alliances. (36) Philonotidion seriataeHinterlang 1992 Typus: Cratoneuro-PhilonotidetumGeissler 1976 (holotypus) (=) Cardamino-MontionBraun-Blanquet 1925 Typus: Bryetum schleicheriBraun-Blanquet 1925 [≡ Montio fontanae-Bryetum schleicheriBraun-Blanquet 1925 nom. corr. et invers. (alternative name)] (holotypus) (=) Cardamino-MontionBraun-Blanquet 1926 nom. superfl. [≡ Cardamino-MontionBraun-Blanquet 1925] (=) Mniobryo-Epilobion hornemanniiNordhagen 1943 Typus: Mniobryo-Epilobietum hornemanniiNordhagen 1943 (lectotypus selected by Zechmeister & Mucina 1994) (=) MontionMaas 1959 nom. superfl. [≡ Cardamino-MontionBraun-Blanquet 1925] Taxonomic reference: Euro+Med PlantBase (http://europlusmed.org; accessed 4 January 2024)

Structural, ecological and biogeographical attributes of European vegetation alliances

AbstractThe first comprehensive phytosociological classification of all vegetation types in Europe (EuroVegChecklist; Applied Vegetation Science, 2016, 19, 3–264) contained brief descriptions of each type. However, these descriptions were not standardized and mentioned only the most distinct features of each vegetation type. The practical application of the vegetation classification system could be enhanced if users had the option to select sets of vegetation types based on various combinations of structural, ecological, and biogeographical attributes. Based on a literature review and expert knowledge, we created a new database that assigns standardized categorical attributes of 12 variables to each of the 1106 alliances dominated by vascular plants defined in EuroVegChecklist. These variables include dominant life form, phenological optimum, substrate moisture, substrate reaction, salinity, nutrient status, soil organic matter, vegetation region, elevational vegetation belt, azonality, successional status and naturalness. The new database has the potential to enhance the usefulness of phytosociological classification for researchers and practitioners and to help understand this classification to non‐specialists.

Classification and nomenclature of temperate forest types in Mexico

Aims: To analyze and synthesize the principal contributions to the creation of a classification of established forests in sub-humid, temperate climatic regions in Mexico. Methods: Documentary analysis and review of works on the classification of the vegetation of Mexico, with emphasis from 1950 to the present. To identify and analyze the terms frequently used to refer to these plant communities, the following was done: literature search, analysis of frequencies and co-occurrences of these terms that appeared in the titles of the documents. A list of associations of these communities was compiled through a documentary review. Results: Vegetation classification proposals, both nationally and internationally, tend towards the standardization of criteria and nested hierarchical integration at various levels based on physiognomic, climatic, phenological and floristic attributes. The two highest levels of organization in these proposals are based on major vegetation, defined by vegetation forms and climatic criteria as “temperate forests”. Meanwhile lower levels, including the level of associations, are based on their floristic composition. The most frequently used term to refer to these plant communities, according to the documents used in the search, is “temperate forest”, although other terms frequently used are “coniferous forest”, “pine forest”, and “oak forest”. Conclusions: Knowledge about the classification of vegetation in Mexico dates back to pre-Hispanic times. However, it was not until the second half of the 20th century that solid proposals that are influential today were put forward. Given the high biological diversity of the country, it is still a pending task to characterize and make an inventory of the diversity at the level of associations that form this type of temperate forests. Taxonomic reference: Villaseñor (2016). Abbreviations: FVT = Trans-Mexican Volcanic Belt; SECLAVEMEX = Mexican Vegetation Classification System; SMO = Sierra Madre Oriental; SMOc = Sierra Madre Occidental; SMS = Sierra Madre del Sur (SMS).

THE STRENGTHS AND LIMITATIONS OF VEGETATION MAPS FOR DETECTING CONSERVATION PERFORMANCE IN NAPA COUNTY'S NATURAL VEGETATION AND AGRICULTURAL LANDS

We report the second edition of Napa County's vegetation map using California's Manual of Vegetation classification system and aerial imagery from 2016. It covers 2053.54 km2, increases polygon resolution by 23.8%, contains 35,244 polygons, and has 71 landcover types, 60 of which are dominated by natural vegetation. It has an overall error rate of 2.6%, includes canopy density measures for 24 vegetation types, and contains explicit measures of structures in the Wildland Urban Interface (WUI). We compared the new map to the one based on 1993 imagery to ask how effective Napa county's agricultural preservation and conservation regulations have been at preventing conversion of natural vegetation across the county and of agricultural lands within and beyond the county's Agricultural Preserve, established in 1968. We examined net change and change detected using a transition matrix, to seek concealed landcover conversion. The net change shows small increases in Agriculture and Urban extents. However, the transition matrix found substantial Urban expansion into Agriculture, 12.9 km2 overall and 226.9 ha (2.24%) within the Agricultural Preserve; and 30 km2 of agricultural expansion into natural vegetation, including 595 ha of high conservation-value oak woodlands. Due to increased map resolution in the 2016 map, we identified stands of natural vegetation (Oak Woodlands, Grassland, and Chaparral) in areas previously mapped as Agriculture and Urban, as well as Oak Woodlands in areas previously mapped as other vegetation types. Their inclusion in net measures of landcover change substantially obscures actual loss in natural vegetation types and agricultural lands. The net loss for Oak Woodlands is 191 ha, but the transition matrix identifies 782 ha converted to urban or agriculture, which suggests only moderate performance for the county's conservation measures. We also found rates of vegetation change in wildfire-burned areas three times higher than in unburned areas. Conversion of agricultural lands to urban was about 2.3% in the Agricultural Preserve, but 6.3% beyond, showing that county zoning slowed urban expansion. We found changes in spatial resolution and mapping details between vegetation map editions made net measures of landcover change inaccurate, and that transition matrix-analyses provided more accurate accounting and identification of mapping methods errors.

Vegetation classification of Stipa steppes in China, with reference to the International Vegetation Classification

Aims: The vegetation classification system of China (China-VCS) is not completed. Stipa steppes are the most important steppes in China. Here we made optimal use of available plot data to classify Stipa steppes into associations in a way that is consistent with International Vegetation Classification. Study Area: the Songnen Plain, Inner Mongolian Plateau, Loess Plateau, Tibetan Plateau, and the northwest mountain areas of China. Methods: We used 1337 plots to partition the Stipa steppes of China into clusters using hierarchical clustering. Supervised noise clustering was used to improve the classifications at the group, alliance, and association levels. Non-metric multidimensional scaling ordination was used to visualize the homogeneity of plots within each cluster, and we overlaid site and climatic vectors. Diagnostic species were identified for each cluster using Indicator Species Analysis. Results: We defined five biogeographic groups, 26 alliances, 91 associations, and 12 communities of Stipa steppes of China. The Stipa-dominated alliances in the framework of the current China-VCS were verified, but the four vegetation subformations of Tussock Steppe were not completely supported by this study. Conclusions: This is the first systematical and comprehensive classification for Stipa steppes in China based on plot data. Our classification used a set of dominant species and diagnostic species to define biogeogrpahic groups, alliances and associations, ensuring compatibility with the International Vegetation Classification. Taxonomic reference: Flora Reipublicae Popularis Sinicae, Flora of China Abbreviations: AMT = Annual Mean Temperature; AP = Annual Precipitation; China-VCS = vegetation classification system of China; IVC = international vegetation classification; MTCQ = Mean Temperature of Coldest Quarter; MTWQ = Mean Temperature of Warmest Quarter; NC = noise clustering; NMDS = non-metric multidimensional scaling; PDQ = Precipitation of Driest Quarter; PS = Precipitation Seasonality; PWQ = Precipitation of Wettest Quarter; TS = Temperature Seasonality

Subtropical-temperate forested wetlands of coastal south-eastern Australia – an analysis of vegetation data to support ecosystem risk assessment at regional, national and global scales

Context Forested wetlands occurring on fluvial sediments are among the most threatened ecosystems in south-east Australia. The first quantitative diagnosis of forested wetland types in NSW was completed in 2005. Since then, there has been a three-fold increase in survey data on coastal floodplains, vegetation classification systems have been developed in New South Wales, Queensland and Victoria, and methods for the assessment of ecosystem conservation risks have been adopted by the International Union for the Conservation of Nature (IUCN). Aims To ensure an evidence base that can support conservation decisions and national conservation assessments, there is a need to review and update the classification of forested wetlands and integrate classification schemes across jurisdictions. Methods We evaluated the efficacy of a multi-stage clustering strategy, applied to data from different sources with largely unknown methodological idiosyncrasies, to retrieve ecologically meaningful clusters. We assessed the veracity and robustness of the 2005 classification of forest wetlands as a framework for national risk assessments over an expanded range. Key results We derived a quantitative, cross-jurisdictional classification of forested wetlands based on a synthesis of 5173 plot samples drawn from three states and identified the status of our units in relation to IUCN's Global Ecosystem Typology. Conclusions Our analyses support the retention of the five legacy types which are the basis for threatened ecosystem listings under the NSW Biodiversity Conservation Act 2016 and Commonwealth Environment Protection and Biodiversity Conservation Act 1999. Implications Our results will support revised assessments of current listings and facilitate their integration at state, national and global scales.

A novel biome concept and classification system based on bioclimate and vegetation – a Neotropical assay

The knowledge of biomes as large-scale ecosystem units has benefited from advances in the ecological and evolutionary sciences. Despite this, a universal biome classification system that also allows a standardized nomenclature has not yet been achieved. We propose a comprehensive and hierarchical classification method and nomenclature to define biomes based on a set of bioclimatic variables and their corresponding vegetation structure and ecological functionality. This method uses three hierarchical biome levels: Zonal biome (Macrobiome), Biome and Regional biome. Biome nomenclature incorporates both bioclimatic and vegetation characterization (i.e. formation). Bioclimate characterization basically includes precipitation rate and thermicity. The description of plant formations encompasses vegetation structure, physiognomy and foliage phenology. Since the available systems tend to underestimate the complexity and diversity of tropical ecosystems, we have tested our approach in the biogeographical area of the Neotropics. Our proposal includes a bioclimatic characterization of the main 16 Neotropical plant formations identified. This method provides a framework that (1) enables biome distribution and changes to be projected from bioclimatic data; (2) allows all biomes to be named according to a globally standardized scheme; and (3) integrates various ecological biome approaches with the contributions of the European and North American vegetation classification systems. Taxonomic reference: Jørgensen et al. (2014). Dedication: This work is dedicated to the memory of and in homage to Prof. Dr. Salvador Rivas-Martínez.

A discussion on the Improvement of Chinese Vegetation Classification System and Nomenclature

A discussion on the Improvement of Chinese Vegetation Classification System and Nomenclature

The current state of the steppe vegetation classification system in the Volga region

Steppe vegetation in the Volga region is one of the predominant types of vegetation. At present, it has survived only in areas inconvenient for human economic and recreational activities. Its research is necessary to understand the structure and composition of zonal vegetation and distribution patterns to predict changes in the modern vegetation cover. The relevance of studying the steppe vegetation of the Volga region is also due to the creation of the project “Vegetation of Russia”. We studied the steppe vegetation of the Volga region in the period from 2011 to 2020 from the standpoint of the J. Braun-Blanquet approach. Field studies covered all the diversity of the steppe vegetation represented in the region-herb-rich grass steppes, herb-grass steppes, grass steppes and desert and their psammophytic, petrophytic and halophytic edaphic variants within Ulyanovsk, Penza, Samara, Saratov, and Volgograd Oblasts, the western part of Orenburg Oblast and the northern part of Astrakhan Oblast. The syntaxonomic analysis of our own materials and literature data made it possible to establish 65 associations, 47 subassociations and 2 variants. They are assigned to the class Festuco-Brometea, the orders Festucetalia valesiacae, Helictotricho-Stipetalia, Tanaceto achilleifolii-Stipetalia lessingianae and the alliances Festucion valesiacae, Agropyrion pectinati, Centaureion sumensis, Stipion korshinskyi and Tanaceto achilleifolii-Stipion lessingianae.

Determination of burn severity models ranging from regional to national scales for the conterminous United States

Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R2 = 0.55, P < 0.001) for dNBR and poor (R2 = 0.30, P < 0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR R2¯ = 0.56 and 0.00–0.83 R2 range; NBR R2¯ = 0.43 and 0.00–0.82 R2 range) and NVC (dNBR R2¯ = 0.55 and 0.15–0.78 R2 range; NBR R2¯ = 0.41 and 0.00–0.79 R2 range) were on average the same or better than the CONUS models for dNBR and NBR, with the strongest fit models exhibiting R2 ≥ 0.70, whereas larger scale regional models R2¯ ranged from 0.28 to 0.5. However, variation in accuracy among landcover types indicate that dNBR and NBR regression models could be used to effectively estimate CBI for future fires in certain regions, while for other regions models may require additional field observations or alternative spectral transformations. Our decision tree schema can be used to help users determine which scale is likely to produce the most accurate results using our models. The CBI regression models developed here, paired with the decision tree, provide users with a simple method to estimate burn severity in units of CBI for any fire within CONUS with moderate to high levels of confidence and provide a template for further development of models with new data going forward.

Different Causal Factors Occur between Land Use/Cover and Vegetation Classification Systems but Not between Vegetation Classification Levels in the Highly Disturbed Jing-Jin-Ji Region of China

Land use/cover and vegetation patterns are influenced by many ecological factors. However, the effect of various factors on different classification systems and different levels of the same system is unclear. We conducted a redundancy analysis with 10 landscape metrics and ecological factors in four periods (1986–2005/2007, 1991–2005/2007, 1996–2005/2007, 2001–2005/2007) to explore their effects on the land use/cover system, vegetation group and vegetation type, and formation and subformation levels of the vegetation classification system in the Jing-Jin-Ji region. Soil, temperature and precipitation from 1986–2005, 1991–2005, and 2001–2005 were the important causal factors, and anthropogenic disturbance and atmospheric factors in 1996–2005 were causal factors at the land use/cover level. The total explained variance from 1996–2005 and 2001–2005 was higher than that from 1986–2005 and 1991–2005 at the land use/cover level. Causal factors and the variance explained by causal factors at the vegetation group, vegetation type, and formation and subformation levels were similar but different in the land use/cover system. Geography, soil and anthropogenic disturbance were the most important causal factors at the three vegetation levels, and the total explained variance from 2001–2007 was higher than that from 1986–2007, 1991–2007, and 1996–2007 at the three vegetation levels. In environmental research, natural resource management and urban or rural planning, geographic factors should be considered at the vegetation group, vegetation type and formation and subformation levels while atmospheric and temperature factors should be considered at the land use/cover level.

Classification of forest and shrubland vegetation in Mediterranean Turkey

AbstractQuestionsWhat are the main vegetation types of forest and shrubland vegetation at the alliance level in Mediterranean Turkey? What is their syntaxonomical position? Can we integrate them into the European vegetation classification system? Which environmental factors are the main drivers of the floristic differentiation of vegetation types?LocationSouthern and western Turkey.MethodsWe collected 4,717 vegetation plots of forest and shrubland vegetation in Mediterranean Turkey and performed an unsupervised classification of this data set. We described vegetation types based on the classification results, expert knowledge and information from the literature. We defined diagnostic species and prepared distribution maps for each vegetation type. To support the interpretation of the vegetation types, we determined the most important environmental variables using canonical correspondence analysis.ResultsThe studied vegetation was divided into 21 types related to three vegetation belts: (a) thermo‐ and meso‐mediterranean, comprising coniferous (Pinus brutia, Pinus pinea) and sclerophyllous forests, as well as macchia, garrigue and phrygana; (b) supra‐mediterranean, comprising Pinus nigra subsp. pallasiana forests, thermophilous deciduous forests dominated by various oak species and Ostrya carpinifolia, and forests dominated by temperate species such as Fagus orientalis; and (c) oro‐mediterranean, comprising forests and shrublands dominated by Abies cilicica, Cedrus libani, Juniperus excelsa and Juniperus communis subsp. nana. Elevation was identified as the main environmental driver of the vegetation pattern. Among climatic variables, the most important are the mean temperatures (annual and of driest, coldest, and warmest quarters), minimum temperature of winter, precipitation of warmest and driest quarters and precipitation seasonality. These factors indicate the decreasing effect of the Mediterranean climate with increasing elevation.ConclusionsThe vegetation of Mediterranean Turkey is arranged along climatic gradients depending on elevation and the distance from the Mediterranean Sea. Most vegetation types in this area correspond to the syntaxa accepted in EuroVegChecklist, while others were described as new.

Australian vegetation classification and the International Vegetation Classification framework: an overview with case studies

Recent advances in conceptual frameworks in vegetation classifications, such as the EcoVeg approach that underpins the International Vegetation Classification (IVC) developed by NatureServe staff and colleagues, offer opportunities to enhance national classification initiatives. National level initiatives provide an important stepping-stone between international units and subnational units. Australia has a long history of developing various vegetation typologies at local to regional scales, but ecologists recognise the need for an Australia-wide, plot-based vegetation classification system that incorporates the principles of the EcoVeg approach, and thereby helps build an international classification system. Using two case studies, we provide a comparison of various structures and criteria for relevant Australian classifications in the context of the IVC, and exemplify how Australian classifications of forest, shrublands, grasslands, and deserts could potentially link into the IVC hierarchy to illustrate the capacity of the IVC to summarise the full range of Australian vegetation at a broad formation (biome) scale. We then discuss how the IVC might inform future work towards an Australian vegetation classification system and, vice versa, the implications of an Australian vegetation classification for IVC development.

Aligning quantitative vegetation classification and landscape scale mapping: updating the classification approach of the Regional Ecosystem classification system used in Queensland

Vegetation classification systems form a base for conservation management and the ecological exploration of the patterns and drivers of species’ distributions. A standardised system crossing administrative and geographical boundaries is widely recognised as most useful for broad-scale management. The Queensland Government, recognising this, uses the Regional Ecosystem (RE) classification system and accompanying mapping as a state-wide standardised vegetation classification system. This system informs legislation and policy at local, state and national levels, underpinning decisions that have wide-ranging implications for biodiversity and people’s livelihoods. It therefore needs to be robust from a scientific and legal perspective. The current approach in the RE system for identifying vegetation communities relies on expert-based class definition procedures. This is in contrast to best practice, which is based on quantitative procedures. This paper discusses the RE system in a global context and outlines the updated approach that incorporates quantitative class definition procedures, synthesises the research behind the updated approach and discusses its implications and implementation.

Overview and compatibility of Northern Territory plot-based vegetation data with the National Vegetation Classification System and future vegetation typologies

Plot-based vegetation data have been collected in the Northern Territory since the 1950s. The purpose of data collection broadly includes land resource and vegetation community mapping, flora surveys, habitat assessment for fauna surveys, rare and threatened species surveys, development assessment and monitoring. These datasets were stored in disparate database systems until 2012. Currently over 423 surveys, 69 000 geocoded plots, comprising almost 838 164 flora records, are maintained using a corporate database. The data are of varying degrees of attribute detail including floristics, structure, strata and associated environmental information. The original territory-wide standardised vegetation classification was based on the 1:1 000 000 vegetation mapping project where 112 broad vegetation types were described from 2245 plots using an intuitive appraisal of numerical analytical techniques. This approach continues to be used; however, a standard method would be desirable to classify plot data into meaningful vegetation types in the Northern Territory. The National Vegetation Information System (vegetation hierarchy) was used to qualitatively assess the level of detail of vegetation plot data. Only 9% of the plot data can be assigned to sub-association level, and 28% to the association level. In addition to reviewing the compatibility of plot data to the National Vegetation Information System, we also provide examples of how it can conform with the International Vegetation Classification.

Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Surface biomass characterization plays a key role in wildfire management. It allows classifying vegetation fuels flammability for fire risk analysis, to define silvicultural prescriptions for fire hazard reduction, to plan prescribed burning, or to model fire behavior and its effects, such as greenhouse gas and pollutant emissions. To facilitate fuel classification and analysis of potential fire behavior and effects in Italy, we harmonized 634 measurements of surface wildland fuels from Alpine, temperate and Mediterranean environments. The dataset provides quantitative data for duff, fine dead fuels and downed woody material, live grasses and shrubs fuel components. Surface fuel data were harmonized by subdividing loads (Mg ha-1) to standard size classes for dead (0-6, 6-25 and 25-75 mm) and live (0-6, 6-25 mm) fuels, collecting percent cover and depth/height (cm) of the various fuel components, and classifying observations into 19 fuelbed categories. To ensure comparability with existing vegetation classification systems, we classified each observation according to the European Fuel Map, the Corine Land Cover classes (level IV), the European Forest Types, and the forest categories of the Italian National Forest Inventory. The dataset and a photo description of each fuelbed category are available as Supplementary material. This dataset is the first step to develop several products at the national scale such as: (i) fuel type classification and mapping; (ii) carbon stock and wildfire emission estimates; (iii) calibration of fuel models for the simulation of fire behavior and effects.

Review and prospect of vegetation research in Sichuan

<p indent="0mm">This paper reviews the vegetation research history in Sichuan Province and analyzes the influencing factors of vegetation change in the past <sc>40 years.</sc> Furthermore, a new cartographic classification system of Sichuan vegetation (e.g., vegetation type groups, vegetation types, alliances) is proposed by referring to recent field surveys, literature, and several significant vegetation classification systems in China. Additionally, some expectations for future research on Sichuan vegetation are proposed. First, the Sichuan Vegetation Classification System should be consistent with the national classification system, and regional characteristics can be reflected in individual units. Second, new techniques and methods can be used to supplement the investigation of vegetation types and areas in Sichuan Province. Finally, the revision and compilation of Sichuan vegetation and the Sichuan vegetation map should begin as soon as possible. Meanwhile, a renewable database should be established to lay a foundation for the frequent updating of vegetation maps in the future.

Spatiotemporal distribution of grassland NPP in Gansu province, China from 1982 to 2011 and its impact factors.

The modified Carnegie–Ames–Stanford Approach (CASA) model based on the comprehensive and sequential classification system of grasslands (CSCS, a unique vegetation classification system) was used to determine grassland net primary production (NPP) in Gansu province from 1982 to 2011 and its spatio-temporal variability. The relationship between NPP and climate drivers was analyzed. The results showed that annual NPP of grasslands in Gansu province averaged 139.30 gC m-2 yr -1 during the study period. NPP decreased from southeast to northwest across the province. Grassland NPP showed an increasing trend during the period 1982–2011, and the increase rate over the whole period was 92.91%. The highest NPP appeared in summer with more precipitation and higher cumulative temperature conditions; while the lowest values existed in winter. The largest correlation coefficient was found between the average annual NPP and the average annual precipitation (r = 0.77), followed by annual NPP and solar radiation (r = 0.70) or NDVI (r = 0.69), Annual NPP had no significant correlation with annual cumulative temperature (>0°C) or moisture index (K-value). Thus, precipitation is the major controlling factor on the average annual NPP in Gansu grassland. Solar radiation and NDVI also have important effects on grassland NPP in Gansu. These results may provide basic information for sustainable development and utilization of grassland and for the improvement and protection of the ecological environment as well.

Optimal transformation of species cover for vegetation classification

AbstractAimsVegetation‐plot sampling usually involves estimating species cover. For classifying plots to vegetation types, covers are often transformed to decrease the effect of dominant species. However, it remains unclear which transformation is optimal. We suggest that for vegetation classification, optimal is such transformation that contributes to creating clusters of plots in an unsupervised classification that are most similar to the widely accepted vegetation types, e.g., phytosociological associations. Here our aim is to find and recommend such optimal transformation by testing a range of transformation options against the national vegetation classifications of three European countries.LocationCzech Republic, The Netherlands, Great Britain.MethodsThree national datasets of vegetation plots with species cover information, classified to associations or community types of the respective national vegetation classification systems, were analysed. From each dataset, multiple subsets of plots were selected randomly, each subset representing a vegetation‐plot table containing several similar associations/community types. Species cover values in these subsets were subjected to various transformations (power transformation, logarithmic transformation and pseudo‐species cut levels). Then each subset was classified by an agglomerative classification method (beta‐flexible clustering with different beta values), and the classification was compared with the units of the national vegetation classification using the adjusted Rand index.ResultsPower transformations of percentage covers with an exponent between 0.3 and 0.6 produced the best match between the unsupervised classifications and the national vegetation classifications. This result did not depend on the classification method used. A similar degree of matching was achieved with some cut levels of pseudo‐species and with logarithmic transformation of percentage cover.ConclusionsIf an unsupervised classification of vegetation plots aims at defining vegetation types that are close to the phytosociological associations accepted in national vegetation classifications, the best transformation is close to the square‐root of percentage cover (i.e., power transformation with exponent 0.5).