Vegetation mapping of the Great Fish River basin,South Africa: Integrating spatial and multi‐spectral remote sensing techniques

Abstract. We present a remote sensing based vegetation mapping technique well suited to a heterogeneous, semi‐arid environment. 10 structural vegetation classes were identified and described on the ground. Using Landsat‐TM from two different seasons and a combination of three conventional classification techniques (including a multi‐temporal classification) we were unsuccessful in delineating all of the desired vegetation classes. We then employed a simple tex‐tural classification index, known as the Moving Standard Deviation Index (MSDI), that has been used to map degradation status. MSDI measures spatial variations in the landscape and is calculated by passing a 3 × 3 standard deviation filter across the Landsat‐TM red band. High MSDI values are associated with degraded or disturbed rangelands whilst low MSDI values are associated with undisturbed rangeland. A combination of two conventional multi‐spectral techniques and MSDI were used to produce a final vegetation classification at an accuracy of 84 %. MSDI successfully discriminated between two contrasting vegetation types of identical spectral properties and significantly strengthened the accuracy of the classification. We recommend the use of a tex‐tural index such as MSDI to supplement conventional vegetation classification techniques in heterogeneous, semi‐arid or arid environments.     

Are landscape attributes a useful shortcut for classifying vegetation in the tropics? A case study of La Amistad International Park

Effective vegetation classification schemes identify the processes determining species assemblages and support the management of protected areas. They can also provide a framework for ecological research. In the tropics, elevation‐based classifications dominate over alternatives such as river catchments. Given the existence of floristic data for many localities, we ask how useful floristic data are for developing classification schemes in species‐rich tropical landscapes and whether floristic data provide support for classification by river catchment. We analyzed the distribution of vascular plant species within 141 plots across an elevation gradient of 130 to 3200 m asl within La Amistad National Park. We tested the hypothesis that river catchment, combined with elevation, explains much of the variation in species composition. We found that annual mean temperature, elevation, and river catchment variables best explained the variation within local species communities. However, only plots in high‐elevation oak forest and Páramo were distinct from those in low‐ and mid‐elevation zones. Beta diversity did not significantly differ in plots grouped by elevation zones, except for low‐elevation forest, although it did differ between river catchments. None of the analyses identified discrete vegetation assemblages within mid‐elevation (700–2600 m asl) plots. Our analysis supports the hypothesis that river catchment can be an alternative means for classifying tropical forest assemblages in conservation settings.     

Are habitat types compatible with floristically‐derived associations?

Abstract. The habitat type system developed by R. Daubenmire has been widely adopted throughout the western United States. Habitat types result from a site classification derived from the classification of late seral plant communities using selected indicator species. It has been suggested that the classification of late successional vegetation used to derive habitat types does not substantially differ from phytosociological classification in the sense of Braun‐Blanquet approach, and that habitat types can be adopted in their present form into floristically‐based vegetation classifications. Despite the many commonalities between the two systems, however, the classification methods, and specifically the use of indicator species in the habitat type system, yield a significantly different classification than the phytosociological approach. This is demonstrated in the comparison of a habitat type classification with the results of a recent phytosociological classification of forest vegetation in the northern Salish Mountains of Montana.     

Relevance of Global Forest Change Data Set to Local Conservation: Case Study of Forest Degradation in Masoala National Park,Madagascar

A global data set on forest cover change was recently published and made freely available for use (Hansen et al. 2013. Science 342: 850–853). Although this data set has been criticized for inaccuracies in distinguishing vegetation types at the local scale, it remains a valuable source of forest cover information for areas where local data is severely lacking. Masoala National Park, in northeastern Madagascar, is an example of a region for which very little spatially explicit forest cover information is available. Yet, this extremely diverse tropical humid forest is undergoing a dramatic rate of forest degradation and deforestation through illegal selective logging of rosewood and ebony, slash‐and‐burn agriculture, and damage due to cyclones. All of these processes result in relatively diffuse and small‐scale changes in forest cover. In this paper, we examine to what extent Hansen et al.'s global forest change data set captures forest loss within Masoala National Park by comparing its performance to a locally calibrated, object‐oriented classification approach. We verify both types of classification with substantial ground truthing. We find that both the global and local classifications perform reasonably well in detecting small‐scale slash‐and‐burn agriculture, but neither performs adequately in detecting selective logging. We conclude that since the use of the global forest change data set requires very little technical and financial investment, and performs almost as well as the more resource‐demanding, locally calibrated classification, it may be advantageous to use the global forest change data set even for local conservation purposes.     

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.     

The management of vegetation classifications with fuzzy clustering

Questions: Does fuzzy clustering provide an appropriate numerical framework to manage vegetation classifications? What is the best fuzzy clustering method to achieve this? Material: We used 531 relevés from Catalonia (Spain), belonging to two syntaxonomic alliances of mesophytic and xerophytic montane pastures, and originally classified by experts into nine and 13 associations, respectively. Methods: We compared the performance of fuzzy C‐means (FCM), noise clustering (NC) and possibilistic C‐means (PCM) on four different management tasks: (1) assigning new relevé data to existing types; (2) updating types incorporating new data; (3) defining new types with unclassified relevés; and (4) reviewing traditional vegetation classifications. Results: As fuzzy classifiers, FCM fails to indicate when a given relevé does not belong to any of the existing types; NC might leave too many relevés unclassified; and PCM membership values cannot be compared. As unsupervised clustering methods, FCM is more sensitive than NC to transitional relevés and therefore produces fuzzier classifications. PCM looks for dense regions in the space of species composition, but these are scarce when vegetation data contain many transitional relevés. Conclusions: All three models have advantages and disadvantages, although the NC model may be a good compromise between the restricted FCM model and the robust but impractical PCM model. In our opinion, fuzzy clustering might provide a suitable framework to manage vegetation classifications using a consistent operational definition of vegetation type. Regardless of the framework chosen, national/regional vegetation classification panels should promote methodological standards for classification practices with numerical tools.     

Integrating a global agro-climatic classification with bioregional boundaries in Australia

Aim Stratification of major differences in the biophysical features of landscapes at the continental scale is necessary to collectively assess local observations of landscape response to management actions for consistency and difference. Such a stratification is an important step in the development of generalizations concerning how landscapes respond to different management regimes. As part of the development of a comparative framework for this purpose, we propose a climate classification adapted from an existing broad scale global agro‐climatic classification, which is closely aligned with natural vegetation formations and common land uses across Australia. Location The project considered landscapes across the continent of Australia. Methods The global agro‐climatic classification was adapted by using elevation‐dependent thin plate smoothing splines to clarify the spatial extents of the 18 global classes found in Australia. The clarified class boundaries were interpolated from known classes at 822 points across Australia. These classes were then aligned with the existing bioregional classification, Interim Biogeographic Regionalization for Australia IBRA 5.1. Results The aligned climate classes reflect major patterns in plant growth temperature and moisture indices and seasonality. These in turn reflect broad differences in cropping and other land use characteristics. Fifty‐two of the 85 bioregions were classified entirely into one of the 18 agro‐climatic classes. The remaining bioregions were classified according to sub‐bioregional boundaries. A small number of these sub‐bioregions were split to better reflect agro‐climatic boundaries. Main conclusions The agro‐climatic classification provided an explicit global context for the analysis. The topographic dependence of the revised climate class boundaries clarified the spatial extents of poorly sampled highland classes and facilitated the alignment of these classes with the bioregional classification. This also made the classification amenable to explicit application. The bioregional and subregional boundaries reflect discontinuities in biophysical features. These permit the integrated classification to reflect major potential differences in landscape function and response to management. The refined agro‐climatic classification and its integration with the IBRA bioregions are both available for general use and assessment.     

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.     

Defining functional biomes and monitoring their change globally

Biomes are important constructs for organizing understanding of how the worlds’ major terrestrial ecosystems differ from one another and for monitoring change in these ecosystems. Yet existing biome classification schemes have been criticized for being overly subjective and for explicitly or implicitly invoking climate. We propose a new biome map and classification scheme that uses information on (i) an index of vegetation productivity, (ii) whether the minimum of vegetation activity is in the driest or coldest part of the year, and (iii) vegetation height. Although biomes produced on the basis of this classification show a strong spatial coherence, they show little congruence with existing biome classification schemes. Our biome map provides an alternative classification scheme for comparing the biogeochemical rates of terrestrial ecosystems. We use this new biome classification scheme to analyse the patterns of biome change observed over recent decades. Overall, 13% to 14% of analysed pixels shifted in biome state over the 30‐year study period. A wide range of biome transitions were observed. For example, biomes with tall vegetation and minimum vegetation activity in the cold season shifted to higher productivity biome states. Biomes with short vegetation and low seasonality shifted to seasonally moisture‐limited biome states. Our findings and method provide a new source of data for rigorously monitoring global vegetation change, analysing drivers of vegetation change and for benchmarking models of terrestrial ecosystem function.     

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.     

Spatial variability of above‐ground net primary production in Uruguayan grasslands: a remote sensing approach

Question: How does above‐ground net primary production (ANPP) differ (estimated from remotely sensed data) among vegetation units in sub‐humid temperate grasslands? Location: Centre‐north Uruguay. Methods: A vegetation map of the study area was generated from LANDSAT imagery and the landscape configuration described. The functional heterogeneity of mapping units was analysed in terms of the fraction of photosynthetically active radiation absorbed by green vegetation (fPAR), calculated from the normalized difference vegetation index (NDVI) images provided by the moderate resolution imaging spectroradiometer (MODIS) sensor. Finally, the ANPP of each grassland class was estimated using NDVI and climatic data. Results: Supervised classification presented a good overall accuracy and moderate to good average accuracy for grassland classes. Meso‐xerophytic grasslands occupied 45% of the area, Meso‐hydrophytic grasslands 43% and Lithophytic steppes 6%. The landscape was shaped by a matrix of large, unfragmented patches of Meso‐xerophytic and Meso‐hydrophytic grasslands. The region presented the lowest anthropic fragmentation degree reported for the Rio de la Plata grasslands. All grassland units showed bimodal annual fPAR seasonality, with spring and autumn peaks. Meso‐hydrophytic grasslands showed a radiation interception 10% higher than the other units. On an annual basis, Meso‐hydrophytic grasslands produced 3800 kg dry matter (DM) ha^?1 yr^?1 and Meso‐xerophytic grasslands and Lithophytic steppes around 3400 kg·DM·ha^?1·yr^?1. Meso‐xerophytic grasslands had the largest spatial variation during most of the year. The ANPP temporal variation was higher than the fPAR variability. Conclusions: Our results provide valuable information for grazing management (identifying spatial and temporal variations of ANPP) and grassland conservation (identifying the spatial distribution of vegetation units).     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

Implementation of a hierarchical global vegetation classification in ecosystem function models

Abstract. We propose an alternative approach for the currently used biogeographic global vegetation classifications. A hierarchical vegetation classification system is proposed for consistent and routine monitoring of global vegetation. Global vegetation is first defined into six classes based on plant canopy structure and dynamics observable by remote sensing from satellites. Additional biome variability is then represented through a remote sensing derived leaf area index map, and direct climate data sets driving an ecosystem model to compute and map net primary production and evapotranspiration. Simulation results from an ecosystem function model suggest that the six canopy structure-based classes are sufficient to represent global variability in these parameters, provided the spatio-temporal variations in Leaf Area Index and climate are characterized accurately. If a bioclimatically based classification is needed for other purposes, our six class approach can be expanded to a possible 21 classes using archived climatic zones. For example, tropical, subtropical, temperate and boreal labels are defined by absolute minimum temperature. Further separation in each class is possible through changes in water availability defined by precipitation and/or soils. The resulting vegetation classes correspond to many of the existing, conventional global vegetation schemes, yet retain the measure of actual vegetation possible because remote sensing first defines the six biome classes in our classification. Vegetation classifications are no longer an end product but a source of initializing data for global ecosystem function models. Remote sensing with biosphere models directly calculates the ecological functions previously inferred from vegetation classifications, but with higher spatial and temporal accuracy.     

A quantitative analysis of photometric data from aerial photographs for vegetation survey

This paper describes the application of quantitative density analysis to black and white aerial photographs for vegetation survey using a Quantimet 720 image analyser. The photometric data are analysed using both a supervised and an unsupervised classification strategy. Floristic data collected from an independent ground survey, are used to categorise those vegetation classes of interest against which the photometric data classifications are assessed. The preliminary results obtained suggest that broad classes of vegetation types may be distinguished automatically from their grey scale distribution patterns.     

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.     

Early Pleistocene vegetation change in upland south‐eastern Australia

Aim This study aims to improve our understanding of the late Cenozoic history of Australian rain forest and sclerophyll biomes by presenting a detailed pollen record demonstrating the floristic composition and orbital‐scale patterns of change in forest communities of upland south‐eastern Australia, during the Early Pleistocene. The record is examined in order to shed light on the nature of the transition from rain forest‐dominated ‘Tertiary’ Australian vegetation to open‐canopied ‘Quaternary’ vegetation. Location Stony Creek Basin (144.13° E, 37.35° S, 550 m a.s.l), a small, infilled palaeolake in the western uplands of Victoria, Australia. Methods A c. 40‐m‐long sediment core was recovered from the infilled palaeolake. Palynology was used to produce a record of changing vegetation through time. Multivariate analyses provided a basis for interpreting the composition of rain forest and sclerophyll forest communities and for identifying changes in these communities over successive insolation cycles. Results Early Pleistocene upland south‐eastern Australian vegetation was characterized by orbital‐scale, cyclic alternation between rain forest and sclerophyll forests. Individual intervals of forest development underwent patterns of sequential taxon expansion that recurred in successive vegetation cycles. Diverse rain forests included a number of angiosperm and gymnosperm taxa now extinct regionally to globally. Sclerophyll forests were also diverse, and occurred under warm and wet climate conditions. Main conclusions The Stony Creek Basin record demonstrates that as recently as c. 1.5 Ma diverse rain forests persisted in southern Australia beyond the modern continental range of rain forest. The importance of conifers in these rain forests emphasizes that they have no modern Australian analogue. Alternation in dominance between these forests and diverse, sclerophyllous open canopied forests was apparently driven by changes in seasonality, and may have been promoted by fire.     

Probabilistic key for identifying vegetation types in the field: A new method and Android application

Quick identification of vegetation types in the field, based on species composition but not requiring time‐consuming plot sampling, is often needed for vegetation mapping, conservation assessment, teaching and other applications of vegetation classification. Here, we propose a new method that identifies the probability of belonging to the units of an established vegetation classification for vegetation stands encountered in the field. The method is based on calculating the probability that a few species observed in the field would co‐occur in a priori defined vegetation types, using the existing information on species occurrence frequency in these types. The method has been implemented in a freely available Android application called Probabilistic Vegetation Key, which makes it possible to employ it in the field using smartphones or tablets, even in the absence of internet access.     

Environmental and anthropogenic determinants of vegetation distribution across Africa

Aim To assess the influence of natural environmental factors and historic and current anthropogenic processes as determinants of vegetation distributions at a continental scale. Location Africa. Methods Boosted regression trees (BRTs) were used to model the distribution of African vegetation types, represented by remote‐sensing‐based land‐cover (LC) types, as a function of environmental factors. The contribution of each predictor variable to the best models and the accuracy of all models were assessed. Subsequently, to test for anthropogenic vegetation transformation, the relationship between the number of BRT false presences per grid cell and human impact was evaluated using hurdle models. Finally, the relative contributions of environmental, current and historic anthropogenic factors on vegetation distribution were assessed using regression‐based variation partitioning. Results Deserts and evergreen forests were best predicted by environmental variables, though most other LC classes were also relatively well predicted by the environment. Annual precipitation emerged as the most important determinant of all LC classes. At low rainfall levels, LC classes with increasing woody cover replaced each other as rainfall increased, while LC class rainfall optima overlapped at high rainfall levels. With some exceptions, anthropogenic factors had a relatively small influence on the distribution of most LC classes. However, anthropogenic factors did have an influence on the inaccuracies in BRT models, and these models provided an indication of which LC classes have been most reduced by transformation. Main conclusions Here we show, for the first time, how environmental and anthropogenic factors influence vegetation distribution across Africa. LC classes at rainfall extremes are best predicted by the environment. In addition, we corroborate, also for the first time, the much‐stated claim that rainfall is the most important variable for the distribution of African vegetation for all African vegetation types. Finally, we indicate how anthropogenic drivers affect LC distributions.     

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.