Braun-Blanquet meets EcoVeg: a formation and division level classification of European phytosociological units

Aims: To link the Braun-Blanquet units of the EuroVegChecklist (EVC) with the upper levels of the International Vegetation Classification (IVC), and to propose a division level classification for Europe. Study area: Europe. Methods: We established a tabular linkage between EVC classes and IVC formations and identified mismatches between these two levels. We then proposed IVC division level units to organize EVC classes. Results: We organized the EVC classes into 21 formations and 30 divisions. We flagged classes that did not fit comfortably within an existing formation, either because its content corresponded to more than one formation or because it did not fit any formation description. In a few cases, we split EVC classes because they seemed too heterogenous to be assigned to a single formation. Conclusions: The IVC approach adds a set of physiognomic and ecological criteria that effectively organizes the EVC classes, which are already being increasingly informed by physiognomy. Therefore, the formation concepts are relatively natural extensions of concepts already embedded in the classes. However, physiognomic placement of Braun-Blanquet classes can be difficult when the sampling of the vegetation is at finer grain than usual in the respective formation (tall-scrub, annual pioneer communities). Some EVC classes seem too heterogenous to fit into the IVC formation system. Delimitation of these classes has often been a matter of debate for many decades, and the IVC perspective might help to solve these intricate issues. In other cases, mismatches between phytosociological classes and IVC formations might better be solved by emending the current formation concepts. Abbreviations: BB = Braun-Blanquet; EVC = EuroVegChecklist; IVC = International Vegetation Classification.

Poplar box woodlands of Eastern Australia: an assessment of a threatened ecological community within the IVC framework

Aims: Ecosystems nationally at risk in Australia are listed under the Environmental Protection and Biodiversity Act (EPBC Act), and many cross State jurisdictional boundaries. The determination of these ecosystems across the State boundaries are based on expert knowledge. The International Vegetation Classification has the potential to be useful as a cross-jurisdictional hierarchy which also gives global perspective to ecosystems. Study Area: All bioregions that include Eucalyptus populnea as a dominant or major component of woodlands across the species known distribution. Methods: We use plot-based data (455 plots) from two states (Queensland and New South Wales) in eastern Australia and quantitative classification methods to assess the definition and description for the Poplar Box Woodland ecosystem type (hereafter “ecological community” or “community”) that is listed as endangered under the EPBC Act. Analyses were conducted using kR-CLUSTER methods to generate alliances. Within these alliances, analyses were undertaken to define associations using agglomerative hierarchical clustering and similarity profile testing (SIMPROF). We then explore how assigning this community into the IVC hierarchy may provide a mechanism for linking Australian communities, defined at the association and alliance levels, to international communities at risk. Results: We define three alliances and 23 associations based on the results of floristic analysis. Using the standard rule-set of the IVC system, we found that the IVC hierarchy was a useful instrument in correlating ecological communities across jurisdictional boundaries where different classification systems are used. It is potentially important in giving a broader understanding of communities that may be at risk continentally and globally. Conclusions: We conclude that the IVC hierarchy can incorporate Australian communities at the association level into useful units at higher levels, and provides a useful classification tool for Australian ecosystems. Taxonomic reference: PlantNET (http://plantnet/10rbgsyd.nsw.gov.au/) [accessed June 2019]. Abbreviations: EPBC Act = Environmental Protection and Biodiversity Act; IVC = International Vegetation Classification; NMDS = non-metric multidimensional scaling; NSW = New South Wales; PCT = Plant Community Type; QLD = Queensland; RE = Regional Vegetation Community; SIMPER = similarity percentage analysis; SIMPROF = Similarity profile analysis.

Object-Based Wetland Vegetation Classification Using Multi-Feature Selection of Unoccupied Aerial Vehicle RGB Imagery

Wetland vegetation is an important component of wetland ecosystems and plays a crucial role in the ecological functions of wetland environments. Accurate distribution mapping and dynamic change monitoring of vegetation are essential for wetland conservation and restoration. The development of unoccupied aerial vehicles (UAVs) provides an efficient and economic platform for wetland vegetation classification. In this study, we evaluated the feasibility of RGB imagery obtained from the DJI Mavic Pro for wetland vegetation classification at the species level, with a specific application to Honghu, which is listed as a wetland of international importance. A total of ten object-based image analysis (OBIA) scenarios were designed to assess the contribution of five machine learning algorithms to the classification accuracy, including Bayes, K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), multi-feature combinations and feature selection implemented by the recursive feature elimination algorithm (RFE). The overall accuracy and kappa coefficient were compared to determine the optimal classification method. The main results are as follows: (1) RF showed the best performance among the five machine learning algorithms, with an overall accuracy of 89.76% and kappa coefficient of 0.88 when using 53 features (including spectral features (RGB bands), height information, vegetation indices, texture features, and geometric features) for wetland vegetation classification. (2) The RF model constructed by only spectral features showed poor classification results, with an overall accuracy of 73.66% and kappa coefficient of 0.70. By adding height information, VIs, texture features, and geometric features to construct the RF model layer by layer, the overall accuracy was improved by 8.78%, 3.41%, 2.93%, and 0.98%, respectively, demonstrating the importance of multi-feature combinations. (3) The contribution of different types of features to the RF model was not equal, and the height information was the most important for wetland vegetation classification, followed by the vegetation indices. (4) The RFE algorithm effectively reduced the number of original features from 53 to 36, generating an optimal feature subset for wetland vegetation classification. The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection. The results illustrate that the combination of UAV-based RGB imagery and the OBIA approach provides a straightforward, yet powerful, approach for high-precision wetland vegetation classification at the species level, in spite of limited spectral information. Compared with satellite data or UAVs equipped with other types of sensors, UAVs with RGB cameras are more cost efficient and convenient for wetland vegetation monitoring and mapping.

Identifying Factors That Influence Accuracy of Riparian Vegetation Classification and River Channel Delineation Mapped Using 1 m Data

Riparian vegetation delineation includes both the process of delineating the riparian zone and classifying vegetation within that zone. We developed a holistic framework to assess riparian vegetation delineation that includes evaluating channel boundary delineation accuracy using a combination of pixel- and object-based metrics. We also identified how stream order, riparian zone width, riparian land use, and image shadow influenced the accuracy of delineation and classification. We tested the framework by evaluating vegetation vs. non-vegetation riparian zone maps produced by applying random forest classification to aerial photographs with a 1 m pixel size. We assessed accuracy of the riparian vegetation classification and channel boundary delineation for two rivers in the northeastern United States. Overall accuracy for the channel boundary delineation was generally above 80% for both sites, while object-based accuracy revealed that 50% of delineated channel was less than 5 m away from the reference channel. Stream order affected channel boundary delineation accuracy while land use and image shadows influenced riparian vegetation classification accuracy; riparian zone width had little impact on observed accuracy. The holistic approach to quantification of accuracy that considers both channel boundary delineation and vegetation classification developed in this study provides an important tool to inform riparian management.

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.

RSPD: A Novel Remote Sensing Index of Plant Biodiversity Combining Spectral Variation Hypothesis and Productivity Hypothesis

Plant diversity (PD) plays an important role in maintaining the healthy function of an ecosystem through affecting the productivity, stability, and nutrient utilization of a terrestrial ecosystem. Remote sensing is a vital way to monitor the status and changes of PD. Most of the existing methods rely on a field botany survey to construct a statistical relationship between PD and remote sensing observations. However, a field botany survey is too costly to be applied widely. In this study, we constructed a new remote sensing index of PD (RSPD), combining the spectral variation hypothesis and productivity hypothesis. Concretely, the RSPD integrated the multi-band spectral reflectance and several spectral greenness, moisture, and red-edge vegetation indices with the principles of Shannon information entropy and Euclidean distance. The RSPD was evaluated by comparing the classical coefficient of variation (CV) method and the Shannon and Simpson diversity indices based on vegetation classification results. Two cases were selected, where Case I was in Beijing and Case II was located in part of Huai’an, China. Sentinel-2 data in three years of 2016, 2018, and 2020 and higher-resolution Pléiades-1 data in 2018 were also utilized. The results demonstrate that: (1) the RSPD is basically consistent with the CV in spatiotemporal variation; (2) the RSPD outperforms the CV as compared with Shannon and Simpson diversity indices that are based on vegetation classification results with Sentinel-2 and Pléiades-1 data; (3) the RSPD outperforms the CV as compared with visual interpretations with Google Earth image. The suggested index can reflect the richness and evenness of plant species, which is inherent in its calculation formula. Moreover, it has a great potential for large-scale regional and long-term series monitoring.

Quantifying the Representation of Plant Communities in the Protected Areas of the U.S.: An Analysis Based on the U.S. National Vegetation Classification Groups

Plant communities represent the integration of ecological and biological processes and they serve as an important component for the protection of biological diversity. To measure progress towards protection of ecosystems in the United States for various stated conservation targets we need datasets at the appropriate thematic, spatial, and temporal resolution. The recent release of the LANDFIRE Existing Vegetation Data Products (2016 Remap) with a legend based on U.S. National Vegetation Classification allowed us to assess the conservation status of plant communities of the U.S. The map legend is based on the Group level of the USNVC, which characterizes the regional differences in plant communities based on dominant and diagnostic plant species. By combining the Group level map with the Protected Areas Database of the United States (PAD-US Ver 2.1), we quantified the representation of each Group. If the mapped vegetation is assumed to be 100% accurate, using the Aichi Biodiversity target (17% land in protection by 2020) we found that 159 of the 265 natural Groups have less than 17% in GAP Status 1&2 lands and 216 of the 265 Groups fail to meet a 30% representation target. Only four of the twenty ecoregions have > 17% of their extent in Status 1&2 lands. Sixteen ecoregions are dominated by Groups that are under-represented. Most ecoregions have many hectares of natural or ruderal vegetation that could contribute to future conservation efforts and this analysis helps identify specific targets and opportunities for conservation across the U.S.