The contribution of vegetation survey and mapping to Herbarium collections and botanical knowledge: a case study from Queensland.

A history of pollen mapping and surveillance: The relations between natural history and clinical allergy

This article looks at innovation as a product of the interaction between scientific and indigenous knowledge (IK). The innovation process raises questions about how knowledge on medicinal plants (MPs) is acquired, commoditized and politicized. By invoking the experiences of two related Indian non-governmental organizations (NGOs), the article examines attempts made to promote IK in MPs through botanical knowledge and knowledge based on policies of biodiversity conservation, global health and development. The case studies further help shed light on how local knowledge is reinvented to fit into new global networks. Making traditional medicine (TRM) more visible through NGOs helps promote IK in MPs; it also has the effect of disbanding TRM from embodied knowledge and daily practices.

The distribution of different vegetation types is important information for landscape management, especially in the context of tackling global environmental change. Vegetation types can be mapped using satellite and airborne passive remote sensing. However, spectrally similar yet structurally different vegetation types, like different tree-dominated land covers, are often challenging to map using spectral information alone. We examined the potential of vertical vegetation structure acquired in the global ecosystem dynamics investigation (GEDI) mission that harnesses a space-borne waveform lidar sensor in vegetation mapping across a heterogeneous tropical landscape in Cambodia. We extracted 121 waveform metrics from Level-1B and Level-2A data products at 1062 locations across five key vegetation types. After reducing the relative height variables’ dimensionality through simple linear regressions, we developed a Random Forest classifier to predict vegetation classes based on 23 GEDI metrics. We then used this model to classify the vegetation types across more than 77 000 GEDI footprints in the study area. GEDI metrics alone were useful in identifying vegetation types with 81% accuracy. Cropland/grassland class had the highest prediction accuracy (user’s accuracy [UA] = 89%; producer’s accuracy [PA] = 91%), while dry deciduous forest had the lowest accuracy (UA = 73%; PA = 69%). By comparing the GEDI-only classification with an optical-radar map, we found that structural and topographic information from GEDI Level-1B and Level-2A can complement the spectral information in assessing natural habitats that neighbor other vegetation types in a heterogeneous landscape. The highest classification accuracy at the footprint scale was obtained from the combination of GEDI, Sentinel-1, and Sentinel-2 (88.3%). We also demonstrated how wall-to-wall vegetation mapping is possible by combining the three data sources. These findings expand the potential use of GEDI waveform lidar data in supporting the development of policy-relevant maps that depict the distribution of forests together with other vegetation types.

A new approach to vegetation sample selection, classification and mapping is described that accounts for rare and restricted vegetation communities. The new method (data-informed sampling and mapping: D-iSM) builds on traditional preferential sampling and was developed to guide conservation and land-use planning. It combines saturation coverage of vegetation point data with a preferential sampling design to produce locally accurate vegetation classifications and maps. Many existing techniques rely entirely or in part on random sampling, modelling against environmental variables, or on assumptions that photo-patterns detected through aerial photographic interpretation or physical landscape features can be attributed to a specific vegetation type. D-iSM uses ground data to inform both classification and mapping phases of a project. The approach is particularly suited to local- and regional-scale situations where disputes between conservation and development often lead to poor planning decisions, as well as in circumstances where highly restricted vegetation types occur within a wider mosaic of more common communities. Benefits of the D-iSM approach include more efficient and more representative floristic sampling, more realistic and repeatable classifications, increased user accuracy in vegetation mapping and increased ability to detect and map rare vegetation communities. Case studies are presented to illustrate the method in real-world classification and mapping projects.

When modelling soil acidification at the European scale, it is inevitable that both model and data have varying degrees of associated uncertainty. The present study attempted to quantify the uncertainty in long‐term forecasts of soil solution concentrations of Al3+ and NO−3 concentrations resulting from the uncertainty in small resolution European‐scale maps and input data, using the Netherlands as a case study. Large‐scale forecasts were made with a relatively simple dynamic process‐oriented model, SMART2. Model outputs were considered as block median concentrations and the block areal fractions in which concentrations exceeded a critical level. Sources of uncertainty considered included (i) uncertainty in soil and vegetation maps (categorical data), and (ii) uncertainty in soil and vegetation‐related parameters (continuous data). The uncertainty in model outputs was quantified by an efficient two‐step Monte Carlo simulation approach, which takes spatial correlation into account. The uncertainty in the input data at the European scale led to major uncertainties in the predicted Al3+ concentration. Uncertainties in the areas where the Al3+ concentration exceeded the maximum allowable concentration were much smaller. The uncertainties in soil‐related parameters contributed most to the uncertainty in the A3+ concentration, whereas the uncertainty contributed by the soil and vegetation maps was negligible. For the NO−3 concentration, however, the uncertainty originated mainly from the soil and vegetation maps. Evaluation of the different error sources is of great practical significance, as it identifies which sources need further improvement. The present study shows that the uncertainty contribution of the different error sources depends greatly on the model output considered.

Estimating the fitness of a local discrete-structured population: From uncertainty to an exact number

This essay is a case study about information gathering and knowledge production in early modern Malabar and the Netherlands with the aim to review the historiography about the making of the Hortus Malabaricus. It focuses on the making of the twelve volumes of the Hortus Malabaricus and analyses the different stages of its production as well as the role of a Dutch servant of the Verenigde Oost-Indische Compagnie (VOC) in transforming information from Malabar into European knowledge. Why Malabar did not equally benefit from the exchange of information? We shall assess Dutch overseas expansion and the impact of trade in global knowledge production, and discuss why Europe has allegedly led the path in the making of early modern science, since approximately 1500. While several scholars embrace the idea of ‘the rise of Europe’, others, first and foremost Andre Gunder Frank, have called for a review of this subject. We will employ this case study to further elucidate certain aspects of knowledge production and the ‘great divergence’ in economic conditions of South Asia and Europe which started to become evident in the mid-eighteenth century. While comparing the social and economic conditions in Malabar and the Netherlands, we will attempt to understand why a centralisation of knowledge took place in the Netherlands and not in Malabar.

Inducing flooding index for vegetation mapping in water-land ecotone with Sentinel-1 & Sentinel-2 images: A case study in Dongting Lake, China

This study aims at finding out thescientif is method about roadside planting in national park. Vegetation survey was practised on the Ome Road (Chichibu-tama national park, Tokyo prefecture) and the Odaigahara Drive-way (Yoshino-kumano national park, Nara prefecture).From the point of phytosociological view, the location (Standort) of the roads was mentioned. Consideration studied here is as follows.1. The relation between natural park planning and phytosociological survey.(i) Natural park planning is to be based on phytnsociological results and vegetation map, to conserve forest communities, which are the most important elements of landscape in natural park. Vegetation map in scale 1: 10, 000-50, 000 with vegetation units of association (Assoziation) and subassociation (Subassoziation) is suitable.(ii) After natural park plan is authorized, location of road must be adapted in detail to natural environment around the road. Phytosociolgical research is also necessary to this purpose. Road must be located carefully, not to injure forest communities and to promote restoration of vegetation. Vegetation map in scale 1: 2, 000-10, 000 with vegetation units of association, subassociation, and variety (Variante) is suitable.2. The relation between planning for roadside planting and phytosociological survey.(i) After road is located, vegetation survey is requested to organize the road and surrounding forest communities phytosociologically. Forest communities and their mantle and sleeve communities must be researched to make up for 1.-(i) and (ii). The process of planning for roadside planting is as follows.(1) Analysis and diagnosis of the planting environment on roadside(a) To make existing vegetation map(b) To make potential natural vegetation map of today(2) Selection of the usefull species(a) To systematize the potential natural vegetations of today and their substitution vegetations(b) To select the usefull species in main species of the vegetations(c) To group the species according to each use Combining (1) and (2), the planning for roadside planting is made.(ii) Finally, the problems of maintenance remain. After planting on the roadside, it is necessary to research the process of succession periodically and to make a suitable management on each stage of succession.

This article describes the vegetation mapping procedure adopted for the Rosyanka Carbon Supersite in Kaliningrad Province (Russia). To achieve the research objectives of the Carbon Supersite Programme which include the assessment of greenhouse gas (GHG) emissions, monitoring of ecosystem changes and modelling of the rewetting process, a detailed basemap of vegetation is required. The GIS-based vegetation map prepared includes over 100 polygon features assigned to 28 vegetation classification units comprising 6 vegetation types and 22 plant community categories, the latter approximating to associations. The mapped phytosociological units can be converted to ecology-based ones which can be used, in combination with additional data, to assign GHG flux values to the mapped units and thus to assess emission/sequestration rates at different peatland sites. Thus, the results of our investigation provide options for developing GHG flux estimation methodologies, e.g. GEST approach or ‘vegetation - water level proxy’ approach. We also outline possibilities for further applications of the vegetation map in relation to carbon supersite purposes. The fine-scale geobotanical map provides high-resolution cartographic material that can be correlated to land cover classes in other types of vegetation cover maps that may be required for research relating to peatland restoration, and the mapped phytosociological units may serve as a basis for increasing resolution to reveal further detail of the spatial heterogeneity of the vegetation cover.

In order to emphasize the importance of vegetation mapping for nature conservation purposes a case study in Terceira island (Azores) is presented, in which the importance of the natural vegetation of the eastern slope of Santa Barbara volcano (which is part of the Site of Community Importance of Santa Barbara–Pico Alto) is evaluated through the elaboration of its vegetation map. Fourteen (14) different natural vegetation types were identified: grasslands (1 type), peat bogs (2 types), scrubs (2), forests (5), successional vegetation (3) and vegetation of rocky slopes (1). All communities are protected under the Habitat and Species Directive (EC/92/43) and most of them are endemic to the Azores Islands. This fact, together with the significant number of Azorean endemic taxa (18), Macaronesian endemic taxa (5) and species protected under the Habitat and Species Directive (7), gives this area an important conservation value that justifies future protection actions. Vegetation mapping is an important tool for the characterization, evaluation and implementation of managing plans of natural areas of the Azores islands. The use of a floristic-based classification, supported by multivariate analysis and structural data, is an efficient methodology for the construction of these maps. The data collected comprise an important set of information about the distribution and abundance of natural vegetation types and endemic and rare species. This information was not available until now and is indispensable for the elaboration of management plans of Special Zones for Conservation that will be part of the NATURA 2000 network.

Modelling pre-clearing vegetation distribution using GIS-integrated statistical, ecological and data models: A case study from the wet tropics of Northeastern Australia

Conservation evaluation of large areas ( > 10 000 km2) in Australia requires detailed mapping of vegetation types. Predicting the original vegetation cover of extensive cleared areas in an explicit, consistent and repeatable manner necessitates the use of statistical modelling. This paper describes an integrated approach to vegetation mapping in a region of New South Wales, Australia. The approach uses separate statistical models for each tree and shrub species to predict the vegetation composition in each grid cell in a geographic information system (GIS). Allocation of these grid cells to communities allows communities that no longer exist in the remaining remnants of woodland to be defined. Examples of use of this information for management are presented. This paper addresses the practical considerations which constrain the way statistical modelling can be used for vegetation mapping in an applied project. Constraints include: (1) data availability (use of sampling to fill gaps in existing data), (2) the effects of cover abundance values, (3) availability of GIS predictors, (4) data management, (5) current generalised additive model methods and (6) prediction methods. Careful attention to the practicality of all components of a vegetation mapping study is essential if modern methods are to be applied in regional studies which must provide functional products for land managers with limited resources, skills and finances at their disposal.

Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning.

We use a geodatabase to investigate the distribution patterns of an important subset of floristic reports recorded for the Parco Nazionale delle Foreste Casentinesi, Monte Falterona, Campigna in the northern Apennines, Italy. This database was analysed using spatial statistical techniques and a digital elevation model. Significant relationships between species presence, sampling effort and species richness were then analysed in relation to topographical variables and to an existing vegetation map. Report-based rarefaction techniques were used to compare areas having different numbers of species recorded. Overall, the analysis shows that some areas of the park are richer in species of conservation interest than others, and that these have been more intensely investigated. Meanwhile, for other areas, botanical knowledge is scarce or even absent. This has led to clustering and redundancy of floristic data in some areas. The study confirms that the existence of a complete and up-to-date geodatabase creates a valuable resource which enables information gaps to be bridged. Such gaps often exist in biological databases for rare and narrowly distributed species. The wider application of these analyses should also give useful indications of how the incidences of these species of conservation interest are associated with particular environmental variables.