Australian Vegetation Attribute Manual Version 7.0 Chapter 2.0 NVIS concepts and standard procedures Australian Vegetation Attribute Manual Version 7.0 2.1 Vegetation The NVIS framework provides a comprehensive means of describing and representing vegetation types, based on establishing relationships between structural and floristic data in a relational database management system. It is assumed that field surveys have already been conducted and that observational data has been classified into meaningful vegetation types, which are typically the mapping units used in the region (for example, see Thackway et al ., 2008). This section primarily deals with the non- spatial vegetation data used to describe each vegetation type, but also includes a short section introducing vegetation-specific metadata used to describe the methods used to survey, classify and map the vegetation of a region. As discussed in the Section 1.5 (Scope of the Australian Vegetation Attribute Manual), this manual is not intended to be a guide for the collection of vegetation data in the field. Mapping standards generally follow accepted thematic polygon or multi- attribute raster data in the natural resources management sector. However, NVIS accepts the best available vegetation descriptions and spatial data from a number of state, territory and non-government systems. Where necessary, and particularly in relation to data gaps, NVIS will accept the multi-attribute spatial layers resulting from aerial photo interpretation for incorporation into derived products. However, these data result from an earlier task of the vegetation mapping workflow; incorporating classified data is the predominant and most-efficient approach. The approach to describing vegetation types in NVIS is derived from field survey methods, such as those described above, in that a vegetation profile is first interpreted into a simple model with several layers or strata (upper (U), mid (M) and ground (G)) which have particular structural and floristic properties. These strata are traditionally defined manually, but can be defined from instrumented sources, such as LiDAR 1 or the product of post-survey analyses. In NVIS, a vegetation type has usually been derived by classifying data from multiple observational points extending over a study area which has been stratified according to one or many variables. The stratified or experimental units tend to have variability around a central tendency. Once classified, the vegetation type can be analysed or interpreted into strata, against which any structural and floristic information is recorded in simple measures. In some cases, there is enough primary field data to use these to establish strata following the field surveys. Strata can often be characterised from reflective remote sensing and the challenge of vegetation mapping includes matching a vegetation type recognisable in the field with patterns, reflectance levels and/or environmental variables. 1 Quadros, N. and Keysers, J. (2015). Airborne LiDAR acquisition and validation. In: AusCover Good Practice Guidelines: A technical handbook supporting calibration and validation activities of remotely sensed data product. (Eds A. Held, S. Phinn, M. Soto-Berrelov and S. Jones) pp. 268- 301. Version 1.2 TERN AusCover, ISBN 978-0-646-94137-0. Australian Vegetation Attribute Manual Version 7.0 For the purposes of NVIS, vegetation types are regarded as definitive when all of the vegetation of a region has been classified and mapped (Thackway et al., 2009. For the purposes of regulation, mapping and monitoring, many jurisdictions have moved or are moving towards the description of vegetation types according to the concept of “definitive vegetation types2”, which can be identified and described independently of the spatial units in which they occur. However, it is recognised that, as more data becomes available, often at a finer scale, classifications may change to absorb greater variability than initially anticipated. 2.1.1 Key concepts 2.1.1.1 Dominance The concept of dominance is central to the construction of NVIS vegetation descriptions. The proposition is that a particular vegetation type can be represented by information that summarises each property in a simple way. In NVIS, this is generally achieved by truncating copious amounts of data that have been ranked, using a vegetation characteristic, to reflect decreasing “dominance 3” within each domain of comparison. This ranking of relative dominance is used for comparisons of the structural properties of strata (Section 2.1.4.3), species and genera within a stratum (Sections 2.1.7.2 and 2.1.7.3) and growth forms within a stratum (Section 2.1.5). The dominance of the (sub-)stratum 4 provides a useful summary of the vegetation description at the simpler levels (Levels 1 to 3) in the NVIS vegetation hierarchy, and because multiple interpretations of the concept exist 5, interpreters creating NVIS descriptions should use a consistent protocol for the assignment of the dominant stratum for each vegetation type . The dominance of a (sub-)stratum can be indicated by its estimated relative biomass (or coarse surrogate, such as cover times height) across a vegetation type. Among other benefits, delineation of the dominant stratum enables the description of a particular vegetation type to be progressively simplified in the NVIS vegetation hierarchy, as discussed in Section 2.1.3 (The NVIS vegetation hierarchy). Another example of the use of dominance is when comparing species listed in an NVIS record. A summary of the type can be generated by documenting the few species that dominate each stratum, in order of decreasing dominance, as measured by suitable vegetation characteristic such as foliage cover or frequency – see Section 2.1.7.2 (Component Data for Species) for further details. Other properties of the vegetation type, particularly values such as height and cover scales are represented by median, modal, average or enumerated values, which are documented in the metadata for each input dataset and in record-level metadata. 2 For example: Plant Community Types in NSW (NSW OEH, 2017); Regional Ecosystems in Queensland (Neldner et al., 2017); Ecological Vegetation Classes in Victoria (Woodgate et al., 1994; Anon (2017). 3 Probably better termed “relative dominance”. 4 Strata occur in NVIS Level 5; substrata occur in NVIS Level 6. 5 Kershaw, K.A. and Looney, J.H.H. (1985). Quantitative and Dynamic Plant Ecology. 3rd Edn. Edward Arnold, Melbourne. Australian Vegetation Attribute Manual Version 7.0 Such values can contribute to further interpretation and description of each vegetation type 6. 2.1.1.2 Vegetation strata Figure 2 provides a schematic representation of vegetation layering in a hypothetical vegetation community. These layers may be interpreted into strata which share heights and growth form parameters (Walker and Hopkins, 1990). In complex vegetation structural types, each of the three NVIS Level 5 strata (U, M and G) may be further divided into substrata to a maximum of nine layers in NVIS Level 6 as shown in Table 4 (NVIS (sub-)stratum codes and descriptions), though in less structurally complex vegetation types, not all of the strata need be present. Figure 1 Vegetation profiles for two different vegetation types. The diagram illustrates the flexibility in assignment of substrata (U1, U2, etc.—see Table 4 for NVIS (sub-)stratum codes and descriptions) at NVIS Level 6. These can be interpreted into three simple strata (as per NVIS Level 5: Upper or Tree, Mid or Shrub and Ground). 6 The comprehensive recording of variability in a vegetation type is beyond the scope of NVIS. The user is referred to relevant site data relating to each vegetation type. Australian Vegetation Attribute Manual Version 7.0 As an example of interpreting layers from field data, Figure 3 (Graphical summary of cover values for an example NVIS Description at Level 5) shows a graphical summary of percentage cover in each of three strata for a Eucalyptus populnea grassy woodland. Figure 2 Graphical summary of cover values for an example NVIS description at Level 5. The tree stratum, U, has a Height Class of 7 and a cover code mid-point of 35% (Cover Code i); the shrub stratum, M, has a Height Class of 4 and a cover code mid-point of 0.125% (and is hence barely visible on the graph; Cover Code ‘bi’) and the ground stratum, G, has a Height Class of 1 and a cover code mid-point of 65% (Cover Code c). See Table 5 for a list of Height Classes and Table 7 for Cover Codes. 2.1.1.3 Notation A number of notation processes have been developed for the data supplier to code Level 5 and 6 data with enough data for the automated generation of simpler vegetation descriptions from the detailed data. These include: • Dominant stratum • Dominant genus or genera 7 within a stratum 7 Genera, but not species, are promoted in the Information Hierarchy Australian Vegetation Attribute Manual Version 7.0 • Listing species in sub/strata according to decreasing dominance • Dominant growth form within a stratum • Listing growth forms in sub/strata according to decreasing dominance • An attribute to generate the “+/-“ delimiter between species, commonly used to indicate relatively lower frequencies of occurrence in the vegetation type, compared with the usual comma “,”. Further details are provided in the following sections. 2.1.2 Data set documentation and information reliability The NVIS framework provides a comprehensive means of describing and representing vegetation types, based on establishing relationships between structural and floristic information in a Relational Database Management System. Each dataset provided as input to NVIS needs to have appropriate metadata supplied. This should be in the form of completed attributes in the Dataset Information part of the NVIS database (DATA_SET, Reference and MAPPING_SOURCE tables). The DATA_SET table shown in Appendix C1 (Entity relationship diagram), in particular, has attributes to describe the scope of each input dataset, validation techniques and data accuracy. The data provider must utilise existing metadata, publications and/or unpublished material associated with the dataset to complete these attributes.