Greater Hunter Native Vegetation Mapping

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Greater Hunter Native Vegetation Mapping supplied by NSW Office of Water on 13/05/2014

The GHM geodatabase builds on a wealth of information and previous mapping from the

Hunter region. Existing field data, mapping, classification and remote sensing interpretation

were augmented with new survey data to produce the vegetation community classification

used in this project. The classification used a series of well documented analyses as well as

expert review to achieve its end-point.

The GHM geodatabase contains two principal vegetation layers. The GHM Vegetation

Type layer and the Canopy Cover (v2) layer (individual tree crowns or clumps of tree

crowns). The GHM also contains field plot localities, associated species information and plotspecific

photographs. Data specific to each polygon (e.g. crown cover) and to each native

vegetation community type (e.g. common name, scientific name) are included. Polygons, the

fundamental spatial units, are built from computer-based feature recognition which delineates

landscapes patterns.

The GHM Vegetation Type map is built by attributing individual polygons with vegetation type

from the GHM floristic classification through a multi-stage process. The process includes

visual interpretation of SPOT 5 and ADS40 imagery as well as species distribution modelling

and expert review. The project included a review of existing mapping and classification and

established equivalences between these and the GHM Classification. VIS ID 3855

Dataset History

Vegetation patterns at the stand scale were delineated using automated feature recognition

software. Definiens eCognition was used to define segments with low internal variation (low

heterogeneity). Pan-sharpened SPOT5 data (5m) from multiple years formed the basis of

the segmentation. The data had been pre-processed to accentuate the range of spectral

responses or colours. The spatial resolution is 5m and the minimum mappable unit was set to

400m2. The polygon boundaries have been smoothed and narrow slivers were eliminated.

There were two stages in the feature recognition approach. The first stage was optimised

to differentiate woody and non-woody vegetation. The second stage was optimised to

differentiate vegetation patterns within the extent of woody vegetation. The first stage

employed multi-temporal pan-sharpened SPOT - 5 data (5m). Only the red band (610-680nm)

from each SPOT image was used to maximise the characteristic stability of woody vegetation

over time. Each object was then classified as woody, non-woody and 'other' using the

Crown Cover v2 layer and visual interpretation. For stage two the boundaries within the

woody vegetation were dissolved and new objects were created within their boundaries

using stretched, multi-temporal imagery. The contrast of all bands was increased using an

adaptive equalisation stretch to maximise the separability of discrete vegetation patches within

mosaics.

The vegetation map was created by attributing vegetation polygons with a vegetation type.

There are multiple stages involved but the fundamental steps are as follows:

Survey sites that meet quality criteria are assigned a GHM type label using PATN

analysis. Vegetation map units were defined using a hierarchical modelling approach that

included the manual allocation of Keith Formation using visual identification, the use of a

species distribution model to calculate the probability of GHM type in each polygon using

environmental layers and a set of expert rules is developed to combine the formation

classification and the modelled results. The results undergo visual quality assurance, again

using manual image interpretation.

Dataset Citation

NSW Office of Environment and Heritage (2014) Greater Hunter Native Vegetation Mapping. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/8f575981-3730-4989-84ce-c90204158406.