This dataset is a modelled dataset of the quality of Terrestrial Native Vegetation as per the "Habitat Hectares" approach (Parkes et al, 2003). Specifically it is a model of the "Habitat Score" which is an index comprising ten (10) separate metrics that are weighted and summed. 
Seven (7) of these are site-based vegetation condition metrics, referred to collectively as the "Site Score"  and three(3) are related to the spatial context of the site and referred to collectively as the "Landscape Score". The Site Score comprises 75% of the final quality score and the Landscape Score makes up the other 25%.
Site Score Components: Large Tree Score (10), Canopy Cover Score (5), Understorey Score (25), Litter Score (5), Log Score (5), Weed Score (15), Recruitment Score (10)
Landscape Score Components: Patch Size/Shape (10), Neighbourhood &Connectivity (15)

This dataset is a modelled dataset of the quality of Terrestrial Native Vegetation as per the "Habitat Hectares" approach (Parkes et al, 2003). Specifically it is a model of the "Habitat Score" which is an index comprising ten (10) separate metrics that are weighted and summed. 
Seven (7) of these are site-based vegetation condition metrics, referred to collectively as the "Site Score"  and three(3) are related to the spatial context of the site and referred to collectively as the "Landscape Score". The Site Score comprises 75% of the final quality score and the Landscape Score makes up the other 25%.
Site Score Components: Large Tree Score (10), Canopy Cover Score (5), Understorey Score (25), Litter Score (5), Log Score (5), Weed Score (15), Recruitment Score (10)
Landscape Score Components: Patch Size/Shape (10), Neighbourhood &Connectivity (15)

To provide a strategic view of native vegetation quality across the state. 
Site Condition ranges are best considered as relative rather than actual values and site based habitat hectare assessments are required to ascertain actual vegetation condition at site

To provide a consistent assessment of landscape context across the state; and where applicable to replace the field assessment of landscape context that is part of a site-based habitat hectare assessment

Accurate

In general the modelled dataset tends to underestimate the condition of vegetation at high quality sites and overestimate the condition of vegetation at low quality sites.  As such, it is inappropriate to use the data for informing decisions of a statutory nature.  

The Landscape Context class accuracy is dependent on the accuracy of the source extent dataset.(Landscape Context processing steps for methodology)

The same approach to assessing site condition is applied across the state, however the site score for naturally treeless vegetation (such as grasslands and shrublands) was rescaled to compensate for the absence of tree related site score components.  
The same approach for calculating Landscape Context has been applied to the complete NV2005_EXTENT dataset

Dataset Source: Site Score:
Each of the 7 site component models have been constructed using "Site Score" data collected from approximately 18,000 sites across Victoria.  Only terrestrial sites were surveyed - wetland condition/quality was not assessed in the field.  At these same locations the values of many environmental (climate, soils, terrain etc) and spectral data (landsat bands) were extracted within a GIS).  Relationships between site score components assessed at point locations during field survey and the environmental variables, were modelled using artificial neural networks.  Neural networks were built for the individual condition variables using the Neural Networks module within the Statistica software package (StatSoft Inc., Tulsa, Oklahoma).  For each output the modelling procedure involved the following steps.  The input data (environmental variables) and output data (site component score indices) were standardized to means of zero and units of standard deviation.  The dataset was then divided into training (half of the field data), selection (one quarter of the field data) and test data (one quarter of the field data).  The training data were used to build the neural network, the selection data were used to check that the procedure was not over-fitting (i.e. to prevent models fitting just the idiosyncrasies of the training data) and the test data were used to validate the final fit of the model.  Models were built from 200 random starts and the 50 models with the best statistical fit (highest R2 values) were saved.  The best performed site component models based on both statistical fit, input variable parsimony and visual assessment were selected.  These models were then used to predict and map site component scores for every 25 m grid across Victoria.  These models were summed to produce an initial "Site Score" model.  Finally the site score for naturally treeless vegetation (such as grasslands and shrublands) was rescaled to compensate for the absence of tree related site score components.  

Landscape Score:
Dataset was generated from a spatial analysis of NVE_2005.


Dataset Originality: Derived

                            
                              Relationship to other Datasets: NV2005_EXTENT is part of a series of native vegetation datsets, as follows:
NV2005_EXTENT - Modelled extent of native vegetation (very broad categories)
NV1750_EVC - Mapped pre 1750 ecological vegetation classes (EVCs) distribution
NV1750_EVCBCS - Mapped pre1750 EVC distribution with Bioregional Conservation Status derived from NV1750_EVC and VBIOREGION100 and depletion statistics, also contains  pre1750 geographic occurrence of EVCs
NV2005_EVCBCS - Extant EVC distribution with Bioregional Conservation Status derived from NV1750_EVCBCS and NV2005_EXTENT , also contains pre1750 geographic occurrence 
NV2005_QUAL - Modelled Native Vegetation Quality - includes modelled site condition and patch-based landscape context
NV2005_LSIMP - Landscape Scale native vegetation importance and connectivity derived from NV2005_EVCBCS, NV2005_QUAL and landscape connectivity

Current Design Issues: In general the modelled dataset tends to underestimate the condition of vegetation at high quality sites and overestimate the condition of vegetation at low quality sites.  As such, it is inappropriate to use the data for informing decisions of a statutory nature.  


Landscape context is wholly dependent on the accuracy of the Native Vegetation dataset upon which it is based , in this case NV2005_EXTENT.

This version scores 80% of the landscape context score, a further 20% ie. 5 points associated with low disturbance history (such as areas of old growth) has not yet been included.


Future Design Issues: Inclusion of scores for low disturbance

Related Documents: None