Dataset

Greater Hunter Native Vegetation Mapping

data.gov.au
Bioregional Assessment Program (Owned by)
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=http://data.gov.au/dataset/cebae2f1-882b-452c-b643-f69e71bee518&rft.title=Greater Hunter Native Vegetation Mapping&rft.identifier=8f575981-3730-4989-84ce-c90204158406&rft.publisher=data.gov.au&rft.description=Greater Hunter Native Vegetation Mapping - Data File## **Abstract** \n\nThis dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.\n\n\n\nGreater Hunter Native Vegetation Mapping supplied by NSW Office of Water on 13/05/2014\n\n\n\nThe GHM geodatabase builds on a wealth of information and previous mapping from the\n\nHunter region. Existing field data, mapping, classification and remote sensing interpretation\n\nwere augmented with new survey data to produce the vegetation community classification\n\nused in this project. The classification used a series of well documented analyses as well as\n\nexpert review to achieve its end-point.\n\nThe GHM geodatabase contains two principal vegetation layers. The GHM Vegetation\n\nType layer and the Canopy Cover (v2) layer (individual tree crowns or clumps of tree\n\ncrowns). The GHM also contains field plot localities, associated species information and plotspecific\n\nphotographs. Data specific to each polygon (e.g. crown cover) and to each native\n\nvegetation community type (e.g. common name, scientific name) are included. Polygons, the\n\nfundamental spatial units, are built from computer-based feature recognition which delineates\n\nlandscapes patterns.\n\nThe GHM Vegetation Type map is built by attributing individual polygons with vegetation type\n\nfrom the GHM floristic classification through a multi-stage process. The process includes\n\nvisual interpretation of SPOT 5 and ADS40 imagery as well as species distribution modelling\n\nand expert review. The project included a review of existing mapping and classification and\n\nestablished equivalences between these and the GHM Classification. VIS ID 3855\n\n## **Dataset History** \n\nVegetation patterns at the stand scale were delineated using automated feature recognition\n\nsoftware. Definiens eCognition was used to define segments with low internal variation (low\n\nheterogeneity). Pan-sharpened SPOT5 data (5m) from multiple years formed the basis of\n\nthe segmentation. The data had been pre-processed to accentuate the range of spectral\n\nresponses or colours. The spatial resolution is 5m and the minimum mappable unit was set to\n\n400m2. The polygon boundaries have been smoothed and narrow slivers were eliminated.\n\nThere were two stages in the feature recognition approach. The first stage was optimised\n\nto differentiate woody and non-woody vegetation. The second stage was optimised to\n\ndifferentiate vegetation patterns within the extent of woody vegetation. The first stage\n\nemployed multi-temporal pan-sharpened SPOT - 5 data (5m). Only the red band (610-680nm)\n\nfrom each SPOT image was used to maximise the characteristic stability of woody vegetation\n\nover time. Each object was then classified as woody, non-woody and 'other' using the\n\nCrown Cover v2 layer and visual interpretation. For stage two the boundaries within the\n\nwoody vegetation were dissolved and new objects were created within their boundaries\n\nusing stretched, multi-temporal imagery. The contrast of all bands was increased using an\n\nadaptive equalisation stretch to maximise the separability of discrete vegetation patches within\n\nmosaics.\n\nThe vegetation map was created by attributing vegetation polygons with a vegetation type.\n\nThere are multiple stages involved but the fundamental steps are as follows:\n\nSurvey sites that meet quality criteria are assigned a GHM type label using PATN\n\nanalysis. Vegetation map units were defined using a hierarchical modelling approach that\n\nincluded the manual allocation of Keith Formation using visual identification, the use of a\n\nspecies distribution model to calculate the probability of GHM type in each polygon using\n\nenvironmental layers and a set of expert rules is developed to combine the formation\n\nclassification and the modelled results. The results undergo visual quality assurance, again\n\nusing manual image interpretation.\n\n## **Dataset Citation** \n\nNSW 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.&rft.creator=Bioregional Assessment Program&rft.date=2019&rft.coverage=POLYGON ((152.8057 -33.59344, 152.8057 -30.99843, 149.5012 -30.99843, 149.5012 -33.59344, 152.8057 -33.59344))&rft_rights=Creative Commons Attribution 3.0 Australia, http://creativecommons.org/licenses/by/3.0/au/, (c) Office of Environment and Heritage NSW&rft_subject=Gloucester subregion&rft_subject=Hunter subregion&rft_subject=New South Wales&rft_subject=biota&rft.type=dataset&rft.language=English Access the data

Licence & Rights:

Other view details
Creative Commons Attribution 3.0 Australia, Http://creativecommons.org/licenses/by/3.0/au/, (c) Office of Environment and Heritage Nsw

Creative Commons Attribution 3.0 Australia, http://creativecommons.org/licenses/by/3.0/au/, (c) Office of Environment and Heritage NSW

Brief description

## **Abstract** \n\nThis dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.\n\n\n\nGreater Hunter Native Vegetation Mapping supplied by NSW Office of Water on 13/05/2014\n\n\n\nThe GHM geodatabase builds on a wealth of information and previous mapping from the\n\nHunter region. Existing field data, mapping, classification and remote sensing interpretation\n\nwere augmented with new survey data to produce the vegetation community classification\n\nused in this project. The classification used a series of well documented analyses as well as\n\nexpert review to achieve its end-point.\n\nThe GHM geodatabase contains two principal vegetation layers. The GHM Vegetation\n\nType layer and the Canopy Cover (v2) layer (individual tree crowns or clumps of tree\n\ncrowns). The GHM also contains field plot localities, associated species information and plotspecific\n\nphotographs. Data specific to each polygon (e.g. crown cover) and to each native\n\nvegetation community type (e.g. common name, scientific name) are included. Polygons, the\n\nfundamental spatial units, are built from computer-based feature recognition which delineates\n\nlandscapes patterns.\n\nThe GHM Vegetation Type map is built by attributing individual polygons with vegetation type\n\nfrom the GHM floristic classification through a multi-stage process. The process includes\n\nvisual interpretation of SPOT 5 and ADS40 imagery as well as species distribution modelling\n\nand expert review. The project included a review of existing mapping and classification and\n\nestablished equivalences between these and the GHM Classification. VIS ID 3855\n\n## **Dataset History** \n\nVegetation patterns at the stand scale were delineated using automated feature recognition\n\nsoftware. Definiens eCognition was used to define segments with low internal variation (low\n\nheterogeneity). Pan-sharpened SPOT5 data (5m) from multiple years formed the basis of\n\nthe segmentation. The data had been pre-processed to accentuate the range of spectral\n\nresponses or colours. The spatial resolution is 5m and the minimum mappable unit was set to\n\n400m2. The polygon boundaries have been smoothed and narrow slivers were eliminated.\n\nThere were two stages in the feature recognition approach. The first stage was optimised\n\nto differentiate woody and non-woody vegetation. The second stage was optimised to\n\ndifferentiate vegetation patterns within the extent of woody vegetation. The first stage\n\nemployed multi-temporal pan-sharpened SPOT - 5 data (5m). Only the red band (610-680nm)\n\nfrom each SPOT image was used to maximise the characteristic stability of woody vegetation\n\nover time. Each object was then classified as woody, non-woody and 'other' using the\n\nCrown Cover v2 layer and visual interpretation. For stage two the boundaries within the\n\nwoody vegetation were dissolved and new objects were created within their boundaries\n\nusing stretched, multi-temporal imagery. The contrast of all bands was increased using an\n\nadaptive equalisation stretch to maximise the separability of discrete vegetation patches within\n\nmosaics.\n\nThe vegetation map was created by attributing vegetation polygons with a vegetation type.\n\nThere are multiple stages involved but the fundamental steps are as follows:\n\nSurvey sites that meet quality criteria are assigned a GHM type label using PATN\n\nanalysis. Vegetation map units were defined using a hierarchical modelling approach that\n\nincluded the manual allocation of Keith Formation using visual identification, the use of a\n\nspecies distribution model to calculate the probability of GHM type in each polygon using\n\nenvironmental layers and a set of expert rules is developed to combine the formation\n\nclassification and the modelled results. The results undergo visual quality assurance, again\n\nusing manual image interpretation.\n\n## **Dataset Citation** \n\nNSW 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.

Full description

Greater Hunter Native Vegetation Mapping - Data File
Click to explore relationships graph

Spatial Coverage And Location

text: POLYGON ((152.8057 -33.59344, 152.8057 -30.99843, 149.5012 -30.99843, 149.5012 -33.59344, 152.8057 -33.59344))

Subjects

User Contributed Tags    

Login to tag this record with meaningful keywords to make it easier to discover

Identifiers