The City of Melbourne maintains more than 80,000 trees. This dataset details the location, species and lifespan of Melbourne's urban forest by precinct. To explore Melbourne's tree data and learn more about the life expectancy and diversity of trees in your city, check out our interactive tree map http://melbourneurbanforestvisual.com.au/, You can download the City of Melbourne's Urban forest Strategy and the summary of your precinct's consultation from the attachments section by selecting the 'About' button

Under the Urban Forest Strategy, there are ten Precinct Plans that provide detailed information about how planting will occur in local streets to meet the objectives of the strategy. A planting schedule for streets in each precinct has been developed in consultation with the community and is based on:\r
-\tCommunity priorities\r
-\tLocation and density of vulnerable residents\r
-\tOpportunities for new planting\r
-\tHot and very hot streets, as shown in thermal imaging \r
-\tUseful Life Expectancy of existing trees\r
-\tExisting canopy cover\r
\r
This dataset shows the anticipated timeframe for tree planting in each street across the City of Melbourne. This timing is provisional and subject to change. The schedule for some streets may be moved forward or delayed by capital works, developments or renewal projects that impact on tree planting or survival. \r
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To learn more about the Urban Forest Strategy and Precinct Plans, visit http://www.melbourne.vic.gov.au/sustainability/urbanforest/pages/urbanforest.aspx

Tree canopy within City of Melbourne mapped using 2021 high resolution multi-spectral imagery. The canopy polygons represent actual tree canopy extents on both private and public property across the city.

Tree canopy within City of Melbourne mapped using 2018 aerial photos. The canopy polygons represent actual tree canopy extents on both private and public property across the city.

This dataset displays the tree planting plan locations within the municipal boundary of the City of Melbourne. This dataset was uploaded for the Digital Transformation Project at the City of Melbourne.

Tree canopy within City of Melbourne mapped using 2008 aerial photos and LiDAR. The canopy polygons represent actual tree canopy extents on both private and public property across the city. The data is considered accurate for 2011. Changes in tree canopy are expected to have occurred since that time.

This dataset contains spatial polygons which represent tree canopy areas across the City of Melbourne. It can be easily mapped using the geometry column.

Tree canopy polygons have been derived by ArborCarbon from high-resolution airborne multispectral imagery. ArborCarbon collected this imagery using the ArborCam, a unique 11-band airborne multispectral camera system optimized for the accurate detection of vegetation and subtle changes in vegetation condition.

The high-resolution airborne imagery datasets were geometrically corrected and orthorectified using the City of Melbourne’s publicly available 2018 aerial imagery and a Digital Terrain Model supplied by the City. A Digital Surface Model was generated from the acquired imagery for the full extent of the City, enabling the stratification of vegetation into a range of height categories. All vegetation >3m above the ground was classified as canopy (excluding vegetation on building rooftops).

Spatial input data to parameterise the stream bank erosion module of the dSedNet model to simulate sediment generation and transport in the Western Port catchment for a 2018-19 study commissioned by Melbourne Water. Lineage: Melbourne Water provided tree canopy polygon data for riparian zones (0-200m stream buffer) in Western Port. These data represented presence/absence of tree canopy and did not capture vegetation with low vertical projection e.g. grass, shrub. Tree canopy was mapped at a fine scale (e.g. 1: 5000) from remote sensing and aerial image digitisation and captured considerable detail. The proportional area of tree canopy occurring within 1 ha grid cells was calculated across Westernport and resampled to a 20m grid for input to the model. While tree cover alone is not necessarily representative of riparian vegetation that stabilises stream banks that also includes low standing types, it was the only suitable data available to the project at the time as remote sensing products were either too coarse or contained insufficient spatial coverage. The workflow for generating riparian vegetation density estimates from Melbourne Water's riparian tree canopy data was executed within the ArcGIS (version 10.2) environment.

Raw_data_GCB. This dataset contains raw measurements of tree growth for ten urban tree species planted in seven Australian cities. For each tree, the dataset provides annual tree-ring width (mm) and annual basal area increment (cm²/year). The species included are: Acer negundo, Celtis australis, Gleditsia triacanthos, Jacaranda mimosifolia, Liquidambar styraciflua, Magnolia grandiflora, Platanus acerifolia, Pyrus calleryana, Robinia pseudoacacia, and Ulmus parvifolia. The cities represented are Adelaide (South Australia), Mandurah (Western Australia), Melbourne and Mildura (Victoria), Parramatta, Penrith, and Sydney (New South Wales).

Climate_data_GCB. This dataset includes climate data for seven Australian cities: Adelaide (South Australia), Mandurah (Western Australia), Melbourne and Mildura (Victoria), Parramatta, Penrith, and Sydney (New South Wales). Variables include annual precipitation, precipitation of the driest and warmest months, precipitation of the driest quarter, maximum temperature of the warmest month, mean annual temperature, minimum temperature of the coldest month, and two drought indices: the de Martonne Aridity Index and the Pinna Combinative Index.

Calculated_data_GCB. This dataset provides summary statistics for each city × species combination, including the mean interseries correlation (r̄, or r-bar) and the Expressed Population Signal (EPS).

Tree canopy within City of Melbourne mapped using 2016 aerial photos and LiDAR. The canopy polygons represent actual tree canopy extents on both private and public property across the city.

This dataset includes sap flux (cm3 cm-2 hr-1) measurements from instrumented trees at the Delaware Forested site (Milford Neck) for the CZNet Coastal Cluster. Tree sap flux reflects tree water use within xylem, rather than flow of tree sap (within phloem). Twelve Pinus taeda (loblolly pine) trees were initially selected for instrumentation. Selection was based on high canopy integrity and large DBH that was uniform across the site and reflected tree maturity. Selected trees were instrumented with heat pulse velocimetry (HPV) sap flow sensors (Implexx model, Edaphic Scientific, Melbourne, Australia). Sensors consist of three equidistant 30 mm-long probes: a central heating probe, a downstream, and an upstream probe. Each probe contains an inner and outer thermistor for functional redundancy and cross-sectional averaging. The Implexx HPV sensors work functionally similar to thermal dissipation probes. Sensors were installed in tree trunks at 1 m in height above the soil surface on the south side of the tree trunk. Bark thickness was shaved to <0.5 cm thickness in a small area for the probe installation so that thermistors were installed in sapwood. Data from sensors was collected every 30 minutes with a CR300 datalogger (Campbell Scientific). Power was supplied with a 7W solar panel and stored in a 12V sealed lead-acid battery. Because the datalogger batteries occasionally died or failed to charge by the solar power (particularly in mid-summer, when the forest canopy was full), there may be gaps in the record.

This dataset contains details on the plaques located at the Shrine of Remembrance of Reserve​. Many of these plaques are located under trees, and where relevant, this dataset also contains details of the tree next to the plaque.

This dataset stores measurements from two field campaigns in 20 environmental plantings aged 4–35 years old in Australia where stem diameters, crown radii, and heights of individual trees and shrubs were collected, and above- and below-ground woody biomass (AGB, BGB) were calculated. The first measurement was undertaken by The University of Melbourne in collaboration with Land Life Company between March and June 2022 covering 14 sites aged 21-35 years in Victoria, Australia. The purpose of this field campaign was to re-evaluate the biomass carbon stock of sites previously assessed in 2000 by CSIRO within the stem diameter database (SDD; Paul et al. 2020). For each site, to ensure representative coverage, the sampling area was divided into equal-sized sectors and within each sector a random transect of 0.05 ha (100 m length by 5 m width) was laid out based on a restricted random sampling design. Two to five transects were measured per site, depending on either CSIRO last measurement plots or to optimize for the project's resources. In total, the dataset contains 48 transects and 4703 unique tree or shrub measurements. Stem diameters were measured with a diameter tape (>10 cm) or a stepped diameter gauge (<10 cm), and height (measured with a laser hypsometer) and crown radius (estimated using a measuring tape) were assessed for only a few representative trees (selected to encompass the full diameter range). The second field campaign was undertaken by Land Life Company between June and July 2023 covering six environmental planting sites aged 4-23 years in Victoria, Australia. The purpose of this campaign was to collect crown data in two directions (perpendicular and parallel to the seeding line). For each site, 14-20 transects of 20 m length were measured (following the seeding line and avoiding gaps or areas of dead plants). In total, the data contains 108 transects and 1341 unique tree and shrub measurements. Crown radius was measured with a tape for each tree or shrub and in addition, stem diameter (measured with a caliper) and height (measured with a laser hypsometer) were also collected. For both datasets, to calculate AGB, stem diameters of multi-stem trees and shrubs were grouped into a single diameter equivalent using the equation of Penman et al. (2003), and then specific plant functional type (PFT) allometric equations (Paul et al. 2015, Paul et al. 2018) were used to derive above and below-ground biomass. Funding: School of Ecosystem and Forest Sciences, The University of Melbourne (Bennett, L T; Byrne, P; Karopoulos, A) Research Project agreement between The University of Melbourne and Land Life According to Paul e... Based on global dig... Calculated Calculated accordin... Calculated accordin... Calculated accordin... Campaign Carbon capture proj... Comment Crown radius Date Time of event Diameter tape for s... ELEVATION Environmental plant... Field experiment Height aboveground Investigator Laser hypsometer Latitude of event Location of event Longitude of event Model accuracy Multiple investigat... Parallel to the see... Perpendicular to th... Plant functional type Principal investigator Reference in GBIF Revegetation Site Species Stem diameter Transect number Tree Tree ID Tree height Tree shrub Tree shrub biomass Uniform resource lo... Woodland restoration Woody biomass aboveground belowground biomass carbon sequestration productivity status stem diameter total

Invasive plants can lead to significant changes in the abundance and diversity of the existing flora. Restoration programs, therefore, largely focus on the recovery of the vegetation. Faunal responses have received less attention. Here we examined whether or not bird communities recovered following removal of a native, invasive tree in South Eastern Australia with a view to evaluating whether this could be used as a tool for assessing the effectiveness of the remediation programs. Pittosporum undulatum is an Australian native tree that has become highly invasive in areas well outside its original range within Australia and in many other regions of the world. In the Azores, for example, it is associated with changes in bird communities. In Australia, high density P. undulatum is the cause of major declines in biodiversity of flora and the total suppression of regeneration of Eucalyptus. Its removal from nature reserves across temperate south eastern Australia has been effective in allowing many plant communities to recover, but the impact on birds is unknown. We compared the species richness, density and functionality of local resident bird communities across original remnant vegetation and invaded areas with those that had been cleared of invasive P. undulatum populations at different times. Areas infested with P. undulatum had fewer carnivorous birds but overall there did not appear to be any strong influence on species richness, or density. However, when invaded areas were examined at a finer scale by partitioning the observations into ‘above’ or ‘within’ and ‘below’ the dense P. undulatum canopy, strong differences were detected with fewer birds and fewer species relative to the diversity and abundance of birds in the Eucalyptus overstory. Our work demonstrates that while P. undulatum provides habitat for birds, there is a difference in the relative proportions of different functional groups. This is important, particularly in light of the long-term decline in ground dwelling bird communities across the temperate regions of the continent. We conclude that birds are a useful bioindicator of habitat health and that the inclusion of avian monitoring programs could improve the evaluation of the efficacy of restoration projects.

Methods Site selection and bird surveys

Ten sites across peri-urban areas of Melbourne, in south-eastern Australia, were selected to evaluate their bird communities (Table 1). The vegetation at these sites has been surveyed and analysed in a previous study (O’Leary et al. 2018 Forest Ecology and Management 408: 112-120). Sites were selected to contain (1) an area of uninvaded, remnant vegetation supporting a Eucalyptus overstorey of varying species composition consistent with local conditions (“reference control”); (2) an area currently infested by P. undulatum (“invaded”); and (3) a formerly invaded area where P. undulatum had been removed (“cleared”). Two sites (Ferntree Gully and Sherbrooke Forest) lacked invaded patches but are included here for analyses that do not depend on a sequence from control to invaded to cleared patches. We used Ecological Vegetation Class (EVC) mapping supported by on-ground observations to ensure that the vegetation patches within a site supported similar vegetation (DELWP 2017). Sites ranged in size from 1–12 ha. The management area at each site was characterised as having a severe P. undulatum infestation (30–70% canopy cover) prior to removal work.

Bird surveys were conducted on three separate mornings at each site from mid-May to late June of 2017. Surveys were conducted within the first three hours after sunrise. Following a modified version of the process established by Loyn (1986) and outlined in Loyn et al. (2007), i.e. 10 minutes surveying time was implemented for each hectare of sampling area at each site, to a maximum of 20 minutes. This timeframe has been observed as appropriate to survey bird communities within south eastern Australian forests, whilst reducing the risk of bias towards conspicuous species with distinctive and/or frequent calls.

All birds observed by sight and call within and below the vegetation canopy were identified to species level. Birds flying overhead were not included in the study. Bird species relative abundance was determined by dividing the total number of birds observed over the three surveys at each site. Surveys distinguished bird use of the habitat within or below the P. undulatum canopy (PU) from use of the overstorey (NPU) due to the predicted large effects of dense P. undulatum canopies. Density was determined by dividing the numbers of birds by the area surveyed.

Values of five traits reflecting ecological functionality (life history, habitat preference, and feeding guild) were extracted for each bird species identified (Higgins et al. 2006 Handbook of Australian, New Zealand and Antarctic Birds, Vol 1-7, Oxford University Press, Melbourne, Australia.).

Statistical analysis

Differences in bird species richness and individual abundance among reference, invaded, and cleared vegetation types and between the P. undulatum understory and overstorey in invaded sites were tested with one-way ANOVAs. To compare species richness in individual feeding guilds across the three vegetation types, we used Kruskal-Wallace tests, due to the smaller numbers of species involved in each guild. The effect of time since P. undulatum removal was tested with linear regression on relativized measures of species richness and individual abundance (richness or abundance in cleared patch divided by richness or abundance in the corresponding reference control). To summarize the differences and similarities among bird communities in the three vegetation types we employed principal components analysis (PCA) of bird species presence/absence data using the prcomp function in R (R Core Team 2017). Loadings on the first two principal components were examined to account for the contribution of individual species to the community configuration. We repeated the PCA using bird abundance data using nonmetric multi-dimensional scaling (NMDS) and checked that the results were biologically meaningful as suggested by Björklund (201, Evolution, 73(10), 2151-2158). Since all results were in qualitative agreement, we present PCA of the presence-absence data here. Scores on the first two principal components were compared among reference, invaded, and cleared vegetation types using Wilcoxon signed-rank tests.

PCA was also used to investigate the functional response of bird communities to P. undulatum infestation and removal. Mean functional trait values in the species assemblage were calculated from the traits of individual species weighted by the abundance of individuals of the species. All data were centred and scaled to unit variance prior to analysis. All analyses were conducted using the R statistical platform (R Core Team 2017).

Processing

The data was originally assembled in csv files for analysis in R and they are provided as supplementary data in that form. Here we have collected the files into a single Excel file for ease of upload.

The number of asterisks indicate model fit (*Low fit, **Moderate fit, ***Good fit). Bold letters outline the best models which had the best distribution fit, the least number of covariates, and the lowest Akaike International Criterion (AIC) and both global and Partial deviance (G.Dev and P.Dev, respectively). The explanatory variables include Mean Minimum Temperature (MMinTemp), Mean Maximum Temperature (MMaxTemp), Highest Temperature (HTemp), Mean Temperature (MTemp), Rainfall (Rain) and Sun Exposure (SunExp) recorded per month at each weather station, as well as the monthly Southern Oscillation Index (SOI). The number after the variable refers to number of months prior (lag) the variable was associated to Jacaranda flowering, and cs indicates smooth variables.

The Vicmap Vegetation Tree Urban represents trees as points across Metropolitan Melbourne and the urban environment within four regional councils: Wangaratta, Sale, Shepparton and Ballarat.

This product is derived from machine learning of high resolution aerial photography with no post processing human intervention.

This dataset is a definitive view of native vegetation extent and condition across Melbourne’s VC68 urban growth areas and incomplete Precinct planning areas within the Melbourne Urban Growth Boundary, time-stamped as at 13 December 2012. The area covered by the timestamping dataset aligns with the extent of the Biodiversity Conservation Strategy (BCS) for Melbourne’s Growth Corridors (DSE 2012).

This dataset wholly replaces the previously released NV2011_TS_GA and includes the following changes: -inclusion of Number of Large and Very Large Trees in Surveyed remnant Patches -Remnant Patches have new Unique IDs to remedy issues in previous dataset

The dataset only provides a view of native vegetation within the extent outlined above. Where a parcel is partially within the extent, the view of native vegetation only applies to areas applicable within the BCS.

Biomass estimates for shrubland-dominated ecosystems in southern California have, to date, been limited to national or statewide efforts which can underestimate the amount of biomass; are limited to one-time snapshots; or estimate aboveground live biomass only. We developed a consistent, repeatable method to assess four vegetative biomass pools from 2001-2023 for our southern California study area (totaling 6,441,208 ha), defined by the Level IV Ecoregions (Bailey 2016) that intersect with USDA Forest Service lands (Figure 1). We first generated aboveground live biomass estimates (Schrader-Patton and Underwood 2021), and then calculated belowground, standing dead, and litter biomass pools using field data in the peer-reviewed literature (Schrader-Patton et al. 2022) (Figure 2). Over half (52.3%) of the study area is shrubland, and our method accounts for three post-fire shrub regeneration strategies: obligate resprouting, obligate seeding, and facultative seeding shrubs. We also generate biomass estimates for trees and herbs, giving a total of five life form/life history types. These data provide an important contribution to the management of shrubland-dominated ecosystems to assess the impacts of wildfire and management activities, such as fuel management and restoration, and for monitoring carbon storage over the long term. The biomass data are a key input into the online web mapping tool SoCal EcoServe, developed for US Department of Agriculture Forest Service resource managers to help evaluate and assess the impacts of wildfire on a suite of ecosystem services including carbon storage. The tool is available at https://manzanita.forestry.oregonstate.edu/ecoservices/ and described in Underwood et al. (2022). REFERENCES Bailey, R.G. 2016. Bailey's ecoregions and subregions of the United States, Puerto Rico, and the U.S. Virgin Islands. Forest Service Research Data Archive. (Fort Collins, Colorado). https://doi.org/10.2737/RDS-2016-0003 Schrader-Patton, C.C. and E.C. Underwood. 2021. New biomass estimates for chaparral-dominated southern California landscapes. Remote Sensing, 13, 1581. https://doi.org/10.3390/rs13081581 Schrader-Patton et al. 2022. “Estimating Wildfire Impacts on the Biomass of Southern California’s Chaparral Shrublands.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Underwood et al. 2022. “Estimating the Impacts of Wildfire on Chaparral Shrublands in Southern California using an Online Web Mapping Tool.” Proceedings for the Fire and Climate Conference May 23-27, 2022, Pasadena, California, USA and June 6-10, 2022, Melbourne, Australia. Published by the International Association of Wildland Fire, Missoula, Montana, USA. Methods METHODS We generated spatial estimates of above ground live biomass (AGLBM, in kg/m2) for 2000-2021 for our southern California study area. The study area, totaling 6,441,208 ha, is defined by the 42 Level IV Ecoregions (Bailey 2016) that intersect the four southern US Department of Agriculture (USDA) National Forests in southern California (Figure 1). We created biomass raster layers (30m spatial resolution) by modeling a set of covariates (Normalized Difference Vegetation Index [NDVI], precipitation, solar radiation, actual evapotranspiration, aspect, slope, climatic water deficit, elevation, flow accumulation, landscape facets, hydrological recharge and runoff, and soil type) to predict AGLBM using 766 field plots of biomass from the USDA Forest Service Forest Inventory and Analysis (FIA); the Landfire Reference Database (LFRDB) plot data; and other research plots. The dates of field data spanned 2001-2012. The NDVI raster data were derived from Landsat TM/ETM+/OLI multispectral satellite data (onboard Landsat 5, 7, and 8, respectively). NDVI data were composited from all available Landsat images for the months of July and August for each year 2001-2023. We also downloaded annual precipitation data for each water year (October 1 - September 30) 2001-2021 from PRISM (http://www.prism.oregonstate.edu/). For each field plot, we extracted the raster values for all covariates; NDVI and precipitation data were matched to the year of plot visit. We predicted AGLBM using the set of 17 covariates (Random Forest [RF] algorithm in R statistical computing software). To create an AGLBM raster surface for each year 2001-2023, we used NDVI and precipitation raster data specific to each year in the RF (using predict function in the R raster module) (see Schrader-Patton and Underwood 2021 for details). To estimate other shrubland biomass pools (standing dead, litter, and below ground) we employed a multi-step process: 1) First, we segregated the study area by community type using the California Wildlife Habitat Relationships (CWHR) data (Mayer and Laudenslayer 1988). The wildland vegetation of the study area (excluding agricultural, urban, water, and barren classes) contains 45 CWHR classes, 14 of which are >=0.75% of the wildland vegetation in the study area. We divided these 14 classes into shrubland dominated versus non-shrubland dominated types (annual grass, oak, conifer, mixed hardwood) (Table 1). Table 1. The Community types (WHR class) that are >= 0.75% of all wildland vegetation in the study area and their % area of the southern California ecoregion

Community type (WHR class)

Vegetation type

Percent of wildland vegetation in study area

Mixed Chaparral

Shrub

29.2

Annual Grassland

Annual grass

15.9

Desert Scrub

Shrub

12.7

Coastal Scrub

Shrub

12.5

Coastal Oak Woodland

Oak

6.4

Chamise-Redshank Chaparral

Shrub

6.0

Pinyon-Juniper

Conifer

2.5

Montane Hardwood

Mixed hardwood

2.3

Blue Oak Woodland

Oak

2.0

Sierran Mixed Conifer

Conifer

1.2

Juniper

Conifer

1.1

Montane Hardwood-Conifer

Mixed hardwood-conifer

1.1

Montane Chaparral

Shrub

1.0

Sagebrush

Shrub

0.9

2) Second, for the shrubland types we determined the per pixel proportion of biomass by three plant life forms: tree, shrub, and herb. We further subdivided the shrub life form into three life history classes based on shrub post-fire regeneration strategies: Obligate Resprouters (OR), obligate seeders (OS), and facultative seeders (FS), providing five plant types in total. We created rasters depicting the proportion of biomass in each of the five plant types by first calculating the proportion of biomass in each type for the plots used in Schrader-Patton and Underwood (2021). The plot data contained individual plant species, crown width and height measurements. Using these measurements, we estimated the biomass for each individual plant within the plot by applying published allometric equations (see Schrader-Patton and Underwood 2021 for details). The individual plants in the plots were classified into the five plant types and the proportion of biomass in each type were calculated for each plot. A multinomial model was used to relate these proportions to a suite of geophysical and remote sensing variables which, in turn, was applied to raster surfaces of these predictors. The final outputs were raster maps of the proportion of biomass by life form (tree, shrub, herb) and, for shrubs, the proportion of biomass by life history type (OR, OS, and FS) (Underwood et al. in review). 3) Third, we estimated the standing dead, litter, and below ground biomass pools by either applying fractions of AGLBM gleaned the available published literature or by using biomass estimates in existing spatial datasets. The specific method used was dependent on the percentage of the WHR class in the study area and the vegetation type (shrub or non-shrub) (Figure 2).
a) For shrubland types >= 0.75% of all wildland vegetation in the study area (Mixed Chaparral, Desert Scrub, Coastal Scrub, Chamise Redshank Chaparral, Montane Chaparral, and Sagebrush), we used the proportion of the five plant types as a basis for applying the AGLBM factors from the literature. For litter estimates, we applied AGLBM factor of 0.78 (derived from Bohlman et al. 2018) to Mixed chaparral, Chamise-Redshank Chaparral, and Coastal scrub WHR classes. These shrubland types also contained tree and herb biomass. We estimated the litter and standing dead biomass for these plant types by multiplying the plant type proportion by AGLBM (Tree and herb AGLBM), or by the North American Wildland Fuels Database (NAWFD, Pritchard et al. 2018) litter biomass (Tree and herb litter and standing dead biomass), or by literature-derived factors (Tree and herb belowground biomass). Sagebrush, Montane chaparral, and Desert scrub were assigned litter biomass from the NAWFD data as these WHR types had no litter estimates in the literature.
b) For non-shrubland types >= 0.75% all wildland vegetation in the study area (Coastal Oak Woodland, Pinyon-Juniper, Montane Hardwood, Blue Oak Woodland, Sierran Mixed Conifer, Juniper, and Montane Hardwood-Conifer), the snag and litter NAWFD biomass estimates were used for standing dead and litter estimates, respectively. For belowground biomass, we used AGLBM factors from the literature based on the gross vegetation type (Oak, Conifer, or Mixed) and amount of total per pixel AGLBM. For example, for Oak WHR types (Coastal Oak Woodland, Blue Oak Woodland) <= 7 kg/m2 we used an AGLBM factor of 0.46 (see Mokany et al. 2006 for breakdown by class breaks). c) For all the remaining WHR classes (each < 0.75% of all wildland vegetation in the study area) and Annual Grasslands, we used the NAWFD snag and litter estimates (standing dead and litter biomass), and the California Air Resources Board (CARB, Battles et al. 2014) for our belowground estimates. The above ground, litter, standing dead, and below ground biomass raster layers for each

3D point cloud representing all physical features (e.g. buildings, trees and terrain) across City of Melbourne. The data has been encoded into a .las file format containing geospatial coordinates and RGB values for each point. The download is a zip file containing compressed .las files for tiles across the city area.

The geospatial data has been captured in Map Grid of Australia (MGA) Zone 55 projection and is reflected in the xyz coordinates within each .las file. Also included are RGB (Red, Green, Blue) attributes to indicate the colour of each point.

Capture Information - Capture Date: May 2018 - Capture Pixel Size: 7.5cm ground sample distance - Map Projection: MGA Zone 55 (MGA55) - Vertical Datum: Australian Height Datum (AHD) - Spatial Accuracy (XYZ): Supplied survey control used for control (Madigan Surveying) – 25 cm absolute accuracy

Limitations: Whilst every effort is made to provide the data as accurate as possible, the content may not be free from errors, omissions or defects.

Sample Data: For an interactive sample of the data please see the link below. https://cityofmelbourne.maps.arcgis.com/apps/webappviewer3d/index.html?id=b3dc1147ceda46ffb8229117a2dac56dPreview:Download:A zip file containing the .las files representing tiles of point cloud data across City of Melbourne area. Download Point Cloud Data (4GB)