Office of Environment and Heritage (2011-2019), N. (2021). Woody Extent and Foliage Projective Cover - Spot, OEH algorithm, NSW. Version 1.0. Terrestrial Ecosystem Research Network. Dataset. https://dx.doi.org/10.25901/dcje-yt71
Woody Extent and Foliage Projective Cover - Spot, OEH algorithm, NSW
Ver: 1.0
Status of Data: completed
Update Frequency: Data not provided
Security Classification: unclassified
Record Last Modified: 2025-12-02
Viewed 11802 times
Accessed 220 times
Dataset Created: 2011-01-01
Dataset Published: 2021-09-23
Data can be accessed from the following links:
HTTPPoint-of-truth metadata URLHTTPCloud Optimised GeoTIFFs - Woody extent and foliage projective cover NSWWMSwoody_extent_fpcHTTPLandscape Data Visualiser - Woody extent and foliage projective cover NSWHTTPnswwoodyvegetationextentfpc2011factsheet1.pdfHTTPnswwoodyvegetationextentfpc2011factsheet2_dTwsjzV.pdfHTTPro-crate-metadata.json
How to cite this collection:
This dataset contains maps of woody vegetation extent and woody foliage projective cover (FPC) for New South Wales at 5 metre resolution.
Woody vegetation is a key feature of our landscape and an integral part of our society. We value it because it contributes to the economy, protects the land, provides us with recreation, and gives refuge to the unique and diverse range of fauna that we regard so highly. Yet it poses a significant threat to us in times of fire and storm. So information about trees is vital for a range of business, property planning, monitoring, risk assessment, and conservation activities.
The datasets are:
Woody vegetation extent. A presence/absence map showing areas of trees and shrubs, taller than two metres, that are visible at the resolution of the imagery used in the analysis. This shows the location, extent, and density of foliage cover for stands of woody vegetation, enabling identification of small features such as trees in paddocks and scattered woodlands through to the largest expanses of forest in the State. Woody extent products contain 'bcu' in the file name.
Woody foliage projective cover (FPC). FPC is a measure of the proportion of the ground area covered by foliage (or photosynthetic tissue) held in a vertical plane and is a measure of canopy density. Woody FPC products contain 'bcv' in the file name.
Both mosaics and tiles are available, along with a shape file that identifies the location of the tiles.
Woody vegetation is a key feature of our landscape and an integral part of our society. We value it because it contributes to the economy, protects the land, provides us with recreation, and gives refuge to the unique and diverse range of fauna that we regard so highly. Yet it poses a significant threat to us in times of fire and storm. So information about trees is vital for a range of business, property planning, monitoring, risk assessment, and conservation activities.
The datasets are:
Woody vegetation extent. A presence/absence map showing areas of trees and shrubs, taller than two metres, that are visible at the resolution of the imagery used in the analysis. This shows the location, extent, and density of foliage cover for stands of woody vegetation, enabling identification of small features such as trees in paddocks and scattered woodlands through to the largest expanses of forest in the State. Woody extent products contain 'bcu' in the file name.
Woody foliage projective cover (FPC). FPC is a measure of the proportion of the ground area covered by foliage (or photosynthetic tissue) held in a vertical plane and is a measure of canopy density. Woody FPC products contain 'bcv' in the file name.
Both mosaics and tiles are available, along with a shape file that identifies the location of the tiles.
Credit
We at TERN acknowledge the Traditional Owners and Custodians throughout Australia, New Zealand and all nations. We honour their profound connections to land, water, biodiversity and culture and pay our respects to their Elders past, present and emerging. We owe a debt of gratitude to the numerous Science Division staff and volunteers who edited the maps. Thanks too, to the following organisations:
Airbus Defence and Space for SPOT imagery data
NSW Land and Property Information for ADS40 data
NSW Land and Property Information and a number of commercial vendors for Lidar data
Joint Remote Sensing Research Program
Purpose
What can the maps be used for? The maps are intended for use in rural landscapes and are suited to many applications including:
- property planning
- vegetation mask for topographic maps
- local government planning
- risk assessment, such as in fire-prone areas
- native vegetation mapping
- habitat identification and mapping
- property planning
- vegetation mask for topographic maps
- local government planning
- risk assessment, such as in fire-prone areas
- native vegetation mapping
- habitat identification and mapping
Lineage
Data not provided.
Method DocumentationData not provided.
Procedure Steps
1.
Image Data:
The source data was SPOT5 High Resolution Geometric (HRG) satellite imagery. It consists of 4 multispectral bands (10 m pixels), and a panchromatic band (2.5 m pixels). A time series of one image per year for the period 2008 to 2011 was acquired during dry periods where the contrast between woody vegetation and the ground cover is high. A total 1256 images were used. The images were registered with ground control. The multispectral imagery was corrected for atmospheric and bi-directional reflectance effects and sharpened to 5 m pixels using the panchromatic imagery. The images were masked for cloud, cloud shadow, topographic shadow, and water.
2.
Foliage Projective Cover (FPC):
An estimate of FPC was derived for every clear pixel in every image. This required a multiple linear regression model that related the multi-spectral reflectance to a reference data set of FPC. Each pixel contained up to 5 observations of FPC and reflectance over time. The probability of a pixel containing woody vegetation was determined using a binomial logistic regression model. The model parameters were the mean FPC, mean red reflectance, variation in FPC over time, and the climate variable vapour pressure deficit. The model was trained using 25930 observations of woody vegetation presence or absence. These points were interpreted from ADS40 aerial imagery where available (0.5 m pixels) and SPOT5 HRG panchromatic images (2.5 m pixels).
3.
Mapping woody vegetation:
Woody vegetation extent was mapped by applying a threshold to the probability images, with further editing by trained analysts. The mean FPC value over time was used to attribute each woody pixel.
4.
Accuracy assessment:
Two comparisons with independently-derived datasets of woody vegetation extent were performed as described in the Data Quality section.
Temporal Coverage
From 2011-01-01 to 2011-12-31
Spatial Resolution
Data not provided.
Vertical Extent
Data not provided.
Data Quality Assessment Scope
OEH staff conducted two comparisons with independent observations of woody vegetation extent. The first comparison used fine-detailed maps of woody-vegetation extent derived from airborne Lidar surveys. The state-wide map of extent had an overall accuracy of 90.1%.
The second comparison used 6670 image-interpreted points of woody vegetation presence or absence. The points were gathered from images with 2.5 m pixels. The overall accuracy was 88%. The spatial variation in accuracy across the state, reported by Local Land Service region, is listed in the table below.
Care should be taken when interpreting the maps. Incorrect classification is most likely to occur where it is difficult to distinguish trees greater than two metres in height from other types of vegetation. Such vegetation includes sparse woodlands, low shrubs, chenopods, heath, wetlands, and irrigated pastures and crops. Also, woody vegetation is only detected about half of the time when the fol
The second comparison used 6670 image-interpreted points of woody vegetation presence or absence. The points were gathered from images with 2.5 m pixels. The overall accuracy was 88%. The spatial variation in accuracy across the state, reported by Local Land Service region, is listed in the table below.
Care should be taken when interpreting the maps. Incorrect classification is most likely to occur where it is difficult to distinguish trees greater than two metres in height from other types of vegetation. Such vegetation includes sparse woodlands, low shrubs, chenopods, heath, wetlands, and irrigated pastures and crops. Also, woody vegetation is only detected about half of the time when the fol
Data Quality Report
Data not provided.
Data Quality Assessment Outcome
Accuracy assessment results are provided in the table below.
| Local Land Service |
Points | Lidar | Local Land Service |
Points | Lidar |
|---|---|---|---|---|---|
| North Coast | 95.8% | 93.6% | Riverina | 89.0% | 93.0% |
| Northern Tablelands | 91.8% | 89.0% | Hunter | 88.7% | 85.3% |
| South East | 91.6% | 94.5% | North West | 88.3% | 89.0% |
| Central Tablelands | 91.0% | 86.8% | Murray | 84.8% | 90.3% |
| Greater Sydney | 90.6% | 89.1% | Western | 77.5% | 88.6% |
| Central West | 89.8% | 88.3% |
ANZSRC - FOR
GCMD Sciences
Horizontal Resolution
Parameters
Platforms
Temporal Resolution
User Defined
trees
Supplemental Information
The coordinate reference systems for the files are NSW Lamberts Conformal Conic (mosaic). GDA94 MGA zones 54, 55, and 56 (tiles)
Resource Specific Usage
Data not provided.
Environment Description
Data not provided.
Terrestrial Ecosystem Research Network
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