Large-area, high-resolution tree cover mapping with multi-temporal SPOT5 imagery, New South Wales, Australia

Fisher, Adrian, Day, Michael, Gill, Tony, Roff, Adam, Danaher, Tim and Flood, Neil (2016) Large-area, high-resolution tree cover mapping with multi-temporal SPOT5 imagery, New South Wales, Australia. Remote Sensing, 8 6: 515.1-515.23. doi:10.3390/rs8060515

Author Fisher, Adrian
Day, Michael
Gill, Tony
Roff, Adam
Danaher, Tim
Flood, Neil
Title Large-area, high-resolution tree cover mapping with multi-temporal SPOT5 imagery, New South Wales, Australia
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2016-01-01
Sub-type Article (original research)
DOI 10.3390/rs8060515
Open Access Status DOI
Volume 8
Issue 6
Start page 515.1
End page 515.23
Total pages 23
Place of publication Basel, Switzerland
Publisher M D P I AG
Language eng
Formatted abstract
Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l'Observation de la Terre 5 (SPOT5) High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW), an area of over 800,000 km2. Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008-2011) for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC), a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (±0.51% standard error), while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r2 = 0.90) with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and is already widely used for natural resource management in the state.
Keyword SPOT5
Foliage projective cover
Tree cover
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
HERDC Pre-Audit
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 5 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 5 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 28 Jun 2016, 21:30:43 EST by Adrian Fisher on behalf of School of Geography, Planning & Env Management