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Remote Sens. 2016, 8(6), 515; doi:10.3390/rs8060515

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

1
Joint Remote Sensing Research Program, School of Geography, Planning and Environmental Management, University of Queensland, Brisbane, QLD 4072, Australia
2
Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, NSW 2052, Australia
3
Office of Environment and Heritage, 10 Valentine Ave, Parramatta, NSW 2150, Australia
4
Remote Sensing Centre, Science Delivery, Department of Science, Information Technology and Innovation, 41 Boggo Road, Dutton Park, QLD 4102, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Anu Swatantran, Nicolas Baghdadi and Prasad S. Thenkabail
Received: 1 March 2016 / Revised: 2 June 2016 / Accepted: 9 June 2016 / Published: 18 June 2016
View Full-Text   |   Download PDF [5513 KB, uploaded 18 June 2016]   |  

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. View Full-Text
Keywords: SPOT5; Landsat; lidar; foliage projective cover; tree cover SPOT5; Landsat; lidar; foliage projective cover; tree cover
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Fisher, A.; Day, M.; Gill, T.; Roff, A.; Danaher, T.; Flood, N. Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia. Remote Sens. 2016, 8, 515.

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