A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series. Issue 3 (1st February 2017)

• Record Type: Journal Article
• Authors: Gill, Tony; Johansen, Kasper; Phinn, Stuart; Trevithick, Rebecca; Scarth, Peter; Armston, John
• Is Part Of: International journal of remote sensing. Volume 38:Issue 3(2017)
• Page Start: 679
• Page End: 705
• Publication Date: 2017-02-01
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2016.1266112
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: There is a significant need to provide nationwide consistent information for land managers and scientists to assist with property planning, vegetation monitoring applications, risk assessment, and conservation activities at an appropriate spatial scale. We created maps of woody vegetation cover of Australia using a consistent method applied across the continent, and made them accessible. We classified pixels as woody or not woody, quantified their foliage projective cover, and classed them as forest or other wooded lands based on their cover density. The maps provide, for the first time, cover density estimates of Australian forests and other wooded lands with the spatial detail required for local-scale studies. The maps were created by linking field data, collected by a network of collaborators across the continent, to a time series of Landsat-5 TM and Landsat-7 ETM+ images for the period 2000–2010. The fractions of green vegetation cover, non-green vegetation cover, and bare ground were calculated for each pixel using a previously developed spectral unmixing approach. Time series statistics, for the green vegetation cover, were used to classify each pixel as either woody or not using a random forest classifier. An estimate of woody foliage projective cover was made by calibration with field measurements, and woody pixels classified as forest where the foliage cover was at least 0.1. Validation of the foliage projective cover with field measurements gave a coefficient of determination, R 2, of 0.918 and root mean square error of 0.070. The user's and producer's accuracies for areas mapped as forest were high at 92.2% and 95.9%, respectively. The user's and producers's accuracies were lower for other wooded lands at 75.7% and 61.3%, respectively. Further research into methods to better separate areas with sparse woody vegetation from those without woody vegetation is needed. The maps provide information that will assist in gaining a better understanding of our natural environment. Applications range from the continental-scale activity of estimating national carbon stocks, to the local scale activities of assessing habitat suitability and property planning.