Research Papers

Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery

[+] Author Affiliations
John D. Armston, Robert J. Denham

Department of Environment and Resource Management, Remote Sensing Center, Climate Building, Indooroopilly, QLD 4068 Australia

Tim J. Danaher

Deartment of Enviornment and Climate Change, Information Sciences Branch, Suite 3, Alstonville Plaza, Alstonville, NSW 2477 Australia

Peter F. Scarth

Department of Environment and Resource Management, Remote Sensing Center, Climate Building, Indooroopilly, QLD 4068 Australia

Trevor N. Moffiet

Faculty of Science & Information Technology, University of Newcastle, University Drive, Callaghan, NSW 2308 Australia

J. Appl. Remote Sens. 3(1), 033540 (August 11, 2009). doi:10.1117/1.3216031
History: Received July 23, 2008; Revised March 30, 2009; Accepted July 31, 2009; August 11, 2009; Online August 11, 2009
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Abstract

The detection of long term trends in woody vegetation in Queensland, Australia, from the Landsat-5 TM and Landsat-7 ETM+ sensors requires the automated prediction of overstorey foliage projective cover (FPC) from a large volume of Landsat imagery. This paper presents a comparison of parametric (Multiple Linear Regression, Generalized Linear Models) and machine learning (Random Forests, Support Vector Machines) regression models for predicting overstorey FPC from Landsat-5 TM and Landsat-7 ETM+ imagery. Estimates of overstorey FPC were derived from field measured stand basal area (RMSE 7.26%) for calibration of the regression models. Independent estimates of overstorey FPC were derived from field and airborne LiDAR (RMSE 5.34%) surveys for validation of model predictions. The airborne LiDAR-derived estimates of overstorey FPC enabled the bias> and variance> of model predictions to be quantified in regional areas. The results showed all the parametric and machine learning models had similar prediction errors (RMSE < 10%), but the machine learning models had less bias> than the parametric models at greater than ~60% overstorey FPC. All models showed greater than 10% bias> in plant communities with high herbaceous or understorey FPC. The results of this work indicate that use of overstorey FPC products derived from Landsat-5 TM or Landsat-7 ETM+ data in Queensland using any of the regression models requires the assumption of senescent or absent herbaceous foliage at the time of image acquisition.

© 2009 Society of Photo-Optical Instrumentation Engineers

Citation

John D. Armston ; Robert J. Denham ; Tim J. Danaher ; Peter F. Scarth and Trevor N. Moffiet
"Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery", J. Appl. Remote Sens. 3(1), 033540 (August 11, 2009). ; http://dx.doi.org/10.1117/1.3216031


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