Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/915985

OpenURL Link
Title
Prediction and validation of foliage projective cover from Landsat-5 TM and Landsat-7 ETM+ imagery
Author/Creator
Armston, John D.;  Denham, Robert J.;  Danaher, Tim J.;  Scarth, Peter F.;  Moffiet, Trevor N.
Institution
The University of Newcastle. Faculty of Science & Information Technology, School of Mathematical and Physical Sciences
Description
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.
Relation
Journal of Applied Remote Sensing Vol. 3
Publisher Link
http://dx.doi.org/10.1117/1.3216031
Date
2009
Publisher
International Society for Optical Engineering (SPIE)
Keyword(s)
airborne LiDAR;  fractional cover;  machine learning;  regression;  stand basal area
Resource Type
journal article
Identifier
http://hdl.handle.net/1959.13/915985
Identifier
ISSN:1931-3195
Reviewed
Reviewed
72 Visitors 87 Hits 0 Downloads
Save/E-mail Citation
Citation Format
E-mail Address
Subject
"Journal of Applied Remote Sensing"
 
OR