Mr. John David Armston Profile

John David Armston

at Australia Queensland Government

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SPIE Journal Paper | 1 August 2009

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

John Armston, Robert Denham, Tim Danaher, Peter Scarth, Trevor Moffiet

JARS, Vol. 3, Issue 01, 033540, (August 2009) https://doi.org/10.1117/12.10.1117/1.3216031

KEYWORDS: LIDAR, Data modeling, Earth observing sensors, Calibration, Sensors, Landsat, Vegetation, Error analysis, Machine learning, Data acquisition

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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.

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