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Sensors 2014, 14(8), 15348-15370; doi:10.3390/s140815348

Intra-and-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral RapidEye Sensor and Advanced Machine Learning Algorithms

1
Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
2
University of Witwatersrand, Geography and Environmental Studies Division School of Geography, Archaeology and Environmental Studies, Room 102-Bernard Price Building, East Campus, Braamfontein Johannesburg 2050, South Africa
*
Author to whom correspondence should be addressed.
Received: 26 April 2014 / Revised: 14 June 2014 / Accepted: 6 August 2014 / Published: 20 August 2014
(This article belongs to the Section Remote Sensors)
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Abstract

The quantification of aboveground biomass using remote sensing is critical for better understanding the role of forests in carbon sequestration and for informed sustainable management. Although remote sensing techniques have been proven useful in assessing forest biomass in general, more is required to investigate their capabilities in predicting intra-and-inter species biomass which are mainly characterised by non-linear relationships. In this study, we tested two machine learning algorithms, Stochastic Gradient Boosting (SGB) and Random Forest (RF) regression trees to predict intra-and-inter species biomass using high resolution RapidEye reflectance bands as well as the derived vegetation indices in a commercial plantation. The results showed that the SGB algorithm yielded the best performance for intra-and-inter species biomass prediction; using all the predictor variables as well as based on the most important selected variables. For example using the most important variables the algorithm produced an R2 of 0.80 and RMSE of 16.93 t·ha−1 for E. grandis; R2 of 0.79, RMSE of 17.27 t·ha−1 for P. taeda and R2 of 0.61, RMSE of 43.39 t·ha−1 for the combined species data sets. Comparatively, RF yielded plausible results only for E. dunii (R2 of 0.79; RMSE of 7.18 t·ha−1). We demonstrated that although the two statistical methods were able to predict biomass accurately, RF produced weaker results as compared to SGB when applied to combined species dataset. The result underscores the relevance of stochastic models in predicting biomass drawn from different species and genera using the new generation high resolution RapidEye sensor with strategically positioned bands. View Full-Text
Keywords: bag fraction; biosphere-atmospheric interactions; learning rate; high resolution RapidEye imagery; tree complexity; variable importance and variable selection bag fraction; biosphere-atmospheric interactions; learning rate; high resolution RapidEye imagery; tree complexity; variable importance and variable selection
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Dube, T.; Mutanga, O.; Elhadi, A.; Ismail, R. Intra-and-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral RapidEye Sensor and Advanced Machine Learning Algorithms. Sensors 2014, 14, 15348-15370.

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