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Remote Sens. 2013, 5(4), 1524-1548; doi:10.3390/rs5041524
Article

Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data

* ,
 and
Tropical Research Institute (IICT), Travessa do Conde da Ribeira, 9, Lisboa 1300-142, Portugal
* Author to whom correspondence should be addressed.
Received: 16 January 2013 / Accepted: 20 March 2013 / Published: 25 March 2013
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Abstract

The quantification of forest above-ground biomass (AGB) is important for such broader applications as decision making, forest management, carbon (C) stock change assessment and scientific applications, such as C cycle modeling. However, there is a great uncertainty related to the estimation of forest AGB, especially in the tropics. The main goal of this study was to test a combination of field data and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) backscatter intensity data to reduce the uncertainty in the estimation of forest AGB in the Miombo savanna woodlands of Mozambique (East Africa). A machine learning algorithm, based on bagging stochastic gradient boosting (BagSGB), was used to model forest AGB as a function of ALOS PALSAR Fine Beam Dual (FBD) backscatter intensity metrics. The application of this method resulted in a coefficient of correlation (R) between observed and predicted (10-fold cross-validation) forest AGB values of 0.95 and a root mean square error of 5.03 Mg·ha−1. However, as a consequence of using bootstrap samples in combination with a cross validation procedure, some bias may have been introduced, and the reported cross validation statistics could be overoptimistic. Therefore and as a consequence of the BagSGB model, a measure of prediction variability (coefficient of variation) on a pixel-by-pixel basis was also produced, with values ranging from 10 to 119% (mean = 25%) across the study area. It provides additional and complementary information regarding the spatial distribution of the error resulting from the application of the fitted model to new observations.
Keywords: above-ground biomass; carbon; ALOS PALSAR; bagging stochastic gradient boosting; Miombo savanna woodland; Mozambique above-ground biomass; carbon; ALOS PALSAR; bagging stochastic gradient boosting; Miombo savanna woodland; Mozambique
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Carreiras, J.M.B.; Melo, J.B.; Vasconcelos, M.J. Estimating the Above-Ground Biomass in Miombo Savanna Woodlands (Mozambique, East Africa) Using L-Band Synthetic Aperture Radar Data. Remote Sens. 2013, 5, 1524-1548.

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