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.
Highlights
Forests play an important role in the global carbon (C) cycle, and their relation to anthropogenic and climate changes have been recognized in the literature (e.g., [1,2])
Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) backscatter intensity data to reduce the uncertainty in the estimation of forest above-ground biomass (AGB) in the Miombo savanna woodlands of Mozambique (East Africa)
The mean, minimum, maximum and standard deviation were computed based on the Advanced Land Observing Satellite (ALOS) PALSAR backscatter intensity data extracted over a 50 m radius
Summary
Forests play an important role in the global carbon (C) cycle, and their relation to anthropogenic and climate changes have been recognized in the literature (e.g., [1,2]). Mapping and understanding the spatial distribution of forest above-ground biomass (AGB) using remote-sensing methods is an important and challenging task [11,12,13]. These maps can be used to monitor forests (deforestation, regrowth and degradation processes), to estimate and model greenhouse gas emissions and the effects of conservation actions, sustainable management and enhancement of C stocks [4,14]. The region is mostly covered by Miombo forests, the most extensive tropical savanna woodland formation of Africa that extends across some of the world’s poorest countries [22] and directly supports the livelihoods of the local populations. The best location for the installment of the plantation in a ~10,000 ha parcel of land was decided, complying with the sustainability criteria listed in the Directive 2009/28/EC of the European
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