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.

Spatial and temporal patterns of large fire (>100 ha) incidence in Portugal over the period 1984–2004 were modeled using extreme value statistics, namely the Peaks Over Threshold approach, which uses the Generalized Pareto Distribution (GPD) as a model. The original dataset includes all fires larger than 5 ha (30 616 fires) that were observed in Portugal during the study period, mapped from Landsat satellite imagery. The country was divided into eight regions, considered internally homogeneous from the perspective of their fire regimes and respective environmental correlates. The temporal analysis showed that there does not appear to be any trend in the incidence of very large fires, but revealed a cyclical behavior in the values of the GPD shape parameter, with a period in the range of 3 to 5 years. Spatial analysis highlighted strong regional differences in the incidence of large fires, and allowed the calculation of return levels for a range of fire sizes. This analysis was affected by the presence of a few outlying observations, which may correspond to clusters of contiguous fire scars, resulting in artificially large burned areas. We discuss some of the implications of our findings in terms of consequences for fire management aimed at preventing the occurrence of extremely large fires, and present ideas for extending the present study.

We screened the algorithm 2 (308 K threshold) European Space Agency (ESA) World Fire Atlas (WFA), for the period 1997–2002, using ancillary land cover, night-lights and volcanic activity datasets, combined with statistical techniques to detect the occurrence of space-time clusters of anomalous observations. The WFA is built using night time data from the Along Track Scanning Radiometer (ATSR) onboard the Second European Remote-Sensing Satellite (ERS-2). The spatial resolution of the data is 1 km and the satellite revisiting period is 3 days at the equator. The WFA is the first and longest archive of global fire observations and has been used in numerous biomass burning studies. Known limitations of the WFA are the inclusion of warm surfaces, gas flares, and city lights, and an underestimation of actual global fire activity, due to the time of satellite overpass. Nevertheless, it has been considered that the WFA contains a relatively small proportion of observations that do not correspond to vegetation fires, which is not corroborated by our findings. During the study period, the annual percentage of false alarms and non-vegetation fires varied from a minimum value of 20.6% in 1997 to a maximum of 27.9% in 1998. Gas flares and hot bare soils are the major sources of false alarms and non-vegetation fires.