Abstract
Secondary forests play an important role in restoring carbon and biodiversity lost previously through deforestation and degradation and yet there is little information available on the extent of different successional stages. Such knowledge is particularly needed in tropical regions where past and current disturbance rates have been high but regeneration is rapid. Focusing on three areas in the Brazilian Amazon (Manaus, Santarém, Machadinho d'Oeste), this study aimed to evaluate the use of single-date Landsat Thematic Mapper (TM) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data in the 2007–2010 period for i) discriminating mature forest, non-forest and secondary forest, and ii) retrieving the age of secondary forests (ASF), with 100m×100m training areas obtained by the analysis of an extensive time-series of Landsat sensor data over the three sites. A machine learning algorithm (random forests) was used in combination with ALOS PALSAR backscatter intensity at HH and HV polarizations and Landsat 5 TM surface reflectance in the visible, near-infrared and shortwave infrared spectral regions. Overall accuracy when discriminating mature forest, non-forest and secondary forest is high (95–96%), with the highest errors in the secondary forest class (omission and commission errors in the range 4–6% and 12–20% respectively) because of misclassification as mature forest. Root mean square error (RMSE) and bias when retrieving ASF ranged between 4.3–4.7years (relative RMSE=25.5–32.0%) and 0.04–0.08years respectively. On average, unbiased ASF estimates can be obtained using the method proposed here (Wilcoxon test, p-value>0.05). However, the bias decomposition by 5-year interval ASF classes showed that most age estimates are biased, with consistent overestimation in secondary forests up to 10–15years of age and underestimation in secondary forests of at least 20years of age. Comparison with the classification results obtained from the analysis of extensive time-series of Landsat sensor data showed a good agreement, with Pearson's coefficient of correlation (R) of the proportion of mature forest, non-forest and secondary forest at 1-km grid cells ranging between 0.97–0.98, 0.96–0.98 and 0.84–0.90 in the 2007–2010 period, respectively. The agreement was lower (R=0.82–0.85) when using the same dataset to compare the ability of ALOS PALSAR and Landsat 5 TM data to retrieve ASF. This was also dependent on the study area, especially when considering mapping secondary forest and retrieving ASF, with Manaus displaying better agreement when compared to the results at Santarém and Machadinho d'Oeste.
Highlights
Land use and land cover change, and conversion from forest to non-forest, is the second largest source of carbon dioxide emissions after fossil fuel combustion, accounting for 9% of annual emissions between 2004 and 2013 (Le Quéré et al., 2015)
Variables obtained from ALOS PALSAR have lower discrimination capability when compared to those obtained from Landsat 5 Thematic Mapper (TM)
The study recognized the ability of combining ALOS PALSAR dualpol and Landsat 5 TM surface reflectance data to map mature forest, non-forest and secondary forest, with overall accuracy of 95–96% across the Brazilian Amazon in the 2007–2010 period, but with higher errors in the secondary forest class because of misclassification as mature forest
Summary
Land use and land cover change, and conversion from forest to non-forest (i.e., deforestation), is the second largest source of carbon dioxide emissions after fossil fuel combustion, accounting for 9% of annual emissions between 2004 and 2013 (Le Quéré et al., 2015). Deforestation across tropical and subtropical biomes was estimated to account for over 60% of total deforestation between 2000 and 2012 (Hansen et al, 2013), with almost two thirds in areas with high tree cover (N 75%). This has severe consequences in terms of carbon stocks depletion (Harris et al, 2012) and losses of biodiversity (Laurance et al, 2014; Lewis et al, 2015). Rates of deforestation observed in the late 1990s and early 2000s were successfully reduced by a combination of stronger forest monitoring-based law enforcement, expansion of protected areas, and interventions at the supply chain level (Nepstad et al, 2014)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
