%0 Journal Article
%4 sid.inpe.br/mtc-m21b/2017/04.18.17.12
%2 sid.inpe.br/mtc-m21b/2017/04.18.17.12.22
%@doi 10.1016/j.rse.2017.03.016
%@issn 0034-4257
%T Mapping major land cover types and retrieving the age of secondary forests in the Brazilian Amazon by combining single-date optical and radar remote sensing data
%D 2017
%8 June
%9 journal article
%A Carreiras, João M. B.,
%A Jones, Joshua,
%A Lucas, Richard M.,
%A Shimabukuro, Yosio Edemir,
%@affiliation University of Sheffield
%@affiliation Aberystwyth University
%@affiliation University of New South Wales
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress j.carreiras@sheffield.ac.uk
%@electronicmailaddress
%@electronicmailaddress
%@electronicmailaddress yosio.shimabukuro@inpe.br
%B Remote Sensing of Environment
%V 194
%P 16-32
%K Age of secondary forests, ALOS PALSAR, Amazon, Landsat TM, Random forests, Tropical secondary forests.
%X 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 20072010 period for i) discriminating mature forest, non-forest and secondary forest, and ii) retrieving the age of secondary forests (ASF), with 100 m × 100 m 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 (9596%), with the highest errors in the secondary forest class (omission and commission errors in the range 46% and 1220% respectively) because of misclassification as mature forest. Root mean square error (RMSE) and bias when retrieving ASF ranged between 4.34.7 years (relative RMSE = 25.532.0%) and 0.040.08 years 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 1015 years of age and underestimation in secondary forests of at least 20 years 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 0.970.98, 0.960.98 and 0.840.90 in the 20072010 period, respectively. The agreement was lower (R = 0.820.85) 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.
%@language en
%3 carreiras_mapping.pdf