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Open AccessArticle

Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon

National Institute for Space Research (INPE)—Remote Sensing Division, Av. dos Astronautas 1758, Jd Granja, São José dos Campos, São Paulo 12227-010, Brazil
National Centre for Earth Observation (NCEO), University of Sheffield, Western Bank, Sheffield S10 2TN, UK
College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4RJ, UK
Lancaster Environment Centre, Lancaster University, Lancaster LA1 4YQ, UK
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(1), 59;
Received: 25 October 2018 / Revised: 26 November 2018 / Accepted: 30 November 2018 / Published: 29 December 2018
Secondary forests (SF) are important carbon sinks, removing CO2 from the atmosphere through the photosynthesis process and storing photosynthates in their aboveground live biomass (AGB). This process occurring at large-scales partially counteracts C emissions from land-use change, playing, hence, an important role in the global carbon cycle. The absorption rates of carbon in these forests depend on forest physiology, controlled by environmental and climatic conditions, as well as on the past land use, which is rarely considered for retrieving AGB from remotely sensed data. In this context, the main goal of this study is to evaluate the potential of polarimetric (quad-pol) ALOS-2 PALSAR-2 data for estimating AGB in a SF area. Land-use was assessed through Landsat time-series to extract the SF age, period of active land-use (PALU), and frequency of clear cuts (FC) to randomly select the SF plots. A chronosequence of 42 SF plots ranging 3–28 years (20 ha) near the Tapajós National Forest in Pará state was surveyed to quantifying AGB growth. The quad-pol data was explored by testing two regression methods, including non-linear (NL) and multiple linear regression models (MLR). We also evaluated the influence of the past land-use in the retrieving AGB through correlation analysis. The results showed that the biophysical variables were positively correlated with the volumetric scattering, meaning that SF areas presented greater volumetric scattering contribution with increasing forest age. Mean diameter, mean tree height, basal area, species density, and AGB were significant and had the highest Pearson coefficients with the Cloude decomposition (λ3), which in turn, refers to the volumetric contribution backscattering from cross-polarization (HV) (ρ = 0.57–0.66, p-value < 0.001). On the other hand, the historical use (PALU and FC) showed the highest correlation with angular decompositions, being the Touzi target phase angle the highest correlation (Φs) (ρ = 0.37 and ρ = 0.38, respectively). The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing to the NL model (R2 adj. = 0.51; RMSE = 38.7 Mg ha−1) bias = 2.1 ± 37.9 Mg ha−1 by incorporate the angular decompositions, related to historical use, and the contribution volumetric scattering, related to forest structure, in the model. The MLR uses six variables, whose selected polarimetric attributes were strongly related with different structural parameters such as the mean forest diameter, basal area, and the mean forest tree height, and not with the AGB as was expected. The uncertainty was estimated to be 18.6% considered all methodological steps of the MLR model. This approach helped us to better understand the relationship between parameters derived from SAR data and the forest structure and its relation to the growth of the secondary forest after deforestation events. View Full-Text
Keywords: backscattering; L-band; SAR polarimetry; microwave; Chapman-Richards model; tropical forest backscattering; L-band; SAR polarimetry; microwave; Chapman-Richards model; tropical forest
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MDPI and ACS Style

Cassol, H.L.G.; Carreiras, J.M.B.; Moraes, E.C.; Aragão, L.E.O.C.; Silva, C.V.J.; Quegan, S.; Shimabukuro, Y.E. Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon. Remote Sens. 2019, 11, 59.

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