Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 30 July 2018 / Approved: 31 July 2018 / Online: 31 July 2018 (05:02:29 CEST)

How to cite: Cassol, H.L.G.; Shimabukuro, Y.E.; Moraes, E.C.; Carreiras, J.M.D.B.; Aragão, L.E.D.O.C.E.; Silva, C.V.D.J.; Quegan, S. Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon. Preprints 2018, 2018070604 (doi: 10.20944/preprints201807.0604.v1). Cassol, H.L.G.; Shimabukuro, Y.E.; Moraes, E.C.; Carreiras, J.M.D.B.; Aragão, L.E.D.O.C.E.; Silva, C.V.D.J.; Quegan, S. Retrieving Secondary Forest Aboveground Biomass from Polarimetric ALOS-2 PALSAR-2 Data in the Brazilian Amazon. Preprints 2018, 2018070604 (doi: 10.20944/preprints201807.0604.v1).

Abstract

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 full polarimetric ALOS-2 PALSAR-2 data for estimating AGB by taking into account the past-land use of SF areas in the Brazilian Amazon. We surveyed a chronosequence of 42 SF plots (20 ha) near the Tapajós National Forest in Pará state to quantifying AGB growth rates. We explored the full polarimetric data testing three regression models including non-linear (NL), multiple linear regressions models (MLR), and the semi-empirical extended water cloud model (EWCM). The results showed that the intensity of previous use has affected the structure of SF by reducing the AGB accumulation and being noticeable by several polarimetric attributes. The combination of multiple prediction variables with MLR improved the AGB estimation by 70% comparing amongst other models (R² adj. = 0.51; RMSE = 13.2 Mg ha-1) bias = 2.1 ± 37.9 Mg ha-1. The error propagation of the MLR model was estimated to be 15%.

Subject Areas

backscattering; L-band; SAR polarimetry; microwave; Chapman-Richards model; tropical forest

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