Abstract
This research has used the L-band radar from ALOS-2 PALSAR-2 and field work data for evaluation of seasonal effects of backscattering intensity on retrieval forest biomass in the tropics. The effects of seasonality and HH, and HV polarizations of the SAR data on the biomass were analyzed. The dry season HV polarization could explain 61% of the biomass in this study region. The dry season HV backscattering intensity was highly sensitive to the biomass compared to the rainy season backscattering intensity. The SAR data acquired in the rainy season with humid and wet canopies were not very sensitive to the in situ biomass. Strong dependence of the biomass estimates with season of SAR data acquisition confirmed that the choice of right season SAR data is very important for improving the satellite based estimates of the biomass. This research expects that the results obtained in this research will contribute to monitoring of the quantity and quality of forest biomass in Vietnam and other tropical countries.
Highlights
Global monitoring of forest carbon is urgently needed for the United Nation program on Reducing Emissions from Deforestation and Degradation (REDD+), a financial payment mechanism for environmental services (Stone & León, 2011; UN-REDD Vietnam, 2012)
The sensitivity of biomass with the backscattering intensity of the HH and HV polarizations for the dry season was analyzed using the coefficient of determination (R2) and Root Mean Square Error (RMSE)
The relationship between rainy season HV polarization. This analysis suggests that dry season SAR data is more important for estimating the biomass than the rainy season data
Summary
Forests sequestrate atmospheric carbon dioxide in the form of biomass during photosynthesis (IPCC, 2003; FAO, 2009; Way & Pearcy, 2012). Forest biomass has an important role in the global carbon cycle (Brown, 1997; IPCC 2006; Gibbs et al, 2007). Accurate monitoring of forest biomass and CO2 sequestration rates are immensely important for increasing understanding of global carbon cycles, improving climate change forecasting models, and climate change mitigation and adaptation strategies (FAO, 1997; GCOS, 2006; Gibbs et al, 2007; FAO, 2009, 2010; Stone & León, 2011). Estimating biomass from satellite data is challenging due to the diverse nature of forests, especially tropical forests (Lefsky et al, 2002; Lu, 2006; Gibbs et al, 2007; FAO, 2010; Sinha et al, 2015)
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