Modelling Aboveground Biomass of Miombo Woodlands in Niassa Special Reserve, Northern Mozambique
نویسندگان
چکیده
Aboveground biomass (AGB) estimation plays a crucial role in forest management and carbon emission reporting, especially for developing countries wishing to address REDD+ projects. Both passive active remote-sensing technologies can provide spatially explicit information of AGB by using limited number field samples, thus reducing the substantial budgetary cost inventories. The aim current study was estimate Niassa Special Reserve (NSR) fusion optical (Landsat 8/OLI Sentinel 2A/MSI) radar (Sentinel 1B ALOS/PALSAR-2) data. performance multiple linear regression models relate ground with different combinations sensor data assessed root-mean-square error (RMSE), Akaike Bayesian criteria (AIC BIC). mean stock (CS) estimated from were at 56 Mg ha?1 (ranging 11 95 ha?1) 28 MgC ha?1, respectively. best model 63 ± 20.3 NSR, ranging 0.6 200 (r2 = 87.5%, AIC 123, BIC 51.93). We obtained an RMSE % 20.46 ha?1. this within range that reported existing literature miombo woodlands. vegetation indices derived Landsat/OLI 2A/MSI, backscatter ALOS/PALSAR-2 is good predictor AGB.
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ژورنال
عنوان ژورنال: Forests
سال: 2022
ISSN: ['1999-4907']
DOI: https://doi.org/10.3390/f13020311