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- W3115750080 abstract "Mangrove forest plays an important role in ecosystem and the need to conserve this is essential in our time as global warming was realized. One way to monitor this forest is to estimate its aboveground biomass (AGB). This study aims to estimate the AGB of the largest single mangrove forest in the Philippines located in the municipality of Del Carmen, Siargao Islands using remotely sensed Interferometric Synthetic Aperture Radar (IfSAR) datasets and Sentinel-1 image. There were three predictor variables derived from the Sentinel -1 image used for modeling the AGB: the backscatter value from VV polarization, backscatter value from VH polarization, and the combination of the backscatter values from VV and VH polarizations. Also, the predictor variable from IfSAR dataset that was used is the canopy height. Linear regression between the field-measured AGB and the predictor variables was used to generate AGB model, and the coefficient of determination (r <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> ) and root mean square error (RMSE) were determined. The results show that the model derived from IfSAR data obtained a coefficient of determination of 0.42 and RMSE of 19.39 Mg/ha. However, the combination of the two polarizations derived from Sentinel-1 image was more accurate in estimating AGB since it obtained the highest r <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> of 0.43 and lowest RMSE of 12.65 Mg/ha. Based on the best model the estimated above ground biomass of whole mangrove forest ranged from 73.37 Mg/ha to 225.51 Mg/ha with an average of 140.2 Mg/ha. The AGB model derived in this study can be used for multi-temporal monitoring of mangroves in the study area." @default.
- W3115750080 created "2021-01-05" @default.
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- W3115750080 date "2020-11-16" @default.
- W3115750080 modified "2023-09-26" @default.
- W3115750080 title "Aboveground Biomass Estimation of Mangroves Using Sentinel-1 Image and IFSAR Data: A Comparison" @default.
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- W3115750080 doi "https://doi.org/10.1109/tencon50793.2020.9293801" @default.
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