Performance of Various Speckle Filter Methods in Modelling Forest Aboveground Biomass using Sentinel-1 Data: Case Study of Barru Regency, South Sulawesi

摘要

Noise in SAR imagery was produced due to different backscatter responses from the objects in the earth’s surface. Thisresulted in a grainy image, usually known as “salt and pepper” noise, which reduces the capability to identify an objectfrom radar imagery. Therefore, speckle filtering was conducted to decrease this noise from SAR imagery. This studyaims to assess the performance of different types of speckle filters, especially when used to construct forest abovegroundbiomass (AGB) model from Sentinel-1 data in Barru Regency, South Sulawesi. There were 4 filters used in this studyi.e. Frost, Gamma-MAP, Median, and Refined Lee. AGBmodelwas constructed by using dual-polarization C-band SARof Sentinel- 1 data and ground inventory plots. 23 plots were collected in the field and the allometric equation was usedto calculate the biomass value of the field survey data then cross-validation models were generated by using biomassvalue and backscatter data from VV and VH polarization. Quality control was performed by comparing the coefficientof determination (R2) of those filters. The result shows that Frost filter, especially on VH polarization was chosen as thebest- fit model to estimate the AGB based on the higher value of R2 (0.3464158) and RMSE (33.5231). The resultdemonstrated the Frost filter as the best filter for retaining and/or enhancing the backscatter signal in Sentinel-1 data tobe used in vegetation biophysical modelling.