ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume IV-3
23 Apr 2018
 | 23 Apr 2018


Reginald Jay Labadisos Argamosa, Ariel Conferido Blanco, Alvin Balidoy Baloloy, Christian Gumbao Candido, John Bart Lovern Caboboy Dumalag, Lady Lee Carandang Dimapilis, and Enrico Camero Paringit

Keywords: Mangrove forest, Above ground biomass, Sentinel-1, Synthetic aperture radar, Grey level co-occurrence matrix, Random forest, Machine learning

Abstract. Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23 cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75 cm to 7.5 cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a−c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r2 of 0.79 and an RMSE of 0.44 Mg using only four features, namely, σ°VH GLCM variance, σ°VH GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.