ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume IV-3
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 13–20, 2018
https://doi.org/10.5194/isprs-annals-IV-3-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 13–20, 2018
https://doi.org/10.5194/isprs-annals-IV-3-13-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  23 Apr 2018

23 Apr 2018

MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

Reginald Jay Labadisos Argamosa1, Ariel Conferido Blanco1,2, Alvin Balidoy Baloloy1, Christian Gumbao Candido1, John Bart Lovern Caboboy Dumalag1, Lady Lee Carandang Dimapilis1, and Enrico Camero Paringit2 Reginald Jay Labadisos Argamosa et al.
  • 1Training Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, Philippines
  • 2Department of Geodetic Engineering, University of the Philippines, Diliman, 1001, Philippines

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.