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

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Ariel Conferido Blanco^1¹  Reginald Jay Labadisos Argamosa^1² 

[1] Department of Geodetic Engineering, University of the Philippines, Diliman, 1001, Philippines^2;Training Center for Applied Geodesy and Photogrammetry, University of the Philippines, Diliman, 1001, Philippines^1

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

DOI  :  10.5194/isprs-annals-IV-3-13-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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.

【 授权许可】

CC BY