Abstract
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.
Highlights
Mangrove forest only represent roughly 0.7% of the total world forest
The methodology is categorized into the following procedures: Sentinel-1 pre-processing, computing for the grey level co-occurrence matrix (GLCM) textures, creating the main data matrix, extracting new information from Principal Components Analysis (PCA), selecting important features, and modelling above ground biomass (AGB) using Random Forest (RF)
The total and average AGB with respect to the area is higher in a dense mangrove forest as compared to the sparse mangrove forest
Summary
Mangrove forest only represent roughly 0.7% of the total world forest. It stores about 20 petagrams of Carbon in its ecosystem (Jones et al, 2014). There have been global records stating that there is a significant mangrove forest cover loss in the past five decades. The loss is mainly attributed to anthropogenic activities such as land conversions (mangrove forests to agriculture/aquaculture), over extraction of timber products, and coastal population increase, among others. These land cover changes lead to the emission of the stored carbon in the above and below ground carbon deposits. There is a need to monitor these changes which can be done by accurate and large-scale monitoring schemes such as the use of remote sensing systems
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