Estimation and mapping of above ground biomass and carbon of Bwindi impenetrable National Park using ALOS PALSAR data

Mangrove has the most carbon rich forests in the tropics. Mapping and monitoring biomass of mangrove forest is very important to manage ecosystem and field survey of mangrove biomass and productivity is very difficult due to muddy soil condition, heavy weight of the wood, very large area and tidal effect on mangrove area. Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) is available for identification and monitoring mangrove forest. The objective of this research is to investigate the impact of tidal height on characteristics of HH and HV derived from ALOS PALSAR for estimation above ground biomass of mangrove forest. Methodology consists of collecting of tidal height data in the study area, ALOS-PALSAR time series data, region of interest (ROI) on mangrove forest, characterization of HH and HV and impact analysis of tidal height on HH and HV. The result of this research has showed the impact of tidal height on characteristics HH and HV on mangrove forest types derived from ALOS-PALSAR and proposed the model for estimation aboveground biomass of mangrove forest.

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

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-13-2018 ◽

2018 ◽

Vol IV-3 ◽

pp. 13-20 ◽

Cited By ~ 2

Author(s):

Reginald Jay Labadisos Argamosa ◽

Ariel Conferido Blanco ◽

Alvin Balidoy Baloloy ◽

Christian Gumbao Candido ◽

John Bart Lovern Caboboy Dumalag ◽

...

Keyword(s):

Random Forest ◽

Principal Components ◽

Mangrove Forest ◽

Above Ground Biomass ◽

Alos Palsar ◽

Forest Mapping ◽

Ground Biomass ◽

Sar Data ◽

Volumetric Objects ◽

Occurrence Matrix

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&thinsp;cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75&thinsp;cm to 7.5&thinsp;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&minus;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&thinsp;Mg using only four features, namely, &sigma;&deg;VH GLCM variance, &sigma;&deg;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.

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