Multi-sensor Approach for the Estimation of Above-Ground Biomass of Mangroves

Mangroves are woody halophytes thriving in muddy substratum along the coastal areas of the tropics and sub-tropics. They are often credited for their exceptional carbon sequestration capability. Estimating above-ground biomass (AGB) through field survey is tedious, particularly in a hostile environment like a mangrove ecosystem. However, the quantification of AGB is made possible with the help of continued advancements in sensor technology and computational algorithms. This research attempts to model the AGB of mangroves present in Bhitarkanika, Odisha, using a multi-sensor approach. We utilized multispectral Sentinel-2 (SM) and Landsat-8 (LO), and hyperspectral Airborne Visible Infra-Red Imaging Spectrometer-Next Generation (AN) datasets in our analysis. The mangrove biomass was calculated for 42 sample plots from a field survey using species specific and common allometric equations. After data-specific preprocessing; six feature sets namely reflectance bands, band ratios, vegetation indices (VIs), texture-based Gray Level Co-occurrence Matrix (GLCM) features of reflectance, band ratios and VIs were extracted for each dataset. The co-located set of features derived from each dataset were regressed against the AGB estimated using field methods of 42 sample plots (1) independently for each feature set, (2) in a combination of feature sets for each dataset and (3) in a combination of the feature sets of all three datasets as a multi-sensor approach. Feature selection techniques were used to get the best possible output of combined AN, SM and LO datasets. The results show that the combination of textural features gave better prediction models than independent sets of features. Also, Genetic Algorithm (GA) and Recursive Feature Elimination CV (RFECV) proved to be better feature selectors than other classical approaches. AN, SM and LO resulted in the R2 value of 0.41, 0.85 and 0.35 with RMSE of 356.81, 195.49 and 366.84 t/ha, respectively; while, the multisensory approach yielded a maximum R2 value of 0.7 and RMSE of 244.86 t/ha. The results show that the structural information of vegetation canopy obtained from textural parameters of different input bands has improved the regression model to predict the biomass.