High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm

The saturation problem associated with the use of NDVI for biomass estimation in high canopy density vegetation is a well known phenomenon. Recent field spectroscopy experiments have shown that narrow band vegetation indices computed from the red edge and the NIR shoulder can improve the estimation of biomass in such situations. However, the wide scale unavailability of high spectral resolution satellite sensors with red edge bands has not seen the up-scaling of these techniques to spaceborne remote sensing of high density biomass. This paper explored the possibility of estimate biomass in a densely vegetated wetland area using normalized difference vegetation index (NDVI) computed from WorldView-2 imagery, which contains a red edge band centred at 725 nm. NDVI was calculated from all possible two band combinations of WorldView-2. Subsequently, we utilized the random forest regression algorithm as variable selection and a regression method for predicting wetland biomass. The performance of random forest regression in predicting biomass was then compared against the widely used stepwise multiple linear regression. Predicting biomass on an independent test data set using the random forest algorithm and 3 NDVIs computed from the red edge and NIR bands yielded a root mean square error of prediction (RMSEP) of 0.441 kg/m2 (12.9% of observed mean biomass) as compared to the stepwise multiple linear regression that produced an RMSEP of 0.5465 kg/m2 (15.9% of observed mean biomass). The results demonstrate the utility of WorldView-2 imagery and random forest regression in estimating and ultimately mapping vegetation biomass at high density – a previously challenging task with broad band satellite sensors.