Regional Scale Monitoring of Vegetation Biomass in River Floodplains Using Imaging Spectroscopy and Ecological Modeling

Monitoring of natural vegetation succession is essential for optimal management of river floodplains in the Netherlands. As the maximum discharge capacity depends on the hydraulic resistance of vegetation, a key biophysical parameter for floodplain monitoring is vegetation biomass. The objective of this study is to investigate the feasibility of mapping above- ground biomass and vegetation structure of heterogeneous canopies in river floodplains using imaging spectroscopy. We establish linear predictive models between vegetation indices derived from airborne imaging spectrometer (HyMap) data and field measurements of biomass (n = 21) for a floodplain in the Netherlands. Results for broad-band and narrow-band derived Vis (e.g., NDVI, SAVI, WDVI and RSR) and a multivariate approach using PLS were compared using a cross-validation procedure to assess the prediction power of the regression models. Results showed that regression models could be improved when differences in vegetation structure were taken into account. Vegetation biomass maps for the floodplain were prepared in two steps. First a classification of plant functional types (PFT) was made using mixture tuned match filtering (MTMF). In a second step, the best regression models were used to map the spatial distribution of vegetation biomass. The results demonstrate the necessity to use a PFT based approach for biomass assessment, improving the quality of the prediction significantly over conventional approaches.