The potential of field hyperspectral remote sensing data for non-destructive assessment of hay meadow biomass and vascular plant diversity has been investigated. Spectrometric and agronomic data were acquired at peak biomass over 34 sites distributed at diverse elevation and slopes over an area of 220 km2 in the Central Alps (Valtellina, Northern Italy). Different modelling approaches were tested to evaluate the predictive performance of spectral measurements: (i) the use of two band ratios of reflectance as input in ordinary least square regression models and (ii) the use of all reflectance bands as input in multivariate partial least square regression models. Each model was subjected to leave-one-out cross-validation and evaluated using the cross-validated coefficient of determination and the root mean square error. Fresh biomass and fuel moisture content were predicted with an average error of
Fava, F., Parolo, G., Colombo, R., Gusmeroli, F., Della Marianna, G., Monteiro, A., et al. (2010). Fine-scale assessment of hay meadow productivity and plant diversity in the European Alps using field spectrometric data. AGRICULTURE, ECOSYSTEMS & ENVIRONMENT, 137(1-2), 151-157 [10.1016/j.agee.2010.01.016].
Fine-scale assessment of hay meadow productivity and plant diversity in the European Alps using field spectrometric data
FAVA, FRANCESCO PIETRO;COLOMBO, ROBERTO;
2010
Abstract
The potential of field hyperspectral remote sensing data for non-destructive assessment of hay meadow biomass and vascular plant diversity has been investigated. Spectrometric and agronomic data were acquired at peak biomass over 34 sites distributed at diverse elevation and slopes over an area of 220 km2 in the Central Alps (Valtellina, Northern Italy). Different modelling approaches were tested to evaluate the predictive performance of spectral measurements: (i) the use of two band ratios of reflectance as input in ordinary least square regression models and (ii) the use of all reflectance bands as input in multivariate partial least square regression models. Each model was subjected to leave-one-out cross-validation and evaluated using the cross-validated coefficient of determination and the root mean square error. Fresh biomass and fuel moisture content were predicted with an average error of
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