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Standing forage biomass (SFB) and the percent of standing biomass composed of forbs (PCTF) were modeled across the growing season. Samples representing stages of plant maturity from early vegetative to dormant were collected from grazed and ungrazed native tallgrass paddocks using a 0.5 X 0.5 m quadrat. Total biomass was measured during all years of the study (1992-1995). Grass and forb biomass were measured separately during 1995. Height of canopy closure also was measured during 1995. Before clipping, plots were scanned with a multispectral radiometer. Models were prepared using simple regression, multiple regression (MR), or a commercial neural network (NN) computer program. Potential inputs to MR and NN models of SFB and PCTF included Julian day of harvest (JD), range site, canopy closure height (CH), incident radiation, spectral reflectance values (RFV) at 8 discreet bandwidths, and the normalized difference vegetation index (NDVI). The NDVI alone accounted for little variability (R2 = 0.13) in SFB during all years of the study. The optimal MR model for the same data set (SFB = 3.5[JD] - 43.7[460 nm RFV] + 1099[NDVI] - 992; R2 = 0.62) accounted for a greater amount of the variability in SFB. The capacity to describe variation in SFB for the 1995 data with MR was improved when CH was included as a variable (R2 = 0.58 versus 0.78). A NN model accounted for the most variation in SFB across the entire study (R2 = 0.76). During 1995, the capability of a NN to account for variation in SFB within the training data was similar whether or not CH was included as an input (R2 = 0.86); however, prediction of SFB from validation data using the same NN was improved by using CH as an input variable. Little variation in PCTF was accounted for by a MR model (R2 = 0.23); however, a considerably larger proportion of the variation in PCTF was accounted for when an NN was used (R2 = 0.59). Seasonal changes in SFB and PCTF were described with an acceptable degree of accuracy by forage reflectance characteristics that were adjusted for time of season and canopy complexity. Moreover, when provided with the same potential inputs, NN predicted SFB and PCTF from validation data more accurately than MR models.

Radiometry for Predicting Tallgrass Prairie Biomass Using Regression and Neural Models