In addition, this imagery serves as a backdrop for information surveyed in the field, and as the source for multi-spectral data for remote sensing classification and yield estimation. These locations were digitized on screen by scanning in a picture of their precise positions, and later refined using GPS data from within the beds. Large areas of low yield occurred between sampling points, leading to the consensus that a new sampling method was needed. In 1997, ground sampling was modified to better reflect the heterogeneous conditions of the cranberry beds. Field data collected at each point include parameters such as number, weight and condition of fruit, upright density, canopy height, soil characteristics, and pH. Figure 1.
Crosses identify sprinkler heads, and filled circles identify smart sampling points.
A stepwise multiple regression was done using the spectral classes with the highest correlation coefficients to predict bed yield. In-situ ground data for two test beds were overlain onto the NDVI surfaces to visually assess variations in observed data within the beds. The results of the unsupervised classification on all beds on this farm for 1996 are shown in Figure 2. Major yield components based on this technique are identifiable on the imagery as verified by field observations. The dark gray pixels shown in the lower left-hand part of the bed are lower lying areas with poor drainage that are wet.