Our results show that a decision tree classifier achieved 60% accuracy. Voting classification increased accuracy by 8% to 68% for the two major class categories. Wrapper based feature selection identified 17% (21 out of 126 bands) of the original wavebands, with which comparable accuracy to using all the bands was achieved but computation time was dramatically reduced by 86% at 99 boosting trials. A comparison was made to use the 22 best wavebands chosen by an independent but comparable study by Thenkabail et el. (2004). We found similar accuracies at 68% only that the machine learning feature selection focused more on early shortwave infrared bands. More than one-third, eight out of 21, of the selected wavebands falls into the region of early shortwave infrared region (1.3-1.9 μm) which is sensitive to the moisture content of vegetation or soil, and has been identified as useful for estimating vegetation stresses. Only 3 selected bands fall into the presumably important near-infrared (0.75-1.05 μm) and far near-infrared (1.05-1.30 μm) ranges. These results point to the importance of the shortwave infrared for mapping of Biological Valuation Map. To show the usefulness of hyperspectral approach, multi-spectral analysis using six similated Landsat TM bands were conducted to compare with HyMap inputs. The accuracy was 48.6% (without boosting) compared to 60.2% using 126 hyperspectral bands.