Extracting forest resource information from remotely sensed and ancillary data : use of an expert system

Using a hypothetical example, the paper demonstrates how the original classification decision rule may cause the amount of land in each cover type to be misestimated. An alternative is proposed and discussed which should provide better area estimates for each type, though locational accuracy may be decreased. The aesthetic quality of the classified image is also reduced because of the "salt and peppering" that results from the use of the probabil­ istically based rule. INTRODUCTION ^ kidmore and turner (1988) presented and discussed a super^ vised nonparametric classifier which was used with multispectral SPOT data to improve classification accuracy compared to either a maximum-likelihood classifier or a Euclidean distance classifier for Monterey pine (Pinus radiata D. Don) plantations in Canberra, Australia. To utilize the classifier, a training field is selected from an unclassified image for each land-cover type of interest. For each training sample extracted, a unique Ndimensional space is created in which each dimension rep­ resents one spectral band of data, and each "edge" of the space is dimensioned by the possible range of pixel brightness values. For example, 7-band 8-bit Thematic Mapper data would result in the creation of a series of seven-dimensional spaces with each dimension or "side" ranging from 0 to 255. (The space, there­ fore, would have a total of 256 raised to the 7th power cells, a number which may be reduced by using a "collapsing factor.") For each training sample, the number of pixels in each cell po­ sition of the appropriate N-dimensional space is tallied based on the brightness value of each band for each pixel. A proba­ bility is then assigned to each cell position in a particular Ndimensional space relative to the number of pixels tallied in that cell position over all N-dimensional spaces. The image is then classified by assigning each unknown pixel to the cover type for which the probability is the greatest at the appropriate cell position. However, because unknown pixels are not distributed to each cover type in the same proportion as they occur in the training samples, this decision rule may cause the amount of land in a particular type to be misestimated. To demonstrate this and describe an alternative decision rule, an intentionally simplistic example will be utilized so that laborious computations are min­ imized. The simplicity of the example does not decrease the validity of the points discussed, however. In this example, a tract of land of 1000 hectares is available on which it is known that two cover types exist, with each type comprising exactly half — 500 hectares — of the area. A cloudfree digital image of the area has been obtained using a sensor which recorded spectral reflectance values in two spectral bands using 1-bit data and a pixel size of 1 hectare. To classify this image using the nonparametric classifier, two training samples of 100 pixels each have been extracted from areas on the image which are known to be perfectly representative of Type 1 and Type 2, respectively. For each type, the pixels were tallied in two-dimensional space based on brightness values and spectral band numbers with the following results: Type 1 Type 2 Band 1 Band 1 Number of Pixels Band 2 Band 2 Utilizing the procedures described, the following probabilities result for each cell position of the two-dimensional spaces: Tvpe 1 Type 2 Band 1 Band 1 0 1 0 1 (50/60) = 0.83 (25/55) = 0.45 (15/35) = 0.43 (10/50) = 0.20 (10/60) = 0.17 (30/55) = 0.55 (20/35) = 0.57 40/50) = 0.80 Given that the 200 pixels selected in the training fields are ex­ actly representative of each type, the brightness values of the 1000 pixels can be expected to be distributed: