ESTIMATION OF FOREST PARAMETERS THROUGH FUZZY CLASSIFICATION OF TM DATA

Several studies have investigated the utility of Landsat 5 TM imagery to estimate forest parameters such as stand composition and density. R egression equations have generally been used to relate these parameter s to the radiance responses of the TM channels. Such a method is not f easible in highly complex landscapes, where forest mixtures and terrai n irregularities may obscure the existence of simple relationships, In the current paper a fuzzy approach to the problem is presented based on a multi-step procedure, First, some typical forest plots with known features are spectrally identified. A Maximum Likelihood fuzzy classi fication with nonparametric priors is then applied to the study images , so as to derive fuzzy membership grades for all pixels with respect to the typical plots, Finally, these grades are used to compute the es timates of the forest parameters by a weighted average strategy, The m ethod was tested on a complex, rugged area in Tuscany mainly covered b y deciduous and coniferous forests. Two TM scenes and accurate ground references taken in spring and summer 1991 were utilized for the testi ng, The first results, statistically evaluated in comparison with thos e of a more usual multivariate regression procedure, are quite encoura ging, The possible application of the fuzzy approach to other cases of environmental monitoring is finally discussed,