FRACTAL ANALYSIS FOR FOREST TYPE DISCRIMINATION —A CASE STUDY USING IRS-1C LISS-111 SATELLITE DATAV. Krishna Prasad, Yogesh Kant and K.V.S. Badarinath
In the present study, an attempt has been made to distinguish different forest types, viz., dry deciduous mixed dry deciduous and mixed scrub forest stands using Fractal methods. IRS-IC LISS-III satellite data with 23.5 m resolution has been used to characterize different forest types. Three fractal methods, viz., line divider method, patch method and variogram method have been used to compute the fractal dimension of the forest stands. Ground based survey has been conducted for identifying different forest types through stratified random sampling. Taxonomic and phytosociological studies have been done for studying community characteristics of different forest stands. Textural windows corresponding to the forest stands have been identified from IRS-IC LISS-Ill data, and the fractal methods have been implemented on the stands for identifying textural homogenity and heterogenity between different forest stands. Results of the study suggested that fractal analysis of forest stand texture can vary markedly within a single spectral class. Line divider method has been found to discriminate the forest stands more distinctly than the other two methods. It has been inferred that, high resolution data sets in conjunction with fractal methods can be used effectively for studying forest structural attributes.
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