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Estimation of forest parameters through Fuzzy classification of TM Data
 Maisam Toosi M.sc. Student, Dept. of Remote Sensing Eng. Email: maisam_toosi@yahoo.com M. J. Valadan Zouj Assistant Professor, Dept. of Remote Sensing Eng. Email: valadan_zouj@kntu.ac.ir Address: faculty of Geodesy and Geomatic Eng., K. N. TOOSI University of Technology Vali Asr St., Vanak Sq., Tehran, Iran, Post Box: 15875-15433 Fax: ++98 21 878 6213, Tel: ++ 98 21 877 9473-5 1. Introduction In the last few years the high resolution multispectral data acquired by the landsat 5 thematic mapper (TM) have been used increasingly in forest monitoring and mapping. Several forest inventors have been obtained for the identification of forest dominant species even in irregular landscapes. On the other hand, the estimation of forest parameters such as actual stand composition and density has been successful only when there was not a complex combination of these parameters and other environmental factors, such as terrain irregularities. The usual estimation methods are based on maximum likelihood with PCI Geomatica software have been set up between forest parameters measured on the ground and the relevant spectral responses in the individual TM channels or combinations of these. The rationale for these analyses is that the variations in forest parameters affect the spectral behavior in a way which can be easily modeled by simple relationships. Unfortunately, this assumption does not always hold, especially when other factors affect the spectral behaviors of the forest examined. In effect, if the signatures of the forests are disturbed by mixtures in composition, terrain irregularities, under story influences, act. The existence of simple relationships between stand parameters and spectral responses can be seriously hampered, or even completely obscured. It can be note, however, that even if simple relationships do not exist some spectral information is likely to be present in the remotely sensed scenes about the parameters examined. Actually, the high resolution TM multispectral data acquired at different times of the year contain a great deal of information about the condition of vegetation. The extraction and processing of this information therefore becomes the fundamental issue. A new methodology is proposed here for the estimation of forest parameters in highly complex landscapes based on a “fuzzy” classification approach. The theory of fuzzy sets is intrinsically suited to dealing with mixed, spectrally undefined cases, and can be adapted to the specific problem as will be shown below. The comparison between results of PCI system and fuzzy method and also by study on TM image, acceptable result can be derived which if can be used in future researches. 2: Study Area, Ground and Satellite Data The study area, of about 15.3×15.3 km in size, is located at approximately 36ْ 32′ north latitude, 51 ْ 07′ East longitudes. This is part of the mazandaran forest on Alborz mountain chain in the north of IRAN. There are several maps of this area, like Iranian geography organization map (scale: 1/50000) and national cartographic center map (scale: 1/25000). The TM frame fore the present research was taken on the 28 of July, 1987. Also PCI Geomatica (8.1) was used for analysis that was powerful software. Study Area  Figure 1: color composite of TM Image 3: Methodology: A-Data processing with PCI software: The processing of the ground and remotely sensed data was carried out on a P4 computer system. The software consists is PCI Geomatica (8.1) for general processing. A scene of 512 ×512 pixels was extracted from frame and georeferenced by a polynomial interpolation algorithm trained on ground control points. In this process supervise method classification was implemented.  Figure 2: PCI Analysis
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