Forestry Department, Faculty of Natural Resources Statistical analysis of satellite remote sensing data for forest inventory and mapping in north of Iran S. A. Bonyad Forestry Department, Faculty of Natural Resources Guilan University, Guilan, Rasht, Iran. Tel: 98 182322 3023 Fax: 98 182 322102 Email- bonyad@guilan.ac.ir

1. Introduction  The objectives of forest inventory are: To define the geographical location of forests To map the forest stands To stratify the forest To estimate forest stand parameters To produce reliable information for forest management To assign probability to forest maps  

Remote sensing data for forest inventory has two options: Aerial photos Satellite imagery

Main satellite image data sources for forest inventory : Landsat TM with 7 bands +Pan Landsat ETM+ with 7 bands +Pan IRS Liss3 SPOT, Multispectral and 1 Panchromatic bands,

Forest inventory requirements: Remotely sensed data forest stands and A suitable classification technique

2. Materials and Methods Study area The natural forest stands of Zanjan province were selected as the study area. Satellite image database. Landsat ETM+ 20. 5. 2002 30m 6 bands Landsat Pan 20. 5. 2002 15m 1 band

Data Analysis Methods Statistical ANOVA and MANOVA techniques:  Wilks’ test  Hotelling’s T2  Principal Components Analysis (PCA) Factor Analysis Also: Vegetation index : DVI , NDVI ,… Maximum liklelihood classification (MLC) technique

3. Results. The preliminary and PCA results are presented in Table 1 and 2 respectively. Correlated data

Uncorrelated PCA data

PCA eigen-channels

Vegetation index for forest inventory Followings Vegetation index were used for forest inventory

Raster GIS A RGIS file created contained 18 image layers for forest inventory analysis :

KIA RGB bands combination

Figure 2. Forest inventory map for Forest stands 50km

Classification results

4. Conclusions The PCA eigen-channels, Vegetation index, Factor Analysis are useful for forest inventory, classification and mapping. The statistical multivariate analysis of variance (MANOVA) techniques are useful to map the forest stands and to estimate stand parameters.

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