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 Remote sensing data for forest inventory has two optionsinventory has two options::

Aerial photos Satellite imagery

Main satellite image data sources for for forest inventory forest inventory :

Landsat TM with 7 bands +Pan Landsat ETM+ with 7 bands +Pan IRS Liss3 SPOT, Multispectral and 1 Panchromatic bands,

Forest inventoryForest 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 dataCorrelated data

Uncorrelated PCA data Uncorrelated PCA data

PCA eigen-channelsPCA eigen-channels

Vegetation indexVegetation index for forest for forest inventoryinventory

Followings Vegetation index were used for forest inventory

Raster GISRaster 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

0 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.

Thank you