Estimation of Forest Canopy Using Remote Sensing and Geostatistics (Case Study: Marivan Baghan Forests)

Subject Areas : natural resorces

Sasan Vafaei ¹ , Rahim Maleknia ² , Hamed Naghavi ³ , Omid Fathizadeh ⁴

1 - Ph.D., Forestry, Faculty of Natural Resources, Lorestan University, Khoram Abad, Iran *(Corresponding Author)
2 - Assistant Prof., Faculty of Agriculture and Natural Resources, University of Lorestan, Khoram Abad, Iran
3 - Assistant Prof., Faculty of Agriculture and Natural Resources, University of Lorestan, Khoram Abad, Iran.
4 - Assistant Prof., Department of Forestry, Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, Ahar, Iran.

Received: 2016-08-25 Accepted : 2017-07-19 Published : 2022-03-21

Keywords: Multiple Regression, Kriging, Zagros forest, Landsat, canopy cover,

Abstract :

Background and Objective: Updated information in quantitative and qualitative properties of forests are useful in describing ecosystem sustainability, and designing management and conservative plans. According to importance of canopy cover parameter in the Zagros region and cost and time consuming processes of field measurement methods, in this study performance of remote sensing and geostatistics techniques to estimate forest canopy cover of Baghan region, Marivan city, were investigated.Material and Methodology:  First, the number of 89 plots (each 0.1 Hectare) were selected based on random sampling method. In each plot, information of tree crown and center geographic coordinates of that plot were recorded. Remote sensing method was carried out using Landsat satellite images (TM) and multiple linear regression model. After image processing, spectral values of the corresponding field plots were extracted from the original images and synthetic bands composed of vegetation indices and principle component analysis. In geostatistic method, the estimation was performed using ordinary kriging from a fitted exponential model to the semivariogram.Findings: The calculated variograms of canopy cover showed relatively strong spatial autocorrelation fitted by exponential models and cross-validation results showed an unbiased estimation of canopy estimation. Compared with the remote sensing method (with R2= 0/52 and RMSE= 16/47), the results indicated that Kriging model (RMSE= 9.21, R2= 0.69) showed a more accurate estimation of forest canopy cover.Discussion and Conclusion: The results showed that geostatistics techniques can be used as an efficient tool for mapping the forest canopy in the same regions (Zagros Forest).

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