Preparation of Forest Density Map Using SPOT-7 and Sentinel-2 Multiplex Sensors in South Zagros )Case study: Fars Province, Dalaki Dadin Area(

Document Type : Original Article

Authors

1 Assistant Professor of Forestry, Faculty of Agriculture and Natural Resources, Islamic Azad University, Chalous Branch

2 Ph.D. Student, in Forestry, Faculty of Agriculture and Natural Resources, Islamic Azad University, Chalous Branch

Abstract

The purpose of this study is to prepare a forest density map using the images of SPOT 7 and Sentinel 2 multispectral sensors in South Zagros, Dalki Dadin Basin, Fars Province, in order to evaluate and compare them with each other. First, a forest and non-forest area map was prepared, and then a forest density map was prepared in four levels: 25-5, 25-50, 50-75, and 75% and above. In order to make the classification correct, the ground reality map based on the interpretation of Ortho's digital photos of the 80s with a scale of 1:40000 was used. Examining the forest, non-forested classification map showed that the Sentinel 2 image with PCA-1-8 band composition and using the maximum likelihood classification algorithm with an overall accuracy of 96.3% and kappa coefficient of 0.91 compared to the spot image 7 By combining PCA-1-3 bands and using neural classifier algorithm with an overall accuracy of 87.57% and kappa coefficient of 0.7, it has a better result. Among the maps obtained from forest classification into four density classes, the map obtained from Sentinel 2 image with neural classifier with PCA-3-8 band composition and with kappa coefficient of 0.72 and accuracy of 88.36 percent ratio shown in Spot 7, the map obtained from the neural classifier with 2-4-3 band composition and 0.64 kappa coefficient and 78.74 percent accuracy had the highest accuracy. Also, after merging the image of SPOT7 and SPOT7-Pan, the map obtained by PC method using the neural classifier with PCA-2-4 band combination with Kappa coefficient 0.75 and 89.26% accuracy has the highest accuracy and map. The result of classifying the forest into four density classes, the result of the PC method using neural classifier with PCA-2-4 band combination and Kappa coefficient of 0.37 and accuracy of 50.60% had the highest accuracy. The overall results showed that, according to the extracted information, the Sentinel 2 image is more accurate for producing forest cover maps in four density classes.

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References

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Applied Soil Research
Volume 12, Issue 2 - Serial Number 2
September 2024
Pages 111-122

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History

  • Receive Date: 08 May 2023
  • Revise Date: 30 September 2023
  • Accept Date: 01 October 2023

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