Urmia University Applied Soil Research 2423-7116 12 2 2024 08 22 Preparation of Forest Density Map Using SPOT-7 and Sentinel-2 Multiplex Sensors in South Zagros )Case study: Fars Province, Dalaki Dadin Area( Preparation of Forest Density Map Using SPOT-7 and Sentinel-2 Multiplex Sensors in South Zagros )Case study: Fars Province, Dalaki Dadin Area( 111 122 121533 10.30466/asr.2024.121533 FA Farid Kazemnejadf Assistant Professor of Forestry, Faculty of Agriculture and Natural Resources, Islamic Azad University, Chalous Branch Reza Abedinzadegan Abdi Ph.D. Student, in Forestry, Faculty of Agriculture and Natural Resources, Islamic Azad University, Chalous Branch Majid Eshagh Nimvari Assistant Professor of Forestry, Faculty of Agriculture and Natural Resources, Islamic Azad University, Chalous Branch Ali Sheikh Al-Islami Assistant Professor of Forestry, Faculty of Agriculture and Natural Resources, Islamic Azad University, Chalous Branch Journal Article 2023 05 08 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. 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. https://asr.urmia.ac.ir/article_121533_fa386b6f57616fd1c0c5eeafc71ce560.pdf