Mapping forest density using Sentinel‐2 and SPOT‐7 multispectral sensor images—A case study from South Zagros forests of Fars province, Iran - Abdi, Reza Abedinzadegan; Kazemnejad, Farid; Nimvari, Majid Eshagh; Sheikh al‐Islami, Ali (Agrosystems, geosciences & environment, 2025)
This study proposed to compare Sentinel‐2 and SPOT 7 multispectral instrument (MSI) images to create a forest canopy density model (FCDm) in the Dalki Dadin area in the South Zagros forests of Fars province, Iran. First, a forest and non‐forest area map was prepared, and then an FCDm was prepared in four categories: 5%–25%, 25%–50%, 50%–75%, and >75%. In this research, the classification of satellite images was done using a parallelepiped classifier, traditional Mahalanobis distance classifier (MDC), maximum‐likelihood classification (MLC), and artificial neural network (ANN) algorithms using appropriate band sets in ENVI 5.3 software. To classify correctly, the ground reality map was implemented based on the interpretation of ortho digital photos of the 80s with a scale of 1:25,000‐scale topographic map. The overall accuracy and kappa coefficient for Sentinel‐2 images (with band combination [BC] of principal component analysis (PCA)‐1‐8 using the MLC algorithm) and the SPOT 7 image (with the BC of PCA‐1‐3 and the use of ANN classification algorithm) were obtained equal to 96.3%, 0.91%, and 87.57%, 0.70, respectively. Therefore, the Sentinel‐2 image has had better results compared to the SPOT 7 image to prepare the forest and non‐forest classification map. Furthermore, the overall accuracy and kappa coefficient for the Sentinel‐2 image (with BC of PCA‐3‐8 using the MLC algorithm) and the SPOT 7 image (with the BC of 2‐3‐4 and the use of ANN classification algorithm) were obtained equal to 88.36%, 0.72%, and 78.74%, 0.64, respectively. Therefore, the Sentinel‐2 image has had better results compared to the SPOT 7 image to provide the forest classification map. Also, after the integration of SPOT7 and SPOT7‐Pan image, the map obtained by PCA method using an ANN classifier with BC of PCA‐2‐4 with a kappa coefficient of 0.75 and accuracy of 89.26% had the highest accuracy. Also, the maps obtained from forest classification into four density classes obtained by PCA method using ANN with BC of PCA‐2‐4 and with a kappa coefficient of 0.37 and accuracy of 59.60% had the highest accuracy. The overall results showed that, according to the extracted information, the Sentinel‐2 image has more appropriate accuracy for producing FCDm in four density classes. Plain Language Summary This study purposed to compare Sentinel‐2 and SPOT 7 multispectral instrument (MSI) images to create a forest canopy density model (FCDm) in the Dalki Dadin area in the South Zagros forests of Fars province, Iran. First, a forest and non‐forest area map was prepared, and then an FCDm was prepared in four categories: 5%–25%, 25%–50%, 50%–75%, and >75%. In this research, the classification of satellite images was done using a parallelepiped classifier, traditional Mahalanobis distance classifier (MDC), maximum‐likelihood classification, and artificial neural network (ANN) algorithms using appropriate band sets in ENVI 5.3 software. To classify correctly, the ground reality map was implemented based on the interpretation of ortho digital photos of the 80s with a scale of 1:25,000‐scale topographic map. The overall accuracy and kappa coefficient for Sentinel‐2 images (with band combination of principal component analysis‐1‐8 using the maximum likelihood).