Investigation of the Effect of Main and Artificial Bands of Sentinel 2 satellite Images on Estimation of Quantitative Characteristics of Zagros Forests

 Estimation of forest habitat characteristics is a necessary issue in order to collect information for sustainable forest management (Ahmadi et al., 2020). Data collection methods require a lot of time and money. Therefore, it is always tried to use complementary methods, with lower costs and acceptable accuracy, using the achievements obtained in various scientific fields (Sivanpillai et al., 2006). Sentinel 2 is a new generation optical satellite for Earth monitoring developed by the European Space Agency with new spectral capabilities, wide coverage and good spatial and temporal resolution for data continuity and enhanced Landsat and Spot missions (Wang et al., 2017). When the size of the population is not very large, the application of each of the simple random, classification and systematic methods leads to a more or less similar result. But when the size of the community increases, these methods are associated with problems such as: preparing a sampling framework, high cost of surveying sample units with high dispersion and preparing a sampling plan from units far from each other (Zubair, 2007). The cluster method is one of the recommended methods for large areas in which instead of one sample plot, several sample plots are harvested in one part of the study area (Yim et al., 2015). Among the researches done on the mentioned subjects are the research of Kleinn (1994), Ismaili et al. (1396), Behera et al. (2021), Sibanda et al. (2021), Praticò et al. (2021), Nazariani et al. (1400) and Dabija et al. (2021). Although studies on estimating quantitative forest characteristics using distance measurement data and nonparametric algorithms in Zagros forests may have been done extensively, the effect of main and artificial bands to estimate canopy characteristics and density (number Per hectare) using Sentinel 2 images in the forests of Watershed Orfi Olad Ghobad Koohdasht with the aim of selecting the optimal cluster design to save time and money to achieve forest inventory has not been reported, so in this study, we tried to investigate this issue.

 In order to conduct the present study, a part of the Zagros forests located 35 km north of Koohdasht city, named Watershed Olad Ghobad was selected. Sampling points were determined in a regular-random manner using a grid with dimensions of 600 × 500 meters. Then, at each sampling point, 16 different cluster sampling designs with four circular and square subplots were designed and implemented. The radius of the circular subplots was 15 meters, the diameter of the square sample was 37 meters and the distance between the subplots was 60 meters. Then, the information on the characteristics of the number per hectare and canopy of trees including the number, of two large and small canopy diameters per sample was measured. In this study, Sentinel 2 sensor images related to August 6, 2021, equivalent to summer 1400, were used at the L1C correction level. This level of correction is geometrically error-free due to the reference ground and because their reflection is at the upper level of the atmosphere. In the present study, four bands (2-blue band, 3-green band, 4-red band, and 8-near-infrared band) of this sensor with a resolution of 10 meters were used. In general, Sentinel 2 image preprocessing operations involve radiometric and geometric correction. The image processing also includes various operations such as grading, texture analysis, band integration, and fabrication of plant features (Naghavi, 2014). In addition to the main bands, artificial bands were created by applying appropriate processing, which was used in the modeling process. Spectral values ​​equivalent to ground plots were extracted from the main and artificial bands and used as an independent variable in the models. In order to evaluate and fit the regression models, 25% of the data were randomly selected (Lu et al, 2004) and excluded from the evaluation data set. The validity of statistical models was evaluated using the coefficient of determination of the mean squared error squared, bias, mean squared error, and squared percentage. In total, ArcGIS software was used to implement the sample parts on the image, ENVI software was used for image processing and STATISTICA software was used for modeling.

In this method, during data validation, the results showed the characteristic of number per hectare of cluster 16 and the characteristic of canopy cover of cluster 15 with a coefficient of explanation (0.66) and (0.59), respectively, it has the highest accuracy. The results obtained from the application of the nearest neighbor algorithm with four criteria of Euclidean distance, Euclidean square, Manhattan, and Chapichev showed that for the number of characteristics per hectare, the Euclidean distance criterion with cluster 16 and for the canopy characteristic of the Euclidean distance criterion with cluster three, respectively (R2 = 0.59 and RMSE=5.70%) and (R2 = 0.62 and RMSE= 12.30%). The accuracy and efficiency of the support vector machine algorithm are influenced by the type of kernel used. The results of different kernels by considering different cluster sampling designs in the backup vector machine method showed for the characteristic number of linear kernel trees and 13 cluster sampling designs with an explanation coefficient of 0.72 and for the canopy characteristic. The linear kernel and the cluster sampling design of seven with a coefficient of determination of 0.65 have the best results. Evaluation of the artificial neural network model showed that the MLP algorithm is more suitable than the RBF algorithm in estimating the studied characteristics with its high accuracy and average squared percentage. Based on this, among the 16 designs used with the MLP algorithm, they showed the most suitable results for the number of characteristics per hectare of cluster six with a coefficient of reflection of 0.86 and for the canopy characteristic of cluster 10 with a coefficient of reflection of 0.76, respectively. Based on the values ​​of the coefficient of explanation and the lowest squared percentage of the mean squares of error, the most appropriate model was selected from the four types of algorithms studied in modeling and the results showed both characteristics of the artificial neural network model respectively (with MLP algorithms MLP 80-20-1 and MLP 80-11-1) presented optimal results with explanation coefficients of 0.86 and 0.76.

The modeling results with four studied algorithms for the canopy characteristic showed that the artificial neural network model algorithm with a cluster sampling design of 10 with an explanation coefficient of 0.76 was the most suitable method. The results are consistent with the study (Yim et al., 2015;) and show the superiority of using cluster sampling, nonparametric modeling of the artificial neural networks and Sentinel 2 images in the structure of the forest ecosystem. Yim et al. (2015) acknowledged that in natural environments, the correlation between sub-plots and habitat conditions in terms of their shape and size should be more sensitive to forest structure. According to the study of Sivanpillai et al. (2006) in poorer masses, due to the presence of more gaps in the canopy, absorption and distribution occur. In contrast, Dabija et al. (2021) compared support vector machine and stochastic forest algorithms for canopy mapping using Sentinel-2 and Landsat 8 satellite imagery to evaluate regional and spatial classification and development in three different regions. Catalonia, Poland, and Romania paid. The results showed that Sentinel-2 satellite images were better than Landsat 8 data inaccuracy (8-10%) in land cover classification and radial-based support vector algorithm than in random forest with accuracy (6-7%). Function. Nazariani et al. (1400) also had the stochastic forest algorithm as the most suitable model for estimating the canopy characteristic, which is not consistent with the results of the present study. The reason for the difference can be found in the type of algorithm obtained and the accuracy achieved.