Estimation of above-ground biomass of Arabdagh reforested stands, Golestan province using Sentinel-2 satellite data

Document Type : Research Paper

Authors

1 PhD in Forest Management, General Directorate of Forest Management and Development, Syria.

2 Associate Professor, Department of Forest Management, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran.

Abstract

Background and objectives: Today, reforested stands are one of the most important sources of forest carbon storage and one of the factors that reduce the process of destruction of natural areas. Above-ground biomass (AGB) plays an essential role in sustainable forest management and reducing global warming and is an important source of information. Allometric equations are an important tool for quantifying above-ground biomass in forests. In recent years, remote sensing techniques using non-parametric methods such as the Random Forest algorithm have been widely used to estimate forest tree biomass. In this research, the ability of Sentinel 2 data using the random forest algorithm to estimate the above-ground biomass of Arabdagh reforested stands in Golestan province was evaluated.

Materials and methods: In this study, 180 circular sample plots with an area of 400 square meters were measured using the cluster sampling method and the diameter at breast height (DBH) and tree height (H) were measured. Also, the exact coordinates of the centers of the sample plots were recorded using DGPS. Then, using the prepared allometric equations, the above-ground biomass of trees was calculated. In this study, Sentinel 2 pre-processed radiometric and geometrical data were used, and based on that, different vegetation indices were prepared. In the implementation of the random forest algorithm, the relationship between the characteristics of biomass as a dependent variable and the spectral values of vegetation indices as independent variables were investigated. Modeling was done using 75% of sample plots (135 sample plots) with random forest algorithm and validation of estimates was done using 25% of sample plots (45 sample plots).


Results: The results showed that NDVI and GNDVI indices had the highest correlation in the estimation of above-ground biomass and the random forest algorithm with 310 trees and 5 predictors and the percentage root mean square error of 35.83% and the coefficient of determination 0.51 was able to estimate the above-ground biomass of Arabdagh reforested stands. Also, the results showed that using the data of Sentinel 2, the random forest algorithm has estimated the above-ground biomass of trees more than the actual values. There is no significant difference at the 95% probability level between the estimated and real above-ground biomass values (p-value > 0.05). Also, among the independent variables used.
Conclusion: The results of this research showed that Sentinel 2 data has been able to estimate the above-ground biomass of Arabdagh reforested stands with acceptable accuracy. According to the results of this article, it can be said that the information of the main bands and spectral indices played an important role in the estimation of above-ground biomass.

Keywords

Main Subjects

References

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Journal of Wood and Forest Science and Technology
Volume 30, Issue 4
December 2024
Pages 93-110

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