Modeling aboveground carbon stock of Zagros forests using field data and Landsat 8 imagery

Document Type : Complete scientific research article

Authors

Abstract

Abstract
Background and objectives: Information on aboveground carbon (AGC) is important for managing forests at local level, land management at regional levels, and carbon emissions reporting at national and international levels; therefore, there is a critical need for low-cost and time-saving approaches for quantifying of AGC. According to this, the estimation of aboveground carbon stock from remotely-sensed data has currently attracted a lot of attention. We assessed the capability of Landsat 8-derived spectral variables for AGC estimates in Zagros coppice oak forests by four non-parametric modeling including: random forest (RF), Cubist, Multivariate adaptive regression spline (MARS) and artificial neural networks (ANNs)
Materials and methods:
The study was carried out in part of Zagros forest, in Kermanshah Province. The values of aboveground carbon (AGC) terrestrial references was determined using field measurement data collected in two sites, Gahvareh (very low degraded (LD) site) and SarfiruzAbad (highly degraded (HD) site). Totally, 124 plots (30×30 meters) surveyed in two studied sites by the systematically-gridded plot design and AGC was calculated by developed species-specific allometric equations for Brant oak trees. For modeling AGC using the remotely-sensed data, we used different Landsat 8 derived variables such as single raw bands, simple band ratios, vegetation indices, principle component analysis and tasseled cap as independent variables and calculated AGC values in plots as dependent variable. The assessment of accuracies of four used non-parametric modeling methods: RF, Cubist, MARS and ANNs and was evaluated by “Leave-one-out” cross validation via criteria such as coefficient of variation (R2), root mean square error (RMSE) and bias.
Results: The results showed the accuracy of AGC estimates in LD site was better than HD site. The comparison of used modeling methods revealed that there are not significant difference in performances and accuracies of used non-parametric approaches. In additional, using the total plots from two test sites in one model caused the increase the results for HD site estimates.
Conclusion: Results showed R2 and relative RMSE values of approximately 0.7 and 32% cross-validated (combined two studied sites) for modeling AGC using Landsat derived variables, which reveals the high potential of Landsat 8 images and non-parametric modeling methods for quantifying AGC in cost and time saving approach for Zagros forests.
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Keywords

Main Subjects


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