Paper Information

Journal:   JOURNAL OF RS AND GIS FOR NATURAL RESOURCES (JOURNAL OF APPLIED RS AND GIS TECHNIQUES IN NATURAL RESOURCE SCIENCE)   winter 2019 , Volume 9 , Number 4 (33) #a00503; Page(s) 73 To 89.
 
Paper: 

The effect of digital preprocessing and modeling method on an estimation of aboveground carbon stock of Zagros forests using Landsat 8 imagery

 
 
Author(s):  SAFARI A., SOHRABI H.*
 
* College of Forestry, Department of Natural Resources & Marine Sciences, Tarbiat Modares University
 
Abstract: 
The aim of this study, was to evaluate the effectiveness of different preprocessing methods and modeling techniques on the accuracy of aboveground carbon stock estimates in two forest stands with different degradation levels (Gahvareh forest and SarfiruzAbad), in Zagros forests in Kurdistan province. Comparison of different digital pre-processing methods on Landsat 8 images was carried out in different scenarios of radiometric, atmospheric, topographic and their combination. In each scenario, we used four modeling methods included linear regression, generalized additive model, random forest, and support vector machine. In most cases, radiometric correction with improved correction coefficient was 0. 71 (R2adj=0. 71) and the root means square error of 30% (RMSe%=0. 30) was outperformed. Comparison of four modeling methods indicates the lower accuracy of estimates in the SarfiruzAbad area with more degradation severity (R2adj=0. 58) compared to the less damaged Gahvareh area (RMSe%=0. 74). The random forest method for Gahvareh area and linear regression and a generalized additive model for SarfiruzAbad provides better results, respectively. However, our findings showed that selection of suitable preprocessing and modeling method have a noticeable effect on the accuracies of characteristics estimates in forest ecosystems by Landsat imagery.
 
Keyword(s): Images preprocessing, Statistical modeling method,Landsat 8,Aboveground carbon stock,Zagros forests
 
References: 
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