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Estimation of aboveground tree carbon stock using SPOT-HRG data (a case study: Darabkola forests)

Journal of Forestry Research 2017年第6期28卷 1172-1179页

作者： Masoumeh Fatholahi Asghar Fallah Seyed Mohammad Hojjati siavash kalbi Department of Natural Resource Sari University of Agricultural Sciences and Natural Resources SariMazandaran 48151 Iran

Forests are among the most important carbon sinks on earth. However, their complex structure and vast areas preclude accurate estimation of forest carbon *** sets from forest monitoring using advanced satellite imagery are now used in international policy *** sets enable tracking of emissions of COinto the atmosphere caused by deforestation and other types of land-use changes. The aim of this study is to determine the capability of SPOT-HRG Satellite data to estimate aboveground carbon stock in a district of Darabkola research and training forest, Iran. Preprocessing to eliminate or reduce geometric error and atmospheric error were performed on the images. Using cluster sampling, 165 sample plots were taken. Of 165 plots, 81 were in natural habitats, and 84 were in forest plantations. Following the collection of ground data, biomass and carbon stocks were quantified for the sample plots on a per hectare basis. Nonparametric regression models such as support vector regression were used for modeling purposes with different kernels including linear, sigmoid, polynomial, and radial basis *** results showed that a third-degree polynomial was the best model for the entire studied areas having an root mean square error, bias and accuracy, respectively, of 38.41,5.31, and 62.2; 42.77, 16.58, and 57.3% for the best polynomial for natural forest; and 44.71, 2.31, and 64.3%for afforestation. Overall, these results indicate that SPOTHRG satellite data and support vector machines are useful for estimating aboveground carbon stock.

关键词： Aboveground carbon stock Support vector machine SPOT-HRG Darabkola

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Mixed-effects modeling for tree height prediction models of Oriental beech in the Hyrcanian forests

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Journal of Forestry Research 2018年第5期29卷 1195-1204页

作者： siavash kalbi Asghar Fallah Pete Bettinger Shaban Shataee Rassoul Yousefpour Sari Agriculture and Natural Resource University SariMazandaran Iran Warnell School of Forestry and Natural Resources University of Georgia Athens GA USA Gorgan Agriculture and Natural Resource University Gorgan Golestan Iran Faculty of Environment and Natural Resources University of Freiburg Freiburg Germany

Height–diameter relationships are essential elements of forest assessment and modeling *** this work,two linear and eighteen nonlinear height–diameter equations were evaluated to find a local model for Oriental beech（Fagus orientalis Lipsky） in the Hyrcanian Forest in *** predictive performance of these models was first assessed by different evaluation criteria： adjusted R^2（R^2_（adj））,root mean square error（RMSE）,relative RMSE（%RMSE）,bias,and relative bias（%bias） *** best model was selected for use as the base mixed-effects *** parameters for test plots were estimated with different tree selection *** show that the Chapman–Richards model had better predictive ability in terms of adj R^2（0.81）,RMSE（3.7 m）,%RMSE（12.9）,bias（0.8）,%Bias（2.79） than the other ***,the calibration response,based on a selection of four trees from the sample plots,resulted in a reduction percentage for bias and RMSE of about 1.6–2.7%.Our results indicate that the calibrated model produced the most accurate results.

关键词： Random effects Tree height Calibration Sangdeh forest Chapman–Richards model Oriental beech

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