Remote sensing quantification on forest biomass based on SAR polarization decomposition and Landsat data

Li Ming-ze; Yu Xin-tong; Gao Yuan-ke; Fan Wen-yi

doi:10.13332/j.1000-1522.20170284

Journal of Beijing Forestry University > 2018 > 40(2): 1-10. > DOI: 10.13332/j.1000-1522.20170284

Li Ming-ze, Yu Xin-tong, Gao Yuan-ke, Fan Wen-yi. Remote sensing quantification on forest biomass based on SAR polarization decomposition and Landsat data[J]. Journal of Beijing Forestry University, 2018, 40(2): 1-10. DOI: 10.13332/j.1000-1522.20170284

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Remote sensing quantification on forest biomass based on SAR polarization decomposition and Landsat data

1.
School of Forestry, Northeast Forestry University, Harbin 150040, Heilongjiang, China
2.
Maoershan Experimental Forest Farm, Northeast Forestry University, Harbin 150611, Heilongjiang, China

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Received Date: August 21, 2017
Revised Date: December 10, 2017
Published Date: January 31, 2018

Graphical Abstract

Abstract

Abstract

ObjectiveForest biomass is one of the important indexes to evaluate the structure, function and productivity of forest ecosystem, and accurate forest biomass estimation on regional scale has great significance in understanding the current forest status and scientific forest management. This study aims to quantify regional scale forest biomass through the polarimetric SAR and Landsat5 TM.
MethodFirstly, SAR data was polarized by polarization decomposition. Then 51 parameters from 45 polarization decomposition parameters and 6 TM bands were used as predictor variables with forest biomass W as response variables, the best model was used in the research area finally.
ResultTwo methods were implemented for model construction: (1) stepwise regression, the final model includes two variables with R² of 0.534, predicting accuracy of 67.51% and RMSE of 43.21 t/ha; (2) Optimal subset method, Bootstrap was applied to select 9 parameters. Then, we got 511 models by optimal subset method and cross-validation was used for model validation. The final model had 5 parameters(TM_band4, Neumann_delta_mod, Neumann_psi, TSVM_psi, TSVM_tau_m3), R² of 0.768 2, simulating accuracy of 88.32%, simulating RMSE of 14.98 t/ha, test accuracy of 86.21%, test RMSE of 19.14 t/ha, Mallows'Cp of 5.249 5 and AIC of 256.504 5 t/ha. We used optimal subset method to build forest biomass estimation model and acquired forest biomass distributing map.
ConclusionThe results show that C band polarimetric SAR and optical Landsat5 TM data can get accurate estimation of forest biomass.
- forest biomass,
- SAR,
- polarization decomposition,
- Bootstrap,
- optimal subset

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References

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