• 中国中文核心期刊
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  • 第二届国家期刊奖提名奖

Citation:

Site-Based Remote Sensing Estimation of Chinese Fir Biomass

  • Received Date: 2015-11-18
  • [Objective] To understand the influence of site quality in the remote sensing Chinese fir biomass estimation. [Method] Based on the forest resource management inventory data and TM image of Jiande city obtained in 2007, the biomass of Chinese fir was calculated by forest volume-biomass conversion factor continuous function method and the site quality was evaluated by site class method. Four biomass estimation models for different site classes (good, moderate, poor and no ranking) were compared and the accuracy of them was tested. [Result] (1) The performance of regression model based on the first principal component analysis of TM remote sensing image is the best, the determination coefficients R2 is higher than 0.69 and the maximum is 0.855. (2) Verifying the model accuracy by reserved independent samples, the whole model accuracy without site class is 87.78% and the accuracies of good, moderate, poor site quality models are respectively 97.37%, 95.82%, and 98.23%. [Conclusion] Distinguishing different site qualities could improve remote sensing estimation precision of Chinese fir biomass. The research results provide with an improved method for the remote sensing estimation of forest biomass, and a reference for improving the remote sensing estimation accuracy of forest biomass and carbon storage.
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Site-Based Remote Sensing Estimation of Chinese Fir Biomass

  • 1. Co-Innovation for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, Jiangsu, China
  • 2. College of Forest Resources and Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu, China

Abstract: [Objective] To understand the influence of site quality in the remote sensing Chinese fir biomass estimation. [Method] Based on the forest resource management inventory data and TM image of Jiande city obtained in 2007, the biomass of Chinese fir was calculated by forest volume-biomass conversion factor continuous function method and the site quality was evaluated by site class method. Four biomass estimation models for different site classes (good, moderate, poor and no ranking) were compared and the accuracy of them was tested. [Result] (1) The performance of regression model based on the first principal component analysis of TM remote sensing image is the best, the determination coefficients R2 is higher than 0.69 and the maximum is 0.855. (2) Verifying the model accuracy by reserved independent samples, the whole model accuracy without site class is 87.78% and the accuracies of good, moderate, poor site quality models are respectively 97.37%, 95.82%, and 98.23%. [Conclusion] Distinguishing different site qualities could improve remote sensing estimation precision of Chinese fir biomass. The research results provide with an improved method for the remote sensing estimation of forest biomass, and a reference for improving the remote sensing estimation accuracy of forest biomass and carbon storage.

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