Nonparametric Model for Remote Sensing Estimating the Volume of Spruce-Fir Forest in Shangri-La

Xiaoxue Lang; Yanhong Xu; Qingtai Shu; Zhuoya Zhang; Fuming Xie; Li Zi

doi:10.11929/j.swfu.201811019

Journal of Southwest Forestry University > 2019 > 39(1): 146-151. > DOI: 10.11929/j.swfu.201811019

Xiaoxue Lang, Yanhong Xu, Qingtai Shu, Zhuoya Zhang, Fuming Xie, Li Zi. Nonparametric Model for Remote Sensing Estimating the Volume of Spruce-Fir Forest in Shangri-La[J]. Journal of Southwest Forestry University, 2019, 39(1): 146-151. DOI: 10.11929/j.swfu.201811019

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Nonparametric Model for Remote Sensing Estimating the Volume of Spruce-Fir Forest in Shangri-La

1.
College of Forestry, Southwest Forestry University, Kunming Yunnan 650224, China
2.
College of Ecotourism, Southwest Forestry University, Kunming Yunnan 650224, China

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Received Date: August 12, 2018
Revised Date: November 28, 2018
Available Online: December 28, 2018
Published Date: December 31, 2018

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Abstract

Selected the typical forest types of spruce-fir forests in Shangri-La City, located in Diqing of Northwest Yunnan, as the research objects. Using Landsat 8 remote sensing image data combined with ground angle control plot survey data, the BPNN and SVM estimation models of spruce-fir were established and compared. The results show that the precision of the SVM model is obviously better than that of the BPNN model, its R²、rRMSE and P are 0.67, 27.91% and 77.09% respectively. The statistical error between the total volume of spruce-fir forests in Shangri-La City and the results of the second-class survey of traditional forest resources is only 1.14% by using SVM remote sensing estimation model, which shows that SVM estimation model could provide support for forest resources.
- nonparametric model,
- BPNN model,
- SVM model,
- forest stock volume,
- spruce-fir forest,
- Shangri-La

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