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Scientia Silvae Sinicae ›› 2015, Vol. 51 ›› Issue (1): 140-149.doi: 10.11707/j.1001-7488.20150117

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Estimating Forest Above-Ground Biomass in the Upper Reaches of Heihe River Basin Using Multi-Spectral Remote Sensing

Guo Yun1,2, Li Zengyuan1, Chen Erxue1, Tian Xin1, Ling Feilong2   

  1. 1. Institute of Forest Resources Information Techniques, CAF Beijing 100091;
    2. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry Education, Fuzhou University Fuzhou 350002
  • Received:2014-01-23 Revised:2014-08-18 Online:2015-01-25 Published:2015-01-23

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Abstract:

[Objective]Forest biomass is the main source of energy and nutrients of the forest ecosystem operation. Qilian Mountain forest reserve at the upper reaches of Heihe River Basin was selected as the research area. The forest inventory data, Landsat-5 TM images and ASTER GDEM products were used as data sources. The purpose of this paper is to explore the effect of terrain on the estimation of forest above-ground biomass (AGB) and select appropriate method for the inversion of forest AGB. [Method]First, a decision-tree classifier was constructed by taking into account of the special habitat of Picea crassifolia and the sensitivity of the green vegetation for ratio vegetation index, and the different responses of various objects on the texture features. The land-cover types of the research area was divided into two categories: forest (Picea crassifolia)—non-forest. The accuracy assessment of classification map was obtained by using field inventory data and high-resolution image of Google Earth (The overall accuracy of the classification is 90.39%, and the Kappa coefficient is 0.81). Then, the forest AGB was estimated using the multiple linear stepwise regression and k-NN. The k-NN was implemented by combining with RF algorithm. The change of the estimation accuracy before and after the topographic correction was analyzed. And the estimation accuracy of two different retrieval methods were compared with the forest survey data. Finally, the grade distribution of regional forest AGB was performed by the optimal estimation method. [Result]The estimation accuracy of multiple linear regression was R2=0.31, RMSE=34.41 thm-2 before SCS + C topographic correction. But it was R2=0.46, RMSE=30.51 thm-2 after SCS + C topographic correction. The optimal k-NN produced higher cross-validation accuracy (R2=0.54, RMSE=26.62 thm-2) by using the data after SCS + C topographic correction than the outcome before SCS + C topographic correction. At the same time, it performed better than the effect of the multiple linear stepwise Regression. The regional forest AGB which was performed by the optimized k-NN (window sampling size was 7×7; distance measures was Mahalanobis Distance; k was 3) showed that the total of forest AGB of Picea crassifolia was 8.4×107 t in this region, and the average was 96.20 thm-2. [Conclusion]The appropriate terrain correction with SCS + C model could effectively eliminate the influence of the change of incident angle of the sun in complex terrain area. It could improve the estimation accuracy of the models. Compared with multiple linear stepwise regression, the optimal k-NN could avoid the phenomenon of learning and the problem of sample imbalances in the case of limited samples.

Key words: Landsat-5 TM, SCS+C terrain correction, multiple linear stepwise regression, k-NN, forest above-ground biomass(AGB), estimation of remote sensing

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