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Scientia Silvae Sinicae ›› 2015, Vol. 51 ›› Issue (5): 46-55.doi: 10.11707/j.1001-7488.20150506

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Remote Sensing Estimation of Aboveground Forest Carbon Storage in Daxing'an Mountains Based on KNN Method

Qi Yujiao, Li Fengri   

  1. School of Forestry, Northeast Forestry University Harbin 150040
  • Received:2014-06-05 Revised:2015-03-01 Online:2015-05-25 Published:2015-06-11

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Abstract:

【Objective】 Forest is the major terrestrial carbon pool. Accurate assessment of forest carbon storage and its spatial distribution is the key to investigating the terrestrial carbon cycle. 【Method】Based on the PSPs data from continuous forest resource inventory and Landsat5 TM in 2010, the k-nearest neighbor (KNN) method was used to estimate, at the pixel level, the aboveground carbon storage in Daxing'an Mountains of Heilongjiang Province. The field PSP data and its corresponding satellite image information were reassigned using a multi-criteria approach in east, south, northand middle regions. The accuracy estimation of different forests before and after the reassignment was also evaluated according to the data of PSPs. In view of the phenomenon that the pixel level KNN estimation having the large values underestimated and small values overestimated, the histogram matching method was used to adjust the variation range of the estimation results. Then, further correction treatment was applied to each region according to the regression equations of field data and the estimation data from the histogram matching until the spatial distribution map of forest carbon storage was drawn.【Result】Overall, Euclidean distance was better than Mahalanobis in our study area at the pixel level of KNN estimation. The root mean square error decreased with the increase of the nearest neighbor k, whereas, the tendency was slow down and gradually stabilized when k is greater than 6. The estimate accuracy was improved significantly at the pixel level in each forest type when the coordinate errors was corrected, and the average root mean square error was reduced from 17.23 to 14.3 t·hm-2.After histogram matching, the phenomenon of underestimation for high value and overestimation for low value was greatly improved in each region, and the correlation between filed data and estimation data was enhanced obviously. However, high value area (carbon storage value was larger than 20 t·hm-2) was overestimated evidently. The mean value, standard deviation, histogram and cumulative frequency distribution graph of the final corrected values through the further correction treatment were more close to those of the field values, and the overestimation in high value area was also well corrected. 【Conclusion】 The integration of forest inventory plot data, satellite image data with the KNN method has gradually become a popular approach for spatial continuous estimation of forest vegetation parameters over large regions. Compared with the regression model established by the spectral value and forest parameters, KNN method is more focuses on the nonlinear dependence between forest parameters and spectral values. However, the KNN estimation method is not only influenced by the distance metric standard, the nearest neighbor k and the image band selection, but it also has the problems such as the location errors of field plots with respect to the satellite image, the tendency to having a suppressed variation range at the pixel level, which make this method subjected to a certain application restrictions. This study indicated that if these impact factors were reasonably corrected, it would be more conducive to the accurate estimation and inversion of forest parameters at regional scale.

Key words: KNN, forest aboveground carbon storage, remote sensing, coordinate registration, histogram matching

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