Version 1
: Received: 3 October 2017 / Approved: 3 October 2017 / Online: 3 October 2017 (16:33:25 CEST)
How to cite:
Xie, S.; Wang, W.; liu, Q.; Meng, J.; Zhao, T.; Huang, G. Estimation of Forest Stand Parameters Using SPOT-5 Satellite Images and Topographic Information. Preprints2017, 2017100017. https://doi.org/10.20944/preprints201710.0017.v1
Xie, S.; Wang, W.; liu, Q.; Meng, J.; Zhao, T.; Huang, G. Estimation of Forest Stand Parameters Using SPOT-5 Satellite Images and Topographic Information. Preprints 2017, 2017100017. https://doi.org/10.20944/preprints201710.0017.v1
Xie, S.; Wang, W.; liu, Q.; Meng, J.; Zhao, T.; Huang, G. Estimation of Forest Stand Parameters Using SPOT-5 Satellite Images and Topographic Information. Preprints2017, 2017100017. https://doi.org/10.20944/preprints201710.0017.v1
APA Style
Xie, S., Wang, W., liu, Q., Meng, J., Zhao, T., & Huang, G. (2017). Estimation of Forest Stand Parameters Using SPOT-5 Satellite Images and Topographic Information. Preprints. https://doi.org/10.20944/preprints201710.0017.v1
Chicago/Turabian Style
Xie, S., Tianzhong Zhao and Guosheng Huang. 2017 "Estimation of Forest Stand Parameters Using SPOT-5 Satellite Images and Topographic Information" Preprints. https://doi.org/10.20944/preprints201710.0017.v1
Abstract
In recent years, remote sensing technology has been widely used to predict forest stand parameters. In order to compare the effects of different features of remote sensing images and topographic information on the prediction of forest stand parameters, multivariate stepwise regression analysis method was used to build estimation models for important forest stand parameters by using textural and spectral features as well as topographic information of SPOT-5 satellite images in northeastern Heilongjiang Province in China as independent variables. The study results show that the optimal window to predict forest stand parameters using textural features of SPOT-5 satellite image is 9×9; the ability of textural features was better than that of spectral features in terms of predicting forest stand parameters; with the inclusion of topographic information, the accuracy of prediction of all models was improved, of which elevation has the most significant effect. The highest accuracy was achieved when predicting the stand volume (SV) (R2adj=0.820), followed by basal area (BA) (R2adj =0.778), accuracy of both above models exceeded 75%. The results show that models combined use of textural, spectral features and topographic information of SPOT-5 images have a good application prospect in predicting forest stand parameters.
Keywords
forest stand parameters; SPOT-5 satellite image; textural and spectral features; topographic information; estimation model
Subject
Biology and Life Sciences, Forestry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
