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Open AccessArticle

Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging

by Lin Chen 1,2, Chunying Ren 1,*, Bai Zhang 1 and Zongming Wang 1,3
1
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
National Earth System Science Data Center, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Forests 2020, 11(3), 296; https://doi.org/10.3390/f11030296
Received: 23 January 2020 / Revised: 25 February 2020 / Accepted: 5 March 2020 / Published: 6 March 2020
Quantifying stand volume through open-access satellite remote sensing data supports proper management of forest stand. Because of limitations on single sensor and support vector machine for regression (SVR) as well as benefits from hybrid models, this study innovatively builds a hybrid model as support vector machine for regression kriging (SVRK) to map stand volume of the Changbai Mountains mixed forests covering 171,450 ha area based on a small training dataset (n = 928). This SVRK model integrated SVR and its residuals interpolated by ordinary kriging. To determine the importance of multi-sensor predictors from ALOS and Sentinel series, the increase in root mean square error (RMSE) of SVR was calculated by removing the variable after the standardization. The SVRK model achieved accuracy with mean error, RMSE and correlation coefficient in –2.67%, 25.30% and 0.76, respectively, based on an independent dataset (n = 464). The SVRK improved the accuracy of 9% than SVR based on RMSE values. Topographic indices from L band InSAR, backscatters of L band SAR, and texture features of VV channel from C band SAR, as well as vegetation indices of the optical sensor were contributive to explain spatial variations of stand volume. This study concluded that SVRK was a promising approach for mapping stand volume in the heterogeneous temperate forests with limited samples. View Full-Text
Keywords: ALOS-2 L band SAR; Sentinel-1 C band SAR; Sentinel-2 MSI; ALOS DSM; stand volume; support vector machine for regression; ordinary kriging ALOS-2 L band SAR; Sentinel-1 C band SAR; Sentinel-2 MSI; ALOS DSM; stand volume; support vector machine for regression; ordinary kriging
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Chen, L.; Ren, C.; Zhang, B.; Wang, Z. Multi-Sensor Prediction of Stand Volume by a Hybrid Model of Support Vector Machine for Regression Kriging. Forests 2020, 11, 296.

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