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Open AccessArticle

Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images

1,2,3, 1,2,3,*, 1,2,3,4, 1,2,3 and 1,2,3
1
Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forest and Technology, Changsha 410004, China
2
Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China
3
Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China
4
Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1872; https://doi.org/10.3390/rs11161872
Received: 30 June 2019 / Revised: 4 August 2019 / Accepted: 7 August 2019 / Published: 10 August 2019
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Abstract

For the planning and sustainable management of forest resources, well-managed plantations are of great significance to mitigate the decrease of forested areas. Monitoring these planted forests is essential for forest resource inventories. In this study, two ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images and ground measurements were employed to estimate growing stem volume (GSV) of Chinese fir plantations located in a hilly area of southern China. To investigate the relationships between forest GSV and polarization characteristics, single and fused variables were derived by the Yamaguchi decomposition and the saturation value of GSV was estimated using a semi-exponential empirical model as a base model. Based on the estimated saturation values and relative root mean square error (RRMSE), the single and fused characteristics and corresponding models were selected and integrated, which led to a novel saturation-based multivariate method used to improve the GSV estimation and mapping of Chinese fir plantations. The new findings included: (1) All the original polarimetric characteristics, statistically, were not significantly correlated with the forest GSV, and their logarithm and ratio transformation fused variables greatly improved the correlations, thus the estimation accuracy of the forest GSV. (2) The logarithm transformation of surface scattering resulted in the greatest saturation, value but the logarithm transformation of double-bounce scattering resulted in the smallest RRMSE of the GSV estimates. (3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of RRMSE. (4) The saturation-based multivariate method led to more accurate estimates of the forest GSV than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables. This implied that the novel saturation-based multivariate method provided greater potential to improve the estimation and mapping of the forest GSV. View Full-Text
Keywords: polarimetric SAR; Yamaguchi decomposition; growing stem volume; saturation-based multivariate method; Chinese fir plantation polarimetric SAR; Yamaguchi decomposition; growing stem volume; saturation-based multivariate method; Chinese fir plantation
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Long, J.; Lin, H.; Wang, G.; Sun, H.; Yan, E. Mapping Growing Stem Volume of Chinese Fir Plantation Using a Saturation-based Multivariate Method and Quad-polarimetric SAR Images. Remote Sens. 2019, 11, 1872.

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