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

Jiangping Long,Hua Sun,Guangxing Wang,Enping Yan,Hui Lin

doi:10.3390/rs11161872

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.

Highlights

Through carbon sequestration and biomass accumulation, planted forests play a major role in mitigation of global climate change, due to the reduction of natural forests and increase of plantations during the past four decades
(3) Compared with the single transformations, the fused variables led to more reasonable saturation values and obviously reduced the values of relative root mean square error (RRMSE). (4) The saturation-based multivariate method led to more accurate estimates of the forest growing stem volumes (GSV) than the univariate method, with the smallest value (29.64%) of RRMSE achieved using the set of six variables
The powers of scatterings derived by the Yamaguchi decomposition were used to estimate the saturation values and forest GSV of Chinese fir plantations located in the hilly area of southern China using two quad-polarimetric PALSAR-2 images

Summary

Introduction

Through carbon sequestration and biomass accumulation, planted forests play a major role in mitigation of global climate change, due to the reduction of natural forests and increase of plantations during the past four decades. It is often difficult to directly obtain forest above ground biomass (AGB) for large areas. Rather than what is done for natural forests, instead, the AGB of plantations is usually derived by first measuring tree heights and diameters at breast height and calculating tree growing stem volumes (GSV) using allometric equations and multiplying GSV with biomass expansion factors; that is, gravity coefficients by tree species. Remote sensing images have been widely used to map GSV on regional scales by combining them with field measurements of GSV at plot level. Because of the impacts of clouds, fogs and moisture, it is very hard to acquire adequate optical images for estimation of forest GSV in southern China, in which there is a subtropical monsoon climate. With the capacity to penetrate forest canopies and interact with forest structures, quad-polarimetric SAR images provide great potential to improve the accuracy of forest GSV monitoring and assessment [6,7,8,9]

Results

Discussion

Conclusion