Estimating the Growing Stem Volume of the Planted Forest Using the General Linear Model and Time Series Quad-Polarimetric SAR Images.

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

doi:10.3390/s20143957

Abstract

Increasing the area of planted forests is rather important for compensation the loss of natural forests and slowing down the global warming. Forest growing stem volume (GSV) is a key indicator for monitoring and evaluating the quality of planted forest. To improve the accuracy of planted forest GSV located in south China, four L-band ALOS PALSAR-2 quad-polarimetric synthetic aperture radar (SAR) images were acquired from June to September with short intervals. Polarimetric characteristics (un-fused and fused) derived by the Yamaguchi decomposition from time series SAR images with different intervals were considered as independent variables for the GSV estimation. Then, the general linear model (GLM) obeyed the exponential distribution were proposed to retrieve the stand-level GSV in plantation. The results show that the un-fused power of double bounce scatters and four fused variables derived from single SAR image is highly sensitive to the GSV, and these polarimeric characteristics derived from the time series images more significantly contribute to improved estimation of GSV. Moreover, compared with the estimated GSV using the semi-exponential model, the employed GLM model with less limitations and simple algorithm has a higher saturation level (nearly to 300 m3/ha) and higher sensitivity to high forest GSV values than the semi-exponential model. Furthermore, by reducing the external disturbance with the help of time average, the accuracy of estimated GSV is improved using fused polarimeric characteristics, and the estimation accuracy of forest GSV was improved as the images increase. Using the fused polarimetric characteristics (Dbl×Vol/Odd) and the GLM, the minimum RRMSE was reduced from 33.87% from single SAR image to 24.42% from the time series SAR images. It is implied that the GLM is more suitable for polarimetric characteristics derived from the time series SAR images and has more potential to improve the planted forest GSV.

Highlights

With the growing area of planted forests, it is of great significant to reduce the carbon dioxide emission and slow down the global warming, due to the decrease of natural forests
This study proposed the general linear model (GLM) for the stand-level forest growing stock volume (GSV) estimation using the time series
The un-fused and fused polarimetric characteristics were derived by Yamaguchi decomposition and the polarization response was analyzed with the GSV in the planted Chinese fir forest

Summary

Introduction

With the growing area of planted forests, it is of great significant to reduce the carbon dioxide emission and slow down the global warming, due to the decrease of natural forests. The growing stock volume (GSV), defined as the total stem volume of living trees, is a basic key indicator for monitoring the planted forest resource at regional scales [4,5,6]. Optical remote sensing techniques have been widely used to monitor the forest resource by establishing the relationship between in-situ GSV and characteristics derived from remote sensing images [5,6]. Disturbed by clouds, fog and mist, the high-quality optical images are hardly acquired in mountain areas, which are the major distribution regions for the planted forest. Microwave remote sensing technology, which is less affected by weather conditions, has ability to measure the forest GSV by their penetration depth related to wavelength [8,9,10]

Methods

Results

Discussion

Conclusion