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

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

doi:10.3390/s20143957

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

Sensors (Basel). 2020 Jul 16;20(14):3957. doi: 10.3390/s20143957.

Authors

Jiangping Long^{1

2

3}, Hui Lin^{1

2}, Guangxing Wang^{1

2

4}, Hua Sun^{1

2}, Enping Yan^{1

2}

Affiliations

¹ Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province, Changsha 410004, China.
² Key Laboratory of State Forestry Administration on Forest Resources Management and Monitoring in Southern Area, Changsha 410004, China.
³ Department of Surveying Engineering, College of Civil Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
⁴ Department of Geography and Environmental Resources, Southern Illinois University, Carbondale, IL 62901, USA.

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.

Keywords: Yamaguchi decomposition; general linear model; growing stem volume; planted forest; polarimetric SAR.

MeSH terms

China
Forests*
Linear Models
Radar*
Trees / growth & development*

Abstract

MeSH terms

Grants and funding