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

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.

Evaluating the Transferability of Spectral Variables and Prediction Models for Mapping Forest Aboveground Biomass Using Transfer Learning Methods

“…Previous studies have demonstrated the strong correlation between forest aboveground biomass (AGB) and vegetation indices derived from optical imagery, indicating their potential for mapping forest AGB [3][4][5][6][7][8][13][14][15]36]. Similarly, our study also confirmed the feasibility of using optical remote sensing data for mapping forest AGB.…”

“…Regarded as the largest organic carbon reservoir, forests play an indispensable role in promoting the global carbon cycle and buffering global warming [1][2][3]. Forest aboveground biomass (AGB) is viewed as one of the most important indicators for assessing the carbon sequestration capacity [4][5][6][7]. Therefore, it is meaningful to accurately and efficiently map the forest AGB and obtain the spatial distribution information for guiding rational forest management activities [8][9][10].…”

“…Furthermore, mapping forest AGB in different regions and different years also requires corresponding ground samples that match the acquisition time of the remote sensing images. However, it is rather difficult to obtain so many ground-measured samples for dynamic monitoring at large scales [5,[7][8][9]19,20].…”

The Role of Time-Series L-Band SAR and GEDI in Mapping Sub-Tropical Above-Ground Biomass

“…Their study assesses that, using multitemporal L-band SAR acquisitions, the %RMSE (percentage of mean AGB) improves from 135% to 89% for Mondah and from around 60% to 45-50% for Lope forest test sites. Using four ALOS-2 acquisitions over a Chinese fir-dominated forest, the AGB estimation accuracy improved from a %RMSE of 33.8% for a single acquisition to 24.4% when multitemporal data were used (Long et al, 2020). However, over a Pine and Eucalyptusdominated semiarid forest in Australia, Tanase et al (2014) report that the multitemporal approach provides more reliable AGB estimates but did not improve the AGB estimation accuracy significantly.…”

Inversion of Coniferous Forest Stock Volume Based on Backscatter and InSAR Coherence Factors of Sentinel-1 Hyper-Temporal Images and Spectral Variables of Landsat 8 OLI

“…Microwave radar is an advanced active remote sensing technology not easily affected by climatic factors, such as clouds, fog, and solar radiation, and can realize all-weather earth observations [31][32][33]. Depending on the wavelength and frequency, microwave radar signals can penetrate the forest canopy to different degrees and obtain comprehensive in-formation on the forest structure at different levels and orientations [34][35][36][37][38][39]. The microwave radar remote sensing technologies commonly used in FSV estimation include synthetic aperture radar (SAR), interferometric radar (InSAR), and polarization interferomet-ric radar (PolInSAR) [40,41].…”