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

Abstract

Forest stock volume (FSV) is a basic data source for estimating forest carbon sink. It is also a crucial parameter that reflects the quality of forest resources and forest management level. The use of remote sensing data combined with a support vector regression (SVR) algorithm has been widely used in FSV estimation. However, due to the complexity and spatial heterogeneity of the forest biological community, in the FSV high-value area with dense vegetation, the optical re-mote sensing variables tend to be saturated, and the sensitivity of synthetic aperture radar (SAR) backscattering features to the FSV is significantly reduced. These factors seriously affect the ac-curacy of the FSV estimation. In this study, Landsat 8 (L8) Operational Land Imager multispectral images and C-band Sentinel-1 (S1) hyper-temporal SAR images were used to extract three re-mote sensing feature datasets: spectral variables (L8), backscattering coefficients (S1), and inter-ferometric SAR factors (S1-InSAR). We proposed a feature selection method based on SVR (FS-SVR) and compared the FSV estimation performance of FS-SVR and stepwise regression analysis (SRA) on the aforementioned three remote sensing feature datasets. Finally, an estima-tion model of coniferous FSV was constructed using the SVR algorithm in Wangyedian Forest Farm, Inner Mongolia, China, and the spatial distribution map of coniferous FSV was predicted. The experimental results show the following: (1) The coherence amplitude and DSM data ob-tained based on S1 images contain information relat-ed to forest canopy height, and the hy-per-temporal S1 image data significantly enrich the diversity of S1-InSAR feature factors. There-fore, the S1-InSAR dataset has a better FSV response than remote sensing factors such as the S1 backscattering coefficient and L8 vegetation index, and the corresponding root mean square er-ror (RMSE) and relative RMSE (rRMSE) values reached 47.6 m3/ha and 20.9%, respectively. (2) The integrated dataset can provide full play to the synergy of the L8, S1, and S1-InSAR remote sensing data. Its RMSE and rRMSE values are 44.3 m3/ha and 19.4% respectively. (3) The proposed FS-SVR method can better select remote sensing variables suitable for FSV estimation than SRA. The average value of the rRMSE (23.17%) based on the three datasets was 13.8% lower than that of the SRA method (26.87%). This study provides new insights into forest FSV retrieval based on active and passive multisource remote sensing joint data.

Biomass Estimation and Saturation Value Determination Based on Multi-Source Remote Sensing Data

“…In this case, the R 2 obtained by SVR is significantly better than the other two models. This is because SVR can handle the strong nonlinear relationship between target parameters and satellite variables and achieve better estimation performance, even with limited sample data [96,97]. RF is a good choice when using horizontal structural indices or combining multiple structural indices as a variable.…”

“…Although the Pearson correlation coefficient selection variable method can simply and quickly select the characteristic variables that are linearly correlated with AGB, it is based on linear correlation and cannot fully and accurately describe the real relationship between biomass and remote sensing variables in complex forest environments. The RF feature selection method screens relatively key feature factors based on specific evaluation criteria, without considering the combination effect relationship between feature factors, which can lead to the selection of variables that do not better reflect the relationship with AGB [96]. LASSO uses constraint forms to identify smaller subsets of estimated variance and predictor variables with good variable selection and regularization capabilities [103].…”

Mapping Growing Stem Volume Using Dual-Polarization GaoFen-3 SAR Images in Evergreen Coniferous Forests

“…Recently, satellite-based SAR sensors with various bands (X, C, and L band) and various polarization (single, dual, and quad) SAR images have been widely applied in mapping Remote Sens. 2023, 15, 2253 2 of 18 forest parameters, such as TerraSAR-X, Rardarsat-2, ALSO-2 PALSAR, and Sentinel-1 [7,13]. Furthermore, previous results have shown that changes in wavelength and types of polarization modes resulted in differences in both analytical workflows and the achieved estimation performances [9,14].…”

Predicción de cosechas de setas silvestres en bosques mediterráneos utilizando sensores remotos activos y pasivos

“…SAR data may be used alone for characterization of forest structure at the stand level (e.g., Gómez et al, 2021), but it may produce synergies with optical data (e.g., Li et al, 2022), and even be advantageous under persistently cloudy circumstances. Although backscatter intensity is more frequently employed, interferometric approaches provide further opportunities, by exploiting the phase difference and the scene coherence between various data acquisitions.…”

“…En las últimas décadas se han desarrollado una gran variedad de técnicas con multitud de aplicaciones en diversos campos (Tsokas et al, 2022), por lo que se clasifican los usos y aplicaciones de los datos SAR en tres categorías en función de su objetivo final: (i) mapeo y clasificación de terrenos: identifican y clasifican el tipo de superficie (Dostálová et al, 2018;Nikaein et al, 2021), el monitoreo forestal (Pulella et al, 2020a;Ygorra et al, 2021) y los derrames de petróleo (Dasari et al, 2022), por ejemplo; (ii) recuperación de parámetros: como la estimación de humedad (Barrett et al, 2009;Dorigo et al, 2017;Paloscia et al, 2013), el cálculo de la biomasa aérea (Cartus et al, 2022;Cartus y Santoro, 2019;Ghosh y Behera, 2021) o la estructura del dosel vegetal (Li et al, 2022;Pinto et al, 2013); y (iii) detección de objetos: para localizarlos e identificarlos en las imágenes, como el petróleo en el mar (Chaudhary y Kumar, 2020).…”