Forest growing stock volume (GSV) extraction using synthetic aperture radar (SAR) images has been widely used in climate change research. However, the relationships between forest GSV and polarimetric SAR (PolSAR) data in the mountain region of central China remain unknown. Moreover, it is challenging to estimate GSV due to the complex topography of the region. In this paper, we estimated the forest GSV from advanced land observing satellite-2 (ALOS-2) phased array-type L-band synthetic aperture radar (PALSAR-2) full polarimetric SAR data based on ground truth data collected in Youxian County, Central China in 2016. An integrated three-stage (polarization orientation angle, POA; effective scattering area, ESA; and angular variation effect, AVE) correction method was used to reduce the negative impact of topography on the backscatter coefficient. In the AVE correction stage, a strategy for fine terrain correction was attempted to obtain the optimum correction parameters for different polarization channels. The elements on the diagonal of covariance matrix were used to develop forest GSV prediction models through five single-variable models and a multi-variable model. The results showed that the integrated three-stage terrain correction reduced the negative influence of topography and improved the sensitivity between the forest GSV and backscatter coefficients. In the three stages, the POA compensation was limited in its ability to reduce the impact of complex terrain, the ESA correction was more effective in low-local incidence angles area than high-local incidence angles, and the effect of the AVE correction was opposite to the ESA correction. The data acquired on 14 July 2016 was most suitable for GSV estimation in this study area due to its correlation with GSV, which was the strongest at HH, HV, and VV polarizations. The correlation coefficient values were 0.489, 0.643, and 0.473, respectively, which were improved by 0.363, 0.373, and 0.366 in comparison to before terrain correction. In the five single-variable models, the fitting performance of the Water-Cloud analysis model was the best, and the correlation coefficient R² value was 0.612. The constructed multi-variable model produced a better inversion result, with a root mean square error (RMSE) of 70.965 m³/ha, which was improved by 22.08% in comparison to the single-variable models. Finally, the space distribution map of forest GSV was established using the multi-variable model. The range of estimated forest GSV was 0 to 450 m³/ha, and the mean value was 135.759 m³/ha. The study expands the application potential of PolSAR data in complex topographic areas; thus, it is helpful and valuable for the estimation of large-scale forest parameters.

The 3-D structure of forests is an important indicator for evaluating forest health and can provide data support for ecological monitoring and protection. Synthetic aperture radar (SAR) tomography (TomoSAR) is an important technology for forest structure estimation using the multibaseline (MB) SAR data stacks. The spectral estimators, such as the Capon method, are usually used to estimate the 3-D reflectivity along elevation direction. In this letter, a modified Capon (M-Capon) method is proposed to improve the estimated profiles of ground and canopy scatterers in the elevation direction, including the estimation accuracy and resolution. This method combines the ideas of the CLEAN algorithm with the Capon method and uses iteration to improve the resolution and accuracy of the estimation. The effectiveness of the M-Capon method is demonstrated using the simulated data and the MB SAR data acquired by the P-band airborne SAR system over the Saihanba Forest Farm in Hebei, China.

In this article, a multilevel model (MLM) for forest height inversion is introduced and investigated using X-band single-pass interferometric synthetic aperture radar (InSAR) coherence data. Compared with the two-level model (TLM), as a generalized model, the MLM is more in line with the characteristics of forest structure and the scattering mechanism for X-band data. Based on the MLM model, three simplified MLM models were derived: TLMm, SINC, and MTND. An improved method of calculating coherence considering the assumptions of the MLM model is proposed. At two test sites, airborne (CASMSAR) and spaceborne (TanDEM-X) X-band single-pass InSAR data and LiDAR H100 data were used to verify the proposed new approach. The results showed that the MTND model can obtain more reliable and accurate inversion results compared to the SINC model and the TLMm. With airborne InSAR data, the MTND model’s highest accuracy was 82.56%, and the root mean square error (RMSE) was 2.4 m. With spaceborne InSAR data, the highest inversion accuracy of the MTND model was 73.55%, with an RMSE of 4.92 m. The inversion accuracy of forest height can be effectively improved by ensuring that the theoretical models and calculations of coherence obey the same assumptions.

In recent decades, most methods proposed for radiometric slope correction involved the backscattering intensity values in synthetic aperture radar (SAR) data. However, these methods are not fully applicable to quad-polarimetric SAR (PolSAR) matrix data. In this paper, we propose a three-step semi-empirical radiometric terrain correction approach for PolSAR forest area data. The three steps of terrain effects correction are: polarisation orientation angle (POA), effective scattering area (ESA), and angular variation effect (AVE) corrections. We propose a novel method to determine adaptively the “n” value in the third step by minimising the correlation coefficient between corrected backscattering coefficients and the local incidence angle; we then constructed the correction coefficients matrix and used it to correct PolSAR matrix data. PALSAR-2 HBQ (L-band, quad-polarisation) data were used to verify the proposed method. After three-step correction, differences between front and back slopes were significantly reduced. Our results indicate that POA, ESA, and AVE corrections are indispensable steps to producing PolSAR data. In the POA correction step, horizontal–vertical (HV) polarisation was maximally influenced by the POA shift. The max deviation of the POA correction was greater than 1 dB for HV polarisation and approximately 0.5 dB for HH/VV polarisation at an intermediate shift angle (±20°). Based on Light Detection and Ranging (LiDAR)-derived forest aboveground biomass (AGB) data, we analysed the relationship between forest AGB and backscattering coefficient; the correlation was improved following the terrain correction. HV polarisation had the best correlation with forest AGB (R = 0.81) and the correlation improved by approximately 0.3 compared to the uncorrected data.

Forest canopy height is an essential parameter in estimating forest aboveground biomass (AGB), growing stock volume (GSV), and carbon storage, and it can provide necessary information in forest management activities. Light direction and ranging (LiDAR) is widely used for estimating canopy height. Considering the high cost of acquiring LiDAR data over large areas, we took a two-stage up-scaling approach in estimating forest canopy height and aimed to develop a method for quantifying the uncertainty of the estimation result. Based on the generalized hierarchical model-based (GHMB) estimation framework, a new estimation framework named RK-GHMB that makes use of a geostatistical method (regression kriging, RK) was developed. In this framework, the wall-to-wall forest canopy height and corresponding uncertainty in map unit scale are generated. This study was carried out by integrating plot data, sampled airborne LiDAR data, and wall-to-wall Ziyuan-3 satellite (ZY3) stereo images. The result shows that RK-GHMB can obtain a similar estimation accuracy (r = 0.92, MAE = 1.50 m) to GHMB (r = 0.92, MAE = 1.52 m) with plot-based reference data. For LiDAR-based reference data, the accuracy of RK-GHMB (r = 0.78, MAE = 1.75 m) is higher than that of GHMB (r = 0.75, MAE = 1.85 m). The uncertainties for all map units range from 1.54 to 3.60 m for the RK-GHMB results. The values change between 1.84 and 3.60 m for GHMB. This study demonstrates that this two-stage up-scaling approach can be used to monitor forest canopy height. The proposed RK-GHMB approach considers the spatial autocorrelation of neighboring data in the second modeling stage and can achieve a higher accuracy.

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.