Figure 2 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Growing stock volume distribution for all sample plots (left) and plots used for SAR-GSV modelling (right).
Source publication
While products generated at global levels provide easy access to information on forest growing stock volume (GSV), their use at regional to national levels is limited by temporal frequency, spatial resolution, or unknown local errors that may be overcome through locally calibrated products. This study assessed the need, and utility, of developing l...
Contexts in source publication
Context 1
... DBH, H, and GSV over the in-situ data, reached 96 cm, 50 m, and 1577 m 3 ha −1 , respectively, depending on the sample plot (Table 1). As small pockets of large trees influence average values, when relatively small areas are assessed, the PSP-wise GSV was high (>1000 m 3 ha −1 ) for some plots (Figure 2). Table 1. ...
Context 2
... NFI plots used in this study were covered within 13 ALOS PALSAR-2 frames and six ascending and descending Sentinel-1 relative orbits (Figure 1). 104 L-band ALOS PALSAR-2 dual-polarized (HH and HV polarizations) datasets were provided by JAXA Figure 2. Growing stock volume distribution for all sample plots (left) and plots used for SAR-GSV modelling (right). ...
Similar publications
Time series of SAR imagery combined with reference ground data can be suitable for producing forest inventories. Copernicus Sentinel-1 imagery is particularly interesting for forest mapping because of its free availability to data users; however, temporal dependencies within SAR time series that can potentially improve mapping accuracy are rarely e...
Citations
... Currently, the commonly used methods for the inversion of forest GSV via active microwave remote sensing can be roughly divided into two categories: nonparametric and parametric models [25]. The nonparametric models introduce various field and microwave data parameters into the model and continuously train and correct the model to obtain the accumulation result [26][27][28][29][30]. Such models can often be highly accurate by combining a large amount of measured data with machine learning algorithms. ...
... Such models can often be highly accurate by combining a large amount of measured data with machine learning algorithms. To date, most studies have combined C-or L-band synthetic aperture radar (SAR) data with nonparametric machine learning algorithms to calculate GSV or biomass, mainly including random forest (RF) [26,27], support vector regression (SVR) [28], artificial neural network (ANN) [31], deep neural network (DNN) [27], and bagging stochastic gradient boosting (BagSGB) [32]. These methods are flexible and highly precise but produce poor interpretation and are prone to overfitting. ...
Forest growing stock volume (GSV) is an essential aspect of ecological carbon stock monitoring. The successive launches of spaceborne microwave satellites have provided a broader way to use microwave remote sensing to monitor forest accumulation. Currently, the inversion parameterization models of active microwave remote sensing stock volume mainly include the interferometric water cloud (IWCM), BIOMASAR, and Siberia. Among them, the IWCM introduces backscattering and coherence, the BIOMASAR model only introduces backscattering, and the Siberia model only introduces coherence. Although these three models combine the backscatter coefficient and coherence of SAR to estimate volume accumulation, the performance of the models has not been evaluated at the same time in the same area. Therefore, this article starts from the perspective of the three combinations of coherence and backscattering, relies on three models that do not require measured data, and evaluates the accuracy of the models’ overall inversion of GSV. In addition, we combine precipitation meteorological information, vegetation types, and seasonal variation to separately explore model performance. The comparison results show that the IWCM model is relatively stable in the process of stock volume inversion and is more sensitive to the vegetation types of coniferous and deciduous forests. The influence of seasons and precipitation on the model is weak, and the accuracy of the multi-time-series model is slightly improved. The Siberia model has a good storage volume inversion effect in this study area, but the multiple time series did not improve the model accuracy. The BIOMASAR model is simple, and its performance was slightly inferior in this study area. Precipitation can negatively affect BIOMASAR. The model results for multiple time series outperform those for single time. In summary, the stability of IWCM is more suitable for research with unknown information. The BIOMASAR model is simple, does not require coherence calculations, and is ideal for the estimation of large-scale national or world-level storage distributions. The Siberian model performs better in small regions and smaller spatiotemporal baselines.
... Some of these environmental conditions were already presented extensively in a very recent study conducted throughout the same forest boundaries used in this paper, but the objective of which focused on analyzing forest vegetation density dynamics through NDVI (Prȃvȃlie et al. 2022). Many other geographical and ecological details on Romanian forests can be found in other studies (Bouriaud et al. 2019;Dumitraşcu et al. 2020;Kucsicsa et al. 2020;Tanase et al. 2021). ...
... Forests field survey data, acquired from the national forest inventory (NFI) database (NFI 2012(NFI , 2018 (Fig. 1b), were also included in the spatial modelling of national forest biomass. The NFI database, initiated in 2008, has a density of 4 9 4 km in hilly and mountainous regions, and of 2 9 2 km in plain areas, where forest cover is lower (Tanase et al. 2021). NFI surveys are based on squareshaped sampling grids (clusters) with 250-m sides, the corners of which feature four circular permanent sampling plots (PSPs) (Fig. 1c). ...
... NFI surveys are based on squareshaped sampling grids (clusters) with 250-m sides, the corners of which feature four circular permanent sampling plots (PSPs) (Fig. 1c). Each PSP consists of three concentric circles (Fig. 1c), with areas of 200 m 2 , 500 m 2 and 2000 m 2 (Marin et al. 2010;Tanase et al. 2021). ...
Forest biomass controls climate stability, many ecological processes and various ecosystem services. This study analyzes for the first time the recent changes (1987–2018) of forest above-ground live biomass (AGB) in Romania, based on a complex volume of remote sensing and forest inventory data that were modelled yearly using a series of sophisticated statistical algorithms. Subsequently, after modelling interannual AGB data, yearly raster values (~ 2 billion total pixel values) were explored as trends over the 32 years, using the Sen's slope estimator and Mann–Kendall test. A large volume of climate data was also processed in this study, in order to detect possible statistical relationships between climate and forest biomass, after 1987. Results showed a mean multiannual value of forest biomass of ~ 185 t/ha and a total AGB amount (stock) of about 1.25 billion tons (~ 1249 million tons or megatonnes/Mt) across Romania. Regarding forest biomass changes, findings revealed increasing and decreasing AGB trends that account for ~ 70% and 30%, respectively, of the countrywide forest biomass changes. However, it was found that about half (~ 48%) of all positive AGB trends are statistically significant, while negative AGB trends have a statistical confidence on only one-fifth (~ 21%) of their spatial footprint in Romania. Overall, upon averaging and summing up all statistically significant values of positive and negative trends, an average AGB increase of ~ 3 t/ha/yr and a total forest biomass gain of ~ 205 Mt were found in Romania, over the entire 1987–2018 period. The various regional statistics highlight a more complex picture of AGB changes across the country. The analysis of interannual eco-climate data indicated a low to moderate climate signal in AGB changes, revealing that climate change is not a major driving force of AGB dynamics, at least according to the data and methodology applied in this study. The results can be useful to governmental forestry, climate and sustainable development policies in Romania.
... Phenology will be made visible and classification facilitated. We find crop tomography in [21,22], growing stocks in forests in [23], multiband polarimetry in [24,25,26,27,28,29] and the enhanced separation of the effects due to surface rugosity and soil moisture in [30,31,32]. Views at different frequencies, even if multi pass and non-simultaneous, might enhance by much the classification accuracy, even if the results obtained up to now are not always satisfying. ...
... They also estimated the FSV in damaged forest areas, and the results suggested that multitemporal Sentinel-1 data have good potential for estimating the overall FSV. In research on FSV estimation based on Cband-and L-band SAR images, the findings of Tanase et al. [44] demonstrated that the FSV estimation performance of C-band and L-band SAR data is almost the same, and the synergy between the two data is limited. Purohit et al. [40] used Landsat 8 OLI and Senti-nel-1A images to accurately predict the spatial distribution of AGB of different forest types in the foothills of the Indian Himalayas, indicating that the coordination of optical remote sensing variables and radar backscatter data can effectively improve the accuracy of forest AGB estimation. ...
... It describes the interaction between the incident radar electromagnetic waves and ground objects by using statistical methods to measure the scattering ability of ground objects [46]. As shown in Equation (1), the backscattering coefficient Sigma 0 (σ 0 ) can be expressed as the average scattering cross-section corresponding to the unit-effective scattering unit area, which is a dimensionless quantity [39,42,44]. ...
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.
