Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

Tanase, Mihai A.; Borlaf-Mena, Ignacio; Santoro, Maurizio; Aponte, Cristina; Marin, Gheorghe; Apostol, Bogdan; Badea, Ovidiu

doi:10.3390/f12070944

Cited by 6 publications

(5 citation statements)

References 49 publications

(77 reference statements)

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“…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].…”

Section: Sar Feature Variable Extractionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ye²,

Long³

et al. 2022

Remote Sensing

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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.

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Section: Sar Feature Variable Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ye²,

Long³

et al. 2022

Remote Sensing

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“…Random forests (RF), support vector machines(SVM), bagging stochastic gradient boosting (BagSGB), neural networks, etc. are the commonly used algorithms in those non-parametric models [14], [17], [18], [19], [20]. Moreover, the non-parametric methods cannot be reproduced by equations, and exploration algorithms have flaws in establishing relationships between backscatter observations and GSV variables of interest [16].…”

Section: Introductionmentioning

confidence: 99%

Backscatter/FVC Space: A Method for Estimating Forest Growing Stock Volume Combining SAR and Optical Remote Sensing

Zhang,

Sun,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The forest is an important part of carbon resources. Forest growing stock volume (GSV) is an important parameter of forest. The Water Cloud Model (WCM) is a simple equation that describes the interaction between ground objects and electromagnetic waves. It has also been applied in the estimation of forest GSV. When estimating GSV, the WCM equation parameters are usually calculated using least squares, but the least squares method relies on field reference data. The subsequent WCM development algorithm BIOMASAR uses a sliding window method that does not rely on measured data. However, the sliding window method is inefficient and can easily lead to missing pixels. We designed the backscatter/FVC feature space based on WCM and BIOMASAR to estimate forest GSV. Comparing with the NFI reference data set and the BIOMASAR algorithm results in the study area, the method is evaluated from three aspects: accuracy, efficiency, and texture. The results show that this method does not rely on actual reference data, and the efficiency is increased from 1661s in the sliding window to 663s. The correlation with the NFI reference data is 0.45, the RMSE is 116.2327 m³/ha, and the RRMSE is 64.86%. The accuracy is better than the BIOMASAR sliding window GSV results in this study area, and compared with Google Earth images of the same period, it is also more consistent with the field texture. In short, the backscatter/FVC feature space can efficiently obtain forest GSV estimates more consistent with field conditions without relying on measured data.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Active Microwave Models of Forest Growing Stock Volume Based on the Idea of the Water Cloud Model

Zhang

Sun

et al. 2023

Remote Sensing

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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.

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Growing Stock Volume Retrieval from Single and Multi-Frequency Radar Backscatter

Cited by 6 publications

References 49 publications

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

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

Backscatter/FVC Space: A Method for Estimating Forest Growing Stock Volume Combining SAR and Optical Remote Sensing

Comparison of Three Active Microwave Models of Forest Growing Stock Volume Based on the Idea of the Water Cloud Model

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