Forest Aboveground Biomass Estimation Using Multi-Features Extracted by Fitting Vertical Backscattered Power Profile of Tomographic SAR

Wan, Xiaofeng; Li, Zengyuan; Chen, Erxue; Zhao, Lei; Zhang, Wangfei; Xu, Kunpeng

doi:10.3390/rs13020186

Cited by 11 publications

(4 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The features extracted by the two neural networks are different. Through feature projection, the classification features of the main network are strengthened, thereby improving the text classification effect [14]. FP net can be integrated with existing LSTM, CNN, Transformer, and BERT neural networks.…”

Section: Fp Netmentioning

confidence: 99%

Feature Mining of News Communication Topic Elements Based on BERT Model

Zheng

2023

Sociology

View full text Add to dashboard Cite

In order to solve the problems of lack of standardization, fuzzy semantics and sparse features in news topic texts, a feature mining of news communication topic elements based on the BERT model is proposed. In the research, multi-layer fully connected layer feature extraction is performed on the output of the news topic text in the BERT model, and the final extracted text features are purified by the feature projection method to enhance the classification effect.Then the feature projection network is fused in the hidden layer inside the BERT model for feature projection, so as to enhance and purify the classification features through the feature projection of the hidden layer. Experiments are performed on Toutiao, Sohu News, THUC News-L, and THUC News-S datasets. The experimental results show that compared with the baseline BERT method, the two methods have better performance in terms of accuracy and macro-average F1 value, and the highest accuracy is 86. 96%, 86. 17%, 94. 40% and 93.73%, respectively, which verifies the feasibility and effectiveness of the proposed method. It is concluded that the proposed method for news topic text classification combining BERT and FP net is effective and efficient.

show abstract

Section: Fp Netmentioning

confidence: 99%

Feature Mining of News Communication Topic Elements Based on BERT Model

Zheng

2023

Sociology

View full text Add to dashboard Cite

show abstract

“…The optical vegetation index has been used as a horizontal structure to examine changes in canopy cover [8,9]. The profiles obtained by the tomographic reflect certain changes in the vertical and horizontal directions of the forest stand and the variability of the reflectivity is regarded as the forest structure [3,[10][11][12]. However, the majority of current remote sensing-based research on forest structure ignores the impact that the overall complexity of the forest structure has on vegetation characteristics in favor of modeling individual horizontal or vertical structures [13].…”

Section: Introductionmentioning

confidence: 99%

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Sa,

Fan

2024

Remote Sensing

View full text Add to dashboard Cite

Modeling forest structure using multi-source satellite data is beneficial to understanding the relationship between vertical and horizontal structure and image features to provide more comprehensive and abundant information for the study of forest structural complexity. This study investigates and models forest structure as a multivariate structure based on sample data and active-passive remote sensing data (Landsat8, Sentinel-2A, and ALOS-2 PALSAR) from the Saihanba Forest in Hebei Province, Northern China, to measure forest structural complexity, relying on a relationship-driven model between field and satellite data. In this study, we considered the effects of the role of satellite variables in different vertical structure types and horizontal structure ranges, used two methods to stepwise select significant variables (stepwise forward selection and Pearson correlation coefficient), and employed a multivariate modeling technique (redundancy analysis) to derive a forest composite structure index (FSI), combining both horizontal and vertical structure attributes. The results show that optical texture can better represent forest structure characteristics, polarization interferometric radar information can represent the vertical structure information of forests, and combining the two can represent 77% of the variance of multiple forest structural attributes. The new FSI can explain 93% of the relationship between stand structure and satellite variables, and the linear fit R2 to the measured data reaches 0.91, which largely shows the situation of the measured data. The generated forest structure map more accurately reflects the complexity of the forest structure in the Saihanba Forest, achieving a supplementary explanation of the measured data.

show abstract

“…Many non-parametric models have been explored for forest AGB estimation, such as random forest (RF) (Yadav et al, 2021), k-nearest neighbors (kNN) (Wan et al, 2021;Andras et al, 2022;Beaudoin et al, 2022), support vector machine (SVM) (Mountrakis et al, 2010;Christoffer et al, 2013), and maximum entropy (MaxEnt) (Wang et al, 2022;Zhao et al, 2022). Although the nonparametric models can provide an excellent fitting effect, it is still hard to improve the precision influence caused by overestimation and underestimation.…”

Section: Introductionmentioning

confidence: 99%

Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China

Zhang

Liu

et al. 2023

Front. For. Glob. Change

View full text Add to dashboard Cite

It is a challenge to reduce the uncertainties of the underestimation and overestimation of forest aboveground biomass (AGB) which is common in optical remote sensing imagery. In this study, four models, namely, the linear stepwise regression (LSR), artificial neural network (ANN), quantile regression (QR), and quantile regression neural network (QRNN) were used to estimate Pinus densata forest AGB data by collecting 146 sample plots combined with Landsat 8-Operational Land Imager (OLI) images in Shangri-La City, Yunnan Province, southwestern China. The results showed that compared with the LSR, the R2 and mean square error (RMSE) of the ANN, QR, and QRNN had improved significantly. In particular, the QRNN was able to significantly improve the situation of overestimation and underestimation when we estimated forest biomass, which had the highest R2 (0.971) and lowest RMSE (9.791 Mg/ha) for the whole biomass segment. Meanwhile, through model validation, we found that the QRNN had the highest R2 (0.761) and lowest RMSE (6.486 Mg/ha) on the biomass segment of <40 Mg/ha. Furthermore, it had the highest R2 (0.904) and lowest RMSE (9.059 Mg/ha) on the biomass segment of >160 Mg/ha, which offered great potential for improving the estimation accuracy of the Pinus densata forest AGB. In conclusion, the QRNN, combining the advantages of QR and ANN, provides great potential for reducing the precision influence caused by the overestimation and underestimation in forest AGB estimation using optical remote sensing data.

show abstract

Forest Aboveground Biomass Estimation Using Multi-Features Extracted by Fitting Vertical Backscattered Power Profile of Tomographic SAR

Cited by 11 publications

References 47 publications

Feature Mining of News Communication Topic Elements Based on BERT Model

Feature Mining of News Communication Topic Elements Based on BERT Model

Forest Structure Mapping of Boreal Coniferous Forests Using Multi-Source Remote Sensing Data

Improving the accuracy of forest aboveground biomass using Landsat 8 OLI images by quantile regression neural network for Pinus densata forests in southwestern China

Contact Info

Product

Resources

About