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.
In order to evaluate forest quality and carbon stocks and improve our understanding of ecosystems and carbon cycling processes, the accurate measurement of aboveground biomass (AGB) and other forest ...characteristics is crucial. This paper considers the response differences between the bands obtained from Landsat 8 and Sentinel-2A sensors, respectively, and combines the exhaustive combination of spectral indices with normalization and ratio techniques to establish suitable weights for the bands in the vegetation index using relative sensitivity and noise equivalent (NE) to improve the saturation effect between the vegetation index and forest parameters (canopy closure (CC), forest stand density (S), basal area (BA), and AGB) and extend the linear relationship between them. This paper also considers the effects of window size, direction, and principal component analysis on texture features, adds weight to textures and combines textures using linear correlation and NE, establishes texture indices to improve the limitations of information contained in individual texture features, analyzes the potential of texture features to evaluate each forest parameter under different conditions, and better captures the variation of forest parameters. In this paper, we only analyze the planted coniferous forest in Saihanba to avoid the differences in electromagnetic wave effects that are difficult to judge and analyze because of the differences in leaf size and leaf orientation between coniferous and broad-leaf forests. In contrast, the vegetation indices and texture indices obtained from Sentinel-2A could better estimate each vegetation parameter, and the linear estimation of each vegetation parameter using the new texture index reached an R2 above 0.65. The results of this study indicate that Sentinel-2A and Landsat 8 are promising remote sensing datasets for estimating vegetation parameters at the regional scale, and Sentinel-2A data can be employed as the primary source of earth observation data for assessing forest resources in the Saihanba area.
Forest biomass estimation is undoubtedly one of the most pressing research subjects at present. Combining multi-source remote sensing information can give full play to the advantages of different ...remote sensing technologies, providing more comprehensive and rich information for aboveground biomass (AGB) estimation research. Based on Landsat 8, Sentinel-2A, and ALOS2 PALSAR data, this paper takes the artificial coniferous forests in the Saihanba Forest of Hebei Province as the object of study, fully explores and establishes remote sensing factors and information related to forest structure, gives full play to the advantages of spectral signals in detecting the horizontal structure and multi-dimensional synthetic aperture radar (SAR) data in detecting the vertical structure, and combines environmental factors to carry out multivariate synergistic methods of estimating the AGB. This paper uses three variable selection methods (Pearson correlation coefficient, random forest significance, and the least absolute shrinkage and selection operator (LASSO)) to establish the variable sets, combining them with three typical non-parametric models to estimate AGB, namely, random forest (RF), support vector regression (SVR), and artificial neural network (ANN), to analyze the effect of forest structure on biomass estimation, explore the suitable AGB of artificial coniferous forests estimation of machine learning models, and develop the method of quantifying saturation value of the combined variables. The results show that the horizontal structure is more capable of explaining the AGB compared to the vertical structure information, and that combining the multi-structure information can improve the model results and the saturation value to a great extent. In this study, different sets of variables can produce relatively superior results in different models. The variable set selected using LASSO gives the best results in the SVR model, with an R2 values of 0.9998 and 0.8792 for the training and the test set, respectively, and the highest saturation value obtained is 185.73 t/ha, which is beyond the range of the measured data. The problem of saturation in biomass estimation in boreal medium- and high-density forests was overcome to a certain extent, and the AGB of the Saihanba area was better estimated.
Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In ...this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.
The smoking rate in Thailand has been steadily decreasing for decades alongside the government’s tobacco control policies. However, evidence of whether the decrease to date has occurred equally ...across all population groups is scarce. Therefore, this study aimed to examine the changes in the socioeconomic patterns of smoking among male adults in Thailand from 2001 to 2021. This study employed a pooled cross-sectional design with 296,011 male adults aged 15 years or older from the Health and Welfare Survey 2001 ( n = 74,003), 2003 ( n = 14,940), 2006 ( n = 25,088), 2009 ( n = 26,370), 2013 ( n = 26,919), 2015 ( n = 52,904), and 2021 ( n = 75,787). Descriptive analysis and binary logistic regression were used. The results indicated that the smoking rate decreased by approximately 25% from 46.81% in 2001 to 35.01% in 2021. This decrease was significantly greater in high- and low-level socioeconomic groups than in mid-level groups. Specifically, high- and low-income, high- and low-educated, older, married and divorced, employed, and urban and rural people exhibited larger decreases in smoking rates than middle-income, middle-educated, middle-aged and younger, single, unemployed, and Bangkok metropolitan people, respectively. Additionally, the smoking rates of low-income groups decreased as cigarette retail prices increased, whereas those of high-income groups decreased regardless of tobacco control policies. The government’s price policy and health awareness may have significantly influenced the decrease in smoking rates of the low- and high-level socioeconomic groups, respectively. Therefore, the government should continue its price policy and public relations practices to further decrease smoking rates.
This paper considers extinction coefficient changes with height caused by the inhomogeneous distribution of scatterers in heterogeneous forests and uses the InSAR phase center height histogram and ...Gaussian function to fit the normalized extinction coefficient curve so as to reflect the vertical structure of the heterogeneous forest. Combining polarization decomposition based on the physical model and the PolInSAR parameter inversion method, the ground and volume coherence matrices can be separated based on the polarization characteristics and interference coherence diversity. By combining the new abovementioned parameters, the semi-empirical improved RVoG inversion model can be used to both quantify the effects of temporal decorrelation on coherence and phase errors and avoid the effects of small vertical wavenumbers on the large temporal baseline of spaceborne data. The model provided robust inversion for the height of the coniferous forest and enhanced the parameter estimation of the forest structure. This study addressed the influence of vertical structure differences on the extinction coefficient, though the coherence of the ground and volume in sparse vegetation areas could not be accurately estimated, and the oversensitivity of temporal decorrelation caused by inappropriate vertical wavenumbers. According to this method we used spaceborne L-band ALOS-2 PALSAR data on the Saihanba forest in Hebei Province acquired in 2020 for the purpose of height inversion, with a temporal baseline range of 14–70 days and the vertical wavenumber range of 0.01–0.03 rad/m. The results are further validated using sample data, with R2 reaching 0.67.
The soil aggregate is the fundamental unit of soil structure. The fractionation characteristics and influencing factors of phosphorus (P) in soil aggregates inherently link its geochemical ...characteristics and recycling mechanism. This work investigated the fractionation characteristics of inorganic P in cold temperate forest soils and studied the impacts of recovery periods after forest fires and soil aggregate protection mechanisms on P fractionation. Our results showed that the TP, active P, stable P, and total organic carbon (TOC) contents varied with increasing recovery years after forest fire disturbance. The TP content in the coarse particulate organic matter fraction (cPOM) exhibited an increasing trend with the number of recovery years. Redundancy analysis (RDA) and correlation analysis indicated that TOC played a crucial role in influencing the dynamics of P fractionation during the recovery process. The order of TP levels in different soil aggregate fractions was as follows: μClay > dClay > LF > cPOM > dSilt > μSilt > iPOM, with significant contributions from the cPOM and dSilt fractions. The ranking of P fractions in bulk soils was as follows: ACa-P > Fe-P > Oc-P > Or-P > De-P > Al-P > Ex-P. The protective mechanism of soil aggregates had a more significant effect on TOC than TP, with the order of protective abilities being: Phy×biochem-protected > Biochem-protected > Phy-protected > Non-protected mechanism. TOC and recovery years emerged as critical factors influencing the dynamics of different P fractions during post-fire recovery. Soil aggregate protection mechanisms demonstrated significantly higher effects on TOC than on TP. This study provides insights into the fractionation mechanisms of P in the soil–forest ecosystem of the Greater Khingan Mountains, contributing to the sustainable development and utilization of cold temperate forest ecosystems.
The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the ...regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R2 increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R2 = 0.82, rRMSE = 18.06%).
The indiscriminate use of nitrogen fertiliser (NF) is a obstruction to improve soil quality and crop yields. However, the effect of biochar and NF on soil microbial ecosystem (SME) and crop yields is ...unknown. A five-year field experiment in China aimed to evaluate the effects of biochar and nitrogen fertiliser (NF) combination on soil structure, C-to-N ratio (CNR), microbial biomass, and spring maize yield. Biochar and NF were applied at different rates, and the combined application resulted in a soil solid-liquid-gas ratio closer to the ideal value. The use of biochar alone and in combination with NF significantly increased soil's C, N, and CNR. A moderate application of biochar and NF resulted in favourable biological and chemical properties of the soil. The application of biochar and NF at moderate levels led to an increase in SME, with the B8N150 producing the highest yield. The highest yield of B8N150 represents a 24.25% increase compared to the unfertilized control and a 9.04% increase compared to B0N150. Moderate use of biochar and NF could be beneficial in areas with similar climatic conditions.
Late or chronic active antibody-mediated rejection (AMR) associated with
donor-specific antibodies (dnDSA) after renal transplantation is a great clinical challenge because it is often resistant to ...conventional therapies. Daratumumab, an anti-CD38 monoclonal antibody that can deplete plasma cells, may be effective for the treatment of late or chronic active AMR.
We designed a novel regimen that included early intensive therapy with daratumumab plus plasmapheresis (PP)/intravenous immunoglobulins (IVIG) and later maintenance therapy with daratumumab alone, and used this regimen to treat late or chronic active AMR in two kidney transplant recipients with extremely high levels of anti-DQ7 dnDSA.
Both patients had a limited clinical response to the early treatment with rituximab and PP/IVIG (with or without splenic irradiation); however, they had a remarkable decrease in anti-DQ7 DSA (MFI value from ~20,000 to ~5,000) after 2-3 months of intensive therapy with daratumumab plus PP/IVIG. Over 20 months of follow-up, patient 1 maintained a low DSA (as low as 1,572) and normal renal function on daratumumab maintenance therapy. Patient 2 retained a low DSA and improved renal function and pathological lesions within one year after treatment but then deteriorated because of acute T cell-mediated rejection.
Our daratumumab-based regimen has shown promising results in the treatment of refractory late active or chronic active AMR in renal transplant recipients with high-level dnDSA. This may provide a reference for better use of daratumumab in the treatment of late or chronic active AMR.
