Forest Height Inversion by Combining Single-Baseline TanDEM-X InSAR Data with External DTM Data

Forest canopy height estimation is essential for forest management and biomass estimation. In this study, we aimed to evaluate the capacity of TanDEM-X interferometric synthetic aperture radar (InSAR) data to estimate canopy height with the assistance of an external digital terrain model (DTM). A ground-to-volume ratio estimation model was proposed so that the canopy height could be precisely estimated from the random-volume-over-ground (RVoG) model. We also refined the RVoG inversion process with the relationship between the estimated penetration depth (PD) and the phase center height (PCH). The proposed method was tested by TanDEM-X InSAR data acquired over relatively homogenous coniferous forests (Teruel test site) and coniferous as well as broadleaved forests (La Rioja test site) in Spain. Comparing the TanDEM-X-derived height with the LiDAR-derived height at plots of size 50 m × 50 m, the root-mean-square error (RMSE) was 1.71 m (R2 = 0.88) in coniferous forests of Teruel and 1.97 m (R2 = 0.90) in La Rioja. To demonstrate the advantage of the proposed method, existing methods based on ignoring ground scattering contribution, fixing extinction, and assisting with simulated spaceborne LiDAR data were compared. The impacts of penetration and terrain slope on the RVoG inversion were also evaluated. The results show that when a DTM is available, the proposed method has the optimal performance on forest height estimation.This work was supported in part by the National Natural Science Foundation of China under Grant 41820104005, Grant 42030112, and Grant 41904004, Hunan Natural Science Foundation under Grant 2021JJ30808, and in part by the Spanish Ministry of Science and Innovation, Agencia Estatal de Investigacion, under Projects PID2020-117303GB-C22/AEI/10.13039/501100011033 and PROWARM (PID2020-118444GA-I00/AEI/10.13039/501100011033)

Calibrating Multimodal Learning

Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely unexplored. In this paper, through extensive empirical studies, we identify current multimodal classification methods suffer from unreliable predictive confidence that tend to rely on partial modalities when estimating confidence. Specifically, we find that the confidence estimated by current models could even increase when some modalities are corrupted. To address the issue, we introduce an intuitive principle for multimodal learning, i.e., the confidence should not increase when one modality is removed. Accordingly, we propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous methods. This technique could be flexibly equipped by existing models and improve the performance in terms of confidence calibration, classification accuracy, and model robustness

A Comparison of Sentinel-1 Biased and Unbiased Coherence for Crop Monitoring and Classification

Synthetic Aperture Radar (SAR) holds significant potential for applications in crop monitoring and classification. Interferometric SAR (InSAR) coherence proves effective in monitoring crop growth. Currently, the coherence based on the maximum likelihood estimator is biased towards low coherence values. Therefore, the main aim of this work is to access the performance of Sentinel-1 time-series biased coherence and unbiased coherence in crop monitoring and classification. This study was conducted during the 2018 growing season (April-October) in Komoka, an agricultural region in southwestern Ontario, Canada, primarily cultivating three crops: soybean, corn, and winter wheat. To verify the ability of coherence to monitor crops, a linear correlation coefficient between temporal coherence and dual polarimetric radar vegetation index (DpRVI) was fitted. The results revealed a stable correlation between temporal coherence and DpRVI time-series, with the highest correlation observed for soybean (0.7 < R < 0.8), followed by wheat and corn. Notably, unbiased coherence of the VV channel exhibited the highest correlation (R > 0.75). In addition, we applied unbiased coherence to crop classification. The results show that unbiased coherence exhibits very promising classification performance, with the overall accuracy (84.83%) and kappa coefficient (0.76) of VV improved by 8.35% and 0.12, respectively, over biased coherence, and the overall accuracy (73.25%) and kappa coefficient (0.57) of VH improved by 7.56% and 0.14, respectively, over biased coherence, and all crop classification accuracies were also effectively improved. This study demonstrates the feasibility of coherence monitoring of crops and provides new insights in enhancing the higher separability of crops

The Role of CSR in Promoting Energy-Specific Pro-Environmental Behavior among Hotel Employees

Mitigating environmental crises requires efforts to reduce carbon emission at every level and segment of an economy. In this respect, the energy sector is blamed for increasing greenhouse gas emissions (GHG) throughout the globe. Specifically, it was specified that electrical energy contributes to 35% of the world’s GHG emissions. Without a doubt, the topics related to clean and green energies remained a part of academic discussion; however, a critical knowledge gap exists in most studies. That is, most of the prior literature focused only on the production side (supply side) of electrical energy, neglecting the consumption side (consumption at the level of individuals). Given that a significant amount of electricity has been consumed by the individuals in buildings (homes, offices, or others) for heating and cooling purposes, it is important to promote a target-specific (energy-specific) pro-environmental behavior (TSPEB) of individuals. However, such a debate did not receive any significant attention previously. Further, psychological factors such as employees’ environmental commitment (EEC) and green self-efficacy (GSE) were identified as critical mediators to drive different employees’ outcomes, but the mediating effect of EEC and GSE was not tested earlier to foster TSPEB in a CSR framework. The data for the current work were collected from employees of different hotels in a developing country by employing a survey strategy (n = 383). The structural equation modeling was used to analyze the data, which confirmed that hospitality employees’ CSR perceptions could improve TSPEB. The statistical results also confirmed the significant mediating effects of EEC and GSE. The finding of this study will help the hospitality sector to improve its efforts for de-carbonization by improving the energy consumption behavior of employees as an outcome of CSR

Complex Networks Approach for Analyzing the Correlation of Traditional Chinese Medicine Syndrome Evolvement and Cardiovascular Events in Patients with Stable Coronary Heart Disease

This is a multicenter prospective cohort study to analyze the correlation of traditional Chinese medicine (TCM) syndrome evolvement and cardiovascular events in patients with stable coronary heart disease (CHD). The impact of syndrome evolvement on cardiovascular events during the 6-month and 12-month follow-up was analyzed using complex networks approach. Results of verification using Chi-square test showed that the occurrence of cardiovascular events was positively correlated with syndrome evolvement when it evolved from toxic syndrome to Qi deficiency, blood stasis, or sustained toxic syndrome, when it evolved from Qi deficiency to blood stasis, toxic syndrome, or sustained Qi deficiency, and when it evolved from blood stasis to Qi deficiency. Blood stasis, Qi deficiency, and toxic syndrome are important syndrome factors for stable CHD. There are positive correlations between cardiovascular events and syndrome evolution from toxic syndrome to Qi deficiency or blood stasis, from Qi deficiency to blood stasis, or toxic syndrome and from blood stasis to Qi deficiency. These results indicate that stable CHD patients with pathogenesis of toxin consuming Qi, toxin leading to blood stasis, and mutual transformation of Qi deficiency and blood stasis are prone to recurrent cardiovascular events

The Role of CSR in Promoting Energy-Specific Pro-Environmental Behavior among Hotel Employees

Mitigating environmental crises requires efforts to reduce carbon emission at every level and segment of an economy. In this respect, the energy sector is blamed for increasing greenhouse gas emissions (GHG) throughout the globe. Specifically, it was specified that electrical energy contributes to 35% of the world’s GHG emissions. Without a doubt, the topics related to clean and green energies remained a part of academic discussion; however, a critical knowledge gap exists in most studies. That is, most of the prior literature focused only on the production side (supply side) of electrical energy, neglecting the consumption side (consumption at the level of individuals). Given that a significant amount of electricity has been consumed by the individuals in buildings (homes, offices, or others) for heating and cooling purposes, it is important to promote a target-specific (energy-specific) pro-environmental behavior (TSPEB) of individuals. However, such a debate did not receive any significant attention previously. Further, psychological factors such as employees’ environmental commitment (EEC) and green self-efficacy (GSE) were identified as critical mediators to drive different employees’ outcomes, but the mediating effect of EEC and GSE was not tested earlier to foster TSPEB in a CSR framework. The data for the current work were collected from employees of different hotels in a developing country by employing a survey strategy (n = 383). The structural equation modeling was used to analyze the data, which confirmed that hospitality employees’ CSR perceptions could improve TSPEB. The statistical results also confirmed the significant mediating effects of EEC and GSE. The finding of this study will help the hospitality sector to improve its efforts for de-carbonization by improving the energy consumption behavior of employees as an outcome of CSR

Nicotinic Receptor β2 Determines Nk Cell-Dependent Metastasis In A Murine Model Of Metastatic Lung Cancer

Cigarette smoke exposure markedly compromises the ability of the immune system to protect against invading pathogens and tumorigenesis. Nicotine is a psychoactive component of tobacco products that acts as does the natural neurotransmitter, acetylcholine, on nicotinic receptors (nAChRs). Here we demonstrate that natural killer (NK) cells strongly express nAChR β2. Nicotine exposure impairs the ability of NK cells to kill target cells and release cytokines, a process that is largely abrogated by nAChR β2 deficiency. Further, nicotinic suppression of NF-κB-induced transcriptional activity in NK cells is dependent on nAChR β2. This nAChR subtype also plays a large role in the NK cell-mediated control of melanoma lung metastasis, in a murine lung metastasis model exposed to nicotine. Our findings suggest nAChR β2 as a prominent pathway for nicotine induced impairment of NK cell functions which contributes to the occurrence of smoking-related pathologies. © 2013 Hao et al

Rice Crop Height Inversion from TanDEM-X PolInSAR Data Using the RVoG Model Combined with the Logistic Growth Equation

The random volume over ground (RVoG) model has been widely used in the field of vegetation height retrieval based on polarimetric interferometric synthetic aperture radar (PolInSAR) data. However, to date, its application in a time-series framework has not been considered. In this study, the logistic growth equation was introduced into the PolInSAR method for the first time to assist in estimating crop height, and an improved inversion scheme for the corresponding RVoG model parameters combined with the logistic growth equation was proposed. This retrieval scheme was tested using a time series of single-pass HH-VV bistatic TanDEM-X data and reference data obtained over rice fields. The effectiveness of the time-series RVoG model based on the logistic growth equation and the convenience of using equation parameters to evaluate vegetation growth status were analyzed at three test plots. The results show that the improved method can effectively monitor the height variation of crops throughout the whole growth cycle and the rice height estimation achieved an accuracy better than when single dates were considered. This proved that the proposed method can reduce the dependence on interferometric sensitivity and can achieve the goal of monitoring the whole process of rice height evolution with only a few PolInSAR observations.This research was funded in part by the National Natural Science Foundation of China (grant nos. 41820104005, 42030112, 41904004) and in part by the and the Spanish Ministry of Science and Innovation (grant no. PID2020-117303GB-C22)