Wilkes Land and Totten Glacier (TG) in East Antarctica (EA) have been losing ice mass significantly since 1989. There is a lack of knowledge of long-term mass balance in the region which hinders the estimation of its contribution to global sea level rise. Here we show that this acceleration trend in TG has occurred since the 1960s. We reconstruct ice flow velocity fields of 1963-1989 in TG from the first-generation satellite images of ARGON and Landsat-1&4, and build a five decade-long record of ice dynamics. We find a persistent long-term ice discharge rate of 68 ± 1 Gt/y and an acceleration of 0.17 ± 0.02 Gt/y2 from 1963 to 2018, making TG the greatest contributor to global sea level rise in EA. We attribute the long-term acceleration near grounding line from 1963 to 2018 to basal melting likely induced by warm modified Circumpolar Deep Water. The speed up in shelf front during 1973-1989 was caused by a large calving front retreat. As the current trend continues, intensified monitoring in the TG region is recommended in the next decades.
It is imperative to quantitatively analyze the long-term temporal and spatial characteristics of the urban heat island (UHI) effect on cities for applications, such as urban expansion and environmental protection. Owing to the high spatial resolution and availability of long time-series data, remote sensing images from Landsat satellites are widely used for land surface temperature (LST) retrieval. However, limited by the satellite revisit cycle and image quality, the use of multisource Landsat images in a long-term study of the UHI effect is inevitable. Nonetheless, owing to the differences among multisource sensors, such as Landsat-7 and Landsat-8, there may be apparent deviations in the LST results retrieved from different sensor data, which are obtained from the same area and under similar circumstances. Consequently, it is necessary to build a relationship between the LST results generated from multisource Landsat sensors for future research on the UHI effect. In this study, Shenzhen city was studied to explore the fitting relationship between the corresponding LST products from Landsat-7 and Landsat-8 images obtained from adjacent dates with similar climatic conditions. Furthermore, factors affecting the fitting models, such as land cover types, seasonal and inter-annual differences, were analyzed. The constructed fitting model had a strong relationship with land cover types but a relatively weak relationship with seasonal and inter-annual differences; this indicates that a pseudo Landsat-8-based LST product can be generated from a Landsat-7-based LST product using a model fitted by a Landsat-7/8 pair obtained from adjacent years (or different seasons). Finally, by considering the consistency between LST products from multisource Landsat images, the spatiotemporal variations in the UHI effect in Shenzhen can be accurately explored using long time-series data.
Hot Temperature , Urbanization , Cities , Temperature , Environmental Monitoring/methods
Platform attitude jitter is inevitable during a satellite flight. The jitter reduces the quality of push-broom optical images, resulting in geometric deformation and image blur. This paper proposes an approach that offers jitter effect compensation and quality improvement for push-broom optical images based on dynamic point spread function (PSF) estimation and iterative image restoration considering the effect of terrain relief and time delay integration (TDI). First, the attitude jitter is precisely detected and estimated by considering the terrain relief. The jitter information is then used to establish the dynamic PSF of each image line considering the changing jitter values and TDI effect. Finally, based on the constructed PSF, the image is improved by iterative restoration using the optimal-window Richardson-Lucy algorithm. The method is validated by both simulation and real data experiments. In the simulation experiment, we apply jitter with different amplitudes and frequencies to generate the degraded images and then restore the image using the proposed restoration method. The results show that the proposed method can effectively restore images affected by jitter. In addition, real data experiments are carried out with multispectral remote sensing images from the ZY-3 satellite. The results show that in addition to the improvement of the radiometric quality, the geometric quality is also significantly improved in both the across-track and the along-track directions. The experimental results validated that the proposed method outperformed other methods without considering the terrain and TDI effect.
The COVID-19 pandemic is the most serious catastrophe since the Second World War. To predict the epidemic more accurately under the influence of policies, a framework based on Independently Recurrent Neural Network (IndRNN) with fine-tuning are proposed for predict the epidemic development trend of confirmed cases and deaths in the United Stated, India, Brazil, France, Russia, China, and the world to late May, 2021. The proposed framework consists of four main steps: data pre-processing, model pre-training and weight saving, the weight fine-tuning, trend predicting and validating. It is concluded that the proposed framework based on IndRNN and fine-tuning with high speed and low complexity, has great fitting and prediction performance. The applied fine-tuning strategy can effectively reduce the error by up to 20.94% and time cost. For most of the countries, the MAPEs of fine-tuned IndRNN model were less than 1.2%, the minimum MAPE and RMSE were 0.05%, and 1.17, respectively, by using Chinese deaths, during the testing phase. According to the prediction and validation results, the MAPEs of the proposed framework were less than 6.2% in most cases, and it generated lowest MAPE and RMSE values of 0.05% and 2.14, respectively, for deaths in China. Moreover, Policies that play an important role in the development of COVID-19 have been summarized. Timely and appropriate measures can greatly reduce the spread of COVID-19; untimely and inappropriate government policies, lax regulations, and insufficient public cooperation are the reasons for the aggravation of the epidemic situations. The code is available at https://github.com/zhhongsh/COVID19-Precdiction. And the prediction by IndRNN model with fine-tuning are now available online (http://47.117.160.245:8088/IndRNNPredict).
Attitude jitter causes image motion and degrades geometric accuracy of high-resolution satellite images. This work studies the mechanism of the attitude jitter effect on the imaging geometry of three-line-array push-broom sensors onboard satellites, which is a typical configuration used for topographic mapping. Based on a rigorous physical imaging model, we derived quantitative models of the geometric effect of attitude jitter in the roll, pitch, and yaw angles on the image distortions of the forward, nadir, and backward view sensors, and the accuracy of the derived models is validated through comprehensive experiments and analyses. The experimental results reveal the following. First, the attitude jitter in the roll angle dominates the cross-track image deviation; it does not affect the along-track geometry of the nadir-view sensor but marginally affects the off-nadir-view sensors, and the image distortions share a linear relationship with the image column coordinates. Second, the attitude jitter in the pitch angle dominates the along-track image deviation, and the image distortions in the off-nadir-view images are relatively larger than those in the nadir-view images. The attitude jitter in the pitch angle does not affect the cross-track geometry of the nadir-view sensor but marginally affects the off-nadir-view sensors, and the image distortions share a linear relationship with the image column coordinates. Finally, the attitude jitter in the yaw angle mainly causes the cross-track image deviation in the off-nadir-view sensors, and the along-track image geometries of all the three view sensors are marginally affected to the same extent by the yaw angle variation.
Spirobichroman-based polymers with high gas permeability and selectivity are promising for their applications as membranes in gas separation. In this study, three spirobichroman-based polyimides (PIs; 6FDA-FH, 6FDA-DH, and 6FDA-MH) were synthesised by the polyreaction between diamines containing different substituents (benzene ring, pyridine ring, and methyl group) and 4,4'-(hexafluoroisopropylidene)-diphthalic anhydride (6FDA). The physical properties, gas transport behaviour, d-spacing, dihedral angle of molecules, and fractional free volume of the PIs were investigated through experiments and molecular simulations. The PIs exhibited excellent thermal stability and good solubility in common organic solvents. The gas permeability of the PIs was investigated; the results highlighted the critical role of the substituents in the enhancement of the gas separation performance of polymer membranes. Detailed analysis of the PIs showed that 6FDA-FH exhibits the highest gas permeability. This can be ascribed to the loose packing of the polymer chain owing to the increased dihedral angle between the two planes. However, the methyl substituent in 6FDA-MH disrupts the polymer chain packing rather than changing the dihedral angle between the two planes, thus enhancing the gas permeability of 6FDA-MH. Furthermore, 6FDA-DH exhibited the highest CO2/CH4 selectivity, which is attributed to the CO2 affinity of the polymer containing the pyridine unit.
To optically capture and analyze the structure and changes of the flow field of a weak airflow object with high accuracy, this study proposes novel weak flow field extraction methods based on background-oriented schlieren. First, a fine background pattern texture and a sensor network layout were designed to satisfy the requirement of weak flow field extraction. Second, the image displacement was extracted by calculating the correlation matrix in the frequency domain for a particle image velocimetry algorithm, and further calculations were performed for the density field using Poisson's equation. Finally, the time series baseline stacking method was proposed to obtain the flow field changes of weak airflow structures. A combustion experiment was conducted to validate the feasibility and accuracy of the proposed method. The results of a quad-rotor unmanned aerial vehicle experiment showed that the clear, uneven, and continuous quantitative laminar flow field could be obtained directly, which overcame the interference of the weak airflow, large field of view, and asymmetrical steady flow.
Bioinspired fibrous materials have emerged as a unique class of matrix for fabrication of fiber-shaped nanomaterial assemblies. Here, we report a novel functional fiber-shaped nanohybrid for efficient removal of antimonite via in situ synthesis of ferric oxides anchored to silk nanofibril. The silk nanofibril matrix played important roles in the growth of ferric oxides via metal-ligand interactions. The achieved nanocomposites had high surface areas and activity with more functional groups, contributing to superior antimonite elimination. The nanocomposite achieved a maximum removal capacity of 159.9 mg/g toward antimonite. And the common interfering ions of SO42-, NO3-, CO32-, PO43- and SiO32- exhibited negligible influence on antimonite removal. The mechanism study point that two factors are closely involved: surface complexation and hydrogen bonding. Benefiting from the low cost and environmental-friendly nature of silk fibroin as well as excellent removal capacity and high selectivity, it suggests that the nanohybrids might be promising for antimonite extraction from contaminated water.
Antimony , Fibroins , Ferric Compounds , Silk , Water Purification
The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China's epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country's total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.
COVID-19/epidemiology , COVID-19/transmission , Models, Biological , Pandemics , Cities/epidemiology , Hong Kong/epidemiology , Humans , Macau/epidemiology , Spatial Analysis
Rapid urban expansion often leads to substantial encroachment on ecological lands and destruction of natural environments. We developed a new cellular automata model (named CACEO) that uses cross-entropy optimization (CEO) to reproduce and project urban expansion into coastal areas and to assess urban encroachment on ecological lands. The CEO algorithm automatically searches for the near-optimal CA parameters and is capable of objectively parameterizing CA models to predict multi-objective scenarios. We calibrated CACEO by simulating urban expansion at Wenzhou from 1995 to 2005, validated the model from 2005 to 2015 using real data, and then predicted urban expansion for 2025 and 2035. End-state overall accuracies were 93.8% for 2005 and 94.4% for 2015, while figure-of-merit metrics were 27.9% for 2005 and 19.1% for 2015. We predicted four different scenarios to year 2025 and 2035: (1) a business-as-usual (BAU)-scenario using benchmark settings; (2) a District-scenario based on a district-oriented urban development strategy; (3) a Road-scenario based on a road network-oriented urban development strategy; and (4) a Coast-scenario based on a coast-oriented urban development strategy. Each scenario predicts a substantially different pattern of urban encroachment on ecological land and significant loss of farmland, forest, wetland and grassland. These scenarios should be useful in adjusting urban development strategies at Wenzhou and elsewhere.
Conservation of Natural Resources , Urban Renewal , Algorithms , China , Entropy , Forests
Automatic fine registration of multisensor images plays an essential role in many remote sensing applications. However, it is always a challenging task due to significant radiometric and textural differences. In this paper, an enhanced subpixel phase correlation method is proposed, which embeds phase congruency-based structural representation, L1-norm-based rank-one matrix approximation with adaptive masking, and stable robust model fitting into the conventional calculation framework in the frequency domain. The aim is to improve the accuracy and robustness of subpixel translation estimation in practical cases. In addition, template matching using the enhanced subpixel phase correlation is integrated to realize reliable fine registration, which is able to extract a sufficient number of well-distributed and high-accuracy tie points and reduce the local misalignment for coarsely coregistered multisensor remote sensing images. Experiments undertaken with images from different satellites and sensors were carried out in two parts: tie point matching and fine registration. The results of qualitative analysis and quantitative comparison with the state-of-the-art area-based and feature-based matching methods demonstrate the effectiveness and reliability of the proposed method for multisensor matching and registration.
With the current extensive availability of synthetic-aperture radar (SAR) datasets with high temporal (e.g., a repeat cycle of a few or a dozen days) and spatial resolution (e.g., in the order of â¼1 m), radar remote sensing possesses an increasing potential for the monitoring of glacier surface motion thanks to the nearly weather and time-independent advantages. This paper proposes a robust subpixel frequency-based image correlation method for dense matching and integrates the improved matching into a workflow of glacier surface motion estimation using SAR intensity images with specific pre-processing and post-processing steps. The proposed matching method combines complex edge maps and local upsampling in the frequency domain for subpixel intensity tracking, which ensure the accuracy and robustness of glacier surface motion estimation. Experiments were carried out with TerraSAR-X and Sentinel-1 images covering two glacier areas in pole and alpine regions. The results of the monitoring and investigation of glacier motion validate the feasibility and reliability of the presented motion estimation method based on subpixel gradient correlation. The comparative results using both simulated and real SAR data indicate that the proposed matching method outperforms commonly used correlation-based matching methods in terms of matching accuracy and the ability to obtain correct matches.
Attitude jitter is a crucial factor that limits the imaging quality and geo-positioning accuracy of high-resolution optical satellites, which has attracted significant research interests in recent years. However, few researchers have attempted to retrieve the dynamic characteristics and time-varying trends of a satellite attitude jitter. This paper presents a novel processing framework for detecting, estimating, and investigating time-varying attitude jitter in long strips based on a time-frequency analysis with the input from either an attitude sensor or an optical imaging sensor. Attitude angle signals containing attitude jitter information are detected from attitude data through generating the Euler angles relative to the orbit coordinate system, or from image data through high-accuracy dense matching between parallax observations, correction of integration time variation and frequency domain-based deconvolution. Variational mode decomposition is adopted to extract the separate band-limited periodic components, and Hilbert spectral analysis is integrated to estimate the instantaneous attributes for each time sample and the varying trends for the entire duration. Experiments with three sets of ZiYuan-3 long-strip datasets were carried out to test the novel processing framework of attitude jitter. The experimental results indicate that the processing framework could reveal the dynamic jitter characteristics, and the mutual validations of different data sources demonstrate the effectiveness of the proposed method.
Land use change affected by wide ranges of human activities is a key driver of global climate change. In the last three decades, China has experienced unprecedented land use change accompanied by increasing environmental problems. There is a pressing need to project and analyze long-term land use scenarios that are critical for land use planning and policymaking. Using GlobeLand30 data, we examined China's land use change from 2000 to 2010, and developed a novel LandCA model for scenario projections from 2020 to 2050. The observed and projected land use change (2000-2050) was analyzed in terms of the interval, category, and transition levels. Our findings show that land Exchange intensity is more than 3 times greater than land Quantity intensity from 2000 to 2050, and the overall rate of land use change will decelerate from 2010 to 2050. During 2000-2010, the loss of built-up land to other categories was 12.7% while the gain was 32.5%, with a growth rate 3.4 times larger than that during 2010-2050. The total amount of cultivated land continuously decreases but will not violate the Chinese "Cultivated Land Red-Line Restriction" by 2050. We speculate that the government's goal of 26% forest cover by 2050 may not be achieved, as a result of strict land use policies preventing the transformation from cultivated land to forests. This study contributes to new evaluations of long-term land use change in China for the government to adjust policies and regulations for sustainable development.
Conservation of Natural Resources , Forests , China , Climate Change , Human Activities , Humans
Driven by increasing urban demand, spatially-varying urban expansion has led to significant ecosystem degradation in China and elsewhere. Spatial nonstationarity affects the relationship between urban expansion and ecosystem service value (ESV) loss, but its significance has been under-emphasized. To study the spatially-heterogeneous ESV loss, we integrated cellular automata (CA) with geographically weighted regression (GWR) in a model that considers the relationships between urban expansion and its driving factors. We used ten GWR bandwidths to construct the CAGWR models for reproducing rapid urban expansion at Chongqing from 2005 to 2010. We then used the CAGWR model with the best bandwidth to predict future urban scenarios out to 2030. Our modeling shows that CAGWR is strongly sensitive to bandwidth, and that the overall accuracy and Figure-of-Merit are maximized with a ~2 km2 bandwidth (about 150 samples). We examined ESV losses in eleven ecosystem classes and found that climate regulation and water flow regulation are the dominant drivers of ESV loss. From 2010 to 2030, Chongqing's urban area will increase by about 87%, resulting in substantial encroachment on agricultural land, dryland and shrubs, causing significant ESV losses of about 38%. Our results constitute an early warning of ecosystem degradation caused by massive urban development. This study improves our understanding of spatially-varying urban expansion and related ESV losses in rapidly developing areas and should help improve urban planning regulation and regional policy for sustainable development to maintain environmentally-friendly cities.
Clinical experience suggests increased incidences of neonatal jaundice when air quality worsens, yet no studies have quantified this relationship. Here we reports investigations in 25,782 newborns showing an increase in newborn's bilirubin levels, the indicator of neonatal jaundice risk, by 0.076 (95% CI: 0.027-0.125), 0.029 (0.014-0.044) and 0.009 (95% CI: 0.002-0.016) mg/dL per µg/m3 for PM2.5 exposure in the concentration ranges of 10-35, 35-75 and 75-200 µg/m3, respectively. The response is 0.094 (0.077-0.111) and 0.161 (0.07-0.252) mg/dL per µg/m3 for SO2 exposure at 10-15 and above 15 µg/m3, respectively, and 0.351 (0.314-0.388) mg/dL per mg/m3 for CO exposure. Bilirubin levels increase linearly with exposure time between 0 and 48 h. Positive relationship between maternal exposure and newborn bilirubin level is also quantitated. The jaundice-pollution relationship is not affected by top-of-atmosphere incident solar irradiance and atmospheric visibility. Improving air quality may therefore be key to lowering the neonatal jaundice risk.
Air Pollutants/toxicity , Air Pollution/analysis , Jaundice, Neonatal/chemically induced , Maternal Exposure/adverse effects , Particulate Matter/toxicity , Prenatal Exposure Delayed Effects , Air Pollutants/analysis , Beijing , Bilirubin/blood , Female , Humans , Infant, Newborn , Particulate Matter/analysis , Pregnancy
In this work, a novel Fe3O4@C@MnO2 hybrid was successfully synthesized via facile method. The morphology, structure, chemical composition, magnetic behavior and EM wave absorbing performance of the hybrid were systematically investigated. Results indicate that the hybrid possesses uniform hierarchical and mesoporous structure. The magnetic saturation(Ms) value of the hybrid is 19.8 emu g-1, which is beneficial to improve magnetic loss. According to its reflection loss curve, the hybrid performs superior EM wave absorption capacity, with a minimum reflection loss value and effective absorbing bandwidth of -35â¯dB and 5â¯GHz when the specimen thickness is 2.7â¯mm. The excellent performance of this hybrid can mainly be attributed to its ideal matching of magnetic loss and dielectric loss, large specific surface area, mesoporous structure and interfacial polarizations. Such new material has the potential to be a superior electromagnetic wave absorber, or applied as a functional filler to modify resin matrix.
Quantifying the contribution of driving factors is crucial to urban expansion modeling based on cellular automata (CA). The objective of this study is to compare individual-factor-based (IFB) models and multi-factor-based (MFB) models as well as examine the impacts of each factor on future urban scenarios. We quantified the contribution of driving factors using a generalized additive model (GAM), and calibrated six IFB-DE-CA models and fifteen MFB-DE-CA models using a differential evolution (DE) algorithm. The six IFB-DE-CA models and five MFB-DE-CA models were selected to simulate the 2005-2015 urban expansion of Hangzhou, China, and all IFB-DE-CA models were applied to project future urban scenarios out to the year 2030. Our results show that terrain (DEM) and population density (POP) are the two most influential factors affecting urban expansion of Hangzhou, indicating the dominance of biophysical and demographic drivers. All DE-CA models produced defensible simulations for 2015, with overall accuracy exceeding 89%. The IFB-DE-CA models based on DEM and POP outperformed some MFB-DE-CA models, suggesting that multiple factors are not necessarily more effective than a single factor in simulating present urban patterns. The future scenarios produced by the IFB-DE-CA models are substantially shaped by the corresponding factors. These scenarios can inform urban modelers and policy-makers as to how Hangzhou city will evolve if the corresponding factors are individually focused. This study improves our understanding of the effects of driving factors on urban expansion and future scenarios when incorporating the factors separately.
Environmental Monitoring/methods , Models, Theoretical , Urbanization/trends , Algorithms , China , Cities , Forecasting
Land ecological security (LES) refers to the environmental health and sustainability of the land resources and ecosystems, which are substantially affected by biophysical and socio-economic factors. We assess the spatiotemporal patterns of LES in Ningbo city on the southeast coast of China from 1975 to 2015 and explore the effects of driving factors. Expert evaluation is used to estimate the LES score for each 2×2km grid and map the patterns by Kriging. Five levels of LES are used: very secure, secure, neutral, insecure and very insecure. A generalized additive model (GAM) captures the relationships between LES and driving factors, and identifies the dominant factors. Our results show that the Ningbo LES has been deteriorating since 1975, and it is now very insecure in Ningbo city center and the central area of the satellite city Cixi. The dominant factors affecting LES are distances to city center (Dcity), district center (Ddistrict) and road networks (Droads), and the moving window built-up area (Dndbi). Among these, Dndbi is most important as inferred by the highest explained deviance of the GAM. This study improves our understanding of the spatiotemporal patterns of LES in Ningbo and how LES changes. As a result, it provides insight to help local governments optimize land-use configuration, potentially improving the environment and ecosystems and creating a more environmentally friendly city.
