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The recently emerged beech leaf disease (BLD) is causing the decline and death of American beech in North America. First observed in Northeast Ohio, USA, in 2012, BLD had been documented in 10 northeastern US states and the Canadian province of Ontario as of July 2022. A foliar nematode has been implicated as the causal agent, along with some bacterial taxa. No effective treatments have been documented in the primary literature. Irrespective of potential treatments, prevention and prompt eradication (AKA rapid responses) remain the most cost-effective approaches to the management of forest tree disease. For these approaches to be feasible, however, it is necessary to understand the factors that contribute to BLD spread and use them in estimation of risk. Here, we conducted an analysis of BLD risk across Northern Ohio, Western Pennsylvania, Western New York, and Northern West Virginia, USA. In the absence of symptoms, an area cannot necessarily be deemed free of BLD, i.e., absence of BLD cannot be certain, due to its fast spread and the lag in symptom expression (latency) after infection. Therefore, we employed two widely used presence-only species distribution models (SDMs), one-class support vector machine (OCSVM) and maximum entropy (Maxent), to predict the spatial pattern of BLD risk, based on BLD presence records and associated environmental variables. Our results show that both methods work well for BLD environmental risk modeling purposes, but Maxent outperforms OCSVM with respect to both the quantitative receiver operating characteristics (ROC) analysis and the qualitative evaluation of the spatial risk maps. Meanwhile, the Maxent model provides a quantification of variable contribution for different environmental factors, indicating that meteorological (isothermality and temperature seasonality) and land cover type (closed broadleaved deciduous forest) factors are likely key contributors to BLD distribution. Moreover, the future trajectories of BLD risk over our study area in the context of climate change were investigated by comparing the current and future risk maps obtained by Maxent. In addition to offering the ability to predict where the disease may spread next, our work contributes to the epidemiological characterization of BLD, providing new lines of investigation to improve ecological or silvicultural management. Furthermore, this study shows strong potential for extension of environmental risk mapping over the full American beech distribution range, so that proactive management measures can be put in place. Similar approaches can be designed for other significant or emerging forest pest problems, contributing to overall management efficiency and efficacy.

Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

The latest MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Aerosol Optical Depth (AOD) products provide AOD datasets at 3 km and 10 km spatial resolutions retrieved by t...

Background Glioblastoma (GB) is the most common and aggressive brain and central nervous system malignancy. MicroRNAs (miRNAs) have been demonstrated to be predictors of prognostic outcomes, playing an important role in the pathogenesis and progression of GB. We aim to identify the potential miRNAs in GB. Methods GSE103228 was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed miRNAs (DE-miRNAs) using the Student’s t-test. Potential target genes for DE-miRNAs were predicted using miRTarBase, and their functions were analyzed using Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The protein-protein interaction (PPI) network was constructed using the STRING database and visualized using Cytoscape to identify a hub target gene-miRNA network. Furthermore, the expression of GB target genes was verified using University of Alabama Cancer (UALCAN) database. Results A total of 49 DE-miRNAs were identified in GB including 30 down-regulated miRNAs and 19 up-regulated miRNAs. Our analysis predicted 1,118 and 1,063 potential target genes from the top three most up-regulated and down-regulated DE-miRNAs, respectively, that were enriched in several GB-related pathways including the cancer pathway. ACTB and MYC were considered to be hub genes in our PPI networks. Conclusions MiR-218-5p and miR-148a-3p regulated most of the hub genes and miR-148a-3p appeared to be a prognostic biomarker.

Human-perceived wind chill describes the combined effects of wind velocity and low temperature, strongly related to human health and natural environment. Although long-term trends in the air or ambient temperature over China under global warming have been well studied in the literature, the changes in human-perceived wind chill conditions, especially under possible urbanization effects, are still not completely known. This paper investigates the changes of wind chill over China and quantifies the associated urbanization effect by examining nearly 2000 meteorological stations during 1961–2014 using the generalized additive model (GAM). Results show that the winter wind chill temperature (WCT) in China exhibits more prominent raising trends than the air temperature, i.e., 0.623 and 0.349 °C per decade, respectively. The wind speed (V) and wind chill days (WCD) decreased by 0.149 m/s and 1.970 days per decade, respectively. These trends become more substantial in densely populated and highly urbanized areas such as the North China Plain. The expansion of urban built-up area induces additional warming (reducing) to the increase (decrease) in WCT (WCD). On average, an increase from 0% to 100% in the urban fraction induced 0.290 ± 0.067 °C higher WCT (± denotes the 95% confidence interval), along with a reduction in V and WCD by 0.052 ± 0.014 m/s and 3.513 ± 0.387 days, respectively; whereas, the presence of the grassland and forest significantly diminishes the WCT and increases the WCD and surface V. It is expected that wind chill over China tends to be weakened under glocal warming and local urbanization in the near future. Our results have important implications for climate change mitigation, urban planning, landscape design, and air pollution abatement.

Convolutional neural network (CNN) has led to significant progress in object detection. In order to detect the objects in various sizes, the object detectors often exploit the hierarchy of the multi-scale feature maps called feature pyramid, which is readily obtained by the CNN architecture. However, such feature maps do not fully consider the supplementary effect of contextual information on semantics. In this work, we proposed a feature fusion method of residual attention based on the SSD benchmark network call Improved SSD to make full use of context information to improve the characterization ability of feature maps. Besides, we use the residual attention mechanism to reinforce the key features to further improve the detector performance. The experiment result on benchmark dataset PASCAL VOC shows that the map of the proposed method with input image sizes of 300×300 and 512×512 is 78.8% and 80.7%.

Summertime extreme heat events exert severe impacts on the natural environment and human society, especially in densely populated and highly urbanized regions. While previous studies have focused on independent heat day/night, there is a lack of attention to the changes in compound events with cooccurring daytime and nighttime extreme temperature on the same day. In this study, we examine the spatio-temporal changes of summertime compound heat extremes (including compound heat day and compound heatwave) across China, with a particular focus on 20 major urban agglomerations (UAs), and quantify the urbanization effects on these changes. We find that the frequency and fraction of compound heat events show obvious spatial disparities across China. The compound heat events occur more frequently in highly populated and urbanized areas such as the Pearl River Delta. Moreover, the frequency and fraction of compound heat events have significantly increased in recent decades in most parts of China, especially in more developed UAs. These intensifying trends have even accelerated in more recent decades. Our further investigations suggest that most UAs of China experienced an intensifying urbanization effect on compound heat events, and few UAs in northwestern and central China (e.g., UAs of the north Tianshan mountain and the middle reaches of the Yangtze River) display a weakening effect of urbanization. Our findings highlight the important role of urbanization in increasing compound heat extremes and suggest that the increasing threats of compound events in urban areas should be given more attention under the context of global warming and local urbanization.

Spatial and temporal satellite image fusion (STIF) has provided a feasible alternative for generating imagery with both high spatial and temporal resolution, thus expanding the applications of existing satellite sensors. However, a critical challenge confronting the further development of STIF is to systematically and robustly address complex land surface changes, which include land cover changes without shape changes (e.g., crop rotation) and land cover changes with shape changes (e.g., urban expansion), in addition to conventional land surface changes (e.g., phenological changes of vegetation). This paper presents the Robust Adaptive Spatial and Temporal Fusion Model (RASTFM) to tackle this challenge with one prior pair of MODIS-Landsat images. In RASTFM, land surface changes are reorganized into non-shape changes (including phenological changes and land cover changes without shape changes) and shape changes (i.e., land cover changes with shape changes), which are handled differently. However, both non-shape changes and shape changes are predicted through a Non-Local Linear Regression (NL-LR) of the subject pixel's similar neighbors. A regression based high-pass modulation is also performed as a post-processing step to improve both the spatial details and spectral fidelity of the predicted Landsat image. Unlike other STIF models (e.g., the Spatial and Temporal Adaptive Reflectance Fusion Model, STARFM), RASTFM can find similar neighboring pixels more precisely through a non-local searching strategy and derives the weights of similar neighbors more rigorously via a linear regression model. As both non-shape and shape changes are treated based on the regression of similar neighboring pixels, the land surface changes are processed in a unified manner. Experiments that use one simulated and three actual MODIS-Landsat datasets featured by different types of land surface changes were conducted to demonstrate the performance of RASTFM. Comparisons with the state-of-the-art STIF models, including weighted function, unmixing and dictionary-learning methods, show that NL-LR based RASTFM can capture the land surface changes in various landscapes more accurately and robustly in a unified manner, thereby facilitating the continuous and detailed monitoring of complex and diverse land surface dynamics.

Air pollution with high concentrations of fine particulate matter (PM2.5) poses severe threats to human health. Accurate estimation of PM2.5 concentrations can timely assist relevant agencies to conduct air pollution treatment and provide essential data sources for epidemiological research related to PM2.5 exposure. Although China has established a network for monitoring ground-level PM2.5 concentrations over the past decades, the limited available records from the sparsely-located PM2.5 monitoring sites hinder the fine-resolution research of air pollution. Many studies have been conducted to fill the data gap caused by sparsely distributed monitoring sites, but the accuracy of different models varies greatly. In recent years, machine learning models have become the preferred choices due to their high estimation accuracy. However, the estimation accuracy may differ significantly in different study areas with different models, and there are few studies on model performance evaluation regarding the Pearl River Delta (PRD) region of China. This study evaluated the performance of six machine learning models for estimating PM2.5 concentrations in PRD from August 2014 to December 2019. Moreover, multi-source data were adopted for reliable daily PM2.5 concentration estimation, including meteorology, vegetation, topography, and point of interest (POI). The results show that the tree-structured models (i.e., Random Forest (RF) and Gradient Boosting Regression Tree (GBRT)) generally produce better estimations than other models. Two neural network models (i.e., Back Propagation Neural Network (BPNN) and Elman Neural Network (ENN)) show a similar estimation accuracy. Additionally, the Generalized Additive Model (GAM) generally gives the worst performance, followed by the Support Vector Machines (SVM) model. RF is thus highly recommended based on the estimation accuracy, while GBRT is also a promising model for daily PM2.5 estimation in PRD. Our study provides a reference for selecting an appropriate model for daily PM2.5 concentration estimation in PRD and other regions with climate background.

Toxic Cyanobacteria-rich Harmful Algal Blooms (CyanoHABs) are severe environmental issues impacting water environment, aquatic life populations, and surrounding wild and human lives. Given their fast-changing variations in space and time, frequent monitoring of CyanoHABs with sufficient spatial details and coverage is needed to understand their impacts. However, current monitoring activities based on in situ or satellite data do not meet this requirement due to the limited spatial coverages of buoy measurements and the compromise between spatial resolution and temporal frequency of satellite observations. In this study, we develop a Spatial-Temporal Image Fusion (STIF) approach to enable high spatial-temporal resolution monitoring of CyanoHABs. The proposed approach consists of two steps: (1) a new CyanoHAB spectral index called Broad Wavelength Algae Index (BWAI) is developed for fine-but-sparse (Landsat) and coarse-but-frequent (MODIS) satellite images, and (2) the Landsat and MODIS derived BWAI images are fused by the Robust Adaptive Spatial and Temporal Fusion Model (RASTFM) to generate fine-and-frequent Landsat-like BWAI images. Our results show that the proposed BWAI index is with higher similarity and correlation with the reference Cyanobacteria Index (CI) images and in situ observations than the comparative algae indices devised for broad wavelength sensors. Moreover, the 30-m Landsat-like BWAI image series provide more accurate and detailed results than their 500-m MODIS counterparts and greatly improve the temporal frequency over Landsat-based algae indices, demonstrating the contribution of the improved spatial-temporal resolution achieved by the proposed STIF approach. Consequently, this research fills the gap of high spatiotemporal resolution monitoring of CyanoHABs and paves a new way of assessing water environment in a timely and detailed manner.

Unprecedented urbanization in China facilitates the rapid development of urban agglomerations (UAs) and may exert prominent effects on regional climate and environment change. By analyzing a set of 27 extreme temperature and precipitation indices, this study examines the changes in extreme climate events in 20 UAs in China and evaluates the urbanization effects using a dynamic classification of urban and rural stations by time-varying land use/cover maps. The regional differences of the urbanization effects on extreme climate events are also investigated by a k-means clustering. It is found that, for both temperature and precipitation extremes, the urban and rural areas exhibit remarkably distinct changes and demonstrate significant urbanization effect, which also varies across different climate backgrounds. Urbanization profoundly contributes to increasing hot extremes and reducing cold extremes in most UAs, while it seems to pose the opposite effects in several UAs of arid and high-latitude regions. On average, the urbanization effect accounts for around 30% of the total change in extreme temperature events over the urban core areas of 20 UAs. On the other hand, the urbanization effects on extreme precipitation indices display stronger regional discrepancies than temperature extremes. Urbanization tends to have weakening effects on extreme precipitation events in UAs over coastal regions and intensifying influences on those in central/west China. It causes more (less) frequent and more (less) intense precipitation in UAs of inland central/west (coastal) areas. Our findings provide a systematic understanding of the urbanization effects on extreme climate and may have important implications for the mitigation of urban disasters.

Cryptocercus is a genus of sub-social wood-feeding cockroaches that represents the sister group to the eusocial termites. We generated mitochondrial (12S+16S rRNA, COII), nuclear (28S rRNA) and Blattbacterium endosymbiont (16S+23S rRNA) sequence data for 8 new Chinese species, and combined these with previously available data to undertake the most extensive analysis of phylogenetic relationships within the genus to date. As expected, phylogenetic relationships among Blattabacterium strains were found to be congruent with those of their hosts. Three major clades were found to exist in Asian populations, one representing taxa from the Hengduan mountains in Southwestern China, a second including taxa from Russia, Korea, Northeastern China, and Yunnan in the Hengduan Mountains, and a third including taxa from the Qinling Mountains and Daba Mountains in Central China. A molecular dating analysis using 7 termite fossils to calibrate the molecular clock indicated that the divergence of American and Asian Cryptocercus occurred 55.09Ma (41.55-72.28Ma 95% CI), and that the radiations of American and Asian taxa occurred 28.48Ma (20.83-37.95Ma 95% CI) and 20.97Ma (15.78-27.21Ma 95% CI) respectively. Reconstruction of ancestral geographic distributions using S-DIVA suggested Cryptocercus was originally distributed across both continents, as opposed to ancestral migration of Cryptocercus from one continent to the other. The last common ancestor of Asian Cryptocercus was inferred to have existed in Central China. An examination of male chromosome numbers in Asian Cryptocercus showed that diploid numbers vary from 2n=15 to 2n=41, and indicates the presence of eight new species. Our study represents the most comprehensive phylogenetic and biogeographic study yet performed for this important group of cockroaches.

It is known that rapid expansion in urban area exerts prominent effects on surface or near-surface temperature and the urban heat island effect. However, whether and how urban land expansion influences atmospheric humidity, a key indicator closely relating to human comfort and urban environment, warrant further investigations. By taking Guangdong Province as an example, in this study we examined the change in atmospheric moisture over South China during 1961–2014 and evaluated the urban expansion effects. We found a prominent drying tendency over Guangdong, as characterized by decreasing relative and specific humidities and increasing vapor pressure deficit. The drying trend has a positive correlation with urban expansion speed and exhibited regional differences, with particularly stronger trends in more urbanized and populated areas such as the Pearl River Delta. Using a dynamic classification of urban / rural stations based on time-varying high-resolution land use / land cover maps, we estimated that urbanization contributed to approximately 50% of the total drying trend in the urban areas of Guangdong. Possible mechanisms and processes underlying the urban expansion effect were also discussed. We suggested that the changes in atmospheric moisture and the urban expansion effect should be considered in future climate change mitigation, urban planning, and landscape design to support sustainable development in urban areas.

High spatiotemporal resolution concentration of fine particulate matter (PM2.5) enables accurate and detailed air quality monitoring, especially for metropolitan cities with high levels of population density. Although ground air quality monitoring stations can provide timely and accurate observations, they are usually very sparsely distributed, and cannot provide PM2.5 concentration data with continuous spatial coverage. Instead, satellite observations, e.g., Landsat 8/Thermal Infrared Sensor (TIRS) and Terra/Moderate Resolution Imaging Spectroradiometer (MODIS), can both obtain data with continuous coverage. However, there is a trade-off between satellite sensors' spatial and temporal resolution. Hence, this study presents an estimation model for PM2.5 concentrations that combines these multi-source data to produce high spatiotemporal resolution concentration maps in urban area. The approach is tested on New York City, NY, USA. Specifically, we first use cloud-free MODIS thermal band images and the corresponding ground-station PM2.5 records to build a local PM2.5 prediction model. Then, we exploit a spatiotemporal image fusion technique to obtain Landsat-like thermal band image series from Landsat 8/TIRS (100 m spatial resolution) and Terra/MODIS (1 km spatial resolution) sensors. Finally, we convert the fused high spatiotemporal resolution thermal band images to PM2.5 concentration maps by the prediction model from step 1. The validation between the estimated and the real PM2.5 values shows that the detailed Landsat-like high spatial resolution PM2.5 estimations are more accurate than the original blurred MODIS one.

Spatial, Temporal, and Spectral Resolution (STSR) are three key characteristics of Earth observation satellite sensors; however, any single satellite sensor cannot provide Earth observations with high STSR simultaneously because of the hardware technology limitations of satellite sensors. On the other hand, a conflicting circumstance is that the demand for high STSR has been growing with the remote sensing application development. Although image fusion technology provides a feasible means to overcome the limitations of the current Earth observation data, the current fusion technologies cannot enhance all STSR simultaneously and provide high enough resolution improvement level. This study proposes a Hybrid Spatial-Temporal-Spectral image Fusion Model (HSTSFM) to generate synthetic satellite data with high STSR simultaneously, which blends the high spatial resolution from the panchromatic image of Landsat-8 Operational Land Imager (OLI), the high temporal resolution from the multi-spectral image of Moderate Resolution Imaging Spectroradiometer (MODIS), and the high spectral resolution from the hyper-spectral image of Hyperion to produce high STSR images. The proposed HSTSFM contains three fusion modules: (1) spatial-spectral image fusion; (2) spatial-temporal image fusion; (3) temporal-spectral image fusion. A set of test data with both phenological and land cover type changes in Beijing suburb area, China is adopted to demonstrate the performance of the proposed method. The experimental results indicate that HSTSFM can produce fused image that has good spatial and spectral fidelity to the reference image, which means it has the potential to generate synthetic data to support the studies that require high STSR satellite imagery., {"references":["Tong, X., Zhao, W., Xing, J., & Fu, W. (2016, July). Status and development of China High-Resolution Earth Observation System and application. In Geoscience and Remote Sensing Symposium (IGARSS), 2016 IEEE International (pp. 3738-3741). IEEE.","The future missions of NASA's Earth Observing System, https://eospso.nasa.gov/future-missions.","Zhang, Y. (2004). Understanding image fusion. Photogrammetric engineering and remote sensing, 70(6), 657-661.","Gao, F., Masek, J., Schwaller, M., & Hall, F. (2006). On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance. Geoscience and Remote Sensing, IEEE Transactions on, 44(8), 2207-2218.","Zhang, Y. (1999). A new merging method and its spectral and spatial effects. International Journal of Remote Sensing, 20(10), 2003-2014.","Winter, M. E., Winter, E. M., Beaven, S. G., & Ratkowski, A. J. (2007, March). Hyperspectral image sharpening using multispectral data. In Aerospace Conference, 2007 IEEE (pp. 1-9). IEEE.","Song, H., Huang, B., Zhang, K., & Zhang, H. (2014). Spatio-spectral fusion of satellite images based on dictionary-pair learning. Information Fusion, 18, 148-160.","Huang, B., & Song, H. (2012). Spatiotemporal reflectance fusion via sparse representation. Geoscience and Remote Sensing, IEEE Transactions on, 50(10), 3707-3716.","Song, H., & Huang, B. (2013). Spatiotemporal satellite image fusion through one-pair image learning. IEEE Transactions on Geoscience and Remote Sensing, 51(4), 1883-1896.\n[10]\tZhao, Y., & Huang, B. (2016, August). A Two-step Spatio-Temporal satellite image Fusion Model for temporal changes of various LULC under one-pair prior images scenario. In Signal Processing, Communications and Computing (ICSPCC), 2016 IEEE International Conference on (pp. 1-5). IEEE.\n[11]\tShen, H., Meng, X., & Zhang, L. (2016). An Integrated Framework for the Spatio–Temporal–Spectral Fusion of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 54(12), 7135-7148.\n[12]\tHuang, B., Zhang, H., Song, H., Wang, J., & Song, C. (2013). Unified fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial–temporal–spectral earth observations. Remote sensing letters, 4(6), 561-569.\n[13]\tZhang, L., Fu, D., Sun, X., Chen, H., & She, X. (2016, April). A spatial-temporal-spectral blending model using satellite images. In IOP Conference Series: Earth and Environmental Science (Vol. 34, No. 1, p. 012042). IOP Publishing.\n[14]\tGevaert, C. M., & García-Haro, F. J. (2015). A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote sensing of Environment, 156, 34-44.\n[15]\tZhu, X., Helmer, E. H., Gao, F., Liu, D., Chen, J., & Lefsky, M. A. (2016). A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172, 165-177.\n[16]\tZhang, Y. (2008). U.S. Patent No. 7,340,099. Washington, DC: U.S. Patent and Trademark Office."]}

Plenty of researches have been presented to combine satellite remote sensing observations with ground-level air quality monitoring sites measurements together to accomplish spatially continuous observation of air pollutant, such as using Aerosol Optical Depth (AOD) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) and ground observed fine particulate matter (PM 2.5 ) to estimate the PM 2.5 concentrations that has the same spatial coverage and continuity with MODIS AOD. However, because the temporal resolution of MODIS, i.e. twice in daytime, is too limited to accomplish real-time air quality monitoring. Hence, geostationary satellite with higher temporal resolution is introduced in this study, e.g., Multi-functional Transport Satellite 2 (MTSAT-2), which provides Earth observations for every half an hour or even more frequently. This paper presents an integrated spatial-temporal-spectral image fusion model to blend the spatial, temporal and spectral resolution of MODIS and MTSAT-2 images to generate the synthetic data with high spatial, temporal and spectral resolution simultaneously, which can provide more comprehensive data for real-time air quality monitoring. The fusion process involves MTSAT-2 visual band sharpening, spatial-temporal fusion, and temporal-spectral fusion, which aims to improve spatial, temporal, and spectral resolution respectively. The proposed image fusion model was tested on one set of MODIS and MTSAT-2 images within the Pearl River Delta (PRD) region, China. Experimental results indicate that, the advantages of the spatial, temporal, and spectral resolution of these two satellites are merged quite well, which can produce synthetic half-hourly MODIS-like images for real-time air quality monitoring.

This paper proposes a two-step spatio-temporal fusion model (TSTFM) for generating synthetic satellite remote sensing images with high-spatial and high-temporal resolution (HSaHTeR) based on one pair of prior images, which contain one low-spatial but high-temporal resolution (LSaHTeR) image and one high-spatial but low-temporal resolution (HSaLTeR) image. Considering both phenology and type surface temporal changes, the two steps in TSTFM are adopted to handle these two kinds of changes respectively, which are based on weighted mean and example-based image super-resolution approaches accordingly. In addition, a relative radiometric normalization process is conducted before performing the two-step spatio-temporal fusion (STF) process, which aims to calibrate radiometric differences of different kinds of satellite sensors. The proposed method was tested on two sets of test data: surface with mainly LULC phenology changes and surface with primarily LULC type changes. Experimental results show that TSTFM can capture both phenology and type changes efficiently and precisely even with one-pair prior images, and it can also maintain its robustness when facing extremely complex LULC.

The spatial consistency of low-resolution multi-source remote sensing data is of great importance for their combination in global change research. Currently, many methods are developed for the precise geometric correction of single kind of low-resolution data. However, the spatial consistency correction method is still need to be developed when many different kinds of low-resolution sensors' data are taken into consideration altogether, which is aimed to make their spectral data become consistent in geo-location. MODIS surface reflectance products, as they are of high accuracy of geo-location and data quality among low-resolution data, the spectral data of the multi-source low-resolution sensors are corrected to be consistency with it, which contain the level 1B data of NOAA/AVHRR, FY-3/VIRR, FY-3/MERSI, FY-2/VISSR. The proposed method in this paper can conducted spatial consistency correction on multi-source low-resolution remote sensing data precisely, efficiently, and automatically, which is based on contour point coarse matching and contour precise matching.

Based on 96 completed surveys from logistics customers, the author conducted a analysis between customer loyalty and logistics cost in evaluating the service quality attributes. It was found that the top gaps were all related to the functional quality (friendly interactions between customers, and logistics firms), not technical quality (e.g., the extra services provided, better price, or preferred rules and regulations). This study offered an approach to balance the logistics cost, customer satisfaction and customer loyalty under acceptable service quality for logistics practitioners. Switching barriers are also discussed to manifest that customer loyalty enhancement is not the only way to keep the customer from switching to logistics competitors.

The study on the development of guidelines to select appropriate strategies and to improve the performance when given a particular strategy became a critical question in supply chain management (SCM) focused by scholars. To achieve this objective, this research aims to found a simulation test bed in the SCM, which can enhance SCM capability to model diverse supply chain scenarios realistically. By the analysis of user interface, schedule planning and updating, system initialization, time-phased replenishment execution, DRP/MRP strategies, a simulation test bed for supply chain simulator approach is delivered in the papers.