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Abstract Background Particulate matter (PM), including the major risk factor for lung cancer (LC), greatly impacts human health. Although numerous studies have highlighted spatiotemporal patterns and PM-LC associations, these studies have not been well-reviewed. Thus, we examined epidemiological studies linked with PM-LC and provided concise, up-to-date data. Methods We used certain keywords to review articles published in PubMed, Web of Science, Scopus, and Google Scholar until 30th June 2024 and identified 1474 research articles. We then filtered the research articles based on our criteria and ultimately dropped down to 30 for this review. Results Out of the thirty reviewed studies on the PM-LC relation, twenty-four focused on PM2.5, four on PM10, and two on both, indicating that approximately 80% of the respondents were inclined toward fine particles and their health impacts. The study revealed that 22 studies used visualization, 12 used exploration, and 15 used modeling methods. A strong positive relationship was reported between LC and PM2.5, ranging from 1.04 to 1.60 (95% CI) for a 10 µg/m3 increase in PM2.5 exposure. However, compared to PM2.5, PM10 was found to have a significantly less positive association. Conclusions Very few studies have used advanced spatiotemporal methods to examine the association between LC and PM. Advanced spatiotemporal analysis techniques should be employed to explore this association in specific geographical locations. Further research should utilize spatiotemporal epidemiological approaches to study the link between PM and lung cancer.

BackgroundDespite examining the role of an association between particulate matter and lung cancer in low-income countries, studies on the association between long-term exposure to particulate matter and lung cancer risk are still contradictory. This study investigates the spatiotemporal distribution patterns of lung cancer incidence and potential association with particulate matter (PM2.5) in Bagmati province, Nepal.MethodsWe performed a spatiotemporal study to analyze the LC – PM2.5 association, using LC and annual mean PM2.5 concentration data from 2012 to 2021. The study assessed the global spatial autocorrelation test using global Moran's I, applied hotspot analysis. A bivariate statistical analysis was performed to evaluate the association, we also applied the geographically weighted regression model (GWR) to look for possible relationships.ResultsThe annual mean crude incidence rate (CIR) and standardized incidence rate (SIR) were 5.16, and 6.09 respectively. The study reveals an increasing trend with notable municipal-level spatial variations. Bhaktapur municipality exhibits the highest CIR (243.88), followed by Panchkhal and Sunapati. Males consistently exhibit higher rates, particularly in middle-aged and older adult populations. Bhaktapur displayed the highest CIR in males (171.9) but very low in females (72). The spatial analysis identified concentration trends and hotspots developed in the Bhaktapur, Panchkhal, and Sunapati municipalities. The SIR showed fluctuating patterns of continuous rise until 2019, decrease in 2020, and rise again thereafter. Similar fluctuation association patterns were observed with PM2.5, the r-squared value consistently fluctuated during the study period.ConclusionIn this study, we found an association between PM2.5 exposure and lung cancer incidence. The findings underscore the need for targeted public health interventions, highlighting the role of PM2.5. Future research is suggested to explore the relationship between lung cancer distribution and various environmental risk factors for effective control and prevention. Addressing air pollution could potentially reduce future lung cancer risk.

Satellite pose estimation (PE) is crucial for space missions and orbital maneuvering. High-accuracy satellite PE could reduce risks, enhance safety, and help achieve the objectives of close proximity and docking operations for autonomous systems by reducing the need for manual control in the future. This article presents a joint iterative satellite PE and particle swarm optimization (PE-PSO) method. The PE-PSO method uses the number of batches derived from satellite PE as the number of particles and keeps the number of epochs from the satellite PE process as the number of epochs for PSO. The objective function of PSO is the training function of the implemented network. The output obtained from the previous objective function is applied to update the new positions of the particles, which serve as the inputs of the current training function. The PE-PSO method is tested on synthetic Soyuz satellite image datasets acquired from the Unreal Rendered Spacecrafts On-Orbit Datasets (URSOs) under different preset hyperparameters. The proposed method significantly reduces the incurred loss, especially during the batch-processing operation of each epoch. The results illustrate the accuracy improvement attained by the PE-PSO method over epoch processing, but its time consumption is not distinct from that of the conventional method. In addition, PE-PSO achieves better performance by reducing the mean position estimation error by 13.1% and the mean orientation estimation error on the testing dataset by 29.1% based on the pretrained weights of Common Objects in Context (COCO). Additionally, PE-PSO improves the accuracy of the Soyuz_hard-based weight by 7.8% and 0.3% in terms of the mean position estimation error and mean orientation estimation error, respectively.

Ecological vulnerability assessment is crucial for environment protection, ecological restoration and resource utilization. However, many former studies have limitations in the indicator system of the assessment, which were not comparable for different types of ecologically vulnerable areas. It is difficult to apply directly to the ecological vulnerability assessment of different types and in various regions. Aiming to solve these problems, the study proposed a well-established and comprehensive indicator system for ecological vulnerability assessment and conducted ecological vulnerability assessment application of five types of typical ecologically vulnerable areas of China based on remote sensing, meteorological, geographic and other data. The results show that the average EVIs value of Zhangbei County ranging from 0.525 to 0.559 are the highest among the five research areas during the four periods, followed by Zoige region and Xiamen bay. However, the region with the lowest average EVI value varies. In 2005 and 2015, it was Taihe County, while in 2010 and 2020, it was the Sanjiangyuan region. The variation of average EVIs in the five typical areas presents slight fluctuation and remains generally stable from 2005 to 2020. It indicates that the environmental protection measures and projects undertaken by the Chinese government in recent years have had a striking effect, curbing the trend of ecological environment deterioration.

Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management.

PurposeA mouse model of irradiation (IR)-induced heart injury was established to investigate the early changes in cardiac function after radiation and the role of cardiac macrophages in this process.MethodsCardiac function was evaluated by heart-to-tibia ratio, lung-to-heart ratio and echocardiography. Immunofluorescence staining and flow cytometry analysis were used to evaluate the changes of macrophages in the heart. Immune cells from heart tissues were sorted by magnetic beads for single-cell RNA sequencing, and the subsets of macrophages were identified and analyzed. Trajectory analysis was used to explore the differentiation relationship of each macrophage subset. The differentially expressed genes (DEGs) were compared, and the related enriched pathways were identified. Single-cell regulatory network inference and clustering (SCENIC) analysis was performed to identify the potential transcription factors (TFs) which participated in this process.ResultsCardiac function temporarily decreased on Day 7 and returned to normal level on Day 35, accompanied by macrophages decreased and increased respectively. Then, we identified 7 clusters of macrophages by single-cell RNA sequencing and found two kinds of stage specific macrophages: senescence-associated macrophage (Cdkn1ahighC5ar1high) on Day 7 and interferon-associated macrophage (Ccr2highIsg15high) on Day 35. Moreover, we observed cardiac macrophages polarized over these two-time points based on M1/M2 and CCR2/major histocompatibility complex II (MHCII) expression. Finally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses suggested that macrophages on Day 7 were characterized by an inflammatory senescent phenotype with enhanced chemotaxis and inflammatory factors, while macrophages on Day 35 showed enhanced phagocytosis with reduced inflammation, which was associated with interferon-related pathways. SCENIC analysis showed AP-1 family members were associated with IR-induced macrophages changes.ConclusionWe are the first study to characterize the diversity, features, and evolution of macrophages during the early stages in an IR-induced cardiac injury animal model.

Exchange coupling interaction in sintered magnetic materials is generally isotropic. In this study, the anisotropic exchange coupling interaction was found in sintered oblate cylindrical SrFe12O19 (SrM) specimens obtained by the SrM nanopowders synthesized via a hydrothermal method. According to Henkel plots, the exchange coupling interaction between hard–hard magnetic grains was found in both as-pressed and sintered specimens. However, the exchange coupling interaction can only be found in the in-plane direction but not in the out-of-plane direction for the sintered specimens. By building a model of a grain configuration, this anisotropy of the exchange coupling interaction was ascribed to the vertically arranged plate-like SrM grains with micrometers in width but nanometers in thickness, which was confirmed by morphologies of cross sections in fractured specimens.

As an essential ecological barrier in the north of China, the grassland degradation situation in the Xilin Gol League of Inner Mongolia has remained severe in recent years. Based on data spanning from 2010 to 2022, this study utilizes a set of ten indicators, encompassing altitude, temperature, precipitation, wind speed, FVC, LAI, FRAR, NPP, ET, and above-ground biomass, to construct a Bayesian model for assessing ecological risks associated with vegetation change in the Xilin Gol League. In the case of the most crucial parameter, above-ground biomass, we integrate spectral bands, vegetation indices, and ground-truth data, employing the Random Forest algorithm to estimate the above-ground biomass in the Xilin Gol League. The study further applies the Mann-Kendall trend test and Sen's slope estimation to analyze changes in this parameter and employs it as an indicator for assessing grassland degradation. The results showed that (1) Overall, the risk level decreases gradually from the west to the east and aligns with the grassland types of the Xilin Gol League. (2) In terms of spatial and temporal distribution, medium and high-risk areas are primarily located in the western region of the Xilin Gol League, showing a distinct westward shift from 2010 to 2022. (3) Areas with higher grassland degradation risk are characterized by lower altitudes, higher temperatures, less precipitation, lower vegetation coverage, and reduced potential evapotranspiration. (4) Under the pressures of both climate warming and vegetation change, there has been a significant shift in grassland degradation risk levels in the Xilin Gol League. The application of the Bayesian model effectively reveals intricate causal relationships among various variables, such as climate, terrain, and vegetation dynamics. This study could provide guidance for the prevention and control of grassland degradation in the Xilin Gol League.

Long-term exposure to high concentrations of fine particles can cause irreversible damage to people’s health. Therefore, it is of extreme significance to conduct large-scale continuous spatial fine particulate matter (PM2.5) concentration prediction for air pollution prevention and control in China. The distribution of PM2.5 ground monitoring stations in China is uneven with a larger number of stations in southeastern China, while the number of ground monitoring sites is also insufficient for air quality control. Remote sensing technology can obtain information quickly and macroscopically. Therefore, it is possible to predict PM2.5 concentration based on multi-source remote sensing data. Our study took China as the research area, using the Pearson correlation coefficient and GeoDetector to select auxiliary variables. In addition, a long short-term memory neural network and random forest regression model were established for PM2.5 concentration estimation. We finally selected the random forest regression model (R2 = 0.93, RMSE = 4.59 μg m−3) as our prediction model by the model evaluation index. The PM2.5 concentration distribution across China in 2021 was estimated, and then the influence factors of high-value regions were explored. It is clear that PM2.5 concentration is not only related to the local geographical and meteorological conditions, but also closely related to economic and social development.

Grassland desertification stands as an ecological concern globally. It is crucial for desertification prevention and control to comprehend the variation in area and severity of desertified grassland (DGL), clarify the intensities of conversion among DGLs of different desertification levels, and explore the spatial and temporal driving factors of desertification. In this study, a Desertification Difference Index (DDI) model was constructed based on albedo-EVI to extract desertification information. Subsequently, intensity analysis, the Geo-detector model, and correlation analysis were applied to analyze the dynamics and driving factors of desertification. The results showed the following: (1) Spatially, the DGL in Xilingol exhibited a zonal distribution. Temporally, the degree of DGL decreased, with the proportion of severely and moderately desertified areas decreasing from 51.77% in 2000 to 37.23% in 2020, while the proportion of nondesertified and healthy areas increased from 17.85% in 2000 to 37.40% in 2020; (2) Transition intensities among different desertification levels were more intense during 2000–2012, stabilizing during 2012–2020; (3) Meteorological factors and soil conditions primarily drive the spatial distribution of DDI, with evapotranspiration exhibiting the most significant influence (q-value of 0.83), while human activities dominate interannual DDI variations. This study provides insights into the conversion patterns among different desertification levels and the divergent driving forces shaping desertification in both spatial and temporal dimensions in Xilingol.

Although numerous epidemiological studies have demonstrated a relationship between environmental factors and chronic diseases, there is a lack of comprehensive population health vulnerability assessment studies from the perspective of environmental exposure, population sensitivity and adaptation on a regional scale. To address this gap, this study focused on six high-mortality chronic diseases in China and constructed an exposure–sensitivity–adaptability framework-based index system using multivariate data. The constructed system effectively estimated health vulnerability for the chronic diseases. The R-square between vulnerability and mortality rates for respiratory diseases and malignant tumors exceeded 0.7 and was around 0.6 for the other four chronic diseases. In 2020, Chongqing exhibited the highest vulnerability to respiratory diseases. For heart diseases, vulnerability values exceeding 0.5 were observed mainly in northern and northeastern provinces. Vulnerability values above 0.5 were observed in Jiangsu, Shanghai, Tianjin, Shandong and Liaoning for cerebrovascular diseases and malignant tumors. Shanghai had the highest vulnerability to endogenous metabolic diseases, and Tibet exhibited the highest vulnerability to digestive system diseases. The main related factor analysis results show that high temperature and humidity, severe temperature fluctuations, serious air pollution, high proportion of middle-aged and elderly population, as well as high consumption of aquatic products, red meat and eggs increased health vulnerability, while increasing per capita educational resources helped reduce vulnerability.

Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. Using the Mann–Kendall methods, Pearson correlation, and the Geodetector, this study investigated the spatiotemporal variation and driving factors of GPP from 2000 to 2020. The results showed that (1) in terms of spatial distribution, GPP showed a trend of “low-high-low” regions, with low values for grassland and arable land and a high value for forest land. The growth trend is fast in forest areas, while the growth trend is not obvious in cultivated areas. The regions with significant growth accounted for 68.73% of the whole region. (2) The whole region shows a growth rate of 2.07 g C∙m−2∙yr−1, showing obvious seasonality, with a slow growth trend in spring and autumn and a fast growth trend in summer. (3) The driving factors of GPP spatial differentiation in the Beijing-Tianjin-Hebei region were land surface temperature, land use type, and nighttime light data, while precipitation and downward surface shortwave radiation show no strong explanatory power for the spatial differentiation of GPP, which means that these two factors have less driving force on the spatial differentiation of GPP. The interaction of LUCC with the other factors presents two-factor enhancement, while the LST interaction with the other three factors presents non-linear enhancement. This study could provide a theoretical basis for the sustainable development of the Beijing-Tianjin-Hebei Region.

The combination of natural environment changes and human activities affects the growth of grasslands. In order to quantitatively assess the causes of spatial and temporal variation of grasslands in Xilingol, this study assessed the spatial and temporal evolution patterns of grassland health based on MOD13A1 long time series Normalized Difference Vegetation Index (NDVI) data from 2000–2019 using trend analysis. The geodetector model was used to explore the dominant drivers of spatial variation in grassland NDVI, combined with 34 factors covering natural environmental changes and human disturbances over the same period. The results show that the grasslands of Xilingol showed an overall recovery trend from 2000 to 2019, with an average annual NDVI growth rate of 0.0028/a, a monthly increasing rate of 0.0005/month, and 68.06% of the grassland at an average recovery level. Moisture-dominated natural climate change factors, such as Growing Season Precipitation (Prep2), Annual Mean Water Vapor Pressure (WVP), and Annual Mean Relative Humidity (RH), were the underlying cause of grassland health changes during the study period, with the highest explanatory factor being growing season precipitation (q value of 0.59 on a multi-year average). The influence of primary production value among human activities was greater, and the explanatory factor of tertiary production value showed an increasing trend. The interactions among natural and anthropogenic factors significantly enhances their explanatory credibility for NDVI, with the type of interaction dominated by the two-factor enhancement. Risk detection of the top 10 dominant drivers in terms of q statistic were carried out to obtain the threshold range of each driver in the high zone of grassland NDVI, which can provide a scientific reference for the sustainable restoration of grassland.

Dysentery is a major infectious diseases that affects public health and the quality of life in Jiangsu Province. Since the development of the railways, the incidence of dysentery in the general population living near railway lines has increased. This research investigates the relationship between the geographical distribution of the railways and the incidence of dysentery in Jiangsu Province, and suggests a method for predicting dysentery in such areas. We used previously published literature, statistics, and known physiological properties of Shigella to analyse bacterial dysentery data for Jiangsu Province for the period from 2001 to 2012. The results indicate that railways could be indicative of the transmission of dysentery. Analysis of the incidence of dysentery in relation to the distribution of the railways using linear regression found that the incidence of dysentery in Jiangsu Province for 2001–2012 was linearly related to the buffer distance from the particular railway line. Furthermore, both regression parameters were affected by meteorological parameters, and the railway site was linearly related to the incidence of dysentery in cities along the railway.

The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km2. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery.

Although leaf area index (LAI) is one of the essential parameters employed to monitor global vegetation, no global LAI products which use a fine spatial resolution yet exist. To remedy this, we herein outline an adapted LAI retrieval algorithm which employs HuanJing-1 charge-coupled device (HJ-1 CCD) data, which was originally determined using Landsat thematic mapper (TM) data. Validation of this adapted LAI retrieval algorithm via field measurements demonstrates that errors for ∼72% of the sample sites were within a range of 0.2 for the Bashang Grassland in Hebei Province, and within 1.0 for the Taihe region in Jiangxi Province. In addition, the correlation coefficients (R) of HJ-1 LAI and ground-measured LAI data were similar to those of MODIS LAI and ground-measured LAI datasets for both regions. These results demonstrate the potential for transposing a mature Landsat LAI-retrieval algorithm to HJ-1 data. Furthermore, we generated 30 m HJ-1 LAI datasets for China for the summer of 2012. An analytical comparison between retrieved HJ-1 LAI and MODIS LAI data showed the coherence of the LAI distribution. Finally, a specific post-forest fire area was mapped using HJ-1 LAI datasets.

Monitoring the dynamics of inundation areas in wetlands over contiguous years is important because it influences wetland ecosystem monitoring. However, because the variable nature of wetlands tends to hamper monitoring change analyses, the potential for misinterpretation increases. The Kalman filter (KF) or extended Kalman filter (EKF), which uses recursive processing based on the former information, can be applied to time-series remote sensing imagery. In the experiment, a periodic triangle function of two modulated parameters is treated as the system model, and Normalized Difference Vegetation Index (NDVI) time-series data are used for the measurement model in the correction processes of the state estimation. A decision metric is computed from the mean and amplitude sequence, which results from the state estimation filter. Consequently, an optimal threshold is calculated using a minimum error thresholding algorithm based on a pre-labelled sample. NDVI time-series data from Poyang Lake, China – derived from 250-m Moderate Resolution Imaging Spectroradiometer satellite data obtained from January 2009 to December 2013 – are applied to monitor the dynamics of inundation changes. The results show that the EKF achieves satisfactory results, with 85.52% accuracy in the year 2009, while the KF has an accuracy of 84.16% during that same time.

Taking the Tibetan Plateau (TP) as a study area, we developed an algorithm to generate long-term four-level snow disaster products (1982–2012) using a new daily snow depth product with a spatial resolution of 0.05° using AVHRR archival reflectance products (AVH09C1-version4) from Land Long-Term Data Record and passive microwave snow depth products. The total classification agreement of our products reached 83.6%, improved from 69.1%. R-square reached 0.62, which showed a good agreement with field data. Based on the products, we obtained annual snow disaster results during 1982–2012. The results indicated that in 1983, 1985, 1997, 1998 and 2008, a large part of the TP suffered extremely severe snow disaster. The annual variation of light and moderate snow disaster areas is much stable than severe and extremely severe areas. After 1999, annual extremely severe areas are more stable and smaller than before. Some areas suffered severe snow disaster in 1985, 1997 and 1998, while in other years they presented a normal status. A large part of the middle-east TP suffered extremely severe snow disaster almost every year. The information within population-filtered counties was extracted to support the development of the husbandry and the urban and rural planning for government.

It is an indisputable fact that wetlands in northern China are subject to increasing pressures from climate change and other human-mediated activities, including the wetland in the Ordos Larus relictus National Nature Reserve. Dynamic monitoring the wetland's state and changes have an irreplaceable role in its protection and management but have rarely been performed systematically. In this study, the wetland land-cover changes during 1991–2014 were analyzed using three land-cover components (vegetation, soil and water) acquired through linear spectral unmixing. Then, the temporal ecological changes were predicted for the next 5–100 years using cellular automata and Markov chain models. The results showed that, under the natural and anthropogenic effects, the wetland is suffering continuous degradation. From 2000 to 2005, a dramatic change characterized by a rapid reduction in water area and a significant increase in vegetation coverage occurred. The water area decreased to its historic minimum of 1.74 km2 in 2010. The prediction results indicated that the water area will further decrease to less than 0.1 km2 over the next 35 years. Considering the steady decline in precipitation over the past 50 years, halting destructive human activities and artificially intervening with wetland management are reasonable alternatives to prevent additional degradation.

To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.

Aiming at studying the ecological security in Wenchuan, China, where the ecological system had been greatly damaged by the earthquake in 2008, an evaluation indicators system was established based on the pressure–state–response (PSR) model, and the indicators were derived from remote sensing images, meteorological data, and statistical data. Ecological security indexes in 2007, 2008, and 2013 were calculated through a comprehensive index method. Then the spatio-temporal analysis on ecological security was conducted. Particularly, the changes of ecological security were centrally analyzed concerning topography, land use types, vegetation coverage, as well as ecological PSR. It is found that the ecological security in Wenchuan presented a distinct vertical-belt distribution. It was damaged by the earthquake, however, it had been improved obviously by 2013, which still did not reach the level in 2007. Meanwhile, the epicentre, the Minjiang River and the southwestern mountains have typically regional characteristics, where the changes of ecological security were analyzed elaborately. The ecological security of these regions had been differently restored compared with 2007. However, there were still some regions with unsafe ecological security. Finally, the studied area was divided into four regions with different protection strategies and some recommendations were provided for further ecological protection.

Abstract Background Due to recent emergence, dengue is becoming one of the major public health problems in Nepal. The numbers of reported dengue cases in general and the area with reported dengue cases are both continuously increasing in recent years. However, spatiotemporal patterns and clusters of dengue have not been investigated yet. This study aims to fill this gap by analyzing spatiotemporal patterns based on monthly surveillance data aggregated at district. Methods Dengue cases from 2010 to 2014 at district level were collected from the Nepal government’s health and mapping agencies respectively. GeoDa software was used to map crude incidence, excess hazard and spatially smoothed incidence. Cluster analysis was performed in SaTScan software to explore spatiotemporal clusters of dengue during the above-mentioned time period. Results Spatiotemporal distribution of dengue fever in Nepal from 2010 to 2014 was mapped at district level in terms of crude incidence, excess risk and spatially smoothed incidence. Results show that the distribution of dengue fever was not random but clustered in space and time. Chitwan district was identified as the most likely cluster and Jhapa district was the first secondary cluster in both spatial and spatiotemporal scan. July to September of 2010 was identified as a significant temporal cluster. Conclusion This study assessed and mapped for the first time the spatiotemporal pattern of dengue fever in Nepal. Two districts namely Chitwan and Jhapa were found highly affected by dengue fever. The current study also demonstrated the importance of geospatial approach in epidemiological research. The initial result on dengue patterns and risk of this study may assist institutions and policy makers to develop better preventive strategies.

The primary goal of this paper is to discuss an integrated approach to efficiently obtaining earthquake damage information. We developed a framework to rapidly obtain earthquake damage information using post-earthquake airborne optical images. The framework is a standard process that includes data selection, preprocessing, damage factor identification, damage factor evaluation and the development of an earthquake damage information map. We can obtain damage information on severely affected regions using this framework, which will aid in planning rescue and rehabilitation efforts following disasters. We used the integrated approach to obtain damage information using the Lushan earthquake (magnitude 7.0, 20 April 2013) as a case study. The result were as follows: (1) 644 collapsed buildings and 4599 damaged buildings accounted for 13.90% and 96.24%, respectively, of the total number of buildings in the study area; (2) 334 landslides (total area of 691,674.5 m2) were detected and were found at greater probabilities at elevations of 1400–1500 m and higher slope; (3) no secondary disasters, such as barrier lakes, were detected; (4) 15 damaged sections (total of 306 m) were detected in the lifelines, and road sections that are at a high risk of damage (total of 2.4 km) were identified; and (5) key structures, including Yuxi River Dam and three bridges, were intact. Integrating the earthquake damage factor information generated a comprehensive Lushan earthquake damage information map. The integrated approach was proven to be effective using the Lushan earthquake as a case study and can be applied to assess earthquake damage to facilitate efficient rescue efforts.

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research.

Land degradation reflected by vegetation is a commonly used practice to monitor desertification. To retrieve important information for ecosystem management accurate assessment of desertification is necessary. The major factors that drive vegetation dynamics in arid and semi-arid regions are climate and anthropogenic activities. Progression of desertification is expected to exacerbate under future climate change scenarios, through precipitation variability, increased drought frequency and persistence of dry conditions. This study examined spatiotemporal vegetation dynamics in arid regions of Sindh, Pakistan, using annual and growing season Normalized Difference Vegetation Index (NDVI) data from 2000 to 2017, and explored the climatic and anthropogenic effects on vegetation. Results showed an overall upward trend (annual 86.71% and growing season 82.7%) and partial downward trend (annual 13.28% and growing season 17.3%) in the study area. NDVI showed the highest significant increase in cropland region during annual, whereas during growing season the highest significant increase was observed in savannas. Overall high consistency in future vegetation trends in arid regions of Sindh province is observed. Stable and steady development region (annual 48.45% and growing 42.80%) dominates the future vegetation trends. Based on the Hurst exponent and vegetation dynamics of the past, improvement in vegetation cover is predicted for a large area (annual 44.49% and growing 30.77%), and a small area is predicted to have decline in vegetation activity (annual 0.09% and growing 3.04%). Results revealed that vegetation growth in the study area is a combined result of climatic and anthropogenic factors; however, in the future multi-controls are expected to have a slightly larger impact on annual positive development than climate whereas positive development in growing season is more likely to continue in future under the control of climate variability.

Global land degradation and sustainable development has become a serious challenge for the terrestrial ecosystems. Shrub plays a crucial role in global ecosystem protection, ecological reconstruction, which is especially important in arid and semi-arid sandland ecosystem. Shrub above ground biomass (AGB) is a proxy of carbon sequestration capacity. Shrub AGB in Mu Us Sandland was estimated using different methods based on Landsat Thematic Mapper (TM) data, topography data, combined with in situ survey data. Linear regression model, multiple stepwise regression model, machine learning model and geometric optical model were used to estimate shrub biomass in combination with in situ data, respectively and their effects were validated and compared. Results showed that shrub AGB predicted from one multiple stepwise regression model with Ratio Vegetation Index (RVI) and Brightness from K-T transformation as input variables reached highest accuracy. For both high and low shrub coverage regions, shrub AGB distribution maps derived from this multiple stepwise regression model achieved higher precision. All these findings will provide a scientific support for ecological sustainable development in eco-vulnerable ecosystems. Keywords: Above ground biomass (AGB), Arid and semi-arid ecosystem, Multiple stepwise regression, Machine learning, Geometric optical model

This study analysed the response in the size of wetlands to a sudden turn from drought to flood along the Yangtze River system in China. The wetlands and their spatial distributions in the provinces of Hubei, Hunan, Jiangxi, Anhui and Jiangsu for three periods (April and May 2010, April and May 2011 and June and July 2011) were identified by the artificial visual interpretation of charge-coupled device images obtained in 2010 and 2011 by the HJ-1A and HJ-1B satellites. The sizes of the wetland areas of all five provinces in the three periods were 30,087.10, 23,427.68 and 31,346.49 km2, respectively, with the largest changes occurring in Jiangxi. Changes in the mean standardized precipitation index (SPI) and its patterns of spatial distribution had similar trends to the changes in the size of the wetlands of each province in the three periods. SPI was positively correlated (R2 = 0.360, p < 0.05) with changes in the proportions of the wetlands during the drought. The mean SPI of the final period after the flood was positively correlated (R2 = 0.406, p < 0.01) with changes in the proportions of wetlands. Poyang Lake and Dongting Lake were impacted most by the flood, and Hongze Lake and Caohu Lake were the least affected.

Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., continental and global) is problematic because existing LiDAR instruments are still data-limited and expensive. This paper proposes a new approach based on an artificial neural network (ANN) for modeling of forest canopy heights over the China continent. Our model ingests spaceborne LiDAR metrics and multiple geospatial predictors including climatic variables (temperature and precipitation), forest type, tree cover percent and land surface reflectance. The spaceborne LiDAR instrument used in the study is the Geoscience Laser Altimeter System (GLAS), which can provide within-footprint forest canopy heights. The ANN was trained with pairs between spatially discrete LiDAR metrics and full gridded geo-predictors. This generates valid conjugations to predict heights over the China continent. The ANN modeled heights were evaluated with three different reference data. First, field measured tree heights from three experiment sites were used to validate the ANN model predictions. The observed tree heights at the site-scale agreed well with the modeled forest heights (R = 0.827, and RMSE = 4.15 m). Second, spatially discrete GLAS observations and a continuous map from the interpolation of GLAS-derived tree heights were separately used to evaluate the ANN model. We obtained R of 0.725 and RMSE of 7.86 m and R of 0.759 and RMSE of 8.85 m, respectively. Further, inter-comparisons were also performed with two existing forest height maps. Our model granted a moderate agreement with the existing satellite-based forest height maps (R = 0.738, and RMSE = 7.65 m (R2 = 0.52, and RMSE = 8.99 m). Our results showed that the ANN model developed in this paper is capable of estimating forest heights over the China continent with a satisfactory accuracy. Forth coming research on our model will focus on extending the model to the estimation of woody biomass.

Flood disasters are closely associated with an increased risk of infection, particularly from waterborne diseases. Most studies of waterborne diseases have relied on the direct determination of pathogens in contaminated water to assess disease risk. In contrast, this study aims to use an indirect assessment that employs a back propagation neural network (BPNN) for modelling diarrheal outbreaks using data from remote sensing and dissolved-oxygen (DO) measurements to reduce cost and time. Our study area is in Ayutthaya province, which was very severely affected by the catastrophic 2011 Thailand flood. BPNN was used to model the relationships among the parameters of the flood and the water quality and the risk of people becoming infected. Radarsat-2 scenes were utilized to estimate flood area and duration, while the flood water quality was derived from the interpolation of DO samples. The risk-ratio function was applied to the diarrheal morbidity to define the level of outbreak detection and the outbreak periods. Tests of the BPNN prediction model produced high prediction accuracy of diarrheal-outbreak risk with low prediction error and a high degree of correlation. With the promising accuracy of our approach, decision-makers can plan rapid and comprehensively preventive measures and countermeasures in advance.

Fine-scale population distribution is increasingly becoming a research hotspot owing to its high demand in many applied fields. It is of great significance in urban emergency response, disaster assessment, resource allocation, urban planning, market research, and transportation route design. This study employed land cover, building address, and housing price data, and high-resolution stereo pair remote sensing images to simulate fine-scale urban population distribution. We firstly extracted the residential zones on the basis of land cover and Google Earth data, combined them with building information including address and price. Then, we employed the stereo pair analysis method to obtain the building height on the basis of ZY3-02 high-resolution satellite data and transform the building height into building floors. After that, we built a sophisticated, high spatial resolution model of population density. Finally, we evaluated the accuracy of the model using the survey data from 12 communities in the study area. Results demonstrated that the proposed model for spatial fine-scale urban population products yielded more accurate small-area population estimation relative to high-resolution gridded population surface (HGPS). The approach proposed in this study holds potential to improve the precision and automation of high-resolution population estimation.

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

Accurate information regarding forest volume plays an important role in estimating afforestation, timber harvesting, and forest ecological services. Traditionally, operations on forest growing stock volume using field measurements are labor-intensive and time-consuming. Recently, remote sensing technology has emerged as a time-cost efficient method for forest inventory. In the present study, we have adopted three procedures, including samples expanding, feature selection, and results generation and evaluation. Extrapolating the samples from Light Detection and Ranging (LiDAR) scanning is the most important step in satisfying the requirement of sample size for nonparametric methods operation and result in accuracy improvement. Besides, mean decrease Gini (MDG) methodology embedded into Random Forest (RF) algorithm served as a selector for feature measure; afterwards, RF and K-Nearest Neighbor (KNN) were adopted in subsequent forest volume prediction. The results show that the retrieval of Forest volume in the entire area was in the range of 50−360 m3/ha, and the results from the two models show a better consistency while using the sample combination extrapolated by the optimal threshold value (2 × 10−4), leading to the best performances of RF (R2 = 0.618, root mean square error, RMSE = 43.641 m3/ha, mean absolute error, MAE = 33.016 m3/ha), followed by KNN (R2 = 0.617, RMSE = 43.693 m3/ha, MAE = 32.534 m3/ha). The detailed analysis that is discussed in the present paper clearly shows that expanding image-derived LiDAR samples helps in refining the prediction of regional forest volume while using satellite data and nonparametric models.

The ultimate goal of our multi-article series is to demonstrate the Allometric Scaling and Resource Limitation (ASRL) approach for mapping tree heights and biomass. This third article tests the feasibility of the optimized ASRL model over China at both site (14 meteorological stations) and continental scales. Tree heights from the Geoscience Laser Altimeter System (GLAS) waveform data are used for the model optimizations. Three selected ASRL parameters (area of single leaf, α; exponent for canopy radius, η; and root absorption efficiency, γ) are iteratively adjusted to minimize differences between the references and predicted tree heights. Key climatic variables (e.g., temperature, precipitation, and solar radiation) are needed for the model simulations. We also exploit the independent GLAS and in situ tree heights to examine the model performance. The predicted tree heights at the site scale are evaluated against the GLAS tree heights using a two-fold cross validation (RMSE = 1.72 m; R2 = 0.97) and bootstrapping (RMSE = 4.39 m; R2 = 0.81). The modeled tree heights at the continental scale (1 km spatial resolution) are compared to both GLAS (RMSE = 6.63 m; R2 = 0.63) and in situ (RMSE = 6.70 m; R2 = 0.52) measurements. Further, inter-comparisons against the existing satellite-based forest height maps have resulted in a moderate degree of agreements. Our results show that the optimized ASRL model is capable of satisfactorily retrieving tree heights over continental China at both scales. Subsequent studies will focus on the estimation of woody biomass after alleviating the discussed limitations.

In recent years, air pollution has become an important public health concern. The high concentration of fine particulate matter with diameter less than 2.5 µm (PM2.5) is known to be associated with lung cancer, cardiovascular disease, respiratory disease, and metabolic disease. Predicting PM2.5 concentrations can help governments warn people at high risk, thus mitigating the complications. Although attempts have been made to predict PM2.5 concentrations, the factors influencing PM2.5 prediction have not been investigated. In this work, we study feature importance for PM2.5 prediction in Tehran’s urban area, implementing random forest, extreme gradient boosting, and deep learning machine learning (ML) approaches. We use 23 features, including satellite and meteorological data, ground-measured PM2.5, and geographical data, in the modeling. The best model performance obtained was R2 = 0.81 (R = 0.9), MAE = 9.93 µg/m3, and RMSE = 13.58 µg/m3 using the XGBoost approach, incorporating elimination of unimportant features. However, all three ML methods performed similarly and R2 varied from 0.63 to 0.67, when Aerosol Optical Depth (AOD) at 3 km resolution was included, and 0.77 to 0.81, when AOD at 3 km resolution was excluded. Contrary to the PM2.5 lag data, satellite-derived AODs did not improve model performance.

A methodology to generate spatially continuous fields of tree heights with an optimized Allometric Scaling and Resource Limitations (ASRL) model is reported in this first of a multi-part series of articles. Model optimization is performed with the Geoscience Laser Altimeter System (GLAS) waveform data. This methodology is demonstrated by mapping tree heights over forested lands in the continental USA (CONUS) at 1 km spatial resolution. The study area is divided into 841 eco-climatic zones based on three forest types, annual total precipitation classes (30 mm intervals) and annual average temperature classes (2 °C intervals). Three model parameters (area of single leaf, α, exponent for canopy radius, η, and root absorption efficiency, γ) were selected for optimization, that is, to minimize the difference between actual and potential tree heights in each of the eco-climatic zones over the CONUS. Tree heights predicted by the optimized model were evaluated against GLAS heights using a two-fold cross validation approach (R2 = 0.59; RMSE = 3.31 m). Comparison at the pixel level between GLAS heights (mean = 30.6 m; standard deviation = 10.7) and model predictions (mean = 30.8 m; std. = 8.4) were also performed. Further, the model predictions were compared to existing satellite-based forest height maps. The optimized ASRL model satisfactorily reproduced the pattern of tree heights over the CONUS. Subsequent articles in this series will document further improvements with the ultimate goal of mapping tree heights and forest biomass globally.

The ultimate goal of this multi-article series is to develop a methodology to generate continuous fields of tree height and biomass. The first paper demonstrated the need for Allometric Scaling and Resource Limitation (ASRL) model optimization and its ability to generate spatially continuous fields of tree heights over the continental USA at coarse (1 km) spatial resolution. The objective of this second paper is to provide an assessment of that approach at site scale, specifically at 12 FLUXNET sites where more accurate data are available. Estimates of tree heights from the Geoscience Laser Altimeter System (GLAS) waveform data are used for model optimization. Amongst the five possible GLAS metrics that are representative of tree heights, the best metric is selected based on how closely the metric resembles field-measured and Laser Vegetation Imaging Sensor tree heights. In the optimization process, three parameters of the ASRL model (area of single leaf, α; exponent for canopy radius, η; and root absorption efficiency, γ) are simultaneously adjusted to minimize the difference between model predictions and observations at the study sites (distances to valid GLAS footprints ≤ 10 km). Performance of the optimized ASRL model was evaluated through comparisons to the best GLAS metric of tree height using a two-fold cross validation approach (R2 = 0.85; RMSE = 1.81 m) and a bootstrapping approach (R2 = 0.66; RMSE = 2.60 m). The optimized model satisfactorily performed at the site scale, thus corroborating results presented in part one of this series. Future investigations will focus on generalizing these results and extending the model formulation using similar allometric concepts for the estimation of woody biomass.

Forest canopy height plays an important role in forest management and ecosystem modeling. There are a variety of techniques employed to map forest height using remote sensing data but it is still necessary to explore the use of new data and methods. In this study, we demonstrate an approach for mapping canopy heights of poplar plantations in plain areas through a combination of stereo and multispectral data from China’s latest civilian stereo mapping satellite ZY3-02. First, a digital surface model (DSM) was extracted using photogrammetry methods. Then, canopy samples and ground samples were selected through manual interpretation. Canopy height samples were obtained by calculating the DSM elevation differences between the canopy samples and ground samples. A regression model was used to correlate the reflectance of a ZY3-02 multispectral image with the canopy height samples, in which the red band and green band reflectance were selected as predictors. Finally, the model was extrapolated to the entire study area and a wall-to-wall forest canopy height map was obtained. The validation of the predicted canopy height map reported a coefficient of determination (R2) of 0.72 and a root mean square error (RMSE) of 1.58 m. This study demonstrates the capacity of ZY3-02 data for mapping the canopy height of pure plantations in plain areas.

Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data.

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

Burnt forest recovery is normally monitored with a time-series analysis of satellite data because of its proficiency for large observation areas. Traditional methods, such as linear correlation plotting, have been proven to be effective, as forest recovery naturally increases with time. However, these methods are complicated and time consuming when increasing the number of observed parameters. In this work, we present a random forest variable importance (RF-VIMP) scheme called multilevel RF-VIMP to compare and assess the relationship between 36 spectral indices (parameters) of burnt boreal forest recovery in the Great Xing’an Mountain, China. Six Landsat images were acquired in the same month 0, 1, 4, 14, 16, and 20 years after a fire, and 39,380 fixed-location samples were then extracted to calculate the effectiveness of the 36 parameters. Consequently, the proposed method was applied to find correlations between the forest recovery indices. The experiment showed that the proposed method is suitable for explaining the efficacy of those spectral indices in terms of discrimination and trend analysis, and for showing the satellite data and forest succession dynamics when applied in a time series. The results suggest that the tasseled cap transformation wetness, brightness, and the shortwave infrared bands (both 1 and 2) perform better than other indices for both classification and monitoring.

With the economic growth and increasing urbanization in the last three decades, the air quality over China has continuously degraded, which poses a great threat to human health. The concentration of fine particulate matter (PM2.5) directly affects the mortality of people living in the polluted areas where air quality is poor. The Beijing-Tianjin-Hebei (BTH) region, one of the well organized urban regions in northern China, has suffered with poor air quality and atmospheric pollution due to recent growth of the industrial sector and vehicle emissions. In the present study, we used the back propagation neural network model approach to estimate the spatial distribution of PM2.5 concentration in the BTH region for the period January 2014–December 2016, combining the satellite-derived aerosol optical depth (S-DAOD) and meteorological data. The results were validated using the ground PM2.5 data. The general method including all PM2.5 training data and 10-fold cross-method have been used for validation for PM2.5 estimation (R2 = 0.68, RMSE = 20.99 for general validation; R2 = 0.54, RMSE = 24.13 for cross-method validation). The study provides a new approach to monitoring the distribution of PM2.5 concentration. The results discussed in the present paper will be of great help to government agencies in developing and implementing environmental conservation policy.

Increasing trends of urbanization lead to vegetation degradation in big cities and affect the urban thermal environment. This study investigated (1) the cooling effect of urban green space spatial patterns on Land Surface Temperature (LST); (2) how the surrounding environment influences the green space cool islands (GCI), and vice versa. The study was conducted in two Asian capitals: Beijing, China and Islamabad, Pakistan by utilizing Gaofen-1 (GF-1) and Landsat-8 satellite imagery. Pearson’s correlation and normalized mutual information (NMI) were applied to investigate the relationship between green space characteristics and LST. Landscape metrics of green spaces including Percentage of Landscape (PLAND), Patch Density (PD), Edge Density (ED), and Landscape Shape Index (LSI) were selected to calculate the spatial patterns of green spaces, whereas GCI indicators were defined by Green Space Range (GR), Temperature Difference (TD), and Temperature Gradient (TG). The results indicate that both vegetation composition and configuration influence LST distributions; however, vegetation composition appeared to have a slightly greater effect. The cooling effect can be produced more effectively by increasing green space percentage, planting trees in large patches with equal distribution, and avoiding complex-shaped green spaces. The GCI principle indicates that LST can be decreased by increasing the green space area, increasing the water body fraction, or by decreasing the fraction of impervious surfaces. GCI can also be strengthened by decreasing the fraction of impervious surfaces and increasing the fraction of water body or vegetation in the surrounding environment. The cooling effect of vegetation and water could be explained based on their thermal properties. Beijing has already enacted the green-wedge initiative to increase the vegetation canopy. While designing the future urban layout of Islamabad, the construction of artificial lakes within the urban green spaces would also be beneficial, as is the case with Beijing.

Andrews first proposed an equation to visualize the structures within data in 1972. Since then, this equation has been used for data transformation and visualization in a wide variety of fields. However, it has yet to be applied to satellite image data. The effect of unwanted, or impure, pixels occurring in these data varies with their distribution in the image; the effect is greater if impurity pixels are included in a classifier’s training set. Andrews’ curves enable the interpreter to select outlier or impurity data that can be grouped into a new category for classification. This study overcomes the above-mentioned problem and illustrates the novelty of applying Andrews’ plots to satellite image data, and proposes a robust method for classifying the plots that combines Dempster-Shafer theory with fuzzy set theory. In addition, we present an example, obtained from real satellite images, to demonstrate the application of the proposed classification method. The accuracy and robustness of the proposed method are investigated for different training set sizes and crop types, and are compared with the results of two traditional classification methods. We find that outlier data are easily eliminated by examining Andrews’ curves and that the proposed method significantly outperforms traditional methods when considering the classification accuracy.

PurposeOral fluoropyrimidine (S-1, capecitabine) has been considered as an important part of various regimens. We aimed to evaluate the efficacy and safety of S-1-based therapy versus capecitabine -based therapy in gastrointestinal cancers.MethodsEligible studies were identified from Pubmed, EMBASE. Additionally, abstracts presented at American Society of Clinical Oncology (ASCO) conferences held between 2000 and 2013 were searched to identify relevant clinical trials. The outcome included overall survival (OS), progression-free survival (PFS), overall response rate (ORR), disease control rate (DCR) and advent events.ResultsA total of 6 studies (4 RCTs and 2 retrospective analysis studies) containing 790 participants were included in this meta-analysis, including 401 patients in the S-1-based group and 389 patients in the capecitabine-based group. Results of our meta-analysis indicated that S-1-based and capecitabine-based regimens showed very similar efficacy in terms of PFS (HR 0.92, 95% CI 0.78-1.09, P = 0.360), OS (HR 1.01, 95% CI 0.84-1.21, P = 0.949), ORR (HR 1.04, 95% CI 0.87-1.25, P = 0.683) and DCR (HR 1.02, 95% CI 0.94-1.10, P = 0.639). There was also no significant difference in toxicity between regimens other than mild more hand-foot syndrome in capecitabine-based regimens.ConclusionBoth the S-1-based and capecitabine-based regimens are equally active and well tolerated, and have the potential of backbone chemotherapy regimen in further studies of gastrointestinal cancers.

BACKGROUND: To date, the association between phospholipase C epsilon 1 (PLCE1) rs2274223 A>G and risk of digestive tract cancer (DTC) remains inconclusive. To derive a more precise estimation of the association, we conducted a meta-analysis on all eligible case-control studies involving 8281 cases and 10,532 controls. METHODS: A comprehensive search was conducted to identify all eligible studies of PLCE1 rs2274223 polymorphism and digestive tract cancer risk. The pooled odds ratio (OR) and the 95% confidence interval (95% CI) were calculated using a fixed or random effect model. Heterogeneity, publication bias, and sensitivity analysis were also explored. RESULTS: Overall, the PLCE1 rs2274223 A>G polymorphism was associated with risk of DTC in all genetic models (GA vs. AA: OR = 1.21, 95% CI = 1.14-1.29, PG polymorphism was significantly associated with increased risk of DTC, especially among Asian populations. Due to some minor limitations, our findings should be confirmed in further studies.

BACKGROUND: Myelin transcription factor 1 (MYT1) and its homologue MYT1-like (MYT1L) are the two main members of MYT/NZF family transcription factors, which are highly related, share a high degree of identity and show similar regulatory functions in neural development. There are evidences from several cytology experiments showing that MYT1 is associated with carcinoma. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we genotyped 944 surgically resected gastric cancer patients by the SNaPshot method to explore the association of MYT1L rs17039396 polymorphism with survival of gastric cancer in a Chinese population. We found that cardia cancer patients carrying MYT1L rs17039396 GG genotype survived for a significantly shorter time than those carrying the GA genotype. This significance was enhanced in the dominant model (GG vs. GA/AA, log-rank P = 0.001), suggesting a potential protect role of the variant A allele. Multivariate Cox regression analyses showed that the AG/GG genotypes were associated with a significantly decreased risk of death from gastric cancer (adjusted hazard ratio (HR) = 0.57, 95% confidence interval (CI) = 0.40-0.81). Stratification analyses further showed that such protective effect was statistically significant in subgroups of patients with tumor size ≤5 cm (adjusted HR = 0.34, 95%CI = 0.19-0.64), well-moderate gastric cancer (adjusted HR = 0.59, 95%CI = 0.35-0.98), no lymph-node metastasis (adjusted HR = 0.49, 95%CI = 0.31-0.76), no distant metastasis (adjusted HR = 0.59, 95%CI = 0.41-0.84). CONCLUSIONS/SIGNIFICANCE: In conclusion, these data represents the first demonstration that MYT1L rs17039396 variants could indentified as a favorable prognostic indicator for gastric cancer, particularly among the cardia gastric cancer. Further validation in other larger studies with different ethnic populations and functional evaluations are needed.