Retrieval Of Environmental Parameters Over Water Areas Using MODIS Data

In this study, new and simple algorithms to retrieve cirrus cloud, sediment and aerosol optical depth (AOD) from Moderate Resolution Radiospectrometer (MODIS) imagery is suggested and demonstrated. Techniques used in this thesis are totally new and never been used before by others researchers. The presences of cirrus cloud have been detected utilizing gradient technique algorithm. The algorithm is based on the gradient connecting the 1.38 and 1.24 μm lines of the log– log graph of apparent reflectance against the MODIS wavelength. This algorithm has been tested over the Yellow Sea, Gulf of Martaban, Mediterranean Sea and Atlantic Ocean. The result of this algorithm is then compared with the band ratio technique and the accuracy was determined with percentage difference below 10%. The presence of sediment reflectance in the MODIS imagery not only saturate the ocean color channels, but also enhance the reflectance of near infrared channels used in the atmospheric correction algorithm. This leads to the overestimation of the aerosol contributions and underestimation of the derived water-leaving reflectance in the visible channels. In this study, a new and simple method to detect and mask the presence of sediment reflectance in the MODIS imagery is proposed. The algorithm is based on the difference of the gradient of the line connecting the 0.47 and 2.13 μm channels and 0.47 and 0.66 μm channels of a log–log graph of the apparent reflectance values against their MODIS wavelengths. A sample results over Yellow Sea, Gulf of Martaban and Arabian Sea was demonstrated. The accuracy of the algorithm was tested and determined

DISCRIMINATING SEDIMENT AND CLEAR WATER OVER CAOSTAL WATER USING GD TECHNIQUE

Currently two algorithms are being used routinely by the MODIS Atmosphere and Ocean Team in order to distinguish sediment influence and clear water pixels over turbid water area. These two algorithms require complicated computational analyses. In this paper, a simple algorithm based on empirical technique to detect the sediment-influenced pixels over coastal waters is proposed as an alternative to these two algorithms. This study used apparent reflectance acquired from MODIS L1B product. This algorithm is based on the gradient difference of the line connecting the 0.47- and 1.24-qm channels and 0.47- and 0.66-qm channels of a log-log graph of the apparent reflectance values against MODIS wavelengths. Over clear-water areas (deep blue sea), the 0.47-, 0.66- and 1.24-qm channels fitted very well in line with correlation R > 0.99. Over turbid waters, a substantial increase of 0.66 qm in the reflectance leads to a low correlation value. By computing the difference between the gradient of the line connecting 0.47 and 0.66 qm and the gradient of the line connecting 0.47 and 1.24 qm, the threshold to discriminate turbid and shallow coastal waters from clear-water pixels can be obtained. If the gradient difference is greater than 0, the pixels were then marked as sediment-influenced pixels. This proposed algorithm works well for MODIS Terra and Aqua sensor. The comparison of this algorithm with an established algorithm also showed a good agreement

Euphotic Depth Zone Variation in Peninsular Malaysia Maritime

This study is conducted in Peninsular Malaysia maritime to investigate the euphotic zone depth (Zeu) variation and the possible suspended matter that may contribute to the variation. The Zeu data were acquired from the MODIS Aqua satellite from November 2002 to September 2013. The result shows that the Zeu along the Malaysia maritime are highly seasonal-dependent. The lowest Zeu values are observed during the northeast monsoon season (NEMS) in the east coast Peninsular Malaysia and during the southwest monsoon season (SWMS) for the west coast area. Chlorophyll- a (Chl-a) and colored dissolved organic matter (CDOM) are found to be the contributing factors for the coastal line and open water area. While, sediment only contributes to the area located along the coastal line where lower Zeu values are observed

A Comparison between Ratio and Gradient Technique in Discriminating Cirrus Clouds from Tropospheric Aerosols over Water in MODIS Data

This study aims to compare between ratio technique (RT) and gradient technique (GT) to distinguish cirrus cloud from tropospheric aerosol over water in MODIS data. Both techniques make use of 1.375 µm and 1.240 µm band and are applied to five different scenes. The outcomes from both techniques are compared using an error matrix in which revealing that the GT has a very high agreement with RT in distinguishing cirrus cloud from tropospheric aerosol in MODIS data

Naïve Bayes Classification Of High-Resolution Aerial Imagery

In this study, the performance of Naïve Bayes classification on a high-resolution aerial image captured from a UAV-based remote sensing platform is investigated. K-means clustering of the study area is initially performed to assist in selecting the training pixels for the Naïve Bayes classification. The Naïve Bayes classification is performed using linear and quadratic discriminant analyses and by making use of training set sizes that are varied from 10 through 100 pixels. The results show that the 20 training set size gives the highest overall classification accuracy and Kappa coefficient for both discriminant analysis types. The linear discriminant analysis with 94.44% overall classification accuracy and 0.9395 Kappa coefficient is found higher than the quadratic discriminant analysis with 88.89% overall classification accuracy and 0.875 Kappa coefficient. Further investigations carried out on the producer accuracy and area size of individual classes show that the linear discriminant analysis produces a more realistic classification compared to the quadratic discriminant analysis particularly due to limited homogenous training pixels of certain objects

Monitoring Land Cover Changes In The Tropics Using Satellite Remote Sensing Data

Changes in land cover are inevitable phenomena that occur in all parts of the world. Land cover changes can occur due to natural phenomena that include runoff, soil erosion and sedimentation besides man-made phenomena that include deforestation, urbanization and conversion of land covers to suit human needs. Several works on change detection have been carried out elsewhere, however there were lack of effort in analyzing the issues that affect the performance of existing change detection techniques. The study presented in this paper aims to detect changes of land covers by using remote sensing satellite data. The study involves detection of land cover changes using remote sensing techniques. This makes use satellite data taken at different times over a particular area of interest. The data has resolution of 30 m and records surface reflectance at approximately 0.4 to 0.7 micrometers wavelengths. The study area is located in Selangor, Malaysia and occupied with tropical land covers including coastal swamp water, sediment plumes, urban, industry, water, bare land, cleared land, oil palm, rubber and coconut. Initially, region of interests (ROI) were drawn on each of the land covers in order to extract the training pixels. Landsat satellite bands 1, 2, 3, 4, 5 and 7 were then used as the input for the three supervised classification methods namely Support Vector Machine (SVM), Maximum Likelihood (ML) and Neural Network (NN). Different sizes of training pixels were used as the input for the classification methods so that the performance can be better understood. The accuracy of the classifications was then assessed by analyzing the classifications with a set of reference pixels using a confusion matrix. The classification methods were then used to identify the conversion of land cover from year 2000 to 2005 within the study area. The outcomes of the land cover change detection were reported in terms quantitative and qualitative analyses. The study shows that SVM gives a more accurate and realistic land cover change detection compared to ML and NN mainly due to not being much influenced by the size of the training pixels. The findings of the study serve as important input for decision makers in managing natural resources and environment in the tropics systematically and efficiently

DEVELOPMENT OF REGIONAL TSS ALGORITHM OVER PENANG USING MODIS TERRA (250 M) SURFACE REFLECTANCE PRODUCT

Total suspended sediment (TSS) plays a significant role in the environment. Many researchers show that TSS has a high correlation with the red portion of the visible light spectrum. The correlation is highly dependent on geography of the study area. The aim of this study was to develop specific algorithms utilizing corrected MODIS Terra 250-m surface reflectance (Rrs) product (MOD09) to map TSS over the Penang coastal area. Field measurements of TSS were performed during two cruise trips that were conducted on 8 December 2008 and 29 January 2010 over the Penang coastal area. The relationship between TSS and the surface reflectance of MOD09 was analysed using regression analysis. The developed algorithm showed that Rrs are highly correlated with the in-situ TSS with R2 is 0.838. The result shows that the Rrs product could be used to estimate TSS over the Penang area

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Discriminating sediment and clear water over coastal water using GD technique

Currently two algorithms are being used routinely by the MODIS Atmosphere and Ocean Team in order to distinguish sediment influence and clear water pixels over turbid water area. These two algorithms require complicated computational analyses. In this paper, a simple algorithm based on empirical technique to detect the sediment-influenced pixels over coastal waters is proposed as an alternative to these two algorithms. This study used apparent reflectance acquired from MODIS L1B product. This algorithm is based on the gradient difference of the line connecting the 0.47- and 1.24-μm channels and 0.47- and 0.66-μm channels of a log-log graph of the apparent reflectance values against MODIS wavelengths. Over clear-water areas (deep blue sea), the 0.47-, 0.66- and 1.24-μm channels fitted very well in line with correlation R > 0.99. Over turbid waters, a substantial increase of 0.66 μm in the reflectance leads to a low correlation value. By computing the difference between the gradient of the line connecting 0.47 and 0.66 μm and the gradient of the line connecting 0.47 and 1.24 μm, the threshold to discriminate turbid and shallow coastal waters from clear-water pixels can be obtained. If the gradient difference is greater than 0, the pixels were then marked as sediment-influenced pixels. This proposed algorithm works well for MODIS Terra and Aqua sensor. The comparison of this algorithm with an established algorithm also showed a good agreement