scholarly journals Techniques of SNP Genotyping and Remote Sensing Applied for Elucidation of the Contribution of Polygene and Environmental Factors to Inter-individual Variation in Skin Pigmentation Phenotype

2005 ◽  
Vol 24 (4) ◽  
pp. 483-486 ◽  
Author(s):  
Sumiko Anno ◽  
Susumu Kudo ◽  
Keita Hamasaki ◽  
Kazunari Matsumura ◽  
Takushi Yamamoto ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Individual Variation ◽  
Skin Pigmentation ◽  
Snp Genotyping ◽  
Pigmentation Phenotype

Association of genetic variants with skin pigmentation phenotype among populations of west Maharashtra, India

2016 ◽  
Vol 28 (5) ◽  
pp. 610-618 ◽  
Author(s):  
Manjari Jonnalagadda ◽  
Heather Norton ◽  
Shantanu Ozarkar ◽  
Shaunak Kulkarni ◽  
Richa Ashma
Keyword(s):  
Genetic Variants ◽  
Skin Pigmentation ◽  
Pigmentation Phenotype

scholarly journals A Machine Learning Approach for Estimating the Trophic State of Urban Waters Based on Remote Sensing and Environmental Factors

2021 ◽  
Vol 13 (13) ◽  
pp. 2498
Author(s):  
Shijie Zhu ◽  
Jingqiao Mao
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Water Temperature ◽  
Trophic State ◽  
Trophic Levels ◽  
Water Bodies ◽  
Trophic State Index ◽  
Urban Water ◽  
Estimation Model ◽  
Input Variables

To improve the accuracy of remotely sensed estimates of the trophic state index (TSI) of inland urban water bodies, key environmental factors (water temperature and wind field) were considered during the modelling process. Such environmental factors can be easily measured and display a strong correlation with TSI. Then, a backpropagation neural network (BP-NN) was applied to develop the TSI estimation model using remote sensing and environmental factors. The model was trained and validated using the TSI quantified by five water trophic indicators obtained for the period between 2018 and 2019, and then we selected the most appropriate combination of input variables according to the performance of the BP-NN. Our results demonstrate that the optimal performance can be obtained by combining the water temperature and single-band reflection values of Sentinel-2 satellite imagery as input variables (R2 = 0.922, RMSE = 3.256, MAPE = 2.494%, and classification accuracy rate = 86.364%). Finally, the spatial and temporal distribution of the aquatic trophic state over four months with different trophic levels was mapped in Gongqingcheng City using the TSI estimation model. In general, the predictive maps based on our proposed model show significant seasonal changes and spatial characteristics in the water trophic state, indicating the possibility of performing cost-effective, RS-based TSI estimation studies on complex urban water bodies elsewhere.

scholarly journals An Approach to Minimize Atmospheric Correction Error and Improve Physics-Based Satellite-Derived Bathymetry in a Coastal Environment

2020 ◽  
Vol 12 (17) ◽  
pp. 2752
Author(s):  
Christopher O. Ilori ◽  
Anders Knudby
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Complete Solution ◽  
Atmospheric Correction ◽  
Florida Keys ◽  
Radiative Transfer Model ◽  
Inversion Method ◽  
Transfer Model ◽  
Landsat 8 ◽  
Depth Range

Physics-based radiative transfer model (RTM) inversion methods have been developed and implemented for satellite-derived bathymetry (SDB); however, precise atmospheric correction (AC) is required for robust bathymetry retrieval. In a previous study, we revealed that biases from AC may be related to imaging and environmental factors that are not considered sufficiently in all AC algorithms. Thus, the main aim of this study is to demonstrate how AC biases related to environmental factors can be minimized to improve SDB results. To achieve this, we first tested a physics-based inversion method to estimate bathymetry for a nearshore area in the Florida Keys, USA. Using a freely available water-based AC algorithm (ACOLITE), we used Landsat 8 (L8) images to derive per-pixel remote sensing reflectances, from which bathymetry was subsequently estimated. Then, we quantified known biases in the AC using a linear regression that estimated bias as a function of imaging and environmental factors and applied a correction to produce a new set of remote sensing reflectances. This correction improved bathymetry estimates for eight of the nine scenes we tested, with the resulting changes in bathymetry RMSE ranging from +0.09 m (worse) to −0.48 m (better) for a 1 to 25 m depth range, and from +0.07 m (worse) to −0.46 m (better) for an approximately 1 to 16 m depth range. In addition, we showed that an ensemble approach based on multiple images, with acquisitions ranging from optimal to sub-optimal conditions, can be used to estimate bathymetry with a result that is similar to what can be obtained from the best individual scene. This approach can reduce time spent on the pre-screening and filtering of scenes. The correction method implemented in this study is not a complete solution to the challenge of AC for satellite-derived bathymetry, but it can eliminate the effects of biases inherent to individual AC algorithms and thus improve bathymetry retrieval. It may also be beneficial for use with other AC algorithms and for the estimation of seafloor habitat and water quality products, although further validation in different nearshore waters is required.

scholarly journals Dispersion of tongka langit banana in Buru and Seram, Maluku Province, Indonesia, based on topographic and climate factors

2020 ◽  
Vol 21 (5) ◽  
Author(s):  
HALVINA GRASELA SAIYA ◽  
Adriana Hiariej ◽  
ANNEKE PESIK ◽  
ELIZABETH KAYA ◽  
MEITTY LOUISE HEHANUSSA ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Remote Sensing And Gis ◽  
Climate Factors ◽  
Climate Data ◽  
Climate Condition ◽  
Climate Conditions ◽  
Shuttle Radar Topographic Mission ◽  
Wet Climate ◽  
Gis Methods

Abstract. Saiya HG, Hiariej A, Pesik A, Kaya E, Hehanussa ML, Puturuhu F. 2020. Dispersion of tongka langit banana in Buru and Seram, Maluku Province, Indonesia, based on topographic and climate factors. Biodiversitas 21: 2035-2046. The aim of this research is to understand the dispersion of tongka langit banana as one of the important endemic species in Maluku and also to know the topographic and climate factors hypothetically influencing the dispersion of tongka langit banana. The associated environmental factors are an initial approach that can be used to assess why the species only exists in certain locations. The data of coordinates were collected from survey activity; meanwhile, the slope and contour data were from the Shuttle Radar Topographic Mission (SRTM); and the climate data were from Meteorology, Climatology, and Geophysical Agency through statistic data publication. Then, all data were analyzed using Remote Sensing and GIS methods. The results showed that in Buru Island, tongka langit bananas were found in four locations, with climate condition was rather wet and found on slope grade of 0-8% and 8-15%. Whereas in Seram Island, tongka langit bananas were found in fifteen locations, with wet climate conditions, and on the same condition of slope as those found in Buru island.

scholarly journals Leptospirosis, Climate and Satellite-based Environmental Factors: A Temporal Modeling

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Pandji W. Dhewantara ◽  
Wenbiao Hu ◽  
Wenyi Zhang ◽  
Wenwu Yin ◽  
Fan Ding ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Factors ◽  
Land Surface Temperature ◽  
Early Warning ◽  
Physical Environment ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remotely Sensed ◽  
Environmental Data

ObjectiveTo quantify the effects of climate variability, selected remotely-sensed environmental factors on human leptospirosis in the high-risk counties in China.IntroductionLeptospirosis is a zoonotic disease caused by the pathogenic Leptospira bacteria and is ubiquitously distributed in tropical and subtropical regions. Leptospirosis transmission driven by complex factors include climatic, environmental and local social conditions 1. Each year, there are about 1 million cases of human leptospirosis reported globally and it causes approximately 60,000 people lost their lives due to infection 2. Yunnan Province and Sichuan Province are two of highly endemic areas in the southwest China that had contributed for 47% of the total national reported cases during 2005-2015 3. Factors underlying local leptospirosis transmission in these two areas is far from clear and thus hinder the efficacy of control strategies. Hence, it is essential to assess and identify local key drivers associated with persistent leptospirosis transmission in that areas to lay foundation for the development of early-warning systems. Currently, remote sensing technology provides broad range of physical environment data at various spatial and temporal scales 4, which can be used to understand the leptospirosis epidemiology. Utilizing satellite-based environmental data combined with locally-acquired weather data may potentially enhance existing surveillance programs in China so that the burden of leptospirosis could be reduced.MethodsThis study was carried out in two counties situated in different climatic zone in the southwestern China, Mengla and Yilong County (Fig 1). Total of 543 confirmed leptospirosis cases reported during 2006-2016 from both counties were used in this analysis. Time series decomposition was used to explore the long-term seasonality of leptospirosis incidence in two counties during the period studied. Monthly remotely-sensed environmental data such as normalized difference vegetation index (NDVI), modified normalized water difference index (MNDWI) and land surface temperature (LST) were collected from satellite databases. Climate data include monthly precipitation and relative humidity (RH) data were obtained from local weather stations. Lagged effects of rainfall, humidity, normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and land surface temperature (LST) on leptospirosis was examined. Generalized linear model with negative binomial link was used to assess the relationships of climatic and physical environment factors with leptospirosis. Best-fitted model was determined based on the lowest information criterion and deviance.ResultsLeptospirosis incidence in both counties showed strong and unique annual seasonality. Bi-modal temporal pattern was exhibited in Mengla County while single epidemic curve was persistently demonstrated in Yilong County (Fig 2). Total of 10 and 20 models were generated for Mengla and Yilong County, respectively. After adjusting for seasonality, final best-fitted models indicated that rainfall at lag of 6-month (incidence rate ratio (IRR)= 0.989; 95% confidence interval (CI) 0.985-0.993, p<0.001) and current LST (IRR=0.857, 95%CI:0.729-0.929, p<0.001) significantly associated with leptospirosis in Mengla County (Table 1). While in Yilong, rainfall at 1-month lag, MNDWI (5-months lag) and LST (3-months lag) were associated with an increased incidence of leptospirosis with a risk ratio of 1.013 (95%CI: 1.003-1.023), 7.960 (95%CI: 1.241-47.66) and 1.193 (95%CI:1.095-1.301), respectively.ConclusionsOur study identified lagged effect and relationships of weather and remotely-sensed environmental factors with leptospirosis in two endemic counties in China. Rainfall in combination with satellite derived physical environment factors such as flood/water indicator (MNDWI) and temperature (LST) could help explain the local epidemiology as well as good predictors for leptospirosis outbreak in both counties. This would also be an avenue for the development of leptospirosis early warning system in to support leptospirosis control in China.References1. Haake, D. A. , Levett, P. N. Leptospirosis in humans. Current Topics in Microbiology and Immunology 2015, 387, 65-97.2. Costa, F. et al. Global Morbidity and Mortality of Leptospirosis: A Systematic Review. PLOS Neglected Tropical Diseases 2015, 9, e0003898.3. Dhewantara, P. W. et al. Epidemiological shift and geographical heterogeneity in the burden of leptospirosis in China. Infectious Diseases of Poverty 2018, 7, 57.4. Herbreteau, V., Salem, G., Souris, M., Hugot, J. P. & Gonzalez, J. P. Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. Health & Place 2007, 13, 400-403. 

scholarly journals Application of Remote Sensing in Natural Sciences

2021 ◽  
Author(s):  
Ehsan Atazadeh ◽  
Mostafa Mahdavifard
Keyword(s):  
Biological Sciences ◽  
Remote Sensing ◽  
Environmental Factors ◽  
Nutrient Status ◽  
Natural Sciences ◽  
Wide Area ◽  
Density Mapping ◽  
Status Assessment ◽  
Depth Sounding ◽  
Conservation And Management

Generally, the term biomass is used for all materials originating from photosynthesis. However, biomass can equally apply to animals. Conservation and management of biomass is very important. There are various ways and methods for biomass evaluation. One of these methods is remote sensing. Remote sensing provides information about biomass, but also about biodiversity and environmental factors estimation over a wide area. The great potential of remote sensing has received considerable attention over the last few decades in many different areas in biological sciences including nutrient status assessment, weed abundance, deforestation, glacial features in Arctic and Antarctic regions, depth sounding of coastal and ocean depths, and density mapping.

Monitoring seasonal variations of colored dissolved organic matter for the Saginaw River based on Landsat-8 data

2018 ◽  
Vol 19 (1) ◽  
pp. 274-281 ◽  
Author(s):  
Jiang Chen ◽  
Weining Zhu ◽  
Yuhan Zheng ◽  
Yong Q. Tian ◽  
Qian Yu
Keyword(s):  
Remote Sensing ◽  
Organic Matter ◽  
Environmental Factors ◽  
Dissolved Organic Matter ◽  
Spatial Resolution ◽  
Seasonal Variations ◽  
Pearson Correlation ◽  
Landsat 8 ◽  
Colored Dissolved Organic Matter ◽  
Positive Role

Abstract Remote sensing is an effective tool for studying CDOM (colored dissolved organic matter) variations and its relevant environmental factors. Monitoring CDOM distribution and dynamics in small water is often limited by the coarse spatial resolution of traditional ocean color sensors. In this study, because of its high spatial resolution of 30 m, Landsat-8 data were used to assess seasonal variations of CDOM in the Saginaw River, by using an empirical statistic model. Pearson correlation analysis between CDOM variations and other environmental factors, such as temperature, discharge, and dissolved oxygen, shows that temperature was negatively correlated to CDOM variations and discharge played a positive role. We also calculated the monthly mean aCDOM(440) (the absorption coefficient of CDOM at 440 nm) for the Saginaw River between April and November from 2013 to 2016. This study demonstrates a good example for future applications in small waters: observing CDOM variations and other relevant environmental factors change by using Landsat remote sensing, so that we can know more about water quality and ecosystem health of small waters as well as the climate change impact on regional watersheds.

scholarly journals A One Health perspective to identify environmental factors that affect Rift Valley fever transmission in Gezira state, Central Sudan

2019 ◽  
Vol 47 (1) ◽  
Author(s):  
Rania Salah Eldien Bashir ◽  
Osama Ahmed Hassan
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Environmental Factors ◽  
Soil Type ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Logistic Regression Model ◽  
Multilevel Logistic Regression ◽  
Valley Fever ◽  
Multilevel Logistic Regression Model

Abstract Background Rift Valley fever (RVF) is a zoonotic viral vector-borne disease that affects both animals and humans and leads to severe economic consequences. RVF outbreaks are triggered by a favorable environment and flooding, which enable mosquitoes to proliferate and spread the virus further. RVF is endemic to Africa and has spread to Saudi Arabia and Yemen. There is great concern that RVF may spread to previously unaffected geographic regions due to climate change. We aimed to better understand the spatiotemporal pattern of the 2007 RVF outbreak at the human–animal–environment interface and to determine environmental factors that may have effects on RVF occurrence in Gezira state, Sudan. Materials and methods We compiled epidemiological, environmental, and spatiotemporal data across time and space using remote sensing and a geographical information system (GIS). The epidemiological data included 430 RVF human cases as well as human and animal population demographic data for each locality. The cases were collected from 41 locations in Gezira state. The environmental data represent classified land cover during 2007, the year of the RVF outbreak, and the average of the Normalized Difference Vegetation Index (NDVI) for 6 months of 2007 is compared with those of 2010 and 2014, when there was no RVF outbreak. To determine the effect of the environmental factors such as NDVI, soil type, and RVF case’s location on the Blue Nile riverbank on RVF incidence in Gezira state, a multilevel logistic regression model was carried out. Results We found that the outbreak in Gezira state occurred as a result of interaction among animals, humans, and the environment. The multilevel logistic regression model (F = 43,858, df = 3, p = 0.000) explained 23% of the variance in RVF incidence due to the explanatory variables. Notably, soil type (β = 0.613, t = 11.284, p = 0.000) and NDVI (β = − 0.165, t = − 3.254, p = 0.001) were the explanatory environmental factors that had significant effects on RVF incidence in 2007 in Gezira state, Sudan. Conclusions Precise remote sensing and the GIS technique, which rely on environmental indices such as NDVI and soil type that are satellite-derived, can contribute to establishing an early warning system for RVF in Sudan. Future preparedness and strengthening the capacity of regional laboratories are necessary for early notification of outbreaks in animals and humans.

scholarly journals Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1576 ◽  
Author(s):  
Li Zhu ◽  
Lianghao Huang ◽  
Linyu Fan ◽  
Jinsong Huang ◽  
Faming Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Environmental Factors ◽  
Recurrent Neural Network ◽  
Landslide Susceptibility ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Predictive Rate

Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

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