Spatio-temporal modelling of the duration of the cotton cycle in the State Of Goiás, Brazil

The objective of this work was to develop and validate a mathematical model to estimate the duration of cotton (Gossypium hirsutum L. r. latifolium hutch) cycle in the State of Goiás, Brazil, by applying the method of growing degree-days (GD), and considering, simultaneously, its time-space variation. The model was developed as a linear combination of elevation, latitude, longitude, and Fourier series of time variation. The model parameters were adjusted by using multiple-linear regression to the observed GD accumulated with air temperature in the range of 15°C to 40°C. The minimum and maximum temperature records used to calculate the GD were obtained from 21 meteorological stations, considering data varying from 8 to 20 years of observation. The coefficient of determination, resulting from the comparison between the estimated and calculated GD along the year was 0.84. Model validation was done by comparing estimated and measured crop cycle in the period from cotton germination to the stage when 90 percent of bolls were opened in commercial crop fields. Comparative results showed that the model performed very well, as indicated by the Pearson correlation coefficient of 0.90 and Willmott agreement index of 0.94, resulting in a performance index of 0.85.

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Spatio-temporal modeling of surface runoff in ungauged sub-catchments of Subarnarekha river basin using SWAT

MAUSAM ◽

10.54302/mausam.v72i3.1309 ◽

2021 ◽

Vol 72 (3) ◽

pp. 597-606

Author(s):

CHINMAYA PANDA ◽

DWARIKA MOHAN DAS ◽

B. C. SAHOO ◽

B. PANIGRAHI ◽

K. K. SINGH

Keyword(s):

Surface Runoff ◽

Assessment Tool ◽

Nutrient Loss ◽

Coefficient Of Determination ◽

Annual Rainfall ◽

Model Parameters ◽

Runoff Generation ◽

Temporal Modeling ◽

Spatio Temporal ◽

R Factors

In this present study, Soil and Water Assessment Tool (SWAT) embedded with ArcGIS interface has been used to simulate the surface runoff from the un-gauged sub-catchments in the upper catchment of Subarnarekha basin. Model calibration and validation were performed with the help of Sequential Uncertainty Fitting (SUFI-2) in-built in the SWAT-CUP package (SWAT Calibration Uncertainty Programs). The model was calibrated for a period from 1996 to 2008 with 3 years warm up period (1996-1998) and validated for a period of 5 years from 2009 to 2013. The model evaluation was performed by Nash - Sutcliffe coefficient (NSE), Coefficient of determination (R2) and Percentage Bias (PBIAS). The degree of uncertainty was evaluated by P and R factors. Basing upon the R2, NSE and PBIAS values respectively, of the order of 0.90, 0.90 and -12%, during calibration and 0.85, 0.83 and -15% during validation, substantiate performance of the model. All uncertainties of model parameters have been well taken by the P and R factors respectively, of the order of 0.95 and 0.77 during calibration and 0.82 and 0.87 during validation. The runoff generation from 19 sub-catchments of Adityapur catchment varies from 29.2-44.1% of the annual rainfall and average surface runoff simulated for the entire catchment is 545 mm. As the surface runoff generated in most of the sub-catchments amounts to above 30% of rainfall, it is recommended for adequate number of structural interventions at appropriate locations in the catchment to store the rainfall excess for providing irrigation, recharging groundwater and restricting the sediment and nutrient loss.

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Evaluation of allergic response using dynamic thermography

Opto-Electronics Review ◽

10.1515/oere-2015-0007 ◽

2015 ◽

Vol 23 (1) ◽

Author(s):

E. Rokita ◽

T. Rok ◽

G. Tatoń

Keyword(s):

Allergic Reaction ◽

Skin Prick Test ◽

High Sensitivity ◽

Allergic Response ◽

Coefficient Of Determination ◽

Model Parameters ◽

Prick Test ◽

Skin Reactivity ◽

Heated Area ◽

Spatio Temporal

AbstractSkin dynamic termography supplemented by a mathematical model is presented as an objective and sensitive indicator of the skin prick test result. Termographic measurements were performed simultaneously with routine skin prick tests. The IR images were acquired every 70 s up to 910 s after skin prick. In the model histamine is treated as the principal mediator of the allergic reaction. Histamine produces vasolidation and the engorged vessels are responsible for an increase in skin temperature. The model parameters were determined by fitting the analytical solutions to the spatio-temporal distributions of the differences between measured and baseline temperatures. The model reproduces experimental data very well (coefficient of determination = 0.805÷0.995). The method offers a set of parameters to describe separately skin allergic reaction and skin reactivity. The release of histamine after allergen injection is the best indicator of allergic response. The diagnostic parameter better correlates with the standard evaluation of a skin prick test (correlation coefficient = 0.98) than the result of the thermographic planimetric method based on temperature and heated area determination (0.81). The high sensitivity of the method allows for determination of the allergic response in patients with the reduced skin reactivity.

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Retrieval of Land Surface Temperature Diurnal Cycle model parameters from Kalpana-1 VHRR data over India

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-8-933-2014 ◽

2014 ◽

Vol XL-8 ◽

pp. 933-936

Author(s):

D. B. Shah ◽

M. R. Pandya ◽

A. Gujrati ◽

H. J. Trivedi ◽

R. P. Singh

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Thermal Inertia ◽

Global Scale ◽

Surface Processes ◽

Coefficient Of Determination ◽

Maximum Temperature ◽

Model Parameters ◽

Indian Landmass

Land Surface Temperature (LST) is an important parameter in the land surface processes on regional and global scale. The Land Surface Temperature Diurnal (LSTD) cycle of different land cover is an excellent indicator of the surface processes and their interaction with planetary boundary layer. The Kalpana-1 very high resolution radiometer (VHRR) LST product is available with 30 minute spatial resolution and 0.1 degree temporal resolution. A study was carried out with an objective to determine the LSTD parameters directly from K1-VHRR monthly averaged LST observations over Indian landmass. In this analysis, a harmonic function is fitted to LSTD from the K1-VHRR observations, where cosine term describing the effect of sun and exponential term represents decay of LST during nighttime. Using LSTD parameters, one can directly know the temperature amplitude, residual temperature and time of maximum temperature for each pixel. The LSTD parameters fitting accuracy in root mean square error (RMSE) and coefficient of determination (R2) ranges between 0.5–2.5 K and 0.90–0.99 respectively for most of the pixels over Indian landmass. These LSTD parameters may bring new insight for estimation of thermal inertia and also useful in cloud screening algorithms.

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Climate change and its impact on Forest Fire in the state of Himachal Pradesh and Uttarakhand states of India: Remote Sensing and GIS Analysis

Contemporary Trends in Geoscience ◽

10.2478/ctg-2018-0016 ◽

2018 ◽

Vol 7 (2) ◽

pp. 229-246 ◽

Cited By ~ 3

Author(s):

Firoz Ahmad ◽

Laxmi Goparaju

Keyword(s):

Remote Sensing ◽

Forest Fire ◽

Pearson Correlation ◽

Fire Frequency ◽

Remote Sensing And Gis ◽

Himachal Pradesh ◽

Significant Rise ◽

The State ◽

Annual Precipitation ◽

Maximum Temperature

Abstract We have examined the climate and forest fire data using Remote Sensing and GIS in the state of Himachal Pradesh and Uttarakhand states of India. The significant high forest fire events were observed in district of Pauri Garhwal (22.4%) followed by Naini Tal (16.4%), Tehri Garhwal (8.5%), Almora (7.7%), Chamoli (5.8%), Dehra Dun (4.6%), Uttarkashi (4.3%), Champawat (4.2%), Haridwar (3.6%), Una (3.4%), Bageshwar (3.1%), Udham Singh Nagar (2.9%), Sirmaur (2.7%), Solan (2.3%), Kangra (2.1%), Pithoragarh (1.7%) and Shimla (1.2%). The LULC forest category “Deciduous Broadleaf Forest” occupied 17.2% of total forest area and retain significantly high forest fire percent equivalent to 44.7% of total forest fire events. The study revealed that 79% of forest fire incidence was found in the month of April and May. The fire frequency was found highest in the month of April (among all months) whereas it was spread over the five grids (in the count) where the fire frequencies were greater than 100. The average monthly analysis (from January to June) for maximum temperature (°C), precipitation (mm), solar radiation (MJ/m^2), wind velocity (meter/sec.), wet-days frequency (number of days) and evapotranspiration (mm/day) were found to be in the range of (9.90 to 26.44), (26.06 to 134.71), (11738 to 24119), (1.397 to 2.237), (1.46 to 5.12) and (3.96 to 8.46) respectively. Rapid climate/weather severities which significantly enhance the forest fire events were observed in the month of April and May. The analysis of the Pearson Correlation Coefficient (PCC) values of climate parameters showed a significant correlation with forest fire events. The analysis of predicted (2050) climate anomalies data (RCP-6) for the month of April and annual precipitation manifest the significant rise in April temperature and reduction in annual precipitation observed over large part of high forest fire grids will certainly impact adversely to the future forest fire scenario.

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Comparison of Spatial Interpolation Methods for Mapping Daily Air Temperature

10.5194/egusphere-egu21-13970 ◽

2021 ◽

Author(s):

Lasyamayee L Sahoo ◽

Subashisa Dutta

Keyword(s):

Minimum Temperature ◽

Evaluation Method ◽

Heat Waves ◽

Interpolation Method ◽

Climatic Variability ◽

Labour Productivity ◽

The State ◽

Coefficient Of Determination ◽

Maximum Temperature ◽

Interpolation Methods

The sparsely distributed meteorological centers fails to provide enough information regarding spatial patterns. Even at places where dense meteorological stations are available, it is difficult to develop realistic gridded data due to the complex topography and climatic variability. Some of the climate as well as hydrological model require spatially continuous datasets as inputs. It is possible to obtain a continuous surface of raster datasets with the help of interpolation methods where each value is assigned based on surrounding values using specific mathematical formulas. For present study, various interpolation methods, like Inverse distance weighted, ordinary krigging, thin plate smoothing spline; has been compared for maximum and minimum temperature. Error in the interpolated data was analyzed by independent cross validation method, in which measurements like root mean square error (RMSE), mean squared relative error (MSRE), coefficient of determination (r2) and coefficient of efficiency (CE) were adopted for performance evaluation. Method with minimum error was chosen for developing the final map. It provides an effective way for mapping the meteorological variables in a topographically diverse region. In this case, an Indian state Odisha is chosen as study area. The state consists of 10 different agro-climatic zones and sees several weather systems across the year. The area suffers with floods, drought, heat waves and costal erosion almost every year with variable intensity. Strong heat waves in summer affect the human health, agriculture, construction efficiency and labour productivity. As three-fourth of the state is filled with mountains and high lands, monitoring network is sparsely distributed. Despite small latitudinal difference, temperature changes considerably with respect to both space and time. Here interpolation method plays a vital role to avoid uncertainty in modelling. Based on the generated maps, vulnerable areas on the basis of maximum temperature in summer and minimum temperature in winter is identified. Several indicators and vulnerability indices has been used.

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Two-Stage Water Jet Landing Point Prediction Model for Intelligent Water Shooting Robot

Sensors ◽

10.3390/s21082704 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2704

Author(s):

Yunhan Lin ◽

Wenlong Ji ◽

Haowei He ◽

Yaojie Chen

Keyword(s):

Pitch Angle ◽

Back Propagation ◽

Water Jet ◽

The State ◽

Model Parameters ◽

Propagation Time ◽

Back Propagation Algorithm ◽

Angle Error ◽

Landing Point ◽

Propagation Algorithm

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time.

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Consistent Long-Term Monthly Coastal Wetland Vegetation Monitoring Using a Virtual Satellite Constellation

Remote Sensing ◽

10.3390/rs13030438 ◽

2021 ◽

Vol 13 (3) ◽

pp. 438

Author(s):

Subrina Tahsin ◽

Stephen C. Medeiros ◽

Arvind Singh

Keyword(s):

Vegetation Index ◽

Coastal Wetland ◽

Thermal Emission ◽

Mean Squared Error ◽

Wetland Vegetation ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Vegetation Monitoring ◽

Time Space

Long-term monthly coastal wetland vegetation monitoring is the key to quantifying the effects of natural and anthropogenic events, such as severe storms, as well as assessing restoration efforts. Remote sensing data products such as Normalized Difference Vegetation Index (NDVI), alongside emerging data analysis techniques, have enabled broader investigations into their dynamics at monthly to decadal time scales. However, NDVI data suffer from cloud contamination making periods within the time series sparse and often unusable during meteorologically active seasons. This paper proposes a virtual constellation for NDVI consisting of the red and near-infrared bands of Landsat 8 Operational Land Imager, Sentinel-2A Multi-Spectral Instrument, and Advanced Spaceborne Thermal Emission and Reflection Radiometer. The virtual constellation uses time-space-spectrum relationships from 2014 to 2018 and a random forest to produce synthetic NDVI imagery rectified to Landsat 8 format. Over the sample coverage area near Apalachicola, Florida, USA, the synthetic NDVI showed good visual coherence with observed Landsat 8 NDVI. Comparisons between the synthetic and observed NDVI showed Root Mean Squared Error and Coefficient of Determination (R2) values of 0.0020 sr−1 and 0.88, respectively. The results suggest that the virtual constellation was able to mitigate NDVI data loss due to clouds and may have the potential to do the same for other data. The ability to participate in a virtual constellation for a useful end product such as NDVI adds value to existing satellite missions and provides economic justification for future projects.

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Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design

Sustainability ◽

10.3390/su13095207 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5207

Author(s):

Zed Zulkafli ◽

Farrah Melissa Muharam ◽

Nurfarhana Raffar ◽

Amirparsa Jajarmizadeh ◽

Mukhtar Jibril Abdi ◽

...

Keyword(s):

Pearson Correlation ◽

Extreme Rainfall ◽

Peninsular Malaysia ◽

Coefficient Of Determination ◽

Index Insurance ◽

Weather Index ◽

Basis Risk ◽

Insurance Contracts ◽

Weather Index Insurance ◽

Streamflow Statistics

Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia.

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A Raman algorithm to estimate human age from protein structural variations in autopsy skin samples: a protein biological clock

Scientific Reports ◽

10.1038/s41598-021-85371-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Daisuke Miyamori ◽

Takeshi Uemura ◽

Wenliang Zhu ◽

Kei Fujikawa ◽

Takaaki Nakaya ◽

...

Keyword(s):

Protein Folding ◽

Age Estimation ◽

Mean Squared Error ◽

Pearson Correlation ◽

Biological Clock ◽

Estimation Method ◽

Forensic Identification ◽

Random Coil ◽

Coefficient Of Determination ◽

Skin Samples

AbstractThe recent increase of the number of unidentified cadavers has become a serious problem throughout the world. As a simple and objective method for age estimation, we attempted to utilize Raman spectrometry for forensic identification. Raman spectroscopy is an optical-based vibrational spectroscopic technique that provides detailed information regarding a sample’s molecular composition and structures. Building upon our previous proof-of-concept study, we measured the Raman spectra of abdominal skin samples from 132 autopsy cases and the protein-folding intensity ratio, RPF, defined as the ratio between the Raman signals from a random coil an α-helix. There was a strong negative correlation between age and RPF with a Pearson correlation coefficient of r = 0.878. Four models, based on linear (RPF), squared (RPF2), sex, and RPF by sex interaction terms, were examined. The results of cross validation suggested that the second model including linear and squared terms was the best model with the lowest root mean squared error (11.3 years of age) and the highest coefficient of determination (0.743). Our results indicate that the there was a high correlation between the age and RPF and the Raman biological clock of protein folding can be used as a simple and objective forensic age estimation method for unidentified cadavers.

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Are Saliva Tests Important in the Prediction of Carious Disease?

Applied Sciences ◽

10.3390/app11135932 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5932

Author(s):

Daniela Luminita Ichim ◽

Liliana Sachelarie ◽

Alexandra Burlui

Keyword(s):

Health Promotion ◽

Pearson Correlation ◽

Coefficient Of Determination ◽

Data Series ◽

Study Group ◽

Complex Phenomenon ◽

Oral Health Promotion ◽

Group 4 ◽

Carious Lesions ◽

Pearson Correlation Test

(1) Background: The appearance and progression of carious lesions represent a complex phenomenon of interactions of microbial factors (the action of bacteria on the tooth), of the factors related to the host, to the diet, and to the time factor. Which hasan influence on the rate of microbismof the oral cavity on the installation of carious disease? (2) Methods: In order to correctly assess the cariogenic risk of an individual, it is recommended to perform twoor more tests based on different principles (microbiological, clinical, epidemiological). The representative data series for the investigation were analyzed statistically and by applying the Pearson correlation test considering the coefficient of determination R for all pairs of data series. (3) Results: Salivary tests played animportant role in establishing control sessions, in carrying out prophylactic caries therapy, and establishing prognosis. The existence of a statistical associationwas confirmed between the prevalence of dental caries and the results of salivary tests for the study group. (4) Conclusions: The results of the saliva tests can be used in oral health promotion.

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