scholarly journals Study on paddy phenomics ecosystem and yield estimation using space-borne multi sensor remote sensing data

2021 ◽  
Vol 21 (2) ◽  
pp. 171-175
Author(s):  
MAHESH PALAKURU ◽  
KIRAN YARRAKULA
Keyword(s):  
Rice Yield ◽  
Remote Sensing Data ◽  
Coefficient Of Determination ◽  
Polynomial Method ◽  
Yield Estimation ◽  
Phenological Stages ◽  
Modis Evi ◽  
Order Polynomial ◽  
L Band

In the present study three phenological stages of rice namely transplanting stage, heading stage and harvesting stages were derived from MODIS EVI data. SMAP L-band was used to identify the puddling field. The performance of the estimated phenological stages from MODIS EVI and SMAP were evaluated with field data and root mean square error (RMSE) was calculated. The rice yield estimation was also performed by application of second order polynomial method. The performance of the polynomial model showed good results with the coefficient of determination of 0.74. 

A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation

2014 ◽  
Vol 197 ◽  
pp. 52-64 ◽  
Author(s):  
N.T. Son ◽  
C.F. Chen ◽  
C.R. Chen ◽  
V.Q. Minh ◽  
N.H. Trung
Keyword(s):  
Comparative Analysis ◽  
Large Scale ◽  
Rice Yield ◽  
Yield Estimation ◽  
Modis Evi

scholarly journals Kombination von Landesforstinventar- und Fernerkundungsdaten für Kleingebietsschätzungen

2011 ◽  
Vol 162 (9) ◽  
pp. 290-299 ◽  
Author(s):  
Katharina Steinmann ◽  
Christian Ginzler ◽  
Adrian Lanz
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
National Forest Inventory ◽  
Aerial Images ◽  
Coefficient Of Determination ◽  
National Forest ◽  
Target Variable ◽  
Ancillary Data ◽  
Timber Volume

Combining data from the Swiss National Forest Inventory and from remote sensing for small-area estimations in forestry A design-unbiased small estimator was tested in this study. This estimator combines terrestrial data from the Swiss National Forest Inventory (LFI) with ancillary data from stereo aerial images and airborne laser scanner (ALS) data. The estimator was tested for the two target variables: the percentage of forest and the timber volume. The efficiency of the estimator depends on the model precision of the target variable obtained with remote sensing data and other ancillary spatial data, which can potentially explain the spatial variation of the target variable. Canopy heights derived from stereo aerial images (ADS40) and ALS data were used as ancillary data. Regression models for the timber volume and the forest/non-forest decision of the LFI samples were calibrated within the cantons Appenzell Inner Rhodes and Appenzell Outer Rhodes and provided a coefficient of determination of roughly 60%. Adding the forest/non-forest decision from the aerial photo interpretation of the LFI as an explanatory variable slightly improved the models and allowed to gain a coefficient of determination of 65% for the timber volume and 85% for the forest/non-forest decision. Within the forest area, the canopy height models explained nearly 40% (ALS data) and 20% (ADS40 data) of the observed timber volume variability. This case study shows that using remote sensing data can increase the precision (in terms of the standard error) of the timber volume estimation in canton Appenzell Inner Rhodes by roughly 30%. The same is valid for the estimation of the percentage of forest. A reduction in the standard error of about 10% for the latter estimation was obtained by using the aerial images and nearly 25% using the ALS data.

scholarly journals Viscosity and Sensory Acceptability of Almond Milkshake as Influenced by Sugar, Almond Paste and Corn Flour-A Response Surface Study

2021 ◽  
Vol 10 (3) ◽  
pp. 2483-2493
Keyword(s):  
Response Surface ◽  
Coefficient Of Determination ◽  
Confirmatory Test ◽  
Corn Flour ◽  
Sensory Score ◽  
Order Polynomial ◽  
Predicted Values ◽  
Experimental Values ◽  
Central Composite

The effect of variables such as sugar, almond paste, and cornflour on viscosity and a sensory score of almond milkshake samples were studied by response surface methodology. The central composite design was used to obtain optimum levels of variables. The values of viscosity and sensory scores obtained from different experiment runs were 170-1085cps and 6.2-7.7. The second-order polynomial model suggested by design expert software for viscosity and a sensory score of almond milkshake showed R2 (coefficient of determination) of 0.9871 and 0.9590, respectively. Whereas model F-values for viscosity and a sensory score of almond milkshake were 84.9 and 26.02, respectively. Optimum levels of sugar, almond paste, and cornflour suggested by models were 8%, 1% & 2%, respectively. Experimental values of responses obtained from the confirmatory test were almost similar to predicted values of responses suggested by models.

Statistical method of steepest improvement of response

2018 ◽  
Vol 84 (11) ◽  
pp. 74-87
Author(s):  
V. B. Bokov
Keyword(s):  
Statistical Method ◽  
Linear Model ◽  
Response Prediction ◽  
Initial Experiment ◽  
First Order ◽  
Prediction Response ◽  
Conditional Extremum ◽  
Order Polynomial ◽  
Limiting Values

A new statistical method for response steepest improvement is proposed. This method is based on an initial experiment performed on two-level factorial design and first-order statistical linear model with coded numerical factors and response variables. The factors for the runs of response steepest improvement are estimated from the data of initial experiment and determination of the conditional extremum. Confidence intervals are determined for those factors. The first-order polynomial response function fitted to the data of the initial experiment makes it possible to predict the response of the runs for response steepest improvement. The linear model of the response prediction, as well as the results of the estimation of the parameters of the linear model for the initial experiment and factors for the experiments of the steepest improvement of the response, are used when finding prediction response intervals in these experiments. Kknowledge of the prediction response intervals in the runs of steepest improvement of the response makes it possible to detect the results beyond their limits and to find the limiting values of the factors for which further runs of response steepest improvement become ineffective and a new initial experiment must be carried out.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Variations in Triterpenoid Deposition in Cuticular Waxes during Development and Maturation of Selected Fruits of Rosaceae Family

2020 ◽  
Vol 21 (24) ◽  
pp. 9762
Author(s):  
Soyol Dashbaldan ◽  
Cezary Pączkowski ◽  
Anna Szakiel
Keyword(s):  
Metabolic Pathways ◽  
Cuticular Waxes ◽  
Rosa Rugosa ◽  
Phenological Stages ◽  
Aronia Melanocarpa ◽  
Fruit Samples ◽  
Black Chokeberry ◽  
Rosaceae Family ◽  
Fruit Surface

The process of fruit ripening involves many chemical changes occurring not only in the mesocarp but also in the epicarp, including changes in the triterpenoid content of fruit cuticular waxes that can modify the susceptibility to pathogens and mechanical properties of the fruit surface. The aim of the study was the determination of the ripening-related changes in the triterpenoid content of fruit cuticular waxes of three plant species from the Rosaceae family, including rugosa rose (Rosa rugosa), black chokeberry (Aronia melanocarpa var. “Galicjanka”) and apple (Malus domestica var. “Antonovka”). The triterpenoid and steroid content in chloroform-soluble cuticular waxes was determined by a GC-MS/FID method at four different phenological stages. The profile of identified compounds was rather similar in selected fruit samples with triterpenoids with ursane-, oleanane- and lupane-type carbon skeletons, prevalence of ursolic acid and the composition of steroids. Increasing accumulation of triterpenoids and steroids, as well as the progressive enrichment of the composition of these compounds in cuticular wax during fruit development, was observed. The changes in triterpenoid content resulted from modifications of metabolic pathways, particularly hydroxylation and esterification, that can alter interactions with complementary functional groups of aliphatic constituents and lead to important changes in fruit surface quality.

scholarly journals A Multimethod Analysis for Average Annual Precipitation Mapping in the Khorasan Razavi Province (Northeastern Iran)

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 592
Author(s):  
Mehdi Aalijahan ◽  
Azra Khosravichenar
Keyword(s):  
Regional Planning ◽  
Interpolation Method ◽  
Absolute Error ◽  
Rain Gauge ◽  
Annual Precipitation ◽  
Coefficient Of Determination ◽  
Distance Weighting ◽  
Average Annual Precipitation ◽  
Inverse Distance

The spatial distribution of precipitation is one of the most important climatic variables used in geographic and environmental studies. However, when there is a lack of full coverage of meteorological stations, precipitation estimations are necessary to interpolate precipitation for larger areas. The purpose of this research was to find the best interpolation method for precipitation mapping in the partly densely populated Khorasan Razavi province of northeastern Iran. To achieve this, we compared five methods by applying average precipitation data from 97 rain gauge stations in that province for a period of 20 years (1994–2014): Inverse Distance Weighting, Radial Basis Functions (Completely Regularized Spline, Spline with Tension, Multiquadric, Inverse Multiquadric, Thin Plate Spline), Kriging (Simple, Ordinary, Universal), Co-Kriging (Simple, Ordinary, Universal) with an auxiliary elevation parameter, and non-linear Regression. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R2) were used to determine the best-performing method of precipitation interpolation. Our study shows that Ordinary Co-Kriging with an auxiliary elevation parameter was the best method for determining the distribution of annual precipitation for this region, showing the highest coefficient of determination of 0.46% between estimated and observed values. Therefore, the application of this method of precipitation mapping would form a mandatory base for regional planning and policy making in the arid to semi-arid Khorasan Razavi province during the future.

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