scholarly journals Assessment of the Rice Panicle Initiation by Using NDVI-Based Vegetation Indexes

2021 ◽  
Vol 11 (21) ◽  
pp. 10076
Author(s):  
Joon-Keat Lai ◽  
Wen-Shin Lin
Keyword(s):  
Regression Model ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Polynomial Regression ◽  
Fertilizer Application ◽  
Quadratic Polynomial ◽  
Green Ring ◽  
Vegetation Indexes ◽  
Initiation Stage ◽  
Polynomial Regression Model

The assessment of rice panicle initiation is crucial for the management of nitrogen fertilizer application that affects yield and quality of grain. The occurrence of panicle initiation could be determined via either green ring, internode-elongation, or a 1–2 mm panicle, and was observed through manual dissection. The quadratic polynomial regression model was used to construct the model of the trend of normalized difference vegetation index-based vegetation indexes (NDVI-based VIs) between pre-tillering and panicle differentiation stages. The slope of the quadratic polynomial regression model tended to be alleviated in the period in which the panicle initiation stage should occur. The results indicated that the trend of the NDVI-based VIs was correlated with panicle initiation. NDVI-based VIs could be a useful indicator to remotely assess panicle initiation.

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals Polynomial Regression Model Analysis of Abdominal Wall Tension and Intra-abdominal Pressure in Critically ill Patients

2021 ◽  
Author(s):  
Hao Tang ◽  
Dongchu Zhao ◽  
Chuan Zhang ◽  
Xiaoying Huang ◽  
Dong Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Model ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Polynomial Regression ◽  
Prognostic Indicators ◽  
Wall Tension ◽  
Abdominal Hypertension ◽  
Abdominal Compliance ◽  
Polynomial Regression Model

Abstract BackgroundAbdominal wall tension (AWT) plays an important role in the pathogenesis of abdominal compliance (AC). This study uses a polynomial regression model to analyze the correlation between intra-vesical pressure(IVP) and AWT in critically ill patients and provides new ideas for the diagnosis and treatment of critically ill patients with intra-abdominal hypertension（IAH）.MethodsA retrospective analysis was conducted in critically ill patients who met the inclusion criteria and were admitted to the Department of intensive care unit of Daping Hospital of Army Medical University from March 14, 2019, to May 23, 2020. According to the IVP on the first day of ICU admission and death within 28 days, the patients were divided into the IAH group (IVP ≥12 mmHg), the non-IAH group, the survival group and the nonsurvival group. The demographic and clinical data, prognostic indicators, AWT and IVP on days 1-7 after entering the ICU, IAH risk factors, and 28-day death risk factors were collected.ResultsA total of 100 patients were enrolled, with an average age of 45.59±11.4 years. There were 55 males (55%), 30 patients from departments of internal medicine (30%), 43 patients from surgery departments (43%), and 27 trauma patients (27%). In the IAH group, there were 50 patients (29 males, 58%), with an average age of 45.28±12.27 years; there were 50 patients (26 males, 52%) in the non-IAH group, with an average age of 45.90±10.58 years. The IVP on the 1st day and the average IVP within 7 days of the IAH group was 18.99（17.52，20.77）mmHg and 19.43（16.87，22.25）mmHg, respectively, which was higher than that of the non-IAH group [ 6.14（3.48，8.70）mmHg, 6.66（2.74，9.08）mmHg], p＜0.001. The AWT on the 1st day and the average AWT within 7 days of the IAH group was 2.89±0.32 N/mm and 2.82±0.46 N/mm, respectively, which was higher than that of the non-IAH group [(2.45±0.29)N/mm,(2.43±0.39)N/mm],p＜0.001.The polynomial regression models showed that the average AWT and IVP on the 1st day and within 7 days were AWTday1 = -2.450×10-3IVP2+9.695×10-2 IVP+2.046，r=0.667（p＜0.0001），and AWTmean = -2.293×10-3IVP2+9.273×10-2 IVP+2.081, respectively. The logistic regression analysis showed that AWTday1 of 2.73-2.97 N/mm increased the patient's 28-day mortality risk (OR: 6.834; 95%: 1.105-42.266, p=0.010).ConclusionsThere is a nonlinear correlation between AWT and IVP in critically ill patients, and a high AWT may indicate poor prognosis.

scholarly journals Forecasting COVID-19 cases in Algeria using logistic growth and polynomial regression models

2021 ◽  
Author(s):  
Mohamed LOUNIS ◽  
Babu Malavika
Keyword(s):  
Regression Model ◽  
Logistic Model ◽  
Polynomial Regression ◽  
Logistic Growth ◽  
The Novel ◽  
Logistic Growth Model ◽  
Polynomial Regression Models ◽  
Using Data ◽  
Novel Coronavirus ◽  
Polynomial Regression Model

Abstract The novel Coronavirus respiratory disease 2019 (COVID-19) is still expanding through the world since it started in Wuhan (China) on December 2019 reporting a number of more than 84.4 millions cases and 1.8 millions deaths on January 3rd 2021.In this work and to forecast the COVID-19 cases in Algeria, we used two models: the logistic growth model and the polynomial regression model using data of COVID-19 cases reported by the Algerian ministry of health from February 25th to December 2nd, 2020. Results showed that the polynomial regression model fitted better the data of COVID-19 in Algeria the Logistic model. The first model estimated the number of cases on January, 19th 2021 at 387673 cases. This model could help the Algerian authorities in the fighting against this disease.

scholarly journals Combining Global Geopotential Models, Digital Elevation Models, and GNSS/Leveling for Precise Local Geoid Determination in Some Mexico Urban Areas: Case Study

2021 ◽  
Vol 10 (12) ◽  
pp. 819
Author(s):  
Norberto Alcantar-Elizondo ◽  
Ramon Victorino Garcia-Lopez ◽  
Xochitl Guadalupe Torres-Carillo ◽  
Guadalupe Esteban Vazquez-Becerra
Keyword(s):  
Regression Model ◽  
Numerical Integration ◽  
Urban Areas ◽  
Polynomial Regression ◽  
Geodetic Network ◽  
Geoid Undulation ◽  
Geopotential Model ◽  
Commission Error ◽  
Standard Deviations ◽  
Polynomial Regression Model

This work shows improvements of geoid undulation values obtained from a high-resolution Global Geopotential Model (GGM), applied to local urban areas. The methodology employed made use of a Residual Terrain Model (RTM) to account for the topographic masses effect on the geoid. This effect was computed applying the spherical tesseroids approach for mass discretization. The required numerical integration was performed by 2-D integration with 1DFFT technique that combines DFT along parallels with direct numerical integration along meridians. In order to eliminate the GGM commission error, independent geoid undulations values obtained from a set of GNSS/leveling stations are employed. A corrector surface from the associated geoid undulation differences at the stations was generated through a polynomial regression model. The corrector surface, in addition to the GGM commission error, also absorbs the GNSS/leveling errors as well as datum inconsistencies and systematic errors of the data. The procedure was applied to five Mexican urban areas that have a geodetic network of GNSS/leveling points, which range from 166 to 811. Two GGM were evaluated: EGM2008 and XGM2019e_2159. EGM2008 was the model that showed relatively better agreement with the GNSS/leveling stations having differences with RMSE values in the range of 8–60 cm and standard deviations of 5–8 cm in four of the networks and 17 cm in one of them. The computed topographic masses contribution to the geoid were relatively small, having standard deviations on the range 1–24 mm. With respect to corrector surface estimations, they turned out to be fairly smooth yielding similar residuals values for two geoid models. This was also the case for the most recent Mexican gravity geoid GGM10. For the three geoid models, the second order polynomial regression model performed slightly better than the first order with differences up to 1 cm. These two models produced geoid correction residuals with a standard deviation in one test area of 14 cm while for the others it was of about 4–7 cm. However, the kriging method that was applied for comparison purposes produced slightly smaller values: 8 cm for one area and 4–6 cm for the others.

Sign in / Sign up

Export Citation Format

Share Document