Mapping of

Land cover map of North Sinai was produced based on the FAO-Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is covering a total area of 20,310.4 km2 (203,104 hectare). The landscape classification was based on SPOT4 data acquired in 2011 using combined multispectral bands of 20 m spatial resolution. Geographic Information System (GIS) was used to manipulate the attributed layers of classification in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include bare rock, bare soils (stony, very stony and salt crusts), loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing). The artificial surfaces include linear and non-linear features

Retrieving leaf area index from SPOT4 satellite data

A research project was conducted as collaboration between the National Authority for Remote Sensing and Space Sciences (NARSS) in Egypt and the Institute of Remote Sensing Applications (IRSA), Chinese Academy of Sciences. The objective of this study is to generate normalized difference vegetation index (NDVI)–leaf area index (LAI) statistical inversion models for three rice varieties planted in Egypt (Giza-178, Sakha-102, and Sakha-104) using the data of two rice growing seasons. Field observations were carried out to collect LAI field measurements during 2008 and 2009 rice seasons. The SPOT4 satellite data acquired in rice season of 2008 and 2009 conjunction with field observations dates were used to calculate the vegetation indices values. Statistical analyses were performed to confirm the assumptions of inversion modeling for plant variables and to get reliable models that fit the inversion relationship between LAI and NDVI. The inversion process resulted in three NDVI–LAI models adequate to predict LAI with 95% confidence for the three different rice varieties. The accuracy of the generated models ranged between 50% in the case of Sakha-104 and 82% in the case of Giza-178. LAI maps were produced from NDVI imageries based on the generated models

Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta

The objective of the current work is to generate statistical empirical rice yield estimation models under the local conditions of the Egyptian Nile delta. The methodology is based on regressing measured yield with satellite derived spectral information or leaf area index (LAI). LAI field measurements and spectral information from SPOT data collected during two crop seasons are examined against measured yield to generate the yield models. Near-infrared and red bands, six vegetation indices and LAI of 100 points are used as the main inputs for the modeling process while 20 points of the same are used for validation process. Nine models are generated and tested against the observed yield. Comparing the generated models show relatively higher superiority of (LAI-yield) and (infrared-yield) models over the rest of the models with (0.061) and (0.090) as a standard error of estimate and (0.945) and (0.883) as coefficient of determinations between modeled and observed yield. The models are applicable a month before harvest for similar regions with same conditions

Relation of Procollagen Type III Amino Terminal Propeptide Level to Sepsis Severity in Pediatrics

Background: Sepsis is still the main etiology of mortality in pediatric intensive care units (PICUs). Therefore, we performed this study to evaluate the value of procollagen Type III amino-terminal propeptide (PIIINP) as a biomarker for sepsis severity diagnosis and mortality. Method: A prospective study was carried out on 170 critically ill children admitted into the PICU and 100 controls. The performed clinical examinations included calculation of the pediatric risk of mortality. Serum PIIINP was withdrawn from patients at admission and from the controls. Results: PIIINP level was significantly more increased in sepsis, severe sepsis, and septic shock than among the controls (p < 0.001). PIIINP was significantly higher in severe sepsis and septic shock (568.3 (32.5–1304.7) and 926.2 (460.6–1370), respectively) versus sepsis (149.5 (29.6–272.9)) (p < 0.001). PIIINP was significantly increased in non-survivors (935.4 (104.6–1370)) compared to survivors (586.5 (29.6–1169)) (p < 0.016). ROC curve analysis exhibited an area under the curve (AUC) of 0.833 for PIIINP, which is predictive for sepsis, while the cut-off point of 103.3 ng/mL had a sensitivity of 88% and specificity of 82%. The prognosis of the AUC curve for PIIINP to predict mortality was 0.651; the cut-off of 490.4 ng/mL had a sensitivity of 87.5% and specificity of 51.6%. Conclusions: PIIINP levels are increased in sepsis, with significantly higher levels in severe sepsis, septic shock, and non-survivors, thus representing a promising biomarker for pediatric sepsis severity and mortality

Multi-Sensor Remote Sensing to Estimate Biophysical Variables of Green-Onion Crop (<i>Allium cepa</i> L.) under Different Sources of Magnesium in Ismailia, Egypt

Foliar feeding has been confirmed to be the fastest way of dealing with nutrient deficiencies and increasing the yield and quality of crop products. The synthesis of chlorophyll and photosynthesis are directly related to magnesium (Mg), which operates in the improvement of plant tissues and enhances the appearance of plants. This study aimed to analyze the correlation between two biophysical variables, including the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and seven spectral vegetation indices. The spectral indices under investigation were Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Disease–Water Stress Index (DSWI), Modified Chlorophyll Absorption Ratio Index (MCARI), the Red-Edge Inflection Point Index (REIP), and Pigment-Specific Simple Ratio (PSSRa). These indices were derived from Sentinel-2 data to investigate the impact of applying foliar applications of Mg from various sources in the production of green-onion crops. The biophysical variables were derived using field measurements and Sentinel-2 data under the effects of different sources of Mg foliar sprays. The correlation coefficient between field-measured LAI and remotely sensed, calculated LAI was 0.72 in two seasons. Concerning FAPAR, it was found that the correlation between remotely sensed calculated FAPAR and field-measured FAPAR was 0.66 in the first season and 0.89 in the second season. The magnesium oxide nanoparticle (nMgO) treatments resulted in significantly higher yields than the different treatments of foliar applications. The LAI and FAPAR variables showed a positive correlation with yield in the first season (October) and in the second season (March). Yield in treatment by nMgO varied significantly from that in the other treatments, ranging from 69-ton ha−1 in the first season to 74.9-ton ha−1 in the second season. Linear regression between LAI and PSSRa showed the highest correlation coefficient (0.90) compared with other vegetation indices in the first season. In the same season, the highest correlation coefficient (0.94) was found between FAPAR and PSSRa. In the second season, the highest accuracy to the estimate LAI was found in the correlation between MCARI and PSSRa, with correlation coefficients of 0.9 and 0.91, respectively. In the second season, the highest accuracy to the estimate FAPAR was found with the correlation between PSSRa, ARVI, and NDVI, with correlation coefficients 0.97 and 0.96, respectively. The highest correlation coefficients between vegetation indices and yield were found with ARVI and NDVI in the first season, and only with NDVI in the second season