REMOTE SENSING TECHNOLOGIES AS A TOOL FOR COTTON LEAFWORM, SPODOPTERA LITTORALIS (BOISD.): PREDICTION OF ANNUAL GENERATIONS

The study was carried out at Menia Governorate during (2014/2015) sugar beet season under field condition. The temperature is an important environmental factor that has an effect on the rate of development, survival and in any other biological and ecological aspects for the cotton leafworm, Spodoptera littoralis (Boisd.). Seasonal abundance of the insect population and predication of field generation throw a light on the temperature influence on insect development in the field. The data obtained in this work showed that the cotton leafworm, S. littoralis had four generations on sugar beet during the period from September 1st to March 1st. The predicted peaks of generations could be detected when the accumulated thermal units reach 524.27 degree days (dd's). The predicted peaks for the four generations detected earlier or later +3 to -2 days than the observed peaks. The expected peaks and the corresponding expected generations for cotton leafworm could be helpful to design the IPM control program

Studying Rain Water Catchment Potentialities in the Northwest Coast of Egypt Using Remote Sensing and Geographic Information System

Sustainable agricultural development of the desert areas of Egypt under the scarcity of irrigation water is a significant national challenge. Existing water harvesting techniques on the northwest coast of Egypt do not ensure the optimal use of rainfall for agricultural purposes. Basin-scale hydrology potentialities were studied to investigate how available annual rainfall could be used in agriculture irrigation to increase crop production. The present study includes data related to agricultural production in the form of geospatial layers including climate, soil, land covers unite and rain water catchment areas. Thematic classification of Sentinal-2 imagery was carried out to produce the land cover and crop maps following based on the (FAO) system of land cover classification. Contour lines and spot height points were used to create a digital elevation model (DEM). Then, DEM was used to delineate basins, sub-basins, and water outlet points using the Soil and Water Assessment Tool (Arc SWAT). Main soil mapping units of the study area identified from Land Master Plan maps it was(Very shallow gravelly and rocky soils and barren rock). Climatic data collected from the Central Laboratory for Climate. The results showed that the study area receives a significant amount of precipitation almost every three years, however, water harvesting methods are inappropriate to store water to be used in agricultural during drought seasons. The amount of precipitation(81.9 mm), surface water runoff(4.46 mm), potential evapotranspiration(70.5 mm), and actual evapotranspiration(7.10 mm) for the years (2004 to 2017) shown as results of (Arc SWAT). The land cover map showed that tree crops (olive and fig) cover 195.8 km2 when herbaceous crops (barley and wheat) cover 154 km2. The maximum elevation was 250 meters above sea level while the lowest one was -3 meters below sea level. The study area receives a massive variable amount of precipitation; however, water harvesting methods are inappropriate to store water for purposes

Rice yield forecasting models using satellite imagery in Egypt

Ability to make yield prediction before harvest using satellite remote sensing is important in many aspects of agricultural decision-making. In this study, canopy reflectance band and different band ratios in form of vegetation indices (VI) with leaf area index (LAI) were used to generate remotely sensed pre-harvest empirical rice yield prediction models. LAI measurements, spectral data derived from two SPOT data acquired on August 24, 2008 and August 23, 2009 and observed rice yield were used as main inputs for rice yield modeling. Each remotely sensed factor was used separately and in combination with LAI to generate the models. The results showed that green spectral band, middle infra-red spectral band and green vegetation index (GVI) did not show sufficient capability as rice yield estimators while other inputs such as red spectral band, near infrared spectral band and vegetation indices that are algebraic ratios from these two spectral bands when used separately or in combined with leaf area index (LAI) produced high accurate rice yield estimation models. The validation process was carried out using two statistical tests; standard error of estimate and the correlation coefficient between modeled and predicted yield. The validation results indicated that using normalized difference vegetation index (NDVI) combined with leaf area index (LAI) produced the model with highest accuracy and stability during the two rice seasons. The generated models are applicable 90 days after planting in any similar environmental conditions and agricultural practices

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

Early detection of the Mediterranean Fruit Fly, Ceratitis capitata (Wied.) in oranges using different aspects of remote sensing applications

Mediterranean Fruit Fly, Ceratitis capitata (Diptera: Tephritidae) is regarded as an important pest of orange (Citrus). Early detection of pest infestations enables the optimal application of preventative and control measures. This study was carried out under laboratory conditions, in order to predict and monitor orange pest infestations. Consequently, the scope was to find a remote sensing application that can help in the prediction of Mediterranean Fruit Fly infestation in oranges with the least loss in production. Spectroscopic and thermal imaging techniques were investigated, as effective tools in determination of pest infestation and damage in orange fruits. According to the findings, the optimum spectral zones that can be used to discriminate and differentiate between healthy (non-infected) orange fruit and infected ones were red and near infrared bands. Six vegetation indices were calculated to analyze the Field Spectral measurements. By calculating the NPCI (Normalized Pigment Chlorophyll Index), it was found that NPCI values for infected orange fruits were higher in comparison to healthy ones. Thermal imaging showed that the infected orange fruit temperatures were on average 0.8 °C higher than that of healthy fruits. As the maximum temperature differential (MTD) between healthy and infected fruits were 23.7–24.5 °C, respectively. These spectral reflectance curves were useful for researchers working on Site-specific crop management, as they can use remote sensing to detect individual fruit infections. Also, this technique should be used as a powerful and non-destructive method for assistance in agriculture

Assessment of Spectroscopic and Morphological Properties of some Fruit Crops under the Influence of Pollution with Heavy Metals Using Remote Sensing Techniques

Dietary exposure to a variety of heavy metals, including Ni, Cd, Cr, Pb, Zn, and Hg, has been identified as a danger to human health through fruits and vegetables, contamination of heavy metals is known as a grave risk to our climate. The study aims to develop empirical models to predict the concentration of heavy metals (Ni, Cd, Cr, Pb, Zn, and Hg) in the leaves of Citrus and Mango crops. The study was carried out in an observation site in Giza governorate that is cultivated by varied herbaceous and tree cover crops. This study area is suffering from severe pollution caused by near industrial district. The sample collected from deferent zones that are divided to six spatial zones and coded by from zone (2, 3, 4, 5, and 6). The distance between each Zone 10 Km that extends from the north to south and covers 60% from the Agriculture area in the Giza governorate. The main inputs of the generated models were spectroscopic remotely sensed data and laboratory analytical measurements of heavy metals in crop leaves. ASD (Analytical Spectral Devices) field spectro-radiometer was used to calculate hyper-spectral vegetation indices. Modeled heavy metal concentrations were tested against laboratory analysis through two common statistical tests; the Correlation of determination (R2) and Root Mean square (RMSE) error between predicted modeled heavy metals. Results shown the correlation coefficient of the generated models, red and near-infrared spectral bands demonstrated high precision and sufficiency for mango and citrus leaves to predict heavy metals. The models produced refer to specific regions with the same conditions. The overall results imply that hyper-spectral vegetation indices could be correlated with heavy metal content, while heavy metal content in plants may be influenced by many others. Remote sensing spectroscopy is a possible and promising technology to track the environmental pressures on agricultural vegetation. Additional ground remote sensing experiments are needed to assess the possibility of hyper-spectral reflectance spectroscopy in monitoring the stress of different types of metals on various plants

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

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

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