Application of Remote Sensing Techniques and Geographic Information Systems to Analyze Land Surface Temperature in Response to Land Use/Land Cover Change in Greater Cairo Region, Egypt

The Greater Cairo Region (GCR), Egypt has experienced rapid urban expansion and broad development over the past several decades. Due to such development, this region faces many environmental consequences. In order to mitigate such consequences, it is essential to examine the historical change to measure the urban sprawl of GCR, and its effect on land surface temperature (LST). The objective of this study is to fulfill this goal. It does so by generating land use/land cover (LULC) maps derived from Landsat 5 TM for 1990 and 2003 and Landsat 8 OLI for 2016, using several classification techniques. A spectral radiance model and a web-based atmospheric correction model were used to successfully evaluate LST from thermal bands of Landsat data. Overall accuracy of Landsat derived land use data were 90.3%, 96.5% and 94.9% for years 1990, 2003 and 2016, respectively. The LULC change analysis revealed vegetation loss to urban land by an amount of 7.73% and from barren lands to urban uses by 8.70% within a 26-year timespan (1990-2016). This rapid urban growth significantly decreases vegetation areas, consequently increasing the LST and modifying the urban microclimate. Results from this study can help policy-makers characterize the evolution of urban construction for future developments.

Evaluation of ventilation performance and energy efficiency of greenhouse fans

In order to investigate fan performance in fan-ventilated greenhouses(Urbana,USA),the effects of guard screen and loose belts on fan ventilation airflow and power consumption in greenhouse operations were examined with four belt-driven fans as trial subjects.The Fans Assessment Numeration System was used to measure the airflow rate.Temperature,relative humidity and power consumption were also monitored.Results show there were significant differences in the airflow rate between the fans with a cleaned and uncleaned guard screen(P<0.05).Power consumption also differed significantly even with the same cooling effect in greenhouse.When fan belts were adjusted to the proper tension,the fan speed and airflow rate were 13.1% and 30.1% higher than those of original belts,respectively.The daily average power consumption for the fan with the original loose belts was 20.4% higher than that with the adjusted belts when the pad was not working and 24.2% higher with pad working.The ventilation performance of fans with identical specifications showed a variation by up to 13.0% in terms of the ventilating efficiency ratio.These results demonstrated that fans should be cleaned routinely,and belt tension should be checked to ensure that fan performance meets specifications.This can reduce the power consumption in greenhouses for environmental control.Moreover,reordering fan staging,so that the most efficient fans are used in areas of greatest demand,can also reduce ventilation energy costs.

Comparison of direct and indirect determinations of dynamic ventilation rate in a modern dairy free stall barn

Reliable estimation of the ventilation rate(VR)in intensive livestock buildings is necessary for studying building environmental control strategies and predicting indoor air quality and air emissions.As direct air exchange measurements are time-consuming and expensive,it is environmentally inefficient to measure livestock building VR continuously in practice.Hence,indirect VR estimation methods have been widely used in modelling environmental control and air emissions,and also to measure airflow in the field.The accuracy of indirect measurement methods needs to be evaluated by comparing with direct measurements.In this study,the direct and indirect methods of determining hourly and daily mean VRs were applied to a mechanically-ventilated dairy free stall barn monitored by the 24-month National Air Emissions Monitoring Study.The direct method was used to continuously monitor fan rotational speeds,and differential static pressures,coupled with periodic in-situ fan performance assessments,to calculate the VR.The indirect method consisted of estimating the VR using CO2 concentration measurements and the CO2 mass balance method.The average daily and hourly means(mean±SD)of directly measured barn ventilation rates for two years were(246±73)m3/s and(245±77)m3/s,respectively.The average daily and hourly means(mean±SD)of barn ventilation rates estimated by the CO2 mass balance method were(287±93.4)m3/s and(287±118)m3/s,respectively.Correlation analyses showed a strong correlation between the indirect CO2 mass balance method and direct measurement methods(r=0.93 for daily means and r=0.85 for hourly means).

Long Lead-Time Streamflow Forecasting Using Oceanic-Atmospheric Oscillation Indices

Climatic variability influences the hydrological cycle that subsequently affects the discharge in the stream. The variability in the climate can be represented by the ocean-atmospheric oscillations which provide the forecast opportunity for the streamflow. Prediction of future water availability accurately and reliably is a key step for successful water resource management in the arid regions. Four popular ocean-atmospheric indices were used in this study for annual streamflow volume prediction. They were Pacific Decadal Oscillation (PDO), El-Nino Southern Oscillation (ENSO), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). Multivariate Relevance Vector Machine (MVRVM), a data driven model based on Bayesian learning approach was used as a prediction model. The model was applied to four unimpaired stream gages in Utah that spatially covers the state from north to south. Different models were developed based on the combinations of oscillation indices in the input. A total of 60 years (1950-2009) of data were used for the analysis. The model was trained on 50 years of data (1950-1999) and tested on 10 years of data (2000-2009). The best combination of oscillation indices and the lead-time were identified for each gage which was used to develop the prediction model. The predicted flow had reasonable agreement with the actual annual flow volume. The sensitivity analysis shows that the PDO and ENSO have relatively stronger effect compared to other oscillation indices in Utah. The prediction results from the MVRVM were compared with the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) where MVRVM performed relatively better.